1 00:00:00,090 --> 00:00:02,430 The following content is provided under a Creative 2 00:00:02,430 --> 00:00:03,820 Commons license. 3 00:00:03,820 --> 00:00:06,030 Your support will help MIT OpenCourseWare 4 00:00:06,030 --> 00:00:10,120 continue to offer high quality educational resources for free. 5 00:00:10,120 --> 00:00:12,660 To make a donation or to view additional materials 6 00:00:12,660 --> 00:00:16,620 from hundreds of MIT courses, visit MIT OpenCourseWare 7 00:00:16,620 --> 00:00:17,992 at ocw.mit.edu. 8 00:00:21,220 --> 00:00:22,470 WILLIAM BONVILLIAN: All right. 9 00:00:22,470 --> 00:00:25,230 Let's dive right into education. 10 00:00:25,230 --> 00:00:29,580 And, you know, this is the other side of the innovation 11 00:00:29,580 --> 00:00:30,620 equation, right? 12 00:00:30,620 --> 00:00:33,870 We've talked in terms of institutions 13 00:00:33,870 --> 00:00:36,150 and linking and connecting institutions, 14 00:00:36,150 --> 00:00:39,810 and we've talked a lot about R&D and the R&D system, 15 00:00:39,810 --> 00:00:42,060 but you know, from the first class on, 16 00:00:42,060 --> 00:00:43,950 Romer taught us that the talent base 17 00:00:43,950 --> 00:00:47,070 is a very critical consideration in innovation. 18 00:00:47,070 --> 00:00:49,440 And how do you build up the talent base? 19 00:00:49,440 --> 00:00:54,090 That's essentially our set of tests today. 20 00:00:54,090 --> 00:00:55,500 So, first, Norm Augustine. 21 00:00:55,500 --> 00:00:58,440 I wanted to acquaint you with Norm Augustine, 22 00:00:58,440 --> 00:01:01,560 because in the science and technology community, 23 00:01:01,560 --> 00:01:04,170 he's known as St. Augustine. 24 00:01:04,170 --> 00:01:09,030 And he is just an incredible stand-up figure 25 00:01:09,030 --> 00:01:13,350 on issues like the importance of federal R&D investment. 26 00:01:13,350 --> 00:01:15,210 He was chairman for a lengthy period 27 00:01:15,210 --> 00:01:18,930 of time of Lockheed Martin. 28 00:01:18,930 --> 00:01:24,390 He won the President's Medal of Technology. 29 00:01:24,390 --> 00:01:30,090 Just a noted innovator, himself, but also a true expert 30 00:01:30,090 --> 00:01:32,700 on R&D issues, R&D policies. 31 00:01:32,700 --> 00:01:37,740 Unfailingly helpful and willing to volunteer for whatever task 32 00:01:37,740 --> 00:01:40,920 the National Academies or in many cases MIT, 33 00:01:40,920 --> 00:01:44,970 or other organizations kind of need help and advice 34 00:01:44,970 --> 00:01:47,360 from a real senior statesman, Augustine 35 00:01:47,360 --> 00:01:49,360 has always been willing to step up to the plate. 36 00:01:49,360 --> 00:01:51,360 So he's kind of a remarkable figure. 37 00:01:51,360 --> 00:01:57,300 He led the rising against the Gathering Storm report 38 00:01:57,300 --> 00:02:02,780 back in 2000-- 39 00:02:02,780 --> 00:02:06,300 around the early 2000s timetable-- 40 00:02:06,300 --> 00:02:07,830 along with people like Chuck Vest, 41 00:02:07,830 --> 00:02:10,080 and a really noted community of other experts, 42 00:02:10,080 --> 00:02:14,760 that made the argument for a very significant R&D increase 43 00:02:14,760 --> 00:02:16,710 for the physical science agencies. 44 00:02:16,710 --> 00:02:20,640 Around that time, we had been doubling NIH. 45 00:02:20,640 --> 00:02:22,830 This report made the case for doubling 46 00:02:22,830 --> 00:02:27,010 the physical science-based R&D areas, as well. 47 00:02:27,010 --> 00:02:29,820 And it was a very influential and important report. 48 00:02:29,820 --> 00:02:32,160 He was one of the real leaders of it, 49 00:02:32,160 --> 00:02:34,530 and he played an important role, along with Chuck Vest, 50 00:02:34,530 --> 00:02:42,050 in helping persuade the latest Bush administration-- 51 00:02:42,050 --> 00:02:45,400 hi, Karen-- to adopt its recommendations. 52 00:02:45,400 --> 00:02:50,350 So there was a period of time of ongoing R&D 53 00:02:50,350 --> 00:02:54,950 increases for the physical science agencies. 54 00:02:54,950 --> 00:02:57,760 So the report had a result. 55 00:02:57,760 --> 00:03:03,180 In addition, he came back and wrote this 2007 report 56 00:03:03,180 --> 00:03:07,000 that just kind of summarized the trouble 57 00:03:07,000 --> 00:03:10,930 the US has got on the Science and Technology education front. 58 00:03:10,930 --> 00:03:14,080 So it's a 2007 snapshot, but it's still 59 00:03:14,080 --> 00:03:17,137 a great little collection of key points. 60 00:03:17,137 --> 00:03:18,970 I'm just going to summarize it very briefly. 61 00:03:21,720 --> 00:03:25,690 Key finding number one was that US children are not 62 00:03:25,690 --> 00:03:29,570 prepared for the 21st century jobs, 63 00:03:29,570 --> 00:03:32,760 and he has data sets, as you know, 64 00:03:32,760 --> 00:03:36,840 to back all these points up. 65 00:03:36,840 --> 00:03:38,670 Children in school are being taught 66 00:03:38,670 --> 00:03:42,600 by teachers that are not trained in the fields of math 67 00:03:42,600 --> 00:03:44,220 and science that they're teaching in, 68 00:03:44,220 --> 00:03:48,300 which is a major underlying problem in those students 69 00:03:48,300 --> 00:03:51,390 picking up those fields. 70 00:03:51,390 --> 00:03:54,960 US children are falling behind their foreign peers 71 00:03:54,960 --> 00:03:58,530 and counterparts in science and technology areas. 72 00:03:58,530 --> 00:04:00,780 The US K through 12 system just is not 73 00:04:00,780 --> 00:04:06,000 performing in a way that's comparable to many other 74 00:04:06,000 --> 00:04:08,650 systems in the world. 75 00:04:08,650 --> 00:04:13,500 And then, kind of a fourth point is that US secondary education 76 00:04:13,500 --> 00:04:16,157 isn't preparing students-- 77 00:04:16,157 --> 00:04:17,490 aside from the teacher problem-- 78 00:04:17,490 --> 00:04:19,860 for math, science, or engineering majors, 79 00:04:19,860 --> 00:04:22,170 and too few students-- 80 00:04:22,170 --> 00:04:23,310 this is the key point-- 81 00:04:23,310 --> 00:04:25,890 are majoring in those disciplines 82 00:04:25,890 --> 00:04:30,120 to yield the talent base that we need 83 00:04:30,120 --> 00:04:32,305 for technology-related careers. 84 00:04:35,400 --> 00:04:37,650 And just to underscore that, the US 85 00:04:37,650 --> 00:04:40,710 ranks 17th among developed nations in the proportion 86 00:04:40,710 --> 00:04:42,990 of college students receiving degrees in science 87 00:04:42,990 --> 00:04:48,040 and engineering, and it fell from third place 88 00:04:48,040 --> 00:04:49,500 30 years before. 89 00:04:49,500 --> 00:04:56,730 So these are dilemmas that the US K through 12 system has. 90 00:04:56,730 --> 00:04:58,890 So that's the K through 12 story. 91 00:04:58,890 --> 00:05:02,250 Then we shift to our friend Paul Romer, 92 00:05:02,250 --> 00:05:07,710 who we visited at the outset of this class. 93 00:05:07,710 --> 00:05:12,360 And as you know, he taught at Stanford, 94 00:05:12,360 --> 00:05:14,250 and then has been teaching at NYU. 95 00:05:14,250 --> 00:05:17,700 He's now chief economist to the World Bank, 96 00:05:17,700 --> 00:05:23,850 where he's been a fascinating critic of economics and changes 97 00:05:23,850 --> 00:05:28,140 and reforms that field needs to go through. 98 00:05:28,140 --> 00:05:32,400 But he came up with prospector theory, and in a way, 99 00:05:32,400 --> 00:05:37,960 what he's playing out here in this pretty noted critique from 100 00:05:37,960 --> 00:05:41,790 2000-- which is still quite widely read-- 101 00:05:41,790 --> 00:05:46,470 his critique is, what happened to the supply? 102 00:05:46,470 --> 00:05:48,480 What are our supply breakdowns? 103 00:05:48,480 --> 00:05:52,440 And then he's identified those supply breakdowns 104 00:05:52,440 --> 00:05:55,230 with higher education institutions. 105 00:05:55,230 --> 00:05:57,490 What are they failing to deliver? 106 00:05:57,490 --> 00:06:02,040 So the issue is that the federal government 107 00:06:02,040 --> 00:06:03,990 policies in science and technology 108 00:06:03,990 --> 00:06:07,470 tend to subsidize demand. 109 00:06:07,470 --> 00:06:10,710 So in the private sector, it's primarily tax incentives, 110 00:06:10,710 --> 00:06:11,880 R&D tax credits. 111 00:06:14,490 --> 00:06:17,700 And you promote demand to encourage 112 00:06:17,700 --> 00:06:19,230 science and engineering talent. 113 00:06:22,380 --> 00:06:26,070 That policy approach, which is astronomically expensive-- 114 00:06:26,070 --> 00:06:29,940 those are very expensive policies-- 115 00:06:29,940 --> 00:06:32,610 that policy approach doesn't inquire 116 00:06:32,610 --> 00:06:38,640 about the supply response that hopefully those subsidies 117 00:06:38,640 --> 00:06:39,510 elicit. 118 00:06:39,510 --> 00:06:43,860 And he goes back and argues that the institutional arrangements 119 00:06:43,860 --> 00:06:48,210 in universities, which are the key institutions on the supply 120 00:06:48,210 --> 00:06:55,270 side, are geared to meeting the needs in a variety of ways. 121 00:06:55,270 --> 00:06:57,270 So there needs to be a new incentive system 122 00:06:57,270 --> 00:07:00,570 to start to turn around the supply side 123 00:07:00,570 --> 00:07:04,890 because the current demand-based tax incentive system just 124 00:07:04,890 --> 00:07:06,720 isn't doing that. 125 00:07:06,720 --> 00:07:09,480 So that's his overall frame. 126 00:07:09,480 --> 00:07:13,140 In the 20th century, he argues, rapid technological progress 127 00:07:13,140 --> 00:07:16,690 drove unprecedented growth. 128 00:07:16,690 --> 00:07:19,170 We know that argument. 129 00:07:19,170 --> 00:07:22,740 And that was fostered by a publicly supported system 130 00:07:22,740 --> 00:07:25,170 of education. 131 00:07:25,170 --> 00:07:28,410 So a steady flow of trained talent, 132 00:07:28,410 --> 00:07:29,850 trained in the scientific method, 133 00:07:29,850 --> 00:07:33,540 was a core policy that evolved over time, 134 00:07:33,540 --> 00:07:40,800 but that public policy approach ignored the structures that 135 00:07:40,800 --> 00:07:43,710 were supposed to deliver that talent base, 136 00:07:43,710 --> 00:07:45,690 i.e. the higher education system, 137 00:07:45,690 --> 00:07:48,060 and the incentives and disincentives 138 00:07:48,060 --> 00:07:51,510 within that higher education structure that make that supply 139 00:07:51,510 --> 00:07:53,250 side problematic. 140 00:07:53,250 --> 00:07:55,620 So he would argue that a governmental-- set 141 00:07:55,620 --> 00:08:00,180 of governmental programs to speed up innovation 142 00:08:00,180 --> 00:08:04,860 gets thwarted by the supply side problem, 143 00:08:04,860 --> 00:08:07,770 in particular by the higher education structure. 144 00:08:07,770 --> 00:08:12,690 So government programs focused on the demand and not 145 00:08:12,690 --> 00:08:17,010 the supply side are going to undermine the innovation 146 00:08:17,010 --> 00:08:18,300 capabilities that we need. 147 00:08:21,210 --> 00:08:25,230 Then he steps back and kind of makes the innovation argument 148 00:08:25,230 --> 00:08:26,250 for us. 149 00:08:26,250 --> 00:08:28,323 He argues that speeding up growth 150 00:08:28,323 --> 00:08:29,740 is really the only way we're going 151 00:08:29,740 --> 00:08:33,720 to be able to cope with the oncoming demographics 152 00:08:33,720 --> 00:08:36,990 that we talked about when we discuss the health care 153 00:08:36,990 --> 00:08:39,450 innovation system. 154 00:08:39,450 --> 00:08:43,289 He says that a conservative estimate of the return on R&D 155 00:08:43,289 --> 00:08:47,280 spending would be a 25% return. 156 00:08:47,280 --> 00:08:50,670 There's arguments that that's low, that it's well over 50%, 157 00:08:50,670 --> 00:08:53,910 but let's accept his 25%. 158 00:08:53,910 --> 00:08:55,320 His argument is that if you-- 159 00:08:55,320 --> 00:08:57,510 in theory, that if you increased R&D spending 160 00:08:57,510 --> 00:09:04,530 by 2% of GDP, voila, we'd get a half percentage 161 00:09:04,530 --> 00:09:05,650 point of growth. 162 00:09:05,650 --> 00:09:06,150 Right? 163 00:09:06,150 --> 00:09:09,660 In an economy that has 0.7% growth at the moment, 164 00:09:09,660 --> 00:09:11,610 this is not a minor concept. 165 00:09:11,610 --> 00:09:12,660 Right? 166 00:09:12,660 --> 00:09:14,660 And the data would tend to-- 167 00:09:14,660 --> 00:09:17,100 it at least in theory-- bear him out. 168 00:09:17,100 --> 00:09:22,290 But then you run into the barrier, right? 169 00:09:22,290 --> 00:09:24,490 You have to look at the full system, 170 00:09:24,490 --> 00:09:27,310 and that means also looking at the talent. 171 00:09:27,310 --> 00:09:33,240 So just increasing the R&D spending per se 172 00:09:33,240 --> 00:09:39,230 doesn't address what he calls the supply side, 173 00:09:39,230 --> 00:09:42,770 and if the total number of scientists and engineers 174 00:09:42,770 --> 00:09:46,130 is fixed, then you limit-- 175 00:09:46,130 --> 00:09:47,770 and this is pure prospector's theory, 176 00:09:47,770 --> 00:09:50,900 as you remember from our first class-- 177 00:09:50,900 --> 00:09:54,050 then you limit the biggest input into innovation, and thus 178 00:09:54,050 --> 00:09:56,600 into growth. 179 00:09:56,600 --> 00:10:00,680 So by not expanding its supply of scientists and engineers, 180 00:10:00,680 --> 00:10:05,810 i.e. the talent base, then the US 181 00:10:05,810 --> 00:10:09,350 is limiting its growth capacity by a pretty significant amount, 182 00:10:09,350 --> 00:10:12,060 he argues, in economic terms. 183 00:10:12,060 --> 00:10:14,630 So what's broken down? 184 00:10:14,630 --> 00:10:16,940 What what's the heart of the problem, here? 185 00:10:16,940 --> 00:10:24,860 And this is a deep critique of higher education, which 186 00:10:24,860 --> 00:10:28,460 was highly controversial when this came out and occasioned 187 00:10:28,460 --> 00:10:33,920 a lot of criticism of Romer, but it's hard to dispute his data, 188 00:10:33,920 --> 00:10:39,170 and you will probably see this from your own experiences 189 00:10:39,170 --> 00:10:43,250 or the experiences of colleagues in other universities. 190 00:10:43,250 --> 00:10:48,020 So what's broken down on the talent supply side? 191 00:10:48,020 --> 00:10:50,780 He argues that universities measure themselves 192 00:10:50,780 --> 00:10:56,025 by their ability to select top SAT scoring students. 193 00:10:56,025 --> 00:10:57,650 In other words, they measure themselves 194 00:10:57,650 --> 00:11:00,560 by the quality input they're able to attract, 195 00:11:00,560 --> 00:11:03,380 not by their output. 196 00:11:03,380 --> 00:11:05,965 There's no output measures in this system. 197 00:11:05,965 --> 00:11:07,340 They're competing with each other 198 00:11:07,340 --> 00:11:09,710 in things like US News and World Report 199 00:11:09,710 --> 00:11:15,800 on the students they attract, not on the student outcomes 200 00:11:15,800 --> 00:11:17,520 that they're creating. 201 00:11:17,520 --> 00:11:20,630 So the traditional liberal arts university, he argues, 202 00:11:20,630 --> 00:11:24,980 faces little pressure to respond to skills needs. 203 00:11:24,980 --> 00:11:28,280 That economic messaging that's inherent in the system 204 00:11:28,280 --> 00:11:33,580 doesn't get translated back to the higher education system. 205 00:11:33,580 --> 00:11:39,170 So, meanwhile, the university has a fixed investment 206 00:11:39,170 --> 00:11:43,050 in its faculty that are teaching in many areas-- 207 00:11:43,050 --> 00:11:46,310 including the sciences, but obviously outside the sciences. 208 00:11:46,310 --> 00:11:49,460 There is internal pressure to maintain 209 00:11:49,460 --> 00:11:52,580 the relative size of those departments, which are-- 210 00:11:52,580 --> 00:11:54,710 they have invested in. 211 00:11:54,710 --> 00:11:57,572 And that, in turn-- 212 00:11:57,572 --> 00:11:59,030 and we'll discuss why in a minute-- 213 00:11:59,030 --> 00:12:01,700 but makes it more difficult for students 214 00:12:01,700 --> 00:12:04,970 to get science degrees. 215 00:12:04,970 --> 00:12:07,460 So the science faculties, he argues, 216 00:12:07,460 --> 00:12:10,610 are happy to do what they call "maintain 217 00:12:10,610 --> 00:12:20,030 professional standards," i.e. have a lot of lower grades. 218 00:12:20,030 --> 00:12:24,050 And effectively what that does is force out of the system 219 00:12:24,050 --> 00:12:26,480 a tremendous amount of talent because, again, they've 220 00:12:26,480 --> 00:12:31,580 got fixed faculty sizes that aren't coordinated with what 221 00:12:31,580 --> 00:12:33,860 national talent supply and needs may 222 00:12:33,860 --> 00:12:35,630 be in science and technology. 223 00:12:35,630 --> 00:12:37,940 There's no pressure on universities 224 00:12:37,940 --> 00:12:41,870 to address those corresponding faculty 225 00:12:41,870 --> 00:12:45,530 sizes to what the national need may or may not be, 226 00:12:45,530 --> 00:12:48,500 and therefore what's happened is essentially 227 00:12:48,500 --> 00:12:53,390 a bifurcated education system, where sciences and engineering 228 00:12:53,390 --> 00:12:55,580 students have one grading system-- 229 00:12:55,580 --> 00:12:57,890 and as you all know, it's a tougher grading system 230 00:12:57,890 --> 00:13:05,510 at liberal arts universities than other majors 231 00:13:05,510 --> 00:13:07,730 in the social sciences and humanities. 232 00:13:07,730 --> 00:13:11,450 And you know, we know that there is significant grade inflation 233 00:13:11,450 --> 00:13:14,340 in the non-science, non-engineering fields, 234 00:13:14,340 --> 00:13:18,890 and that there is limited if any grade inflation in the tougher 235 00:13:18,890 --> 00:13:19,640 technical fields. 236 00:13:19,640 --> 00:13:22,010 So that's what he's talking about, here. 237 00:13:22,010 --> 00:13:24,530 We maintain essentially two grading standards, 238 00:13:24,530 --> 00:13:27,980 the net effect of which is to substantially discourage 239 00:13:27,980 --> 00:13:29,510 entry of talent. 240 00:13:29,510 --> 00:13:32,750 Now this is not in this report, but the National Academy 241 00:13:32,750 --> 00:13:36,590 of Engineering has explored extensively 242 00:13:36,590 --> 00:13:42,290 whether the people dropping out of majors in engineering 243 00:13:42,290 --> 00:13:44,870 are stronger or weaker students than those staying in, 244 00:13:44,870 --> 00:13:47,120 and they can't find any basis for concluding 245 00:13:47,120 --> 00:13:48,965 that they are any weaker. 246 00:13:48,965 --> 00:13:50,840 In other words, we're just driving talent out 247 00:13:50,840 --> 00:13:53,780 of the system through these mechanisms, 248 00:13:53,780 --> 00:13:55,670 is Romer's argument. 249 00:13:55,670 --> 00:13:58,850 So the supply problem-- 250 00:13:58,850 --> 00:14:02,420 by running this bifurcating grading system 251 00:14:02,420 --> 00:14:05,870 to drive out talent, to maintain class size numbers 252 00:14:05,870 --> 00:14:09,350 and make sure you don't have to advise too many people-- 253 00:14:09,350 --> 00:14:14,300 the supply problem, in turn, drives graduate school numbers, 254 00:14:14,300 --> 00:14:17,820 because of course, undergraduate degrees are prerequisites. 255 00:14:17,820 --> 00:14:22,040 So how does US industry cope with this? 256 00:14:22,040 --> 00:14:23,600 Essentially what it's done-- 257 00:14:23,600 --> 00:14:25,430 and this is not bad-- 258 00:14:25,430 --> 00:14:28,310 it's encouraged wholesale immigration 259 00:14:28,310 --> 00:14:31,520 from all over the world to fill the gap, which 260 00:14:31,520 --> 00:14:33,470 it's done very systematically. 261 00:14:33,470 --> 00:14:35,030 And that's why, for example, industry 262 00:14:35,030 --> 00:14:38,720 is so concerned about this H-1B visa set of proposals 263 00:14:38,720 --> 00:14:41,630 that the new administration has recently proposed. 264 00:14:41,630 --> 00:14:43,070 What are the ramifications of that 265 00:14:43,070 --> 00:14:45,710 in terms of access to this talent supply? 266 00:14:45,710 --> 00:14:48,630 Obviously, worldwide talent, as we've talked about before, 267 00:14:48,630 --> 00:14:50,390 is a very important competitive advantage 268 00:14:50,390 --> 00:14:51,800 for the United States. 269 00:14:51,800 --> 00:14:53,870 But here we're in a circumstance where 270 00:14:53,870 --> 00:14:59,870 we're not creating parallel opportunities for folks 271 00:14:59,870 --> 00:15:00,610 in the US. 272 00:15:04,370 --> 00:15:09,110 Moving on with Romer's indictment of higher education, 273 00:15:09,110 --> 00:15:12,560 because that's really what it is, 274 00:15:12,560 --> 00:15:21,770 he argues that PhD programs train graduates 275 00:15:21,770 --> 00:15:31,960 for the academy, but as all of us in this room know, there's-- 276 00:15:31,960 --> 00:15:35,650 there's a complete oversupply of PhDs in terms of what 277 00:15:35,650 --> 00:15:37,090 the academy itself-- 278 00:15:37,090 --> 00:15:40,300 in other words, university teaching-- actually requires. 279 00:15:40,300 --> 00:15:44,710 So then we have to invent new mechanisms to keep this talent 280 00:15:44,710 --> 00:15:45,490 base around. 281 00:15:45,490 --> 00:15:51,880 So we invent long, endless, seven or eight-year graduate 282 00:15:51,880 --> 00:15:54,280 programs. 283 00:15:54,280 --> 00:15:57,520 England somehow manages to get its PhDs done in three years. 284 00:15:57,520 --> 00:15:58,600 Are they worse? 285 00:15:58,600 --> 00:15:59,750 I don't know. 286 00:15:59,750 --> 00:16:02,360 But we have a seven or eight year old graduate-- 287 00:16:02,360 --> 00:16:04,480 eight year long graduate education program, 288 00:16:04,480 --> 00:16:07,780 and then we invent this whole class of essentially 289 00:16:07,780 --> 00:16:11,950 apprentices that we call post-docs-- 290 00:16:11,950 --> 00:16:14,495 another substantial army of people-- 291 00:16:14,495 --> 00:16:16,120 because there's no space in the academy 292 00:16:16,120 --> 00:16:18,130 to accommodate these people. 293 00:16:18,130 --> 00:16:20,230 And yet the training system is not 294 00:16:20,230 --> 00:16:22,900 geared to the location where there 295 00:16:22,900 --> 00:16:26,470 are extensive opportunities-- 296 00:16:26,470 --> 00:16:28,600 whether in established firms or startups-- which 297 00:16:28,600 --> 00:16:32,050 is for industry because the training system is really 298 00:16:32,050 --> 00:16:38,540 geared for entering the academy, not for entering industry. 299 00:16:38,540 --> 00:16:41,350 And obviously, it tends to focus more on basic than applied 300 00:16:41,350 --> 00:16:44,540 for obvious reasons. 301 00:16:44,540 --> 00:16:47,680 So this-- we're just multiplying the problems here, 302 00:16:47,680 --> 00:16:52,600 because the core input institutions, i.e. higher 303 00:16:52,600 --> 00:16:56,550 education, are not organized around the skills problem 304 00:16:56,550 --> 00:16:59,470 society has got. 305 00:16:59,470 --> 00:17:03,490 That's Romer's indictment. 306 00:17:03,490 --> 00:17:08,650 I had the privilege of working with him on legislation, 307 00:17:08,650 --> 00:17:10,900 and when this piece of legislation came out, 308 00:17:10,900 --> 00:17:14,960 there was considerable interest on Capitol Hill, 309 00:17:14,960 --> 00:17:18,640 and we're hearing all the time on Capitol Hill in this era 310 00:17:18,640 --> 00:17:22,000 about, we've got a talent base problem, 311 00:17:22,000 --> 00:17:26,019 and there was considerable interest in the critiques 312 00:17:26,019 --> 00:17:28,569 that Romer had made of the system, 313 00:17:28,569 --> 00:17:30,820 and how do you change the system? 314 00:17:30,820 --> 00:17:34,600 So, like other senior staffers, I 315 00:17:34,600 --> 00:17:37,297 read his stuff and thought gee, how are we 316 00:17:37,297 --> 00:17:38,380 going to turn this around? 317 00:17:38,380 --> 00:17:42,070 We're going to have to spend a fortune on creating 318 00:17:42,070 --> 00:17:49,480 a massive new kind of fellowship program across the country, 319 00:17:49,480 --> 00:17:52,090 on top of what we already have to encourage science 320 00:17:52,090 --> 00:17:54,180 and engineering education. 321 00:17:54,180 --> 00:17:57,420 We'll have to significantly increase that-- 322 00:17:57,420 --> 00:18:01,050 maybe by a factor of two or more. 323 00:18:01,050 --> 00:18:02,780 How are we going to afford this? 324 00:18:02,780 --> 00:18:04,590 Where's that money come from? 325 00:18:04,590 --> 00:18:09,750 So I had a conversation with Romer, and he said, 326 00:18:09,750 --> 00:18:13,470 Bill, you have to think like an economist here. 327 00:18:13,470 --> 00:18:16,360 You have to bribe the gatekeepers. 328 00:18:16,360 --> 00:18:17,790 Right? 329 00:18:17,790 --> 00:18:19,610 So I-- what are you talking about? 330 00:18:19,610 --> 00:18:23,340 And he said, look, figure out who the gatekeepers are 331 00:18:23,340 --> 00:18:24,960 in this entire system and you bribe 332 00:18:24,960 --> 00:18:28,510 them to make them turn around their behavior. 333 00:18:28,510 --> 00:18:33,450 So in this case, it's the departments and the colleges 334 00:18:33,450 --> 00:18:34,950 and universities as a whole-- 335 00:18:34,950 --> 00:18:37,590 it's their administrations. 336 00:18:37,590 --> 00:18:40,350 Bribe them to get them to change their numbers 337 00:18:40,350 --> 00:18:43,140 because if they're not producing enough scientists and engineers 338 00:18:43,140 --> 00:18:47,230 net, then pay them to do this. 339 00:18:47,230 --> 00:18:50,040 And by the way, bribery is a heck of a lot 340 00:18:50,040 --> 00:18:53,370 cheaper than creating a massive new national fellowship, 341 00:18:53,370 --> 00:18:55,480 and much more efficient. 342 00:18:55,480 --> 00:18:58,513 So obviously, bribery is not the apt term here, 343 00:18:58,513 --> 00:19:00,930 but we ended up creating a program at the National Science 344 00:19:00,930 --> 00:19:04,650 Foundation, which Congress passed, called The Step 345 00:19:04,650 --> 00:19:06,930 Education Program, which essentially 346 00:19:06,930 --> 00:19:11,700 offers very significant funding to departments 347 00:19:11,700 --> 00:19:14,160 that guarantee they're going to turn their numbers around, 348 00:19:14,160 --> 00:19:18,840 and then present pathways by which they're going to do so. 349 00:19:18,840 --> 00:19:23,430 In other words, maybe they offer much more one 350 00:19:23,430 --> 00:19:25,260 on one kind of tutorial attention 351 00:19:25,260 --> 00:19:28,440 to keep students in science and engineering. 352 00:19:28,440 --> 00:19:31,180 Maybe they offer fellowships with industry, 353 00:19:31,180 --> 00:19:32,830 so you're guaranteed summer employment 354 00:19:32,830 --> 00:19:35,588 in an interesting, relevant, applied field. 355 00:19:35,588 --> 00:19:37,380 In other words, there may be a whole slew-- 356 00:19:37,380 --> 00:19:38,460 and there turned out to be a lot-- 357 00:19:38,460 --> 00:19:40,830 of ideas on how to begin to turn those numbers around. 358 00:19:40,830 --> 00:19:42,570 AUDIENCE: Are you talking at the undergraduate level? 359 00:19:42,570 --> 00:19:44,302 WILLIAM BONVILLIAN: I'm talking at the undergraduate level. 360 00:19:44,302 --> 00:19:45,540 AUDIENCE: I'm-- 361 00:19:45,540 --> 00:19:46,260 WILLIAM BONVILLIAN: Go ahead, Max. 362 00:19:46,260 --> 00:19:48,635 AUDIENCE: I'm kind of confused how you can simultaneously 363 00:19:48,635 --> 00:19:51,390 have a problem where you don't have enough science engineering 364 00:19:51,390 --> 00:19:55,490 workers but you also have this army of post docs 365 00:19:55,490 --> 00:19:57,690 that you don't know what to do with. 366 00:19:57,690 --> 00:19:59,010 WILLIAM BONVILLIAN: Well, I mean that's kind of the next stage 367 00:19:59,010 --> 00:19:59,640 of the problem. 368 00:19:59,640 --> 00:20:04,320 So his next piece, Max, is innovation and graduate 369 00:20:04,320 --> 00:20:06,600 education training. 370 00:20:06,600 --> 00:20:09,120 So in other words, create curricula that are relevant 371 00:20:09,120 --> 00:20:12,060 not only to training for the academy, 372 00:20:12,060 --> 00:20:15,480 but curricula that are also relevant to training for entry 373 00:20:15,480 --> 00:20:18,900 in the industry, and we're obviously 374 00:20:18,900 --> 00:20:21,840 starting to see some of these things materialize. 375 00:20:21,840 --> 00:20:24,780 So a school like MIT-- but it's by no means alone-- 376 00:20:24,780 --> 00:20:30,920 has a massive entrepreneurship curriculum now available 377 00:20:30,920 --> 00:20:31,958 in all of its schools. 378 00:20:31,958 --> 00:20:33,750 This is not just a business school program, 379 00:20:33,750 --> 00:20:36,400 this is available across the board, 380 00:20:36,400 --> 00:20:39,352 and as you undergraduates know, it's really quite accessible. 381 00:20:39,352 --> 00:20:41,310 There's a substantial number of business majors 382 00:20:41,310 --> 00:20:43,560 who do a lot of entrepreneurship-- 383 00:20:43,560 --> 00:20:46,920 business minors-- who do a lot of entrepreneurship-esque 384 00:20:46,920 --> 00:20:49,290 features in their education. 385 00:20:49,290 --> 00:20:51,060 That's now much better understood 386 00:20:51,060 --> 00:20:53,940 by students going to universities 387 00:20:53,940 --> 00:20:57,420 as an option for what their route ahead might be 388 00:20:57,420 --> 00:20:59,472 than it was even 10 years ago. 389 00:20:59,472 --> 00:21:00,930 So there has been some change here, 390 00:21:00,930 --> 00:21:04,370 but that's another change he would make, 391 00:21:04,370 --> 00:21:08,340 is to make the training much more relevant to entry 392 00:21:08,340 --> 00:21:13,500 into established firms and to startup firms 393 00:21:13,500 --> 00:21:15,580 at the graduate school level. 394 00:21:15,580 --> 00:21:19,740 So overall, he would attempt to use some federal funding here 395 00:21:19,740 --> 00:21:24,960 to, in effect, redress the imbalance in federal demand 396 00:21:24,960 --> 00:21:29,910 and supply programs to create an input of support 397 00:21:29,910 --> 00:21:31,650 on the federal side-- 398 00:21:31,650 --> 00:21:35,640 on the supply side. 399 00:21:35,640 --> 00:21:37,832 All right, so that's two of our three. 400 00:21:37,832 --> 00:21:38,790 You want to pause here? 401 00:21:38,790 --> 00:21:41,820 Because this one's so nicely controversial 402 00:21:41,820 --> 00:21:45,420 that we could discuss it, and you all can disagree with Romer 403 00:21:45,420 --> 00:21:49,620 or indicate how correct he is. 404 00:21:49,620 --> 00:21:51,270 Shall we do a quick pause? 405 00:21:51,270 --> 00:21:52,098 Who's got this one? 406 00:21:52,098 --> 00:21:52,890 You've got it, Max? 407 00:21:52,890 --> 00:21:54,515 Do you want to quickly summarize Romer? 408 00:21:54,515 --> 00:21:55,678 Let's do Augustine, too. 409 00:21:55,678 --> 00:21:56,220 AUDIENCE: Oh. 410 00:21:56,220 --> 00:21:57,303 WILLIAM BONVILLIAN: Great. 411 00:21:57,303 --> 00:21:59,850 AUDIENCE: All right, so Augustine talks about how, 412 00:21:59,850 --> 00:22:02,610 while as a lot of us know, education in the United States 413 00:22:02,610 --> 00:22:05,500 isn't great before the university level. 414 00:22:05,500 --> 00:22:07,290 So he gives a lot of statistics, how 415 00:22:07,290 --> 00:22:09,430 we're doing pretty poorly in math and science 416 00:22:09,430 --> 00:22:14,490 when compared to other countries, how, for whatever 417 00:22:14,490 --> 00:22:17,880 reason, it seems that as people stay 418 00:22:17,880 --> 00:22:21,460 within the American system, the longer they stay in, 419 00:22:21,460 --> 00:22:25,170 the poorer their ability to compete with other countries 420 00:22:25,170 --> 00:22:26,560 is. 421 00:22:26,560 --> 00:22:30,930 So actually, I found out one of the things-- 422 00:22:30,930 --> 00:22:35,010 some of the-- so America has, in the past few decades, 423 00:22:35,010 --> 00:22:37,380 been famous for the concept of a brain drain, 424 00:22:37,380 --> 00:22:41,370 where it would take the best and brightest from other countries, 425 00:22:41,370 --> 00:22:42,297 like India and China-- 426 00:22:42,297 --> 00:22:44,130 they would come over here for our university 427 00:22:44,130 --> 00:22:46,470 system, which is nice. 428 00:22:46,470 --> 00:22:47,340 It's great for us. 429 00:22:47,340 --> 00:22:50,460 It's not great for them, but apparently this trend 430 00:22:50,460 --> 00:22:52,450 is slowing as we find out in this article, 431 00:22:52,450 --> 00:22:54,993 because of some of our more isolationist policies. 432 00:22:54,993 --> 00:22:56,910 And I really appreciated that he mentioned it, 433 00:22:56,910 --> 00:23:01,140 because often a lot of people tried to talk about, 434 00:23:01,140 --> 00:23:03,640 isolationism is bad because we are the world, 435 00:23:03,640 --> 00:23:05,910 but this actually gives a more concrete 436 00:23:05,910 --> 00:23:08,700 reason for why isolationism actually 437 00:23:08,700 --> 00:23:11,400 causes some significant problems to our innovation system 438 00:23:11,400 --> 00:23:14,610 and to our economy. 439 00:23:14,610 --> 00:23:17,460 So one of the questions that I wanted to pose 440 00:23:17,460 --> 00:23:21,540 is, how have all of our educational institutions 441 00:23:21,540 --> 00:23:23,790 at the university level managed to maintain 442 00:23:23,790 --> 00:23:27,990 their stature and their quality despite the fact that lower-- 443 00:23:27,990 --> 00:23:31,080 lower level institutions like high school and below 444 00:23:31,080 --> 00:23:34,320 have been so sub par? 445 00:23:34,320 --> 00:23:38,430 And can we implement some of these characteristics 446 00:23:38,430 --> 00:23:39,450 at these other levels? 447 00:23:42,632 --> 00:23:43,340 Yeah, that's all. 448 00:23:43,340 --> 00:23:45,230 AUDIENCE: I think some of the answer 449 00:23:45,230 --> 00:23:48,840 to that comes from just the inequality across I 450 00:23:48,840 --> 00:23:50,290 through 12 schools. 451 00:23:50,290 --> 00:23:52,640 The US does have some fantastic schools, 452 00:23:52,640 --> 00:23:54,830 it's just who has access to them, 453 00:23:54,830 --> 00:23:58,900 and that tends to favor certain groups over others, 454 00:23:58,900 --> 00:24:01,340 and so instead of being able to tap 455 00:24:01,340 --> 00:24:03,550 into the full potential talent pool that we have, 456 00:24:03,550 --> 00:24:05,080 we're getting the ones who happen 457 00:24:05,080 --> 00:24:07,288 to live in a neighborhood that goes to a good school, 458 00:24:07,288 --> 00:24:10,670 or they have a nice magnet school in their county. 459 00:24:10,670 --> 00:24:12,620 So I think you could also pose the question, 460 00:24:12,620 --> 00:24:14,540 how much better could American universities 461 00:24:14,540 --> 00:24:18,420 be if they had their full talent pool to choose from? 462 00:24:18,420 --> 00:24:19,760 AUDIENCE: That's fair. 463 00:24:19,760 --> 00:24:21,830 AUDIENCE: Yeah, I like that point a lot, 464 00:24:21,830 --> 00:24:23,450 because there are-- 465 00:24:23,450 --> 00:24:27,830 I mean, we definitely have institutions-- higher parable 466 00:24:27,830 --> 00:24:30,110 learning institutions, universities-- in the United 467 00:24:30,110 --> 00:24:33,230 States that are extremely famous, extremely well known, 468 00:24:33,230 --> 00:24:34,670 and extremely well respected. 469 00:24:34,670 --> 00:24:36,080 You all are at one. 470 00:24:36,080 --> 00:24:39,170 But there, if you look-- 471 00:24:39,170 --> 00:24:41,870 that doesn't mean that every single university in the United 472 00:24:41,870 --> 00:24:43,730 States is internationally respected. 473 00:24:43,730 --> 00:24:46,610 There's this huge spectrum of the level of education 474 00:24:46,610 --> 00:24:49,280 or the quality of education that you can get at the university 475 00:24:49,280 --> 00:24:51,380 level, and I think you're exactly right-- that's 476 00:24:51,380 --> 00:24:54,080 the same for K through 12. 477 00:24:54,080 --> 00:24:55,520 It's just there's a huge span. 478 00:24:55,520 --> 00:24:58,790 You just don't necessarily hear about the really bad university 479 00:24:58,790 --> 00:24:59,750 level educations. 480 00:24:59,750 --> 00:25:00,992 [INAUDIBLE] 481 00:25:00,992 --> 00:25:01,770 AUDIENCE: OK. 482 00:25:01,770 --> 00:25:02,270 So-- 483 00:25:02,270 --> 00:25:03,145 AUDIENCE: [INAUDIBLE] 484 00:25:03,145 --> 00:25:05,130 AUDIENCE: So following up on that, then, 485 00:25:05,130 --> 00:25:06,755 I've heard that one of the main reasons 486 00:25:06,755 --> 00:25:10,220 that we-- that, at least for the K through 12 system, people 487 00:25:10,220 --> 00:25:14,600 are-- or governments-- are unable to support 488 00:25:14,600 --> 00:25:16,880 the schools that are doing very well 489 00:25:16,880 --> 00:25:19,168 and punish the ones that are doing poorly. 490 00:25:19,168 --> 00:25:20,960 Is there a way that we could implement this 491 00:25:20,960 --> 00:25:24,770 without jeopardizing the educations of the people who 492 00:25:24,770 --> 00:25:26,410 are already in these systems? 493 00:25:26,410 --> 00:25:30,440 Because schools can't be treated exactly like a free market, 494 00:25:30,440 --> 00:25:32,810 because in a free market, the worst case 495 00:25:32,810 --> 00:25:36,130 is you buy a Zune instead of an iPhone or iPod 496 00:25:36,130 --> 00:25:37,940 and well, OK, that kind of sucks. 497 00:25:37,940 --> 00:25:40,040 But it doesn't suck nearly as much 498 00:25:40,040 --> 00:25:42,500 as it does for the kid who has to go 499 00:25:42,500 --> 00:25:44,570 through a year with a bad teacher, 500 00:25:44,570 --> 00:25:46,842 and sure, maybe the teacher gets fired after, 501 00:25:46,842 --> 00:25:48,800 but that's still a year that this kid has lost, 502 00:25:48,800 --> 00:25:50,990 and that sets them behind, and those-- 503 00:25:50,990 --> 00:25:53,940 that impact can last for the rest of this person's life. 504 00:25:53,940 --> 00:25:59,600 So-- that was a really long question, I'm sorry. 505 00:25:59,600 --> 00:26:04,790 So to phrase that more simply, how can we 506 00:26:04,790 --> 00:26:08,990 implement some sort of free market style 507 00:26:08,990 --> 00:26:17,540 without that inherent impact on a kid's life? 508 00:26:17,540 --> 00:26:20,480 AUDIENCE: Well, I think just to reorganize the question 509 00:26:20,480 --> 00:26:23,310 and some decision making, why are you assuming it's 510 00:26:23,310 --> 00:26:26,357 the teacher's fault that, in a central city school, 511 00:26:26,357 --> 00:26:27,440 they don't do well, right? 512 00:26:27,440 --> 00:26:29,340 Usually it's people who really, really care, 513 00:26:29,340 --> 00:26:31,430 but the students aren't ready to learn. 514 00:26:31,430 --> 00:26:33,130 Because like-- I forget the quote, 515 00:26:33,130 --> 00:26:35,075 but it's combined as like a parachute that 516 00:26:35,075 --> 00:26:37,523 needs to be opened to receive information. 517 00:26:37,523 --> 00:26:39,190 So I think it's a more complex issue, is 518 00:26:39,190 --> 00:26:40,180 the point I'm trying to make. 519 00:26:40,180 --> 00:26:40,460 AUDIENCE: Yeah. 520 00:26:40,460 --> 00:26:41,390 AUDIENCE: [INAUDIBLE] the first time 521 00:26:41,390 --> 00:26:43,932 I've looked into this, because there's a special class called 522 00:26:43,932 --> 00:26:46,358 by Tom Malone, and I think we're going 523 00:26:46,358 --> 00:26:47,900 to be talking about the future of war 524 00:26:47,900 --> 00:26:49,490 next week where he talks a lot about, 525 00:26:49,490 --> 00:26:52,550 now we're in a distributed kind of network system. 526 00:26:52,550 --> 00:26:55,310 And we kind of get through hierarchy systems. 527 00:26:55,310 --> 00:26:58,053 And so like in that class, we posited like, 528 00:26:58,053 --> 00:26:59,720 you know like YouTube has all the videos 529 00:26:59,720 --> 00:27:01,275 and is created by a ton of people. 530 00:27:01,275 --> 00:27:03,650 We talked about like what if somebody made like a YouTube 531 00:27:03,650 --> 00:27:05,750 for education, where the best teachers would get the most 532 00:27:05,750 --> 00:27:06,333 views. 533 00:27:06,333 --> 00:27:08,000 And you could quantify that and pay them 534 00:27:08,000 --> 00:27:09,200 really high salaries, right? 535 00:27:09,200 --> 00:27:13,460 So you'd get like NBA, and NBA player salaries 536 00:27:13,460 --> 00:27:16,160 for doing really great content and a lot of people 537 00:27:16,160 --> 00:27:16,950 would access it. 538 00:27:16,950 --> 00:27:18,710 And pretty much the whole population 539 00:27:18,710 --> 00:27:21,280 would be taught by the best teachers in the country 540 00:27:21,280 --> 00:27:22,310 or in the world. 541 00:27:22,310 --> 00:27:24,860 And then you'd have the personal touch, 542 00:27:24,860 --> 00:27:28,190 like Khan Academy, where it's like online for some subjects 543 00:27:28,190 --> 00:27:29,940 or like [INAUDIBLE] in a really good way. 544 00:27:29,940 --> 00:27:32,060 And then in the actual class system, 545 00:27:32,060 --> 00:27:34,970 you are going through the kind of like the issues 546 00:27:34,970 --> 00:27:36,740 and figuring out what your errors are 547 00:27:36,740 --> 00:27:38,460 so you can learn better. 548 00:27:38,460 --> 00:27:40,820 So I think that's an interesting incentive. 549 00:27:40,820 --> 00:27:42,380 The thing though is like I think this 550 00:27:42,380 --> 00:27:44,965 is more of the dynamics of the market 551 00:27:44,965 --> 00:27:48,920 but the gatekeepers problem than it is like an education problem 552 00:27:48,920 --> 00:27:51,050 or even like the pipeline issues. 553 00:27:51,050 --> 00:27:53,360 If our incentive system is to get the best people 554 00:27:53,360 --> 00:27:55,490 to the school and not care what happens 555 00:27:55,490 --> 00:27:59,630 after, that's kind of like a screwed up system, right? 556 00:27:59,630 --> 00:28:02,570 It's like if your whole-- yeah. 557 00:28:02,570 --> 00:28:04,758 Like we're not focusing on making 558 00:28:04,758 --> 00:28:06,050 the best students in the world. 559 00:28:06,050 --> 00:28:07,910 We're focusing on finding the smartest people up 560 00:28:07,910 --> 00:28:10,160 to that point that they become students at our university. 561 00:28:10,160 --> 00:28:12,702 And our reputation is based on getting the smartest people up 562 00:28:12,702 --> 00:28:13,280 to here. 563 00:28:13,280 --> 00:28:15,590 So it doesn't matter if you have a screwed up economy. 564 00:28:15,590 --> 00:28:18,280 There's always going to be a really top 1%. 565 00:28:18,280 --> 00:28:20,030 Like, it doesn't matter, because like most 566 00:28:20,030 --> 00:28:22,280 of the people at the school will come from all different kinds 567 00:28:22,280 --> 00:28:23,090 of backgrounds. 568 00:28:23,090 --> 00:28:24,590 And they will never say, most likely 569 00:28:24,590 --> 00:28:26,182 it was because of their school. 570 00:28:26,182 --> 00:28:27,890 Like if they come from a poor background, 571 00:28:27,890 --> 00:28:29,310 they're going to say, you know, I just went online, 572 00:28:29,310 --> 00:28:31,160 or went to the library, or I learned from the best people 573 00:28:31,160 --> 00:28:31,785 in their books. 574 00:28:35,450 --> 00:28:38,930 That was even like a response. 575 00:28:38,930 --> 00:28:41,840 WILLIAM BONVILLIAN: Well, you introduced the online idea. 576 00:28:41,840 --> 00:28:46,560 And we're going to jump on that kind at the end of the class. 577 00:28:46,560 --> 00:28:48,490 So we should come back to that Martin. 578 00:28:48,490 --> 00:28:50,050 AUDIENCE: Which I definitely think is an interesting idea. 579 00:28:50,050 --> 00:28:51,677 I think it's easy to frame the access. 580 00:28:51,677 --> 00:28:53,260 All you need is an internet connection 581 00:28:53,260 --> 00:28:55,310 and basically everyone has a cell phone. 582 00:28:55,310 --> 00:28:57,200 AUDIENCE: You know, we'll get to is later. 583 00:28:57,200 --> 00:28:59,700 WILLIAM BONVILLIAN: And there are pros and cons on this too. 584 00:28:59,700 --> 00:29:00,750 AUDIENCE: It's also overdone, right? 585 00:29:00,750 --> 00:29:02,540 There's like a ton of businesses and a ton of organizations 586 00:29:02,540 --> 00:29:03,562 I've seen try to do it. 587 00:29:03,562 --> 00:29:05,270 And like why haven't they figured it out. 588 00:29:05,270 --> 00:29:07,478 It's probably like a policy, incentives, power issue. 589 00:29:12,683 --> 00:29:14,600 AUDIENCE: Yes, I think, just thinking about it 590 00:29:14,600 --> 00:29:16,610 from like also an international perspective, 591 00:29:16,610 --> 00:29:19,820 I think a problem in the US is also like the whole respect 592 00:29:19,820 --> 00:29:21,050 thing for teachers. 593 00:29:21,050 --> 00:29:23,270 Like I think especially at the middle school 594 00:29:23,270 --> 00:29:25,790 or the high school, like the public education system, maybe 595 00:29:25,790 --> 00:29:28,100 there's not as much respect for being a teacher. 596 00:29:28,100 --> 00:29:30,630 Whereas like maybe in Sweden, or like China, 597 00:29:30,630 --> 00:29:32,030 like being a professor, a teacher 598 00:29:32,030 --> 00:29:35,120 is really well respected and considered a really prestigious 599 00:29:35,120 --> 00:29:35,680 job. 600 00:29:35,680 --> 00:29:37,850 So I think that's like some systemic issue that's 601 00:29:37,850 --> 00:29:40,820 also kind of preventing maybe the top talent from going 602 00:29:40,820 --> 00:29:43,660 into teaching, not necessarily at the higher education level. 603 00:29:43,660 --> 00:29:47,030 I think there is a lot of really intelligent academics 604 00:29:47,030 --> 00:29:48,060 in that space. 605 00:29:48,060 --> 00:29:50,240 But definitely, towards the beginning 606 00:29:50,240 --> 00:29:52,460 where children are like really starting out 607 00:29:52,460 --> 00:29:54,860 their educational careers, and that's the fundamental. 608 00:29:54,860 --> 00:29:57,140 And also from what I know like in Europe, 609 00:29:57,140 --> 00:30:00,500 they start kind of specializing pretty early 610 00:30:00,500 --> 00:30:03,110 on in like high school into what kind of track 611 00:30:03,110 --> 00:30:04,790 they want to pursue later on. 612 00:30:04,790 --> 00:30:08,510 So I feel like that could be an interesting way that they're 613 00:30:08,510 --> 00:30:11,210 kind of promoting, for example, engineering 614 00:30:11,210 --> 00:30:15,920 or math or STEM related technical expertise. 615 00:30:15,920 --> 00:30:19,040 Like they're really fostering that from early stage. 616 00:30:19,040 --> 00:30:22,400 So that really helps and sets up the students 617 00:30:22,400 --> 00:30:24,350 to advance later on. 618 00:30:24,350 --> 00:30:26,190 Whereas like a lot of students here 619 00:30:26,190 --> 00:30:30,290 they get like kind of basic introduction to everything. 620 00:30:30,290 --> 00:30:32,450 But then once they get to engineering classes, 621 00:30:32,450 --> 00:30:34,885 some people aren't very prepared. 622 00:30:34,885 --> 00:30:37,010 AUDIENCE: Actually regarding the first part of what 623 00:30:37,010 --> 00:30:38,870 you said about the teachers and trying 624 00:30:38,870 --> 00:30:41,997 to incentivize the best talent in our country 625 00:30:41,997 --> 00:30:44,330 to become a teacher, that's actually my second question. 626 00:30:44,330 --> 00:30:47,330 I was going to ask, well, how could we incentivize them 627 00:30:47,330 --> 00:30:48,290 outside of pay? 628 00:30:48,290 --> 00:30:52,130 Because I've heard that the US education system 629 00:30:52,130 --> 00:30:54,770 is at an all time high for paying, for the amount of money 630 00:30:54,770 --> 00:30:56,900 that we spend per student in the classroom. 631 00:30:56,900 --> 00:31:00,900 So clearly just throwing money at this problem is not enough. 632 00:31:00,900 --> 00:31:02,892 So what can we do? 633 00:31:02,892 --> 00:31:04,850 Is there a way we can either change the culture 634 00:31:04,850 --> 00:31:07,817 or some other aspect? 635 00:31:07,817 --> 00:31:10,400 AUDIENCE: Yeah, I think one of the problems with the US system 636 00:31:10,400 --> 00:31:12,800 is that it's kind of punitive. 637 00:31:12,800 --> 00:31:17,080 Where there's like mass or firing, sorry, 638 00:31:17,080 --> 00:31:18,860 mass firing things, whereas teachers 639 00:31:18,860 --> 00:31:21,897 that are not like performing up to a grade just be like let go. 640 00:31:21,897 --> 00:31:23,480 And that's kind of bad on the students 641 00:31:23,480 --> 00:31:25,160 because there's a lot of turnover. 642 00:31:25,160 --> 00:31:28,550 But I think in other, we can maybe borrow from another model 643 00:31:28,550 --> 00:31:31,400 where other cities, I think what comes to mind 644 00:31:31,400 --> 00:31:35,720 is Shanghai has this program where a lot of teachers 645 00:31:35,720 --> 00:31:37,670 or professors that are not performing as well 646 00:31:37,670 --> 00:31:41,090 are partnered with an educator that has more experience 647 00:31:41,090 --> 00:31:44,840 or has better understanding of how to reach the students. 648 00:31:44,840 --> 00:31:47,450 And they have this kind of like collaborative model, 649 00:31:47,450 --> 00:31:49,370 where they bring up the teachers instead 650 00:31:49,370 --> 00:31:51,712 of trying to just fire them or like dock their pay 651 00:31:51,712 --> 00:31:52,610 or something. 652 00:31:52,610 --> 00:31:55,022 Which I think is probably something 653 00:31:55,022 --> 00:31:57,230 that would be ultimately better for like the students 654 00:31:57,230 --> 00:31:59,240 in general as well. 655 00:31:59,240 --> 00:32:02,570 WILLIAM BONVILLIAN: So Max, why don't we move on now to Grover. 656 00:32:02,570 --> 00:32:03,700 AUDIENCE: Oh, yeah, sure. 657 00:32:03,700 --> 00:32:06,800 That most anybody is going to be closing points here. 658 00:32:06,800 --> 00:32:08,175 AUDIENCE: I just want to bring up 659 00:32:08,175 --> 00:32:10,050 the point that was made in one of the papers, 660 00:32:10,050 --> 00:32:12,530 I forget if it's this one, about having access to looking 661 00:32:12,530 --> 00:32:13,898 at like seeing engineers. 662 00:32:13,898 --> 00:32:16,190 Yeah, like, if you're in a community where you've never 663 00:32:16,190 --> 00:32:18,290 got to interact with engineers or somebody who does STEM, 664 00:32:18,290 --> 00:32:19,190 you're going to see it a certain way, 665 00:32:19,190 --> 00:32:20,780 especially since when you look at the subject, 666 00:32:20,780 --> 00:32:23,150 it's so dry in the classroom versus what you actually 667 00:32:23,150 --> 00:32:24,440 end up doing. 668 00:32:24,440 --> 00:32:26,290 I think that's also a big component, 669 00:32:26,290 --> 00:32:28,790 especially once you structure who you want to become, right? 670 00:32:28,790 --> 00:32:30,207 Because at that age, you're trying 671 00:32:30,207 --> 00:32:32,670 to figure out where you want to be a role model. 672 00:32:32,670 --> 00:32:34,310 Yeah, like an example, Steve Jobs 673 00:32:34,310 --> 00:32:36,242 got to work when he was 12 at HP. 674 00:32:36,242 --> 00:32:37,700 And he said that was the thing that 675 00:32:37,700 --> 00:32:40,040 led him to work on tech, because his whole background, 676 00:32:40,040 --> 00:32:41,320 his dad was a mechanic. 677 00:32:41,320 --> 00:32:43,820 It also made him realize what a good company was, because he 678 00:32:43,820 --> 00:32:46,112 saw how the employees are treated and it made them see, 679 00:32:46,112 --> 00:32:49,040 oh, this is why having the right company culture matters. 680 00:32:49,040 --> 00:32:51,140 That's a lesson he learned at 12 that led him 681 00:32:51,140 --> 00:32:53,630 to change how he saw his life. 682 00:32:53,630 --> 00:32:56,870 AUDIENCE: I think on my end in terms of Augustine, 683 00:32:56,870 --> 00:32:59,450 there is a point he made about American exceptionalism 684 00:32:59,450 --> 00:33:03,080 and our focus on finding spectacular talent, 685 00:33:03,080 --> 00:33:04,440 not just on good talent. 686 00:33:04,440 --> 00:33:06,080 And so, I remember a few weeks ago, 687 00:33:06,080 --> 00:33:08,070 I mentioned the quote where the good 688 00:33:08,070 --> 00:33:09,640 becomes the enemy of the great. 689 00:33:09,640 --> 00:33:11,780 And maybe in the education system, 690 00:33:11,780 --> 00:33:13,890 the great is the enemy of the good. 691 00:33:13,890 --> 00:33:16,550 And perhaps we're sort of not doing a good enough job 692 00:33:16,550 --> 00:33:19,970 of supporting people who do a fine job at pursuing 693 00:33:19,970 --> 00:33:22,370 engineering and science because we're 694 00:33:22,370 --> 00:33:25,560 focused on finding the innovators, the Edisons, 695 00:33:25,560 --> 00:33:27,930 the Jobs, the Gates of the world. 696 00:33:27,930 --> 00:33:29,990 So to create a support infrastructure, 697 00:33:29,990 --> 00:33:32,510 people who might do a fine job at carrying out 698 00:33:32,510 --> 00:33:34,580 their functions I think is ultimately 699 00:33:34,580 --> 00:33:37,320 the task of the education system at the lower level. 700 00:33:37,320 --> 00:33:39,790 And then at the university level of cultivating that talent 701 00:33:39,790 --> 00:33:43,160 to really create spectacular innovation. 702 00:33:43,160 --> 00:33:44,840 AUDIENCE: Yeah, to add on to that point, 703 00:33:44,840 --> 00:33:46,730 I think, yeah, they definitely prefer the Edisons, 704 00:33:46,730 --> 00:33:47,760 but I think that was more in the paper 705 00:33:47,760 --> 00:33:49,633 already talked about breakthrough ideas. 706 00:33:49,633 --> 00:33:51,050 I think this paper was more about, 707 00:33:51,050 --> 00:33:53,592 we have a pipeline issue that we need these kind of employees 708 00:33:53,592 --> 00:33:55,370 so that these fields stay in the US. 709 00:33:55,370 --> 00:33:56,320 And they weren't looking for Edisons. 710 00:33:56,320 --> 00:33:58,112 They were looking for niche, like you know, 711 00:33:58,112 --> 00:34:01,758 you want to discipline and can move ahead and get that job. 712 00:34:01,758 --> 00:34:03,800 WILLIAM BONVILLIAN: So let's shift over to Romer. 713 00:34:03,800 --> 00:34:05,802 AUDIENCE: Sure. 714 00:34:05,802 --> 00:34:06,760 you're just like, oh,-- 715 00:34:06,760 --> 00:34:07,980 WILLIAM BONVILLIAN: Well, we're leading into it. 716 00:34:07,980 --> 00:34:09,030 So we might as well. 717 00:34:09,030 --> 00:34:10,460 That's your pipeline boy. 718 00:34:10,460 --> 00:34:11,500 AUDIENCE: Yeah. 719 00:34:11,500 --> 00:34:15,110 Yeah, so regarding the pipeline, Romer 720 00:34:15,110 --> 00:34:18,380 decided to focus mostly on the undergraduate and graduate 721 00:34:18,380 --> 00:34:21,139 institutions and trying to figure out 722 00:34:21,139 --> 00:34:25,909 how you can increase the supply of the talented scientists 723 00:34:25,909 --> 00:34:29,960 and engineers that exist in the American innovation system. 724 00:34:29,960 --> 00:34:34,530 So one of the questions that I saw 725 00:34:34,530 --> 00:34:37,580 was posed pretty interesting, someone was asking 726 00:34:37,580 --> 00:34:39,770 what metrics or measures could you 727 00:34:39,770 --> 00:34:46,790 use to actually evaluate these new scientists and engineers 728 00:34:46,790 --> 00:34:48,409 as they're coming into these fields 729 00:34:48,409 --> 00:34:51,980 and how can you train them to come into, 730 00:34:51,980 --> 00:34:54,727 to be prepared to go into either industry or academia. 731 00:34:57,203 --> 00:34:59,370 AUDIENCE: I mean, the first thing that comes to mind 732 00:34:59,370 --> 00:35:02,220 is like professional engineer exams that exist already. 733 00:35:02,220 --> 00:35:03,690 That's kind of like the standard. 734 00:35:03,690 --> 00:35:04,910 AUDIENCE: Those aren't required, are they? 735 00:35:04,910 --> 00:35:06,000 AUDIENCE: No, not usually. 736 00:35:06,000 --> 00:35:08,042 Well some companies will require you to get them. 737 00:35:08,042 --> 00:35:09,340 It's generally just like a-- 738 00:35:09,340 --> 00:35:10,650 AUDIENCE: It's a nice little-- 739 00:35:10,650 --> 00:35:12,270 AUDIENCE: Thing you can check. 740 00:35:12,270 --> 00:35:14,728 But like, I don't think it's very like, 741 00:35:14,728 --> 00:35:16,770 before I came to MIT, I'd never even heard of it. 742 00:35:16,770 --> 00:35:18,960 So I don't know if that's something 743 00:35:18,960 --> 00:35:20,690 that carries a ton of weight. 744 00:35:20,690 --> 00:35:24,490 But I don't know. 745 00:35:24,490 --> 00:35:27,000 I don't really like the idea of using standardized tests 746 00:35:27,000 --> 00:35:28,840 as a measure of competency. 747 00:35:28,840 --> 00:35:30,600 But I mean if you're trying to talk 748 00:35:30,600 --> 00:35:33,510 about a large group of people there is no feasible way 749 00:35:33,510 --> 00:35:34,868 to do it other than that. 750 00:35:34,868 --> 00:35:36,660 If you want to talk about all the engineers 751 00:35:36,660 --> 00:35:40,170 that are entering the workforce, like, unfortunately, 752 00:35:40,170 --> 00:35:42,128 numbers are kind of like the only way to do it. 753 00:35:42,128 --> 00:35:43,670 AUDIENCE: Yeah, you can't [INAUDIBLE] 754 00:35:43,670 --> 00:35:44,640 recommendation letters. 755 00:35:44,640 --> 00:35:46,950 Just like everyone can find someone that likes them. 756 00:35:50,860 --> 00:35:52,350 Yes. 757 00:35:52,350 --> 00:35:55,660 AUDIENCE: Might there be a reason why 758 00:35:55,660 --> 00:36:00,130 such extensive licensing exams exist in the medical field 759 00:36:00,130 --> 00:36:03,067 and not in the engineering or life sciences fields? 760 00:36:03,067 --> 00:36:04,900 AUDIENCE: I mean, it's because it's probably 761 00:36:04,900 --> 00:36:07,067 easy to mess up costs a lot of money if you mess up. 762 00:36:07,067 --> 00:36:08,830 So you don't want people to mess up. 763 00:36:08,830 --> 00:36:10,220 But it's pretty easy to mess up in engineering. 764 00:36:10,220 --> 00:36:10,880 AUDIENCE: Yeah. 765 00:36:10,880 --> 00:36:13,380 AUDIENCE: Yeah, but people don't die and don't get lawsuits. 766 00:36:13,380 --> 00:36:14,820 [INTERPOSING VOICES] 767 00:36:19,450 --> 00:36:22,180 AUDIENCE: I mean, to the personal engineering 768 00:36:22,180 --> 00:36:25,150 exams, to what extent does accreditation of universities 769 00:36:25,150 --> 00:36:28,500 already attempt to fill that role that you know, 770 00:36:28,500 --> 00:36:30,010 if you're accepted into and graduate 771 00:36:30,010 --> 00:36:32,170 from an accredited engineering program, 772 00:36:32,170 --> 00:36:34,265 you already have that check mark, that like, OK. 773 00:36:34,265 --> 00:36:34,890 AUDIENCE: Yeah. 774 00:36:34,890 --> 00:36:38,000 So the PE exam is basically just a feather in your cap. 775 00:36:38,000 --> 00:36:40,720 It doesn't really-- or I don't know, a little stamp 776 00:36:40,720 --> 00:36:42,320 on your resume. 777 00:36:42,320 --> 00:36:43,558 Doesn't really-- 778 00:36:43,558 --> 00:36:45,350 AUDIENCE: But is there a fault with the way 779 00:36:45,350 --> 00:36:49,866 that we give that accreditation to things right now? 780 00:36:49,866 --> 00:36:52,040 Is it becoming more meaningless? 781 00:36:52,040 --> 00:36:54,620 If we're graduating maybe an engineer from one school 782 00:36:54,620 --> 00:36:56,780 that is far more capable than another school 783 00:36:56,780 --> 00:37:00,337 that has the same like stand? 784 00:37:00,337 --> 00:37:02,420 AUDIENCE: I mean, you could make the same argument 785 00:37:02,420 --> 00:37:03,753 with like any profession, right? 786 00:37:03,753 --> 00:37:07,880 Like, to go into Stephanie as example of medicine. 787 00:37:07,880 --> 00:37:12,440 So if you have someone who comes from the Harvard vs. name 788 00:37:12,440 --> 00:37:14,240 somewhere you don't like, Harvard. 789 00:37:17,892 --> 00:37:19,850 Yeah, then you can see a difference in quality. 790 00:37:26,543 --> 00:37:28,960 WILLIAM BONVILLIAN: Let me raise a question, [INAUDIBLE].. 791 00:37:28,960 --> 00:37:31,150 It's harder for you folks at MIT to see this 792 00:37:31,150 --> 00:37:34,450 because it's obviously predominantly science 793 00:37:34,450 --> 00:37:37,040 and engineering. 794 00:37:37,040 --> 00:37:41,110 But let me ask Sanam and Steph, how much 795 00:37:41,110 --> 00:37:43,400 do you see the great inflation in a classic, 796 00:37:43,400 --> 00:37:46,360 you know, high quality, liberal arts school 797 00:37:46,360 --> 00:37:49,210 between social science, humanities kinds 798 00:37:49,210 --> 00:37:52,270 of majors and science and engineering majors? 799 00:37:52,270 --> 00:37:54,790 And Lily, you came from one of those institutions. 800 00:37:54,790 --> 00:37:56,480 Please join in. 801 00:37:56,480 --> 00:37:57,260 Is this real? 802 00:37:57,260 --> 00:37:59,140 Is the problem that Romer is seeing here, 803 00:37:59,140 --> 00:38:00,290 is this a real issue? 804 00:38:00,290 --> 00:38:03,530 AUDIENCE: [INAUDIBLE] econ major at Wellesley College. 805 00:38:03,530 --> 00:38:05,670 AUDIENCE: Not at Wellesley, there 806 00:38:05,670 --> 00:38:08,623 is a definite sense of grade inflation there. 807 00:38:08,623 --> 00:38:09,790 AUDIENCE: It is not a sense. 808 00:38:09,790 --> 00:38:10,490 It is a policy. 809 00:38:10,490 --> 00:38:11,698 AUDIENCE: Yes, it's a policy. 810 00:38:11,698 --> 00:38:12,730 It's an actual policy. 811 00:38:12,730 --> 00:38:15,500 Instituted, grade inflation. 812 00:38:15,500 --> 00:38:18,500 But in terms of the disparity between the humanities, 813 00:38:18,500 --> 00:38:24,700 social sciences and STEM fields, I think there is definitely-- 814 00:38:24,700 --> 00:38:27,850 generally, there's the idea that like the STEM fields are 815 00:38:27,850 --> 00:38:34,030 more difficult. So recently we had this policy where 816 00:38:34,030 --> 00:38:37,330 the first semester of your college year shadow graded, 817 00:38:37,330 --> 00:38:39,040 so you're not actually given grades. 818 00:38:39,040 --> 00:38:41,830 And that kind of resulted in a lot of people 819 00:38:41,830 --> 00:38:44,230 entering the STEM fields [INAUDIBLE].. 820 00:38:44,230 --> 00:38:46,240 And that was something that obviously 821 00:38:46,240 --> 00:38:48,220 the humanities professors and social sciences 822 00:38:48,220 --> 00:38:48,970 were very against. 823 00:38:48,970 --> 00:38:51,130 Because if the students coming in 824 00:38:51,130 --> 00:38:53,770 were automatically going to anything outside 825 00:38:53,770 --> 00:38:55,570 of their department, they kind of ended up 826 00:38:55,570 --> 00:38:57,430 staying there because the faculty are great. 827 00:38:57,430 --> 00:38:59,410 So they would continue in that program. 828 00:38:59,410 --> 00:39:01,240 So that was kind of an imbalance that 829 00:39:01,240 --> 00:39:03,765 occurred in the recent years. 830 00:39:03,765 --> 00:39:05,640 WILLIAM BONVILLIAN: So in a way, exactly what 831 00:39:05,640 --> 00:39:07,320 Romer's talking about. 832 00:39:07,320 --> 00:39:08,000 Interesting. 833 00:39:08,000 --> 00:39:10,200 Lily, what was your point? 834 00:39:10,200 --> 00:39:11,820 AUDIENCE: I was a science major. 835 00:39:11,820 --> 00:39:14,110 And I made A's in every single class 836 00:39:14,110 --> 00:39:16,350 I took outside of my own major. 837 00:39:16,350 --> 00:39:17,292 And not As. 838 00:39:20,142 --> 00:39:22,184 WILLIAM BONVILLIAN: What were you thinking, Lily? 839 00:39:22,184 --> 00:39:23,040 [LAUGHING] 840 00:39:23,040 --> 00:39:24,882 AUDIENCE: We started-- 841 00:39:24,882 --> 00:39:27,510 I wanted to do science. 842 00:39:27,510 --> 00:39:29,970 We started out with almost 600 students 843 00:39:29,970 --> 00:39:32,790 in the first semester chemistry course. 844 00:39:32,790 --> 00:39:34,890 And by the second semester chemistry, 845 00:39:34,890 --> 00:39:38,610 so this is chemistry majors, biology majors, and engineering 846 00:39:38,610 --> 00:39:40,170 majors, all have to take that. 847 00:39:40,170 --> 00:39:42,420 They also have to take a second semester of chemistry. 848 00:39:42,420 --> 00:39:46,040 And there were probably less than 400 849 00:39:46,040 --> 00:39:47,340 by the second semester. 850 00:39:47,340 --> 00:39:48,840 So it was a huge weed out class. 851 00:39:48,840 --> 00:39:50,798 AUDIENCE: How many were there in the beginning? 852 00:39:50,798 --> 00:39:53,363 AUDIENCE: Over 600, just over six. 853 00:39:53,363 --> 00:39:55,530 WILLIAM BONVILLIAN: Most schools have notorious weed 854 00:39:55,530 --> 00:39:58,260 out classes that essentially frustrate 855 00:39:58,260 --> 00:39:59,830 the ambitions of a lot of students, 856 00:39:59,830 --> 00:40:03,030 particularly in the premed area. 857 00:40:03,030 --> 00:40:06,430 Obviously notorious, shall we say organic chemistry is 858 00:40:06,430 --> 00:40:08,290 the [INAUDIBLE]. 859 00:40:08,290 --> 00:40:13,560 So this is a dilemma when you've got a societal need 860 00:40:13,560 --> 00:40:15,630 and you've got the institutions that 861 00:40:15,630 --> 00:40:17,940 are supposed to deliver your talent base 862 00:40:17,940 --> 00:40:20,670 just operating off a completely different set of incentives 863 00:40:20,670 --> 00:40:22,050 than what the society may be. 864 00:40:22,050 --> 00:40:24,240 That's the core of Romer's argument here. 865 00:40:24,240 --> 00:40:28,560 And another part of his argument is, the numbers are not that 866 00:40:28,560 --> 00:40:29,350 far off. 867 00:40:29,350 --> 00:40:31,225 In other words, if we just got the people who 868 00:40:31,225 --> 00:40:35,400 wanted to major in science, engineering, and mathematics 869 00:40:35,400 --> 00:40:39,030 to stay in the field, you know, lots of the numbers problems 870 00:40:39,030 --> 00:40:40,620 get a heck of a lot better. 871 00:40:40,620 --> 00:40:43,410 But that 40% or more dropout rate 872 00:40:43,410 --> 00:40:44,860 out of those fields that typically 873 00:40:44,860 --> 00:40:50,070 occurs at the undergraduate level is highly problematic. 874 00:40:50,070 --> 00:40:51,390 AUDIENCE: Yes. 875 00:40:51,390 --> 00:40:53,932 AUDIENCE: I think they've been waiting longer if you want to. 876 00:40:53,932 --> 00:40:55,950 AUDIENCE: Oh, I couldn't see your hands. 877 00:40:55,950 --> 00:40:58,333 AUDIENCE: Yeah, so I started to wonder about a couple 878 00:40:58,333 --> 00:41:00,375 of these choke points, like where actually people 879 00:41:00,375 --> 00:41:02,250 are starting to drop out of these science and engineering 880 00:41:02,250 --> 00:41:02,750 fields. 881 00:41:02,750 --> 00:41:06,170 I know we identified kind of these weed out classes. 882 00:41:06,170 --> 00:41:10,140 And I think kind of looking from like who declares, who ends up 883 00:41:10,140 --> 00:41:13,440 finishing, I think you could probably, 884 00:41:13,440 --> 00:41:16,950 as a whole identify kind of gradient deflation in a couple 885 00:41:16,950 --> 00:41:20,190 of these classes is like very complicated, kind of weed out 886 00:41:20,190 --> 00:41:22,830 classes as reasons why people start to kind of leave 887 00:41:22,830 --> 00:41:24,480 the field and migrate out. 888 00:41:24,480 --> 00:41:27,450 And I think it might be indicative to sort 889 00:41:27,450 --> 00:41:30,378 of each institution kind of where these dropout points are. 890 00:41:30,378 --> 00:41:31,920 But I think en masse, you can kind of 891 00:41:31,920 --> 00:41:36,750 take these like first year classes as kind of places 892 00:41:36,750 --> 00:41:38,220 where students start to fall off. 893 00:41:38,220 --> 00:41:42,140 And then, is it actually-- 894 00:41:42,140 --> 00:41:44,580 so is it part of kind of this college exploratory process, 895 00:41:44,580 --> 00:41:46,497 like kind of figuring out what you want to do? 896 00:41:46,497 --> 00:41:48,980 It's like how you phrase it. 897 00:41:48,980 --> 00:41:50,748 As like, you started as a chemistry major. 898 00:41:50,748 --> 00:41:53,040 But maybe you end up gravitating towards social science 899 00:41:53,040 --> 00:41:55,440 because you don't really like the way that weed out class 900 00:41:55,440 --> 00:41:56,130 is structured? 901 00:41:56,130 --> 00:41:58,650 Or is it part of this indicative problem that we have, 902 00:41:58,650 --> 00:42:00,990 where like we can't graduate and sustain 903 00:42:00,990 --> 00:42:04,270 the people who are actually interested in these fields? 904 00:42:04,270 --> 00:42:07,440 And so I wonder if like, you know, just because you know, 905 00:42:07,440 --> 00:42:08,940 we're not graduating enough, is it 906 00:42:08,940 --> 00:42:10,793 because these classes are actually 907 00:42:10,793 --> 00:42:12,960 structured in a way that prevents people graduating? 908 00:42:12,960 --> 00:42:15,057 Or is it like part of this-- 909 00:42:15,057 --> 00:42:17,265 kind of the way that we brand your college experience 910 00:42:17,265 --> 00:42:18,840 is like you're supposed to be able to explore 911 00:42:18,840 --> 00:42:20,715 and kind of transition in and out, especially 912 00:42:20,715 --> 00:42:23,990 in that first year and experience things. 913 00:42:23,990 --> 00:42:27,030 And my second point was like, there's 914 00:42:27,030 --> 00:42:28,530 a little bit of a difference I would 915 00:42:28,530 --> 00:42:33,030 say in kind of STEM fields, even in that first year. 916 00:42:33,030 --> 00:42:35,280 Because I know a lot of colleges offer the opportunity 917 00:42:35,280 --> 00:42:38,850 to use high school classes to sort 918 00:42:38,850 --> 00:42:42,030 of test out of those first year, maybe even those weed 919 00:42:42,030 --> 00:42:43,590 out classes. 920 00:42:43,590 --> 00:42:45,285 And then, it's sort of an added bonus 921 00:42:45,285 --> 00:42:47,660 as well, because you end up testing out of these classes, 922 00:42:47,660 --> 00:42:51,300 so you use up less semesters to finish your degree. 923 00:42:51,300 --> 00:42:53,520 And so I know that can be like a big reason 924 00:42:53,520 --> 00:42:55,982 and incentive for students to sort of choose 925 00:42:55,982 --> 00:42:57,690 other universities, because they're like, 926 00:42:57,690 --> 00:42:59,970 oh, I'll only be in this school for three years 927 00:42:59,970 --> 00:43:02,430 if I go to maybe the state school over here, 928 00:43:02,430 --> 00:43:04,500 rather than staying in school for four years 929 00:43:04,500 --> 00:43:06,720 at a different university. 930 00:43:06,720 --> 00:43:09,030 And like, is there a way to identify 931 00:43:09,030 --> 00:43:12,780 sort of the relative quality of education 932 00:43:12,780 --> 00:43:15,690 in kind of testing out of that first year of engineering 933 00:43:15,690 --> 00:43:18,235 classes and then finishing in three versus going for four. 934 00:43:18,235 --> 00:43:19,860 Because I feel like if you can graduate 935 00:43:19,860 --> 00:43:22,920 a whole bunch of people, maybe in that three year time span 936 00:43:22,920 --> 00:43:25,823 without like a marginal decrease in quality of education, 937 00:43:25,823 --> 00:43:27,990 we can start looking more to those programs as well. 938 00:43:30,655 --> 00:43:32,530 WILLIAM BONVILLIAN: Max, how about a close up 939 00:43:32,530 --> 00:43:33,540 point on the Romer. 940 00:43:36,130 --> 00:43:40,730 AUDIENCE: So evidently, Romer is evidence 941 00:43:40,730 --> 00:43:45,902 that the education problem is unbelievably complicated. 942 00:43:45,902 --> 00:43:47,360 Throwing money at the issue has not 943 00:43:47,360 --> 00:43:49,760 been sufficient to actually solve it. 944 00:43:49,760 --> 00:43:54,290 And there are issues with the quality, 945 00:43:54,290 --> 00:43:58,490 as was being talked about, the weed out courses that exist, 946 00:43:58,490 --> 00:44:01,950 grade inflation and deflation, standardizing. 947 00:44:01,950 --> 00:44:05,045 So, such an issue will take-- 948 00:44:05,045 --> 00:44:06,920 it's going to take a very long time to solve. 949 00:44:06,920 --> 00:44:09,552 But all of the issues that he has pointed out, 950 00:44:09,552 --> 00:44:10,760 they are definitely solvable. 951 00:44:13,760 --> 00:44:19,580 I'd say the main barrier between us and now and a future 952 00:44:19,580 --> 00:44:22,580 where these issues are solved would be the-- 953 00:44:22,580 --> 00:44:24,260 would be just politics, just trying 954 00:44:24,260 --> 00:44:26,900 to get people to agree to these different programs that 955 00:44:26,900 --> 00:44:28,760 would focus more on the education 956 00:44:28,760 --> 00:44:32,470 and less on the incentives of those teaching. 957 00:44:32,470 --> 00:44:34,220 WILLIAM BONVILLIAN: I think part of what's 958 00:44:34,220 --> 00:44:37,100 interesting about Romer's argument 959 00:44:37,100 --> 00:44:41,540 is that he takes the theory that we've got a talent supply, set 960 00:44:41,540 --> 00:44:43,850 of talent supply issues, and then he 961 00:44:43,850 --> 00:44:48,400 attempts to figure out what are the more specific barriers 962 00:44:48,400 --> 00:44:52,460 to that supply, and then do an institutional analysis of why 963 00:44:52,460 --> 00:44:56,090 these barriers are in effect created at the higher education 964 00:44:56,090 --> 00:44:56,870 system. 965 00:44:56,870 --> 00:44:58,760 And then interestingly, he attempts 966 00:44:58,760 --> 00:45:03,252 to take three or four public policy fixes that 967 00:45:03,252 --> 00:45:04,460 could actually address these. 968 00:45:04,460 --> 00:45:09,140 And I do think that both his article and the Step 969 00:45:09,140 --> 00:45:11,900 Program from the National Science Foundation 970 00:45:11,900 --> 00:45:16,430 created based on the legislation that he 971 00:45:16,430 --> 00:45:18,410 recommended that Congress passed, 972 00:45:18,410 --> 00:45:20,270 and that NSF implemented. 973 00:45:20,270 --> 00:45:22,520 It was never implemented with the funding 974 00:45:22,520 --> 00:45:25,170 scale that was really needed to make a significant difference. 975 00:45:25,170 --> 00:45:28,610 But it started to send a different set of signals 976 00:45:28,610 --> 00:45:32,510 about supply of science and technology talent 977 00:45:32,510 --> 00:45:34,310 to the university system and did I 978 00:45:34,310 --> 00:45:36,800 think have an effect over time. 979 00:45:36,800 --> 00:45:40,080 But these issues are obviously still with us. 980 00:45:40,080 --> 00:45:44,150 So let me move quickly from Romer 981 00:45:44,150 --> 00:45:47,320 to our next reading with Richard Freeman. 982 00:45:47,320 --> 00:45:50,390 And he kind of takes us to the next level 983 00:45:50,390 --> 00:45:51,530 of the supply problem. 984 00:45:51,530 --> 00:45:55,100 Richard Freeman is a quite famous labor economist 985 00:45:55,100 --> 00:45:57,170 at the school up the street. 986 00:45:57,170 --> 00:46:02,900 And he's famous for his wide range and variety of hats. 987 00:46:02,900 --> 00:46:05,960 As you can see, he has a wonderful collection. 988 00:46:05,960 --> 00:46:08,230 So he looks at a different problem. 989 00:46:08,230 --> 00:46:14,460 You know does globalization threaten the-- 990 00:46:14,460 --> 00:46:16,310 and its movement in science and engineering, 991 00:46:16,310 --> 00:46:20,330 threaten US economic leadership? 992 00:46:20,330 --> 00:46:21,650 And you know, his-- 993 00:46:21,650 --> 00:46:26,810 I'll kind of summarize his four key points here. 994 00:46:29,330 --> 00:46:31,880 You know, the underlying issue is 995 00:46:31,880 --> 00:46:35,270 that changes in the global job market for science 996 00:46:35,270 --> 00:46:38,570 and engineering, science and entering workers, S&E workers 997 00:46:38,570 --> 00:46:42,980 are eroding US dominance in science and engineering, which 998 00:46:42,980 --> 00:46:47,180 in turn diminishes a strong comparative advantage 999 00:46:47,180 --> 00:46:50,280 that the US historically held since the end of World War II 1000 00:46:50,280 --> 00:46:52,610 as we've been talking about in the past. 1001 00:46:52,610 --> 00:46:54,500 And he makes four underlying points 1002 00:46:54,500 --> 00:46:56,550 in kind of looking at this dimension. 1003 00:46:56,550 --> 00:46:59,270 In other words, we have Augustine's critique of K 1004 00:46:59,270 --> 00:46:59,900 through 12. 1005 00:46:59,900 --> 00:47:03,710 We have Romer's critique of the higher education system. 1006 00:47:03,710 --> 00:47:06,410 And then Richard Freeman kind of tells us 1007 00:47:06,410 --> 00:47:08,840 what the economic implications are 1008 00:47:08,840 --> 00:47:13,400 of failing to maintain that leadership base in science 1009 00:47:13,400 --> 00:47:14,870 and engineering talent. 1010 00:47:14,870 --> 00:47:18,110 So his point is that the share of the world science 1011 00:47:18,110 --> 00:47:24,290 and engineering graduates in the US is in decline. 1012 00:47:24,290 --> 00:47:26,360 And look, others are understanding this model 1013 00:47:26,360 --> 00:47:30,080 and moving to fill it. 1014 00:47:30,080 --> 00:47:32,450 Second, that the job market has worsened 1015 00:47:32,450 --> 00:47:35,120 for younger workers in science and engineering fields 1016 00:47:35,120 --> 00:47:38,660 relative to many other high level 1017 00:47:38,660 --> 00:47:41,690 occupations, which in turn discourages 1018 00:47:41,690 --> 00:47:44,710 US students in those fields. 1019 00:47:44,710 --> 00:47:47,420 Now he's writing this at the time when 1020 00:47:47,420 --> 00:47:50,845 a startling proportion of MIT students 1021 00:47:50,845 --> 00:47:52,220 and other science and engineering 1022 00:47:52,220 --> 00:47:54,740 graduates in other schools were going 1023 00:47:54,740 --> 00:47:56,870 into the financial sector, financial services, 1024 00:47:56,870 --> 00:47:58,610 and consulting. 1025 00:47:58,610 --> 00:48:00,770 Some of that since he wrote has turned around. 1026 00:48:00,770 --> 00:48:03,870 In other words, there has been an emergence 1027 00:48:03,870 --> 00:48:09,050 at MIT of interest in startups and entrepreneurship, 1028 00:48:09,050 --> 00:48:15,200 to the tune that some 20% to 25% of those of you 1029 00:48:15,200 --> 00:48:17,300 when you graduate, your graduating classmates 1030 00:48:17,300 --> 00:48:19,670 will go into those fields in a way 1031 00:48:19,670 --> 00:48:21,830 that community moved out of financial services 1032 00:48:21,830 --> 00:48:27,170 post 2008 crash and moved over to what 1033 00:48:27,170 --> 00:48:30,170 may be a really important kind of career shift 1034 00:48:30,170 --> 00:48:31,880 in terms of the country's future. 1035 00:48:31,880 --> 00:48:37,850 But that hadn't occurred yet by the time he's writing this. 1036 00:48:37,850 --> 00:48:41,690 So in other words, the extremely high pay 1037 00:48:41,690 --> 00:48:44,180 available to those entering financial services 1038 00:48:44,180 --> 00:48:48,170 and consulting drained talent out of the system 1039 00:48:48,170 --> 00:48:50,720 is his point within the US. 1040 00:48:50,720 --> 00:48:56,780 And then, you know, countries like China and India 1041 00:48:56,780 --> 00:49:01,970 found that they could compete with the US in high tech 1042 00:49:01,970 --> 00:49:05,600 by starting to train substantial numbers of science 1043 00:49:05,600 --> 00:49:07,910 and engineering specialists. 1044 00:49:07,910 --> 00:49:11,810 In other words, they could leapfrog, right, and in effect 1045 00:49:11,810 --> 00:49:14,720 go to substantial parts of their economy being 1046 00:49:14,720 --> 00:49:19,310 quite high tech, even while they were working on bringing up 1047 00:49:19,310 --> 00:49:22,670 what's in effect a developing world economy in the meantime. 1048 00:49:22,670 --> 00:49:24,393 So they hit on this model. 1049 00:49:24,393 --> 00:49:25,810 And it's a really important model. 1050 00:49:25,810 --> 00:49:29,810 But that in turn had an effect on kind of US dominance 1051 00:49:29,810 --> 00:49:30,420 in this field. 1052 00:49:30,420 --> 00:49:32,795 I mean, it's not a bad thing for the world to get better, 1053 00:49:32,795 --> 00:49:37,290 but it doesn't have an effect on the US comparative advantage. 1054 00:49:37,290 --> 00:49:42,680 So to ease the adjustment to a less dominant position 1055 00:49:42,680 --> 00:49:45,710 in science and engineering with the corresponding economic 1056 00:49:45,710 --> 00:49:50,220 ramifications for US economic growth and competitiveness, 1057 00:49:50,220 --> 00:49:51,810 the US is going to have to develop, 1058 00:49:51,810 --> 00:49:55,910 he argues, new labor market and R&D policies 1059 00:49:55,910 --> 00:50:00,220 that start to try to change the science and engineering talent 1060 00:50:00,220 --> 00:50:01,333 numbers. 1061 00:50:01,333 --> 00:50:03,500 So we're going to have to step into the marketplace. 1062 00:50:03,500 --> 00:50:05,540 And you know, this is where Romer's thinking 1063 00:50:05,540 --> 00:50:07,470 would come to bear. 1064 00:50:07,470 --> 00:50:12,170 So let me let me move to Goldin and Katz 1065 00:50:12,170 --> 00:50:19,410 and they further talk about the societal ramifications for what 1066 00:50:19,410 --> 00:50:22,248 we're doing in higher education, right? 1067 00:50:22,248 --> 00:50:23,790 We've talked a bit about this before. 1068 00:50:27,320 --> 00:50:31,710 And they also teach up the street. 1069 00:50:31,710 --> 00:50:35,700 And their book, The Race Between Education and Technology 1070 00:50:35,700 --> 00:50:37,730 was really a quite important one. 1071 00:50:37,730 --> 00:50:40,530 I think it's held up well since it came out in 2009. 1072 00:50:40,530 --> 00:50:44,310 But I had you read a short version, a Milken Institute 1073 00:50:44,310 --> 00:50:47,910 review piece that came out before they put their book out. 1074 00:50:47,910 --> 00:50:51,450 But I do recommend the book to you. 1075 00:50:51,450 --> 00:50:55,230 The gap between wages of educated and less well educated 1076 00:50:55,230 --> 00:50:58,890 workers has been growing since 1980, 1077 00:50:58,890 --> 00:51:01,740 and this expanding wage inequality 1078 00:51:01,740 --> 00:51:03,720 has characterized the US since that time. 1079 00:51:06,300 --> 00:51:09,440 So we're becoming a much more polarized society 1080 00:51:09,440 --> 00:51:14,150 and the lines are drawn on education lines, right? 1081 00:51:14,150 --> 00:51:16,040 And this is, we've talked about this before, 1082 00:51:16,040 --> 00:51:19,760 but this is David Otter's barbell problem, 1083 00:51:19,760 --> 00:51:22,370 that our society is increasingly looking like a barbell. 1084 00:51:22,370 --> 00:51:25,760 And we've got on one bell, a growing 1085 00:51:25,760 --> 00:51:30,140 and successful upper middle class that has the education 1086 00:51:30,140 --> 00:51:31,520 and is able to use that education 1087 00:51:31,520 --> 00:51:35,750 to capture that wealth, a thinning middle, 1088 00:51:35,750 --> 00:51:39,830 and a growing lower end services economy that's 1089 00:51:39,830 --> 00:51:42,050 less well-paid and less well off, 1090 00:51:42,050 --> 00:51:44,120 to which substantial portions of the middle 1091 00:51:44,120 --> 00:51:45,410 are now being shunted. 1092 00:51:45,410 --> 00:51:48,740 And we can start to see this polarization in our economy 1093 00:51:48,740 --> 00:51:51,980 in pretty sharp terms. 1094 00:51:51,980 --> 00:51:56,960 And that's what Katz and Goldin are writing about. 1095 00:51:56,960 --> 00:52:00,410 The wage inequality narrowed in the United States 1096 00:52:00,410 --> 00:52:04,640 significantly from about 1910 through the 1950s. 1097 00:52:04,640 --> 00:52:09,260 Then it stabilized until about the 1980s. 1098 00:52:09,260 --> 00:52:12,920 And then it's grown since that time. 1099 00:52:12,920 --> 00:52:14,780 Why? 1100 00:52:14,780 --> 00:52:16,660 And they argue-- 1101 00:52:16,660 --> 00:52:21,650 I'll attempt to just paint you a quick picture. 1102 00:52:21,650 --> 00:52:28,070 But they argue that there is a race between education 1103 00:52:28,070 --> 00:52:33,080 and technology, and that what happened in the US 1104 00:52:33,080 --> 00:52:37,850 was that there is an ever-growing curve 1105 00:52:37,850 --> 00:52:42,045 of technology knowledge since the Industrial Revolution. 1106 00:52:42,045 --> 00:52:43,670 And we've talked about this previously, 1107 00:52:43,670 --> 00:52:46,220 but I'll reiterate it. 1108 00:52:46,220 --> 00:52:48,500 That an economy requires, in other words, 1109 00:52:48,500 --> 00:52:50,600 it requires an ever-growing level 1110 00:52:50,600 --> 00:52:54,580 of technological sophistication. 1111 00:52:54,580 --> 00:52:59,500 And the genius of the US system was 1112 00:52:59,500 --> 00:53:04,515 to create mass higher education, which we did, 1113 00:53:04,515 --> 00:53:05,890 first through the Land Grant Act. 1114 00:53:05,890 --> 00:53:07,510 Of course that created MIT. 1115 00:53:07,510 --> 00:53:10,470 That was the big enabler for MIT. 1116 00:53:10,470 --> 00:53:12,310 It had initial funding in 1861. 1117 00:53:12,310 --> 00:53:14,500 Its graduating class marched off to the Civil War. 1118 00:53:14,500 --> 00:53:16,360 There was no revenue base. 1119 00:53:16,360 --> 00:53:20,675 In 1862, Congress passed the Land Grant College Act 1120 00:53:20,675 --> 00:53:22,300 and suddenly there was a revenue stream 1121 00:53:22,300 --> 00:53:24,900 to make up for the loss of talent, loss of the tuition 1122 00:53:24,900 --> 00:53:25,400 base. 1123 00:53:25,400 --> 00:53:28,210 So that saved MIT. 1124 00:53:28,210 --> 00:53:31,540 But created public higher education 1125 00:53:31,540 --> 00:53:34,780 really across the country in every state at the time 1126 00:53:34,780 --> 00:53:36,730 extended to new ones. 1127 00:53:36,730 --> 00:53:40,280 So we created that system of mass higher education, really 1128 00:53:40,280 --> 00:53:42,400 through the Land Grant College Act in 1862. 1129 00:53:42,400 --> 00:53:45,490 No other country had done anything like that. 1130 00:53:45,490 --> 00:53:50,710 So we created a talent base that stayed ahead 1131 00:53:50,710 --> 00:53:53,620 of the technological curve, right? 1132 00:53:53,620 --> 00:53:56,050 And that's what you want. 1133 00:53:56,050 --> 00:53:59,080 You want your talent base to stay ahead 1134 00:53:59,080 --> 00:54:03,400 of the increased sophistication of technology in the economy, 1135 00:54:03,400 --> 00:54:07,920 so that that talent base can keep moving that curve up, 1136 00:54:07,920 --> 00:54:08,590 right? 1137 00:54:08,590 --> 00:54:11,110 And that they benefit each other. 1138 00:54:11,110 --> 00:54:14,080 And then, what Katz and Goldin point out, 1139 00:54:14,080 --> 00:54:19,810 is that in the mid '70s, we level that off. 1140 00:54:19,810 --> 00:54:22,300 AUDIENCE: Was there something that caused it in particular? 1141 00:54:22,300 --> 00:54:23,925 Let me get to that in a minute, Martin. 1142 00:54:23,925 --> 00:54:25,930 But we do need to come back to that. 1143 00:54:25,930 --> 00:54:27,400 I don't think we fully understand 1144 00:54:27,400 --> 00:54:29,525 the dimensions of what was happening at that point. 1145 00:54:29,525 --> 00:54:31,120 But there are some thoughts. 1146 00:54:31,120 --> 00:54:35,140 So what happened was that the people that 1147 00:54:35,140 --> 00:54:39,460 continue to ride this curve, they 1148 00:54:39,460 --> 00:54:43,300 stayed up with the technology curve. 1149 00:54:43,300 --> 00:54:47,680 Those that fell off fell behind the curve 1150 00:54:47,680 --> 00:54:51,010 and their incomes correspondingly suffered. 1151 00:54:51,010 --> 00:54:54,630 That's the case that they're making. 1152 00:54:54,630 --> 00:54:57,280 and that these people got left off the curve 1153 00:54:57,280 --> 00:55:00,760 and couldn't keep riding up to take advantage 1154 00:55:00,760 --> 00:55:01,680 of the economic gains. 1155 00:55:01,680 --> 00:55:04,840 So you've got a smaller number of people you know, 1156 00:55:04,840 --> 00:55:06,160 riding this curve up. 1157 00:55:06,160 --> 00:55:09,670 They get the gains. 1158 00:55:09,670 --> 00:55:13,960 Whereas in this period of time between 1910 and 1950, 1159 00:55:13,960 --> 00:55:16,540 everybody at least, a very large part of the population 1160 00:55:16,540 --> 00:55:18,620 was riding that curve and able to benefit. 1161 00:55:18,620 --> 00:55:21,910 So that's the great creation of this mass middle class 1162 00:55:21,910 --> 00:55:24,220 in the United States. 1163 00:55:24,220 --> 00:55:27,760 And the data tends to bear this out. 1164 00:55:27,760 --> 00:55:33,130 So that's their essential equation here on what happened. 1165 00:55:33,130 --> 00:55:35,380 Again, they argue that technological advance 1166 00:55:35,380 --> 00:55:36,350 is the key to growth. 1167 00:55:36,350 --> 00:55:40,000 We know that from this class. 1168 00:55:40,000 --> 00:55:45,670 And that the ebb and flow of wage equality and inequality 1169 00:55:45,670 --> 00:55:48,190 is very much related to the skill 1170 00:55:48,190 --> 00:55:51,010 set you've got to keep riding that technological curve. 1171 00:55:51,010 --> 00:55:55,360 Now look, if anything the growth in that technological curve 1172 00:55:55,360 --> 00:55:59,100 has just gotten steeper with the development of all 1173 00:55:59,100 --> 00:56:02,040 these information technologies. 1174 00:56:02,040 --> 00:56:03,760 You know heaven forbid, a next generation 1175 00:56:03,760 --> 00:56:06,970 if you don't have coding skills, right? 1176 00:56:06,970 --> 00:56:09,460 With the entry of significant amount 1177 00:56:09,460 --> 00:56:12,370 of artificial intelligence in the economy. 1178 00:56:12,370 --> 00:56:15,160 If you're going to stay up, you really need that skill base. 1179 00:56:15,160 --> 00:56:18,950 Otherwise you're going to get left behind. 1180 00:56:18,950 --> 00:56:23,860 So, you know, that's essentially their picture 1181 00:56:23,860 --> 00:56:25,630 of what's been going on here. 1182 00:56:25,630 --> 00:56:33,930 Let me-- that stagnation of education levels around '73 1183 00:56:33,930 --> 00:56:42,760 or so coincides with the period of economic challenge 1184 00:56:42,760 --> 00:56:46,270 from Japan that we talked about in the second manufacturing 1185 00:56:46,270 --> 00:56:47,530 class. 1186 00:56:47,530 --> 00:56:51,250 That's the period where the Rust Belt gets created. 1187 00:56:51,250 --> 00:56:54,250 So the US had been on an ever rising economic curve. 1188 00:56:54,250 --> 00:56:58,180 Suddenly its growth rate fell to the 2% range 1189 00:56:58,180 --> 00:57:05,340 from its historic 3% range and its productivity rate 1190 00:57:05,340 --> 00:57:08,340 strides growth as we've talked about, 1191 00:57:08,340 --> 00:57:11,290 its productivity rate fell to the 1% range. 1192 00:57:11,290 --> 00:57:14,050 So that means there's less real wealth in the economy. 1193 00:57:14,050 --> 00:57:17,530 And that may, Martin, in answer to your question, that 1194 00:57:17,530 --> 00:57:19,720 may have had something to do with our ability 1195 00:57:19,720 --> 00:57:23,510 to keep financing ever-growing education. 1196 00:57:23,510 --> 00:57:29,440 Remember that the public universities provide 1197 00:57:29,440 --> 00:57:31,780 80% of higher education. 1198 00:57:31,780 --> 00:57:35,052 That's the key component of the system. 1199 00:57:35,052 --> 00:57:37,260 You know, places like MIT are all very well and good, 1200 00:57:37,260 --> 00:57:40,000 but that's a 20% share. 1201 00:57:40,000 --> 00:57:44,640 The core talent base are getting trained in public universities. 1202 00:57:44,640 --> 00:57:46,140 And other things have been happening 1203 00:57:46,140 --> 00:57:48,190 to those public universities. 1204 00:57:48,190 --> 00:57:51,310 So they're competing at the state level 1205 00:57:51,310 --> 00:57:55,740 for funding with two major factors. 1206 00:57:55,740 --> 00:57:58,330 One is Medicaid, which the states bear 1207 00:57:58,330 --> 00:58:00,860 a very large portion of and the other is prisons. 1208 00:58:00,860 --> 00:58:03,290 Because we have a massive prison growth. 1209 00:58:03,290 --> 00:58:05,983 And the annual cost of prisoners is 1210 00:58:05,983 --> 00:58:08,150 much higher than putting people in higher education. 1211 00:58:08,150 --> 00:58:10,025 You begin to wonder where your priorities are 1212 00:58:10,025 --> 00:58:12,260 when you think about that. 1213 00:58:12,260 --> 00:58:14,060 So there have been increased pressure 1214 00:58:14,060 --> 00:58:17,300 on state budgets that correspond with that '73 1215 00:58:17,300 --> 00:58:21,680 to like 1990 and 1991, kind of economic decline period that 1216 00:58:21,680 --> 00:58:22,880 may account for it. 1217 00:58:22,880 --> 00:58:27,080 Now the Obama administration understood this curve well. 1218 00:58:27,080 --> 00:58:31,820 And Obama was fluent with Katz and Goldin's work. 1219 00:58:31,820 --> 00:58:36,590 And they made a major effort to increase Pell grants 1220 00:58:36,590 --> 00:58:40,550 and increase the availability of higher education funding. 1221 00:58:40,550 --> 00:58:44,068 So interestingly, there was an improvement in the last decade 1222 00:58:44,068 --> 00:58:45,110 in some of these numbers. 1223 00:58:45,110 --> 00:58:48,170 Some of that was driven by the fact 1224 00:58:48,170 --> 00:58:51,480 that jobs are a disaster in 2007 and 2008. 1225 00:58:51,480 --> 00:58:55,610 So people tended to spend that time if they could afford it 1226 00:58:55,610 --> 00:58:57,740 in higher education locations. 1227 00:58:57,740 --> 00:58:59,300 So that was some of that going on. 1228 00:58:59,300 --> 00:59:01,540 But overall, the Administration made an attempt 1229 00:59:01,540 --> 00:59:03,290 to try and get those numbers turned around 1230 00:59:03,290 --> 00:59:05,520 and they're a bit better. 1231 00:59:05,520 --> 00:59:08,150 And the public universities themselves 1232 00:59:08,150 --> 00:59:10,490 understood their mission and realized 1233 00:59:10,490 --> 00:59:13,340 that they're going to have to increase their employment base 1234 00:59:13,340 --> 00:59:16,760 in order to accomplish this. 1235 00:59:16,760 --> 00:59:18,470 All right, so that's Katz and Goldin. 1236 00:59:18,470 --> 00:59:21,050 I mean there's other important points there, but that's key. 1237 00:59:21,050 --> 00:59:23,270 And then I want to do-- 1238 00:59:23,270 --> 00:59:25,970 you know, we've talked about higher education 1239 00:59:25,970 --> 00:59:28,610 as though it's the only way here. 1240 00:59:28,610 --> 00:59:32,510 But I wanted to introduce some controversy into the debate. 1241 00:59:32,510 --> 00:59:39,530 So William Baumol, very noted economist, taught at Princeton, 1242 00:59:39,530 --> 00:59:43,810 more recent years have been teaching at NYU, you know, 1243 00:59:43,810 --> 00:59:45,230 a remarkable analyst. 1244 00:59:49,110 --> 00:59:51,750 And he's written in many different kind 1245 00:59:51,750 --> 00:59:54,630 of economics fields. 1246 00:59:54,630 --> 00:59:58,770 Baumol does this NBER, National Bureau 1247 00:59:58,770 --> 01:00:04,110 of Economic Research kind of work paper 1248 01:00:04,110 --> 01:00:07,940 that just nails the whole system. 1249 01:00:07,940 --> 01:00:11,970 So I thought we'd introduce a little controversy 1250 01:00:11,970 --> 01:00:15,810 in today's class by putting his piece in front of us. 1251 01:00:15,810 --> 01:00:17,480 He argues that breakthrough innovation 1252 01:00:17,480 --> 01:00:21,920 comes from independent inventors and entrepreneurs, 1253 01:00:21,920 --> 01:00:25,430 that large firms concentrate on incremental innovation. 1254 01:00:25,430 --> 01:00:28,280 We've talked about this bit before. 1255 01:00:28,280 --> 01:00:31,070 And this conclusion is what is startling. 1256 01:00:31,070 --> 01:00:34,480 Education for the mastery of science knowledge 1257 01:00:34,480 --> 01:00:40,500 aids incremental advance, doesn't necessarily 1258 01:00:40,500 --> 01:00:44,640 prepare you for doing the breakthrough, 1259 01:00:44,640 --> 01:00:45,780 entrepreneurial side. 1260 01:00:49,080 --> 01:00:53,640 So his point then, is that kind of standard science education 1261 01:00:53,640 --> 01:00:57,530 may actually impede breakthrough thinking. 1262 01:00:57,530 --> 01:01:00,600 And that large firm R&D requires scientists and engineers 1263 01:01:00,600 --> 01:01:03,330 that are educated in the established 1264 01:01:03,330 --> 01:01:06,540 fields and the established analytical methods 1265 01:01:06,540 --> 01:01:11,370 and that successful innovators and entrepreneurs often 1266 01:01:11,370 --> 01:01:15,670 lack that standard preparation. 1267 01:01:15,670 --> 01:01:17,490 And that that may get them out of the box 1268 01:01:17,490 --> 01:01:21,390 of incremental advance into kind of new territories. 1269 01:01:21,390 --> 01:01:26,170 And he points out that we don't have a system 1270 01:01:26,170 --> 01:01:31,630 and we don't understand breakthrough learning. 1271 01:01:31,630 --> 01:01:33,490 How do we educate for innovation? 1272 01:01:33,490 --> 01:01:39,080 We have no real clue on how to do that. 1273 01:01:39,080 --> 01:01:41,080 And that procedures for incremental learning 1274 01:01:41,080 --> 01:01:42,800 do seem to work. 1275 01:01:42,800 --> 01:01:46,810 But we don't know how to educate for the innovation side-- 1276 01:01:46,810 --> 01:01:48,760 of the innovation system, who's got what. 1277 01:01:48,760 --> 01:01:53,520 He points out the Proctor & Gamble with 7,500 scientists, 1278 01:01:53,520 --> 01:01:56,560 1,250 PhDs, I mean, these kinds of totals start 1279 01:01:56,560 --> 01:02:01,010 to dwarf the size of faculties at MIT and Harvard and Stanford 1280 01:02:01,010 --> 01:02:03,650 and so forth. 1281 01:02:03,650 --> 01:02:05,500 You know, with 22 research centers, 1282 01:02:05,500 --> 01:02:08,260 P&G has in 12 different nations, that's 1283 01:02:08,260 --> 01:02:11,920 a pretty amazing talent base. 1284 01:02:11,920 --> 01:02:14,770 What are they up to? 1285 01:02:14,770 --> 01:02:21,520 Remember that when you look at R&D combined, industry 1286 01:02:21,520 --> 01:02:25,090 spans about 70% of the total spending on R&D. Of course, 1287 01:02:25,090 --> 01:02:29,740 we know that that's D, not R, right? 1288 01:02:29,740 --> 01:02:32,890 But industry has got about a 70% share. 1289 01:02:32,890 --> 01:02:39,220 The federal government, about 30% share, that's R. 1290 01:02:39,220 --> 01:02:42,310 So that gives us an idea of the different size 1291 01:02:42,310 --> 01:02:45,950 of the establishments in the R&D side. 1292 01:02:45,950 --> 01:02:49,540 So we know that industry employs some 64% 1293 01:02:49,540 --> 01:02:52,150 of scientists and engineers, right? 1294 01:02:52,150 --> 01:02:56,050 So industry has got that talent base. 1295 01:02:56,050 --> 01:02:59,830 And he notes that critical breakthrough 1296 01:02:59,830 --> 01:03:03,930 historical innovator figures like Watt, Whitney, Fulton, 1297 01:03:03,930 --> 01:03:08,200 Morris, Edison, the Wright brothers, Wozniak, Jobs, Gates, 1298 01:03:08,200 --> 01:03:13,540 Dell, have no college degrees, and frankly, 1299 01:03:13,540 --> 01:03:15,280 limited scientific training. 1300 01:03:15,280 --> 01:03:17,030 AUDIENCE: Wait, Jobs had a college degree. 1301 01:03:17,030 --> 01:03:19,072 WILLIAM BONVILLIAN: Yes, he had a college degree. 1302 01:03:19,072 --> 01:03:20,170 No, he didn't finish Reed. 1303 01:03:20,170 --> 01:03:21,290 AUDIENCE: No, he left in the first year. 1304 01:03:21,290 --> 01:03:22,270 WILLIAM BONVILLIAN: Yeah, he left. 1305 01:03:22,270 --> 01:03:23,330 He left after a year or two. 1306 01:03:23,330 --> 01:03:24,100 AUDIENCE: I thought he went somewhere else. 1307 01:03:24,100 --> 01:03:24,933 AUDIENCE: He stayed. 1308 01:03:24,933 --> 01:03:27,360 He stayed with Reed and just dropped in on classes, 1309 01:03:27,360 --> 01:03:29,110 but it wouldn't count as getting a degree. 1310 01:03:29,110 --> 01:03:29,830 WILLIAM BONVILLIAN: He was studying things 1311 01:03:29,830 --> 01:03:31,360 like calligraphy that actually turned out 1312 01:03:31,360 --> 01:03:32,960 to be incredibly important for him. 1313 01:03:32,960 --> 01:03:34,752 AUDIENCE: I mean, that's the quote example, 1314 01:03:34,752 --> 01:03:36,505 he went a lot of classes [INAUDIBLE].. 1315 01:03:36,505 --> 01:03:39,090 WILLIAM BONVILLIAN: Right. 1316 01:03:39,090 --> 01:03:40,423 And he spent a year in India. 1317 01:03:40,423 --> 01:03:42,548 AUDIENCE: Yeah, he did a lot of interesting things. 1318 01:03:45,550 --> 01:03:49,900 WILLIAM BONVILLIAN: So Baumol's point is progress requires-- 1319 01:03:49,900 --> 01:03:51,640 we shouldn't underestimate this. 1320 01:03:51,640 --> 01:03:53,710 It's not that one side is bad. 1321 01:03:53,710 --> 01:03:58,090 Progress requires both breakthrough radical advance 1322 01:03:58,090 --> 01:04:00,100 and incremental advance. 1323 01:04:00,100 --> 01:04:03,190 And an example, which I've used before with you all, you 1324 01:04:03,190 --> 01:04:06,220 know, if you're flying across the Atlantic Ocean, 1325 01:04:06,220 --> 01:04:09,520 the Wright brothers, you know, motorized kite 1326 01:04:09,520 --> 01:04:12,070 is a terrific, radical break through advance. 1327 01:04:12,070 --> 01:04:15,070 But I'd rather take the 787, product 1328 01:04:15,070 --> 01:04:17,560 of 10 decades worth of incremental advances when 1329 01:04:17,560 --> 01:04:19,300 I go across the ocean. 1330 01:04:19,300 --> 01:04:21,520 So both are really important here. 1331 01:04:21,520 --> 01:04:23,215 You've got to do both pieces. 1332 01:04:26,620 --> 01:04:30,370 But, a disproportionate share of the breakthroughs do 1333 01:04:30,370 --> 01:04:35,140 seem to come from kind of independent inventors 1334 01:04:35,140 --> 01:04:36,160 or entrepreneurs. 1335 01:04:36,160 --> 01:04:39,947 And large firms tend to specialize on the incremental, 1336 01:04:39,947 --> 01:04:42,280 in part because they want to break up their own business 1337 01:04:42,280 --> 01:04:43,050 models, right? 1338 01:04:43,050 --> 01:04:44,717 They've got established business models. 1339 01:04:44,717 --> 01:04:46,780 They want to contribute to those business models 1340 01:04:46,780 --> 01:04:49,990 rather than wreck their existing business model. 1341 01:04:49,990 --> 01:04:52,240 So that's part of the economic motivation here. 1342 01:04:54,970 --> 01:04:58,990 But education for incremental advance 1343 01:04:58,990 --> 01:05:02,110 may well be different as Baumol points out, than education 1344 01:05:02,110 --> 01:05:05,770 for the novel advance. 1345 01:05:05,770 --> 01:05:07,650 And incremental improvement may well 1346 01:05:07,650 --> 01:05:10,930 require a much greater mastery of demanding 1347 01:05:10,930 --> 01:05:14,650 science and technology information than the novel 1348 01:05:14,650 --> 01:05:16,360 idea. 1349 01:05:16,360 --> 01:05:18,440 So both are essential. 1350 01:05:18,440 --> 01:05:21,700 But then he posts the critical question, 1351 01:05:21,700 --> 01:05:23,920 how do you educate for the original and novel idea 1352 01:05:23,920 --> 01:05:25,510 generation? 1353 01:05:25,510 --> 01:05:33,223 So we've got three new stories on the table here. 1354 01:05:33,223 --> 01:05:35,140 And let's start off discussion, and then we'll 1355 01:05:35,140 --> 01:05:36,610 take a break in a bit. 1356 01:05:36,610 --> 01:05:38,330 First, we've got Richard Freeman. 1357 01:05:38,330 --> 01:05:39,790 AUDIENCE: Yes. 1358 01:05:39,790 --> 01:05:42,190 I wanted to make some strides actually 1359 01:05:42,190 --> 01:05:45,940 on a point to that Baumol made in our conversation of three 1360 01:05:45,940 --> 01:05:49,543 months since Baumol was the one who cited P&G 1361 01:05:49,543 --> 01:05:51,460 as an innovative research and development firm 1362 01:05:51,460 --> 01:05:53,360 with a lot of PhDs. 1363 01:05:53,360 --> 01:05:57,100 One of you posed the question about Freeman. 1364 01:05:57,100 --> 01:05:58,540 This paper lauds the relationship 1365 01:05:58,540 --> 01:06:01,690 in the United States between firms and university research. 1366 01:06:01,690 --> 01:06:03,970 For multi-national companies, what incentives 1367 01:06:03,970 --> 01:06:06,850 do they have to promote US innovation leadership, even 1368 01:06:06,850 --> 01:06:09,010 when they may be based in the US when they operate 1369 01:06:09,010 --> 01:06:10,750 in so many different countries? 1370 01:06:10,750 --> 01:06:12,340 So at the heart of this question is, 1371 01:06:12,340 --> 01:06:16,030 why do we need to invest in America 1372 01:06:16,030 --> 01:06:19,450 if it is that the private sector is not investing and has 1373 01:06:19,450 --> 01:06:22,240 no rational self interest to invest in us 1374 01:06:22,240 --> 01:06:25,370 from an economic perspective? 1375 01:06:25,370 --> 01:06:27,650 That is the implication of this question. 1376 01:06:27,650 --> 01:06:29,108 AUDIENCE: I don't think it's like-- 1377 01:06:29,108 --> 01:06:31,130 I think that we look at it one dimensionally. 1378 01:06:31,130 --> 01:06:32,710 You have to look at it, like, think about it 1379 01:06:32,710 --> 01:06:33,340 like an individual, right? 1380 01:06:33,340 --> 01:06:34,340 Like you're going to be a baby. 1381 01:06:34,340 --> 01:06:35,715 There's a whole gestation period. 1382 01:06:35,715 --> 01:06:37,678 Then there's a time period where you're trying 1383 01:06:37,678 --> 01:06:38,720 to grow as an individual. 1384 01:06:38,720 --> 01:06:41,178 And then there's one when you're ready to get to it, right? 1385 01:06:41,178 --> 01:06:43,410 And like you're very useful. 1386 01:06:43,410 --> 01:06:45,080 So for industry like, they can't really 1387 01:06:45,080 --> 01:06:47,130 handle this whole gestation period, 1388 01:06:47,130 --> 01:06:49,740 you know, one to 18 years old period. 1389 01:06:49,740 --> 01:06:51,890 But they can handle it after, when 1390 01:06:51,890 --> 01:06:54,930 you're ready to go and do a little ramp up skills. 1391 01:06:54,930 --> 01:06:58,930 But to do a whole thing is pretty difficult. 1392 01:06:58,930 --> 01:07:01,140 AUDIENCE: That's an interesting answer. 1393 01:07:01,140 --> 01:07:03,930 WILLIAM BONVILLIAN: I'd add too that the US has an extremely 1394 01:07:03,930 --> 01:07:06,390 decentralized labor market. 1395 01:07:06,390 --> 01:07:10,110 So there is a huge disincentive for employers in the United 1396 01:07:10,110 --> 01:07:12,960 States to offer training. 1397 01:07:12,960 --> 01:07:18,510 Because they offer training, which can be quite expensive, 1398 01:07:18,510 --> 01:07:23,070 investing in you, that in turn equips you to move on. 1399 01:07:23,070 --> 01:07:27,390 So often another employer will buy you out 1400 01:07:27,390 --> 01:07:30,210 for a lower margin with the educational costs. 1401 01:07:30,210 --> 01:07:32,640 But the employer who invested in your education 1402 01:07:32,640 --> 01:07:36,420 won't be able to match that increment. 1403 01:07:36,420 --> 01:07:40,080 So why should they invest in employees 1404 01:07:40,080 --> 01:07:42,540 if the talent is going to go elsewhere? 1405 01:07:42,540 --> 01:07:45,390 And countries like Germany have a much more established set 1406 01:07:45,390 --> 01:07:49,380 of apprenticeship rules and much more heavily unionized, 1407 01:07:49,380 --> 01:07:53,040 80% unionized in the manufacturing sector. 1408 01:07:53,040 --> 01:07:56,130 They have been able to create an apprenticeship system that's 1409 01:07:56,130 --> 01:07:58,800 much more enduring and employer tied. 1410 01:07:58,800 --> 01:08:01,770 That creates tremendous encouragement for employers 1411 01:08:01,770 --> 01:08:06,060 to provide lots of skills training to its employees. 1412 01:08:06,060 --> 01:08:08,700 Indeed, it's really a part of the German education system. 1413 01:08:08,700 --> 01:08:10,470 We have never really been able, because we 1414 01:08:10,470 --> 01:08:14,550 have such a decentralized and almost laissez faire labor 1415 01:08:14,550 --> 01:08:18,899 market, we've never been able to create 1416 01:08:18,899 --> 01:08:22,460 significant incentives for employers to provide education. 1417 01:08:22,460 --> 01:08:24,797 It's a deep structural problem in our system. 1418 01:08:24,797 --> 01:08:26,880 And you know it's one we're going to probably have 1419 01:08:26,880 --> 01:08:28,172 to figure out how to deal with. 1420 01:08:28,172 --> 01:08:31,270 We have created a whole system of community colleges. 1421 01:08:31,270 --> 01:08:32,850 But the burden is on the employee 1422 01:08:32,850 --> 01:08:34,470 to go back and get that education. 1423 01:08:34,470 --> 01:08:37,170 We make that pretty inexpensive. 1424 01:08:37,170 --> 01:08:39,470 AUDIENCE: Going back here about the gatekeeper thing. 1425 01:08:39,470 --> 01:08:42,143 Couldn't we just bribe them? 1426 01:08:42,143 --> 01:08:43,810 WILLIAM BONVILLIAN: Bribe the employees? 1427 01:08:43,810 --> 01:08:44,109 AUDIENCE: Yeah. 1428 01:08:44,109 --> 01:08:44,520 Oh, no. 1429 01:08:44,520 --> 01:08:45,180 WILLIAM BONVILLIAN: Bribe the employers? 1430 01:08:45,180 --> 01:08:45,750 AUDIENCE: Yeah. 1431 01:08:45,750 --> 01:08:46,319 WILLIAM BONVILLIAN: Right, we could. 1432 01:08:46,319 --> 01:08:47,970 AUDIENCE: The education, how much is it? 1433 01:08:47,970 --> 01:08:49,680 WILLIAM BONVILLIAN: I'm sure it's not cheap, right? 1434 01:08:49,680 --> 01:08:50,109 AUDIENCE: Damn it. 1435 01:08:50,109 --> 01:08:50,880 WILLIAM BONVILLIAN: But in other words, 1436 01:08:50,880 --> 01:08:53,460 could we create an incentive for employers 1437 01:08:53,460 --> 01:08:57,450 to provide the education system, even if they weren't 1438 01:08:57,450 --> 01:08:59,729 able to retain their workers. 1439 01:08:59,729 --> 01:09:01,590 And what would that cost? 1440 01:09:01,590 --> 01:09:03,600 So maybe the feds would pick up that cost. 1441 01:09:03,600 --> 01:09:06,569 So in a way, the community college system 1442 01:09:06,569 --> 01:09:08,939 is a way of the government picking up that 1443 01:09:08,939 --> 01:09:13,720 Cost and share it with the employee. 1444 01:09:13,720 --> 01:09:17,560 Because employers can't take the risk mitigating. 1445 01:09:17,560 --> 01:09:20,411 I kind of jumped into the discussion here. 1446 01:09:20,411 --> 01:09:21,953 I want to give it back to you, Steph. 1447 01:09:21,953 --> 01:09:23,569 Get me out of this. 1448 01:09:23,569 --> 01:09:26,529 AUDIENCE: I think that's-- who posed this question? 1449 01:09:26,529 --> 01:09:27,970 Yeah, I think you're right. 1450 01:09:27,970 --> 01:09:31,330 I mean it really gets at the heart of all three readings. 1451 01:09:31,330 --> 01:09:33,580 And I think, in particular, let me 1452 01:09:33,580 --> 01:09:38,920 see if I can parse for a quote here from one of the readings. 1453 01:09:38,920 --> 01:09:41,229 Actually it was in Freeman's reading, when he quoted 1454 01:09:41,229 --> 01:09:43,979 Derek Bok, who is the founder-- 1455 01:09:43,979 --> 01:09:44,640 I think-- 1456 01:09:44,640 --> 01:09:45,550 WILLIAM BONVILLIAN: President of Harvard. 1457 01:09:45,550 --> 01:09:46,859 AUDIENCE: President of Harvard. 1458 01:09:46,859 --> 01:09:49,240 And now the graduate school of education at Harvard 1459 01:09:49,240 --> 01:09:52,720 has a center for innovative learning named after Derek Bok. 1460 01:09:52,720 --> 01:09:55,750 And he claimed towards the end of the piece, 1461 01:09:55,750 --> 01:09:58,330 or rather he was cited towards the end of the piece 1462 01:09:58,330 --> 01:10:01,690 as saying that other countries are facilitating our enterprise 1463 01:10:01,690 --> 01:10:02,660 model. 1464 01:10:02,660 --> 01:10:07,350 And if, I think it's really meritous of consideration, 1465 01:10:07,350 --> 01:10:09,880 that if our education system is not supporting 1466 01:10:09,880 --> 01:10:13,030 the production of scientists and engineers, and firms are you 1467 01:10:13,030 --> 01:10:15,070 know sort of ready and happy to get employees 1468 01:10:15,070 --> 01:10:17,440 from other countries or to move to other countries, 1469 01:10:17,440 --> 01:10:19,840 that we're effectively shooting ourself in a foot, 1470 01:10:19,840 --> 01:10:22,810 by participating in free market principles for education. 1471 01:10:22,810 --> 01:10:24,618 Because we can't provide the labor force 1472 01:10:24,618 --> 01:10:26,410 and we can't support the labor force and we 1473 01:10:26,410 --> 01:10:28,540 can't support incentives financially. 1474 01:10:28,540 --> 01:10:32,470 So at the end of the day, we're preventing ourselves 1475 01:10:32,470 --> 01:10:34,570 on all fronts from innovating, which 1476 01:10:34,570 --> 01:10:38,260 I think is a really important consideration that merits sort 1477 01:10:38,260 --> 01:10:40,880 of being honest with ourselves, as not only a society, 1478 01:10:40,880 --> 01:10:44,430 but also as you know a political economy. 1479 01:10:44,430 --> 01:10:47,050 AUDIENCE: Something throughout all the readings 1480 01:10:47,050 --> 01:10:48,640 that I have not understood. 1481 01:10:48,640 --> 01:10:51,790 How is it that we're spending so much on education, 1482 01:10:51,790 --> 01:10:54,840 like so much money per student, yet teachers 1483 01:10:54,840 --> 01:10:58,240 are not getting any of it and the students 1484 01:10:58,240 --> 01:10:59,940 don't have very high quality educations. 1485 01:10:59,940 --> 01:11:00,940 Where's the money going? 1486 01:11:00,940 --> 01:11:02,315 AUDIENCE: Yeah, Max, I think that 1487 01:11:02,315 --> 01:11:04,210 was addressed by Norman Augustine's piece 1488 01:11:04,210 --> 01:11:06,610 very slightly when he talked about the appropriation 1489 01:11:06,610 --> 01:11:08,590 of funds and where spending goes. 1490 01:11:08,590 --> 01:11:11,110 Specifically Augustine cited that 61% 1491 01:11:11,110 --> 01:11:13,120 of education budgets in schools tend 1492 01:11:13,120 --> 01:11:15,920 to go to education spending sort of broadly. 1493 01:11:15,920 --> 01:11:17,890 And that it actually tends to trend downward 1494 01:11:17,890 --> 01:11:19,390 for a lot of school districts who 1495 01:11:19,390 --> 01:11:22,570 focus on other extracurricular activities like sports. 1496 01:11:22,570 --> 01:11:24,880 And I think he made that very brief note 1497 01:11:24,880 --> 01:11:29,020 about the role of American culture 1498 01:11:29,020 --> 01:11:31,780 in those funding priorities. 1499 01:11:31,780 --> 01:11:35,050 And I think that also was a consideration 1500 01:11:35,050 --> 01:11:37,900 of mine in reading this piece in conjunction 1501 01:11:37,900 --> 01:11:39,010 with the Freeman piece. 1502 01:11:39,010 --> 01:11:43,480 Like, what is the role that our own vision of ourselves 1503 01:11:43,480 --> 01:11:46,300 and how we want to educate our children 1504 01:11:46,300 --> 01:11:51,550 informs our policy decisions and spending down the line. 1505 01:11:51,550 --> 01:11:54,430 And you brought the point earlier that 1506 01:11:54,430 --> 01:11:56,200 how is it that we don't have-- or that we 1507 01:11:56,200 --> 01:11:58,750 have too many jobs that we don't have enough jobs 1508 01:11:58,750 --> 01:12:02,290 but have too many PhDs, but then not have enough PhDs. 1509 01:12:02,290 --> 01:12:05,170 And I think that the point that Bill was trying to make 1510 01:12:05,170 --> 01:12:08,350 is that we have too many PhDs who 1511 01:12:08,350 --> 01:12:10,750 are trying to go into academia, but not enough PhDs who 1512 01:12:10,750 --> 01:12:14,380 are prepared for industry and not enough PhDs generally 1513 01:12:14,380 --> 01:12:15,970 to go into industry. 1514 01:12:15,970 --> 01:12:17,620 Does that clear things up a little? 1515 01:12:17,620 --> 01:12:19,203 AUDIENCE: That clears things up a lot. 1516 01:12:19,203 --> 01:12:20,000 AUDIENCE: OK. 1517 01:12:20,000 --> 01:12:20,770 WILLIAM BONVILLIAN: Yeah, I think I'd 1518 01:12:20,770 --> 01:12:22,080 add an additional point there. 1519 01:12:22,080 --> 01:12:24,840 And this is a complex one that you are free to disagree with. 1520 01:12:24,840 --> 01:12:31,570 But, when we created the higher education system in the US, 1521 01:12:31,570 --> 01:12:35,230 obviously it occurred over the process of centuries. 1522 01:12:35,230 --> 01:12:38,980 And when the federal government, during and following World War 1523 01:12:38,980 --> 01:12:41,230 II, came into a very significant support 1524 01:12:41,230 --> 01:12:45,010 role for that system in addition to the states, 1525 01:12:45,010 --> 01:12:50,170 that was already a quite competitive system. 1526 01:12:50,170 --> 01:12:52,870 And as someone who has spent substantial amount of time 1527 01:12:52,870 --> 01:12:56,860 with MIT's top administrators, I can tell you 1528 01:12:56,860 --> 01:13:01,220 that MIT and other universities do exactly the same thing, 1529 01:13:01,220 --> 01:13:05,320 they watched their university competitors like hawks. 1530 01:13:05,320 --> 01:13:07,120 They know exactly what they're up to. 1531 01:13:07,120 --> 01:13:09,340 They know exactly what they're spending on what. 1532 01:13:09,340 --> 01:13:12,220 They know exactly what their competition models are. 1533 01:13:12,220 --> 01:13:17,080 They're looking very hard at how MIT will compete all the time. 1534 01:13:17,080 --> 01:13:20,350 It is an extremely competitive model. 1535 01:13:20,350 --> 01:13:26,170 It drives a tremendous search for talent at the faculty level 1536 01:13:26,170 --> 01:13:27,670 as well as the student level. 1537 01:13:27,670 --> 01:13:31,660 And that talent base is very well compensated. 1538 01:13:31,660 --> 01:13:35,380 And that is a core way by which you compete for talent 1539 01:13:35,380 --> 01:13:37,330 in a competitive system. 1540 01:13:37,330 --> 01:13:43,090 We don't really have a competitive system in the K-12 1541 01:13:43,090 --> 01:13:44,270 public education system. 1542 01:13:44,270 --> 01:13:46,660 It's essentially a monopoly-based socialist model 1543 01:13:46,660 --> 01:13:48,010 frankly. 1544 01:13:48,010 --> 01:13:52,570 And you know part of the entry of charter school legislation 1545 01:13:52,570 --> 01:13:55,840 was to introduce a competitive model into the K 1546 01:13:55,840 --> 01:13:57,010 through 12 system. 1547 01:13:57,010 --> 01:13:59,630 Now there's a big debate as to how well that's worked. 1548 01:13:59,630 --> 01:14:01,990 But I think it's fair to say overall, 1549 01:14:01,990 --> 01:14:04,820 it's added a significant dose of competition into that system. 1550 01:14:07,160 --> 01:14:07,660 And 1551 01:14:07,660 --> 01:14:09,970 You could look at competition in health care. 1552 01:14:09,970 --> 01:14:12,150 And you could competition and you 1553 01:14:12,150 --> 01:14:15,010 know other large government supported systems too. 1554 01:14:15,010 --> 01:14:17,560 But you know, when we came out of World War II, 1555 01:14:17,560 --> 01:14:21,200 we created a socialist model for caring for veterans 1556 01:14:21,200 --> 01:14:23,750 in federally owned hospitals. 1557 01:14:23,750 --> 01:14:27,790 And we fit into what was already a competitive space 1558 01:14:27,790 --> 01:14:31,390 by supporting students, who in turn were given funding 1559 01:14:31,390 --> 01:14:32,200 and they could go. 1560 01:14:32,200 --> 01:14:33,700 They could take the money with them. 1561 01:14:33,700 --> 01:14:36,140 Didn't go to the universities to fund them. 1562 01:14:36,140 --> 01:14:39,250 So it's introduction of more competitive models 1563 01:14:39,250 --> 01:14:42,280 here may have something to do with improvements 1564 01:14:42,280 --> 01:14:43,400 in higher education. 1565 01:14:43,400 --> 01:14:45,760 At least that was the concept behind introducing 1566 01:14:45,760 --> 01:14:47,200 charter schools. 1567 01:14:47,200 --> 01:14:48,820 AUDIENCE: Could I ask you a followup? 1568 01:14:48,820 --> 01:14:51,160 I know that other countries have normal schools which 1569 01:14:51,160 --> 01:14:52,075 are schools meant to train-- 1570 01:14:52,075 --> 01:14:54,340 WILLIAM BONVILLIAN: Yeah, other countries have socialist models 1571 01:14:54,340 --> 01:14:55,470 and make them work. 1572 01:14:55,470 --> 01:14:58,110 But we're just not as good at that. 1573 01:14:58,110 --> 01:15:00,130 AUDIENCE: What prevented the United States 1574 01:15:00,130 --> 01:15:03,452 from developing normal schools or incorporating normal schools 1575 01:15:03,452 --> 01:15:04,160 into their model? 1576 01:15:06,602 --> 01:15:08,310 WILLIAM BONVILLIAN: I don't want to claim 1577 01:15:08,310 --> 01:15:10,680 to be an expert on the creation of normal schools. 1578 01:15:10,680 --> 01:15:14,340 But in the 19th century, the US did 1579 01:15:14,340 --> 01:15:19,970 create both the institutions for mass higher education, i.e. 1580 01:15:19,970 --> 01:15:23,730 the public universities, and high school education 1581 01:15:23,730 --> 01:15:26,250 in a very short period of time. 1582 01:15:26,250 --> 01:15:28,200 And communities were essentially realizing 1583 01:15:28,200 --> 01:15:30,600 that they had to upgrade the skills 1584 01:15:30,600 --> 01:15:33,420 for an industrial economy as their population shifted 1585 01:15:33,420 --> 01:15:39,950 from farming to working in manufacturing firms primarily. 1586 01:15:39,950 --> 01:15:42,750 And they understood the need for upgraded skill sets. 1587 01:15:42,750 --> 01:15:45,780 And there was an effort across the country 1588 01:15:45,780 --> 01:15:47,040 to create high schools. 1589 01:15:47,040 --> 01:15:50,640 Now some regions, particularly New England, had them. 1590 01:15:50,640 --> 01:15:54,030 But the rest of the country, including the American South, 1591 01:15:54,030 --> 01:15:58,920 is pretty quick to replicate a high school 1592 01:15:58,920 --> 01:16:02,850 model as a fix for a needed skill set. 1593 01:16:02,850 --> 01:16:05,660 So that is a massive social policy 1594 01:16:05,660 --> 01:16:09,018 that was done in a remarkably short period of decades 1595 01:16:09,018 --> 01:16:09,810 across the country. 1596 01:16:09,810 --> 01:16:12,648 So the country is capable of making really major changes. 1597 01:16:17,510 --> 01:16:19,200 I don't think I answered your question. 1598 01:16:19,200 --> 01:16:20,120 AUDIENCE: That's OK. 1599 01:16:20,120 --> 01:16:26,330 I just certainly think it's important to consider the-- 1600 01:16:26,330 --> 01:16:28,740 as it was highlighted I think in several 1601 01:16:28,740 --> 01:16:30,710 of the readings, the role of teachers, 1602 01:16:30,710 --> 01:16:34,580 one, moving forward in the future, two, 1603 01:16:34,580 --> 01:16:38,360 how teachers are currently being educated, three, 1604 01:16:38,360 --> 01:16:43,460 teachers are being compensated and sustained within the model. 1605 01:16:43,460 --> 01:16:46,220 They seem to be sort of an undercurrent of what we're 1606 01:16:46,220 --> 01:16:48,770 talking about, but not something that any of the readings 1607 01:16:48,770 --> 01:16:51,950 are willing to address specifically, and perhaps 1608 01:16:51,950 --> 01:16:54,910 strategically for political reasons.