WEBVTT 00:00:00.090 --> 00:00:02.430 The following content is provided under a Creative 00:00:02.430 --> 00:00:03.820 Commons license. 00:00:03.820 --> 00:00:06.030 Your support will help MIT OpenCourseWare 00:00:06.030 --> 00:00:10.120 continue to offer high quality educational resources for free. 00:00:10.120 --> 00:00:12.660 To make a donation or to view additional materials 00:00:12.660 --> 00:00:16.620 from hundreds of MIT courses, visit MIT OpenCourseWare 00:00:16.620 --> 00:00:17.992 at ocw.mit.edu. 00:00:21.220 --> 00:00:22.470 WILLIAM BONVILLIAN: All right. 00:00:22.470 --> 00:00:25.230 Let's dive right into education. 00:00:25.230 --> 00:00:29.580 And, you know, this is the other side of the innovation 00:00:29.580 --> 00:00:30.620 equation, right? 00:00:30.620 --> 00:00:33.870 We've talked in terms of institutions 00:00:33.870 --> 00:00:36.150 and linking and connecting institutions, 00:00:36.150 --> 00:00:39.810 and we've talked a lot about R&D and the R&D system, 00:00:39.810 --> 00:00:42.060 but you know, from the first class on, 00:00:42.060 --> 00:00:43.950 Romer taught us that the talent base 00:00:43.950 --> 00:00:47.070 is a very critical consideration in innovation. 00:00:47.070 --> 00:00:49.440 And how do you build up the talent base? 00:00:49.440 --> 00:00:54.090 That's essentially our set of tests today. 00:00:54.090 --> 00:00:55.500 So, first, Norm Augustine. 00:00:55.500 --> 00:00:58.440 I wanted to acquaint you with Norm Augustine, 00:00:58.440 --> 00:01:01.560 because in the science and technology community, 00:01:01.560 --> 00:01:04.170 he's known as St. Augustine. 00:01:04.170 --> 00:01:09.030 And he is just an incredible stand-up figure 00:01:09.030 --> 00:01:13.350 on issues like the importance of federal R&D investment. 00:01:13.350 --> 00:01:15.210 He was chairman for a lengthy period 00:01:15.210 --> 00:01:18.930 of time of Lockheed Martin. 00:01:18.930 --> 00:01:24.390 He won the President's Medal of Technology. 00:01:24.390 --> 00:01:30.090 Just a noted innovator, himself, but also a true expert 00:01:30.090 --> 00:01:32.700 on R&D issues, R&D policies. 00:01:32.700 --> 00:01:37.740 Unfailingly helpful and willing to volunteer for whatever task 00:01:37.740 --> 00:01:40.920 the National Academies or in many cases MIT, 00:01:40.920 --> 00:01:44.970 or other organizations kind of need help and advice 00:01:44.970 --> 00:01:47.360 from a real senior statesman, Augustine 00:01:47.360 --> 00:01:49.360 has always been willing to step up to the plate. 00:01:49.360 --> 00:01:51.360 So he's kind of a remarkable figure. 00:01:51.360 --> 00:01:57.300 He led the rising against the Gathering Storm report 00:01:57.300 --> 00:02:02.780 back in 2000-- 00:02:02.780 --> 00:02:06.300 around the early 2000s timetable-- 00:02:06.300 --> 00:02:07.830 along with people like Chuck Vest, 00:02:07.830 --> 00:02:10.080 and a really noted community of other experts, 00:02:10.080 --> 00:02:14.760 that made the argument for a very significant R&D increase 00:02:14.760 --> 00:02:16.710 for the physical science agencies. 00:02:16.710 --> 00:02:20.640 Around that time, we had been doubling NIH. 00:02:20.640 --> 00:02:22.830 This report made the case for doubling 00:02:22.830 --> 00:02:27.010 the physical science-based R&D areas, as well. 00:02:27.010 --> 00:02:29.820 And it was a very influential and important report. 00:02:29.820 --> 00:02:32.160 He was one of the real leaders of it, 00:02:32.160 --> 00:02:34.530 and he played an important role, along with Chuck Vest, 00:02:34.530 --> 00:02:42.050 in helping persuade the latest Bush administration-- 00:02:42.050 --> 00:02:45.400 hi, Karen-- to adopt its recommendations. 00:02:45.400 --> 00:02:50.350 So there was a period of time of ongoing R&D 00:02:50.350 --> 00:02:54.950 increases for the physical science agencies. 00:02:54.950 --> 00:02:57.760 So the report had a result. 00:02:57.760 --> 00:03:03.180 In addition, he came back and wrote this 2007 report 00:03:03.180 --> 00:03:07.000 that just kind of summarized the trouble 00:03:07.000 --> 00:03:10.930 the US has got on the Science and Technology education front. 00:03:10.930 --> 00:03:14.080 So it's a 2007 snapshot, but it's still 00:03:14.080 --> 00:03:17.137 a great little collection of key points. 00:03:17.137 --> 00:03:18.970 I'm just going to summarize it very briefly. 00:03:21.720 --> 00:03:25.690 Key finding number one was that US children are not 00:03:25.690 --> 00:03:29.570 prepared for the 21st century jobs, 00:03:29.570 --> 00:03:32.760 and he has data sets, as you know, 00:03:32.760 --> 00:03:36.840 to back all these points up. 00:03:36.840 --> 00:03:38.670 Children in school are being taught 00:03:38.670 --> 00:03:42.600 by teachers that are not trained in the fields of math 00:03:42.600 --> 00:03:44.220 and science that they're teaching in, 00:03:44.220 --> 00:03:48.300 which is a major underlying problem in those students 00:03:48.300 --> 00:03:51.390 picking up those fields. 00:03:51.390 --> 00:03:54.960 US children are falling behind their foreign peers 00:03:54.960 --> 00:03:58.530 and counterparts in science and technology areas. 00:03:58.530 --> 00:04:00.780 The US K through 12 system just is not 00:04:00.780 --> 00:04:06.000 performing in a way that's comparable to many other 00:04:06.000 --> 00:04:08.650 systems in the world. 00:04:08.650 --> 00:04:13.500 And then, kind of a fourth point is that US secondary education 00:04:13.500 --> 00:04:16.157 isn't preparing students-- 00:04:16.157 --> 00:04:17.490 aside from the teacher problem-- 00:04:17.490 --> 00:04:19.860 for math, science, or engineering majors, 00:04:19.860 --> 00:04:22.170 and too few students-- 00:04:22.170 --> 00:04:23.310 this is the key point-- 00:04:23.310 --> 00:04:25.890 are majoring in those disciplines 00:04:25.890 --> 00:04:30.120 to yield the talent base that we need 00:04:30.120 --> 00:04:32.305 for technology-related careers. 00:04:35.400 --> 00:04:37.650 And just to underscore that, the US 00:04:37.650 --> 00:04:40.710 ranks 17th among developed nations in the proportion 00:04:40.710 --> 00:04:42.990 of college students receiving degrees in science 00:04:42.990 --> 00:04:48.040 and engineering, and it fell from third place 00:04:48.040 --> 00:04:49.500 30 years before. 00:04:49.500 --> 00:04:56.730 So these are dilemmas that the US K through 12 system has. 00:04:56.730 --> 00:04:58.890 So that's the K through 12 story. 00:04:58.890 --> 00:05:02.250 Then we shift to our friend Paul Romer, 00:05:02.250 --> 00:05:07.710 who we visited at the outset of this class. 00:05:07.710 --> 00:05:12.360 And as you know, he taught at Stanford, 00:05:12.360 --> 00:05:14.250 and then has been teaching at NYU. 00:05:14.250 --> 00:05:17.700 He's now chief economist to the World Bank, 00:05:17.700 --> 00:05:23.850 where he's been a fascinating critic of economics and changes 00:05:23.850 --> 00:05:28.140 and reforms that field needs to go through. 00:05:28.140 --> 00:05:32.400 But he came up with prospector theory, and in a way, 00:05:32.400 --> 00:05:37.960 what he's playing out here in this pretty noted critique from 00:05:37.960 --> 00:05:41.790 2000-- which is still quite widely read-- 00:05:41.790 --> 00:05:46.470 his critique is, what happened to the supply? 00:05:46.470 --> 00:05:48.480 What are our supply breakdowns? 00:05:48.480 --> 00:05:52.440 And then he's identified those supply breakdowns 00:05:52.440 --> 00:05:55.230 with higher education institutions. 00:05:55.230 --> 00:05:57.490 What are they failing to deliver? 00:05:57.490 --> 00:06:02.040 So the issue is that the federal government 00:06:02.040 --> 00:06:03.990 policies in science and technology 00:06:03.990 --> 00:06:07.470 tend to subsidize demand. 00:06:07.470 --> 00:06:10.710 So in the private sector, it's primarily tax incentives, 00:06:10.710 --> 00:06:11.880 R&D tax credits. 00:06:14.490 --> 00:06:17.700 And you promote demand to encourage 00:06:17.700 --> 00:06:19.230 science and engineering talent. 00:06:22.380 --> 00:06:26.070 That policy approach, which is astronomically expensive-- 00:06:26.070 --> 00:06:29.940 those are very expensive policies-- 00:06:29.940 --> 00:06:32.610 that policy approach doesn't inquire 00:06:32.610 --> 00:06:38.640 about the supply response that hopefully those subsidies 00:06:38.640 --> 00:06:39.510 elicit. 00:06:39.510 --> 00:06:43.860 And he goes back and argues that the institutional arrangements 00:06:43.860 --> 00:06:48.210 in universities, which are the key institutions on the supply 00:06:48.210 --> 00:06:55.270 side, are geared to meeting the needs in a variety of ways. 00:06:55.270 --> 00:06:57.270 So there needs to be a new incentive system 00:06:57.270 --> 00:07:00.570 to start to turn around the supply side 00:07:00.570 --> 00:07:04.890 because the current demand-based tax incentive system just 00:07:04.890 --> 00:07:06.720 isn't doing that. 00:07:06.720 --> 00:07:09.480 So that's his overall frame. 00:07:09.480 --> 00:07:13.140 In the 20th century, he argues, rapid technological progress 00:07:13.140 --> 00:07:16.690 drove unprecedented growth. 00:07:16.690 --> 00:07:19.170 We know that argument. 00:07:19.170 --> 00:07:22.740 And that was fostered by a publicly supported system 00:07:22.740 --> 00:07:25.170 of education. 00:07:25.170 --> 00:07:28.410 So a steady flow of trained talent, 00:07:28.410 --> 00:07:29.850 trained in the scientific method, 00:07:29.850 --> 00:07:33.540 was a core policy that evolved over time, 00:07:33.540 --> 00:07:40.800 but that public policy approach ignored the structures that 00:07:40.800 --> 00:07:43.710 were supposed to deliver that talent base, 00:07:43.710 --> 00:07:45.690 i.e. the higher education system, 00:07:45.690 --> 00:07:48.060 and the incentives and disincentives 00:07:48.060 --> 00:07:51.510 within that higher education structure that make that supply 00:07:51.510 --> 00:07:53.250 side problematic. 00:07:53.250 --> 00:07:55.620 So he would argue that a governmental-- set 00:07:55.620 --> 00:08:00.180 of governmental programs to speed up innovation 00:08:00.180 --> 00:08:04.860 gets thwarted by the supply side problem, 00:08:04.860 --> 00:08:07.770 in particular by the higher education structure. 00:08:07.770 --> 00:08:12.690 So government programs focused on the demand and not 00:08:12.690 --> 00:08:17.010 the supply side are going to undermine the innovation 00:08:17.010 --> 00:08:18.300 capabilities that we need. 00:08:21.210 --> 00:08:25.230 Then he steps back and kind of makes the innovation argument 00:08:25.230 --> 00:08:26.250 for us. 00:08:26.250 --> 00:08:28.323 He argues that speeding up growth 00:08:28.323 --> 00:08:29.740 is really the only way we're going 00:08:29.740 --> 00:08:33.720 to be able to cope with the oncoming demographics 00:08:33.720 --> 00:08:36.990 that we talked about when we discuss the health care 00:08:36.990 --> 00:08:39.450 innovation system. 00:08:39.450 --> 00:08:43.289 He says that a conservative estimate of the return on R&D 00:08:43.289 --> 00:08:47.280 spending would be a 25% return. 00:08:47.280 --> 00:08:50.670 There's arguments that that's low, that it's well over 50%, 00:08:50.670 --> 00:08:53.910 but let's accept his 25%. 00:08:53.910 --> 00:08:55.320 His argument is that if you-- 00:08:55.320 --> 00:08:57.510 in theory, that if you increased R&D spending 00:08:57.510 --> 00:09:04.530 by 2% of GDP, voila, we'd get a half percentage 00:09:04.530 --> 00:09:05.650 point of growth. 00:09:05.650 --> 00:09:06.150 Right? 00:09:06.150 --> 00:09:09.660 In an economy that has 0.7% growth at the moment, 00:09:09.660 --> 00:09:11.610 this is not a minor concept. 00:09:11.610 --> 00:09:12.660 Right? 00:09:12.660 --> 00:09:14.660 And the data would tend to-- 00:09:14.660 --> 00:09:17.100 it at least in theory-- bear him out. 00:09:17.100 --> 00:09:22.290 But then you run into the barrier, right? 00:09:22.290 --> 00:09:24.490 You have to look at the full system, 00:09:24.490 --> 00:09:27.310 and that means also looking at the talent. 00:09:27.310 --> 00:09:33.240 So just increasing the R&D spending per se 00:09:33.240 --> 00:09:39.230 doesn't address what he calls the supply side, 00:09:39.230 --> 00:09:42.770 and if the total number of scientists and engineers 00:09:42.770 --> 00:09:46.130 is fixed, then you limit-- 00:09:46.130 --> 00:09:47.770 and this is pure prospector's theory, 00:09:47.770 --> 00:09:50.900 as you remember from our first class-- 00:09:50.900 --> 00:09:54.050 then you limit the biggest input into innovation, and thus 00:09:54.050 --> 00:09:56.600 into growth. 00:09:56.600 --> 00:10:00.680 So by not expanding its supply of scientists and engineers, 00:10:00.680 --> 00:10:05.810 i.e. the talent base, then the US 00:10:05.810 --> 00:10:09.350 is limiting its growth capacity by a pretty significant amount, 00:10:09.350 --> 00:10:12.060 he argues, in economic terms. 00:10:12.060 --> 00:10:14.630 So what's broken down? 00:10:14.630 --> 00:10:16.940 What what's the heart of the problem, here? 00:10:16.940 --> 00:10:24.860 And this is a deep critique of higher education, which 00:10:24.860 --> 00:10:28.460 was highly controversial when this came out and occasioned 00:10:28.460 --> 00:10:33.920 a lot of criticism of Romer, but it's hard to dispute his data, 00:10:33.920 --> 00:10:39.170 and you will probably see this from your own experiences 00:10:39.170 --> 00:10:43.250 or the experiences of colleagues in other universities. 00:10:43.250 --> 00:10:48.020 So what's broken down on the talent supply side? 00:10:48.020 --> 00:10:50.780 He argues that universities measure themselves 00:10:50.780 --> 00:10:56.025 by their ability to select top SAT scoring students. 00:10:56.025 --> 00:10:57.650 In other words, they measure themselves 00:10:57.650 --> 00:11:00.560 by the quality input they're able to attract, 00:11:00.560 --> 00:11:03.380 not by their output. 00:11:03.380 --> 00:11:05.965 There's no output measures in this system. 00:11:05.965 --> 00:11:07.340 They're competing with each other 00:11:07.340 --> 00:11:09.710 in things like US News and World Report 00:11:09.710 --> 00:11:15.800 on the students they attract, not on the student outcomes 00:11:15.800 --> 00:11:17.520 that they're creating. 00:11:17.520 --> 00:11:20.630 So the traditional liberal arts university, he argues, 00:11:20.630 --> 00:11:24.980 faces little pressure to respond to skills needs. 00:11:24.980 --> 00:11:28.280 That economic messaging that's inherent in the system 00:11:28.280 --> 00:11:33.580 doesn't get translated back to the higher education system. 00:11:33.580 --> 00:11:39.170 So, meanwhile, the university has a fixed investment 00:11:39.170 --> 00:11:43.050 in its faculty that are teaching in many areas-- 00:11:43.050 --> 00:11:46.310 including the sciences, but obviously outside the sciences. 00:11:46.310 --> 00:11:49.460 There is internal pressure to maintain 00:11:49.460 --> 00:11:52.580 the relative size of those departments, which are-- 00:11:52.580 --> 00:11:54.710 they have invested in. 00:11:54.710 --> 00:11:57.572 And that, in turn-- 00:11:57.572 --> 00:11:59.030 and we'll discuss why in a minute-- 00:11:59.030 --> 00:12:01.700 but makes it more difficult for students 00:12:01.700 --> 00:12:04.970 to get science degrees. 00:12:04.970 --> 00:12:07.460 So the science faculties, he argues, 00:12:07.460 --> 00:12:10.610 are happy to do what they call "maintain 00:12:10.610 --> 00:12:20.030 professional standards," i.e. have a lot of lower grades. 00:12:20.030 --> 00:12:24.050 And effectively what that does is force out of the system 00:12:24.050 --> 00:12:26.480 a tremendous amount of talent because, again, they've 00:12:26.480 --> 00:12:31.580 got fixed faculty sizes that aren't coordinated with what 00:12:31.580 --> 00:12:33.860 national talent supply and needs may 00:12:33.860 --> 00:12:35.630 be in science and technology. 00:12:35.630 --> 00:12:37.940 There's no pressure on universities 00:12:37.940 --> 00:12:41.870 to address those corresponding faculty 00:12:41.870 --> 00:12:45.530 sizes to what the national need may or may not be, 00:12:45.530 --> 00:12:48.500 and therefore what's happened is essentially 00:12:48.500 --> 00:12:53.390 a bifurcated education system, where sciences and engineering 00:12:53.390 --> 00:12:55.580 students have one grading system-- 00:12:55.580 --> 00:12:57.890 and as you all know, it's a tougher grading system 00:12:57.890 --> 00:13:05.510 at liberal arts universities than other majors 00:13:05.510 --> 00:13:07.730 in the social sciences and humanities. 00:13:07.730 --> 00:13:11.450 And you know, we know that there is significant grade inflation 00:13:11.450 --> 00:13:14.340 in the non-science, non-engineering fields, 00:13:14.340 --> 00:13:18.890 and that there is limited if any grade inflation in the tougher 00:13:18.890 --> 00:13:19.640 technical fields. 00:13:19.640 --> 00:13:22.010 So that's what he's talking about, here. 00:13:22.010 --> 00:13:24.530 We maintain essentially two grading standards, 00:13:24.530 --> 00:13:27.980 the net effect of which is to substantially discourage 00:13:27.980 --> 00:13:29.510 entry of talent. 00:13:29.510 --> 00:13:32.750 Now this is not in this report, but the National Academy 00:13:32.750 --> 00:13:36.590 of Engineering has explored extensively 00:13:36.590 --> 00:13:42.290 whether the people dropping out of majors in engineering 00:13:42.290 --> 00:13:44.870 are stronger or weaker students than those staying in, 00:13:44.870 --> 00:13:47.120 and they can't find any basis for concluding 00:13:47.120 --> 00:13:48.965 that they are any weaker. 00:13:48.965 --> 00:13:50.840 In other words, we're just driving talent out 00:13:50.840 --> 00:13:53.780 of the system through these mechanisms, 00:13:53.780 --> 00:13:55.670 is Romer's argument. 00:13:55.670 --> 00:13:58.850 So the supply problem-- 00:13:58.850 --> 00:14:02.420 by running this bifurcating grading system 00:14:02.420 --> 00:14:05.870 to drive out talent, to maintain class size numbers 00:14:05.870 --> 00:14:09.350 and make sure you don't have to advise too many people-- 00:14:09.350 --> 00:14:14.300 the supply problem, in turn, drives graduate school numbers, 00:14:14.300 --> 00:14:17.820 because of course, undergraduate degrees are prerequisites. 00:14:17.820 --> 00:14:22.040 So how does US industry cope with this? 00:14:22.040 --> 00:14:23.600 Essentially what it's done-- 00:14:23.600 --> 00:14:25.430 and this is not bad-- 00:14:25.430 --> 00:14:28.310 it's encouraged wholesale immigration 00:14:28.310 --> 00:14:31.520 from all over the world to fill the gap, which 00:14:31.520 --> 00:14:33.470 it's done very systematically. 00:14:33.470 --> 00:14:35.030 And that's why, for example, industry 00:14:35.030 --> 00:14:38.720 is so concerned about this H-1B visa set of proposals 00:14:38.720 --> 00:14:41.630 that the new administration has recently proposed. 00:14:41.630 --> 00:14:43.070 What are the ramifications of that 00:14:43.070 --> 00:14:45.710 in terms of access to this talent supply? 00:14:45.710 --> 00:14:48.630 Obviously, worldwide talent, as we've talked about before, 00:14:48.630 --> 00:14:50.390 is a very important competitive advantage 00:14:50.390 --> 00:14:51.800 for the United States. 00:14:51.800 --> 00:14:53.870 But here we're in a circumstance where 00:14:53.870 --> 00:14:59.870 we're not creating parallel opportunities for folks 00:14:59.870 --> 00:15:00.610 in the US. 00:15:04.370 --> 00:15:09.110 Moving on with Romer's indictment of higher education, 00:15:09.110 --> 00:15:12.560 because that's really what it is, 00:15:12.560 --> 00:15:21.770 he argues that PhD programs train graduates 00:15:21.770 --> 00:15:31.960 for the academy, but as all of us in this room know, there's-- 00:15:31.960 --> 00:15:35.650 there's a complete oversupply of PhDs in terms of what 00:15:35.650 --> 00:15:37.090 the academy itself-- 00:15:37.090 --> 00:15:40.300 in other words, university teaching-- actually requires. 00:15:40.300 --> 00:15:44.710 So then we have to invent new mechanisms to keep this talent 00:15:44.710 --> 00:15:45.490 base around. 00:15:45.490 --> 00:15:51.880 So we invent long, endless, seven or eight-year graduate 00:15:51.880 --> 00:15:54.280 programs. 00:15:54.280 --> 00:15:57.520 England somehow manages to get its PhDs done in three years. 00:15:57.520 --> 00:15:58.600 Are they worse? 00:15:58.600 --> 00:15:59.750 I don't know. 00:15:59.750 --> 00:16:02.360 But we have a seven or eight year old graduate-- 00:16:02.360 --> 00:16:04.480 eight year long graduate education program, 00:16:04.480 --> 00:16:07.780 and then we invent this whole class of essentially 00:16:07.780 --> 00:16:11.950 apprentices that we call post-docs-- 00:16:11.950 --> 00:16:14.495 another substantial army of people-- 00:16:14.495 --> 00:16:16.120 because there's no space in the academy 00:16:16.120 --> 00:16:18.130 to accommodate these people. 00:16:18.130 --> 00:16:20.230 And yet the training system is not 00:16:20.230 --> 00:16:22.900 geared to the location where there 00:16:22.900 --> 00:16:26.470 are extensive opportunities-- 00:16:26.470 --> 00:16:28.600 whether in established firms or startups-- which 00:16:28.600 --> 00:16:32.050 is for industry because the training system is really 00:16:32.050 --> 00:16:38.540 geared for entering the academy, not for entering industry. 00:16:38.540 --> 00:16:41.350 And obviously, it tends to focus more on basic than applied 00:16:41.350 --> 00:16:44.540 for obvious reasons. 00:16:44.540 --> 00:16:47.680 So this-- we're just multiplying the problems here, 00:16:47.680 --> 00:16:52.600 because the core input institutions, i.e. higher 00:16:52.600 --> 00:16:56.550 education, are not organized around the skills problem 00:16:56.550 --> 00:16:59.470 society has got. 00:16:59.470 --> 00:17:03.490 That's Romer's indictment. 00:17:03.490 --> 00:17:08.650 I had the privilege of working with him on legislation, 00:17:08.650 --> 00:17:10.900 and when this piece of legislation came out, 00:17:10.900 --> 00:17:14.960 there was considerable interest on Capitol Hill, 00:17:14.960 --> 00:17:18.640 and we're hearing all the time on Capitol Hill in this era 00:17:18.640 --> 00:17:22.000 about, we've got a talent base problem, 00:17:22.000 --> 00:17:26.019 and there was considerable interest in the critiques 00:17:26.019 --> 00:17:28.569 that Romer had made of the system, 00:17:28.569 --> 00:17:30.820 and how do you change the system? 00:17:30.820 --> 00:17:34.600 So, like other senior staffers, I 00:17:34.600 --> 00:17:37.297 read his stuff and thought gee, how are we 00:17:37.297 --> 00:17:38.380 going to turn this around? 00:17:38.380 --> 00:17:42.070 We're going to have to spend a fortune on creating 00:17:42.070 --> 00:17:49.480 a massive new kind of fellowship program across the country, 00:17:49.480 --> 00:17:52.090 on top of what we already have to encourage science 00:17:52.090 --> 00:17:54.180 and engineering education. 00:17:54.180 --> 00:17:57.420 We'll have to significantly increase that-- 00:17:57.420 --> 00:18:01.050 maybe by a factor of two or more. 00:18:01.050 --> 00:18:02.780 How are we going to afford this? 00:18:02.780 --> 00:18:04.590 Where's that money come from? 00:18:04.590 --> 00:18:09.750 So I had a conversation with Romer, and he said, 00:18:09.750 --> 00:18:13.470 Bill, you have to think like an economist here. 00:18:13.470 --> 00:18:16.360 You have to bribe the gatekeepers. 00:18:16.360 --> 00:18:17.790 Right? 00:18:17.790 --> 00:18:19.610 So I-- what are you talking about? 00:18:19.610 --> 00:18:23.340 And he said, look, figure out who the gatekeepers are 00:18:23.340 --> 00:18:24.960 in this entire system and you bribe 00:18:24.960 --> 00:18:28.510 them to make them turn around their behavior. 00:18:28.510 --> 00:18:33.450 So in this case, it's the departments and the colleges 00:18:33.450 --> 00:18:34.950 and universities as a whole-- 00:18:34.950 --> 00:18:37.590 it's their administrations. 00:18:37.590 --> 00:18:40.350 Bribe them to get them to change their numbers 00:18:40.350 --> 00:18:43.140 because if they're not producing enough scientists and engineers 00:18:43.140 --> 00:18:47.230 net, then pay them to do this. 00:18:47.230 --> 00:18:50.040 And by the way, bribery is a heck of a lot 00:18:50.040 --> 00:18:53.370 cheaper than creating a massive new national fellowship, 00:18:53.370 --> 00:18:55.480 and much more efficient. 00:18:55.480 --> 00:18:58.513 So obviously, bribery is not the apt term here, 00:18:58.513 --> 00:19:00.930 but we ended up creating a program at the National Science 00:19:00.930 --> 00:19:04.650 Foundation, which Congress passed, called The Step 00:19:04.650 --> 00:19:06.930 Education Program, which essentially 00:19:06.930 --> 00:19:11.700 offers very significant funding to departments 00:19:11.700 --> 00:19:14.160 that guarantee they're going to turn their numbers around, 00:19:14.160 --> 00:19:18.840 and then present pathways by which they're going to do so. 00:19:18.840 --> 00:19:23.430 In other words, maybe they offer much more one 00:19:23.430 --> 00:19:25.260 on one kind of tutorial attention 00:19:25.260 --> 00:19:28.440 to keep students in science and engineering. 00:19:28.440 --> 00:19:31.180 Maybe they offer fellowships with industry, 00:19:31.180 --> 00:19:32.830 so you're guaranteed summer employment 00:19:32.830 --> 00:19:35.588 in an interesting, relevant, applied field. 00:19:35.588 --> 00:19:37.380 In other words, there may be a whole slew-- 00:19:37.380 --> 00:19:38.460 and there turned out to be a lot-- 00:19:38.460 --> 00:19:40.830 of ideas on how to begin to turn those numbers around. 00:19:40.830 --> 00:19:42.570 AUDIENCE: Are you talking at the undergraduate level? 00:19:42.570 --> 00:19:44.302 WILLIAM BONVILLIAN: I'm talking at the undergraduate level. 00:19:44.302 --> 00:19:45.540 AUDIENCE: I'm-- 00:19:45.540 --> 00:19:46.260 WILLIAM BONVILLIAN: Go ahead, Max. 00:19:46.260 --> 00:19:48.635 AUDIENCE: I'm kind of confused how you can simultaneously 00:19:48.635 --> 00:19:51.390 have a problem where you don't have enough science engineering 00:19:51.390 --> 00:19:55.490 workers but you also have this army of post docs 00:19:55.490 --> 00:19:57.690 that you don't know what to do with. 00:19:57.690 --> 00:19:59.010 WILLIAM BONVILLIAN: Well, I mean that's kind of the next stage 00:19:59.010 --> 00:19:59.640 of the problem. 00:19:59.640 --> 00:20:04.320 So his next piece, Max, is innovation and graduate 00:20:04.320 --> 00:20:06.600 education training. 00:20:06.600 --> 00:20:09.120 So in other words, create curricula that are relevant 00:20:09.120 --> 00:20:12.060 not only to training for the academy, 00:20:12.060 --> 00:20:15.480 but curricula that are also relevant to training for entry 00:20:15.480 --> 00:20:18.900 in the industry, and we're obviously 00:20:18.900 --> 00:20:21.840 starting to see some of these things materialize. 00:20:21.840 --> 00:20:24.780 So a school like MIT-- but it's by no means alone-- 00:20:24.780 --> 00:20:30.920 has a massive entrepreneurship curriculum now available 00:20:30.920 --> 00:20:31.958 in all of its schools. 00:20:31.958 --> 00:20:33.750 This is not just a business school program, 00:20:33.750 --> 00:20:36.400 this is available across the board, 00:20:36.400 --> 00:20:39.352 and as you undergraduates know, it's really quite accessible. 00:20:39.352 --> 00:20:41.310 There's a substantial number of business majors 00:20:41.310 --> 00:20:43.560 who do a lot of entrepreneurship-- 00:20:43.560 --> 00:20:46.920 business minors-- who do a lot of entrepreneurship-esque 00:20:46.920 --> 00:20:49.290 features in their education. 00:20:49.290 --> 00:20:51.060 That's now much better understood 00:20:51.060 --> 00:20:53.940 by students going to universities 00:20:53.940 --> 00:20:57.420 as an option for what their route ahead might be 00:20:57.420 --> 00:20:59.472 than it was even 10 years ago. 00:20:59.472 --> 00:21:00.930 So there has been some change here, 00:21:00.930 --> 00:21:04.370 but that's another change he would make, 00:21:04.370 --> 00:21:08.340 is to make the training much more relevant to entry 00:21:08.340 --> 00:21:13.500 into established firms and to startup firms 00:21:13.500 --> 00:21:15.580 at the graduate school level. 00:21:15.580 --> 00:21:19.740 So overall, he would attempt to use some federal funding here 00:21:19.740 --> 00:21:24.960 to, in effect, redress the imbalance in federal demand 00:21:24.960 --> 00:21:29.910 and supply programs to create an input of support 00:21:29.910 --> 00:21:31.650 on the federal side-- 00:21:31.650 --> 00:21:35.640 on the supply side. 00:21:35.640 --> 00:21:37.832 All right, so that's two of our three. 00:21:37.832 --> 00:21:38.790 You want to pause here? 00:21:38.790 --> 00:21:41.820 Because this one's so nicely controversial 00:21:41.820 --> 00:21:45.420 that we could discuss it, and you all can disagree with Romer 00:21:45.420 --> 00:21:49.620 or indicate how correct he is. 00:21:49.620 --> 00:21:51.270 Shall we do a quick pause? 00:21:51.270 --> 00:21:52.098 Who's got this one? 00:21:52.098 --> 00:21:52.890 You've got it, Max? 00:21:52.890 --> 00:21:54.515 Do you want to quickly summarize Romer? 00:21:54.515 --> 00:21:55.678 Let's do Augustine, too. 00:21:55.678 --> 00:21:56.220 AUDIENCE: Oh. 00:21:56.220 --> 00:21:57.303 WILLIAM BONVILLIAN: Great. 00:21:57.303 --> 00:21:59.850 AUDIENCE: All right, so Augustine talks about how, 00:21:59.850 --> 00:22:02.610 while as a lot of us know, education in the United States 00:22:02.610 --> 00:22:05.500 isn't great before the university level. 00:22:05.500 --> 00:22:07.290 So he gives a lot of statistics, how 00:22:07.290 --> 00:22:09.430 we're doing pretty poorly in math and science 00:22:09.430 --> 00:22:14.490 when compared to other countries, how, for whatever 00:22:14.490 --> 00:22:17.880 reason, it seems that as people stay 00:22:17.880 --> 00:22:21.460 within the American system, the longer they stay in, 00:22:21.460 --> 00:22:25.170 the poorer their ability to compete with other countries 00:22:25.170 --> 00:22:26.560 is. 00:22:26.560 --> 00:22:30.930 So actually, I found out one of the things-- 00:22:30.930 --> 00:22:35.010 some of the-- so America has, in the past few decades, 00:22:35.010 --> 00:22:37.380 been famous for the concept of a brain drain, 00:22:37.380 --> 00:22:41.370 where it would take the best and brightest from other countries, 00:22:41.370 --> 00:22:42.297 like India and China-- 00:22:42.297 --> 00:22:44.130 they would come over here for our university 00:22:44.130 --> 00:22:46.470 system, which is nice. 00:22:46.470 --> 00:22:47.340 It's great for us. 00:22:47.340 --> 00:22:50.460 It's not great for them, but apparently this trend 00:22:50.460 --> 00:22:52.450 is slowing as we find out in this article, 00:22:52.450 --> 00:22:54.993 because of some of our more isolationist policies. 00:22:54.993 --> 00:22:56.910 And I really appreciated that he mentioned it, 00:22:56.910 --> 00:23:01.140 because often a lot of people tried to talk about, 00:23:01.140 --> 00:23:03.640 isolationism is bad because we are the world, 00:23:03.640 --> 00:23:05.910 but this actually gives a more concrete 00:23:05.910 --> 00:23:08.700 reason for why isolationism actually 00:23:08.700 --> 00:23:11.400 causes some significant problems to our innovation system 00:23:11.400 --> 00:23:14.610 and to our economy. 00:23:14.610 --> 00:23:17.460 So one of the questions that I wanted to pose 00:23:17.460 --> 00:23:21.540 is, how have all of our educational institutions 00:23:21.540 --> 00:23:23.790 at the university level managed to maintain 00:23:23.790 --> 00:23:27.990 their stature and their quality despite the fact that lower-- 00:23:27.990 --> 00:23:31.080 lower level institutions like high school and below 00:23:31.080 --> 00:23:34.320 have been so sub par? 00:23:34.320 --> 00:23:38.430 And can we implement some of these characteristics 00:23:38.430 --> 00:23:39.450 at these other levels? 00:23:42.632 --> 00:23:43.340 Yeah, that's all. 00:23:43.340 --> 00:23:45.230 AUDIENCE: I think some of the answer 00:23:45.230 --> 00:23:48.840 to that comes from just the inequality across I 00:23:48.840 --> 00:23:50.290 through 12 schools. 00:23:50.290 --> 00:23:52.640 The US does have some fantastic schools, 00:23:52.640 --> 00:23:54.830 it's just who has access to them, 00:23:54.830 --> 00:23:58.900 and that tends to favor certain groups over others, 00:23:58.900 --> 00:24:01.340 and so instead of being able to tap 00:24:01.340 --> 00:24:03.550 into the full potential talent pool that we have, 00:24:03.550 --> 00:24:05.080 we're getting the ones who happen 00:24:05.080 --> 00:24:07.288 to live in a neighborhood that goes to a good school, 00:24:07.288 --> 00:24:10.670 or they have a nice magnet school in their county. 00:24:10.670 --> 00:24:12.620 So I think you could also pose the question, 00:24:12.620 --> 00:24:14.540 how much better could American universities 00:24:14.540 --> 00:24:18.420 be if they had their full talent pool to choose from? 00:24:18.420 --> 00:24:19.760 AUDIENCE: That's fair. 00:24:19.760 --> 00:24:21.830 AUDIENCE: Yeah, I like that point a lot, 00:24:21.830 --> 00:24:23.450 because there are-- 00:24:23.450 --> 00:24:27.830 I mean, we definitely have institutions-- higher parable 00:24:27.830 --> 00:24:30.110 learning institutions, universities-- in the United 00:24:30.110 --> 00:24:33.230 States that are extremely famous, extremely well known, 00:24:33.230 --> 00:24:34.670 and extremely well respected. 00:24:34.670 --> 00:24:36.080 You all are at one. 00:24:36.080 --> 00:24:39.170 But there, if you look-- 00:24:39.170 --> 00:24:41.870 that doesn't mean that every single university in the United 00:24:41.870 --> 00:24:43.730 States is internationally respected. 00:24:43.730 --> 00:24:46.610 There's this huge spectrum of the level of education 00:24:46.610 --> 00:24:49.280 or the quality of education that you can get at the university 00:24:49.280 --> 00:24:51.380 level, and I think you're exactly right-- that's 00:24:51.380 --> 00:24:54.080 the same for K through 12. 00:24:54.080 --> 00:24:55.520 It's just there's a huge span. 00:24:55.520 --> 00:24:58.790 You just don't necessarily hear about the really bad university 00:24:58.790 --> 00:24:59.750 level educations. 00:24:59.750 --> 00:25:00.992 [INAUDIBLE] 00:25:00.992 --> 00:25:01.770 AUDIENCE: OK. 00:25:01.770 --> 00:25:02.270 So-- 00:25:02.270 --> 00:25:03.145 AUDIENCE: [INAUDIBLE] 00:25:03.145 --> 00:25:05.130 AUDIENCE: So following up on that, then, 00:25:05.130 --> 00:25:06.755 I've heard that one of the main reasons 00:25:06.755 --> 00:25:10.220 that we-- that, at least for the K through 12 system, people 00:25:10.220 --> 00:25:14.600 are-- or governments-- are unable to support 00:25:14.600 --> 00:25:16.880 the schools that are doing very well 00:25:16.880 --> 00:25:19.168 and punish the ones that are doing poorly. 00:25:19.168 --> 00:25:20.960 Is there a way that we could implement this 00:25:20.960 --> 00:25:24.770 without jeopardizing the educations of the people who 00:25:24.770 --> 00:25:26.410 are already in these systems? 00:25:26.410 --> 00:25:30.440 Because schools can't be treated exactly like a free market, 00:25:30.440 --> 00:25:32.810 because in a free market, the worst case 00:25:32.810 --> 00:25:36.130 is you buy a Zune instead of an iPhone or iPod 00:25:36.130 --> 00:25:37.940 and well, OK, that kind of sucks. 00:25:37.940 --> 00:25:40.040 But it doesn't suck nearly as much 00:25:40.040 --> 00:25:42.500 as it does for the kid who has to go 00:25:42.500 --> 00:25:44.570 through a year with a bad teacher, 00:25:44.570 --> 00:25:46.842 and sure, maybe the teacher gets fired after, 00:25:46.842 --> 00:25:48.800 but that's still a year that this kid has lost, 00:25:48.800 --> 00:25:50.990 and that sets them behind, and those-- 00:25:50.990 --> 00:25:53.940 that impact can last for the rest of this person's life. 00:25:53.940 --> 00:25:59.600 So-- that was a really long question, I'm sorry. 00:25:59.600 --> 00:26:04.790 So to phrase that more simply, how can we 00:26:04.790 --> 00:26:08.990 implement some sort of free market style 00:26:08.990 --> 00:26:17.540 without that inherent impact on a kid's life? 00:26:17.540 --> 00:26:20.480 AUDIENCE: Well, I think just to reorganize the question 00:26:20.480 --> 00:26:23.310 and some decision making, why are you assuming it's 00:26:23.310 --> 00:26:26.357 the teacher's fault that, in a central city school, 00:26:26.357 --> 00:26:27.440 they don't do well, right? 00:26:27.440 --> 00:26:29.340 Usually it's people who really, really care, 00:26:29.340 --> 00:26:31.430 but the students aren't ready to learn. 00:26:31.430 --> 00:26:33.130 Because like-- I forget the quote, 00:26:33.130 --> 00:26:35.075 but it's combined as like a parachute that 00:26:35.075 --> 00:26:37.523 needs to be opened to receive information. 00:26:37.523 --> 00:26:39.190 So I think it's a more complex issue, is 00:26:39.190 --> 00:26:40.180 the point I'm trying to make. 00:26:40.180 --> 00:26:40.460 AUDIENCE: Yeah. 00:26:40.460 --> 00:26:41.390 AUDIENCE: [INAUDIBLE] the first time 00:26:41.390 --> 00:26:43.932 I've looked into this, because there's a special class called 00:26:43.932 --> 00:26:46.358 by Tom Malone, and I think we're going 00:26:46.358 --> 00:26:47.900 to be talking about the future of war 00:26:47.900 --> 00:26:49.490 next week where he talks a lot about, 00:26:49.490 --> 00:26:52.550 now we're in a distributed kind of network system. 00:26:52.550 --> 00:26:55.310 And we kind of get through hierarchy systems. 00:26:55.310 --> 00:26:58.053 And so like in that class, we posited like, 00:26:58.053 --> 00:26:59.720 you know like YouTube has all the videos 00:26:59.720 --> 00:27:01.275 and is created by a ton of people. 00:27:01.275 --> 00:27:03.650 We talked about like what if somebody made like a YouTube 00:27:03.650 --> 00:27:05.750 for education, where the best teachers would get the most 00:27:05.750 --> 00:27:06.333 views. 00:27:06.333 --> 00:27:08.000 And you could quantify that and pay them 00:27:08.000 --> 00:27:09.200 really high salaries, right? 00:27:09.200 --> 00:27:13.460 So you'd get like NBA, and NBA player salaries 00:27:13.460 --> 00:27:16.160 for doing really great content and a lot of people 00:27:16.160 --> 00:27:16.950 would access it. 00:27:16.950 --> 00:27:18.710 And pretty much the whole population 00:27:18.710 --> 00:27:21.280 would be taught by the best teachers in the country 00:27:21.280 --> 00:27:22.310 or in the world. 00:27:22.310 --> 00:27:24.860 And then you'd have the personal touch, 00:27:24.860 --> 00:27:28.190 like Khan Academy, where it's like online for some subjects 00:27:28.190 --> 00:27:29.940 or like [INAUDIBLE] in a really good way. 00:27:29.940 --> 00:27:32.060 And then in the actual class system, 00:27:32.060 --> 00:27:34.970 you are going through the kind of like the issues 00:27:34.970 --> 00:27:36.740 and figuring out what your errors are 00:27:36.740 --> 00:27:38.460 so you can learn better. 00:27:38.460 --> 00:27:40.820 So I think that's an interesting incentive. 00:27:40.820 --> 00:27:42.380 The thing though is like I think this 00:27:42.380 --> 00:27:44.965 is more of the dynamics of the market 00:27:44.965 --> 00:27:48.920 but the gatekeepers problem than it is like an education problem 00:27:48.920 --> 00:27:51.050 or even like the pipeline issues. 00:27:51.050 --> 00:27:53.360 If our incentive system is to get the best people 00:27:53.360 --> 00:27:55.490 to the school and not care what happens 00:27:55.490 --> 00:27:59.630 after, that's kind of like a screwed up system, right? 00:27:59.630 --> 00:28:02.570 It's like if your whole-- yeah. 00:28:02.570 --> 00:28:04.758 Like we're not focusing on making 00:28:04.758 --> 00:28:06.050 the best students in the world. 00:28:06.050 --> 00:28:07.910 We're focusing on finding the smartest people up 00:28:07.910 --> 00:28:10.160 to that point that they become students at our university. 00:28:10.160 --> 00:28:12.702 And our reputation is based on getting the smartest people up 00:28:12.702 --> 00:28:13.280 to here. 00:28:13.280 --> 00:28:15.590 So it doesn't matter if you have a screwed up economy. 00:28:15.590 --> 00:28:18.280 There's always going to be a really top 1%. 00:28:18.280 --> 00:28:20.030 Like, it doesn't matter, because like most 00:28:20.030 --> 00:28:22.280 of the people at the school will come from all different kinds 00:28:22.280 --> 00:28:23.090 of backgrounds. 00:28:23.090 --> 00:28:24.590 And they will never say, most likely 00:28:24.590 --> 00:28:26.182 it was because of their school. 00:28:26.182 --> 00:28:27.890 Like if they come from a poor background, 00:28:27.890 --> 00:28:29.310 they're going to say, you know, I just went online, 00:28:29.310 --> 00:28:31.160 or went to the library, or I learned from the best people 00:28:31.160 --> 00:28:31.785 in their books. 00:28:35.450 --> 00:28:38.930 That was even like a response. 00:28:38.930 --> 00:28:41.840 WILLIAM BONVILLIAN: Well, you introduced the online idea. 00:28:41.840 --> 00:28:46.560 And we're going to jump on that kind at the end of the class. 00:28:46.560 --> 00:28:48.490 So we should come back to that Martin. 00:28:48.490 --> 00:28:50.050 AUDIENCE: Which I definitely think is an interesting idea. 00:28:50.050 --> 00:28:51.677 I think it's easy to frame the access. 00:28:51.677 --> 00:28:53.260 All you need is an internet connection 00:28:53.260 --> 00:28:55.310 and basically everyone has a cell phone. 00:28:55.310 --> 00:28:57.200 AUDIENCE: You know, we'll get to is later. 00:28:57.200 --> 00:28:59.700 WILLIAM BONVILLIAN: And there are pros and cons on this too. 00:28:59.700 --> 00:29:00.750 AUDIENCE: It's also overdone, right? 00:29:00.750 --> 00:29:02.540 There's like a ton of businesses and a ton of organizations 00:29:02.540 --> 00:29:03.562 I've seen try to do it. 00:29:03.562 --> 00:29:05.270 And like why haven't they figured it out. 00:29:05.270 --> 00:29:07.478 It's probably like a policy, incentives, power issue. 00:29:12.683 --> 00:29:14.600 AUDIENCE: Yes, I think, just thinking about it 00:29:14.600 --> 00:29:16.610 from like also an international perspective, 00:29:16.610 --> 00:29:19.820 I think a problem in the US is also like the whole respect 00:29:19.820 --> 00:29:21.050 thing for teachers. 00:29:21.050 --> 00:29:23.270 Like I think especially at the middle school 00:29:23.270 --> 00:29:25.790 or the high school, like the public education system, maybe 00:29:25.790 --> 00:29:28.100 there's not as much respect for being a teacher. 00:29:28.100 --> 00:29:30.630 Whereas like maybe in Sweden, or like China, 00:29:30.630 --> 00:29:32.030 like being a professor, a teacher 00:29:32.030 --> 00:29:35.120 is really well respected and considered a really prestigious 00:29:35.120 --> 00:29:35.680 job. 00:29:35.680 --> 00:29:37.850 So I think that's like some systemic issue that's 00:29:37.850 --> 00:29:40.820 also kind of preventing maybe the top talent from going 00:29:40.820 --> 00:29:43.660 into teaching, not necessarily at the higher education level. 00:29:43.660 --> 00:29:47.030 I think there is a lot of really intelligent academics 00:29:47.030 --> 00:29:48.060 in that space. 00:29:48.060 --> 00:29:50.240 But definitely, towards the beginning 00:29:50.240 --> 00:29:52.460 where children are like really starting out 00:29:52.460 --> 00:29:54.860 their educational careers, and that's the fundamental. 00:29:54.860 --> 00:29:57.140 And also from what I know like in Europe, 00:29:57.140 --> 00:30:00.500 they start kind of specializing pretty early 00:30:00.500 --> 00:30:03.110 on in like high school into what kind of track 00:30:03.110 --> 00:30:04.790 they want to pursue later on. 00:30:04.790 --> 00:30:08.510 So I feel like that could be an interesting way that they're 00:30:08.510 --> 00:30:11.210 kind of promoting, for example, engineering 00:30:11.210 --> 00:30:15.920 or math or STEM related technical expertise. 00:30:15.920 --> 00:30:19.040 Like they're really fostering that from early stage. 00:30:19.040 --> 00:30:22.400 So that really helps and sets up the students 00:30:22.400 --> 00:30:24.350 to advance later on. 00:30:24.350 --> 00:30:26.190 Whereas like a lot of students here 00:30:26.190 --> 00:30:30.290 they get like kind of basic introduction to everything. 00:30:30.290 --> 00:30:32.450 But then once they get to engineering classes, 00:30:32.450 --> 00:30:34.885 some people aren't very prepared. 00:30:34.885 --> 00:30:37.010 AUDIENCE: Actually regarding the first part of what 00:30:37.010 --> 00:30:38.870 you said about the teachers and trying 00:30:38.870 --> 00:30:41.997 to incentivize the best talent in our country 00:30:41.997 --> 00:30:44.330 to become a teacher, that's actually my second question. 00:30:44.330 --> 00:30:47.330 I was going to ask, well, how could we incentivize them 00:30:47.330 --> 00:30:48.290 outside of pay? 00:30:48.290 --> 00:30:52.130 Because I've heard that the US education system 00:30:52.130 --> 00:30:54.770 is at an all time high for paying, for the amount of money 00:30:54.770 --> 00:30:56.900 that we spend per student in the classroom. 00:30:56.900 --> 00:31:00.900 So clearly just throwing money at this problem is not enough. 00:31:00.900 --> 00:31:02.892 So what can we do? 00:31:02.892 --> 00:31:04.850 Is there a way we can either change the culture 00:31:04.850 --> 00:31:07.817 or some other aspect? 00:31:07.817 --> 00:31:10.400 AUDIENCE: Yeah, I think one of the problems with the US system 00:31:10.400 --> 00:31:12.800 is that it's kind of punitive. 00:31:12.800 --> 00:31:17.080 Where there's like mass or firing, sorry, 00:31:17.080 --> 00:31:18.860 mass firing things, whereas teachers 00:31:18.860 --> 00:31:21.897 that are not like performing up to a grade just be like let go. 00:31:21.897 --> 00:31:23.480 And that's kind of bad on the students 00:31:23.480 --> 00:31:25.160 because there's a lot of turnover. 00:31:25.160 --> 00:31:28.550 But I think in other, we can maybe borrow from another model 00:31:28.550 --> 00:31:31.400 where other cities, I think what comes to mind 00:31:31.400 --> 00:31:35.720 is Shanghai has this program where a lot of teachers 00:31:35.720 --> 00:31:37.670 or professors that are not performing as well 00:31:37.670 --> 00:31:41.090 are partnered with an educator that has more experience 00:31:41.090 --> 00:31:44.840 or has better understanding of how to reach the students. 00:31:44.840 --> 00:31:47.450 And they have this kind of like collaborative model, 00:31:47.450 --> 00:31:49.370 where they bring up the teachers instead 00:31:49.370 --> 00:31:51.712 of trying to just fire them or like dock their pay 00:31:51.712 --> 00:31:52.610 or something. 00:31:52.610 --> 00:31:55.022 Which I think is probably something 00:31:55.022 --> 00:31:57.230 that would be ultimately better for like the students 00:31:57.230 --> 00:31:59.240 in general as well. 00:31:59.240 --> 00:32:02.570 WILLIAM BONVILLIAN: So Max, why don't we move on now to Grover. 00:32:02.570 --> 00:32:03.700 AUDIENCE: Oh, yeah, sure. 00:32:03.700 --> 00:32:06.800 That most anybody is going to be closing points here. 00:32:06.800 --> 00:32:08.175 AUDIENCE: I just want to bring up 00:32:08.175 --> 00:32:10.050 the point that was made in one of the papers, 00:32:10.050 --> 00:32:12.530 I forget if it's this one, about having access to looking 00:32:12.530 --> 00:32:13.898 at like seeing engineers. 00:32:13.898 --> 00:32:16.190 Yeah, like, if you're in a community where you've never 00:32:16.190 --> 00:32:18.290 got to interact with engineers or somebody who does STEM, 00:32:18.290 --> 00:32:19.190 you're going to see it a certain way, 00:32:19.190 --> 00:32:20.780 especially since when you look at the subject, 00:32:20.780 --> 00:32:23.150 it's so dry in the classroom versus what you actually 00:32:23.150 --> 00:32:24.440 end up doing. 00:32:24.440 --> 00:32:26.290 I think that's also a big component, 00:32:26.290 --> 00:32:28.790 especially once you structure who you want to become, right? 00:32:28.790 --> 00:32:30.207 Because at that age, you're trying 00:32:30.207 --> 00:32:32.670 to figure out where you want to be a role model. 00:32:32.670 --> 00:32:34.310 Yeah, like an example, Steve Jobs 00:32:34.310 --> 00:32:36.242 got to work when he was 12 at HP. 00:32:36.242 --> 00:32:37.700 And he said that was the thing that 00:32:37.700 --> 00:32:40.040 led him to work on tech, because his whole background, 00:32:40.040 --> 00:32:41.320 his dad was a mechanic. 00:32:41.320 --> 00:32:43.820 It also made him realize what a good company was, because he 00:32:43.820 --> 00:32:46.112 saw how the employees are treated and it made them see, 00:32:46.112 --> 00:32:49.040 oh, this is why having the right company culture matters. 00:32:49.040 --> 00:32:51.140 That's a lesson he learned at 12 that led him 00:32:51.140 --> 00:32:53.630 to change how he saw his life. 00:32:53.630 --> 00:32:56.870 AUDIENCE: I think on my end in terms of Augustine, 00:32:56.870 --> 00:32:59.450 there is a point he made about American exceptionalism 00:32:59.450 --> 00:33:03.080 and our focus on finding spectacular talent, 00:33:03.080 --> 00:33:04.440 not just on good talent. 00:33:04.440 --> 00:33:06.080 And so, I remember a few weeks ago, 00:33:06.080 --> 00:33:08.070 I mentioned the quote where the good 00:33:08.070 --> 00:33:09.640 becomes the enemy of the great. 00:33:09.640 --> 00:33:11.780 And maybe in the education system, 00:33:11.780 --> 00:33:13.890 the great is the enemy of the good. 00:33:13.890 --> 00:33:16.550 And perhaps we're sort of not doing a good enough job 00:33:16.550 --> 00:33:19.970 of supporting people who do a fine job at pursuing 00:33:19.970 --> 00:33:22.370 engineering and science because we're 00:33:22.370 --> 00:33:25.560 focused on finding the innovators, the Edisons, 00:33:25.560 --> 00:33:27.930 the Jobs, the Gates of the world. 00:33:27.930 --> 00:33:29.990 So to create a support infrastructure, 00:33:29.990 --> 00:33:32.510 people who might do a fine job at carrying out 00:33:32.510 --> 00:33:34.580 their functions I think is ultimately 00:33:34.580 --> 00:33:37.320 the task of the education system at the lower level. 00:33:37.320 --> 00:33:39.790 And then at the university level of cultivating that talent 00:33:39.790 --> 00:33:43.160 to really create spectacular innovation. 00:33:43.160 --> 00:33:44.840 AUDIENCE: Yeah, to add on to that point, 00:33:44.840 --> 00:33:46.730 I think, yeah, they definitely prefer the Edisons, 00:33:46.730 --> 00:33:47.760 but I think that was more in the paper 00:33:47.760 --> 00:33:49.633 already talked about breakthrough ideas. 00:33:49.633 --> 00:33:51.050 I think this paper was more about, 00:33:51.050 --> 00:33:53.592 we have a pipeline issue that we need these kind of employees 00:33:53.592 --> 00:33:55.370 so that these fields stay in the US. 00:33:55.370 --> 00:33:56.320 And they weren't looking for Edisons. 00:33:56.320 --> 00:33:58.112 They were looking for niche, like you know, 00:33:58.112 --> 00:34:01.758 you want to discipline and can move ahead and get that job. 00:34:01.758 --> 00:34:03.800 WILLIAM BONVILLIAN: So let's shift over to Romer. 00:34:03.800 --> 00:34:05.802 AUDIENCE: Sure. 00:34:05.802 --> 00:34:06.760 you're just like, oh,-- 00:34:06.760 --> 00:34:07.980 WILLIAM BONVILLIAN: Well, we're leading into it. 00:34:07.980 --> 00:34:09.030 So we might as well. 00:34:09.030 --> 00:34:10.460 That's your pipeline boy. 00:34:10.460 --> 00:34:11.500 AUDIENCE: Yeah. 00:34:11.500 --> 00:34:15.110 Yeah, so regarding the pipeline, Romer 00:34:15.110 --> 00:34:18.380 decided to focus mostly on the undergraduate and graduate 00:34:18.380 --> 00:34:21.139 institutions and trying to figure out 00:34:21.139 --> 00:34:25.909 how you can increase the supply of the talented scientists 00:34:25.909 --> 00:34:29.960 and engineers that exist in the American innovation system. 00:34:29.960 --> 00:34:34.530 So one of the questions that I saw 00:34:34.530 --> 00:34:37.580 was posed pretty interesting, someone was asking 00:34:37.580 --> 00:34:39.770 what metrics or measures could you 00:34:39.770 --> 00:34:46.790 use to actually evaluate these new scientists and engineers 00:34:46.790 --> 00:34:48.409 as they're coming into these fields 00:34:48.409 --> 00:34:51.980 and how can you train them to come into, 00:34:51.980 --> 00:34:54.727 to be prepared to go into either industry or academia. 00:34:57.203 --> 00:34:59.370 AUDIENCE: I mean, the first thing that comes to mind 00:34:59.370 --> 00:35:02.220 is like professional engineer exams that exist already. 00:35:02.220 --> 00:35:03.690 That's kind of like the standard. 00:35:03.690 --> 00:35:04.910 AUDIENCE: Those aren't required, are they? 00:35:04.910 --> 00:35:06.000 AUDIENCE: No, not usually. 00:35:06.000 --> 00:35:08.042 Well some companies will require you to get them. 00:35:08.042 --> 00:35:09.340 It's generally just like a-- 00:35:09.340 --> 00:35:10.650 AUDIENCE: It's a nice little-- 00:35:10.650 --> 00:35:12.270 AUDIENCE: Thing you can check. 00:35:12.270 --> 00:35:14.728 But like, I don't think it's very like, 00:35:14.728 --> 00:35:16.770 before I came to MIT, I'd never even heard of it. 00:35:16.770 --> 00:35:18.960 So I don't know if that's something 00:35:18.960 --> 00:35:20.690 that carries a ton of weight. 00:35:20.690 --> 00:35:24.490 But I don't know. 00:35:24.490 --> 00:35:27.000 I don't really like the idea of using standardized tests 00:35:27.000 --> 00:35:28.840 as a measure of competency. 00:35:28.840 --> 00:35:30.600 But I mean if you're trying to talk 00:35:30.600 --> 00:35:33.510 about a large group of people there is no feasible way 00:35:33.510 --> 00:35:34.868 to do it other than that. 00:35:34.868 --> 00:35:36.660 If you want to talk about all the engineers 00:35:36.660 --> 00:35:40.170 that are entering the workforce, like, unfortunately, 00:35:40.170 --> 00:35:42.128 numbers are kind of like the only way to do it. 00:35:42.128 --> 00:35:43.670 AUDIENCE: Yeah, you can't [INAUDIBLE] 00:35:43.670 --> 00:35:44.640 recommendation letters. 00:35:44.640 --> 00:35:46.950 Just like everyone can find someone that likes them. 00:35:50.860 --> 00:35:52.350 Yes. 00:35:52.350 --> 00:35:55.660 AUDIENCE: Might there be a reason why 00:35:55.660 --> 00:36:00.130 such extensive licensing exams exist in the medical field 00:36:00.130 --> 00:36:03.067 and not in the engineering or life sciences fields? 00:36:03.067 --> 00:36:04.900 AUDIENCE: I mean, it's because it's probably 00:36:04.900 --> 00:36:07.067 easy to mess up costs a lot of money if you mess up. 00:36:07.067 --> 00:36:08.830 So you don't want people to mess up. 00:36:08.830 --> 00:36:10.220 But it's pretty easy to mess up in engineering. 00:36:10.220 --> 00:36:10.880 AUDIENCE: Yeah. 00:36:10.880 --> 00:36:13.380 AUDIENCE: Yeah, but people don't die and don't get lawsuits. 00:36:13.380 --> 00:36:14.820 [INTERPOSING VOICES] 00:36:19.450 --> 00:36:22.180 AUDIENCE: I mean, to the personal engineering 00:36:22.180 --> 00:36:25.150 exams, to what extent does accreditation of universities 00:36:25.150 --> 00:36:28.500 already attempt to fill that role that you know, 00:36:28.500 --> 00:36:30.010 if you're accepted into and graduate 00:36:30.010 --> 00:36:32.170 from an accredited engineering program, 00:36:32.170 --> 00:36:34.265 you already have that check mark, that like, OK. 00:36:34.265 --> 00:36:34.890 AUDIENCE: Yeah. 00:36:34.890 --> 00:36:38.000 So the PE exam is basically just a feather in your cap. 00:36:38.000 --> 00:36:40.720 It doesn't really-- or I don't know, a little stamp 00:36:40.720 --> 00:36:42.320 on your resume. 00:36:42.320 --> 00:36:43.558 Doesn't really-- 00:36:43.558 --> 00:36:45.350 AUDIENCE: But is there a fault with the way 00:36:45.350 --> 00:36:49.866 that we give that accreditation to things right now? 00:36:49.866 --> 00:36:52.040 Is it becoming more meaningless? 00:36:52.040 --> 00:36:54.620 If we're graduating maybe an engineer from one school 00:36:54.620 --> 00:36:56.780 that is far more capable than another school 00:36:56.780 --> 00:37:00.337 that has the same like stand? 00:37:00.337 --> 00:37:02.420 AUDIENCE: I mean, you could make the same argument 00:37:02.420 --> 00:37:03.753 with like any profession, right? 00:37:03.753 --> 00:37:07.880 Like, to go into Stephanie as example of medicine. 00:37:07.880 --> 00:37:12.440 So if you have someone who comes from the Harvard vs. name 00:37:12.440 --> 00:37:14.240 somewhere you don't like, Harvard. 00:37:17.892 --> 00:37:19.850 Yeah, then you can see a difference in quality. 00:37:26.543 --> 00:37:28.960 WILLIAM BONVILLIAN: Let me raise a question, [INAUDIBLE].. 00:37:28.960 --> 00:37:31.150 It's harder for you folks at MIT to see this 00:37:31.150 --> 00:37:34.450 because it's obviously predominantly science 00:37:34.450 --> 00:37:37.040 and engineering. 00:37:37.040 --> 00:37:41.110 But let me ask Sanam and Steph, how much 00:37:41.110 --> 00:37:43.400 do you see the great inflation in a classic, 00:37:43.400 --> 00:37:46.360 you know, high quality, liberal arts school 00:37:46.360 --> 00:37:49.210 between social science, humanities kinds 00:37:49.210 --> 00:37:52.270 of majors and science and engineering majors? 00:37:52.270 --> 00:37:54.790 And Lily, you came from one of those institutions. 00:37:54.790 --> 00:37:56.480 Please join in. 00:37:56.480 --> 00:37:57.260 Is this real? 00:37:57.260 --> 00:37:59.140 Is the problem that Romer is seeing here, 00:37:59.140 --> 00:38:00.290 is this a real issue? 00:38:00.290 --> 00:38:03.530 AUDIENCE: [INAUDIBLE] econ major at Wellesley College. 00:38:03.530 --> 00:38:05.670 AUDIENCE: Not at Wellesley, there 00:38:05.670 --> 00:38:08.623 is a definite sense of grade inflation there. 00:38:08.623 --> 00:38:09.790 AUDIENCE: It is not a sense. 00:38:09.790 --> 00:38:10.490 It is a policy. 00:38:10.490 --> 00:38:11.698 AUDIENCE: Yes, it's a policy. 00:38:11.698 --> 00:38:12.730 It's an actual policy. 00:38:12.730 --> 00:38:15.500 Instituted, grade inflation. 00:38:15.500 --> 00:38:18.500 But in terms of the disparity between the humanities, 00:38:18.500 --> 00:38:24.700 social sciences and STEM fields, I think there is definitely-- 00:38:24.700 --> 00:38:27.850 generally, there's the idea that like the STEM fields are 00:38:27.850 --> 00:38:34.030 more difficult. So recently we had this policy where 00:38:34.030 --> 00:38:37.330 the first semester of your college year shadow graded, 00:38:37.330 --> 00:38:39.040 so you're not actually given grades. 00:38:39.040 --> 00:38:41.830 And that kind of resulted in a lot of people 00:38:41.830 --> 00:38:44.230 entering the STEM fields [INAUDIBLE].. 00:38:44.230 --> 00:38:46.240 And that was something that obviously 00:38:46.240 --> 00:38:48.220 the humanities professors and social sciences 00:38:48.220 --> 00:38:48.970 were very against. 00:38:48.970 --> 00:38:51.130 Because if the students coming in 00:38:51.130 --> 00:38:53.770 were automatically going to anything outside 00:38:53.770 --> 00:38:55.570 of their department, they kind of ended up 00:38:55.570 --> 00:38:57.430 staying there because the faculty are great. 00:38:57.430 --> 00:38:59.410 So they would continue in that program. 00:38:59.410 --> 00:39:01.240 So that was kind of an imbalance that 00:39:01.240 --> 00:39:03.765 occurred in the recent years. 00:39:03.765 --> 00:39:05.640 WILLIAM BONVILLIAN: So in a way, exactly what 00:39:05.640 --> 00:39:07.320 Romer's talking about. 00:39:07.320 --> 00:39:08.000 Interesting. 00:39:08.000 --> 00:39:10.200 Lily, what was your point? 00:39:10.200 --> 00:39:11.820 AUDIENCE: I was a science major. 00:39:11.820 --> 00:39:14.110 And I made A's in every single class 00:39:14.110 --> 00:39:16.350 I took outside of my own major. 00:39:16.350 --> 00:39:17.292 And not As. 00:39:20.142 --> 00:39:22.184 WILLIAM BONVILLIAN: What were you thinking, Lily? 00:39:22.184 --> 00:39:23.040 [LAUGHING] 00:39:23.040 --> 00:39:24.882 AUDIENCE: We started-- 00:39:24.882 --> 00:39:27.510 I wanted to do science. 00:39:27.510 --> 00:39:29.970 We started out with almost 600 students 00:39:29.970 --> 00:39:32.790 in the first semester chemistry course. 00:39:32.790 --> 00:39:34.890 And by the second semester chemistry, 00:39:34.890 --> 00:39:38.610 so this is chemistry majors, biology majors, and engineering 00:39:38.610 --> 00:39:40.170 majors, all have to take that. 00:39:40.170 --> 00:39:42.420 They also have to take a second semester of chemistry. 00:39:42.420 --> 00:39:46.040 And there were probably less than 400 00:39:46.040 --> 00:39:47.340 by the second semester. 00:39:47.340 --> 00:39:48.840 So it was a huge weed out class. 00:39:48.840 --> 00:39:50.798 AUDIENCE: How many were there in the beginning? 00:39:50.798 --> 00:39:53.363 AUDIENCE: Over 600, just over six. 00:39:53.363 --> 00:39:55.530 WILLIAM BONVILLIAN: Most schools have notorious weed 00:39:55.530 --> 00:39:58.260 out classes that essentially frustrate 00:39:58.260 --> 00:39:59.830 the ambitions of a lot of students, 00:39:59.830 --> 00:40:03.030 particularly in the premed area. 00:40:03.030 --> 00:40:06.430 Obviously notorious, shall we say organic chemistry is 00:40:06.430 --> 00:40:08.290 the [INAUDIBLE]. 00:40:08.290 --> 00:40:13.560 So this is a dilemma when you've got a societal need 00:40:13.560 --> 00:40:15.630 and you've got the institutions that 00:40:15.630 --> 00:40:17.940 are supposed to deliver your talent base 00:40:17.940 --> 00:40:20.670 just operating off a completely different set of incentives 00:40:20.670 --> 00:40:22.050 than what the society may be. 00:40:22.050 --> 00:40:24.240 That's the core of Romer's argument here. 00:40:24.240 --> 00:40:28.560 And another part of his argument is, the numbers are not that 00:40:28.560 --> 00:40:29.350 far off. 00:40:29.350 --> 00:40:31.225 In other words, if we just got the people who 00:40:31.225 --> 00:40:35.400 wanted to major in science, engineering, and mathematics 00:40:35.400 --> 00:40:39.030 to stay in the field, you know, lots of the numbers problems 00:40:39.030 --> 00:40:40.620 get a heck of a lot better. 00:40:40.620 --> 00:40:43.410 But that 40% or more dropout rate 00:40:43.410 --> 00:40:44.860 out of those fields that typically 00:40:44.860 --> 00:40:50.070 occurs at the undergraduate level is highly problematic. 00:40:50.070 --> 00:40:51.390 AUDIENCE: Yes. 00:40:51.390 --> 00:40:53.932 AUDIENCE: I think they've been waiting longer if you want to. 00:40:53.932 --> 00:40:55.950 AUDIENCE: Oh, I couldn't see your hands. 00:40:55.950 --> 00:40:58.333 AUDIENCE: Yeah, so I started to wonder about a couple 00:40:58.333 --> 00:41:00.375 of these choke points, like where actually people 00:41:00.375 --> 00:41:02.250 are starting to drop out of these science and engineering 00:41:02.250 --> 00:41:02.750 fields. 00:41:02.750 --> 00:41:06.170 I know we identified kind of these weed out classes. 00:41:06.170 --> 00:41:10.140 And I think kind of looking from like who declares, who ends up 00:41:10.140 --> 00:41:13.440 finishing, I think you could probably, 00:41:13.440 --> 00:41:16.950 as a whole identify kind of gradient deflation in a couple 00:41:16.950 --> 00:41:20.190 of these classes is like very complicated, kind of weed out 00:41:20.190 --> 00:41:22.830 classes as reasons why people start to kind of leave 00:41:22.830 --> 00:41:24.480 the field and migrate out. 00:41:24.480 --> 00:41:27.450 And I think it might be indicative to sort 00:41:27.450 --> 00:41:30.378 of each institution kind of where these dropout points are. 00:41:30.378 --> 00:41:31.920 But I think en masse, you can kind of 00:41:31.920 --> 00:41:36.750 take these like first year classes as kind of places 00:41:36.750 --> 00:41:38.220 where students start to fall off. 00:41:38.220 --> 00:41:42.140 And then, is it actually-- 00:41:42.140 --> 00:41:44.580 so is it part of kind of this college exploratory process, 00:41:44.580 --> 00:41:46.497 like kind of figuring out what you want to do? 00:41:46.497 --> 00:41:48.980 It's like how you phrase it. 00:41:48.980 --> 00:41:50.748 As like, you started as a chemistry major. 00:41:50.748 --> 00:41:53.040 But maybe you end up gravitating towards social science 00:41:53.040 --> 00:41:55.440 because you don't really like the way that weed out class 00:41:55.440 --> 00:41:56.130 is structured? 00:41:56.130 --> 00:41:58.650 Or is it part of this indicative problem that we have, 00:41:58.650 --> 00:42:00.990 where like we can't graduate and sustain 00:42:00.990 --> 00:42:04.270 the people who are actually interested in these fields? 00:42:04.270 --> 00:42:07.440 And so I wonder if like, you know, just because you know, 00:42:07.440 --> 00:42:08.940 we're not graduating enough, is it 00:42:08.940 --> 00:42:10.793 because these classes are actually 00:42:10.793 --> 00:42:12.960 structured in a way that prevents people graduating? 00:42:12.960 --> 00:42:15.057 Or is it like part of this-- 00:42:15.057 --> 00:42:17.265 kind of the way that we brand your college experience 00:42:17.265 --> 00:42:18.840 is like you're supposed to be able to explore 00:42:18.840 --> 00:42:20.715 and kind of transition in and out, especially 00:42:20.715 --> 00:42:23.990 in that first year and experience things. 00:42:23.990 --> 00:42:27.030 And my second point was like, there's 00:42:27.030 --> 00:42:28.530 a little bit of a difference I would 00:42:28.530 --> 00:42:33.030 say in kind of STEM fields, even in that first year. 00:42:33.030 --> 00:42:35.280 Because I know a lot of colleges offer the opportunity 00:42:35.280 --> 00:42:38.850 to use high school classes to sort 00:42:38.850 --> 00:42:42.030 of test out of those first year, maybe even those weed 00:42:42.030 --> 00:42:43.590 out classes. 00:42:43.590 --> 00:42:45.285 And then, it's sort of an added bonus 00:42:45.285 --> 00:42:47.660 as well, because you end up testing out of these classes, 00:42:47.660 --> 00:42:51.300 so you use up less semesters to finish your degree. 00:42:51.300 --> 00:42:53.520 And so I know that can be like a big reason 00:42:53.520 --> 00:42:55.982 and incentive for students to sort of choose 00:42:55.982 --> 00:42:57.690 other universities, because they're like, 00:42:57.690 --> 00:42:59.970 oh, I'll only be in this school for three years 00:42:59.970 --> 00:43:02.430 if I go to maybe the state school over here, 00:43:02.430 --> 00:43:04.500 rather than staying in school for four years 00:43:04.500 --> 00:43:06.720 at a different university. 00:43:06.720 --> 00:43:09.030 And like, is there a way to identify 00:43:09.030 --> 00:43:12.780 sort of the relative quality of education 00:43:12.780 --> 00:43:15.690 in kind of testing out of that first year of engineering 00:43:15.690 --> 00:43:18.235 classes and then finishing in three versus going for four. 00:43:18.235 --> 00:43:19.860 Because I feel like if you can graduate 00:43:19.860 --> 00:43:22.920 a whole bunch of people, maybe in that three year time span 00:43:22.920 --> 00:43:25.823 without like a marginal decrease in quality of education, 00:43:25.823 --> 00:43:27.990 we can start looking more to those programs as well. 00:43:30.655 --> 00:43:32.530 WILLIAM BONVILLIAN: Max, how about a close up 00:43:32.530 --> 00:43:33.540 point on the Romer. 00:43:36.130 --> 00:43:40.730 AUDIENCE: So evidently, Romer is evidence 00:43:40.730 --> 00:43:45.902 that the education problem is unbelievably complicated. 00:43:45.902 --> 00:43:47.360 Throwing money at the issue has not 00:43:47.360 --> 00:43:49.760 been sufficient to actually solve it. 00:43:49.760 --> 00:43:54.290 And there are issues with the quality, 00:43:54.290 --> 00:43:58.490 as was being talked about, the weed out courses that exist, 00:43:58.490 --> 00:44:01.950 grade inflation and deflation, standardizing. 00:44:01.950 --> 00:44:05.045 So, such an issue will take-- 00:44:05.045 --> 00:44:06.920 it's going to take a very long time to solve. 00:44:06.920 --> 00:44:09.552 But all of the issues that he has pointed out, 00:44:09.552 --> 00:44:10.760 they are definitely solvable. 00:44:13.760 --> 00:44:19.580 I'd say the main barrier between us and now and a future 00:44:19.580 --> 00:44:22.580 where these issues are solved would be the-- 00:44:22.580 --> 00:44:24.260 would be just politics, just trying 00:44:24.260 --> 00:44:26.900 to get people to agree to these different programs that 00:44:26.900 --> 00:44:28.760 would focus more on the education 00:44:28.760 --> 00:44:32.470 and less on the incentives of those teaching. 00:44:32.470 --> 00:44:34.220 WILLIAM BONVILLIAN: I think part of what's 00:44:34.220 --> 00:44:37.100 interesting about Romer's argument 00:44:37.100 --> 00:44:41.540 is that he takes the theory that we've got a talent supply, set 00:44:41.540 --> 00:44:43.850 of talent supply issues, and then he 00:44:43.850 --> 00:44:48.400 attempts to figure out what are the more specific barriers 00:44:48.400 --> 00:44:52.460 to that supply, and then do an institutional analysis of why 00:44:52.460 --> 00:44:56.090 these barriers are in effect created at the higher education 00:44:56.090 --> 00:44:56.870 system. 00:44:56.870 --> 00:44:58.760 And then interestingly, he attempts 00:44:58.760 --> 00:45:03.252 to take three or four public policy fixes that 00:45:03.252 --> 00:45:04.460 could actually address these. 00:45:04.460 --> 00:45:09.140 And I do think that both his article and the Step 00:45:09.140 --> 00:45:11.900 Program from the National Science Foundation 00:45:11.900 --> 00:45:16.430 created based on the legislation that he 00:45:16.430 --> 00:45:18.410 recommended that Congress passed, 00:45:18.410 --> 00:45:20.270 and that NSF implemented. 00:45:20.270 --> 00:45:22.520 It was never implemented with the funding 00:45:22.520 --> 00:45:25.170 scale that was really needed to make a significant difference. 00:45:25.170 --> 00:45:28.610 But it started to send a different set of signals 00:45:28.610 --> 00:45:32.510 about supply of science and technology talent 00:45:32.510 --> 00:45:34.310 to the university system and did I 00:45:34.310 --> 00:45:36.800 think have an effect over time. 00:45:36.800 --> 00:45:40.080 But these issues are obviously still with us. 00:45:40.080 --> 00:45:44.150 So let me move quickly from Romer 00:45:44.150 --> 00:45:47.320 to our next reading with Richard Freeman. 00:45:47.320 --> 00:45:50.390 And he kind of takes us to the next level 00:45:50.390 --> 00:45:51.530 of the supply problem. 00:45:51.530 --> 00:45:55.100 Richard Freeman is a quite famous labor economist 00:45:55.100 --> 00:45:57.170 at the school up the street. 00:45:57.170 --> 00:46:02.900 And he's famous for his wide range and variety of hats. 00:46:02.900 --> 00:46:05.960 As you can see, he has a wonderful collection. 00:46:05.960 --> 00:46:08.230 So he looks at a different problem. 00:46:08.230 --> 00:46:14.460 You know does globalization threaten the-- 00:46:14.460 --> 00:46:16.310 and its movement in science and engineering, 00:46:16.310 --> 00:46:20.330 threaten US economic leadership? 00:46:20.330 --> 00:46:21.650 And you know, his-- 00:46:21.650 --> 00:46:26.810 I'll kind of summarize his four key points here. 00:46:29.330 --> 00:46:31.880 You know, the underlying issue is 00:46:31.880 --> 00:46:35.270 that changes in the global job market for science 00:46:35.270 --> 00:46:38.570 and engineering, science and entering workers, S&E workers 00:46:38.570 --> 00:46:42.980 are eroding US dominance in science and engineering, which 00:46:42.980 --> 00:46:47.180 in turn diminishes a strong comparative advantage 00:46:47.180 --> 00:46:50.280 that the US historically held since the end of World War II 00:46:50.280 --> 00:46:52.610 as we've been talking about in the past. 00:46:52.610 --> 00:46:54.500 And he makes four underlying points 00:46:54.500 --> 00:46:56.550 in kind of looking at this dimension. 00:46:56.550 --> 00:46:59.270 In other words, we have Augustine's critique of K 00:46:59.270 --> 00:46:59.900 through 12. 00:46:59.900 --> 00:47:03.710 We have Romer's critique of the higher education system. 00:47:03.710 --> 00:47:06.410 And then Richard Freeman kind of tells us 00:47:06.410 --> 00:47:08.840 what the economic implications are 00:47:08.840 --> 00:47:13.400 of failing to maintain that leadership base in science 00:47:13.400 --> 00:47:14.870 and engineering talent. 00:47:14.870 --> 00:47:18.110 So his point is that the share of the world science 00:47:18.110 --> 00:47:24.290 and engineering graduates in the US is in decline. 00:47:24.290 --> 00:47:26.360 And look, others are understanding this model 00:47:26.360 --> 00:47:30.080 and moving to fill it. 00:47:30.080 --> 00:47:32.450 Second, that the job market has worsened 00:47:32.450 --> 00:47:35.120 for younger workers in science and engineering fields 00:47:35.120 --> 00:47:38.660 relative to many other high level 00:47:38.660 --> 00:47:41.690 occupations, which in turn discourages 00:47:41.690 --> 00:47:44.710 US students in those fields. 00:47:44.710 --> 00:47:47.420 Now he's writing this at the time when 00:47:47.420 --> 00:47:50.845 a startling proportion of MIT students 00:47:50.845 --> 00:47:52.220 and other science and engineering 00:47:52.220 --> 00:47:54.740 graduates in other schools were going 00:47:54.740 --> 00:47:56.870 into the financial sector, financial services, 00:47:56.870 --> 00:47:58.610 and consulting. 00:47:58.610 --> 00:48:00.770 Some of that since he wrote has turned around. 00:48:00.770 --> 00:48:03.870 In other words, there has been an emergence 00:48:03.870 --> 00:48:09.050 at MIT of interest in startups and entrepreneurship, 00:48:09.050 --> 00:48:15.200 to the tune that some 20% to 25% of those of you 00:48:15.200 --> 00:48:17.300 when you graduate, your graduating classmates 00:48:17.300 --> 00:48:19.670 will go into those fields in a way 00:48:19.670 --> 00:48:21.830 that community moved out of financial services 00:48:21.830 --> 00:48:27.170 post 2008 crash and moved over to what 00:48:27.170 --> 00:48:30.170 may be a really important kind of career shift 00:48:30.170 --> 00:48:31.880 in terms of the country's future. 00:48:31.880 --> 00:48:37.850 But that hadn't occurred yet by the time he's writing this. 00:48:37.850 --> 00:48:41.690 So in other words, the extremely high pay 00:48:41.690 --> 00:48:44.180 available to those entering financial services 00:48:44.180 --> 00:48:48.170 and consulting drained talent out of the system 00:48:48.170 --> 00:48:50.720 is his point within the US. 00:48:50.720 --> 00:48:56.780 And then, you know, countries like China and India 00:48:56.780 --> 00:49:01.970 found that they could compete with the US in high tech 00:49:01.970 --> 00:49:05.600 by starting to train substantial numbers of science 00:49:05.600 --> 00:49:07.910 and engineering specialists. 00:49:07.910 --> 00:49:11.810 In other words, they could leapfrog, right, and in effect 00:49:11.810 --> 00:49:14.720 go to substantial parts of their economy being 00:49:14.720 --> 00:49:19.310 quite high tech, even while they were working on bringing up 00:49:19.310 --> 00:49:22.670 what's in effect a developing world economy in the meantime. 00:49:22.670 --> 00:49:24.393 So they hit on this model. 00:49:24.393 --> 00:49:25.810 And it's a really important model. 00:49:25.810 --> 00:49:29.810 But that in turn had an effect on kind of US dominance 00:49:29.810 --> 00:49:30.420 in this field. 00:49:30.420 --> 00:49:32.795 I mean, it's not a bad thing for the world to get better, 00:49:32.795 --> 00:49:37.290 but it doesn't have an effect on the US comparative advantage. 00:49:37.290 --> 00:49:42.680 So to ease the adjustment to a less dominant position 00:49:42.680 --> 00:49:45.710 in science and engineering with the corresponding economic 00:49:45.710 --> 00:49:50.220 ramifications for US economic growth and competitiveness, 00:49:50.220 --> 00:49:51.810 the US is going to have to develop, 00:49:51.810 --> 00:49:55.910 he argues, new labor market and R&D policies 00:49:55.910 --> 00:50:00.220 that start to try to change the science and engineering talent 00:50:00.220 --> 00:50:01.333 numbers. 00:50:01.333 --> 00:50:03.500 So we're going to have to step into the marketplace. 00:50:03.500 --> 00:50:05.540 And you know, this is where Romer's thinking 00:50:05.540 --> 00:50:07.470 would come to bear. 00:50:07.470 --> 00:50:12.170 So let me let me move to Goldin and Katz 00:50:12.170 --> 00:50:19.410 and they further talk about the societal ramifications for what 00:50:19.410 --> 00:50:22.248 we're doing in higher education, right? 00:50:22.248 --> 00:50:23.790 We've talked a bit about this before. 00:50:27.320 --> 00:50:31.710 And they also teach up the street. 00:50:31.710 --> 00:50:35.700 And their book, The Race Between Education and Technology 00:50:35.700 --> 00:50:37.730 was really a quite important one. 00:50:37.730 --> 00:50:40.530 I think it's held up well since it came out in 2009. 00:50:40.530 --> 00:50:44.310 But I had you read a short version, a Milken Institute 00:50:44.310 --> 00:50:47.910 review piece that came out before they put their book out. 00:50:47.910 --> 00:50:51.450 But I do recommend the book to you. 00:50:51.450 --> 00:50:55.230 The gap between wages of educated and less well educated 00:50:55.230 --> 00:50:58.890 workers has been growing since 1980, 00:50:58.890 --> 00:51:01.740 and this expanding wage inequality 00:51:01.740 --> 00:51:03.720 has characterized the US since that time. 00:51:06.300 --> 00:51:09.440 So we're becoming a much more polarized society 00:51:09.440 --> 00:51:14.150 and the lines are drawn on education lines, right? 00:51:14.150 --> 00:51:16.040 And this is, we've talked about this before, 00:51:16.040 --> 00:51:19.760 but this is David Otter's barbell problem, 00:51:19.760 --> 00:51:22.370 that our society is increasingly looking like a barbell. 00:51:22.370 --> 00:51:25.760 And we've got on one bell, a growing 00:51:25.760 --> 00:51:30.140 and successful upper middle class that has the education 00:51:30.140 --> 00:51:31.520 and is able to use that education 00:51:31.520 --> 00:51:35.750 to capture that wealth, a thinning middle, 00:51:35.750 --> 00:51:39.830 and a growing lower end services economy that's 00:51:39.830 --> 00:51:42.050 less well-paid and less well off, 00:51:42.050 --> 00:51:44.120 to which substantial portions of the middle 00:51:44.120 --> 00:51:45.410 are now being shunted. 00:51:45.410 --> 00:51:48.740 And we can start to see this polarization in our economy 00:51:48.740 --> 00:51:51.980 in pretty sharp terms. 00:51:51.980 --> 00:51:56.960 And that's what Katz and Goldin are writing about. 00:51:56.960 --> 00:52:00.410 The wage inequality narrowed in the United States 00:52:00.410 --> 00:52:04.640 significantly from about 1910 through the 1950s. 00:52:04.640 --> 00:52:09.260 Then it stabilized until about the 1980s. 00:52:09.260 --> 00:52:12.920 And then it's grown since that time. 00:52:12.920 --> 00:52:14.780 Why? 00:52:14.780 --> 00:52:16.660 And they argue-- 00:52:16.660 --> 00:52:21.650 I'll attempt to just paint you a quick picture. 00:52:21.650 --> 00:52:28.070 But they argue that there is a race between education 00:52:28.070 --> 00:52:33.080 and technology, and that what happened in the US 00:52:33.080 --> 00:52:37.850 was that there is an ever-growing curve 00:52:37.850 --> 00:52:42.045 of technology knowledge since the Industrial Revolution. 00:52:42.045 --> 00:52:43.670 And we've talked about this previously, 00:52:43.670 --> 00:52:46.220 but I'll reiterate it. 00:52:46.220 --> 00:52:48.500 That an economy requires, in other words, 00:52:48.500 --> 00:52:50.600 it requires an ever-growing level 00:52:50.600 --> 00:52:54.580 of technological sophistication. 00:52:54.580 --> 00:52:59.500 And the genius of the US system was 00:52:59.500 --> 00:53:04.515 to create mass higher education, which we did, 00:53:04.515 --> 00:53:05.890 first through the Land Grant Act. 00:53:05.890 --> 00:53:07.510 Of course that created MIT. 00:53:07.510 --> 00:53:10.470 That was the big enabler for MIT. 00:53:10.470 --> 00:53:12.310 It had initial funding in 1861. 00:53:12.310 --> 00:53:14.500 Its graduating class marched off to the Civil War. 00:53:14.500 --> 00:53:16.360 There was no revenue base. 00:53:16.360 --> 00:53:20.675 In 1862, Congress passed the Land Grant College Act 00:53:20.675 --> 00:53:22.300 and suddenly there was a revenue stream 00:53:22.300 --> 00:53:24.900 to make up for the loss of talent, loss of the tuition 00:53:24.900 --> 00:53:25.400 base. 00:53:25.400 --> 00:53:28.210 So that saved MIT. 00:53:28.210 --> 00:53:31.540 But created public higher education 00:53:31.540 --> 00:53:34.780 really across the country in every state at the time 00:53:34.780 --> 00:53:36.730 extended to new ones. 00:53:36.730 --> 00:53:40.280 So we created that system of mass higher education, really 00:53:40.280 --> 00:53:42.400 through the Land Grant College Act in 1862. 00:53:42.400 --> 00:53:45.490 No other country had done anything like that. 00:53:45.490 --> 00:53:50.710 So we created a talent base that stayed ahead 00:53:50.710 --> 00:53:53.620 of the technological curve, right? 00:53:53.620 --> 00:53:56.050 And that's what you want. 00:53:56.050 --> 00:53:59.080 You want your talent base to stay ahead 00:53:59.080 --> 00:54:03.400 of the increased sophistication of technology in the economy, 00:54:03.400 --> 00:54:07.920 so that that talent base can keep moving that curve up, 00:54:07.920 --> 00:54:08.590 right? 00:54:08.590 --> 00:54:11.110 And that they benefit each other. 00:54:11.110 --> 00:54:14.080 And then, what Katz and Goldin point out, 00:54:14.080 --> 00:54:19.810 is that in the mid '70s, we level that off. 00:54:19.810 --> 00:54:22.300 AUDIENCE: Was there something that caused it in particular? 00:54:22.300 --> 00:54:23.925 Let me get to that in a minute, Martin. 00:54:23.925 --> 00:54:25.930 But we do need to come back to that. 00:54:25.930 --> 00:54:27.400 I don't think we fully understand 00:54:27.400 --> 00:54:29.525 the dimensions of what was happening at that point. 00:54:29.525 --> 00:54:31.120 But there are some thoughts. 00:54:31.120 --> 00:54:35.140 So what happened was that the people that 00:54:35.140 --> 00:54:39.460 continue to ride this curve, they 00:54:39.460 --> 00:54:43.300 stayed up with the technology curve. 00:54:43.300 --> 00:54:47.680 Those that fell off fell behind the curve 00:54:47.680 --> 00:54:51.010 and their incomes correspondingly suffered. 00:54:51.010 --> 00:54:54.630 That's the case that they're making. 00:54:54.630 --> 00:54:57.280 and that these people got left off the curve 00:54:57.280 --> 00:55:00.760 and couldn't keep riding up to take advantage 00:55:00.760 --> 00:55:01.680 of the economic gains. 00:55:01.680 --> 00:55:04.840 So you've got a smaller number of people you know, 00:55:04.840 --> 00:55:06.160 riding this curve up. 00:55:06.160 --> 00:55:09.670 They get the gains. 00:55:09.670 --> 00:55:13.960 Whereas in this period of time between 1910 and 1950, 00:55:13.960 --> 00:55:16.540 everybody at least, a very large part of the population 00:55:16.540 --> 00:55:18.620 was riding that curve and able to benefit. 00:55:18.620 --> 00:55:21.910 So that's the great creation of this mass middle class 00:55:21.910 --> 00:55:24.220 in the United States. 00:55:24.220 --> 00:55:27.760 And the data tends to bear this out. 00:55:27.760 --> 00:55:33.130 So that's their essential equation here on what happened. 00:55:33.130 --> 00:55:35.380 Again, they argue that technological advance 00:55:35.380 --> 00:55:36.350 is the key to growth. 00:55:36.350 --> 00:55:40.000 We know that from this class. 00:55:40.000 --> 00:55:45.670 And that the ebb and flow of wage equality and inequality 00:55:45.670 --> 00:55:48.190 is very much related to the skill 00:55:48.190 --> 00:55:51.010 set you've got to keep riding that technological curve. 00:55:51.010 --> 00:55:55.360 Now look, if anything the growth in that technological curve 00:55:55.360 --> 00:55:59.100 has just gotten steeper with the development of all 00:55:59.100 --> 00:56:02.040 these information technologies. 00:56:02.040 --> 00:56:03.760 You know heaven forbid, a next generation 00:56:03.760 --> 00:56:06.970 if you don't have coding skills, right? 00:56:06.970 --> 00:56:09.460 With the entry of significant amount 00:56:09.460 --> 00:56:12.370 of artificial intelligence in the economy. 00:56:12.370 --> 00:56:15.160 If you're going to stay up, you really need that skill base. 00:56:15.160 --> 00:56:18.950 Otherwise you're going to get left behind. 00:56:18.950 --> 00:56:23.860 So, you know, that's essentially their picture 00:56:23.860 --> 00:56:25.630 of what's been going on here. 00:56:25.630 --> 00:56:33.930 Let me-- that stagnation of education levels around '73 00:56:33.930 --> 00:56:42.760 or so coincides with the period of economic challenge 00:56:42.760 --> 00:56:46.270 from Japan that we talked about in the second manufacturing 00:56:46.270 --> 00:56:47.530 class. 00:56:47.530 --> 00:56:51.250 That's the period where the Rust Belt gets created. 00:56:51.250 --> 00:56:54.250 So the US had been on an ever rising economic curve. 00:56:54.250 --> 00:56:58.180 Suddenly its growth rate fell to the 2% range 00:56:58.180 --> 00:57:05.340 from its historic 3% range and its productivity rate 00:57:05.340 --> 00:57:08.340 strides growth as we've talked about, 00:57:08.340 --> 00:57:11.290 its productivity rate fell to the 1% range. 00:57:11.290 --> 00:57:14.050 So that means there's less real wealth in the economy. 00:57:14.050 --> 00:57:17.530 And that may, Martin, in answer to your question, that 00:57:17.530 --> 00:57:19.720 may have had something to do with our ability 00:57:19.720 --> 00:57:23.510 to keep financing ever-growing education. 00:57:23.510 --> 00:57:29.440 Remember that the public universities provide 00:57:29.440 --> 00:57:31.780 80% of higher education. 00:57:31.780 --> 00:57:35.052 That's the key component of the system. 00:57:35.052 --> 00:57:37.260 You know, places like MIT are all very well and good, 00:57:37.260 --> 00:57:40.000 but that's a 20% share. 00:57:40.000 --> 00:57:44.640 The core talent base are getting trained in public universities. 00:57:44.640 --> 00:57:46.140 And other things have been happening 00:57:46.140 --> 00:57:48.190 to those public universities. 00:57:48.190 --> 00:57:51.310 So they're competing at the state level 00:57:51.310 --> 00:57:55.740 for funding with two major factors. 00:57:55.740 --> 00:57:58.330 One is Medicaid, which the states bear 00:57:58.330 --> 00:58:00.860 a very large portion of and the other is prisons. 00:58:00.860 --> 00:58:03.290 Because we have a massive prison growth. 00:58:03.290 --> 00:58:05.983 And the annual cost of prisoners is 00:58:05.983 --> 00:58:08.150 much higher than putting people in higher education. 00:58:08.150 --> 00:58:10.025 You begin to wonder where your priorities are 00:58:10.025 --> 00:58:12.260 when you think about that. 00:58:12.260 --> 00:58:14.060 So there have been increased pressure 00:58:14.060 --> 00:58:17.300 on state budgets that correspond with that '73 00:58:17.300 --> 00:58:21.680 to like 1990 and 1991, kind of economic decline period that 00:58:21.680 --> 00:58:22.880 may account for it. 00:58:22.880 --> 00:58:27.080 Now the Obama administration understood this curve well. 00:58:27.080 --> 00:58:31.820 And Obama was fluent with Katz and Goldin's work. 00:58:31.820 --> 00:58:36.590 And they made a major effort to increase Pell grants 00:58:36.590 --> 00:58:40.550 and increase the availability of higher education funding. 00:58:40.550 --> 00:58:44.068 So interestingly, there was an improvement in the last decade 00:58:44.068 --> 00:58:45.110 in some of these numbers. 00:58:45.110 --> 00:58:48.170 Some of that was driven by the fact 00:58:48.170 --> 00:58:51.480 that jobs are a disaster in 2007 and 2008. 00:58:51.480 --> 00:58:55.610 So people tended to spend that time if they could afford it 00:58:55.610 --> 00:58:57.740 in higher education locations. 00:58:57.740 --> 00:58:59.300 So that was some of that going on. 00:58:59.300 --> 00:59:01.540 But overall, the Administration made an attempt 00:59:01.540 --> 00:59:03.290 to try and get those numbers turned around 00:59:03.290 --> 00:59:05.520 and they're a bit better. 00:59:05.520 --> 00:59:08.150 And the public universities themselves 00:59:08.150 --> 00:59:10.490 understood their mission and realized 00:59:10.490 --> 00:59:13.340 that they're going to have to increase their employment base 00:59:13.340 --> 00:59:16.760 in order to accomplish this. 00:59:16.760 --> 00:59:18.470 All right, so that's Katz and Goldin. 00:59:18.470 --> 00:59:21.050 I mean there's other important points there, but that's key. 00:59:21.050 --> 00:59:23.270 And then I want to do-- 00:59:23.270 --> 00:59:25.970 you know, we've talked about higher education 00:59:25.970 --> 00:59:28.610 as though it's the only way here. 00:59:28.610 --> 00:59:32.510 But I wanted to introduce some controversy into the debate. 00:59:32.510 --> 00:59:39.530 So William Baumol, very noted economist, taught at Princeton, 00:59:39.530 --> 00:59:43.810 more recent years have been teaching at NYU, you know, 00:59:43.810 --> 00:59:45.230 a remarkable analyst. 00:59:49.110 --> 00:59:51.750 And he's written in many different kind 00:59:51.750 --> 00:59:54.630 of economics fields. 00:59:54.630 --> 00:59:58.770 Baumol does this NBER, National Bureau 00:59:58.770 --> 01:00:04.110 of Economic Research kind of work paper 01:00:04.110 --> 01:00:07.940 that just nails the whole system. 01:00:07.940 --> 01:00:11.970 So I thought we'd introduce a little controversy 01:00:11.970 --> 01:00:15.810 in today's class by putting his piece in front of us. 01:00:15.810 --> 01:00:17.480 He argues that breakthrough innovation 01:00:17.480 --> 01:00:21.920 comes from independent inventors and entrepreneurs, 01:00:21.920 --> 01:00:25.430 that large firms concentrate on incremental innovation. 01:00:25.430 --> 01:00:28.280 We've talked about this bit before. 01:00:28.280 --> 01:00:31.070 And this conclusion is what is startling. 01:00:31.070 --> 01:00:34.480 Education for the mastery of science knowledge 01:00:34.480 --> 01:00:40.500 aids incremental advance, doesn't necessarily 01:00:40.500 --> 01:00:44.640 prepare you for doing the breakthrough, 01:00:44.640 --> 01:00:45.780 entrepreneurial side. 01:00:49.080 --> 01:00:53.640 So his point then, is that kind of standard science education 01:00:53.640 --> 01:00:57.530 may actually impede breakthrough thinking. 01:00:57.530 --> 01:01:00.600 And that large firm R&D requires scientists and engineers 01:01:00.600 --> 01:01:03.330 that are educated in the established 01:01:03.330 --> 01:01:06.540 fields and the established analytical methods 01:01:06.540 --> 01:01:11.370 and that successful innovators and entrepreneurs often 01:01:11.370 --> 01:01:15.670 lack that standard preparation. 01:01:15.670 --> 01:01:17.490 And that that may get them out of the box 01:01:17.490 --> 01:01:21.390 of incremental advance into kind of new territories. 01:01:21.390 --> 01:01:26.170 And he points out that we don't have a system 01:01:26.170 --> 01:01:31.630 and we don't understand breakthrough learning. 01:01:31.630 --> 01:01:33.490 How do we educate for innovation? 01:01:33.490 --> 01:01:39.080 We have no real clue on how to do that. 01:01:39.080 --> 01:01:41.080 And that procedures for incremental learning 01:01:41.080 --> 01:01:42.800 do seem to work. 01:01:42.800 --> 01:01:46.810 But we don't know how to educate for the innovation side-- 01:01:46.810 --> 01:01:48.760 of the innovation system, who's got what. 01:01:48.760 --> 01:01:53.520 He points out the Proctor & Gamble with 7,500 scientists, 01:01:53.520 --> 01:01:56.560 1,250 PhDs, I mean, these kinds of totals start 01:01:56.560 --> 01:02:01.010 to dwarf the size of faculties at MIT and Harvard and Stanford 01:02:01.010 --> 01:02:03.650 and so forth. 01:02:03.650 --> 01:02:05.500 You know, with 22 research centers, 01:02:05.500 --> 01:02:08.260 P&G has in 12 different nations, that's 01:02:08.260 --> 01:02:11.920 a pretty amazing talent base. 01:02:11.920 --> 01:02:14.770 What are they up to? 01:02:14.770 --> 01:02:21.520 Remember that when you look at R&D combined, industry 01:02:21.520 --> 01:02:25.090 spans about 70% of the total spending on R&D. Of course, 01:02:25.090 --> 01:02:29.740 we know that that's D, not R, right? 01:02:29.740 --> 01:02:32.890 But industry has got about a 70% share. 01:02:32.890 --> 01:02:39.220 The federal government, about 30% share, that's R. 01:02:39.220 --> 01:02:42.310 So that gives us an idea of the different size 01:02:42.310 --> 01:02:45.950 of the establishments in the R&D side. 01:02:45.950 --> 01:02:49.540 So we know that industry employs some 64% 01:02:49.540 --> 01:02:52.150 of scientists and engineers, right? 01:02:52.150 --> 01:02:56.050 So industry has got that talent base. 01:02:56.050 --> 01:02:59.830 And he notes that critical breakthrough 01:02:59.830 --> 01:03:03.930 historical innovator figures like Watt, Whitney, Fulton, 01:03:03.930 --> 01:03:08.200 Morris, Edison, the Wright brothers, Wozniak, Jobs, Gates, 01:03:08.200 --> 01:03:13.540 Dell, have no college degrees, and frankly, 01:03:13.540 --> 01:03:15.280 limited scientific training. 01:03:15.280 --> 01:03:17.030 AUDIENCE: Wait, Jobs had a college degree. 01:03:17.030 --> 01:03:19.072 WILLIAM BONVILLIAN: Yes, he had a college degree. 01:03:19.072 --> 01:03:20.170 No, he didn't finish Reed. 01:03:20.170 --> 01:03:21.290 AUDIENCE: No, he left in the first year. 01:03:21.290 --> 01:03:22.270 WILLIAM BONVILLIAN: Yeah, he left. 01:03:22.270 --> 01:03:23.330 He left after a year or two. 01:03:23.330 --> 01:03:24.100 AUDIENCE: I thought he went somewhere else. 01:03:24.100 --> 01:03:24.933 AUDIENCE: He stayed. 01:03:24.933 --> 01:03:27.360 He stayed with Reed and just dropped in on classes, 01:03:27.360 --> 01:03:29.110 but it wouldn't count as getting a degree. 01:03:29.110 --> 01:03:29.830 WILLIAM BONVILLIAN: He was studying things 01:03:29.830 --> 01:03:31.360 like calligraphy that actually turned out 01:03:31.360 --> 01:03:32.960 to be incredibly important for him. 01:03:32.960 --> 01:03:34.752 AUDIENCE: I mean, that's the quote example, 01:03:34.752 --> 01:03:36.505 he went a lot of classes [INAUDIBLE].. 01:03:36.505 --> 01:03:39.090 WILLIAM BONVILLIAN: Right. 01:03:39.090 --> 01:03:40.423 And he spent a year in India. 01:03:40.423 --> 01:03:42.548 AUDIENCE: Yeah, he did a lot of interesting things. 01:03:45.550 --> 01:03:49.900 WILLIAM BONVILLIAN: So Baumol's point is progress requires-- 01:03:49.900 --> 01:03:51.640 we shouldn't underestimate this. 01:03:51.640 --> 01:03:53.710 It's not that one side is bad. 01:03:53.710 --> 01:03:58.090 Progress requires both breakthrough radical advance 01:03:58.090 --> 01:04:00.100 and incremental advance. 01:04:00.100 --> 01:04:03.190 And an example, which I've used before with you all, you 01:04:03.190 --> 01:04:06.220 know, if you're flying across the Atlantic Ocean, 01:04:06.220 --> 01:04:09.520 the Wright brothers, you know, motorized kite 01:04:09.520 --> 01:04:12.070 is a terrific, radical break through advance. 01:04:12.070 --> 01:04:15.070 But I'd rather take the 787, product 01:04:15.070 --> 01:04:17.560 of 10 decades worth of incremental advances when 01:04:17.560 --> 01:04:19.300 I go across the ocean. 01:04:19.300 --> 01:04:21.520 So both are really important here. 01:04:21.520 --> 01:04:23.215 You've got to do both pieces. 01:04:26.620 --> 01:04:30.370 But, a disproportionate share of the breakthroughs do 01:04:30.370 --> 01:04:35.140 seem to come from kind of independent inventors 01:04:35.140 --> 01:04:36.160 or entrepreneurs. 01:04:36.160 --> 01:04:39.947 And large firms tend to specialize on the incremental, 01:04:39.947 --> 01:04:42.280 in part because they want to break up their own business 01:04:42.280 --> 01:04:43.050 models, right? 01:04:43.050 --> 01:04:44.717 They've got established business models. 01:04:44.717 --> 01:04:46.780 They want to contribute to those business models 01:04:46.780 --> 01:04:49.990 rather than wreck their existing business model. 01:04:49.990 --> 01:04:52.240 So that's part of the economic motivation here. 01:04:54.970 --> 01:04:58.990 But education for incremental advance 01:04:58.990 --> 01:05:02.110 may well be different as Baumol points out, than education 01:05:02.110 --> 01:05:05.770 for the novel advance. 01:05:05.770 --> 01:05:07.650 And incremental improvement may well 01:05:07.650 --> 01:05:10.930 require a much greater mastery of demanding 01:05:10.930 --> 01:05:14.650 science and technology information than the novel 01:05:14.650 --> 01:05:16.360 idea. 01:05:16.360 --> 01:05:18.440 So both are essential. 01:05:18.440 --> 01:05:21.700 But then he posts the critical question, 01:05:21.700 --> 01:05:23.920 how do you educate for the original and novel idea 01:05:23.920 --> 01:05:25.510 generation? 01:05:25.510 --> 01:05:33.223 So we've got three new stories on the table here. 01:05:33.223 --> 01:05:35.140 And let's start off discussion, and then we'll 01:05:35.140 --> 01:05:36.610 take a break in a bit. 01:05:36.610 --> 01:05:38.330 First, we've got Richard Freeman. 01:05:38.330 --> 01:05:39.790 AUDIENCE: Yes. 01:05:39.790 --> 01:05:42.190 I wanted to make some strides actually 01:05:42.190 --> 01:05:45.940 on a point to that Baumol made in our conversation of three 01:05:45.940 --> 01:05:49.543 months since Baumol was the one who cited P&G 01:05:49.543 --> 01:05:51.460 as an innovative research and development firm 01:05:51.460 --> 01:05:53.360 with a lot of PhDs. 01:05:53.360 --> 01:05:57.100 One of you posed the question about Freeman. 01:05:57.100 --> 01:05:58.540 This paper lauds the relationship 01:05:58.540 --> 01:06:01.690 in the United States between firms and university research. 01:06:01.690 --> 01:06:03.970 For multi-national companies, what incentives 01:06:03.970 --> 01:06:06.850 do they have to promote US innovation leadership, even 01:06:06.850 --> 01:06:09.010 when they may be based in the US when they operate 01:06:09.010 --> 01:06:10.750 in so many different countries? 01:06:10.750 --> 01:06:12.340 So at the heart of this question is, 01:06:12.340 --> 01:06:16.030 why do we need to invest in America 01:06:16.030 --> 01:06:19.450 if it is that the private sector is not investing and has 01:06:19.450 --> 01:06:22.240 no rational self interest to invest in us 01:06:22.240 --> 01:06:25.370 from an economic perspective? 01:06:25.370 --> 01:06:27.650 That is the implication of this question. 01:06:27.650 --> 01:06:29.108 AUDIENCE: I don't think it's like-- 01:06:29.108 --> 01:06:31.130 I think that we look at it one dimensionally. 01:06:31.130 --> 01:06:32.710 You have to look at it, like, think about it 01:06:32.710 --> 01:06:33.340 like an individual, right? 01:06:33.340 --> 01:06:34.340 Like you're going to be a baby. 01:06:34.340 --> 01:06:35.715 There's a whole gestation period. 01:06:35.715 --> 01:06:37.678 Then there's a time period where you're trying 01:06:37.678 --> 01:06:38.720 to grow as an individual. 01:06:38.720 --> 01:06:41.178 And then there's one when you're ready to get to it, right? 01:06:41.178 --> 01:06:43.410 And like you're very useful. 01:06:43.410 --> 01:06:45.080 So for industry like, they can't really 01:06:45.080 --> 01:06:47.130 handle this whole gestation period, 01:06:47.130 --> 01:06:49.740 you know, one to 18 years old period. 01:06:49.740 --> 01:06:51.890 But they can handle it after, when 01:06:51.890 --> 01:06:54.930 you're ready to go and do a little ramp up skills. 01:06:54.930 --> 01:06:58.930 But to do a whole thing is pretty difficult. 01:06:58.930 --> 01:07:01.140 AUDIENCE: That's an interesting answer. 01:07:01.140 --> 01:07:03.930 WILLIAM BONVILLIAN: I'd add too that the US has an extremely 01:07:03.930 --> 01:07:06.390 decentralized labor market. 01:07:06.390 --> 01:07:10.110 So there is a huge disincentive for employers in the United 01:07:10.110 --> 01:07:12.960 States to offer training. 01:07:12.960 --> 01:07:18.510 Because they offer training, which can be quite expensive, 01:07:18.510 --> 01:07:23.070 investing in you, that in turn equips you to move on. 01:07:23.070 --> 01:07:27.390 So often another employer will buy you out 01:07:27.390 --> 01:07:30.210 for a lower margin with the educational costs. 01:07:30.210 --> 01:07:32.640 But the employer who invested in your education 01:07:32.640 --> 01:07:36.420 won't be able to match that increment. 01:07:36.420 --> 01:07:40.080 So why should they invest in employees 01:07:40.080 --> 01:07:42.540 if the talent is going to go elsewhere? 01:07:42.540 --> 01:07:45.390 And countries like Germany have a much more established set 01:07:45.390 --> 01:07:49.380 of apprenticeship rules and much more heavily unionized, 01:07:49.380 --> 01:07:53.040 80% unionized in the manufacturing sector. 01:07:53.040 --> 01:07:56.130 They have been able to create an apprenticeship system that's 01:07:56.130 --> 01:07:58.800 much more enduring and employer tied. 01:07:58.800 --> 01:08:01.770 That creates tremendous encouragement for employers 01:08:01.770 --> 01:08:06.060 to provide lots of skills training to its employees. 01:08:06.060 --> 01:08:08.700 Indeed, it's really a part of the German education system. 01:08:08.700 --> 01:08:10.470 We have never really been able, because we 01:08:10.470 --> 01:08:14.550 have such a decentralized and almost laissez faire labor 01:08:14.550 --> 01:08:18.899 market, we've never been able to create 01:08:18.899 --> 01:08:22.460 significant incentives for employers to provide education. 01:08:22.460 --> 01:08:24.797 It's a deep structural problem in our system. 01:08:24.797 --> 01:08:26.880 And you know it's one we're going to probably have 01:08:26.880 --> 01:08:28.172 to figure out how to deal with. 01:08:28.172 --> 01:08:31.270 We have created a whole system of community colleges. 01:08:31.270 --> 01:08:32.850 But the burden is on the employee 01:08:32.850 --> 01:08:34.470 to go back and get that education. 01:08:34.470 --> 01:08:37.170 We make that pretty inexpensive. 01:08:37.170 --> 01:08:39.470 AUDIENCE: Going back here about the gatekeeper thing. 01:08:39.470 --> 01:08:42.143 Couldn't we just bribe them? 01:08:42.143 --> 01:08:43.810 WILLIAM BONVILLIAN: Bribe the employees? 01:08:43.810 --> 01:08:44.109 AUDIENCE: Yeah. 01:08:44.109 --> 01:08:44.520 Oh, no. 01:08:44.520 --> 01:08:45.180 WILLIAM BONVILLIAN: Bribe the employers? 01:08:45.180 --> 01:08:45.750 AUDIENCE: Yeah. 01:08:45.750 --> 01:08:46.319 WILLIAM BONVILLIAN: Right, we could. 01:08:46.319 --> 01:08:47.970 AUDIENCE: The education, how much is it? 01:08:47.970 --> 01:08:49.680 WILLIAM BONVILLIAN: I'm sure it's not cheap, right? 01:08:49.680 --> 01:08:50.109 AUDIENCE: Damn it. 01:08:50.109 --> 01:08:50.880 WILLIAM BONVILLIAN: But in other words, 01:08:50.880 --> 01:08:53.460 could we create an incentive for employers 01:08:53.460 --> 01:08:57.450 to provide the education system, even if they weren't 01:08:57.450 --> 01:08:59.729 able to retain their workers. 01:08:59.729 --> 01:09:01.590 And what would that cost? 01:09:01.590 --> 01:09:03.600 So maybe the feds would pick up that cost. 01:09:03.600 --> 01:09:06.569 So in a way, the community college system 01:09:06.569 --> 01:09:08.939 is a way of the government picking up that 01:09:08.939 --> 01:09:13.720 Cost and share it with the employee. 01:09:13.720 --> 01:09:17.560 Because employers can't take the risk mitigating. 01:09:17.560 --> 01:09:20.411 I kind of jumped into the discussion here. 01:09:20.411 --> 01:09:21.953 I want to give it back to you, Steph. 01:09:21.953 --> 01:09:23.569 Get me out of this. 01:09:23.569 --> 01:09:26.529 AUDIENCE: I think that's-- who posed this question? 01:09:26.529 --> 01:09:27.970 Yeah, I think you're right. 01:09:27.970 --> 01:09:31.330 I mean it really gets at the heart of all three readings. 01:09:31.330 --> 01:09:33.580 And I think, in particular, let me 01:09:33.580 --> 01:09:38.920 see if I can parse for a quote here from one of the readings. 01:09:38.920 --> 01:09:41.229 Actually it was in Freeman's reading, when he quoted 01:09:41.229 --> 01:09:43.979 Derek Bok, who is the founder-- 01:09:43.979 --> 01:09:44.640 I think-- 01:09:44.640 --> 01:09:45.550 WILLIAM BONVILLIAN: President of Harvard. 01:09:45.550 --> 01:09:46.859 AUDIENCE: President of Harvard. 01:09:46.859 --> 01:09:49.240 And now the graduate school of education at Harvard 01:09:49.240 --> 01:09:52.720 has a center for innovative learning named after Derek Bok. 01:09:52.720 --> 01:09:55.750 And he claimed towards the end of the piece, 01:09:55.750 --> 01:09:58.330 or rather he was cited towards the end of the piece 01:09:58.330 --> 01:10:01.690 as saying that other countries are facilitating our enterprise 01:10:01.690 --> 01:10:02.660 model. 01:10:02.660 --> 01:10:07.350 And if, I think it's really meritous of consideration, 01:10:07.350 --> 01:10:09.880 that if our education system is not supporting 01:10:09.880 --> 01:10:13.030 the production of scientists and engineers, and firms are you 01:10:13.030 --> 01:10:15.070 know sort of ready and happy to get employees 01:10:15.070 --> 01:10:17.440 from other countries or to move to other countries, 01:10:17.440 --> 01:10:19.840 that we're effectively shooting ourself in a foot, 01:10:19.840 --> 01:10:22.810 by participating in free market principles for education. 01:10:22.810 --> 01:10:24.618 Because we can't provide the labor force 01:10:24.618 --> 01:10:26.410 and we can't support the labor force and we 01:10:26.410 --> 01:10:28.540 can't support incentives financially. 01:10:28.540 --> 01:10:32.470 So at the end of the day, we're preventing ourselves 01:10:32.470 --> 01:10:34.570 on all fronts from innovating, which 01:10:34.570 --> 01:10:38.260 I think is a really important consideration that merits sort 01:10:38.260 --> 01:10:40.880 of being honest with ourselves, as not only a society, 01:10:40.880 --> 01:10:44.430 but also as you know a political economy. 01:10:44.430 --> 01:10:47.050 AUDIENCE: Something throughout all the readings 01:10:47.050 --> 01:10:48.640 that I have not understood. 01:10:48.640 --> 01:10:51.790 How is it that we're spending so much on education, 01:10:51.790 --> 01:10:54.840 like so much money per student, yet teachers 01:10:54.840 --> 01:10:58.240 are not getting any of it and the students 01:10:58.240 --> 01:10:59.940 don't have very high quality educations. 01:10:59.940 --> 01:11:00.940 Where's the money going? 01:11:00.940 --> 01:11:02.315 AUDIENCE: Yeah, Max, I think that 01:11:02.315 --> 01:11:04.210 was addressed by Norman Augustine's piece 01:11:04.210 --> 01:11:06.610 very slightly when he talked about the appropriation 01:11:06.610 --> 01:11:08.590 of funds and where spending goes. 01:11:08.590 --> 01:11:11.110 Specifically Augustine cited that 61% 01:11:11.110 --> 01:11:13.120 of education budgets in schools tend 01:11:13.120 --> 01:11:15.920 to go to education spending sort of broadly. 01:11:15.920 --> 01:11:17.890 And that it actually tends to trend downward 01:11:17.890 --> 01:11:19.390 for a lot of school districts who 01:11:19.390 --> 01:11:22.570 focus on other extracurricular activities like sports. 01:11:22.570 --> 01:11:24.880 And I think he made that very brief note 01:11:24.880 --> 01:11:29.020 about the role of American culture 01:11:29.020 --> 01:11:31.780 in those funding priorities. 01:11:31.780 --> 01:11:35.050 And I think that also was a consideration 01:11:35.050 --> 01:11:37.900 of mine in reading this piece in conjunction 01:11:37.900 --> 01:11:39.010 with the Freeman piece. 01:11:39.010 --> 01:11:43.480 Like, what is the role that our own vision of ourselves 01:11:43.480 --> 01:11:46.300 and how we want to educate our children 01:11:46.300 --> 01:11:51.550 informs our policy decisions and spending down the line. 01:11:51.550 --> 01:11:54.430 And you brought the point earlier that 01:11:54.430 --> 01:11:56.200 how is it that we don't have-- or that we 01:11:56.200 --> 01:11:58.750 have too many jobs that we don't have enough jobs 01:11:58.750 --> 01:12:02.290 but have too many PhDs, but then not have enough PhDs. 01:12:02.290 --> 01:12:05.170 And I think that the point that Bill was trying to make 01:12:05.170 --> 01:12:08.350 is that we have too many PhDs who 01:12:08.350 --> 01:12:10.750 are trying to go into academia, but not enough PhDs who 01:12:10.750 --> 01:12:14.380 are prepared for industry and not enough PhDs generally 01:12:14.380 --> 01:12:15.970 to go into industry. 01:12:15.970 --> 01:12:17.620 Does that clear things up a little? 01:12:17.620 --> 01:12:19.203 AUDIENCE: That clears things up a lot. 01:12:19.203 --> 01:12:20.000 AUDIENCE: OK. 01:12:20.000 --> 01:12:20.770 WILLIAM BONVILLIAN: Yeah, I think I'd 01:12:20.770 --> 01:12:22.080 add an additional point there. 01:12:22.080 --> 01:12:24.840 And this is a complex one that you are free to disagree with. 01:12:24.840 --> 01:12:31.570 But, when we created the higher education system in the US, 01:12:31.570 --> 01:12:35.230 obviously it occurred over the process of centuries. 01:12:35.230 --> 01:12:38.980 And when the federal government, during and following World War 01:12:38.980 --> 01:12:41.230 II, came into a very significant support 01:12:41.230 --> 01:12:45.010 role for that system in addition to the states, 01:12:45.010 --> 01:12:50.170 that was already a quite competitive system. 01:12:50.170 --> 01:12:52.870 And as someone who has spent substantial amount of time 01:12:52.870 --> 01:12:56.860 with MIT's top administrators, I can tell you 01:12:56.860 --> 01:13:01.220 that MIT and other universities do exactly the same thing, 01:13:01.220 --> 01:13:05.320 they watched their university competitors like hawks. 01:13:05.320 --> 01:13:07.120 They know exactly what they're up to. 01:13:07.120 --> 01:13:09.340 They know exactly what they're spending on what. 01:13:09.340 --> 01:13:12.220 They know exactly what their competition models are. 01:13:12.220 --> 01:13:17.080 They're looking very hard at how MIT will compete all the time. 01:13:17.080 --> 01:13:20.350 It is an extremely competitive model. 01:13:20.350 --> 01:13:26.170 It drives a tremendous search for talent at the faculty level 01:13:26.170 --> 01:13:27.670 as well as the student level. 01:13:27.670 --> 01:13:31.660 And that talent base is very well compensated. 01:13:31.660 --> 01:13:35.380 And that is a core way by which you compete for talent 01:13:35.380 --> 01:13:37.330 in a competitive system. 01:13:37.330 --> 01:13:43.090 We don't really have a competitive system in the K-12 01:13:43.090 --> 01:13:44.270 public education system. 01:13:44.270 --> 01:13:46.660 It's essentially a monopoly-based socialist model 01:13:46.660 --> 01:13:48.010 frankly. 01:13:48.010 --> 01:13:52.570 And you know part of the entry of charter school legislation 01:13:52.570 --> 01:13:55.840 was to introduce a competitive model into the K 01:13:55.840 --> 01:13:57.010 through 12 system. 01:13:57.010 --> 01:13:59.630 Now there's a big debate as to how well that's worked. 01:13:59.630 --> 01:14:01.990 But I think it's fair to say overall, 01:14:01.990 --> 01:14:04.820 it's added a significant dose of competition into that system. 01:14:07.160 --> 01:14:07.660 And 01:14:07.660 --> 01:14:09.970 You could look at competition in health care. 01:14:09.970 --> 01:14:12.150 And you could competition and you 01:14:12.150 --> 01:14:15.010 know other large government supported systems too. 01:14:15.010 --> 01:14:17.560 But you know, when we came out of World War II, 01:14:17.560 --> 01:14:21.200 we created a socialist model for caring for veterans 01:14:21.200 --> 01:14:23.750 in federally owned hospitals. 01:14:23.750 --> 01:14:27.790 And we fit into what was already a competitive space 01:14:27.790 --> 01:14:31.390 by supporting students, who in turn were given funding 01:14:31.390 --> 01:14:32.200 and they could go. 01:14:32.200 --> 01:14:33.700 They could take the money with them. 01:14:33.700 --> 01:14:36.140 Didn't go to the universities to fund them. 01:14:36.140 --> 01:14:39.250 So it's introduction of more competitive models 01:14:39.250 --> 01:14:42.280 here may have something to do with improvements 01:14:42.280 --> 01:14:43.400 in higher education. 01:14:43.400 --> 01:14:45.760 At least that was the concept behind introducing 01:14:45.760 --> 01:14:47.200 charter schools. 01:14:47.200 --> 01:14:48.820 AUDIENCE: Could I ask you a followup? 01:14:48.820 --> 01:14:51.160 I know that other countries have normal schools which 01:14:51.160 --> 01:14:52.075 are schools meant to train-- 01:14:52.075 --> 01:14:54.340 WILLIAM BONVILLIAN: Yeah, other countries have socialist models 01:14:54.340 --> 01:14:55.470 and make them work. 01:14:55.470 --> 01:14:58.110 But we're just not as good at that. 01:14:58.110 --> 01:15:00.130 AUDIENCE: What prevented the United States 01:15:00.130 --> 01:15:03.452 from developing normal schools or incorporating normal schools 01:15:03.452 --> 01:15:04.160 into their model? 01:15:06.602 --> 01:15:08.310 WILLIAM BONVILLIAN: I don't want to claim 01:15:08.310 --> 01:15:10.680 to be an expert on the creation of normal schools. 01:15:10.680 --> 01:15:14.340 But in the 19th century, the US did 01:15:14.340 --> 01:15:19.970 create both the institutions for mass higher education, i.e. 01:15:19.970 --> 01:15:23.730 the public universities, and high school education 01:15:23.730 --> 01:15:26.250 in a very short period of time. 01:15:26.250 --> 01:15:28.200 And communities were essentially realizing 01:15:28.200 --> 01:15:30.600 that they had to upgrade the skills 01:15:30.600 --> 01:15:33.420 for an industrial economy as their population shifted 01:15:33.420 --> 01:15:39.950 from farming to working in manufacturing firms primarily. 01:15:39.950 --> 01:15:42.750 And they understood the need for upgraded skill sets. 01:15:42.750 --> 01:15:45.780 And there was an effort across the country 01:15:45.780 --> 01:15:47.040 to create high schools. 01:15:47.040 --> 01:15:50.640 Now some regions, particularly New England, had them. 01:15:50.640 --> 01:15:54.030 But the rest of the country, including the American South, 01:15:54.030 --> 01:15:58.920 is pretty quick to replicate a high school 01:15:58.920 --> 01:16:02.850 model as a fix for a needed skill set. 01:16:02.850 --> 01:16:05.660 So that is a massive social policy 01:16:05.660 --> 01:16:09.018 that was done in a remarkably short period of decades 01:16:09.018 --> 01:16:09.810 across the country. 01:16:09.810 --> 01:16:12.648 So the country is capable of making really major changes. 01:16:17.510 --> 01:16:19.200 I don't think I answered your question. 01:16:19.200 --> 01:16:20.120 AUDIENCE: That's OK. 01:16:20.120 --> 01:16:26.330 I just certainly think it's important to consider the-- 01:16:26.330 --> 01:16:28.740 as it was highlighted I think in several 01:16:28.740 --> 01:16:30.710 of the readings, the role of teachers, 01:16:30.710 --> 01:16:34.580 one, moving forward in the future, two, 01:16:34.580 --> 01:16:38.360 how teachers are currently being educated, three, 01:16:38.360 --> 01:16:43.460 teachers are being compensated and sustained within the model. 01:16:43.460 --> 01:16:46.220 They seem to be sort of an undercurrent of what we're 01:16:46.220 --> 01:16:48.770 talking about, but not something that any of the readings 01:16:48.770 --> 01:16:51.950 are willing to address specifically, and perhaps 01:16:51.950 --> 01:16:54.910 strategically for political reasons.