1 00:00:02,200 --> 00:00:07,440 Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg Radio. Hey, 2 00:00:07,440 --> 00:00:10,000 Speaker 1: this week on the podcast, I have a special guest. 3 00:00:10,440 --> 00:00:13,680 Speaker 1: Her name is Lisa Cook, and she is an economist 4 00:00:14,040 --> 00:00:18,800 Speaker 1: who has done some absolutely fascinating research on all sorts 5 00:00:18,840 --> 00:00:25,480 Speaker 1: of really interesting things patents, innovation, gender, race, inequality, just 6 00:00:25,640 --> 00:00:30,000 Speaker 1: really really fascinating. You know, Normally, when I prep for 7 00:00:30,080 --> 00:00:33,400 Speaker 1: an interview, I go in kind of knowing a lot 8 00:00:33,479 --> 00:00:36,440 Speaker 1: about the person and maybe I'll find one or two 9 00:00:36,440 --> 00:00:40,480 Speaker 1: little interesting tidbits to ask them. But as I'm doing 10 00:00:40,560 --> 00:00:44,360 Speaker 1: the research and working off of some of the questions 11 00:00:44,720 --> 00:00:47,919 Speaker 1: that bat Nick got to me, we ended up finding 12 00:00:47,960 --> 00:00:54,000 Speaker 1: these really amazing research papers that she put together, some 13 00:00:54,040 --> 00:00:57,760 Speaker 1: stuff that just the data is shocking, and it's amazing 14 00:00:57,760 --> 00:01:01,360 Speaker 1: that nobody thought to even look at this before. She 15 00:01:01,520 --> 00:01:04,959 Speaker 1: found out that the number of African American women who 16 00:01:05,000 --> 00:01:08,600 Speaker 1: were earning their PhD was under one percent of the 17 00:01:08,640 --> 00:01:13,360 Speaker 1: total PhDs. That's a pretty shocking statistic. And her research 18 00:01:14,080 --> 00:01:18,760 Speaker 1: on patents and the impact of racism and what she 19 00:01:18,920 --> 00:01:25,920 Speaker 1: called extra judicial executions or lynchings um has a huge 20 00:01:25,959 --> 00:01:30,520 Speaker 1: impact on on what patents are awarded and subsequent innovation 21 00:01:30,720 --> 00:01:35,280 Speaker 1: in an economy. Some of the stories she told about 22 00:01:36,240 --> 00:01:40,400 Speaker 1: taking what she found two various Nobel laureate economists, expecting 23 00:01:40,440 --> 00:01:43,240 Speaker 1: them to trash her research, and they're all like, this 24 00:01:43,280 --> 00:01:46,800 Speaker 1: is amazing. You gotta go publish this. Milton Friedman, champion 25 00:01:46,880 --> 00:01:50,040 Speaker 1: free market, said, Hey, this is interfering with the free market. 26 00:01:50,040 --> 00:01:52,760 Speaker 1: You have to publish this. This is absolutely fascinating. It's 27 00:01:52,760 --> 00:01:56,720 Speaker 1: a really incredible tale. She's gotten a really fascinating background. 28 00:01:57,280 --> 00:01:59,920 Speaker 1: If you're at all paying attention to the news these days, 29 00:02:00,480 --> 00:02:06,280 Speaker 1: and you're interested in institutional racism or sexism, or why 30 00:02:06,520 --> 00:02:10,840 Speaker 1: the economy works better for some people than others, you're 31 00:02:10,840 --> 00:02:15,639 Speaker 1: gonna find this to be really a fascinating discussion, data based, objective, 32 00:02:15,680 --> 00:02:19,280 Speaker 1: and really really intriguing. So, with no further ado, my 33 00:02:19,440 --> 00:02:25,680 Speaker 1: conversation with Michigan State Universities Lisa Cook. This is Masters 34 00:02:25,680 --> 00:02:30,320 Speaker 1: in Business with Barry Ridholts on Bloomberg Radio. My special 35 00:02:30,360 --> 00:02:33,120 Speaker 1: guest this week is Lisa Cook. She is the professor 36 00:02:33,160 --> 00:02:38,600 Speaker 1: of economics and International Relations at Michigan State University. She 37 00:02:38,919 --> 00:02:41,600 Speaker 1: is a Marshall Scholar who got her pH d in 38 00:02:41,639 --> 00:02:45,840 Speaker 1: economics at Berkeley. She was a researcher for the Council 39 00:02:45,880 --> 00:02:52,920 Speaker 1: of Economic Advisors under President Obama, Lisa Cook, Welcome to Bloomberg. 40 00:02:53,200 --> 00:02:56,120 Speaker 1: Thank you so much so, Lisa, I have to ask 41 00:02:56,240 --> 00:03:01,200 Speaker 1: what drove you towards a career in economics, Verry. That's 42 00:03:01,240 --> 00:03:04,480 Speaker 1: a really good question, and I have a couple of answers. 43 00:03:05,160 --> 00:03:08,640 Speaker 1: One is not as I have looked back on it 44 00:03:08,800 --> 00:03:12,280 Speaker 1: over the last year or so, I think one of 45 00:03:12,320 --> 00:03:16,680 Speaker 1: the important trips that we took every year was to 46 00:03:16,919 --> 00:03:22,280 Speaker 1: a place called Soul City, North Carolina. My cousin, Employed McKissick, 47 00:03:22,400 --> 00:03:25,880 Speaker 1: was starting the city from scratched. He was He was 48 00:03:25,919 --> 00:03:29,079 Speaker 1: a protester along with Martin Luther King and marched with him. 49 00:03:29,160 --> 00:03:33,239 Speaker 1: Was in the class of at Morehouse College, integrated the 50 00:03:33,320 --> 00:03:36,720 Speaker 1: University of North Carolina. You know, just really active on 51 00:03:36,920 --> 00:03:39,520 Speaker 1: many different fronts. But he was building a city from scratch. 52 00:03:40,240 --> 00:03:43,880 Speaker 1: And this is something we saw every summer, from one 53 00:03:44,280 --> 00:03:48,120 Speaker 1: trailer to two trailers, to three trailers, to a building 54 00:03:48,120 --> 00:03:52,040 Speaker 1: that housed IBM, two more buildings. And what he had 55 00:03:52,080 --> 00:03:55,440 Speaker 1: to do was to essentially plan an economy. And I 56 00:03:55,480 --> 00:04:01,600 Speaker 1: think that that notion is something that really started turning 57 00:04:01,680 --> 00:04:03,480 Speaker 1: the wheels for me. How do you how do you 58 00:04:03,560 --> 00:04:06,640 Speaker 1: plan an economy and how do you try to close 59 00:04:06,680 --> 00:04:11,080 Speaker 1: the racial wealth gap. This was a multi cultural effort, 60 00:04:11,400 --> 00:04:15,560 Speaker 1: but the emphasis was trying to create some good jobs 61 00:04:15,600 --> 00:04:19,280 Speaker 1: so that the racial wealth gap would be closed. Now 62 00:04:19,279 --> 00:04:21,479 Speaker 1: this is in the sixties and seventies, so that's that's 63 00:04:21,560 --> 00:04:24,960 Speaker 1: one answer. That's that's upon a lot of reflection recently. 64 00:04:25,520 --> 00:04:29,279 Speaker 1: But I would say that at Oxford, when I was 65 00:04:29,360 --> 00:04:33,440 Speaker 1: trying to decide on one of the three topics I 66 00:04:33,600 --> 00:04:37,080 Speaker 1: was to study, when in Philosophy, Politics and Economics, I 67 00:04:37,160 --> 00:04:41,440 Speaker 1: took this mathematical economics tutorial. It was so much fun. 68 00:04:41,680 --> 00:04:45,720 Speaker 1: It's a grad student teaching it, and I kept telling myself, 69 00:04:45,760 --> 00:04:48,720 Speaker 1: there cannot be a field. Is this much fun? Can't be? 70 00:04:49,000 --> 00:04:52,719 Speaker 1: Can't be? So I kept putting it off. I climbed 71 00:04:52,760 --> 00:04:58,080 Speaker 1: Killamandara with this Cambridge training the economists, and he convinced 72 00:04:58,080 --> 00:05:01,679 Speaker 1: me that no, I should not go off to LSE 73 00:05:02,040 --> 00:05:05,320 Speaker 1: and do a PhD and mathematical logic, which is what 74 00:05:05,520 --> 00:05:09,600 Speaker 1: I was planning. I should do a PhD in economic 75 00:05:09,720 --> 00:05:12,680 Speaker 1: So in five hours he convinced me that this is 76 00:05:13,360 --> 00:05:16,040 Speaker 1: what I should do. So I think that's the answer 77 00:05:16,080 --> 00:05:18,400 Speaker 1: to your question. Did you know you were always heading 78 00:05:18,440 --> 00:05:21,800 Speaker 1: towards academia and I have to point out your doctoral 79 00:05:21,800 --> 00:05:25,960 Speaker 1: advisors were Barry Iken Green and David Rohmer. That's some 80 00:05:26,160 --> 00:05:31,520 Speaker 1: pretty big firepower, that's right. I am not certain that 81 00:05:31,600 --> 00:05:36,599 Speaker 1: I was always headed to academia. I thought I was 82 00:05:36,680 --> 00:05:40,840 Speaker 1: going to be a lawyer, certainly because so many people 83 00:05:41,680 --> 00:05:46,039 Speaker 1: in my family and around me were involved in the 84 00:05:46,120 --> 00:05:50,719 Speaker 1: Civil rights movement. One of the things that I thought 85 00:05:50,720 --> 00:05:54,679 Speaker 1: about all the time was was making sure that voting 86 00:05:54,760 --> 00:05:59,080 Speaker 1: rights were protected, and the way to do that was 87 00:05:59,200 --> 00:06:04,679 Speaker 1: through the law off And it wasn't until uh much later, 88 00:06:05,120 --> 00:06:09,839 Speaker 1: during this period of climbing, for example, UH and trying 89 00:06:09,839 --> 00:06:12,160 Speaker 1: to figure out whether it's going to do economics or not, 90 00:06:12,320 --> 00:06:16,400 Speaker 1: that I rejected the law. But I didn't think I 91 00:06:16,440 --> 00:06:20,000 Speaker 1: was going into academia if I did uh the law. 92 00:06:20,839 --> 00:06:26,839 Speaker 1: I think there's a lot of historiesis among economists and 93 00:06:26,920 --> 00:06:32,520 Speaker 1: among academics in general. So most of my relatives were 94 00:06:32,560 --> 00:06:37,120 Speaker 1: in some form of academia. So I certainly got a 95 00:06:37,120 --> 00:06:39,920 Speaker 1: lot of exposure to it, but I wasn't convinced that 96 00:06:39,920 --> 00:06:45,839 Speaker 1: that's what I wanted wanted to do. My advisors, whether 97 00:06:45,920 --> 00:06:52,000 Speaker 1: they were hormal or informal at um at Spellman and 98 00:06:52,279 --> 00:06:57,080 Speaker 1: at Berkeley, were just off the charts amazing. One of 99 00:06:57,120 --> 00:07:02,240 Speaker 1: them was. It was Donald mental Store who was president 100 00:07:02,320 --> 00:07:06,599 Speaker 1: of Stollman College at the time and at one point 101 00:07:06,839 --> 00:07:12,480 Speaker 1: at the college board and absolute mentor and helped too, 102 00:07:13,760 --> 00:07:17,280 Speaker 1: you know, encourage me to apply for the marshal and 103 00:07:17,360 --> 00:07:22,239 Speaker 1: for the roads and other uh take advantage of other opportunities. 104 00:07:22,800 --> 00:07:29,160 Speaker 1: Marjorite Gant, who was our Study Abroad coordinator, also a 105 00:07:29,240 --> 00:07:35,480 Speaker 1: deep mentor. And then at Berkeley, Uh, it was Paul 106 00:07:35,560 --> 00:07:39,880 Speaker 1: Rohmer in addition to the people on my committee George Acolos, 107 00:07:39,920 --> 00:07:43,640 Speaker 1: when I talked to all the time, especially about Russian 108 00:07:43,960 --> 00:07:47,400 Speaker 1: and the thing about choosing the topic that I did 109 00:07:47,440 --> 00:07:49,680 Speaker 1: to study the Russian banking system. Now this was in 110 00:07:49,720 --> 00:07:53,280 Speaker 1: the early nineties, right, So we were trying to figure 111 00:07:53,280 --> 00:07:58,320 Speaker 1: out what are the best models to analyze the Russian 112 00:07:58,320 --> 00:08:02,680 Speaker 1: economy with? Is this banking system like another? Is it 113 00:08:02,840 --> 00:08:06,440 Speaker 1: forming like others? Can it form like others? How does 114 00:08:06,560 --> 00:08:10,679 Speaker 1: one allocate credit on a market basis when it hasn't 115 00:08:10,720 --> 00:08:15,400 Speaker 1: been before or recently? So I was looking for anybody 116 00:08:15,400 --> 00:08:18,760 Speaker 1: who could elucidate this, and I was just grateful that 117 00:08:18,800 --> 00:08:23,520 Speaker 1: people talk to me and we're willing to serve on 118 00:08:23,600 --> 00:08:27,520 Speaker 1: my committee to help me home these questions. So yeah, 119 00:08:27,680 --> 00:08:34,120 Speaker 1: very very Uh, fantastic person my committee, David Romer. Clearly, 120 00:08:34,160 --> 00:08:38,200 Speaker 1: I liked macro right, I liked international topics, and I 121 00:08:38,480 --> 00:08:41,959 Speaker 1: just had a wonderful time at Berkeley with with all 122 00:08:42,000 --> 00:08:45,800 Speaker 1: of them. You've done some really fascinating research, and we're 123 00:08:45,800 --> 00:08:48,080 Speaker 1: going to talk in a few minutes about your research 124 00:08:48,160 --> 00:08:52,520 Speaker 1: into patents and innovation. But I have to ask about 125 00:08:52,640 --> 00:08:55,600 Speaker 1: what led to the op ed you wrote a couple 126 00:08:55,600 --> 00:08:58,120 Speaker 1: of years ago in the New York Times where you 127 00:08:58,200 --> 00:09:03,720 Speaker 1: pointed out that just yearro point six of economics PhD 128 00:09:03,880 --> 00:09:07,559 Speaker 1: s were awarded to black women. What was that pieces 129 00:09:07,640 --> 00:09:12,640 Speaker 1: genesis and what sort of feedback did it create? There 130 00:09:12,640 --> 00:09:17,520 Speaker 1: were two sources of that article that up ed. The 131 00:09:17,559 --> 00:09:22,559 Speaker 1: first was that the A a American Economic Association had 132 00:09:22,640 --> 00:09:27,119 Speaker 1: done a climate survey and I was on the committee 133 00:09:27,160 --> 00:09:32,160 Speaker 1: to write up the results. And it was astonishing the 134 00:09:32,320 --> 00:09:38,160 Speaker 1: kind of comments, especially that we got from people in 135 00:09:38,200 --> 00:09:41,080 Speaker 1: the profession. Uh. You know, we learned a lot from that, 136 00:09:41,160 --> 00:09:43,840 Speaker 1: as you can recall, we learned a lot about how 137 00:09:44,000 --> 00:09:51,040 Speaker 1: widespread sexual harassment was. We learned that people were not 138 00:09:51,120 --> 00:09:56,240 Speaker 1: feeling included in economics for various reasons. We knew that, uh, 139 00:09:56,640 --> 00:09:59,559 Speaker 1: we suspected that promotion and pay were a problem with 140 00:09:59,600 --> 00:10:03,960 Speaker 1: respect to UH, minority women, but we didn't know the 141 00:10:04,080 --> 00:10:07,560 Speaker 1: extent to which that was the case. And certainly we 142 00:10:07,600 --> 00:10:11,559 Speaker 1: didn't know that African American women were the ones who 143 00:10:11,640 --> 00:10:15,280 Speaker 1: had to take more steps to avoid discrimination and that 144 00:10:15,400 --> 00:10:20,000 Speaker 1: they were the ones reporting the most discrimination. So that 145 00:10:20,240 --> 00:10:24,240 Speaker 1: was worrisome in and of itself. So definitely one of 146 00:10:24,280 --> 00:10:28,360 Speaker 1: the motivations for writing the APT ed to talk more 147 00:10:28,400 --> 00:10:32,120 Speaker 1: about that. And and again the comments were terrified. Uh. 148 00:10:32,200 --> 00:10:35,520 Speaker 1: One of them let the article, if I were to 149 00:10:36,000 --> 00:10:38,959 Speaker 1: advise my son, I would tell them not to go 150 00:10:39,080 --> 00:10:43,240 Speaker 1: into economics, and I regret having gone into it. This 151 00:10:43,320 --> 00:10:46,199 Speaker 1: person implied that she was a black woman. Uh. There 152 00:10:46,240 --> 00:10:50,400 Speaker 1: was another comment that really hit home. Uh, why is 153 00:10:50,440 --> 00:10:54,040 Speaker 1: it that all of these prizes are given and no 154 00:10:54,160 --> 00:10:57,480 Speaker 1: African Americans ever win these prizes like Nobel nan on 155 00:10:57,520 --> 00:11:00,760 Speaker 1: the day that the novel is being awarded, Uh, like 156 00:11:00,880 --> 00:11:03,600 Speaker 1: the Nobel How can that be welcoming? How can that 157 00:11:03,640 --> 00:11:08,040 Speaker 1: be serious? If our research isn't being taken seriously, our 158 00:11:08,080 --> 00:11:12,520 Speaker 1: careers aren't being taken seriously. And the other motivation, So 159 00:11:12,760 --> 00:11:15,760 Speaker 1: one motivation was the climate study. The other one was 160 00:11:15,960 --> 00:11:21,080 Speaker 1: as director of the American Economic Association summer program. I 161 00:11:21,200 --> 00:11:25,760 Speaker 1: noticed that there weren't too many black women, and I thought, Okay, 162 00:11:25,800 --> 00:11:29,240 Speaker 1: I know that black women out number of black men 163 00:11:29,640 --> 00:11:33,600 Speaker 1: and the stem fields and undergraduate so what is going 164 00:11:33,640 --> 00:11:37,760 Speaker 1: on here? So I certainly put it at the top 165 00:11:37,800 --> 00:11:41,480 Speaker 1: of my list to try to recruit more black women 166 00:11:41,559 --> 00:11:44,360 Speaker 1: to apply to the A A Summer program and to 167 00:11:45,080 --> 00:11:49,720 Speaker 1: UH to get them to think more seriously about doing 168 00:11:49,920 --> 00:11:53,520 Speaker 1: a PhD in economics, and this was the way to 169 00:11:53,600 --> 00:11:57,400 Speaker 1: do it. So I had those two sources that led 170 00:11:57,440 --> 00:12:00,480 Speaker 1: to that op ed and would respect to the action. 171 00:12:01,280 --> 00:12:06,800 Speaker 1: The reaction has has largely, I would say, been good, 172 00:12:07,559 --> 00:12:10,080 Speaker 1: UH in the sense that it got a conversation started. 173 00:12:10,360 --> 00:12:13,000 Speaker 1: It doesn't even feel like it's just been a year ago. 174 00:12:13,640 --> 00:12:16,880 Speaker 1: So much has happened in the year. So even if 175 00:12:16,880 --> 00:12:20,240 Speaker 1: people were talking about it in the last year before 176 00:12:20,800 --> 00:12:25,079 Speaker 1: say the work from home period, before the pandemic, before 177 00:12:25,720 --> 00:12:30,760 Speaker 1: UH George Floyd, people were embracing this notion that this 178 00:12:30,880 --> 00:12:33,640 Speaker 1: wasn't just one person. And I think that that in 179 00:12:33,720 --> 00:12:39,240 Speaker 1: economics many of us were often told that, well, that's 180 00:12:39,280 --> 00:12:43,439 Speaker 1: anyosyncratic and you're overreacting. But when you see the results 181 00:12:43,480 --> 00:12:47,920 Speaker 1: from the climate survey from you know, nine thousand respondents. 182 00:12:48,520 --> 00:12:51,440 Speaker 1: We see a pattern, and we see a pattern with 183 00:12:51,559 --> 00:12:57,640 Speaker 1: respect to pay promotion, UH and and the climate in economics. 184 00:12:57,720 --> 00:13:01,960 Speaker 1: So I would say that in hindsight it was it 185 00:13:02,080 --> 00:13:06,800 Speaker 1: was well received because of the kinds of conversation it started. 186 00:13:07,800 --> 00:13:12,400 Speaker 1: The SAD collective has taken off. It was UH. It 187 00:13:12,760 --> 00:13:15,839 Speaker 1: got started as they well, I wouldn't say as a 188 00:13:15,920 --> 00:13:20,240 Speaker 1: result of but certainly the founders are my former students 189 00:13:20,240 --> 00:13:23,880 Speaker 1: in the A A Summer program. But a lot has 190 00:13:23,960 --> 00:13:27,200 Speaker 1: happened in just that short year, and I think I'm 191 00:13:27,240 --> 00:13:29,880 Speaker 1: just grateful to see the movement and hope it is 192 00:13:29,920 --> 00:13:33,719 Speaker 1: sustained within the economics field. There's a lot more going on, 193 00:13:33,920 --> 00:13:38,600 Speaker 1: not just what I'm saying, you know, renaming UH lecturers 194 00:13:38,640 --> 00:13:43,800 Speaker 1: and making the place more welcoming for all kinds of people, 195 00:13:43,880 --> 00:13:49,360 Speaker 1: being more open about mental health again, about being inclusive 196 00:13:49,400 --> 00:13:53,840 Speaker 1: in so many different ways. I think this is just historic. 197 00:13:54,280 --> 00:13:58,280 Speaker 1: This is an historic change for the American Economic Association. 198 00:13:58,720 --> 00:14:02,319 Speaker 1: Quite fascinating. Let's talk a little bit about another piece 199 00:14:02,400 --> 00:14:07,640 Speaker 1: of your research that that I find absolutely fascinating. You've 200 00:14:07,760 --> 00:14:14,199 Speaker 1: documented some pretty dramatic differences in patent rates based on 201 00:14:14,400 --> 00:14:17,480 Speaker 1: race and gender. Tell us a little bit about that. 202 00:14:18,360 --> 00:14:25,080 Speaker 1: This research came from my dissertation emerged from my dissertation 203 00:14:25,440 --> 00:14:31,440 Speaker 1: being written in Moscow and on the Russian banking system. 204 00:14:31,480 --> 00:14:36,600 Speaker 1: I interviewed both bankers and entrepreneurs trying to figure out 205 00:14:36,720 --> 00:14:40,200 Speaker 1: if a Russian banking system could emerge in this post 206 00:14:40,240 --> 00:14:43,120 Speaker 1: Soviet period. So this is the early nineties. It was 207 00:14:43,160 --> 00:14:46,640 Speaker 1: a rough and tumble time when major banker on averages 208 00:14:46,680 --> 00:14:49,880 Speaker 1: are being killed per month. It was a time when 209 00:14:50,520 --> 00:14:54,000 Speaker 1: UH structures were being figured out as long as as 210 00:14:54,000 --> 00:14:58,240 Speaker 1: well as the personalities who would engage in banking in 211 00:14:58,240 --> 00:15:01,880 Speaker 1: in Russia. One thing they post, the questions they post 212 00:15:01,920 --> 00:15:06,120 Speaker 1: to me UH had a couple of features. One common 213 00:15:06,120 --> 00:15:10,680 Speaker 1: one was why can't innovation come to Russia? And I 214 00:15:10,720 --> 00:15:13,520 Speaker 1: thought it was fascinating because it had nothing to do 215 00:15:13,920 --> 00:15:18,480 Speaker 1: with the Russian banking system really, and that they wanted 216 00:15:18,520 --> 00:15:21,640 Speaker 1: to know for me, You're American, You're an economist, to 217 00:15:21,800 --> 00:15:24,040 Speaker 1: tell us why this is the case. And I told 218 00:15:24,080 --> 00:15:26,200 Speaker 1: him I have no answer, really don't have an answer. 219 00:15:26,240 --> 00:15:29,520 Speaker 1: But you know, I kind of looked around and I'm like, Okay, well, 220 00:15:29,960 --> 00:15:32,960 Speaker 1: if I were an inventor, I'm not sure i'd feel 221 00:15:33,080 --> 00:15:36,880 Speaker 1: secure here. I'm not sure. I thought that my ideas 222 00:15:36,920 --> 00:15:40,280 Speaker 1: would be protected. But anyway, I put a put a 223 00:15:40,320 --> 00:15:44,400 Speaker 1: placeholder in it, and I thought, after I finished my dissertation, 224 00:15:44,440 --> 00:15:47,360 Speaker 1: maybe I'll come back to it, and it kept weighing 225 00:15:47,400 --> 00:15:49,560 Speaker 1: on me. This question, I thought was a really good 226 00:15:49,560 --> 00:15:52,440 Speaker 1: one because at the time, the conventional wisdom was that 227 00:15:52,560 --> 00:15:56,760 Speaker 1: if you protected if a country protected intellectual property rights, 228 00:15:57,040 --> 00:16:00,080 Speaker 1: that was sufficient for innovation to come from, then in 229 00:16:00,240 --> 00:16:04,960 Speaker 1: innovation to come. And I thought the Russians deserve a 230 00:16:05,000 --> 00:16:07,920 Speaker 1: better answer than this. So I wondered if there were 231 00:16:08,200 --> 00:16:12,360 Speaker 1: an historical experiment that could inform what the Russians were 232 00:16:12,360 --> 00:16:16,600 Speaker 1: going through. And I thought, well, maybe this period of 233 00:16:16,640 --> 00:16:22,440 Speaker 1: the late eighteen hundreds early nineteen hundreds could be elucidating. 234 00:16:22,800 --> 00:16:25,240 Speaker 1: And I just decided that maybe, you know, maybe I'll 235 00:16:25,240 --> 00:16:27,760 Speaker 1: look at patents to see what's going on there. Of course, 236 00:16:27,760 --> 00:16:29,280 Speaker 1: I thought it was going to be easy to find 237 00:16:29,320 --> 00:16:32,040 Speaker 1: African American patents. Of course it wasn't because races it 238 00:16:32,120 --> 00:16:38,200 Speaker 1: recorded on patent data. So that was a big undertaking 239 00:16:38,240 --> 00:16:42,240 Speaker 1: to match names to people in the patent data set. 240 00:16:42,560 --> 00:16:44,760 Speaker 1: So tell us a little bit how you were able 241 00:16:44,800 --> 00:16:48,960 Speaker 1: to figure out the race of people who were applying 242 00:16:49,000 --> 00:16:52,760 Speaker 1: for patent protection. Hundred and fifty years ago. How are 243 00:16:52,760 --> 00:16:56,640 Speaker 1: you able to get through what was a pretty bare 244 00:16:56,720 --> 00:17:02,680 Speaker 1: bones database within the government to find out who actually, um, 245 00:17:02,880 --> 00:17:06,040 Speaker 1: we're applying for these patents? That was that's a good question. 246 00:17:06,880 --> 00:17:10,840 Speaker 1: I tried everything. I tried everything. So the first thing 247 00:17:10,880 --> 00:17:14,480 Speaker 1: that I did was to try to do what Mo 248 00:17:14,680 --> 00:17:19,000 Speaker 1: Nathan and Trauma Trauma Mo Nathan did and others who 249 00:17:19,080 --> 00:17:23,359 Speaker 1: study black names in the contemporary period. I tried to 250 00:17:23,400 --> 00:17:25,840 Speaker 1: do what they did, and I tried to take data 251 00:17:25,960 --> 00:17:32,439 Speaker 1: from the Census and UH find the most popular names UH, 252 00:17:32,720 --> 00:17:38,920 Speaker 1: ones that were distinctively African American. And I did it 253 00:17:39,040 --> 00:17:42,000 Speaker 1: for this period, but those those don't apply. The names 254 00:17:42,000 --> 00:17:46,359 Speaker 1: they use were post Civil Rights era names, they weren't historical. 255 00:17:46,800 --> 00:17:49,639 Speaker 1: So I decided that I needed to do that for 256 00:17:50,040 --> 00:17:52,520 Speaker 1: the historical period, and that's how I came up with 257 00:17:52,920 --> 00:17:59,120 Speaker 1: the first recording of systematic names that black names historically 258 00:17:59,200 --> 00:18:02,359 Speaker 1: for African Americans. And I did that by using the 259 00:18:02,400 --> 00:18:08,760 Speaker 1: Census again. UH. Luckily, later on my co authors Tuvon 260 00:18:08,880 --> 00:18:12,879 Speaker 1: Logan and John Parman helped to externally validate that list. 261 00:18:13,240 --> 00:18:16,760 Speaker 1: But that was the first time that such a list existed. 262 00:18:16,800 --> 00:18:19,200 Speaker 1: So that was the first thing that I had to do. 263 00:18:19,520 --> 00:18:25,639 Speaker 1: But after I did that only a few people that pattern. 264 00:18:25,760 --> 00:18:28,399 Speaker 1: There were only a few patent taes who had these 265 00:18:29,000 --> 00:18:31,840 Speaker 1: black names, so I was able to capture some, but 266 00:18:31,960 --> 00:18:37,240 Speaker 1: not many. Then I resorted to a common myth about 267 00:18:37,359 --> 00:18:45,040 Speaker 1: African American names related to naming people, especially men, after presidents. 268 00:18:45,440 --> 00:18:48,520 Speaker 1: So started looking for presidents. Okay, so what happens is 269 00:18:48,560 --> 00:18:53,960 Speaker 1: that all men in America have named that are uh, 270 00:18:54,080 --> 00:18:57,520 Speaker 1: those of presidents. So this is you know, this wasn't 271 00:18:57,560 --> 00:19:00,439 Speaker 1: unique to African American. So that didn't help. So I 272 00:19:00,680 --> 00:19:05,840 Speaker 1: just started looking for all of the scientists potential inventors 273 00:19:05,840 --> 00:19:09,320 Speaker 1: I could to try to capture the universe of inventors 274 00:19:09,359 --> 00:19:13,800 Speaker 1: some way, say with directories with articles. I found this 275 00:19:13,880 --> 00:19:19,600 Speaker 1: old UH survey of patent agents and patent attorneys that 276 00:19:19,760 --> 00:19:23,240 Speaker 1: was carried out in UH nine hundred and in nineteen 277 00:19:23,320 --> 00:19:28,960 Speaker 1: thirteen by the Patent Office trying to identify African American patentees. 278 00:19:29,280 --> 00:19:32,800 Speaker 1: Now that was good for that period up to roughly 279 00:19:33,040 --> 00:19:36,879 Speaker 1: nineteen thirteen, but there were some flaws. It didn't actuately 280 00:19:36,920 --> 00:19:40,800 Speaker 1: identify the first African American patent he so there were 281 00:19:40,840 --> 00:19:42,800 Speaker 1: there were a few holes, so I had to fill those. 282 00:19:43,080 --> 00:19:46,199 Speaker 1: But I started looking for everything and I wanted it 283 00:19:46,320 --> 00:19:50,320 Speaker 1: to not be biased towards famous people, because I mean, 284 00:19:50,400 --> 00:19:55,119 Speaker 1: the patent data set itself is not biased towards famous people. 285 00:19:56,000 --> 00:20:00,840 Speaker 1: I looked at obituaries, for example, just because people would 286 00:20:00,880 --> 00:20:04,240 Speaker 1: talk about their family members as inventors and they may 287 00:20:04,280 --> 00:20:08,399 Speaker 1: not have described themselves that way. And say the census, 288 00:20:08,520 --> 00:20:12,200 Speaker 1: I mean Edison identified himself as a machinist, and many 289 00:20:12,240 --> 00:20:16,280 Speaker 1: of these inventors described themselves as a machinists in the 290 00:20:16,520 --> 00:20:20,400 Speaker 1: census data. But if you got family members to talk 291 00:20:20,440 --> 00:20:23,439 Speaker 1: about them, they would describe themselves as inventors. So anyway, 292 00:20:23,480 --> 00:20:27,320 Speaker 1: I tried everything I could and eventually I was able 293 00:20:27,400 --> 00:20:31,239 Speaker 1: to fill this in UM, but it took a lot 294 00:20:31,280 --> 00:20:34,359 Speaker 1: of work. This is pre Google patents. This is before 295 00:20:35,320 --> 00:20:39,080 Speaker 1: this is all of this was digitized. This is ancient 296 00:20:39,240 --> 00:20:43,400 Speaker 1: history now, but things were changing rapidly at the time. 297 00:20:43,440 --> 00:20:45,240 Speaker 1: But that's how I came up with this list of 298 00:20:45,359 --> 00:20:48,680 Speaker 1: African American inventors. And now let's put a little meat 299 00:20:48,720 --> 00:20:52,160 Speaker 1: on the bones. The piece who wrote Violence and Economic 300 00:20:52,200 --> 00:20:57,560 Speaker 1: Activity Evidence from African American Patents eighteen seventy ninety. The 301 00:20:57,680 --> 00:21:02,240 Speaker 1: numbers are pretty astonishing. Patent put in the US per million, 302 00:21:02,400 --> 00:21:05,479 Speaker 1: so on a per capita basis, it's six patents per 303 00:21:05,520 --> 00:21:10,920 Speaker 1: million African Americans, forty patents per million women, and two 304 00:21:11,040 --> 00:21:14,800 Speaker 1: hundred and thirty five patents per million for all others. 305 00:21:14,840 --> 00:21:20,000 Speaker 1: That's really a stark difference, that's right, And that's that's 306 00:21:20,119 --> 00:21:24,639 Speaker 1: for the modern period that includes data up to twenty 307 00:21:25,520 --> 00:21:31,640 Speaker 1: so uh after the nineteen data, but certainly it's still 308 00:21:31,760 --> 00:21:34,440 Speaker 1: the case. The reason why it's still failient is because 309 00:21:34,920 --> 00:21:39,600 Speaker 1: eight nine is still the peak year for African American 310 00:21:39,640 --> 00:21:43,480 Speaker 1: patenting per capita. That's what what are the stark things 311 00:21:43,520 --> 00:21:48,720 Speaker 1: that we learn and this paper that white patenting is 312 00:21:48,840 --> 00:21:54,560 Speaker 1: two orders of magnitude higher, patenting by women is one 313 00:21:54,760 --> 00:21:58,800 Speaker 1: order of magnitude higher. And the size of a patent 314 00:21:58,880 --> 00:22:02,960 Speaker 1: team and a teen for African Americans is the same 315 00:22:03,040 --> 00:22:07,840 Speaker 1: size today if we're using data. That is astonishing because 316 00:22:07,880 --> 00:22:12,800 Speaker 1: for the rest of patent tas these patent teams have exploded, 317 00:22:13,640 --> 00:22:16,920 Speaker 1: especially we're talking say chemical patents, it wouldn't be unusual 318 00:22:16,960 --> 00:22:20,480 Speaker 1: to find twenty or thirty people on patent for uh 319 00:22:20,680 --> 00:22:24,760 Speaker 1: some chemical products. So this is this is one of 320 00:22:24,800 --> 00:22:29,480 Speaker 1: the astonishing things about about this number that is is 321 00:22:29,560 --> 00:22:33,439 Speaker 1: really starks. And one thing that I found in doing 322 00:22:34,200 --> 00:22:38,080 Speaker 1: deep dives to biographies of some of these inventors, say 323 00:22:38,160 --> 00:22:44,399 Speaker 1: Garrett Morgan, he led completely different lives from their counterparts 324 00:22:44,480 --> 00:22:48,320 Speaker 1: like Edison or for for Garrett Morgan it would have 325 00:22:48,320 --> 00:22:53,240 Speaker 1: been Charles Brush and Cleveland. They just had to work 326 00:22:53,400 --> 00:22:56,800 Speaker 1: so many channels, many back channels. For example, to be 327 00:22:56,840 --> 00:22:59,520 Speaker 1: able to sell their wares, he had to dress up 328 00:22:59,560 --> 00:23:02,800 Speaker 1: like a nave of American to be able to display 329 00:23:03,040 --> 00:23:06,920 Speaker 1: his gas mask. Because once it was found out that 330 00:23:06,960 --> 00:23:09,720 Speaker 1: he was African American and this gas mask was being 331 00:23:09,840 --> 00:23:13,560 Speaker 1: used by fire departments, uh oh, across the South, they 332 00:23:13,560 --> 00:23:17,080 Speaker 1: started canceling their orders. And the fire departments all across 333 00:23:17,119 --> 00:23:19,640 Speaker 1: the country, but the ones in the South started canceling 334 00:23:19,640 --> 00:23:23,800 Speaker 1: their orders. He used to hire white men to pretend 335 00:23:23,840 --> 00:23:26,800 Speaker 1: like they were him and go around the country to 336 00:23:26,920 --> 00:23:31,320 Speaker 1: self his gas mask. So he was really adept at 337 00:23:31,640 --> 00:23:38,000 Speaker 1: overcoming is increasing consumers side discrimination in America at the time. 338 00:23:38,560 --> 00:23:42,480 Speaker 1: So how do we explain why patents peaked for African 339 00:23:42,520 --> 00:23:47,840 Speaker 1: Americans in what was so significant about what happened right afterwards? 340 00:23:47,920 --> 00:23:51,600 Speaker 1: I think that it was largely Plusy versus Ferguson. So 341 00:23:51,680 --> 00:23:56,439 Speaker 1: that was in eighteen ninety six, and this was the 342 00:23:56,520 --> 00:24:02,639 Speaker 1: culmination of a long period of repealing pieces of the 343 00:24:02,680 --> 00:24:07,480 Speaker 1: Civil Rights Act of eighteen seventy five. And this was 344 00:24:08,040 --> 00:24:10,720 Speaker 1: you know, this is the Seven States were challenging it 345 00:24:10,880 --> 00:24:13,160 Speaker 1: bit by bit, tipping away at it bit by bit, 346 00:24:14,040 --> 00:24:18,480 Speaker 1: and when plus they happened, it was, you know, it 347 00:24:18,520 --> 00:24:21,159 Speaker 1: was a big blow. Separate but equals, So this is 348 00:24:21,680 --> 00:24:26,480 Speaker 1: ruling on separate but equal um. This is this is 349 00:24:26,520 --> 00:24:29,960 Speaker 1: a big blow because inventors at the time, we're doing 350 00:24:30,000 --> 00:24:33,200 Speaker 1: what everybody else was doing. Right. They were they were 351 00:24:33,920 --> 00:24:37,320 Speaker 1: going to libraries to find patent digests to find out 352 00:24:37,320 --> 00:24:40,600 Speaker 1: what the latest inventions were. They were running into each 353 00:24:40,600 --> 00:24:45,960 Speaker 1: other at their patent attorney's office or uh in the 354 00:24:46,040 --> 00:24:49,840 Speaker 1: downtowns of all of these places where they were working. 355 00:24:50,080 --> 00:24:53,320 Speaker 1: But all of a sudden that wasn't possible anymore. These 356 00:24:53,320 --> 00:24:58,080 Speaker 1: commercial districts became all white. Uh. They weren't able to 357 00:24:58,560 --> 00:25:04,000 Speaker 1: manufacture and sell their inventions. They had to often become 358 00:25:04,840 --> 00:25:09,440 Speaker 1: middlemen and to become wholesalers so that they weren't facing 359 00:25:09,560 --> 00:25:13,760 Speaker 1: the public. Some of them went out of business altogether 360 00:25:13,840 --> 00:25:17,280 Speaker 1: and stopped inventing all together. We have a number of 361 00:25:17,680 --> 00:25:21,040 Speaker 1: stories like that. So I think it was largely this 362 00:25:21,240 --> 00:25:26,000 Speaker 1: growing violence and especially plusy versus instance. And with respect 363 00:25:26,000 --> 00:25:29,920 Speaker 1: to the date, what my friends who are constitutional law 364 00:25:30,000 --> 00:25:33,400 Speaker 1: scholars tell me is that this takes a period of time, 365 00:25:33,480 --> 00:25:36,960 Speaker 1: say two or three years for rulemaking, and when it 366 00:25:37,000 --> 00:25:40,919 Speaker 1: became clear that they couldn't engage in in this, I 367 00:25:40,960 --> 00:25:44,520 Speaker 1: think that they rushed everything they could to the patent 368 00:25:44,520 --> 00:25:47,679 Speaker 1: office and uh and hope for the best, and it 369 00:25:47,800 --> 00:25:51,679 Speaker 1: just never happened. Now, it started recovering after some of 370 00:25:51,680 --> 00:25:58,359 Speaker 1: the violence stopped, but it never got back to the EAK. 371 00:25:58,800 --> 00:26:01,800 Speaker 1: And that's really astonishing because there's there are a lot 372 00:26:01,840 --> 00:26:05,679 Speaker 1: more people, a lot more PhDs and and the natural 373 00:26:05,720 --> 00:26:10,720 Speaker 1: sciences for example, a lot more PhDs and engineering, but 374 00:26:11,480 --> 00:26:15,880 Speaker 1: we don't see this showing up in the patent data. 375 00:26:16,040 --> 00:26:19,719 Speaker 1: So earlier this year, the Tulsa race massacre was in 376 00:26:20,760 --> 00:26:22,760 Speaker 1: the news, the President was going to hold a rally 377 00:26:22,840 --> 00:26:27,000 Speaker 1: on the anniversary of that. What was the impact of 378 00:26:27,080 --> 00:26:32,280 Speaker 1: that events on subsequent patents and innovations in the African 379 00:26:32,280 --> 00:26:36,119 Speaker 1: American community, so very that's a really interesting question. This 380 00:26:36,240 --> 00:26:39,080 Speaker 1: is one of those things where you see the data 381 00:26:39,640 --> 00:26:42,600 Speaker 1: and you have no idea what's going on. Like I was, 382 00:26:42,640 --> 00:26:47,399 Speaker 1: I was thinking, Okay, why is this showing up in 383 00:26:47,440 --> 00:26:51,480 Speaker 1: the data, what like what happened on a national scale. 384 00:26:51,760 --> 00:26:55,320 Speaker 1: I'm thinking, okay, so World War one is over. Yeah. 385 00:26:55,119 --> 00:26:57,640 Speaker 1: I could not put my finger on it. And then 386 00:26:57,680 --> 00:27:01,960 Speaker 1: I was like, ah ha, that's not good. The This 387 00:27:02,160 --> 00:27:07,480 Speaker 1: was the largest racial massacre in US history and it 388 00:27:07,560 --> 00:27:11,480 Speaker 1: had an impact on everybody. John Hope Franklin wrote about it, 389 00:27:11,520 --> 00:27:14,119 Speaker 1: the famous historian, wrote about it. He was actually a 390 00:27:14,200 --> 00:27:20,520 Speaker 1: child UH in that massacre and his family business was 391 00:27:20,600 --> 00:27:24,000 Speaker 1: torn up just like many others and destroyed. Lots of 392 00:27:24,000 --> 00:27:27,320 Speaker 1: people died, and he was saying that there was this 393 00:27:27,600 --> 00:27:34,080 Speaker 1: fear that permeated all African Americans because, as the commission 394 00:27:34,240 --> 00:27:37,680 Speaker 1: that investigated this said, no one was safe. There was 395 00:27:37,760 --> 00:27:41,720 Speaker 1: failure at every level of government, at the local level, 396 00:27:41,760 --> 00:27:45,200 Speaker 1: at the state level, and the president at the time 397 00:27:45,280 --> 00:27:49,640 Speaker 1: refused to UH end the violence that was happening there, 398 00:27:49,720 --> 00:27:53,080 Speaker 1: and the n double a c P President UH went 399 00:27:53,160 --> 00:27:56,840 Speaker 1: to see him to try to negotiate an end to this. 400 00:27:57,040 --> 00:27:59,840 Speaker 1: But it was it was really serious and that it 401 00:28:00,000 --> 00:28:01,760 Speaker 1: showed up in the data, and I couldn't make it, 402 00:28:01,960 --> 00:28:04,800 Speaker 1: couldn't make it go away. It was just that series. 403 00:28:04,840 --> 00:28:07,919 Speaker 1: There was no there was no other year like that. 404 00:28:08,119 --> 00:28:14,000 Speaker 1: Besides besides quite fascinating, let's talk a little bit about 405 00:28:14,080 --> 00:28:18,280 Speaker 1: what's going on in the profession of economics. You've attended 406 00:28:18,359 --> 00:28:23,840 Speaker 1: fairly traditional economics program, you've taught at traditional schools. What 407 00:28:23,920 --> 00:28:27,560 Speaker 1: do you think are some of the bigger changes that's 408 00:28:27,600 --> 00:28:30,399 Speaker 1: taking place today versus back when you were a student. 409 00:28:30,840 --> 00:28:37,360 Speaker 1: There was no conversation about why African Americans are missing 410 00:28:38,040 --> 00:28:42,560 Speaker 1: in economics. When I was in college or even in 411 00:28:42,640 --> 00:28:46,360 Speaker 1: graduate school, there was no conversation like that. Even though 412 00:28:46,480 --> 00:28:50,480 Speaker 1: there was a longstanding program the a a summer program 413 00:28:51,120 --> 00:28:53,560 Speaker 1: which I now direct him, which I did at Stanford, 414 00:28:54,520 --> 00:28:58,120 Speaker 1: there was no broad conversation about that. There was no 415 00:28:58,480 --> 00:29:06,320 Speaker 1: broad conversation about out what was wrong with the economics profession. Um. 416 00:29:06,360 --> 00:29:10,000 Speaker 1: You know, I wish that I had heard the Planet 417 00:29:10,080 --> 00:29:15,640 Speaker 1: Money series that was done about my paper, both the 418 00:29:15,680 --> 00:29:20,720 Speaker 1: topic and my paper, because I think that that there 419 00:29:20,760 --> 00:29:26,280 Speaker 1: would have been more realization by many people and by 420 00:29:26,360 --> 00:29:29,560 Speaker 1: those who were trying to get into the field that 421 00:29:30,000 --> 00:29:35,040 Speaker 1: what they were facing they weren't facing alone. So I 422 00:29:35,560 --> 00:29:39,160 Speaker 1: think that that's the biggest change. There was no letter, 423 00:29:39,840 --> 00:29:43,520 Speaker 1: uh that many people have read, say by Bill Spiggs, 424 00:29:44,160 --> 00:29:48,160 Speaker 1: that was widely circulated. You didn't have a president of 425 00:29:48,320 --> 00:29:52,640 Speaker 1: a felleral reserve Bank at all. Number one, you certainly 426 00:29:52,680 --> 00:29:57,080 Speaker 1: didn't see anybody in that leadership position. Uh. There were 427 00:29:57,080 --> 00:30:02,280 Speaker 1: members of the of the f O m C who 428 00:30:02,400 --> 00:30:07,560 Speaker 1: were African American, certainly, uh Emmitt Rice was one of 429 00:30:07,600 --> 00:30:11,760 Speaker 1: the first ones, and you know I knew about them, 430 00:30:11,800 --> 00:30:16,600 Speaker 1: but it didn't seem tangible, especially for for black women. 431 00:30:16,680 --> 00:30:21,880 Speaker 1: And I think that this open conversation is not something 432 00:30:21,920 --> 00:30:25,840 Speaker 1: that anybody could have anticipated that has moved very far 433 00:30:25,960 --> 00:30:28,560 Speaker 1: in a very short period of time that we would 434 00:30:28,640 --> 00:30:32,960 Speaker 1: even try to count how many African American economists that 435 00:30:33,120 --> 00:30:37,960 Speaker 1: are at the Fellow Reserves. I never heard such questions 436 00:30:38,000 --> 00:30:42,280 Speaker 1: post and certainly never thought, you know that I would 437 00:30:42,360 --> 00:30:45,840 Speaker 1: hear Nobel laureates telling me, you've got to publish this. 438 00:30:45,840 --> 00:30:49,760 Speaker 1: This is this is groundbreaking work. You've got to publish this. 439 00:30:49,880 --> 00:30:53,840 Speaker 1: Had no idea that this would be uh the case, 440 00:30:54,120 --> 00:30:56,800 Speaker 1: or that they would be as interested as they were 441 00:30:57,360 --> 00:31:00,240 Speaker 1: and and proved to be, and certainly grateful for it. 442 00:31:00,320 --> 00:31:03,160 Speaker 1: But I didn't think of it at all when I 443 00:31:03,200 --> 00:31:07,960 Speaker 1: was coming through the educational system. So over the summer, 444 00:31:08,040 --> 00:31:11,960 Speaker 1: a former Federal Reserve researcher, Claudia Psam, She also was 445 00:31:12,000 --> 00:31:16,800 Speaker 1: a researcher at the CEO posted a fiery blog post 446 00:31:17,360 --> 00:31:21,680 Speaker 1: quote economics as a disgrace and lays out a lot 447 00:31:21,800 --> 00:31:25,400 Speaker 1: of the specifics that you're discussing. What are your thoughts 448 00:31:25,520 --> 00:31:29,960 Speaker 1: on her criticism of the profession and what are the 449 00:31:30,080 --> 00:31:35,640 Speaker 1: role of politics in economics? That's a good question. Now 450 00:31:35,680 --> 00:31:38,600 Speaker 1: I have to tell you that I didn't read the 451 00:31:38,640 --> 00:31:43,000 Speaker 1: actual blog post because I I understand that I am 452 00:31:43,160 --> 00:31:46,520 Speaker 1: in it and I really at that time didn't want 453 00:31:46,560 --> 00:31:50,520 Speaker 1: to this, could not deal with it emotionally because I've 454 00:31:50,520 --> 00:31:54,400 Speaker 1: been talking about this a lot, talking about economics the 455 00:31:54,480 --> 00:32:00,240 Speaker 1: economics profession a lot. But Claudia I think is and 456 00:32:00,360 --> 00:32:04,360 Speaker 1: has been a catalyst for a lot of frank discussions 457 00:32:04,400 --> 00:32:09,920 Speaker 1: in economics. And because she's been in this special rarefied 458 00:32:10,360 --> 00:32:15,200 Speaker 1: place of being at the subtle reserves of uh having 459 00:32:15,240 --> 00:32:19,640 Speaker 1: this somb rule you know. Uh, there are very few 460 00:32:19,920 --> 00:32:25,960 Speaker 1: few rules named after in macro named after women having 461 00:32:26,000 --> 00:32:29,720 Speaker 1: been at c A. She has been at the top 462 00:32:29,760 --> 00:32:32,280 Speaker 1: of the field in so many different ways, so she 463 00:32:32,400 --> 00:32:37,600 Speaker 1: has a special place, uh from which she sits and 464 00:32:37,640 --> 00:32:42,120 Speaker 1: can see the profession, the broad profession. And she's also 465 00:32:42,360 --> 00:32:48,120 Speaker 1: not a phrase because she is uh interacted with the 466 00:32:48,160 --> 00:32:55,360 Speaker 1: current and former presidents as the A a Uh, Janet 467 00:32:55,400 --> 00:33:01,480 Speaker 1: Yellen and Ben Bernankee, so she she could address them 468 00:33:01,640 --> 00:33:06,360 Speaker 1: as colleagues, as peers. And I think it has been 469 00:33:06,400 --> 00:33:11,640 Speaker 1: she's fonstered this this very necessary conversation. And she's been 470 00:33:11,680 --> 00:33:17,240 Speaker 1: an advocate for graduate students, for people who have felt 471 00:33:17,960 --> 00:33:23,880 Speaker 1: uh sexually harassed or racially harassed in the economics profession. 472 00:33:24,640 --> 00:33:30,320 Speaker 1: And you know, the ambuts person and then quite engage. 473 00:33:30,360 --> 00:33:34,520 Speaker 1: We finally hired an ombuts person UH at the American 474 00:33:34,560 --> 00:33:38,840 Speaker 1: Economic Association. We needed somebody if we were going to 475 00:33:38,960 --> 00:33:42,360 Speaker 1: say these problems exist, we needed somebody to do something 476 00:33:42,400 --> 00:33:45,080 Speaker 1: about them. It's not perfect yet, that mechanism is not 477 00:33:45,160 --> 00:33:50,280 Speaker 1: perfect yet. But I appreciate people who are as engaged 478 00:33:50,360 --> 00:33:55,080 Speaker 1: as Claudis. And she does a lot to mentor the 479 00:33:55,200 --> 00:33:59,120 Speaker 1: next generation of economists, and not just women, and not 480 00:33:59,200 --> 00:34:03,000 Speaker 1: just at or Americans, not just African American women. She 481 00:34:03,160 --> 00:34:08,480 Speaker 1: spends time during this this season preparing, helping people to 482 00:34:08,560 --> 00:34:11,200 Speaker 1: prepare for the job market. She reads their paper. She 483 00:34:11,480 --> 00:34:16,440 Speaker 1: is providing many different public goods for the profession. And 484 00:34:16,600 --> 00:34:20,920 Speaker 1: I just think that she's she's just a gym and 485 00:34:21,120 --> 00:34:25,120 Speaker 1: the profession that we need more people like her who 486 00:34:25,200 --> 00:34:30,160 Speaker 1: will hold our leaders accountable, point point to the issues, 487 00:34:30,239 --> 00:34:34,720 Speaker 1: but also try to figure out ways to address those issues. 488 00:34:34,840 --> 00:34:38,160 Speaker 1: And she's a problem solver. I mean, thesembile was created 489 00:34:38,680 --> 00:34:42,200 Speaker 1: as a mechanism to solve a problems. She talks about 490 00:34:42,600 --> 00:34:47,360 Speaker 1: automatic stabilizes and writes about this. So she's she's a 491 00:34:47,360 --> 00:34:51,480 Speaker 1: problem solver in every dimension. To appreciate her being in 492 00:34:51,520 --> 00:34:55,600 Speaker 1: the profession, quite interesting. Let's talk a little bit about 493 00:34:55,600 --> 00:34:58,920 Speaker 1: what's going on today with the recession. What is this 494 00:34:59,040 --> 00:35:02,960 Speaker 1: doing too? Some and wealth inequality. I'm not sure it 495 00:35:03,000 --> 00:35:05,600 Speaker 1: could do more. I'm not sure this pandemic could do more. 496 00:35:06,320 --> 00:35:13,239 Speaker 1: It is really driving a serious wedge between those who 497 00:35:13,400 --> 00:35:18,680 Speaker 1: have income and wealth and those who doubt. Whether we're 498 00:35:18,719 --> 00:35:23,400 Speaker 1: talking about those who hold stocks and those who don't. 499 00:35:23,520 --> 00:35:29,760 Speaker 1: And uh, just under of Americans do not hold any stock. 500 00:35:30,280 --> 00:35:34,160 Speaker 1: They are not seeing the gains the stock market has 501 00:35:34,320 --> 00:35:40,000 Speaker 1: largely recovered from uh, the beginning of the pandemic. That 502 00:35:40,160 --> 00:35:42,359 Speaker 1: we did see this in the Great Recession too, so 503 00:35:42,800 --> 00:35:47,160 Speaker 1: that's not so unusual that that stocks would cover before 504 00:35:47,320 --> 00:35:53,680 Speaker 1: employment does. But income inequality is taking a beating. How 505 00:35:53,800 --> 00:35:58,080 Speaker 1: is that because let's say, if we add a racial 506 00:35:58,160 --> 00:36:04,239 Speaker 1: dimension to it, African Americans are subject to occupational segregation. 507 00:36:04,320 --> 00:36:07,759 Speaker 1: So they are in a lot of these front facing 508 00:36:09,520 --> 00:36:15,560 Speaker 1: occupations to which they would be disproportionately exposed to COVID nineteen. 509 00:36:16,200 --> 00:36:20,839 Speaker 1: So that's one way in which they're deciding between a 510 00:36:20,960 --> 00:36:25,640 Speaker 1: job and eating or a job and rent a job 511 00:36:25,680 --> 00:36:28,799 Speaker 1: in their health. So so this is one of the 512 00:36:28,840 --> 00:36:32,560 Speaker 1: ways and which income and equality has come to for 513 00:36:32,560 --> 00:36:36,200 Speaker 1: for wealth inequality, it's even more stark. So as I 514 00:36:36,360 --> 00:36:40,360 Speaker 1: was mentioning, Uh, certainly the stock market is recovering, Uh, 515 00:36:40,520 --> 00:36:45,960 Speaker 1: not as many people among African Americans are in the 516 00:36:46,040 --> 00:36:51,360 Speaker 1: stock market. Uh. Certainly, wealth is orders of magnitude lower 517 00:36:51,520 --> 00:36:55,959 Speaker 1: for African American healthhold compared to white healthhold. But there's 518 00:36:56,000 --> 00:36:59,520 Speaker 1: one thing that I followed that really troubles me, and 519 00:36:59,640 --> 00:37:04,800 Speaker 1: that is small businesses. There are fifty white entrepreneurs for 520 00:37:04,920 --> 00:37:08,319 Speaker 1: every black entrepreneur. And as we know, this is a 521 00:37:08,440 --> 00:37:10,880 Speaker 1: tried and true path to the middle class and to 522 00:37:11,280 --> 00:37:14,920 Speaker 1: wealth accumulation in America. And what we know about black 523 00:37:14,960 --> 00:37:21,960 Speaker 1: businesses is that a disproportionate number are reporting closing permanently 524 00:37:23,239 --> 00:37:27,440 Speaker 1: as a result of the recession, the pandemic and recession, 525 00:37:27,840 --> 00:37:32,600 Speaker 1: and they didn't receive PPP funds. Uh, we're not recording. 526 00:37:32,719 --> 00:37:36,319 Speaker 1: SBA is not recording the applicants, and we don't have 527 00:37:36,360 --> 00:37:39,560 Speaker 1: good demographic data, but from the surveys that have been done, 528 00:37:39,960 --> 00:37:43,239 Speaker 1: we know that when they replied, when they applied, they 529 00:37:43,239 --> 00:37:46,040 Speaker 1: were rejected. You know, this is due to the big 530 00:37:46,040 --> 00:37:50,960 Speaker 1: banks being relied upon to UH to all out these moments, 531 00:37:51,000 --> 00:37:54,200 Speaker 1: but also for the amount they were requesting, they got 532 00:37:54,320 --> 00:37:59,360 Speaker 1: disproportionately less. So what I worry about is this fisher 533 00:38:00,320 --> 00:38:05,560 Speaker 1: growing significantly, not just now but in the future, because 534 00:38:05,680 --> 00:38:10,560 Speaker 1: certainly income now lays the groundwork for WELCO in the future. 535 00:38:11,200 --> 00:38:15,960 Speaker 1: So given the sort of work from home pandemic economy 536 00:38:16,040 --> 00:38:19,680 Speaker 1: that we're living in, some people have been calling this 537 00:38:19,880 --> 00:38:24,520 Speaker 1: a quote she session unquote because so much of it 538 00:38:24,600 --> 00:38:29,400 Speaker 1: is falling disproportionately on the shoulders of women. How accurate 539 00:38:29,480 --> 00:38:32,359 Speaker 1: is that phrase? Is this overstanding it or is there 540 00:38:32,400 --> 00:38:36,480 Speaker 1: really a deep gender fisher as well as a racial fisher. 541 00:38:37,120 --> 00:38:43,520 Speaker 1: Absolutely there's a gender fisher that is that is also 542 00:38:43,760 --> 00:38:47,480 Speaker 1: there with the racial fisher, and we could see this 543 00:38:47,880 --> 00:38:51,279 Speaker 1: in American Time Youth Survey, in the Census. What we 544 00:38:51,360 --> 00:38:58,360 Speaker 1: know about women is that regardless of occupation, regardless of income, 545 00:38:58,920 --> 00:39:03,520 Speaker 1: they spend a disproportionate amount of time on care of 546 00:39:03,640 --> 00:39:07,520 Speaker 1: other individuals in the household. That might be children, that 547 00:39:07,640 --> 00:39:12,160 Speaker 1: might be the elderly, but they spend more time taking 548 00:39:12,200 --> 00:39:14,920 Speaker 1: care of those people. And that happened before. That was 549 00:39:14,960 --> 00:39:19,840 Speaker 1: happening before the pandemic. And women are the ones who 550 00:39:20,000 --> 00:39:23,400 Speaker 1: are having to drop out of the workforce while these 551 00:39:23,960 --> 00:39:27,680 Speaker 1: students are stuck at home. Small children especially are stuck 552 00:39:27,719 --> 00:39:31,400 Speaker 1: at home trying to do their homework or trying to 553 00:39:31,480 --> 00:39:35,720 Speaker 1: make sure that they have their their snacks on time 554 00:39:35,840 --> 00:39:39,600 Speaker 1: and get through their school work. So this is a 555 00:39:39,640 --> 00:39:47,480 Speaker 1: she session. Women are back at their participation rates from 556 00:39:47,520 --> 00:39:50,759 Speaker 1: that is shocking, and were to happen in such a 557 00:39:50,800 --> 00:39:54,080 Speaker 1: short period of time is absolutely shocking. And this is 558 00:39:54,120 --> 00:39:58,360 Speaker 1: going to have lasting scars, leave lasting lasting scars on 559 00:39:58,440 --> 00:40:01,319 Speaker 1: the labor market, because one thing we know is that 560 00:40:01,360 --> 00:40:04,920 Speaker 1: when women are out of the labor force, they slow 561 00:40:04,960 --> 00:40:07,920 Speaker 1: down with respect to you pay, with respect to your promotion. 562 00:40:08,480 --> 00:40:12,000 Speaker 1: And I am just hoping that we will get some 563 00:40:12,080 --> 00:40:16,120 Speaker 1: kind of support for child care to make sure that 564 00:40:16,760 --> 00:40:20,800 Speaker 1: that some of these women can recover and recover more quickly. 565 00:40:21,320 --> 00:40:24,480 Speaker 1: Quite interesting, so the Care's Act was passed at the 566 00:40:24,600 --> 00:40:28,160 Speaker 1: end of the first quarter, it was about three trillion dollars. 567 00:40:28,200 --> 00:40:30,919 Speaker 1: If you would have asked me over the summer, will 568 00:40:30,960 --> 00:40:33,680 Speaker 1: we see a follow up another couple of trillion dollars, 569 00:40:34,520 --> 00:40:37,400 Speaker 1: I would have given you ten to one odds that absolutely. 570 00:40:37,440 --> 00:40:40,239 Speaker 1: The politicians in election here of course they're going to 571 00:40:40,320 --> 00:40:43,840 Speaker 1: pass the second stimulus, But here we are, middle of October, 572 00:40:44,680 --> 00:40:48,279 Speaker 1: no such stimulus passed yet? Did we go far enough 573 00:40:48,840 --> 00:40:51,239 Speaker 1: with the first Cares Act? And are you at all 574 00:40:51,280 --> 00:40:54,279 Speaker 1: surprised that there hasn't been a follow up? What do 575 00:40:54,320 --> 00:40:56,640 Speaker 1: you think is going to happen? And how much stimulus 576 00:40:56,640 --> 00:40:59,600 Speaker 1: has needed given the way the economy is starting to 577 00:40:59,640 --> 00:41:03,480 Speaker 1: apply to and attenuate from from that big bounce back 578 00:41:03,520 --> 00:41:05,839 Speaker 1: we saw over the summer. Barrett, you and I were 579 00:41:05,840 --> 00:41:08,719 Speaker 1: in the same position. That's exactly what I thought too, 580 00:41:09,480 --> 00:41:16,040 Speaker 1: And what I knew was that we if we had 581 00:41:16,080 --> 00:41:21,040 Speaker 1: a national coordinated strategy, we could possibly be seeing our 582 00:41:21,120 --> 00:41:25,839 Speaker 1: way back to normalcy by this time, if we had 583 00:41:25,960 --> 00:41:31,640 Speaker 1: a national mask mandate, for example. But that didn't happen. 584 00:41:32,640 --> 00:41:36,760 Speaker 1: And what I find shocking is this three trillion dollars 585 00:41:36,880 --> 00:41:42,360 Speaker 1: was was a good start, but we are repeating the 586 00:41:42,640 --> 00:41:46,040 Speaker 1: lessons of two thousand and eight, two thousand nine. I 587 00:41:46,120 --> 00:41:49,200 Speaker 1: cannot believe this I was on the Obama transition team 588 00:41:49,239 --> 00:41:53,080 Speaker 1: at the time, and what I was watching was austerity 589 00:41:53,160 --> 00:41:56,600 Speaker 1: being put into place. And and I was also there 590 00:41:56,600 --> 00:42:01,120 Speaker 1: in austerity being put into play when the last thing 591 00:42:01,200 --> 00:42:05,080 Speaker 1: you needed was austerity. You need to throw money at 592 00:42:05,120 --> 00:42:10,760 Speaker 1: people so that yes, they will stay stay afloat, whether 593 00:42:10,800 --> 00:42:15,000 Speaker 1: it's businesses or households or the unemployed, they just need 594 00:42:15,080 --> 00:42:19,840 Speaker 1: to stay afloat while we figure this pandemic out. And 595 00:42:19,840 --> 00:42:22,760 Speaker 1: and that is what is still needed. But we're repeating 596 00:42:22,800 --> 00:42:25,680 Speaker 1: the mistakes of two two nine. What we see is 597 00:42:25,719 --> 00:42:30,720 Speaker 1: state and local governments laying off people and and firing people. 598 00:42:30,880 --> 00:42:33,200 Speaker 1: And this is what we saw before, and that's why 599 00:42:33,280 --> 00:42:37,239 Speaker 1: the recovery has stalled. I can't believe that we're doing 600 00:42:37,400 --> 00:42:41,560 Speaker 1: exactly the same thing. And that's why, like Claudia, I 601 00:42:41,680 --> 00:42:44,759 Speaker 1: believe in automatic stabilizes. We've got to have more so 602 00:42:44,960 --> 00:42:49,680 Speaker 1: that this is not j Pel begging Congress and the 603 00:42:49,719 --> 00:42:53,680 Speaker 1: administration to do something and all of the other that 604 00:42:53,840 --> 00:42:57,960 Speaker 1: officials do. And and it is clear that physical policy 605 00:42:58,080 --> 00:43:01,880 Speaker 1: is what is needed the mom it's ore the Federal 606 00:43:01,920 --> 00:43:05,399 Speaker 1: Reserve aren't there. It's not as if they are are 607 00:43:05,520 --> 00:43:07,920 Speaker 1: boundless in what they can do in the kinds of 608 00:43:07,960 --> 00:43:10,920 Speaker 1: resources they can provide. They've been fast, they've been quick, 609 00:43:11,200 --> 00:43:17,320 Speaker 1: but they can't do everything. So shocked that we haven't 610 00:43:17,360 --> 00:43:24,040 Speaker 1: that the administration and Congress, the Senate Republicans have blocked 611 00:43:24,719 --> 00:43:29,879 Speaker 1: aid to American people, households, and businesses. I think it's 612 00:43:30,000 --> 00:43:36,480 Speaker 1: absolutely unconsortable. There's definitely an eviction and rental crisis that 613 00:43:36,719 --> 00:43:40,480 Speaker 1: is right at our feet. It's right in front of us, 614 00:43:40,800 --> 00:43:44,200 Speaker 1: and we're not doing anything about it. Let me are 615 00:43:44,280 --> 00:43:48,120 Speaker 1: closing and we're not doing anything about it. I'm just 616 00:43:48,320 --> 00:43:51,279 Speaker 1: in shock. So let me step back and ask you 617 00:43:51,320 --> 00:43:55,760 Speaker 1: the thirty thousand foot view question, which is we saw 618 00:43:56,280 --> 00:44:01,600 Speaker 1: a very miserly stimulus at the beginning of oh nine. 619 00:44:01,719 --> 00:44:04,040 Speaker 1: It was I know, this sounds ridiculous to say, about 620 00:44:04,080 --> 00:44:07,800 Speaker 1: eight hundred billion dollars which proved to be way too small. 621 00:44:08,320 --> 00:44:11,200 Speaker 1: We saw the rise of austerity, not just here but 622 00:44:11,239 --> 00:44:15,239 Speaker 1: also in the UK, and then in we saw a 623 00:44:15,480 --> 00:44:20,560 Speaker 1: massive pro cyclical tax cut and stimulus. I thought the 624 00:44:20,640 --> 00:44:23,440 Speaker 1: rule book was, hey, in a deep procession, you want 625 00:44:23,440 --> 00:44:29,160 Speaker 1: to see countercyclical fiscal stimulus, not late cycle pro cyclical 626 00:44:29,239 --> 00:44:32,839 Speaker 1: fiscal stimulus. What's the lesson to be taken away from 627 00:44:32,840 --> 00:44:38,440 Speaker 1: this that somebody is not telling the truth about BUDE 628 00:44:38,560 --> 00:44:43,680 Speaker 1: deficits and about gross because what we know is that 629 00:44:44,200 --> 00:44:48,040 Speaker 1: the short run deviations like two thousand and eight, two 630 00:44:48,080 --> 00:44:51,399 Speaker 1: thousand nine, in order for them to remain short run 631 00:44:51,520 --> 00:44:57,400 Speaker 1: deviations of output, you have to go big, and you 632 00:44:57,480 --> 00:45:01,160 Speaker 1: have to assure the American people that something is on 633 00:45:01,239 --> 00:45:03,880 Speaker 1: the way and that they won't be out there on 634 00:45:03,920 --> 00:45:06,920 Speaker 1: their own. And the same it's true for businesses. It 635 00:45:07,200 --> 00:45:12,640 Speaker 1: is absolutely unconsortable that businesses that through no fall to 636 00:45:12,680 --> 00:45:18,080 Speaker 1: their own are laying out tens of thousands of people. 637 00:45:18,320 --> 00:45:21,560 Speaker 1: The airlines are laying off tens of thousands of people, 638 00:45:22,360 --> 00:45:26,200 Speaker 1: and that's not going to affect just them. Of course, 639 00:45:26,680 --> 00:45:31,400 Speaker 1: their dollar support many other jobs, and not just in 640 00:45:31,440 --> 00:45:34,960 Speaker 1: the cities where there are hubbs. This is this isn't 641 00:45:35,080 --> 00:45:38,880 Speaker 1: this is true in general. So I'm still in shock 642 00:45:39,120 --> 00:45:44,440 Speaker 1: that we are not doing more and that the lessons 643 00:45:44,480 --> 00:45:47,800 Speaker 1: of the Great Recession have not been have not been learned. 644 00:45:47,840 --> 00:45:51,360 Speaker 1: It's almost as if some folks were awake for the 645 00:45:51,440 --> 00:45:53,960 Speaker 1: first two days of econ one oh one and just 646 00:45:54,080 --> 00:45:56,719 Speaker 1: slept through the rest of the semester. They missed the 647 00:45:56,800 --> 00:46:00,920 Speaker 1: chapter on Keynes. So here's the pushback. Hey, listen, we 648 00:46:00,960 --> 00:46:04,600 Speaker 1: don't have access to infinite money. Three trillion dollars is 649 00:46:04,640 --> 00:46:07,040 Speaker 1: a lot, and at a certain point we have to 650 00:46:07,080 --> 00:46:10,560 Speaker 1: start being concerned about the deficit. How do you respond 651 00:46:10,600 --> 00:46:13,480 Speaker 1: to that sort of pushback? We do have to become 652 00:46:13,520 --> 00:46:15,879 Speaker 1: concerned about the deficit, but that time is not now, 653 00:46:16,480 --> 00:46:20,200 Speaker 1: absolutely not now. That would be misplaced. How do you 654 00:46:20,280 --> 00:46:22,680 Speaker 1: how do you sustain growth? How do you how do 655 00:46:22,680 --> 00:46:25,719 Speaker 1: you keep people afloat? How do you keep businesses afloat? 656 00:46:25,800 --> 00:46:29,520 Speaker 1: That's the question we should be asking, and this is 657 00:46:30,000 --> 00:46:34,640 Speaker 1: a common view of economists. Many economists, as you know, 658 00:46:35,120 --> 00:46:37,720 Speaker 1: of all stripes, are saying the same thing. I'm saying 659 00:46:38,400 --> 00:46:42,520 Speaker 1: that we worry about deficits in the future. Let's, you know, 660 00:46:42,600 --> 00:46:45,040 Speaker 1: when the economy gets back on the feet, let's start 661 00:46:45,120 --> 00:46:50,600 Speaker 1: worrying about those deficits until then. It's it's abstract, it's 662 00:46:50,680 --> 00:46:54,680 Speaker 1: you know, why why do you starve a person who's 663 00:46:54,719 --> 00:46:59,720 Speaker 1: already hungry? That that is ridiculous. And if you hobble 664 00:46:59,840 --> 00:47:03,160 Speaker 1: the economy. Now, let's say, for example, we could be 665 00:47:03,360 --> 00:47:07,239 Speaker 1: using p PP to give to our smallest newest businesses. 666 00:47:08,560 --> 00:47:14,200 Speaker 1: If those smallest newest businesses don't stay a lot, we're 667 00:47:14,200 --> 00:47:17,680 Speaker 1: going to permanently hit long run growth, because these are 668 00:47:17,719 --> 00:47:20,239 Speaker 1: some of the businesses that give us innovation. This is 669 00:47:20,280 --> 00:47:24,880 Speaker 1: where innovation comes from. Often it's from the smallest, newest businesses, 670 00:47:25,320 --> 00:47:27,520 Speaker 1: and if we don't help them, what we are doing 671 00:47:27,680 --> 00:47:32,439 Speaker 1: is saying we're not going to contribute innovation to long 672 00:47:33,040 --> 00:47:38,000 Speaker 1: long run growth anymore. And that is really unconscionable. We 673 00:47:38,000 --> 00:47:41,120 Speaker 1: shouldn't do that. We shouldn't penalize the economy that way. 674 00:47:41,560 --> 00:47:43,960 Speaker 1: Quite interesting. I know I only have you for a 675 00:47:43,960 --> 00:47:47,360 Speaker 1: few minutes, so let me jump to my favorite questions 676 00:47:47,360 --> 00:47:50,799 Speaker 1: that I ask all of my guests. Tell us what 677 00:47:50,880 --> 00:47:52,920 Speaker 1: are you streaming? What are you watching or listening to 678 00:47:53,040 --> 00:47:56,480 Speaker 1: these days now that we're all working from home. The 679 00:47:56,520 --> 00:48:01,560 Speaker 1: first thing that I've been streaming has been uh Nollywood 680 00:48:02,040 --> 00:48:06,960 Speaker 1: movies and TV series you know from from Nigeria and 681 00:48:07,200 --> 00:48:10,600 Speaker 1: uh the the TV series that just so I'm really 682 00:48:10,640 --> 00:48:15,120 Speaker 1: interesting is fifty And then there's this movie called Chief Daddy. 683 00:48:15,800 --> 00:48:21,759 Speaker 1: I'm also uh watching Chernobyl and I you know Chernobyl. 684 00:48:22,560 --> 00:48:25,799 Speaker 1: I find fascinating. It was well done. I used to 685 00:48:25,840 --> 00:48:29,920 Speaker 1: live and Russia, so I certainly know the story very 686 00:48:29,960 --> 00:48:32,919 Speaker 1: well and studied the former Soviet Union quite a bit. 687 00:48:33,560 --> 00:48:36,080 Speaker 1: But I can't watch it sometimes because it's so close 688 00:48:36,120 --> 00:48:39,120 Speaker 1: to home, and I think it was an excellent series. 689 00:48:39,239 --> 00:48:44,000 Speaker 1: I'm watching Ship's Creek. Uh so that's that that I'm 690 00:48:44,040 --> 00:48:49,280 Speaker 1: not finished yet keeping it as a mini series for myself, 691 00:48:49,719 --> 00:48:55,160 Speaker 1: and certainly I listened to uh Planet Money and Sereal. 692 00:48:55,400 --> 00:48:59,600 Speaker 1: Those are things that I stream a lot, so to 693 00:48:59,760 --> 00:49:02,640 Speaker 1: the there those of day. You mentioned some of your 694 00:49:02,680 --> 00:49:07,120 Speaker 1: early mentors previously. Tell us who helped shape your career. 695 00:49:07,640 --> 00:49:12,560 Speaker 1: I will uh say from a distance. I told you 696 00:49:12,680 --> 00:49:17,120 Speaker 1: some who were close to home, but you know, Barbara 697 00:49:17,200 --> 00:49:20,680 Speaker 1: Jordan was one I was forced to watch by my 698 00:49:20,840 --> 00:49:27,040 Speaker 1: grandmother her leading the impeachment hearings against Richard Nixon. Had 699 00:49:27,080 --> 00:49:30,080 Speaker 1: no idea who she was, but my grandmother was saying, 700 00:49:30,120 --> 00:49:32,840 Speaker 1: this is how democracy works. You've got to watch this, 701 00:49:32,840 --> 00:49:37,160 Speaker 1: this is how democracy works. So there was something that 702 00:49:37,320 --> 00:49:42,280 Speaker 1: I found incredibly inspiring about Barbara Jordan's and Shirley chishol 703 00:49:43,040 --> 00:49:49,120 Speaker 1: and I tried to understand how they got to know 704 00:49:49,960 --> 00:49:53,719 Speaker 1: what they knew and started taking myself a lot more 705 00:49:53,760 --> 00:49:58,360 Speaker 1: seriously as a person who was interested at some level 706 00:49:58,760 --> 00:50:02,440 Speaker 1: in public service. Quite interesting. Tell us some of your 707 00:50:02,480 --> 00:50:04,840 Speaker 1: favorite books. What what are you reading now and what 708 00:50:05,000 --> 00:50:08,160 Speaker 1: have what ends up on your all time favorite lists. Well, 709 00:50:08,200 --> 00:50:10,920 Speaker 1: you know, one book that is my favorite book of 710 00:50:11,000 --> 00:50:15,360 Speaker 1: the summer is a book that is non fixing, but 711 00:50:15,400 --> 00:50:18,200 Speaker 1: it reads like fixing. It is so good. It is 712 00:50:18,239 --> 00:50:22,359 Speaker 1: the World according to Fanny Davis by Bridget Davis, and 713 00:50:22,560 --> 00:50:27,120 Speaker 1: it is about the underground economy, the numbers runners in Detroit. 714 00:50:27,440 --> 00:50:30,279 Speaker 1: It's going to be turned into a movie and has 715 00:50:30,320 --> 00:50:34,120 Speaker 1: been the subject of a number of MPR interviews. But 716 00:50:34,239 --> 00:50:42,880 Speaker 1: I'm also reading books like um uh Mercia baradns Uh, 717 00:50:43,200 --> 00:50:48,680 Speaker 1: Bara Duran's uh The Color of Money. I'm reading um 718 00:50:48,719 --> 00:50:53,680 Speaker 1: of course, Sandy Drity is from Here to Equality. You know, 719 00:50:53,840 --> 00:50:56,319 Speaker 1: I'm just trying to learn as much as i can 720 00:50:56,440 --> 00:51:00,279 Speaker 1: about the moment. I'm reading The deficit mess said that 721 00:51:00,320 --> 00:51:05,040 Speaker 1: I can understand more about how we think about deficits. 722 00:51:05,120 --> 00:51:08,719 Speaker 1: That's that's one way to think about, uh, deficits. But 723 00:51:09,160 --> 00:51:15,359 Speaker 1: I am also rereading things like Moneyball. I used to 724 00:51:15,400 --> 00:51:20,439 Speaker 1: teach the economics of baseball, and I would look forward 725 00:51:20,480 --> 00:51:23,319 Speaker 1: to teaching it again. But that's just fought that, you know, 726 00:51:23,440 --> 00:51:26,080 Speaker 1: That's that's that's just fun. That's just economics for fun. 727 00:51:26,560 --> 00:51:28,640 Speaker 1: So what sort of advice would you give to a 728 00:51:28,719 --> 00:51:32,960 Speaker 1: recent college graduate who was considering a career in economics. 729 00:51:33,960 --> 00:51:39,239 Speaker 1: I would say to get as prepared as you can. 730 00:51:40,200 --> 00:51:44,040 Speaker 1: And when I say prepared, I don't mean doing five 731 00:51:44,160 --> 00:51:50,759 Speaker 1: or six pre doctors programs. Uh, maybe do do one 732 00:51:50,880 --> 00:51:55,240 Speaker 1: or an r A ship. But get to know your 733 00:51:55,400 --> 00:52:00,480 Speaker 1: professors are. Contact your professors if you've already graduated, and 734 00:52:00,920 --> 00:52:03,719 Speaker 1: read as much about the economy as you can. I 735 00:52:03,760 --> 00:52:09,840 Speaker 1: think being anchored in you know, podcasts like like yours, 736 00:52:10,000 --> 00:52:16,520 Speaker 1: or interviews like yours, reading Bloomberg and f T and 737 00:52:17,120 --> 00:52:21,120 Speaker 1: uh the New York Times Business and Economics sections. I 738 00:52:21,160 --> 00:52:25,279 Speaker 1: think having a point of reference is critical because I 739 00:52:25,320 --> 00:52:28,560 Speaker 1: find that many many students don't. So I think that 740 00:52:28,640 --> 00:52:31,520 Speaker 1: it's critical to have some some frame of reference. And 741 00:52:31,560 --> 00:52:35,120 Speaker 1: I think it would be inspiring and interesting once you do, 742 00:52:35,440 --> 00:52:38,400 Speaker 1: quite interesting. And our final question, what do you know 743 00:52:38,440 --> 00:52:41,320 Speaker 1: about the world of economics today that you wish you 744 00:52:41,400 --> 00:52:44,680 Speaker 1: knew twenty five years ago when you were really first 745 00:52:44,680 --> 00:52:54,000 Speaker 1: getting started. I wish I knew more about how policymakers 746 00:52:54,160 --> 00:52:57,840 Speaker 1: think about policymakers who were not economists and who aren't 747 00:52:57,880 --> 00:53:05,680 Speaker 1: at the FED or Treasure think about how to help people. 748 00:53:06,080 --> 00:53:10,440 Speaker 1: So I'm thinking about Congress now, and I wish I 749 00:53:10,520 --> 00:53:16,400 Speaker 1: knew more about how they thought about the economy, because 750 00:53:17,280 --> 00:53:22,040 Speaker 1: if I understood that then, and frankly, if I understood 751 00:53:22,080 --> 00:53:28,440 Speaker 1: that now, maybe we could affect the way they're thinking 752 00:53:28,520 --> 00:53:33,000 Speaker 1: so that we don't run into this other prolonged recession. 753 00:53:33,440 --> 00:53:38,000 Speaker 1: And you know the danger here, Verry, is that inflation 754 00:53:38,360 --> 00:53:42,720 Speaker 1: is a real worry that can actually happen. We haven't 755 00:53:42,800 --> 00:53:47,600 Speaker 1: met our inflation targets for for almost a decade, only 756 00:53:48,000 --> 00:53:51,160 Speaker 1: you know a handful of times in a decade. That 757 00:53:51,280 --> 00:53:55,400 Speaker 1: could be a real serious problem. So I would like 758 00:53:55,560 --> 00:54:01,440 Speaker 1: to understand better how people who make a go policy think, 759 00:54:01,640 --> 00:54:05,360 Speaker 1: and I wish I could influence it more because the 760 00:54:05,400 --> 00:54:10,799 Speaker 1: American people, American households, businesses, need state, local governments, the 761 00:54:10,960 --> 00:54:14,280 Speaker 1: art need a lot of help right now. Quite interesting. 762 00:54:14,719 --> 00:54:17,600 Speaker 1: Thank you Lisa Cook for being so generous with your time. 763 00:54:17,719 --> 00:54:21,240 Speaker 1: We have been speaking with Professor Lisa Cook. She teaches 764 00:54:21,320 --> 00:54:25,200 Speaker 1: economics and international relations at the James Madison College at 765 00:54:25,200 --> 00:54:30,239 Speaker 1: Michigan State University. If you enjoy this conversation, be sure 766 00:54:30,239 --> 00:54:32,719 Speaker 1: and check out any of the other three hundred and 767 00:54:32,719 --> 00:54:37,160 Speaker 1: fifty or so previous interviews we've had. You can find 768 00:54:37,200 --> 00:54:42,880 Speaker 1: that at iTunes, Spotify, Stitcher, a cast wherever Finder podcasts 769 00:54:42,880 --> 00:54:47,400 Speaker 1: are sold. We love your comments, feedback and suggestions right 770 00:54:47,520 --> 00:54:51,440 Speaker 1: to us at m IB podcast at Bloomberg dot net. 771 00:54:51,960 --> 00:54:54,799 Speaker 1: You could check out my weekly columns on Bloomberg dot 772 00:54:54,880 --> 00:54:59,160 Speaker 1: com slash Opinion. Sign up for our daily reads at 773 00:54:59,239 --> 00:55:01,480 Speaker 1: Ridolts dot tom. Be sure and follow me on Twitter 774 00:55:01,560 --> 00:55:04,799 Speaker 1: at rid Halts. Give us a review at Apple iTunes. 775 00:55:05,400 --> 00:55:07,480 Speaker 1: I would be remiss if I did not thank the 776 00:55:07,600 --> 00:55:11,319 Speaker 1: Cracks staff that helps us put these conversations together each week. 777 00:55:12,000 --> 00:55:15,560 Speaker 1: My audio engineer is Maroufal, Michael Batnick is my head 778 00:55:15,560 --> 00:55:20,160 Speaker 1: of research, Attico val Brund is our project manager, Michael 779 00:55:20,200 --> 00:55:23,839 Speaker 1: Boyle is our producer, and I'm Barry rid Halts. You've 780 00:55:23,840 --> 00:55:27,360 Speaker 1: been listening to Masters and Business on Bloomberg Radio.