WEBVTT 00:00:00.135 --> 00:00:02.490 The following content is provided under a Creative 00:00:02.490 --> 00:00:04.059 Commons license. 00:00:04.059 --> 00:00:06.330 Your support will help MIT OpenCourseWare 00:00:06.330 --> 00:00:10.690 continue to offer high-quality educational resources for free. 00:00:10.690 --> 00:00:13.350 To make a donation or view additional materials 00:00:13.350 --> 00:00:17.280 from hundreds of MIT courses, visit MIT OpenCourseWare 00:00:17.280 --> 00:00:18.480 at ocw.mit.edu. 00:00:28.016 --> 00:00:33.070 PROFESSOR: So today is the fourth and last lecture 00:00:33.070 --> 00:00:36.640 on the sort of microfounded macro models. 00:00:36.640 --> 00:00:39.700 And then next class, we will turn 00:00:39.700 --> 00:00:45.640 to a section on measurement, which is like a halfway point, 00:00:45.640 --> 00:00:48.190 or not really, in terms of total number of lectures. 00:00:48.190 --> 00:00:52.720 But we'll get into them-- to the micro evidence. 00:00:52.720 --> 00:00:55.540 I must say, this is sometime-- 00:00:55.540 --> 00:00:59.140 I said this last time-- in reverse of what I sometimes do. 00:00:59.140 --> 00:01:02.440 I often start with the micro stuff and build it up. 00:01:02.440 --> 00:01:04.150 But this isn't a bad idea, because you'll 00:01:04.150 --> 00:01:09.070 get to see this way how different assumptions matter 00:01:09.070 --> 00:01:11.180 within each of these models as you compare 00:01:11.180 --> 00:01:12.430 the predicted solutions. 00:01:12.430 --> 00:01:17.590 So it hopefully heightens your sensitivity to assumptions. 00:01:17.590 --> 00:01:19.990 And then we'll get into the microdata 00:01:19.990 --> 00:01:22.570 and really scrutinize what we see there. 00:01:22.570 --> 00:01:28.510 This paper, in particular, is part of that conversation. 00:01:28.510 --> 00:01:30.580 The way it's organized in terms of the lectures 00:01:30.580 --> 00:01:34.840 is this paper features mostly something 00:01:34.840 --> 00:01:37.540 other than limited commitment. 00:01:37.540 --> 00:01:42.340 Almost every single model has had sort of very limited credit 00:01:42.340 --> 00:01:46.360 or financing constraints, collateral constraints, 00:01:46.360 --> 00:01:52.060 basically putting a damper on sudden transitions 00:01:52.060 --> 00:01:55.150 and generating growth paths. 00:01:55.150 --> 00:02:00.460 And here, we're going to be looking at transition 00:02:00.460 --> 00:02:02.362 somewhat-- also, steady states. 00:02:02.362 --> 00:02:03.820 The main thing is we're going to be 00:02:03.820 --> 00:02:09.940 featuring on other alternative underpinnings of the models-- 00:02:09.940 --> 00:02:12.850 costly state verification, adverse selection, 00:02:12.850 --> 00:02:14.530 moral hazard. 00:02:14.530 --> 00:02:19.600 Again, the irony, for those of you coming down 00:02:19.600 --> 00:02:21.370 from the advanced macro class, is 00:02:21.370 --> 00:02:26.410 that Alp was featuring costly state verification up there 00:02:26.410 --> 00:02:27.570 early in a lecture. 00:02:27.570 --> 00:02:32.200 And these things are somehow related, 00:02:32.200 --> 00:02:37.860 in the sense that the dynamics that are created with the debt 00:02:37.860 --> 00:02:41.760 contracts and so on have to do with what you assume 00:02:41.760 --> 00:02:45.840 about the information structure, as well as adverse selection 00:02:45.840 --> 00:02:48.280 and so on. 00:02:48.280 --> 00:02:53.460 So you'll be perhaps a little relieved 00:02:53.460 --> 00:02:55.500 to know there isn't as much material 00:02:55.500 --> 00:03:00.090 on the front end of this lecture as there was last time. 00:03:00.090 --> 00:03:03.210 That's kind of a sad comment on the literature. 00:03:03.210 --> 00:03:06.390 Almost everybody assumes limited commitment, 00:03:06.390 --> 00:03:08.930 so there really wasn't much to choose from. 00:03:08.930 --> 00:03:14.640 We sort of scoured the earth to find some relevant papers 00:03:14.640 --> 00:03:17.250 in this genre. 00:03:17.250 --> 00:03:20.610 So there's three, and the third one 00:03:20.610 --> 00:03:24.090 is what I'm going to focus my attention on, the tree, so 00:03:24.090 --> 00:03:25.770 to speak, of the lecture. 00:03:25.770 --> 00:03:28.770 And now we'll look at several trees. 00:03:28.770 --> 00:03:31.420 The first two are a bit different. 00:03:31.420 --> 00:03:33.510 This one is Greenwood, Sanchez, and Wang-- 00:03:33.510 --> 00:03:37.440 Jeremy Greenwood, as of Greenwood and Jovanavic, 00:03:37.440 --> 00:03:43.170 which was a key feature of one of the first two models 00:03:43.170 --> 00:03:47.490 we talked about in lecture 2. 00:03:47.490 --> 00:03:51.480 And he's going to focus on Levine's comment. 00:03:51.480 --> 00:03:55.170 It's kind of cool the way these things come together. 00:03:55.170 --> 00:03:57.870 Levine worried that the finance causes growth. 00:03:57.870 --> 00:04:00.200 Empirical literature doesn't have 00:04:00.200 --> 00:04:06.510 a microfounded view of what really impedes intermediation. 00:04:10.440 --> 00:04:13.650 So that's what this paper is about. 00:04:13.650 --> 00:04:16.589 And it's going to be sort of a costly state verification 00:04:16.589 --> 00:04:22.140 point of view with diminishing returns and exogenous, 00:04:22.140 --> 00:04:23.250 technological progress. 00:04:26.190 --> 00:04:28.320 There's a telling sentence. 00:04:28.320 --> 00:04:30.090 In the paper, Uganda could more than 00:04:30.090 --> 00:04:32.040 double its output if it would adopt best 00:04:32.040 --> 00:04:36.150 practice in the financial sector, which is something 00:04:36.150 --> 00:04:38.610 like the US or Luxembourg. 00:04:38.610 --> 00:04:40.740 Although, there are other things in the model, 00:04:40.740 --> 00:04:43.950 so that that's not enough to get it up to, 00:04:43.950 --> 00:04:47.190 quote unquote, "its total potential output." 00:04:47.190 --> 00:04:49.170 Again, you see this language of gaps 00:04:49.170 --> 00:04:52.170 here that we focused on earlier in one 00:04:52.170 --> 00:04:55.920 of those financial possibilities frontier. 00:04:55.920 --> 00:04:58.380 So this is very much trying to get 00:04:58.380 --> 00:05:03.970 at gaps in terms of the underlying model. 00:05:03.970 --> 00:05:06.345 AUDIENCE: What are these other factors? 00:05:06.345 --> 00:05:06.970 PROFESSOR: Hmm? 00:05:06.970 --> 00:05:08.595 AUDIENCE: What are these other factors? 00:05:08.595 --> 00:05:11.280 PROFESSOR: There's going to be some technological progress 00:05:11.280 --> 00:05:14.430 that varies across countries, which is pretty standard stuff. 00:05:17.310 --> 00:05:19.140 And then this is kind of layered in, 00:05:19.140 --> 00:05:21.450 or it's actually more of the heart of the model, 00:05:21.450 --> 00:05:23.496 given that other. 00:05:23.496 --> 00:05:27.270 Well, we've seen that was done in the China paper as well. 00:05:29.820 --> 00:05:34.530 So another interesting thing, 29% of US growth 00:05:34.530 --> 00:05:38.610 is due to improvements in financial intermediation. 00:05:38.610 --> 00:05:42.090 So this paper both has transition paths in it, 00:05:42.090 --> 00:05:43.770 as well as steady states. 00:05:43.770 --> 00:05:46.750 This is a comment about steady state. 00:05:46.750 --> 00:05:50.830 There's exogenous technological progress going on, 00:05:50.830 --> 00:05:53.490 not at the level of the sort of TFP shocks 00:05:53.490 --> 00:05:55.530 that are in front of the production function, 00:05:55.530 --> 00:05:59.400 but in terms of the monitoring technologies which 00:05:59.400 --> 00:06:04.410 intermediaries can use to verify some private information. 00:06:04.410 --> 00:06:07.060 And that's where the technological progress 00:06:07.060 --> 00:06:11.970 is that's driving, say, steady state US growth rate according 00:06:11.970 --> 00:06:17.001 to their calibrated version of the model. 00:06:17.001 --> 00:06:20.550 Now, there are two key variables here that they focus a lot 00:06:20.550 --> 00:06:24.075 on-- the interest rate spread and the capital output ratio. 00:06:28.590 --> 00:06:30.950 So let's look at them. 00:06:34.370 --> 00:06:37.385 These slides are interest rate spreads. 00:06:40.490 --> 00:06:41.600 This is the US. 00:06:41.600 --> 00:06:44.000 That's Taiwan. 00:06:44.000 --> 00:06:47.450 This is the spread in the US, maybe 3%, 00:06:47.450 --> 00:06:50.300 maybe declining, but not so clear-- 00:06:50.300 --> 00:06:52.010 modestly so. 00:06:52.010 --> 00:06:57.150 And this is sort of the capital to GDP ratio. 00:06:57.150 --> 00:07:01.340 And it's paired to Taiwan, where it's a bit more-- 00:07:01.340 --> 00:07:02.870 at first at least-- 00:07:02.870 --> 00:07:04.640 easier to interpret. 00:07:04.640 --> 00:07:09.110 This spread is high and declining. 00:07:09.110 --> 00:07:11.350 So this is the transition part. 00:07:11.350 --> 00:07:15.710 This is Taiwan experiencing technological progress that's 00:07:15.710 --> 00:07:21.820 lowering, or even I guess it was in the steady state, 00:07:21.820 --> 00:07:24.280 at different levels of technological progress 00:07:24.280 --> 00:07:25.570 in monitoring output. 00:07:29.280 --> 00:07:32.660 And as that happens, as the intermediation system becomes 00:07:32.660 --> 00:07:38.420 more efficient, capital is increasing. 00:07:38.420 --> 00:07:41.770 You can make better use of capital 00:07:41.770 --> 00:07:45.080 because banks have better information, 00:07:45.080 --> 00:07:46.880 and they can lend more appropriately 00:07:46.880 --> 00:07:51.050 and adjust for risk more appropriately. 00:07:51.050 --> 00:07:53.780 Now, even then, this diagram is kind of striking. 00:07:53.780 --> 00:07:59.930 Because you can see that this is steadily rising as the spread 00:07:59.930 --> 00:08:00.700 is coming down. 00:08:00.700 --> 00:08:07.740 But relative to US, the capital to GDP ratio is quite high. 00:08:07.740 --> 00:08:09.195 So you know, just be mindful. 00:08:11.810 --> 00:08:16.400 There's two variables here, and one of them is GDP. 00:08:16.400 --> 00:08:21.080 So low levels of GDP kind of make that ratio high, 00:08:21.080 --> 00:08:25.130 and that's going on and also moving these variables 00:08:25.130 --> 00:08:27.669 as you compare over time and over countries. 00:08:32.669 --> 00:08:40.080 So the idea is that firms, banks, really 00:08:40.080 --> 00:08:42.120 monitor cash flows. 00:08:42.120 --> 00:08:45.330 The efficiency depends on the amount of resources 00:08:45.330 --> 00:08:48.870 you devote to monitoring and to the productivity 00:08:48.870 --> 00:08:52.710 of that monitoring technology. 00:08:52.710 --> 00:08:56.460 And as well, firms have differences ex ante 00:08:56.460 --> 00:09:00.180 in the structure of returns that they face. 00:09:03.040 --> 00:09:06.840 So they get the usual sort of why 00:09:06.840 --> 00:09:11.100 money doesn't flow from low productive firms 00:09:11.100 --> 00:09:12.270 to high productive firms. 00:09:12.270 --> 00:09:14.430 And the answer is this monitoring. 00:09:14.430 --> 00:09:17.190 At any moment in time, there are firms 00:09:17.190 --> 00:09:18.690 with high expected returns. 00:09:18.690 --> 00:09:20.520 They tend to be underfunded. 00:09:20.520 --> 00:09:22.920 But it's underfunded relative to this world 00:09:22.920 --> 00:09:27.290 with complete information. 00:09:27.290 --> 00:09:29.930 Whereas, we're in the constrained second best world. 00:09:32.480 --> 00:09:34.850 Others have low returns and they're overfunded. 00:09:34.850 --> 00:09:40.550 So these are the sort of not so talented wealthier firms. 00:09:40.550 --> 00:09:43.040 That theme keeps coming up in all the lectures. 00:09:43.040 --> 00:09:45.110 In China, it was the state-owned sector. 00:09:45.110 --> 00:09:48.920 And last time it was firms drawing talent, 00:09:48.920 --> 00:09:50.870 and some people having low talent which 00:09:50.870 --> 00:09:57.560 is persisting and so on, which is sort of what's true here. 00:09:57.560 --> 00:09:59.760 And of course, as efficiency increases, 00:09:59.760 --> 00:10:03.090 funds are reallocated from less productive 00:10:03.090 --> 00:10:05.640 to more productive firms, and the interest rate spread 00:10:05.640 --> 00:10:07.070 goes down. 00:10:07.070 --> 00:10:09.510 There's going to be perfect competition in the banking 00:10:09.510 --> 00:10:10.870 sector. 00:10:10.870 --> 00:10:12.540 So the cost of borrowing is going 00:10:12.540 --> 00:10:21.780 to fall because these firms are going to be kind of less 00:10:21.780 --> 00:10:25.560 able to walk away with the money in terms of the monitoring 00:10:25.560 --> 00:10:26.950 technology. 00:10:26.950 --> 00:10:32.670 So here is a picture of the interest rate spread. 00:10:32.670 --> 00:10:38.190 And it kind of goes the way you would think. 00:10:38.190 --> 00:10:40.590 The low interest rate spread countries 00:10:40.590 --> 00:10:45.300 are these highly industrialized countries, 00:10:45.300 --> 00:10:50.130 and they tend to have low capital to GDP ratios, 00:10:50.130 --> 00:10:55.230 in part, because they have high GDP. 00:10:55.230 --> 00:10:59.160 And here's a cross-section of countries 00:10:59.160 --> 00:11:03.510 that vary with respect to the level of income and the capital 00:11:03.510 --> 00:11:04.155 output ratio. 00:11:08.290 --> 00:11:12.410 And this is, again, very similar, 00:11:12.410 --> 00:11:16.280 except this is TFP over here. 00:11:16.280 --> 00:11:22.195 So you can see that interest rate spread is a bad thing, 00:11:22.195 --> 00:11:24.790 and the higher the spread, the worse the system. 00:11:24.790 --> 00:11:29.290 So as the interest rate spread goes down, TFP is going up. 00:11:29.290 --> 00:11:33.130 So TFP is a function of this sort of financial efficiency. 00:11:33.130 --> 00:11:33.930 Yeah, Matt. 00:11:33.930 --> 00:11:36.845 AUDIENCE: What data do they use for the interest rate spreads? 00:11:36.845 --> 00:11:37.470 PROFESSOR: Oh-- 00:11:37.470 --> 00:11:39.760 AUDIENCE: [INAUDIBLE] the interest rate [INAUDIBLE].. 00:11:39.760 --> 00:11:42.880 PROFESSOR: I don't know if they grabbed some IMF statistics. 00:11:42.880 --> 00:11:46.120 I think IMF financial statistics [INAUDIBLE] have that. 00:11:46.120 --> 00:11:49.390 I'm not sure if that's what they used. 00:11:49.390 --> 00:11:52.450 But basically, yeah, it's some measure 00:11:52.450 --> 00:11:54.980 of the difference between the lending rate and the borrowing 00:11:54.980 --> 00:11:55.480 rate. 00:11:55.480 --> 00:11:57.790 And then that sounds good at first, 00:11:57.790 --> 00:12:00.850 until you start thinking about all the many ways you could 00:12:00.850 --> 00:12:04.030 get at the borrowing rate. 00:12:04.030 --> 00:12:08.650 Often, these things ignore the informal sector entirely, 00:12:08.650 --> 00:12:10.430 for example. 00:12:10.430 --> 00:12:16.850 This is-- which this paper is doing as well in the theory. 00:12:16.850 --> 00:12:19.370 And this is the usual growth accounting stuff. 00:12:19.370 --> 00:12:19.870 OK? 00:12:19.870 --> 00:12:23.290 I don't know that we should have had this on the earlier slide. 00:12:23.290 --> 00:12:28.990 You want to predict a country's per capita income. 00:12:28.990 --> 00:12:32.650 It's efficient countries with higher TFP. 00:12:32.650 --> 00:12:36.040 So this is sort of behind the Lucas thing, which 00:12:36.040 --> 00:12:38.950 is why isn't money flowing very quickly 00:12:38.950 --> 00:12:41.470 from low-income countries to high-income countries, 00:12:41.470 --> 00:12:45.130 since high TFP tends to mean high marginal product 00:12:45.130 --> 00:12:47.470 of capital and so on. 00:12:47.470 --> 00:12:48.270 But it's amazing. 00:12:48.270 --> 00:12:49.915 They almost all lie on the line. 00:12:52.515 --> 00:12:54.873 AUDIENCE: Is there a TFP solo residual? 00:12:54.873 --> 00:12:55.540 PROFESSOR: Yeah. 00:12:55.540 --> 00:12:57.850 It's just a solo residual. 00:12:57.850 --> 00:13:01.480 And that's a good comment, because this paper 00:13:01.480 --> 00:13:07.420 is about the different ways to decompose at the firm level 00:13:07.420 --> 00:13:10.300 the sort of coefficient that's hanging 00:13:10.300 --> 00:13:13.000 outside the front of the production function. 00:13:13.000 --> 00:13:21.580 And then add it up the way [INAUDIBLE] were doing. 00:13:21.580 --> 00:13:27.700 So they also do this sort of Rajan/Zingales thing, which 00:13:27.700 --> 00:13:31.720 is to try to calibrate against the US economy, 00:13:31.720 --> 00:13:33.760 they look at the firm size distributions 00:13:33.760 --> 00:13:38.530 and the interest rate spreads in the US 00:13:38.530 --> 00:13:43.000 to get some of their parameters, and then take it 00:13:43.000 --> 00:13:45.070 to these cross-country data. 00:13:51.388 --> 00:13:53.151 AUDIENCE: Sir. 00:13:53.151 --> 00:13:53.818 PROFESSOR: Yeah. 00:13:53.818 --> 00:13:55.762 AUDIENCE: Actually, are [INAUDIBLE] 00:13:55.762 --> 00:13:56.734 distributions similar? 00:13:56.734 --> 00:13:59.180 Or is it developed [INAUDIBLE] country? 00:13:59.180 --> 00:14:00.520 PROFESSOR: No. 00:14:00.520 --> 00:14:03.670 So what they're saying is the US would be a more or less 00:14:03.670 --> 00:14:06.350 efficient financial system. 00:14:06.350 --> 00:14:11.440 So you could sort of think about grabbing 00:14:11.440 --> 00:14:15.160 the relative technology parameters from that, 00:14:15.160 --> 00:14:19.540 and then take those sort of what are 00:14:19.540 --> 00:14:21.250 counterfactual-- those parameters are 00:14:21.250 --> 00:14:23.800 put in the context of a developed country 00:14:23.800 --> 00:14:27.760 with lots of other restrictions. 00:14:27.760 --> 00:14:30.370 So the idea is to get the technology right, 00:14:30.370 --> 00:14:33.700 and then somehow figure out what difference the financial system 00:14:33.700 --> 00:14:35.090 makes. 00:14:35.090 --> 00:14:35.590 Yeah? 00:14:35.590 --> 00:14:37.270 AUDIENCE: What are the main assumptions 00:14:37.270 --> 00:14:39.166 you have to make in your own model 00:14:39.166 --> 00:14:41.626 to get that [INAUDIBLE] distribution [INAUDIBLE].. 00:14:41.626 --> 00:14:43.557 Because I mean, [INAUDIBLE] then it 00:14:43.557 --> 00:14:45.390 seems to be about the distribution of talent 00:14:45.390 --> 00:14:46.880 or distribution of [INAUDIBLE]. 00:14:46.880 --> 00:14:47.547 PROFESSOR: Yeah. 00:14:47.547 --> 00:14:52.540 Each one of these models tries to be really careful about how 00:14:52.540 --> 00:14:56.440 to map the data into the notation and assumptions 00:14:56.440 --> 00:14:57.070 they're making. 00:15:01.210 --> 00:15:05.470 And that's-- here, I can give you a hint of that, 00:15:05.470 --> 00:15:09.820 although I can't get to it in as much detail as I would like. 00:15:15.230 --> 00:15:17.830 So here's this production function. 00:15:17.830 --> 00:15:23.320 There is some aggregate shock that hits all the firms. 00:15:23.320 --> 00:15:27.700 There is some theta, which is an idiosyncratic-specific 00:15:27.700 --> 00:15:28.870 firm-level shock. 00:15:28.870 --> 00:15:31.060 And then we have the usual-- 00:15:31.060 --> 00:15:34.060 Cobb-Douglas actually just noting constant returns 00:15:34.060 --> 00:15:34.900 to scale. 00:15:34.900 --> 00:15:38.690 Different papers differ on that. 00:15:38.690 --> 00:15:45.340 And where do these idiosyncratic shocks come from? 00:15:45.340 --> 00:15:49.840 They're drawn at random from-- 00:15:49.840 --> 00:15:52.300 in this case, some simple two-point distribution. 00:15:52.300 --> 00:15:55.720 It could be high or low. 00:15:55.720 --> 00:15:59.050 But here is over and above that, another source 00:15:59.050 --> 00:16:02.320 of the heterogeneity that different firms 00:16:02.320 --> 00:16:05.090 have different sets of thetas. 00:16:05.090 --> 00:16:07.300 So the whole sort of range domain 00:16:07.300 --> 00:16:11.620 can be different, depending on, quote, "the tau type" 00:16:11.620 --> 00:16:14.140 that these firms are. 00:16:14.140 --> 00:16:17.200 Intermediaries are competitive. 00:16:17.200 --> 00:16:21.010 So they're going to sort of not get any surplus out of this. 00:16:21.010 --> 00:16:23.920 They raise funds from consumers and lend them to firms. 00:16:23.920 --> 00:16:26.740 That's typical intermediation structure. 00:16:26.740 --> 00:16:29.740 And the intermediary knows the firm's type tau. 00:16:29.740 --> 00:16:36.250 However, it doesn't know output. 00:16:36.250 --> 00:16:39.610 It doesn't know the idiosyncratic shock theta. 00:16:39.610 --> 00:16:43.550 And it doesn't know the labor supply. 00:16:43.550 --> 00:16:47.290 So all he's doing here is being careful to throw 00:16:47.290 --> 00:16:51.280 in enough sources of unknown that you can't basically 00:16:51.280 --> 00:16:54.100 back out from the output the unknown things 00:16:54.100 --> 00:16:56.830 on the right-hand side. 00:16:56.830 --> 00:17:00.460 And he probably could have gone with different assumptions, 00:17:00.460 --> 00:17:03.220 but that's certainly the spirit of it. 00:17:03.220 --> 00:17:07.074 Or at least, doesn't observe these things costlessly. 00:17:10.630 --> 00:17:14.260 But there is this monitoring technology. 00:17:20.030 --> 00:17:26.030 The firms experience their true productivity, say, high or low, 00:17:26.030 --> 00:17:30.710 call it for the theta i for a given firm. 00:17:30.710 --> 00:17:35.050 And suppose the firm lies about it and says theta j. 00:17:35.050 --> 00:17:36.560 Theta's not observed nor inferred 00:17:36.560 --> 00:17:40.340 from any of the other ex ante or ex post. 00:17:40.340 --> 00:17:45.680 Then the intermediary devotes labor to monitoring the claim. 00:17:48.620 --> 00:17:51.770 So this technology of verification 00:17:51.770 --> 00:17:53.210 is actually resource using. 00:17:53.210 --> 00:17:54.720 It's another production function. 00:17:58.540 --> 00:18:03.070 And then the size of the loan is determined. 00:18:03.070 --> 00:18:04.810 That's the capitalization of the firm. 00:18:04.810 --> 00:18:06.970 They don't have any wealth on their own. 00:18:06.970 --> 00:18:10.180 That's kind of the scale of the project. 00:18:10.180 --> 00:18:15.550 The level of productivity and monitoring is z. 00:18:15.550 --> 00:18:18.880 So that's kind of like whether it's a completely noisy signal, 00:18:18.880 --> 00:18:24.250 or an extremely accurate signal of the actual true state theta 00:18:24.250 --> 00:18:25.870 i. 00:18:25.870 --> 00:18:29.710 So this object, Pij is-- 00:18:29.710 --> 00:18:33.130 that's the probability that if the firm 00:18:33.130 --> 00:18:35.950 made a counterfactual report of theta j 00:18:35.950 --> 00:18:39.700 and really were a theta i, with this amount of labor 00:18:39.700 --> 00:18:43.480 devoted to monitoring, given this loan size, 00:18:43.480 --> 00:18:46.480 and given this index of the productivity of the technology, 00:18:46.480 --> 00:18:49.000 this is the probability of finding out 00:18:49.000 --> 00:18:54.640 what the truth of it really is. 00:18:54.640 --> 00:19:00.230 And so-- and then this mapping here 00:19:00.230 --> 00:19:03.770 is to provide between productivity 00:19:03.770 --> 00:19:10.300 and financial sector z, which, say, we don't see. 00:19:10.300 --> 00:19:16.160 And the things that we do see, at least 00:19:16.160 --> 00:19:19.470 we, as econometricians, see output and interest rate 00:19:19.470 --> 00:19:19.970 spreads. 00:19:19.970 --> 00:19:24.090 Now, let me pause for a second because you might well 00:19:24.090 --> 00:19:26.160 think of the obvious thing is, wait a minute. 00:19:26.160 --> 00:19:28.410 Didn't you just say output was unobserved? 00:19:28.410 --> 00:19:31.290 Well, this model and others make a distinction 00:19:31.290 --> 00:19:34.080 between the exposed data that we have 00:19:34.080 --> 00:19:37.320 as analysts about firm output, as 00:19:37.320 --> 00:19:41.860 opposed to what the intermediary is seeing along the way. 00:19:41.860 --> 00:19:44.247 So we're imagining we do see output 00:19:44.247 --> 00:19:45.580 and these interest rate spreads. 00:19:45.580 --> 00:19:48.490 Those were the slides I just showed you. 00:19:48.490 --> 00:19:51.370 And then this is where I'm going to-- 00:19:51.370 --> 00:19:53.260 Matt-- going to be a bit vague. 00:19:53.260 --> 00:19:57.700 If you looked at the equations in the notation, 00:19:57.700 --> 00:20:04.950 they can basically invert those equations to get x and z. 00:20:04.950 --> 00:20:07.510 And you can do it at a point in time for a given country, 00:20:07.510 --> 00:20:10.240 or you can do it at two different points in time. 00:20:10.240 --> 00:20:12.220 If you're getting z, you're getting 00:20:12.220 --> 00:20:16.000 a measure of this productivity of the monitoring technology. 00:20:16.000 --> 00:20:17.650 And that's why, for example, they 00:20:17.650 --> 00:20:22.410 are able to make statements about Taiwan's productivity 00:20:22.410 --> 00:20:26.260 increased over whatever it was, 10, 15 years, 00:20:26.260 --> 00:20:30.010 along with the drop in the intermediation spreads. 00:20:30.010 --> 00:20:34.750 They infer that from the drop in the intermediation spreads 00:20:34.750 --> 00:20:38.108 and the capital output ratio. 00:20:38.108 --> 00:20:38.900 AUDIENCE: Question. 00:20:38.900 --> 00:20:40.058 PROFESSOR: Yep. 00:20:40.058 --> 00:20:44.998 AUDIENCE: So the assumption that the probability of getting 00:20:44.998 --> 00:20:48.460 [INAUDIBLE] is decreasing. 00:20:48.460 --> 00:20:49.250 PROFESSOR: Yeah. 00:20:49.250 --> 00:20:52.940 So this is a scale effect. 00:20:52.940 --> 00:20:54.980 This came up in Alp's lecture the other day 00:20:54.980 --> 00:20:59.510 as well, which is if you have a fixed cost of monitoring 00:20:59.510 --> 00:21:02.780 or something, and then the country gets richer and richer, 00:21:02.780 --> 00:21:04.850 and credit is going up and up, then 00:21:04.850 --> 00:21:07.280 basically, the cost of verification 00:21:07.280 --> 00:21:08.970 is going down and down. 00:21:08.970 --> 00:21:13.580 But here, a country's getting richer and richer, 00:21:13.580 --> 00:21:16.970 and you don't want this sort of information imperfection 00:21:16.970 --> 00:21:20.030 to go away other than through investment 00:21:20.030 --> 00:21:21.320 in improved technology. 00:21:21.320 --> 00:21:25.460 So it allows that-- 00:21:25.460 --> 00:21:28.880 I guess that conceptually, it's like bunch of little firms 00:21:28.880 --> 00:21:29.690 all spread out. 00:21:29.690 --> 00:21:32.090 And the bigger the loan size, the more plants 00:21:32.090 --> 00:21:36.052 you got to go to see- something like that. 00:21:36.052 --> 00:21:39.850 AUDIENCE: And is the result partially 00:21:39.850 --> 00:21:41.380 depending on this assumption? 00:21:41.380 --> 00:21:44.133 Because it might be the direction is-- 00:21:44.133 --> 00:21:44.800 PROFESSOR: Yeah. 00:21:44.800 --> 00:21:45.300 Yeah. 00:21:45.300 --> 00:21:47.110 It matters quite a bit. 00:21:47.110 --> 00:21:48.415 Now, this is kind of linear. 00:21:51.280 --> 00:21:53.970 If it's diminishing, the effect eventually goes away. 00:21:53.970 --> 00:21:57.345 If it's convex, then I don't know what happens. 00:21:57.345 --> 00:21:57.970 But it matters. 00:22:03.510 --> 00:22:04.010 All right. 00:22:04.010 --> 00:22:06.320 So that's the spirit of that paper. 00:22:06.320 --> 00:22:10.330 Again, hopefully, it whets your appetite and-- 00:22:10.330 --> 00:22:13.100 or at least you can get some sense of what people 00:22:13.100 --> 00:22:16.915 are doing in the literature. 00:22:16.915 --> 00:22:18.290 And there's one other that I want 00:22:18.290 --> 00:22:22.340 to talk about before we get to the main paper of this lecture. 00:22:22.340 --> 00:22:25.910 And this I discovered recently, actually-- 00:22:25.910 --> 00:22:29.270 "International Capital Flows and Credit Markets-- 00:22:29.270 --> 00:22:31.520 A Tale of Two Frictions." 00:22:31.520 --> 00:22:37.000 Somehow everyone's got taken with Dickens. 00:22:37.000 --> 00:22:40.397 The one we looked at last time was "A Tale of Two Sectors." 00:22:40.397 --> 00:22:42.730 I don't know whether you thought that was clever or not. 00:22:42.730 --> 00:22:44.950 This paper was written after that. 00:22:49.010 --> 00:22:53.590 So it's kind of intriguing, and I'm not 00:22:53.590 --> 00:22:55.270 quite sure I totally believe it. 00:22:55.270 --> 00:23:00.710 But let me try to walk you through it. 00:23:00.710 --> 00:23:04.570 So the idea is we see a lot of capital 00:23:04.570 --> 00:23:07.675 moving around the world, and we see boom/bust cycles. 00:23:10.053 --> 00:23:12.220 And they're going to try to get both of those things 00:23:12.220 --> 00:23:14.140 in into one model. 00:23:14.140 --> 00:23:16.780 So it's ambitious. 00:23:16.780 --> 00:23:19.390 Global imbalances, large and persistent capital 00:23:19.390 --> 00:23:21.940 flows coming out of Asia to the US-- 00:23:21.940 --> 00:23:25.840 we talked about that last time in the-- 00:23:25.840 --> 00:23:27.370 last time in the China paper. 00:23:30.900 --> 00:23:35.130 And there the idea is it's hard to make good use of it 00:23:35.130 --> 00:23:39.690 within China for the reasons that that model and others 00:23:39.690 --> 00:23:41.860 postulate, namely, financial friction. 00:23:41.860 --> 00:23:43.920 So then it should go somewhere else. 00:23:51.750 --> 00:23:58.190 But you could also think about it on the other side of it. 00:23:58.190 --> 00:23:59.780 This is the tricky part. 00:23:59.780 --> 00:24:13.000 Namely, that that money is going to unproductive investment 00:24:13.000 --> 00:24:14.320 in the US. 00:24:14.320 --> 00:24:18.680 So at least that's ex post the judgment. 00:24:18.680 --> 00:24:20.590 So then you have to model sort of what's 00:24:20.590 --> 00:24:24.490 going on with the US interest rate that would allow 00:24:24.490 --> 00:24:28.390 the existence of bad projects and the coexistence 00:24:28.390 --> 00:24:29.965 of bad projects with good projects. 00:24:39.160 --> 00:24:40.380 So they try to do both. 00:24:46.600 --> 00:24:51.930 So again, there's credit market which 00:24:51.930 --> 00:24:54.480 intermediates the money that comes from savers 00:24:54.480 --> 00:24:56.760 and goes to investors. 00:24:56.760 --> 00:25:00.560 Individuals are endowed with some resources 00:25:00.560 --> 00:25:03.780 and with an investment project. 00:25:03.780 --> 00:25:06.240 And they have to decide whether to do the project 00:25:06.240 --> 00:25:09.255 and become an entrepreneur, in which case 00:25:09.255 --> 00:25:11.130 they're going to want some credit to fund it, 00:25:11.130 --> 00:25:14.940 or to forego their project and become savers, in which case 00:25:14.940 --> 00:25:17.340 they supply resources to the credit market. 00:25:17.340 --> 00:25:20.610 Now, this building block is the same on almost all the papers 00:25:20.610 --> 00:25:22.880 that we've looked at. 00:25:22.880 --> 00:25:26.120 It's an occupation choice model of-- 00:25:29.410 --> 00:25:34.120 that's used to address financial issues. 00:25:34.120 --> 00:25:36.760 To give adverse selection a crucial role, 00:25:36.760 --> 00:25:38.170 we assume individual productivity 00:25:38.170 --> 00:25:42.610 is private information and unobservable by the lenders. 00:25:42.610 --> 00:25:47.810 That kind of sounds like the previous Greenwood paper. 00:25:47.810 --> 00:25:49.840 So there is some cross-subsidization 00:25:49.840 --> 00:25:55.670 between high and low productivity entrepreneurs. 00:25:55.670 --> 00:25:59.290 A key assumption-- there's only one interest rate. 00:25:59.290 --> 00:26:01.360 So there's no menu of contracts. 00:26:01.360 --> 00:26:04.825 There's no other ways to separate good from bad. 00:26:07.750 --> 00:26:11.610 And the thing, to say it intuitively, 00:26:11.610 --> 00:26:15.840 about adverse selection is, who wants to borrow 00:26:15.840 --> 00:26:18.120 at a given interest rate? 00:26:18.120 --> 00:26:20.430 Well, that's really attractive for people who 00:26:20.430 --> 00:26:24.040 expect never to repay loans. 00:26:24.040 --> 00:26:26.410 So the lender is going to have to somehow break even 00:26:26.410 --> 00:26:29.590 on those bad types that are indistinguishable 00:26:29.590 --> 00:26:30.490 from the good types. 00:26:30.490 --> 00:26:34.600 And that is a force determining the interest rate. 00:26:38.540 --> 00:26:44.800 Now, the question is whether that kind of perverse dynamics 00:26:44.800 --> 00:26:49.210 would move around with wealth and so on. 00:26:49.210 --> 00:26:50.006 Yes. 00:26:50.006 --> 00:26:52.440 AUDIENCE: Are the lenders endowed with mechanisms 00:26:52.440 --> 00:26:53.691 to prevent that? 00:26:53.691 --> 00:26:55.780 Is there physical collateral, or is there 00:26:55.780 --> 00:26:56.822 some sort of reputation-- 00:26:56.822 --> 00:26:57.920 PROFESSOR: Not here. 00:26:57.920 --> 00:26:58.420 Not here. 00:26:58.420 --> 00:27:01.738 But there is a big literature that argues-- 00:27:01.738 --> 00:27:02.280 AUDIENCE: OK. 00:27:02.280 --> 00:27:03.812 So in this-- this environment has 00:27:03.812 --> 00:27:06.270 the potential for the sort of adverse selection [INAUDIBLE] 00:27:06.270 --> 00:27:08.213 type things. 00:27:08.213 --> 00:27:08.880 PROFESSOR: Yeah. 00:27:08.880 --> 00:27:12.790 There is always an issue in papers and as researchers 00:27:12.790 --> 00:27:16.540 at what-- how much-- how many substantive things you 00:27:16.540 --> 00:27:18.760 want going on at the same time. 00:27:18.760 --> 00:27:22.240 I would say this is among the simpler adverse selection 00:27:22.240 --> 00:27:22.740 thing. 00:27:22.740 --> 00:27:26.770 Personally, I would always think menus of contracts 00:27:26.770 --> 00:27:28.720 could select-- 00:27:28.720 --> 00:27:31.930 allow selection or sort of truth-telling. 00:27:31.930 --> 00:27:35.080 And that's just shut down. 00:27:35.080 --> 00:27:37.420 But Rothschild/Stiglitz shut that down. 00:27:40.120 --> 00:27:43.960 So maybe that's off to a tradition of sorts. 00:27:48.070 --> 00:27:51.640 So what are the implications of this adverse selection? 00:27:54.110 --> 00:27:55.860 The interest rate has to be higher than it 00:27:55.860 --> 00:27:58.890 would have been otherwise. 00:27:58.890 --> 00:28:00.390 But on the other hand, there's a lot 00:28:00.390 --> 00:28:01.680 of borrowing and investment. 00:28:01.680 --> 00:28:04.980 Now, be careful here, because these low productivity 00:28:04.980 --> 00:28:07.750 guys are happy to be borrowers. 00:28:07.750 --> 00:28:09.420 So not all borrowing is a good thing. 00:28:09.420 --> 00:28:12.750 And that's kind of the seeds of the bust getting created. 00:28:16.640 --> 00:28:18.350 Because of adverse selection we have 00:28:18.350 --> 00:28:21.410 this wedge between the equilibrium interest rate 00:28:21.410 --> 00:28:24.740 and the marginal return to investment. 00:28:24.740 --> 00:28:26.630 Again, it's a common wedge. 00:28:26.630 --> 00:28:28.880 We just did an interest rate spread. 00:28:28.880 --> 00:28:31.610 Here's another way to think about that spread. 00:28:31.610 --> 00:28:33.740 That doesn't mean it doesn't matter. 00:28:33.740 --> 00:28:37.770 The underlying assumptions do matter. 00:28:37.770 --> 00:28:42.540 But this phenomenon is pervasive across countries in the data, 00:28:42.540 --> 00:28:45.280 and is a big part of almost all these models. 00:28:45.280 --> 00:28:50.510 So adverse selection means that the economy 00:28:50.510 --> 00:28:53.770 can attract more capital, and boost in 00:28:53.770 --> 00:28:58.230 that capital flows seems odd just to say it there. 00:28:58.230 --> 00:28:59.310 You can see it written. 00:29:02.370 --> 00:29:07.410 But again, the idea is that somehow the interest rate 00:29:07.410 --> 00:29:09.840 is higher than it would have been in the full information 00:29:09.840 --> 00:29:15.822 economy, and outside investors seek the higher interest. 00:29:15.822 --> 00:29:17.530 So that's kind of what they mean by that. 00:29:20.300 --> 00:29:27.310 But the true marginal return lies below the interest rate. 00:29:27.310 --> 00:29:29.710 So something bad is happening. 00:29:29.710 --> 00:29:34.570 And the way it comes out in the bath here is the capital-- 00:29:34.570 --> 00:29:39.900 there's lower aggregate consumption because you've 00:29:39.900 --> 00:29:42.857 used resources inefficiently. 00:29:45.840 --> 00:29:48.720 And then they claim this adverse selection actually 00:29:48.720 --> 00:29:50.400 is a force for volatility. 00:29:54.810 --> 00:29:56.295 This is kind of key. 00:29:56.295 --> 00:29:58.950 The incentives of less productive individuals 00:29:58.950 --> 00:30:02.490 to become entrepreneurs are strongest-- 00:30:02.490 --> 00:30:06.000 there's bad types borrow and don't expect to repay much. 00:30:06.000 --> 00:30:09.180 That force is strongest, ironically, when the capital 00:30:09.180 --> 00:30:12.360 stock is low and income is low. 00:30:18.400 --> 00:30:19.930 And what's that all about? 00:30:19.930 --> 00:30:28.730 Well, basically this debt overhang problem 00:30:28.730 --> 00:30:31.130 is more severe when the capital stock is low, 00:30:31.130 --> 00:30:35.560 because you have to borrow more than you would if the capital 00:30:35.560 --> 00:30:37.990 stock were high, and you could almost self-finance. 00:30:41.260 --> 00:30:45.040 And there's a lot of untalented firms borrowing 00:30:45.040 --> 00:30:49.180 when the total income and capital stock of the economy 00:30:49.180 --> 00:30:49.800 is low. 00:30:53.920 --> 00:30:56.480 Or conversely, as capital and income increases, 00:30:56.480 --> 00:31:00.120 that cross-subsidization is decreasing. 00:31:00.120 --> 00:31:05.560 There's more self-financing, and entrepreneurship 00:31:05.560 --> 00:31:10.400 loses its appeal for less productive individuals. 00:31:10.400 --> 00:31:12.400 Well, I mean, it's actually kind of interesting. 00:31:12.400 --> 00:31:15.790 Because you can see the dynamics of occupation choice 00:31:15.790 --> 00:31:18.260 that we've had in the other papers. 00:31:18.260 --> 00:31:20.830 Who's going to be a firm as opposed to a wage earner 00:31:20.830 --> 00:31:24.760 depends on what happens if you were a firm 00:31:24.760 --> 00:31:25.735 and decided to borrow. 00:31:29.670 --> 00:31:31.770 And then they actually get a boom/bust cycle. 00:31:34.280 --> 00:31:41.520 They claim to show that capital inflows are going to fuel 00:31:41.520 --> 00:31:43.080 this accumulation period. 00:31:43.080 --> 00:31:47.280 And then you'll get a contraction. 00:31:51.900 --> 00:31:55.410 Actually, toward the end, the tale of two obstacles or two 00:31:55.410 --> 00:32:05.450 frictions, is they throw pledgeability on top of it. 00:32:05.450 --> 00:32:09.580 So the more traditional sort of collateral constraint 00:32:09.580 --> 00:32:12.370 reduces investment and lowers the interest rate. 00:32:12.370 --> 00:32:13.390 We've seen that, right? 00:32:13.390 --> 00:32:18.720 You can't borrow as much, so there's less pressure 00:32:18.720 --> 00:32:19.660 on the interest rate. 00:32:19.660 --> 00:32:23.310 So the interest rate would be lower. 00:32:23.310 --> 00:32:27.030 Ironically, that low interest rate, though, 00:32:27.030 --> 00:32:30.300 decreases the returns to savings. 00:32:30.300 --> 00:32:35.550 And why be a wage earner and get a lower return on your savings 00:32:35.550 --> 00:32:38.400 when you can be a not so good entrepreneur 00:32:38.400 --> 00:32:40.040 and borrow money at low interest? 00:32:40.040 --> 00:32:45.310 So you get this sort of, they claim, 00:32:45.310 --> 00:32:48.720 a kind of accelerator or exacerbating feature, 00:32:48.720 --> 00:32:51.990 that the adverse selection is interacting 00:32:51.990 --> 00:32:54.331 with the limited collateral constraint. 00:32:58.660 --> 00:33:05.060 And that should whet your appetite for that paper. 00:33:05.060 --> 00:33:10.220 Now again, I apologize in a way, although it wasn't my intention 00:33:10.220 --> 00:33:16.220 to go through all the details of that paper. 00:33:16.220 --> 00:33:20.180 And it is on the Stellar website. 00:33:20.180 --> 00:33:24.350 And we can study it in more detail. 00:33:24.350 --> 00:33:25.970 But I offered it here just to show 00:33:25.970 --> 00:33:28.370 you the kinds of phenomenon that people 00:33:28.370 --> 00:33:32.030 are trying to address with the interplay 00:33:32.030 --> 00:33:33.880 of financial frictions. 00:33:36.810 --> 00:33:46.690 And then we come to the paper I wish to focus on, 00:33:46.690 --> 00:33:48.930 which is, again, a tale of two frictions. 00:33:48.930 --> 00:33:50.370 But we can't use that title. 00:33:54.850 --> 00:33:59.090 But we're doing it in a different way. 00:33:59.090 --> 00:34:04.330 We allow moral hazard and also limited commitment. 00:34:04.330 --> 00:34:06.460 So on the one hand, we're going to take 00:34:06.460 --> 00:34:10.360 the traditional limited commitment constraint 00:34:10.360 --> 00:34:13.719 and remove it, and insert a moral hazard 00:34:13.719 --> 00:34:15.290 constraint explicitly. 00:34:15.290 --> 00:34:17.469 And this time, I can show you the equations. 00:34:21.300 --> 00:34:23.850 But we also allow-- 00:34:23.850 --> 00:34:28.830 we don't get rid of the capital constraint entirely. 00:34:31.980 --> 00:34:35.159 And then you say, well, what are the rules here 00:34:35.159 --> 00:34:36.480 for making this stuff up? 00:34:39.110 --> 00:34:41.270 And my answer is the data. 00:34:43.989 --> 00:34:48.780 And in particular-- this is the reverse order coming here-- 00:34:48.780 --> 00:34:51.179 you will see a paper that I've written 00:34:51.179 --> 00:34:55.770 with Alex Karaivanov, where we have consumption, output, 00:34:55.770 --> 00:34:59.780 investment, and capital stock data, 00:34:59.780 --> 00:35:08.340 and we actually estimate a variety of financial regimes. 00:35:08.340 --> 00:35:11.100 And it turns out that the so-called limited commitment 00:35:11.100 --> 00:35:16.890 regime fits the data best in the rural data that I have 00:35:16.890 --> 00:35:21.180 and in the Northeast, and the moral hazard regime 00:35:21.180 --> 00:35:25.633 fits better in the urban areas and in the areas 00:35:25.633 --> 00:35:26.550 in and around Bangkok. 00:35:29.470 --> 00:35:32.230 So that's the way to think about these two frictions. 00:35:32.230 --> 00:35:35.050 They're, for us, going to be microfounded. 00:35:35.050 --> 00:35:37.450 Although here, we're just borrowing that and putting it 00:35:37.450 --> 00:35:37.950 in. 00:35:41.035 --> 00:35:41.535 So-- 00:35:46.248 --> 00:35:47.240 AUDIENCE: Sir. 00:35:47.240 --> 00:35:48.065 PROFESSOR: Yep. 00:35:48.065 --> 00:35:49.440 AUDIENCE: So basically, are you-- 00:35:49.440 --> 00:35:52.880 you're going to sort of let these two things interplay, 00:35:52.880 --> 00:36:00.770 and you're going to have them be differently 00:36:00.770 --> 00:36:03.180 important in different places. 00:36:03.180 --> 00:36:03.990 PROFESSOR: Yeah. 00:36:03.990 --> 00:36:06.790 So that's where we end up. 00:36:06.790 --> 00:36:09.570 And where we start is we have an economy, 00:36:09.570 --> 00:36:12.060 and I'll tell you all about the environment. 00:36:12.060 --> 00:36:14.910 And then I'll describe what goes on with the limited commitment 00:36:14.910 --> 00:36:19.200 constraint as if it were the whole economy. 00:36:19.200 --> 00:36:22.590 I'll do the same for moral hazard, the whole economy. 00:36:22.590 --> 00:36:28.130 Then I'll have like 50-50, and use the general equilibrium. 00:36:28.130 --> 00:36:32.810 And the punch line is going to be the general equilibrium 00:36:32.810 --> 00:36:34.370 with the two frictions is not just 00:36:34.370 --> 00:36:39.110 a simple convex combination of the two extremes. 00:36:39.110 --> 00:36:40.820 And the reason that that's going on 00:36:40.820 --> 00:36:43.930 has to do with the general equilibrium. 00:36:43.930 --> 00:36:44.930 So you're going to get-- 00:36:47.953 --> 00:36:50.370 roughly, you're going to get interest rates and wages that 00:36:50.370 --> 00:36:53.520 are somewhat between the two extremes. 00:36:53.520 --> 00:36:56.040 But then given that, the question 00:36:56.040 --> 00:36:59.880 about who is to be a firm or a worker depends on the obstacles 00:36:59.880 --> 00:37:01.860 that you face. 00:37:01.860 --> 00:37:03.780 And actually, what turns out to be 00:37:03.780 --> 00:37:07.710 cool at the parameters we have, which we think are reasonable 00:37:07.710 --> 00:37:16.260 and are largely similar to what Alex and I had, 00:37:16.260 --> 00:37:20.520 that in the places with moral hazard, 00:37:20.520 --> 00:37:23.310 output is substantially higher. 00:37:27.990 --> 00:37:30.140 There are slightly more firms there. 00:37:30.140 --> 00:37:33.120 They're larger. 00:37:33.120 --> 00:37:38.040 Those firms are renting more capital or borrowing more. 00:37:38.040 --> 00:37:44.240 The ratio of credit to GDP, the financing ratio, is higher. 00:37:44.240 --> 00:37:47.460 And they employ more labor. 00:37:47.460 --> 00:37:52.580 Without any geography somehow, we've created these-- 00:37:52.580 --> 00:37:55.250 replicated these stylized facts of development 00:37:55.250 --> 00:37:59.150 when you think about rural areas versus urban areas. 00:37:59.150 --> 00:38:03.080 Money is flowing from savings in rural areas 00:38:03.080 --> 00:38:07.325 to the urbanizing cities. 00:38:07.325 --> 00:38:11.500 Now, I'm not going to claim that we've captured everything going 00:38:11.500 --> 00:38:12.700 on in all these countries. 00:38:12.700 --> 00:38:17.230 But this simple sort of combination 00:38:17.230 --> 00:38:20.470 of financial frictions allows that. 00:38:20.470 --> 00:38:23.150 In that sense, this is very different from the other paper. 00:38:23.150 --> 00:38:24.005 Yes. 00:38:24.005 --> 00:38:27.310 AUDIENCE: And so do you have something that endogenously 00:38:27.310 --> 00:38:32.570 makes these two regimes being relevant in those areas? 00:38:32.570 --> 00:38:36.790 PROFESSOR: That's a good question, and it's-- 00:38:36.790 --> 00:38:40.240 at this point, it's hard to answer. 00:38:40.240 --> 00:38:43.960 I mean, it looks as if either there 00:38:43.960 --> 00:38:46.990 is a legal restriction on collateral, 00:38:46.990 --> 00:38:50.940 or the set of financial institutions that are operating 00:38:50.940 --> 00:38:54.600 in the rural areas is different from what's 00:38:54.600 --> 00:38:58.260 going on in the urban areas. 00:38:58.260 --> 00:39:01.590 And if we were going to follow Jeremy's path, 00:39:01.590 --> 00:39:03.590 we would ask your question. 00:39:03.590 --> 00:39:07.260 We are asking, and we just don't have answers yet, 00:39:07.260 --> 00:39:11.370 as to what is the configuration of the financial service 00:39:11.370 --> 00:39:12.480 providers? 00:39:12.480 --> 00:39:14.760 You'll see there is a heavy distinction-- we've 00:39:14.760 --> 00:39:17.820 already seen, actually, where the commercial banks are 00:39:17.820 --> 00:39:20.850 operating and where this government agricultural bank is 00:39:20.850 --> 00:39:21.420 operating. 00:39:21.420 --> 00:39:24.840 And I think that's probably part of the story. 00:39:24.840 --> 00:39:30.540 But in this paper, we do not take a stand on it. 00:39:30.540 --> 00:39:33.690 We take as given just this sort of difference 00:39:33.690 --> 00:39:36.970 in the financial information regime. 00:39:36.970 --> 00:39:38.580 OK. 00:39:38.580 --> 00:39:48.130 So this, we've already said. 00:39:48.130 --> 00:39:51.720 This, we've already talked about. 00:39:51.720 --> 00:39:54.360 So we get to the model. 00:39:54.360 --> 00:39:59.980 So a household that could decide to be either a wage 00:39:59.980 --> 00:40:05.370 earner or a firm will maximize discounted expected utility. 00:40:05.370 --> 00:40:08.100 Beta is the discount rate. u is the common utility 00:40:08.100 --> 00:40:10.830 function, the arguments of which are 00:40:10.830 --> 00:40:17.420 consumption of this household i, and effort of this household i. 00:40:17.420 --> 00:40:22.110 We'll parameterize this when we need to compute something. 00:40:22.110 --> 00:40:24.720 At this level, it's quite general. 00:40:24.720 --> 00:40:28.440 You can-- if x is equal-- this is like a binary choice. 00:40:28.440 --> 00:40:30.900 You can be a worker or a firm. 00:40:30.900 --> 00:40:33.460 By convention, when x is 1, you're a firm, 00:40:33.460 --> 00:40:36.430 and when x is zero, you're a worker. 00:40:36.430 --> 00:40:39.200 And that's going to be endogenous. 00:40:39.200 --> 00:40:45.950 If you do become a firm, you have this productivity draw z, 00:40:45.950 --> 00:40:48.710 and it evolves over time according to sort 00:40:48.710 --> 00:40:51.590 of a standard Markov process. 00:40:51.590 --> 00:40:54.110 I was rushing toward the end of the lecture last time, 00:40:54.110 --> 00:40:59.960 but this was the thing that was in the [INAUDIBLE] 00:40:59.960 --> 00:41:03.023 paper toward the end, and I guess 00:41:03.023 --> 00:41:04.190 in some of the other papers. 00:41:04.190 --> 00:41:06.350 And finally, we have wealth. 00:41:06.350 --> 00:41:09.180 So at the beginning of the period, 00:41:09.180 --> 00:41:13.680 everyone has some predetermined level of wealth, 00:41:13.680 --> 00:41:16.290 and it's in the bank essentially. 00:41:16.290 --> 00:41:19.710 And so an individual is characterized 00:41:19.710 --> 00:41:23.160 by their initial wealth and their current talent. 00:41:26.490 --> 00:41:29.410 Here is the production function. 00:41:29.410 --> 00:41:33.520 It's diminishing returns to scale in capital 00:41:33.520 --> 00:41:34.540 and hired labor. 00:41:37.520 --> 00:41:41.330 And then we have these sort of things out front here. 00:41:41.330 --> 00:41:44.700 The notation varies from one paper to the next. 00:41:44.700 --> 00:41:49.610 But epsilon is here, an idiosyncratic shock. 00:41:49.610 --> 00:41:55.960 iid over households, and z is that level of productivity 00:41:55.960 --> 00:41:59.830 we already talked about that's evolving over time. 00:41:59.830 --> 00:42:05.730 Now, there's actually-- the effort 00:42:05.730 --> 00:42:09.030 of the entrepreneur is another factor of production. 00:42:09.030 --> 00:42:13.440 And that's basically little e. 00:42:13.440 --> 00:42:18.180 So the way that the effort of the entrepreneur is modeled 00:42:18.180 --> 00:42:20.380 is through this. 00:42:20.380 --> 00:42:25.080 The epsilon, the idiosyncratic productivity, 00:42:25.080 --> 00:42:29.160 depends on how hard the entrepreneur is, quote, 00:42:29.160 --> 00:42:31.710 "working," or thinking, you know, 00:42:31.710 --> 00:42:35.020 staying up at night worrying-- basically, due diligence. 00:42:35.020 --> 00:42:36.900 This is not measured. 00:42:36.900 --> 00:42:40.140 You do see the epsilon, but you don't see the e. 00:42:40.140 --> 00:42:44.670 And that's if-- well, at least in the moral hazard model, 00:42:44.670 --> 00:42:45.720 you don't see the e. 00:42:53.340 --> 00:42:56.510 But I should qualify that we're going 00:42:56.510 --> 00:42:58.100 through the standard environment, 00:42:58.100 --> 00:43:00.890 and there's going to be a full information 00:43:00.890 --> 00:43:03.200 environment with limited commitment in which everything 00:43:03.200 --> 00:43:06.460 is seen and full insurance is possible, 00:43:06.460 --> 00:43:08.510 versus the moral hazard environment which 00:43:08.510 --> 00:43:12.500 is going to be the source of limited insurance, 00:43:12.500 --> 00:43:14.750 and it's going to interact-- 00:43:14.750 --> 00:43:19.460 both frictions interact with productivity. 00:43:19.460 --> 00:43:26.270 So there are banks or financial intermediaries, 00:43:26.270 --> 00:43:32.180 and you should think of them as basically risk syndicates. 00:43:32.180 --> 00:43:38.210 Because not only do they sort of lend out of the wealth 00:43:38.210 --> 00:43:41.310 that households have been depositing with them, 00:43:41.310 --> 00:43:46.250 they can also smooth over idiosyncratic shocks. 00:43:46.250 --> 00:43:49.730 So there's a state contingent payment 00:43:49.730 --> 00:43:51.850 that the banks can make-- 00:43:51.850 --> 00:43:54.200 it could be negative-- 00:43:54.200 --> 00:43:56.600 say, to the households as a function 00:43:56.600 --> 00:44:00.575 of their realized and observed epsilon. 00:44:03.980 --> 00:44:08.440 Now, in some respects, it's still-- 00:44:11.430 --> 00:44:15.420 we're not solving a massive programming problem 00:44:15.420 --> 00:44:17.760 and then capturing the interest rates 00:44:17.760 --> 00:44:21.690 and wages off some first order conditions. 00:44:21.690 --> 00:44:24.150 Instead, we're going to imagine that these risk 00:44:24.150 --> 00:44:28.740 syndicates, these banks, and the firms and the households 00:44:28.740 --> 00:44:33.840 all take as given economy-wide interest rates and wages. 00:44:33.840 --> 00:44:35.280 And like many of the other papers, 00:44:35.280 --> 00:44:39.210 we're going to have to determine those by supply and demand 00:44:39.210 --> 00:44:40.680 mechanically. 00:44:40.680 --> 00:44:45.630 So that's a computational burden, 00:44:45.630 --> 00:44:49.690 but it is one that other papers have faced as well. 00:44:49.690 --> 00:44:52.290 Now, what to do about talent-- 00:44:52.290 --> 00:44:53.880 is it insurable or not? 00:44:53.880 --> 00:44:57.120 Well, we kind of decided to try to be realistic 00:44:57.120 --> 00:44:58.770 and to not let it be insured. 00:45:02.080 --> 00:45:03.170 We could go the other way. 00:45:03.170 --> 00:45:06.640 In some sense, we know exactly how to do it. 00:45:06.640 --> 00:45:09.610 And there's some duals which would be satisfied 00:45:09.610 --> 00:45:11.470 if we had allowed it. 00:45:11.470 --> 00:45:14.530 But we thought it was just on the side of realism 00:45:14.530 --> 00:45:20.510 to say don't worry about your job market. 00:45:23.990 --> 00:45:25.640 We're going to cover that. 00:45:25.640 --> 00:45:28.820 NYT is going to fully insure you. 00:45:28.820 --> 00:45:31.650 Well, of course, we could put moral hazard on the z thing. 00:45:31.650 --> 00:45:34.785 But anyway, we didn't get into that. 00:45:39.510 --> 00:45:44.082 So you're going assign the occupation, induce effort-- 00:45:44.082 --> 00:45:44.790 where did I say-- 00:45:44.790 --> 00:45:47.190 I guess I didn't say it. 00:45:47.190 --> 00:45:49.110 It'll come up in a second, hopefully. 00:45:53.540 --> 00:45:54.040 Yeah. 00:45:54.040 --> 00:45:54.860 It was right here. 00:45:59.400 --> 00:46:01.890 We actually also allow there to be 00:46:01.890 --> 00:46:07.060 some insurance and moral hazard issues on the worker side. 00:46:07.060 --> 00:46:12.430 Now, for simplicity, we give them this same p of e function. 00:46:12.430 --> 00:46:14.960 We could have allowed it to be different. 00:46:14.960 --> 00:46:16.100 What is this? 00:46:16.100 --> 00:46:20.020 Well, the idea is you can sort of put a lot of effort 00:46:20.020 --> 00:46:22.930 into working, and the firm is going 00:46:22.930 --> 00:46:26.300 to observe your total sort of productivity. 00:46:26.300 --> 00:46:30.850 But the firm doesn't see your effort. 00:46:30.850 --> 00:46:33.720 It's like piece rate in some sense. 00:46:33.720 --> 00:46:37.320 So we just got tired of being asymmetric, 00:46:37.320 --> 00:46:41.130 as so many papers are about-- you know, there's an insurance 00:46:41.130 --> 00:46:43.800 problem for intermediaries and wage earners don't 00:46:43.800 --> 00:46:46.410 suffer from things like that. 00:46:50.770 --> 00:46:52.840 So here's the timeline. 00:46:52.840 --> 00:46:55.740 The household comes in with-- 00:46:55.740 --> 00:46:58.680 a certain household i comes in with a certain amount 00:46:58.680 --> 00:47:01.980 of wealth and talent. 00:47:01.980 --> 00:47:06.550 There's going to be an assignment, if you will, 00:47:06.550 --> 00:47:09.150 of who's a worker or a firm. 00:47:09.150 --> 00:47:11.490 Then if a firm-- 00:47:11.490 --> 00:47:15.400 well, if a worker or firm ever gets determined, if a firm-- 00:47:15.400 --> 00:47:18.300 the capitalization and labor hiring of the firm 00:47:18.300 --> 00:47:23.560 gets determined, then the epsilon hits. 00:47:23.560 --> 00:47:24.670 Effort comes first. 00:47:24.670 --> 00:47:32.210 Epsilon comes after that and gets in the way, so to speak, 00:47:32.210 --> 00:47:34.970 especially if effort is not observed. 00:47:34.970 --> 00:47:40.430 So this is like idiosyncratic risk subject to moral hazard 00:47:40.430 --> 00:47:41.510 potentially. 00:47:41.510 --> 00:47:45.770 Then consumption and the level of your savings 00:47:45.770 --> 00:47:47.720 that you're carrying over putting 00:47:47.720 --> 00:47:50.150 into the bank for tomorrow, those things 00:47:50.150 --> 00:47:52.910 are functions potentially of those epsilons. 00:47:52.910 --> 00:47:56.870 So the point is, you do bear the consequences 00:47:56.870 --> 00:48:00.200 in the moral hazard model of shirking. 00:48:00.200 --> 00:48:04.640 If you shirked and your productivity 00:48:04.640 --> 00:48:06.470 were low as a consequence of that, 00:48:06.470 --> 00:48:09.560 you're going to have to take a hit in terms 00:48:09.560 --> 00:48:14.060 of lower consumption and lower savings from tomorrow on. 00:48:23.110 --> 00:48:30.600 So here's the optimization problem really of the bank, 00:48:30.600 --> 00:48:33.360 but it's more like an equilibrium outcome. 00:48:33.360 --> 00:48:38.430 The banks are basically competitive. 00:48:38.430 --> 00:48:40.290 There's free entry. 00:48:40.290 --> 00:48:44.770 And for any sort of cohort fully observed, by the way, 00:48:44.770 --> 00:48:48.450 az sort of people out there-- 00:48:48.450 --> 00:48:52.830 there's a lot of them in every little cubbyhole-- 00:48:52.830 --> 00:48:55.170 the banks compete to offer them contracts. 00:48:55.170 --> 00:48:59.890 In effect, they would bid down things 00:48:59.890 --> 00:49:03.380 until utility is maximized. 00:49:03.380 --> 00:49:07.660 So it's as if they're maximizing the utility of this cohort 00:49:07.660 --> 00:49:10.180 by choice of all these things I just went through. 00:49:14.020 --> 00:49:17.770 And this is the resource constraint 00:49:17.770 --> 00:49:21.650 that the risk syndicate faces. 00:49:21.650 --> 00:49:23.600 Partly it's familiar and partly not. 00:49:23.600 --> 00:49:27.800 Namely, you've got uses and sources. 00:49:27.800 --> 00:49:33.170 The uses of money or resources is in consumption and how much 00:49:33.170 --> 00:49:35.860 you save for next time. 00:49:35.860 --> 00:49:40.740 And the sources comes from if you're 00:49:40.740 --> 00:49:45.680 a firm in this syndicate, your net profits, 00:49:45.680 --> 00:49:48.660 after subtracting off the cost of labor and capital 00:49:48.660 --> 00:49:53.690 and depreciating the capital, and from the other guys 00:49:53.690 --> 00:49:54.920 who are wage earners. 00:49:58.700 --> 00:50:00.920 And as is standard in many models, 00:50:00.920 --> 00:50:07.970 you begin the period with basically the savings 00:50:07.970 --> 00:50:10.250 that you had as a bank. 00:50:10.250 --> 00:50:13.260 But now they've sort of accrued interest. 00:50:13.260 --> 00:50:14.730 So you have principal and interest 00:50:14.730 --> 00:50:16.022 at the beginning of the period. 00:50:16.022 --> 00:50:20.040 Now, it looks like a standard sort of incomplete markets 00:50:20.040 --> 00:50:23.700 model, where you have the choices between saving 00:50:23.700 --> 00:50:26.670 and, quote, "borrowing/lending." 00:50:26.670 --> 00:50:30.550 The difference here and-- well, and some profit maximization 00:50:30.550 --> 00:50:31.050 embedded. 00:50:31.050 --> 00:50:36.220 But the difference really is the summing over epsilon. 00:50:36.220 --> 00:50:42.860 So this is the total sort of assigned consumption 00:50:42.860 --> 00:50:45.890 and savings throughout the whole syndicate, 00:50:45.890 --> 00:50:48.140 the average per capita number. 00:50:48.140 --> 00:50:51.670 And this is the per capita sources. 00:50:51.670 --> 00:50:55.250 The intermediaries in trying to break even on every epsilon 00:50:55.250 --> 00:50:57.170 type in the population-- 00:50:57.170 --> 00:51:00.590 that's the risk-sharing part of it, 00:51:00.590 --> 00:51:02.570 that some people could have higher output 00:51:02.570 --> 00:51:04.410 than other people. 00:51:04.410 --> 00:51:08.100 but subject to incentives or insurance. 00:51:08.100 --> 00:51:11.660 The intermediary will smooth that by assigning, say, 00:51:11.660 --> 00:51:14.840 higher consumption than output for the low epsilon guys, 00:51:14.840 --> 00:51:18.360 and conversely for the high epsilon guys. 00:51:18.360 --> 00:51:18.860 Yes. 00:51:18.860 --> 00:51:21.170 AUDIENCE: So the financial intermediary, they 00:51:21.170 --> 00:51:24.620 can control the distribution between consumption and asset? 00:51:24.620 --> 00:51:25.850 PROFESSOR: Yeah. 00:51:25.850 --> 00:51:26.840 That's all observed. 00:51:26.840 --> 00:51:29.480 AUDIENCE: So household can't bypass that 00:51:29.480 --> 00:51:32.493 by saving themselves. 00:51:32.493 --> 00:51:33.410 PROFESSOR: They don't. 00:51:33.410 --> 00:51:33.910 Yeah. 00:51:33.910 --> 00:51:36.320 We assume not. 00:51:36.320 --> 00:51:45.830 But you could assume that they get 00:51:45.830 --> 00:51:48.170 their sort of assignment of assets, 00:51:48.170 --> 00:51:49.510 and then they act on their own. 00:51:49.510 --> 00:51:50.885 But they would put it in the bank 00:51:50.885 --> 00:51:52.170 because they accrue interest. 00:51:52.170 --> 00:51:55.570 So there's really no loss in letting it sit in the bank. 00:51:55.570 --> 00:52:00.093 Now, we can have banks competing with one another. 00:52:00.093 --> 00:52:01.135 That's a bit more subtle. 00:52:01.135 --> 00:52:01.660 AUDIENCE: [INAUDIBLE] the other way around though, right? 00:52:01.660 --> 00:52:03.100 PROFESSOR: Hmm? 00:52:03.100 --> 00:52:06.110 AUDIENCE: You can't like really-- 00:52:06.110 --> 00:52:06.610 OK. 00:52:06.610 --> 00:52:08.235 Like, the household can choose to save, 00:52:08.235 --> 00:52:12.828 but household can choose to consume too? 00:52:12.828 --> 00:52:13.370 I don't know. 00:52:13.370 --> 00:52:17.450 I feel that here, bank has more control over household, 00:52:17.450 --> 00:52:19.360 and they can say exactly how much you can-- 00:52:19.360 --> 00:52:21.640 PROFESSOR: Well, that's probably a better way 00:52:21.640 --> 00:52:22.720 to think about it anyway. 00:52:22.720 --> 00:52:28.060 Because as you'll see, these information frictions 00:52:28.060 --> 00:52:29.710 and collateral restrictions create 00:52:29.710 --> 00:52:33.947 pressures that make savings more or less than what households 00:52:33.947 --> 00:52:36.280 might want to do if they were just following their Euler 00:52:36.280 --> 00:52:36.790 equation. 00:52:36.790 --> 00:52:39.460 So in the end, I agree with you. 00:52:39.460 --> 00:52:41.200 What's going to be actually interesting 00:52:41.200 --> 00:52:42.670 is that it's going-- 00:52:42.670 --> 00:52:43.920 and you'll see this-- 00:52:43.920 --> 00:52:45.310 it's going to go a different way. 00:52:45.310 --> 00:52:49.060 In the collateral constraint model, 00:52:49.060 --> 00:52:53.050 you would like to borrow more, actually, 00:52:53.050 --> 00:52:55.340 in order to have more assets in the future. 00:52:55.340 --> 00:52:57.190 That's not allowed to happen. 00:52:57.190 --> 00:52:59.890 So they're going to be borrowing-constrained 00:52:59.890 --> 00:53:03.500 in terms of looking at their intertemporal consumption path. 00:53:03.500 --> 00:53:08.467 The guy subject to this sort of incentive moral hazard 00:53:08.467 --> 00:53:10.300 constraint, they're actually going to end up 00:53:10.300 --> 00:53:11.740 being savings-constrained. 00:53:11.740 --> 00:53:13.520 They would like to save more. 00:53:13.520 --> 00:53:18.760 So it's already a hint that the dynamics of the household 00:53:18.760 --> 00:53:21.160 decision problems are very different, 00:53:21.160 --> 00:53:23.710 not just of the firm, but the households. 00:53:27.380 --> 00:53:31.550 So what do we mean by the incentive constraint? 00:53:31.550 --> 00:53:35.570 Well, when effort is unobserved, you 00:53:35.570 --> 00:53:37.850 want to induce the assigned effort. 00:53:37.850 --> 00:53:40.010 So these are the good boys. 00:53:40.010 --> 00:53:45.200 They were recommended to do e, and they actually do it. 00:53:45.200 --> 00:53:48.920 So this is the distribution of output, epsilon, 00:53:48.920 --> 00:53:50.640 that results from it. 00:53:50.640 --> 00:53:54.200 And they go into tomorrow with the assigned levels 00:53:54.200 --> 00:53:57.590 of savings and draw talent. 00:53:57.590 --> 00:54:00.050 But effort is not seen. 00:54:00.050 --> 00:54:03.680 So they actually contemplate some 00:54:03.680 --> 00:54:05.660 out of equilibrium behavior, like doing 00:54:05.660 --> 00:54:10.140 e hat, which is other than e. 00:54:10.140 --> 00:54:12.430 Now, what are the consequences of that? 00:54:12.430 --> 00:54:14.430 Well, first of all, there's a direct consequence 00:54:14.430 --> 00:54:16.830 in the utility function, because shirking 00:54:16.830 --> 00:54:21.780 might be good in terms of higher utility. 00:54:21.780 --> 00:54:24.090 But also, there is a direct consequence 00:54:24.090 --> 00:54:27.220 in terms of the distribution of output. 00:54:27.220 --> 00:54:30.720 So this is sort of the moral hazard induced 00:54:30.720 --> 00:54:33.870 productivity consequence. 00:54:33.870 --> 00:54:38.340 And if you do some typical monotonicity or something, 00:54:38.340 --> 00:54:41.580 then higher effort is associated with likely 00:54:41.580 --> 00:54:44.490 getting higher epsilons. 00:54:44.490 --> 00:54:50.910 So that's a standard-looking incentive constraint. 00:54:50.910 --> 00:54:52.770 The-- yes. 00:54:52.770 --> 00:54:55.540 AUDIENCE: So probably we could introduce 00:54:55.540 --> 00:54:58.530 some costs associated with changing jobs 00:54:58.530 --> 00:55:01.682 between [INAUDIBLE]. 00:55:01.682 --> 00:55:04.399 Does it change the analysis at all? 00:55:08.172 --> 00:55:09.630 PROFESSOR: Well, you get to choose. 00:55:09.630 --> 00:55:10.210 But yeah. 00:55:10.210 --> 00:55:11.400 There's no extra cost. 00:55:14.040 --> 00:55:16.900 It would increase the dimensionality of the state 00:55:16.900 --> 00:55:19.980 space rather enormously, because then you'd not only 00:55:19.980 --> 00:55:23.940 have to worry about the current distribution of wealth 00:55:23.940 --> 00:55:26.370 and talent, you'd have to worry about what these guys were 00:55:26.370 --> 00:55:28.200 doing last period. 00:55:28.200 --> 00:55:31.620 And it's actually hard enough to get any kind of solutions, 00:55:31.620 --> 00:55:33.790 analytic or numeric. 00:55:33.790 --> 00:55:37.860 So I think that's the constraint here is keeping the state. 00:55:37.860 --> 00:55:41.070 I mean, there is-- you'll see that there's already 00:55:41.070 --> 00:55:42.960 a huge distribution of wealth, and we're 00:55:42.960 --> 00:55:46.410 going to have to keep track of how that histogram is evolving 00:55:46.410 --> 00:55:52.250 the population jointly with the talent distribution. 00:55:52.250 --> 00:55:56.960 So hopefully, good enough for starters. 00:55:56.960 --> 00:55:57.460 Oh. 00:55:57.460 --> 00:56:00.090 I'm going to skip this slide. 00:56:00.090 --> 00:56:02.590 I'll tell you what was on it, and then we'll come back to it 00:56:02.590 --> 00:56:04.060 when we get to the microdata. 00:56:07.950 --> 00:56:10.980 Basically, instead of solving directly 00:56:10.980 --> 00:56:15.600 for consumption as a function of epsilon and all of that stuff, 00:56:15.600 --> 00:56:20.950 we trick it into a linear programming problem. 00:56:20.950 --> 00:56:25.680 And we do that by keeping track of histograms of things, 00:56:25.680 --> 00:56:29.220 basically, the joint distribution of consumption, 00:56:29.220 --> 00:56:36.770 epsilon, recommended effort and so on, respecting the timing. 00:56:36.770 --> 00:56:39.860 So in words, hopefully, you would think 00:56:39.860 --> 00:56:41.650 that's kind of equivalent. 00:56:41.650 --> 00:56:43.320 Anyway. 00:56:43.320 --> 00:56:45.290 Now, what's the gain? 00:56:45.290 --> 00:56:48.710 The gain is it literally is a linear program. 00:56:48.710 --> 00:56:52.520 So that little module can be solved as a linear programming 00:56:52.520 --> 00:56:54.350 problem, and that's how we're computing 00:56:54.350 --> 00:56:59.020 the solutions to that part. 00:56:59.020 --> 00:57:03.060 But this isn't the right context to go into the notation. 00:57:03.060 --> 00:57:08.350 We'll do it in a very simple convex cost or a fixed cost 00:57:08.350 --> 00:57:10.510 problem first, and then build up to it later. 00:57:13.350 --> 00:57:16.600 So I'm missing something. 00:57:16.600 --> 00:57:17.100 Oh, yeah. 00:57:17.100 --> 00:57:17.640 This thing. 00:57:20.340 --> 00:57:23.100 So this is the other constraint, that the capitalization 00:57:23.100 --> 00:57:26.200 of the firm is just some proportion of your wealth. 00:57:26.200 --> 00:57:29.620 Lambda could be greater than 1, but it's not infinity. 00:57:29.620 --> 00:57:31.710 So this is the constraint that we had 00:57:31.710 --> 00:57:34.940 on so many of the other papers. 00:57:34.940 --> 00:57:36.540 And you can tell stories and model 00:57:36.540 --> 00:57:39.190 this about running away with a capital and so on. 00:57:39.190 --> 00:57:41.010 But this is the essence of it. 00:57:44.010 --> 00:57:45.110 We could do both. 00:57:45.110 --> 00:57:47.810 We're going to imagine that it's one or the other. 00:57:47.810 --> 00:57:52.260 I mean, both simultaneously for a given firm. 00:57:52.260 --> 00:57:56.040 But we're going to put people in one sector or the other. 00:57:56.040 --> 00:57:59.790 So then we're going to solve for the factors occupation choice, 00:57:59.790 --> 00:58:02.385 labor, hiring, capitalization. 00:58:02.385 --> 00:58:05.610 And you want to think about labor supply-- 00:58:05.610 --> 00:58:07.560 it's just only slightly tricky. 00:58:07.560 --> 00:58:09.470 X means entrepreneur. 00:58:09.470 --> 00:58:11.880 1 minus x means wage earner. 00:58:11.880 --> 00:58:15.780 What's the total labor supply coming from wage earners? 00:58:15.780 --> 00:58:18.750 Well, epsilon is units of labor supply. 00:58:18.750 --> 00:58:20.190 It's induced by the effort. 00:58:23.230 --> 00:58:25.300 There's a histogram. 00:58:25.300 --> 00:58:29.760 So we basically add up overall the epsilons 00:58:29.760 --> 00:58:34.490 at the fractions with which they exist in the population. 00:58:34.490 --> 00:58:36.940 And this is total labor supply. 00:58:36.940 --> 00:58:47.920 And so then the rest is pretty less daunting 00:58:47.920 --> 00:58:49.810 in terms of notation. 00:58:49.810 --> 00:58:54.820 We have-- we're going to equate labor supply to labor demand. 00:58:54.820 --> 00:58:58.780 This is the l being employed within the firms. 00:58:58.780 --> 00:59:01.960 The firms vary in terms of their a and z. 00:59:01.960 --> 00:59:05.380 And everyone's facing these economy-wide wage 00:59:05.380 --> 00:59:07.960 and interest rates. 00:59:07.960 --> 00:59:10.720 But this does depend on the az argument, 00:59:10.720 --> 00:59:12.910 so we have to add up over-- 00:59:12.910 --> 00:59:16.370 or integrate up over-- all the az people in the population. 00:59:16.370 --> 00:59:18.580 So again, in practice, we're going 00:59:18.580 --> 00:59:22.180 to have a large finite number of wealths, 00:59:22.180 --> 00:59:24.120 a finite number of talents, and this is going 00:59:24.120 --> 00:59:28.070 to be some kind of summation. 00:59:28.070 --> 00:59:30.710 This is also the capital clearing constraint. 00:59:30.710 --> 00:59:32.930 All that wealth sitting there-- forget the d. 00:59:32.930 --> 00:59:35.720 Don't know why that's there. 00:59:35.720 --> 00:59:38.360 You have all that wealth sitting in the bank, 00:59:38.360 --> 00:59:45.420 and it's going to be lent out at interest to the firms that 00:59:45.420 --> 00:59:46.560 are renting the capital. 00:59:49.930 --> 00:59:57.370 So we grab some parameters and some functional forms. 00:59:57.370 --> 01:00:00.232 These are pretty traditional. 01:00:00.232 --> 01:00:02.440 I guess we didn't want to do anything unconventional, 01:00:02.440 --> 01:00:05.860 because we want to show what difference the obstacles make 01:00:05.860 --> 01:00:08.650 rather than force you into a utility function 01:00:08.650 --> 01:00:10.390 you don't believe. 01:00:10.390 --> 01:00:15.550 Also, we can grab from the literature values for these 01:00:15.550 --> 01:00:16.900 that people believe. 01:00:16.900 --> 01:00:24.130 I don't mean to belittling calibration all the time. 01:00:24.130 --> 01:00:25.550 Beta is realistic here. 01:00:25.550 --> 01:00:31.760 It's about basically 5%, or 95% of the future is valued. 01:00:31.760 --> 01:00:33.560 Sigma's the degree of risk aversion. 01:00:33.560 --> 01:00:34.700 It's 1.5. 01:00:34.700 --> 01:00:39.080 People have values well within that range-- 01:00:39.080 --> 01:00:44.440 just utility is a bit tricky to parameterize of effort, 01:00:44.440 --> 01:00:46.960 but the power function is the Frisch elasticity. 01:00:46.960 --> 01:00:48.760 People have estimated that. 01:00:48.760 --> 01:00:54.940 This is a simple transition which we made up. 01:00:54.940 --> 01:00:58.750 But in other papers, people talk about entry and exit of firms, 01:00:58.750 --> 01:01:03.380 and we could imagine doing that. 01:01:03.380 --> 01:01:05.300 And they are also similar to the parameters 01:01:05.300 --> 01:01:08.490 that Alex and I estimate would likely [INAUDIBLE] functions. 01:01:08.490 --> 01:01:09.620 So here is the-- 01:01:09.620 --> 01:01:15.070 with the Euler equation, it's at the household side. 01:01:15.070 --> 01:01:18.862 Borrowing-constrained households have a Lagrange multiplier 01:01:18.862 --> 01:01:20.445 on that limited commitment constraint. 01:01:20.445 --> 01:01:27.660 It turns out to be tomorrow, but in expectation, it 01:01:27.660 --> 01:01:31.260 leads to this same phenomenon that the marginal utility 01:01:31.260 --> 01:01:35.580 of consumption is high today and lower in expectation tomorrow. 01:01:35.580 --> 01:01:38.940 So these guys are borrowing-constrained. 01:01:38.940 --> 01:01:39.670 Yes. 01:01:39.670 --> 01:01:42.400 AUDIENCE: Question, sir-- going back to [INAUDIBLE].. 01:01:42.400 --> 01:01:44.825 So when you described it earlier, 01:01:44.825 --> 01:01:48.670 so something that entrepreneur can do to [INAUDIBLE] 01:01:48.670 --> 01:01:50.230 to make productivity high. 01:01:50.230 --> 01:01:52.960 If it was mainly labor monitoring, 01:01:52.960 --> 01:01:55.765 I guess it would maybe sort of really only scale with labor. 01:01:55.765 --> 01:01:57.390 Would that-- how much would that change 01:01:57.390 --> 01:01:59.080 what goes on in the model. 01:01:59.080 --> 01:02:01.360 PROFESSOR: Oh, that's a bit like in the spirit 01:02:01.360 --> 01:02:03.990 of Jeremy's model, and we're not doing that. 01:02:03.990 --> 01:02:06.370 But yes, you could put-- 01:02:06.370 --> 01:02:08.320 it's very much like what Jeremy is doing. 01:02:08.320 --> 01:02:10.900 You could put the technology for monitoring effort 01:02:10.900 --> 01:02:13.420 to create a signal at least of effort, 01:02:13.420 --> 01:02:15.340 and that would mitigate-- that would move us 01:02:15.340 --> 01:02:17.155 more toward the full insurance solution. 01:02:21.870 --> 01:02:28.780 And this is the weird one. 01:02:28.780 --> 01:02:32.370 And I'm not sure if you've seen this yet or not 01:02:32.370 --> 01:02:34.110 in public finance. 01:02:34.110 --> 01:02:38.400 But-- so I'm just going to basically assert 01:02:38.400 --> 01:02:41.460 that the first order condition, when 01:02:41.460 --> 01:02:44.370 you're subject to moral hazard, looks like this. 01:02:44.370 --> 01:02:46.740 And for obvious reasons, this is sometimes 01:02:46.740 --> 01:02:51.380 called the inverse Euler equation, 01:02:51.380 --> 01:02:53.045 because this is 1 over u prime. 01:02:56.210 --> 01:02:59.150 The point here is Jensen's inequality. 01:02:59.150 --> 01:03:02.700 You know, if you replace-- 01:03:02.700 --> 01:03:06.260 if you pull the expectation operator outside of it, 01:03:06.260 --> 01:03:08.540 you'd have 1 over u prime, but the inverse 01:03:08.540 --> 01:03:10.880 would take it back to u prime. 01:03:10.880 --> 01:03:14.540 But when you move that expectation operator outside, 01:03:14.540 --> 01:03:18.706 you're taking an average over the interior object. 01:03:18.706 --> 01:03:19.550 I'm sorry. 01:03:19.550 --> 01:03:22.100 This is an average over the interior object as opposed 01:03:22.100 --> 01:03:25.160 to an integration from the outside. 01:03:25.160 --> 01:03:29.940 So it is basically a classical example of Jensen's inequality, 01:03:29.940 --> 01:03:33.790 and it makes the right-hand side higher. 01:03:33.790 --> 01:03:36.777 That's mechanical, but hopefully-- 01:03:36.777 --> 01:03:38.110 we've already talked about this. 01:03:38.110 --> 01:03:44.720 You can see that in the limited commitment, 01:03:44.720 --> 01:03:48.910 people cannot borrow as much as they want. 01:03:48.910 --> 01:03:52.690 That's going to make the demand for funds less, 01:03:52.690 --> 01:03:54.970 and the interest rate is going to be 01:03:54.970 --> 01:03:58.390 less when all the economy is subject from that limited 01:03:58.390 --> 01:04:00.010 commitment constraint, as opposed 01:04:00.010 --> 01:04:04.060 to the moral hazard economy where households and firms are 01:04:04.060 --> 01:04:05.800 savings-constrained. 01:04:05.800 --> 01:04:07.240 So they would like to save more. 01:04:07.240 --> 01:04:08.390 Savings is less. 01:04:08.390 --> 01:04:09.130 It's scarce. 01:04:09.130 --> 01:04:10.630 You have a higher interest rate. 01:04:10.630 --> 01:04:13.670 So these interest rate numbers kind of make sense. 01:04:13.670 --> 01:04:16.950 Don't be too spooked by the negative interest rate. 01:04:16.950 --> 01:04:20.620 You know, we have a depreciation rate in this economy. 01:04:20.620 --> 01:04:23.230 So it's still sort of on net-- 01:04:23.230 --> 01:04:24.250 it's positive. 01:04:27.180 --> 01:04:32.610 And then you can see, comparing these two different countries, 01:04:32.610 --> 01:04:36.810 GDP, TFP-- 01:04:36.810 --> 01:04:39.450 the TFP dynamics are very different, 01:04:39.450 --> 01:04:42.270 although the numbers, though different, 01:04:42.270 --> 01:04:44.040 are not radically different. 01:04:46.770 --> 01:04:49.740 TFP is dragged down in the limited commitment economy, 01:04:49.740 --> 01:04:53.760 because high productivity firms cannot borrow as much as they 01:04:53.760 --> 01:04:55.030 want. 01:04:55.030 --> 01:04:57.930 They can't exploit their z. 01:04:57.930 --> 01:05:00.690 Whereas, in the moral hazard economy, 01:05:00.690 --> 01:05:04.065 you have to induce effort, and that's kind of a drag. 01:05:10.540 --> 01:05:15.520 I mean, if you were draconian and you had no insurance, 01:05:15.520 --> 01:05:19.400 then yes, people would be working very hard. 01:05:19.400 --> 01:05:21.140 But that's actually not optimal. 01:05:21.140 --> 01:05:24.990 The right thing to do is to have this blend between insurance 01:05:24.990 --> 01:05:27.440 and productivity. 01:05:27.440 --> 01:05:30.760 So partially, it's-- this language people use about moral 01:05:30.760 --> 01:05:33.820 hazard in the banking system and in the press 01:05:33.820 --> 01:05:36.280 and the policymakers, pick that up like, 01:05:36.280 --> 01:05:38.140 let's get rid of moral hazard. 01:05:38.140 --> 01:05:40.930 That's not the right way to think about it. 01:05:40.930 --> 01:05:42.880 Moral hazard is an information problem. 01:05:42.880 --> 01:05:46.210 You want incentives to do as best you can, 01:05:46.210 --> 01:05:47.830 given the moral hazard problem. 01:05:47.830 --> 01:05:51.010 But it's like-- eliminate moral hazard is like, let's 01:05:51.010 --> 01:05:52.450 just shut down all the insurance, 01:05:52.450 --> 01:05:53.490 then you don't have a problem. 01:05:53.490 --> 01:05:55.115 But yeah, well, you got other problems. 01:06:00.160 --> 01:06:01.507 Wages are a bit different. 01:06:01.507 --> 01:06:02.965 They're higher in the moral-- these 01:06:02.965 --> 01:06:05.620 are economy-wide wages and interest rates, of course. 01:06:10.870 --> 01:06:12.790 But you can also see a bigger difference 01:06:12.790 --> 01:06:14.725 in the financing ratio. 01:06:17.290 --> 01:06:22.540 Actually, labor here tends to compensate for the more 01:06:22.540 --> 01:06:26.410 restricted capital, and the drag makes labor lower 01:06:26.410 --> 01:06:27.820 in the moral hazard economy. 01:06:32.260 --> 01:06:35.800 External finance is more limited in the limited commitment 01:06:35.800 --> 01:06:36.340 economy. 01:06:36.340 --> 01:06:36.910 Why? 01:06:36.910 --> 01:06:37.990 Because you can't borrow. 01:06:37.990 --> 01:06:40.660 You run into that lambda constraint. 01:06:40.660 --> 01:06:44.090 There's nothing like that in the moral hazard economy. 01:06:44.090 --> 01:06:48.314 So the external finance to GDP ratio is higher. 01:06:48.314 --> 01:06:52.512 AUDIENCE: So why is the interest rate negative? 01:06:52.512 --> 01:06:54.220 PROFESSOR: That's, again, because there's 01:06:54.220 --> 01:06:58.150 an interest rate going on here. 01:06:58.150 --> 01:07:00.460 I mean capital gets utilized, and it gets-- 01:07:00.460 --> 01:07:01.390 it's depreciating. 01:07:01.390 --> 01:07:05.030 And you've got to basically pay for that somehow. 01:07:05.030 --> 01:07:07.120 So it lowers the net yield. 01:07:09.790 --> 01:07:12.990 And here's interesting-- you know, 01:07:12.990 --> 01:07:16.800 I didn't get to say much at all about Joaquin Blaum's paper 01:07:16.800 --> 01:07:18.330 on inequality. 01:07:18.330 --> 01:07:21.090 But I get to be reminded to say something now, 01:07:21.090 --> 01:07:26.130 because here the wealth inequality curve's generated 01:07:26.130 --> 01:07:29.370 by the different constraints. 01:07:29.370 --> 01:07:33.240 And you can see there's actually more inequality 01:07:33.240 --> 01:07:35.870 in the limited commitment economy 01:07:35.870 --> 01:07:38.386 than there is in the moral hazard economy. 01:07:42.580 --> 01:07:47.053 But again, that has to do with this dispersion of the capital. 01:07:47.053 --> 01:07:49.510 Capital tends to be more-- 01:07:49.510 --> 01:07:52.000 not identical, but more compressed 01:07:52.000 --> 01:07:55.680 in the moral hazard economy. 01:07:55.680 --> 01:07:56.950 And capital is very limited. 01:07:56.950 --> 01:08:04.840 You can't take advantage of your productivity in the other one. 01:08:04.840 --> 01:08:08.050 So the wealth dispersion is playing a bigger role, 01:08:08.050 --> 01:08:10.990 and feeding back in turn to-- 01:08:10.990 --> 01:08:13.990 it's like you can't get the convergences. 01:08:17.010 --> 01:08:19.200 And then we take 50-50. 01:08:19.200 --> 01:08:23.484 Same economy-- now we have the urban/rural of the same size, 01:08:23.484 --> 01:08:23.984 and-- 01:08:26.880 --> 01:08:27.760 AUDIENCE: Question. 01:08:27.760 --> 01:08:29.220 PROFESSOR: Yep. 01:08:29.220 --> 01:08:32.250 AUDIENCE: So in the slides with the parameters, 01:08:32.250 --> 01:08:35.043 you don't talk about lambda. 01:08:35.043 --> 01:08:39.229 How much do you think that to be? 01:08:39.229 --> 01:08:41.340 The-- so the-- how much is the constraint? 01:08:41.340 --> 01:08:43.939 PROFESSOR: I'm not sure I remember the number. 01:08:43.939 --> 01:08:45.180 AUDIENCE: But-- I mean, do you remember how you configured it? 01:08:45.180 --> 01:08:46.290 PROFESSOR: It should have been on that list of parameters? 01:08:46.290 --> 01:08:47.500 It wasn't on that page? 01:08:47.500 --> 01:08:48.000 No. 01:08:48.000 --> 01:08:49.950 I don't remember. 01:08:49.950 --> 01:08:52.830 We probably-- it was probably something like 1.3, 01:08:52.830 --> 01:08:54.703 because that's-- 01:08:54.703 --> 01:08:56.370 but that's right off the top of my head. 01:08:56.370 --> 01:08:56.939 AUDIENCE: How do you-- 01:08:56.939 --> 01:08:58.265 [INAUDIBLE] in the same way? 01:08:58.265 --> 01:08:59.640 Because that's how-- like, that's 01:08:59.640 --> 01:09:00.960 something you estimated somewhere else? 01:09:00.960 --> 01:09:01.979 PROFESSOR: Hopefully, we just drew it 01:09:01.979 --> 01:09:03.810 from this literature, the [INAUDIBLE] paper 01:09:03.810 --> 01:09:06.750 that you've seen before. 01:09:06.750 --> 01:09:08.399 I don't think we matched it with data. 01:09:10.920 --> 01:09:13.522 That's actually hard to pin down in the data. 01:09:13.522 --> 01:09:14.939 AUDIENCE: Would it be particularly 01:09:14.939 --> 01:09:17.010 sensitive to the choice of lambda? 01:09:17.010 --> 01:09:18.660 PROFESSOR: Oh, yeah. 01:09:18.660 --> 01:09:19.160 Yeah. 01:09:19.160 --> 01:09:23.020 Remember the [INAUDIBLE] thing, where lambda goes from-- 01:09:23.020 --> 01:09:25.990 I think it was 1.3 to infinity. 01:09:25.990 --> 01:09:28.410 And at infinity, there's no constraint at all. 01:09:28.410 --> 01:09:30.870 You're back to the full insurance, full productivity 01:09:30.870 --> 01:09:31.392 solution. 01:09:31.392 --> 01:09:32.934 AUDIENCE: So some of the things about 01:09:32.934 --> 01:09:36.647 like the wealth inequality and [INAUDIBLE] 01:09:36.647 --> 01:09:37.689 expect those [INAUDIBLE]. 01:09:37.689 --> 01:09:38.800 PROFESSOR: Oh, yeah. 01:09:38.800 --> 01:09:41.590 Now what-- so what you're not seeing here, 01:09:41.590 --> 01:09:44.380 although we have recently done this, 01:09:44.380 --> 01:09:49.450 is run a whole suite of computations for all kinds 01:09:49.450 --> 01:09:53.170 of-- we put sort of error bands or confidence intervals 01:09:53.170 --> 01:09:59.100 on these just to make sure that none of the things that we want 01:09:59.100 --> 01:10:01.320 to emphasize are-- 01:10:01.320 --> 01:10:03.110 either they're always true, or we're 01:10:03.110 --> 01:10:04.660 going to say they're not always true, 01:10:04.660 --> 01:10:06.700 and they depend on these parameters. 01:10:06.700 --> 01:10:09.060 But you're absolutely right, that just showing 01:10:09.060 --> 01:10:12.710 some simulations looks a bit arbitrary, especially 01:10:12.710 --> 01:10:14.460 for things like lambda, which I'm not even 01:10:14.460 --> 01:10:15.752 remembering in the moment, so-- 01:10:18.680 --> 01:10:20.930 But different things would matter for the moral hazard 01:10:20.930 --> 01:10:21.710 economy too. 01:10:21.710 --> 01:10:25.190 You know, the curvature of the labor supply utility 01:10:25.190 --> 01:10:26.690 that generates labor supply-- 01:10:26.690 --> 01:10:29.630 that's going to be that Frisch elasticity. 01:10:29.630 --> 01:10:33.500 We'll see that again when we get to the sort of microdata. 01:10:33.500 --> 01:10:36.660 And the chi that sits in front of labor disutility-- 01:10:36.660 --> 01:10:41.450 that's a huge number, important number. 01:10:41.450 --> 01:10:44.130 So what are these slides? 01:10:44.130 --> 01:10:45.960 Well, this is a bit like generating 01:10:45.960 --> 01:10:47.670 many, many, many simulations. 01:10:47.670 --> 01:10:52.290 We have many economies, and we vary this fraction 01:10:52.290 --> 01:10:53.460 from zero to one. 01:10:53.460 --> 01:10:56.580 I just showed you the 50-50 economy. 01:10:56.580 --> 01:11:00.510 But this allows everyone to be in limited commitment, everyone 01:11:00.510 --> 01:11:04.500 to be in moral hazard, or anywhere in between. 01:11:04.500 --> 01:11:08.100 And so you can see how GDP, TFP, and so on, 01:11:08.100 --> 01:11:13.380 how these things move around with the fraction 01:11:13.380 --> 01:11:17.760 of the population that are subject to moral hazard. 01:11:17.760 --> 01:11:24.450 And partly, this mirrors what I said before in labor supply. 01:11:24.450 --> 01:11:27.180 But on other things, you can see things aren't monotone. 01:11:27.180 --> 01:11:29.330 So those are surprises. 01:11:29.330 --> 01:11:32.880 It actually goes up, and then it comes down again. 01:11:32.880 --> 01:11:35.190 GDP climbs pretty fast, and then it kind of 01:11:35.190 --> 01:11:37.390 wobbles around a bit. 01:11:37.390 --> 01:11:39.960 We've tried to get rid of as many wobbles as possible 01:11:39.960 --> 01:11:42.900 for worries that they're just computational. 01:11:42.900 --> 01:11:45.480 But some of them-- some of the wobbles remain. 01:11:45.480 --> 01:11:48.970 So I've kind of learned to not pay too much attention. 01:11:48.970 --> 01:11:52.620 But this is not numerical error. 01:11:52.620 --> 01:11:56.952 This is systematic, this drop. 01:11:56.952 --> 01:11:59.160 And you can see what the wages and the interest rates 01:11:59.160 --> 01:12:04.390 are doing, and the fraction of firms for that matter. 01:12:04.390 --> 01:12:11.880 Now, here is back to 1/2, 1/2. 01:12:11.880 --> 01:12:17.370 But within that economy, we have the limited commitment sector 01:12:17.370 --> 01:12:18.540 and the moral hazard sector. 01:12:22.230 --> 01:12:25.680 Obviously, there is economy-wide wages 01:12:25.680 --> 01:12:28.170 and interest rates are the same. 01:12:28.170 --> 01:12:30.760 It's one economy clearing. 01:12:30.760 --> 01:12:33.910 But other things vary across these two columns. 01:12:33.910 --> 01:12:35.740 Probably the most exciting part is 01:12:35.740 --> 01:12:40.610 this, which is let's look at labor, for example. 01:12:40.610 --> 01:12:44.030 Labor employed in the sector 0.38; 01:12:44.030 --> 01:12:47.650 labor supplied by the sector 0.53. 01:12:47.650 --> 01:12:50.500 So they're exporters of labor, if you allow 01:12:50.500 --> 01:12:53.270 me to use that terminology. 01:12:53.270 --> 01:12:56.090 Or people are costlessly migrating 01:12:56.090 --> 01:13:00.260 from the limited commitment sector to the moral hazard. 01:13:00.260 --> 01:13:03.500 And indeed, you'll pick up the other side of this thing, 01:13:03.500 --> 01:13:09.560 basically, labor supplied, 0.46; labor demanded, 0.61. 01:13:09.560 --> 01:13:12.230 Now, you say, why isn't it all zero and one. 01:13:12.230 --> 01:13:13.190 No, no, no, no, no. 01:13:13.190 --> 01:13:14.420 Remember the z's. 01:13:14.420 --> 01:13:15.680 Remember the productivity. 01:13:15.680 --> 01:13:19.400 Every sector has some really inefficient people 01:13:19.400 --> 01:13:21.380 who shouldn't be running firms. 01:13:21.380 --> 01:13:24.350 They're going to supply labor no matter what, 01:13:24.350 --> 01:13:28.880 or at least at these equilibrium wages. 01:13:28.880 --> 01:13:32.210 And the same thing is true with the flow of funds 01:13:32.210 --> 01:13:35.160 if we had the geographic decomposition-- 01:13:35.160 --> 01:13:37.790 and we got Mexico to do this, by the way. 01:13:37.790 --> 01:13:41.180 It's really cool-- through their CNBV, 01:13:41.180 --> 01:13:42.530 their financial regulator. 01:13:47.003 --> 01:13:48.670 And the question there, is money flowing 01:13:48.670 --> 01:13:51.250 to Mexico City from the outlying areas 01:13:51.250 --> 01:13:53.140 as they improve intermediation? 01:13:53.140 --> 01:13:57.040 And what consequences does that have for the outlying areas? 01:13:57.040 --> 01:14:01.540 Well, here, money's flowing from the limited commitments sector 01:14:01.540 --> 01:14:02.840 to the moral hazard sector. 01:14:02.840 --> 01:14:05.650 So this is where the action is, so to speak. 01:14:05.650 --> 01:14:07.390 We don't get a lot of difference in terms 01:14:07.390 --> 01:14:09.700 of the numbers of firms, but you're clearly 01:14:09.700 --> 01:14:12.920 seeing differences in terms of employment size. 01:14:12.920 --> 01:14:18.030 And remember the China paper was like this, where labor-- 01:14:18.030 --> 01:14:22.320 the allocation of labor was playing a huge role. 01:14:39.240 --> 01:14:41.720 So-- sorry. 01:14:41.720 --> 01:14:43.805 We had more results over the weekend. 01:14:48.270 --> 01:14:49.682 We've been trying to get these. 01:14:49.682 --> 01:14:51.390 This, we've had for a long time, but I'll 01:14:51.390 --> 01:14:54.885 show you something else that's quite exciting. 01:14:57.580 --> 01:14:59.300 First of all, speed of transitions-- 01:14:59.300 --> 01:15:05.370 you remember that where a paper was all about the puzzle, which 01:15:05.370 --> 01:15:08.220 is, why don't miracle Asian economies grow 01:15:08.220 --> 01:15:17.220 even faster if you believe they were in a solo world? 01:15:17.220 --> 01:15:18.360 But of course, there is-- 01:15:18.360 --> 01:15:20.290 and then they assume this financial friction, 01:15:20.290 --> 01:15:22.850 which slowed things down. 01:15:22.850 --> 01:15:26.450 The friction you assume is going to make a big difference 01:15:26.450 --> 01:15:28.340 to the speed of transitions. 01:15:28.340 --> 01:15:30.560 Actually, here, even in the steady state, 01:15:30.560 --> 01:15:34.700 you can ask how fast do people go from an az state 01:15:34.700 --> 01:15:37.250 to an a prime z prime state? 01:15:37.250 --> 01:15:40.220 This is some big Markov matrix. 01:15:40.220 --> 01:15:42.230 It's sort of steady state, but it's 01:15:42.230 --> 01:15:45.680 transition from the household level 01:15:45.680 --> 01:15:48.240 from one state to another. 01:15:48.240 --> 01:15:53.540 So you may remember, you can multiply those matrices 01:15:53.540 --> 01:15:57.530 over and over times each other, or you can basically 01:15:57.530 --> 01:16:02.240 do an eigenvalue, eigenvector decomposition. 01:16:02.240 --> 01:16:07.970 And the closer this eigenvalue is to 1, the slower things are. 01:16:07.970 --> 01:16:09.080 Things are moving. 01:16:09.080 --> 01:16:11.120 The next multiplication of the matrix 01:16:11.120 --> 01:16:13.280 is kind of very similar-- 01:16:13.280 --> 01:16:16.190 that times the control [INAUDIBLE] 01:16:16.190 --> 01:16:18.840 very similar to what is in the previous period. 01:16:18.840 --> 01:16:21.650 It's 0.93. 01:16:21.650 --> 01:16:24.260 That's a very-- sorry. 01:16:24.260 --> 01:16:29.060 0.93 for one; 0.98 for the other. 01:16:29.060 --> 01:16:34.070 So there's roughly three more times difference 01:16:34.070 --> 01:16:38.090 in the speed of sort of within steady state transitions going 01:16:38.090 --> 01:16:38.970 on. 01:16:38.970 --> 01:16:43.123 Now, there's a technical reason for this, basically. 01:16:43.123 --> 01:16:44.540 On the limited commitment thing we 01:16:44.540 --> 01:16:46.880 have this forward-looking savings behavior. 01:16:46.880 --> 01:16:48.710 So people are going to save their way out 01:16:48.710 --> 01:16:50.210 of these constraints. 01:16:50.210 --> 01:16:52.370 And they can do it pretty quickly. 01:16:52.370 --> 01:16:55.220 In fact, you're going to see some homework 01:16:55.220 --> 01:17:03.080 and so on motivated by thinking through [INAUDIBLE] papers 01:17:03.080 --> 01:17:05.270 and job market papers and so on, that kind of 01:17:05.270 --> 01:17:12.380 get at this thing, which is-- the twist is, why doesn't money 01:17:12.380 --> 01:17:13.610 flow from the US to India? 01:17:13.610 --> 01:17:17.600 The answer is, why doesn't India save its way out of constraints 01:17:17.600 --> 01:17:19.730 more quickly than they seem to? 01:17:23.320 --> 01:17:26.290 So there's discussion in the literature about that. 01:17:26.290 --> 01:17:27.820 But here we do have constraints. 01:17:27.820 --> 01:17:29.890 It's just that they're different. 01:17:29.890 --> 01:17:32.470 And essentially what's going on here 01:17:32.470 --> 01:17:35.830 is you don't want to-- you don't want that wealth 01:17:35.830 --> 01:17:38.280 to move very fast. 01:17:38.280 --> 01:17:41.880 You're going to balance off the incentives 01:17:41.880 --> 01:17:46.170 of today's consumption versus tomorrow's wealth. 01:17:46.170 --> 01:17:48.570 Wealth moves a little bit, depending on whether epsilon 01:17:48.570 --> 01:17:49.230 is high or low. 01:17:49.230 --> 01:17:50.980 But you don't want it to move a whole lot. 01:17:50.980 --> 01:17:55.350 Because if it did, that's the bulk of your utility. 01:17:55.350 --> 01:17:56.430 That's kind of very-- 01:17:56.430 --> 01:17:59.640 that is actually algebraically in the dual, 01:17:59.640 --> 01:18:02.940 equivalent with discounted expected utility. 01:18:02.940 --> 01:18:05.820 And so if you move that quickly, there's 01:18:05.820 --> 01:18:09.030 a big welfare loss because of the concavity. 01:18:09.030 --> 01:18:11.530 So you don't want it to move fast. 01:18:11.530 --> 01:18:14.700 So that's-- OK. 01:18:14.700 --> 01:18:18.720 And then if you look at growth rates, 01:18:18.720 --> 01:18:22.890 the distribution of growth rates looks like it's all zero. 01:18:22.890 --> 01:18:24.930 Actually, what the point is, the growth rates 01:18:24.930 --> 01:18:26.610 are all very similar to each other, 01:18:26.610 --> 01:18:29.280 because there isn't this big dispersion in the moral hazard 01:18:29.280 --> 01:18:32.663 economy because they don't move that fast. 01:18:32.663 --> 01:18:34.080 But a limited commitment-- there's 01:18:34.080 --> 01:18:36.890 a huge dispersion and growth rate. 01:18:36.890 --> 01:18:40.290 And you can imagine going to data with this kind of stuff. 01:18:40.290 --> 01:18:42.670 We didn't look at this in the data. 01:18:42.670 --> 01:18:44.970 But if you have Panel, you can actually 01:18:44.970 --> 01:18:47.730 look not only at static firm size distributions 01:18:47.730 --> 01:18:52.650 but the growth rates, and get some evidence one way 01:18:52.650 --> 01:18:56.170 or the other for one friction. 01:18:56.170 --> 01:18:57.540 Then finally, the weekend-- 01:19:00.090 --> 01:19:02.880 we've had a devil of a time doing this. 01:19:02.880 --> 01:19:05.250 We start with the whole economy subject 01:19:05.250 --> 01:19:07.590 to a limited commitment constraint. 01:19:07.590 --> 01:19:12.450 And then we move it after 10 periods to a moral hazard. 01:19:12.450 --> 01:19:15.420 Now, this is meant to mimic some kind of financial reform. 01:19:15.420 --> 01:19:17.430 If you want to be generous with me, 01:19:17.430 --> 01:19:19.650 think of there being two constraints, 01:19:19.650 --> 01:19:22.170 and then we managed to get rid of one. 01:19:22.170 --> 01:19:23.880 Well, that's not quite what we're doing. 01:19:23.880 --> 01:19:27.220 We kind of substitute limited commitment for moral hazard. 01:19:27.220 --> 01:19:31.530 Basically, they get rid of a constraining legal system, 01:19:31.530 --> 01:19:34.710 but it turns out that created an information problem. 01:19:34.710 --> 01:19:38.570 Anyway, what's really important is 01:19:38.570 --> 01:19:43.740 that you can actually look at the transition dynamics. 01:19:48.100 --> 01:19:51.130 Well, let me just show you the pictures. 01:19:51.130 --> 01:19:55.770 So here, you're in a steady state, 01:19:55.770 --> 01:19:58.230 and then you do a financial reform. 01:19:58.230 --> 01:19:59.280 But it's not like-- 01:20:01.830 --> 01:20:06.060 Kaboski, they did-- they left the financial structure intact, 01:20:06.060 --> 01:20:09.300 and they did real reforms. 01:20:09.300 --> 01:20:11.970 We're leaving everything in the environment and everything 01:20:11.970 --> 01:20:14.790 intact and doing a financial reform, hopefully 01:20:14.790 --> 01:20:18.150 in the spirit of what Levine might have wanted. 01:20:18.150 --> 01:20:22.260 And then we see the implications of that for deeper levels 01:20:22.260 --> 01:20:23.460 of capitalization. 01:20:23.460 --> 01:20:25.085 And look at the wage rate. 01:20:25.085 --> 01:20:29.310 It's no wonder we had trouble finding it-- 01:20:29.310 --> 01:20:31.710 pops up like that instantaneously. 01:20:34.360 --> 01:20:38.320 Now, let me just say, these things are not easy to compute. 01:20:38.320 --> 01:20:41.380 Because-- and [INAUDIBLE] going to say something 01:20:41.380 --> 01:20:43.480 about that on Friday. 01:20:43.480 --> 01:20:46.030 Essentially, when you're out of the steady state, 01:20:46.030 --> 01:20:49.610 you have to figure out where you're going to go 01:20:49.610 --> 01:20:52.090 and how long it's going to take to get there. 01:20:52.090 --> 01:20:54.280 And then you've got to sort of guess 01:20:54.280 --> 01:20:57.700 about the paths of the interest rates and the wages. 01:20:57.700 --> 01:21:00.730 And of course, any arbitrary guess about how long it takes 01:21:00.730 --> 01:21:03.550 and how they're going to move is quite arbitrary. 01:21:03.550 --> 01:21:05.830 So then you need a systematic way 01:21:05.830 --> 01:21:09.460 to adjust period by period to get in new guesses 01:21:09.460 --> 01:21:12.310 and then to try to iterate. 01:21:12.310 --> 01:21:16.450 [INAUDIBLE] managed to do it. 01:21:16.450 --> 01:21:17.320 We have their code. 01:21:17.320 --> 01:21:19.630 I've shared it with many students 01:21:19.630 --> 01:21:21.850 working on related problems. 01:21:21.850 --> 01:21:25.180 It turned out that their code, which 01:21:25.180 --> 01:21:28.090 is getting better and better because they're playing around 01:21:28.090 --> 01:21:33.030 with it too, works for this limited commitment better-- 01:21:33.030 --> 01:21:34.890 much, much better than it ever worked for us 01:21:34.890 --> 01:21:35.700 with moral hazard. 01:21:35.700 --> 01:21:37.890 We were quite despondent about being 01:21:37.890 --> 01:21:40.620 able to compute the transitions. 01:21:40.620 --> 01:21:43.860 But evidently, now we're in business on that. 01:21:51.220 --> 01:21:55.210 So the summary, back to the big picture, 01:21:55.210 --> 01:21:58.060 is these obstacles matter. 01:21:58.060 --> 01:22:01.330 Ideally, I think, if you can manage, 01:22:01.330 --> 01:22:05.740 use the microdata to try to take a stand on what 01:22:05.740 --> 01:22:07.330 the frictions are. 01:22:07.330 --> 01:22:12.820 And through the dynamics of Euler equations and debt 01:22:12.820 --> 01:22:17.930 overhang and so on, you'll generate these dynamic paths. 01:22:17.930 --> 01:22:21.040 And they're going to be general equilibrium 01:22:21.040 --> 01:22:23.680 consequences of the reforms. 01:22:23.680 --> 01:22:28.360 So it's like doing an experiment on these economies 01:22:28.360 --> 01:22:29.870 to see what would happen. 01:22:29.870 --> 01:22:34.120 And this allows us to begin to answer those questions I went 01:22:34.120 --> 01:22:37.870 over in the introductory lecture about finance causes growth, 01:22:37.870 --> 01:22:38.830 but then what? 01:22:38.830 --> 01:22:40.330 What do we do? 01:22:40.330 --> 01:22:43.450 And how do we engineer it? 01:22:43.450 --> 01:22:45.110 And what would we expect to happen? 01:22:45.110 --> 01:22:48.420 And are there winners and losers, and so on?