The Jordan B. Peterson Podcast - May 11, 2017


Dr Martin Daly


Episode Stats

Length

2 hours and 5 minutes

Words per Minute

157.21117

Word Count

19,694

Sentence Count

821

Misogynist Sentences

12

Hate Speech Sentences

13


Summary

In this episode, Dr. Martin Daly talks with Dr. Jordan B. Peterson about his new book, "Killing the Competition." Dr. Daly is a psychologist at McMaster University in Hamilton, Ontario, and author of many influential papers on evolutionary psychology. His current research topics include an evolutionary perspective on risk taking and interpersonal violence, especially male-male conflict. He and his wife, the late Margaret Wilson, were the former editors-in-chief of the journal Evolution and Human Behavior, and former presidents of the Human Behavior and Evolution Society. He was named a Fellow of the Royal Society of Canada in 1998, and is one of the main researchers of the Cinderella Effect, and has been interviewed many times in the press about it. In this book, he tries to make the case that, no, inequality really is the problem, and that some of the arguments that have been advanced for suggesting that it is a mere correlate of violence are wrong. To support these podcasts, you can donate to Dr. Peterson s PODCAST on his website, which can be found here. To support Daily Wire Plus: Donations are tax-deductible, and can be made in the form of a dollar or dollar amount, and you can get a free copy of the book, Killing the Competition, which is available for purchase here. If you like what you hear, please consider pledging a small monthly fee of $1 or $5 or $10 or $15 or $20 or $50, and we'll send you an autographed copy of Killing The Competition and you'll be entered into the drawing drawing for a chance to win a chance at a place at the Big Dawgs drawing a drawing contest, and receive a prize, too! You can also support the drawing a prize and receive an additional $5,000 in the drawing, plus a free book or two, and a lifetime of listening to the next episode of Daily Wire plus! You'll get a signed copy of his book, which will be delivered to you in the podcast, plus all other prizes throughout the course of the show, plus you'll get an ad-free version of the podcast and access to the show. that's all that's coming in the future, plus shipping and shipping, plus some other goodies, plus an additional shipping and goodies, too. You won't have to pay for the book and an ad on the book is available on the day of the final day, plus I'll be notified when it's available.


Transcript

00:00:00.960 Hey everyone, real quick before you skip, I want to talk to you about something serious and important.
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00:00:51.060 Welcome to the Jordan B. Peterson podcast.
00:00:59.520 To support these podcasts, you can donate to Dr. Peterson's Patreon account, the link to which can be found in the description.
00:01:08.200 Dr. Peterson's self-development programs, Self-Authoring, can be found at self-authoring.com.
00:01:14.100 I'm here talking today with Dr. Martin Daly.
00:01:19.360 Dr. Daly is a professor of psychology at McMaster University in Hamilton, Ontario, and author of many influential papers on evolutionary psychology.
00:01:28.140 His current research topics include an evolutionary perspective on risk-taking and interpersonal violence, especially male-male conflict.
00:01:36.660 He and his wife, the late Margo Wilson, were the former editors-in-chief of the journal Evolution and Human Behavior, and former presidents of the Human Behavior and Evolution Society.
00:01:50.460 He was named a Fellow of the Royal Society of Canada in 1998.
00:01:55.660 Daly is one of the main researchers of the Cinderella effect, and has been interviewed many times in the press about it.
00:02:01.880 So, I'm very pleased to be talking with Dr. Daly this morning.
00:02:06.120 It seems to me that he's one of Canada's most outstanding psychologists, and perhaps you could say that about psychologists in the world.
00:02:14.360 And he's done some incredibly interesting research on the relationship between inequality and male violence, and inequality and other topics, too.
00:02:24.540 So, welcome, Dr. Daly.
00:02:26.940 Thank you, Jordan. It's nice to be talking to you.
00:02:29.760 Well, I'm looking forward to our conversation a lot.
00:02:32.460 So, you just wrote a book, which I'm going to show people, called Killing the Competition.
00:02:39.420 And I just read it. It was very interesting.
00:02:42.880 So, I thought maybe I could get you to start by talking a little bit about the book, and also how you...
00:02:48.760 Tell us the story. That would be a good thing to do.
00:02:51.980 Well, the general issue that is addressed in the book is the relationship between economic inequality, which is usually indexed as income inequality, and homicide rates.
00:03:06.280 And it's been known for a long time by sociologists that income inequality is the single best predictor they've got of homicide rates across countries, across states within the U.S., across cities within the U.S., and some other kinds of jurisdictional comparisons.
00:03:23.100 And there's been controversy about why that is, and whether inequality itself is truly the problem, or whether it's just a correlate of something else.
00:03:34.660 And in this book, I try to make the case that, no, inequality really is the problem, and some of the arguments that have been advanced for suggesting that it's a mere correlate of violence, rather than in some way causal to violence, are wrong.
00:03:46.300 So, can you tell us a little bit about how you calculate inequality, and what the measure is?
00:03:53.080 Yeah. Income inequality, there's a number of different measures that are used by economists, and I'm just borrowing the dominant ones from economists.
00:04:01.640 The number one one is something called the GINI Index, G-I-N-I.
00:04:05.360 I used to assume that that was some kind of acronym, but actually it was the name of an Italian economist.
00:04:11.020 And it's a measure that is, ranges from zero to one.
00:04:16.520 It would be zero if everybody had exactly the same income, or exactly the same wealth, if you're doing wealth inequality.
00:04:23.980 And it would approach one as income or wealth was concentrated more and more in the hands of few and then a single individual.
00:04:32.600 And in principle, it would go to one in the extreme if all wealth were held by Bill Gates and none of the rest of us had anything.
00:04:41.020 And now you analyze the GINI coefficient at different levels of jurisdiction.
00:04:47.760 So, I noted in your work that you've looked at countries and states within countries, and I think that's particularly true in the U.S.
00:04:56.740 So, tell us a little bit about what you found.
00:04:59.200 Yeah, well, within the U.S., and again, this has been known by sociologists for some time, within the U.S. and cross-nationally, the GINI coefficient is a very good predictor of homicide.
00:05:12.680 The correlation tends to be on the order of 0.7 in many studies, which means that the variance in either measure, 50% of it could be accounted for by the variability in the other measure, what I'm saying, between homicide and income inequality.
00:05:28.840 And actually, it even works on the neighborhood level.
00:05:32.180 Well, my late wife, Margo, and I published some analyses in Chicago that showed that income inequality was a very strong predictor of homicide rates across neighborhoods within Chicago.
00:05:41.520 Tell us a little bit about what you did in Chicago, because that research is extremely interesting, and also when you did it.
00:05:47.320 Let's see, we did our work in Chicago in the early 90s, and at that time, Chicago had a very high homicide rate, not the worst in the United States, but one of the worst in the United States, and in fact had more homicides every year than the whole of Canada, which makes it a substantial enough phenomenon that you can sort of look for causal factors or correlates without a lot of stochastic noise.
00:06:14.020 In Chicago, Chicago's divided up into some 77, I believe, neighborhoods by, there's a longstanding tradition of urban sociology in Chicago, and there's these sort of well-recognized 77 neighborhoods.
00:06:28.600 And anyway, for these neighborhoods, we were able to amass a variety of neighborhood-specific information, including on income distributions, on homicides, and so forth, working with the Chicago police, who were collaborators in some of this work.
00:06:44.060 And Margot went to the Illinois Department of Health to try and get information on other death rates and birth rates and demographic structure of each of the neighborhoods.
00:06:58.820 And she wanted to compute the local life expectancy, because the idea that she had was that local life expectancy would affect the extent to which people were willing to sort of escalate dangerously in competitive situations.
00:07:13.620 And that was our construal of what most homicides in Chicago were about, were guys killing each other when dissed in bars, circumstances in which there's some sort of competition and it gets dangerous.
00:07:27.440 And our basic idea there and elsewhere has been that a lot of the variability in homicide rates, the most volatile component of homicide rates, has to do with this male-male competition of where and when does it get dangerous and where and when does it sort of dampen down.
00:07:45.160 And for Chicago, anyway, the Illinois Department of Health had never, nobody had ever computed neighborhood-specific life expectancy, but the data were available to do it, age-specific mortality and so on was available to do it.
00:08:01.080 And so we computed age-specific life expectancy, income inequality, and many other variables that criminologists have considered relevant in past studies, racial heterogeneity and blah, blah, blah, and tried to see what were your best predictors of homicide.
00:08:18.340 And in that particular study, everywhere else we've worked, we've mostly found income inequality to be number one.
00:08:25.360 In that particular study, income inequality was a very good predictor, but the best predictor was male life expectancy at birth or at age 15.
00:08:36.320 And in order to compute, of course, you say homicide rates, homicide reduces male life expectancy.
00:08:41.480 So you have to remove homicide statistically as a cause of death and say life expectancy, net of the impact of homicide, that was our best predictor of homicide rates.
00:08:51.180 So life expectancy is very variable in the city of Chicago, and I assume in other U.S. cities.
00:08:56.900 I mean, in the worst neighborhoods, male life expectancy at birth was down in the 50s, as bad as in the worst countries in the world.
00:09:03.540 So in the best neighborhoods, male life expectancy was up in the, I think it was over 80, or in the high 70s in any case, corresponding to what you might expect in Scandinavia, or the places with the best life expectancy in the world.
00:09:17.140 So it's a huge range.
00:09:18.460 That was our best predictor.
00:09:20.080 Then if you try and do a multivariate analysis where you look for, well, what else predicts some residual variability?
00:09:26.600 And there wasn't much residual variability.
00:09:28.580 The second best, indeed the only secondary predictor that seemed to be statistically significant, was income inequality across the neighborhoods.
00:09:36.360 That was the thrust of our study in Chicago.
00:09:40.140 And I'd love to see more work on life expectancy as a predictor of violence.
00:09:45.160 Of the Université de Montréal, criminologist Mark Wimain tried to do the same thing in Montreal.
00:09:50.920 But he found that in Montreal, the difference in life expectancy for men between the worst and the best neighborhoods was only six years, whereas in Chicago it was 24 years, I think.
00:10:02.340 So what do you think accounted for the vast difference in life expectancy between Chicago and Montreal?
00:10:07.520 And was life expectancy itself associated with income inequality?
00:10:12.480 Oh, yes.
00:10:13.360 I mean, that's part of the problem, of course, in all this kind of research here.
00:10:16.560 It's not experimental research.
00:10:18.020 You don't control independent variables, and everything of potential interest is correlated with everything else.
00:10:23.640 So, you know, income inequality alone accounts for more than half the variance in homicide rates across Chicago neighborhoods.
00:10:30.820 So does life expectancy alone.
00:10:33.640 So does percent below the poverty line alone.
00:10:38.020 You know, but these things are all correlated with each other.
00:10:40.520 And so trying to tease apart what's most important is tricky.
00:10:44.100 Well, so the low life expectancy in Chicago neighborhoods is not due to violence.
00:10:49.580 It's due to it's it's due overwhelmingly to differential disease in Chicago.
00:10:56.320 You know, privatization of medicine in the U.S. was so extreme.
00:11:00.060 At the time we were doing this research, emergency rooms in the worst neighborhoods in Chicago had closed down because they got bankrupt.
00:11:07.260 They didn't have enough money to remain open.
00:11:09.940 And therefore, if you got stabbed or shot in a bad neighborhood in Chicago, you had to be transported somewhere else to try and keep you alive because there was, you know, the hospitals had shut their emergency rooms or had shut down completely.
00:11:23.400 So there's all sorts of factors that contribute to to to differential death rates.
00:11:29.060 But, you know, kids in the worst neighborhood are exposed to high levels of lead.
00:11:33.200 There's some evidence that lead exposure in childhood is a big predictor of variability of life expectancy.
00:11:40.540 All kinds of internal diseases.
00:11:42.620 They were more susceptible to the effects of bad nutrition.
00:11:46.180 They were more susceptible to.
00:11:47.480 So if you if you divide causes of death into so-called external causes, which basically means homicide, suicides and accidents and internal causes, which is more or less synonymous with what we ordinarily think of as disease, internal causes were still the biggest source of differential mortality across neighborhoods.
00:12:07.720 So you could make by the sounds of it, you could make a reasonable case that the social safety net in Canada is flattening out the bottom of the of the income distribution, especially the provision of health care.
00:12:19.880 And, you know, I also was informed a while back that the rate of entrepreneurship in Canada is actually higher than than in the U.S.
00:12:29.860 And part of the reason for that is that because health care is provided, people can take a risk of walking away from their jobs without putting their family completely at risk.
00:12:38.380 And so one of the perverse effects of socialized medicine is that it elevates the rates of entrepreneurship.
00:12:44.240 So I also wanted to mention, you know, your your work was absolutely striking to me because of the effect sizes.
00:12:50.260 Now, for people who don't know about how to compare effect sizes, I should point out that you never see a correlation of 0.7 between any two variables in the social sciences.
00:13:03.100 So this guy named Hemphill, who did an empirical analysis of effect size comparisons about four or five years ago, might be longer than that now.
00:13:11.540 And he concluded that 95 percent of social science studies had effect size of 0.5 or less.
00:13:18.380 And so to see a correlation of 0.7 is absolutely overwhelming when you also take into account that measurement error is decreasing the potency of the relationship to some degree.
00:13:28.880 And when you take into account that that 0.5 represents studies that were published because they got something.
00:13:37.680 Yes, exactly. Exactly. So 0.7 is absolutely overwhelming.
00:13:41.880 I've never seen effect sizes that big between two variables of interest in any other domain that I can recall.
00:13:47.740 And then the other thing that that's worth pointing out, and we can talk about this a little bit, too, is the other thing that's so radical about your research is that it and this this what emerges out of the out of the manner in which the Gini coefficient is is calculated.
00:14:02.920 Because it's only a measure of relative poverty and it's the predictor, you also generated data indicating that places where everyone was relatively poor or, say, relatively working class, like North Dakota and some of the Canadian provinces, had very low homicide rates and also places where everyone was rich.
00:14:24.120 Right. So to reiterate, what you're seeing is that what's driving male homicide is the existence and correct me if I'm wrong, the existence of a steep economic dominance hierarchy that makes it difficult for the young men to obtain status through what you might describe as conventional and socially productive means.
00:14:45.020 And so instead they turn to violence as a means of establishing status and most of that's within race and between young men jockeying for position. Is that all correct?
00:14:55.520 Yeah, I think that's a pretty fair characterization. It's worth stressing, yes, that income inequality is in principle and in practice dissociable from just average income or percent below the poverty line or other measures of so-called absolute deprivation.
00:15:13.660 They're often correlated. You know, income inequality across a certain set of jurisdictions may be fairly strongly correlated with the percent below the poverty line, for example. It'd be surprising if it was not usually correlated. But they're not necessarily, as you said.
00:15:30.760 Yeah, so you demonstrated, or you were one of the first people to demonstrate, were you the first, in fact, maybe, that it wasn't poverty that was causing this kind of crime. It was relative poverty. And that changes the interpretation of the situation absolutely dramatically. So tell us a little bit about why you think the males are competing in this deadly manner. What's driving that behavior?
00:15:52.280 Well, it's very interesting. I think men are sensitive to, are interested in relative position, status, maintaining face in competitive milieus. And in a sense, all milieus are a bit competitive. And the willingness to use violets partly can be thought of as kind of a disdain for the future, or I want my now.
00:16:21.560 I'm willing to do something that threatens my life, like escalate in competition or not back down or not walk away from an insult. Because I'm thinking very short term, the rewards for being passive.
00:16:40.600 You know, if you're a nice, prosperous university student of age 20, you have good life prospects, your chances for eventually becoming well paid, maybe people will laugh at this are still reasonably good, your chances for eventually marrying are still reasonably good.
00:16:59.880 But if you're the same age kind of guy in an urban ghetto with a 48% unemployment rate or something like that, then you have very much more.
00:17:11.080 And with uncertainty about the stability of whatever income you do get with the future unknown, then you're more willing to take a risk now, in the pursuit of status now, in the pursuit of sexual opportunity now, in the pursuit of monetary rewards, legal or illicit now.
00:17:31.480 So, and also the maintenance of face, like social reputation is the one resource you've got.
00:17:39.200 If you've got other resources, you can walk away from threats or disrespect and reap your rewards later.
00:17:50.320 If social status is all you've got, then it becomes an important thing to defend.
00:17:56.100 So, I read some research a while back that looked at the relationship between socioeconomic status among men and number of sexual partners and also socioeconomic status among women and number of sexual partners.
00:18:09.540 And that's another domain where you see these kinds of whopping correlations.
00:18:13.300 So, the correlation between socioeconomic status for men and number of available sexual partners is about 0.6 or 0.7, whereas for women, it's negative 0.12.
00:18:23.920 And so, do you think that it's reasonable to assume that either at the phylogenetic level or the ontogenetic level, either evolutionarily speaking or even as a consequence of rational calculation, that part of the reason that men, or perhaps the main reason that men are engaging in these status competitions is because of female hypergamy?
00:18:46.480 Is that a reasonable hypothesis?
00:18:49.020 Hypergamy, and as you say, simple access, I mean, there is, the association that you mentioned is presumably a very longstanding one.
00:19:00.840 That is to say that men with status and resources have had access to partners for sure and probably multiple partners simultaneously or serially to a degree that men of lower status have not.
00:19:15.540 There's high variance in eventual reproduction among males in mammals generally, and although the situation is less extreme in people than in many other mammals, the same is true for people.
00:19:28.360 I mean, when you say they have high variance, compared to what?
00:19:32.040 Well, high variance compared to women, for example.
00:19:34.900 The variability in eventual reproductive success is lower for women than for men or has been.
00:19:39.720 Now, you say sexual access to women, and I think that's exactly the right level to be looking at in contemporary societies, but the reason why that matters is because ancestrally that translated into differential reproduction.
00:19:52.920 In a modern environment in which, you know, contraceptive technology is available, especially to women, that correlation may be broken down, but the motives to seek sexual opportunity remain relevant.
00:20:08.980 So, one of the things that I wanted to talk to you about, too, is that you made a comment in your book about Adrian Rains, and Adrian Rains has written a book recently about the biological predictors of criminality.
00:20:22.540 And you make a strong case that, in some sense, the turning to violence that's characteristic of men in uncertain situations is rational, because it drives, it actually legitimately drives status increase, and that produces a variety of positive effects.
00:20:42.180 So, in some sense, it's a rational response to a radically uncertain environment where competition is high.
00:20:47.580 Now, Rains would say...
00:20:49.580 Welcome to the Jordan B. Peterson podcast.
00:20:52.540 To support these podcasts, you can donate to Dr. Peterson's Patreon account, the link to which can be found in the description.
00:21:03.340 Dr. Peterson's self-development programs, Self-Authoring, can be found at self-authoring.com.
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00:23:56.740 I'm here talking today with Dr. Martin Daly.
00:24:01.960 Dr. Daly is a professor of psychology at McMaster University in Hamilton, Ontario,
00:24:06.540 and author of many influential papers on evolutionary psychology.
00:24:10.740 His current research topics include an evolutionary perspective on risk-taking and interpersonal violence,
00:24:17.240 especially male-male conflict.
00:24:19.260 He and his wife, the late Margo Wilson, were the former editors-in-chief of the journal Evolution and Human Behaviour,
00:24:28.940 and former presidents of the Human Behaviour and Evolution Society.
00:24:33.060 He was named a Fellow of the Royal Society of Canada in 1998.
00:24:37.240 Daly is one of the main researchers of the Cinderella Effect, and has been interviewed many times in the press about it.
00:24:45.040 So, I'm very pleased to be talking with Dr. Daly this morning.
00:24:48.620 It seems to me that he's one of Canada's most outstanding psychologists,
00:24:52.840 and perhaps you could say that about psychologists in the world.
00:24:56.300 And he's done some incredibly interesting research on the relationship between inequality and male violence and inequality and other topics, too.
00:25:07.140 So, welcome, Dr. Daly.
00:25:09.440 Thank you, Jordan.
00:25:10.740 It's nice to be talking to you.
00:25:12.640 Well, I'm looking forward to our conversation a lot.
00:25:14.980 So, you just wrote a book, which I'm going to show people, called Killing the Competition, and I just read it.
00:25:23.820 It was very interesting.
00:25:25.480 So, I thought maybe I could get you to start by talking a little bit about the book and also how you...
00:25:31.280 Tell us the story.
00:25:32.700 That would be a good thing to do.
00:25:35.600 Well, the general issue that is addressed in the book is the relationship between economic inequality,
00:25:43.380 which is usually indexed as income inequality, and homicide rates.
00:25:49.500 And it's been known for a long time by sociologists that income inequality is the single best predictor they've got of homicide rates across countries,
00:25:59.440 across states within the U.S., across cities within the U.S., and some other kinds of jurisdictional comparisons.
00:26:05.700 And there's been controversy about why that is and whether inequality itself is truly the problem or whether it's just a correlate of something else.
00:26:17.380 And in this book, I try to make the case that, no, inequality really is the problem.
00:26:21.080 And some of the arguments that have been advanced for suggesting that it's a mere correlate of violence rather than in some way causal to violence are wrong.
00:26:28.920 So, can you tell us a little bit about how you calculate inequality and what the measure is?
00:26:35.900 Yeah.
00:26:36.260 Income inequality, there's a number of different measures that are used by economists.
00:26:40.960 And I'm just borrowing the dominant ones from economists.
00:26:44.280 The number one one is something called the GINI Index, G-I-N-I.
00:26:47.960 I used to assume that that was some kind of acronym, but actually it was the name of an Italian economist.
00:26:53.620 And it's a measure that ranges from zero to one.
00:26:59.140 It would be zero if everybody had exactly the same income or exactly the same wealth if you're doing wealth inequality.
00:27:06.520 And it would approach one as income or wealth was concentrated more and more in the hands of a few and then a single individual.
00:27:15.220 And in principle, it would go to one in the extreme if all wealth were held by Bill Gates and none of the rest of us had anything.
00:27:23.620 And now you analyze the GINI coefficient at different levels of jurisdiction.
00:27:30.360 So, I noted in your work that you've looked at countries and states within countries.
00:27:36.480 And I think that's particularly true in the U.S.
00:27:39.340 So, tell us a little bit about what you found.
00:27:41.820 Yeah, well, within the U.S., and again, this has been known by sociologists for some time, within the U.S. and cross-nationally, the GINI coefficient is a very good predictor of homicide.
00:27:55.300 The correlation tends to be on the order of 0.7 in many studies, which means that the variance in either measure, 50 percent of it, could be accounted for by the variability in the other measure, what I'm saying, between homicide and income inequality.
00:28:11.460 And actually, it even works on the neighborhood level.
00:28:14.800 My late wife, Margo, and I published some analyses in Chicago that showed that income inequality was a very strong predictor of homicide rates across neighborhoods within Chicago.
00:28:24.140 Tell us a little bit about what you did in Chicago, because that research is extremely interesting, and also when you did it.
00:28:29.960 Let's see, we did our work in Chicago in the early 90s, and at that time, Chicago had a very high homicide rate, not the worst in the United States, but one of the worst in the United States, and in fact had more homicides every year than the whole of Canada, which makes it a substantial enough phenomenon that you can sort of look for causal factors or correlates without a lot of stochastic noise.
00:28:56.640 In Chicago, Chicago's divided up into some 77, I believe, neighborhoods by, there's a longstanding tradition of urban sociology in Chicago, and there's these sort of well-recognized 77 neighborhoods.
00:29:11.200 And anyway, for these neighborhoods, we were able to amass a variety of neighborhood-specific information, including on income distributions, on homicides, and so forth, working with the Chicago police, who were collaborators in some of this work.
00:29:26.640 And Margot went to the Illinois Department of Health to try and get information on other death rates and birth rates and demographic structure of each of the neighborhoods.
00:29:41.340 And she wanted to compute the life expectancy, because the idea that she had was that local life expectancy would affect the extent to which people were willing to sort of escalate dangerously in competitive situations.
00:29:56.220 And that was our construal of what most homicides in Chicago were about, were guys killing each other when dissed in bars, circumstances in which there's some sort of competition and it gets dangerous.
00:30:10.060 And our basic idea there and elsewhere has been that a lot of the variability in homicide rates, the most volatile component of homicide rates, has to do with this male-male competition of where and when does it get dangerous and where and when does it sort of dampen down.
00:30:27.760 And for Chicago, anyway, the Illinois Department of Health had never, nobody had ever computed neighborhood-specific life expectancy, but the data were available to do it, age-specific mortality and so on was available to do it.
00:30:43.680 And so we computed age-specific life expectancy, income inequality, and many other variables that criminologists have considered relevant in past studies, racial heterogeneity and blah, blah, blah, and tried to see what were your best predictors of homicide.
00:31:00.960 And in that particular study, everywhere else we've worked, we've mostly found income inequality to be number one.
00:31:07.980 In that particular study, income inequality was a very good predictor, but the best predictor was male life expectancy at birth or at age 15.
00:31:18.940 And in order to compute, of course, you say homicide rates, homicide reduces male life expectancy.
00:31:24.100 So you have to remove homicide statistically as a cause of death and say life expectancy, net of the impact of homicide, that was our best predictor of homicide rates.
00:31:33.800 So life expectancy is very variable in the city of Chicago and I assume in other U.S. cities.
00:31:39.520 I mean, in the worst neighborhoods, male life expectancy at birth was down in the 50s, as bad as in the worst countries in the world.
00:31:46.140 So in the best neighborhoods, male life expectancy was up in the, I think it was over 80, or in the high 70s in any case, corresponding to what you might expect in Scandinavia or the places with the best life expectancy in the world.
00:31:59.760 So it's a huge range.
00:32:01.080 That was our best predictor.
00:32:02.700 Then if you try and do a multivariate analysis where you look for, well, what else predicts some residual variability?
00:32:09.220 And there wasn't much residual variability.
00:32:11.180 The second best, indeed the only secondary predictor that seemed to be statistically significant, was income inequality across the neighborhoods.
00:32:18.980 That was the thrust of our study in Chicago.
00:32:22.740 And I'd love to see more work on life expectancy as a predictor of violence.
00:32:27.760 Of the Université de Montréal, criminologist Marc Ouimet tried to do the same thing in Montreal.
00:32:33.520 But he found that in Montreal, the difference in life expectancy for men between the worst and the best neighborhoods was only six years, whereas in Chicago it was 24 years, I think.
00:32:44.940 So what do you think accounted for the vast difference in life expectancy between Chicago and Montreal?
00:32:50.140 And was life expectancy itself associated with income inequality?
00:32:55.100 Oh, yes.
00:32:55.960 I mean, that's part of the problem, of course, in all this kind of research here.
00:32:59.160 It's not experimental research.
00:33:00.620 You don't control independent variables, and everything of potential interest is correlated with everything else.
00:33:06.260 So, you know, income inequality alone accounts for more than half the variance in homicide rates across Chicago neighborhoods.
00:33:13.540 So does life expectancy alone.
00:33:16.660 So does percent below the poverty line alone.
00:33:20.660 You know, but these things are all correlated with each other.
00:33:23.140 And so trying to tease apart what's most important is tricky.
00:33:26.720 So the low life expectancy in Chicago neighborhoods is not due to violence.
00:33:32.260 It's due to it's it's due overwhelmingly to differential disease in Chicago.
00:33:38.940 You know, privatization of medicine in the U.S. was so extreme.
00:33:42.680 At the time we were doing this research, emergency rooms in the worst neighborhoods in Chicago had closed down because they got bankrupt.
00:33:49.880 They didn't have enough money to remain open.
00:33:52.560 And therefore, if you got stabbed or shot in a bad neighborhood in Chicago, you had to be transported somewhere else to try and keep you alive because there was, you know, the hospitals had shut their emergency rooms or had shut down completely.
00:34:06.240 So there's all sorts of factors that contribute to to differential death rates.
00:34:11.520 But, you know, kids in the worst neighborhood are exposed to high levels of lead.
00:34:15.840 There's some evidence that lead exposure in childhoods is a big predictor of variability of life expectancy.
00:34:23.200 All kinds of internal diseases they were more susceptible to the effects of bad nutrition they were more susceptible to.
00:34:30.100 So if you if you divide causes of death into so-called external causes, which basically means homicides, suicides and accidents and internal causes, which is more or less synonymous with what we ordinarily think of as disease, internal causes were still the biggest source of differential mortality across neighborhoods.
00:34:50.440 So you could make, by the sounds of it, you could make a reasonable case that the social safety net in Canada is flattening out the bottom of the of the income distribution, especially the provision of health care.
00:35:02.480 And, you know, I also was informed a while back that the rate of entrepreneurship in Canada is actually higher than than in the U.S.
00:35:12.460 And part of the reason for that is that because health care is provided, people can take a risk of walking away from their jobs without putting their family completely at risk.
00:35:21.000 And so one of the perverse effects of socialized medicine is that it elevates the rates of entrepreneurship.
00:35:26.840 So I also wanted to mention, you know, your your work was absolutely striking to me because of the effect sizes.
00:35:32.880 Now, for people who don't know about how to compare effect sizes, I should point out that you never see a correlation of 0.7 between any two variables in the social sciences.
00:35:45.700 So this guy named Hemphill, who did an empirical analysis of effect size comparisons about four or five years ago, might be longer than that now.
00:35:54.160 And he concluded that 95 percent of social science studies had effect size of 0.5 or less.
00:36:00.980 And so to see a correlation of 0.7 is absolutely overwhelming when you also take into account that measurement error is decreasing the potency of the relationship to some degree.
00:36:11.500 And when you take into account that that 0.5 represents studies that were published because they got something.
00:36:20.280 Yes, exactly. Exactly. So 0.7 is absolutely overwhelming.
00:36:24.340 I've never seen effect sizes that big between two variables of interest in any other domain that I can recall.
00:36:30.380 And then the other thing that that's worth pointing out, and we can talk about this a little bit, too, is the other thing that's so radical about your research is that it and this this what emerges out of the out of the manner in which the Gini coefficient is is calculated.
00:36:45.540 Because it's only a measure of relative poverty and it's the predictor, you also generated data indicating that places where everyone was relatively poor or, say, relatively working class, like North Dakota and some of the Canadian provinces, had very low homicide rates and also places where everyone was rich.
00:37:06.740 Right. So to reiterate, what you're seeing is that what's driving male homicide is the existence and correct me if I'm wrong, the existence of a steep economic dominance hierarchy that makes it difficult for the young men to obtain status through what you might describe as conventional and socially productive means.
00:37:27.620 And so instead they turn to violence as a means of establishing status, and most of that's within race and between young men jockeying for position. Is that all correct?
00:37:38.100 Yeah, I think that's a pretty fair characterization. It's worth stressing, yes, that income inequality is in principle and in practice dissociable from just average income or percent below the poverty line or other measures of so-called absolute deprivation.
00:37:56.280 They're often correlated. You know, income inequality across a certain set of jurisdictions may be fairly strongly correlated with the percent below the poverty line, for example. It would be surprising if it was not usually correlated. But they're not necessarily, as you said.
00:38:13.360 Yeah, so you demonstrated, or you were one of the first people to demonstrate, were you the first, in fact, maybe, that it wasn't poverty that was causing this kind of crime. It was relative poverty. And that changes the interpretation of the situation absolutely dramatically. So tell us a little bit about why you think the males are competing in this deadly manner. What's driving that behavior?
00:38:34.900 Well, it's very interesting. I think men are sensitive to, are interested in relative position, status, maintaining face in competitive milieus. And in a sense, all milieus are a bit competitive. And the willingness to use violets partly can be thought of as kind of a disdain for the future, or I want mine now.
00:39:04.180 Well, I'm willing to do something that threatens my life, like escalate in competition or not back down or not walk away from an insult. Because I'm thinking very short term, the rewards for being passive.
00:39:23.300 You know, if you're, you know, if you're, if you're a nice, prosperous university student of age 20, you have good life prospects, your chances for eventually becoming well paid, maybe people will laugh at this are still reasonably good, your chances for eventually marrying are still reasonably good.
00:39:42.480 But if you're the same age kind of guy in an urban ghetto with a 48% unemployment rate or something like that, then you have very much more.
00:39:53.680 And with uncertainty about the stability of whatever income you do get with with the future unknown, then you're more willing to take a risk now, in the pursuit of status now in the pursuit of sexual opportunity now in the pursuit of monetary rewards, legal or illicit now.
00:40:13.680 And also the maintenance of face like social reputation is the one resource you've got.
00:40:20.680 If you've got other resources, you can walk away from threats or disrespect and reap your rewards later.
00:40:31.680 If social status is all you've got, then it becomes an important thing to defend.
00:40:38.680 So I read some research a while back that looked at the relationship between socioeconomic status among men and number of sexual partners and also socioeconomic status among women and number of sexual partners.
00:40:51.680 And that's another domain where you see these kinds of whopping correlations.
00:40:55.680 So the correlation between socioeconomic status for men and number of available sexual partners is about 0.6 or 0.7, whereas for women it's negative 0.12.
00:41:06.680 And so do you think that it's reasonable to assume that either at the phylogenetic level or the ontogenetic level, either evolutionarily speaking or even as a consequence of rational calculation, that part of the reason that men or perhaps the main reason that men are engaging in these status competitions is because of female hypergamy?
00:41:29.080 Is that a reasonable hypothesis?
00:41:31.620 Hypergamy and, as you say, simple access, I mean, there is the association that you mentioned is presumably a very longstanding one.
00:41:43.620 That is to say that men with status and resources have had access to partners for sure and probably multiple partners simultaneously or serially to a degree that men of lower status have not.
00:41:58.620 High variance in eventual reproduction among males in mammals generally, and although the situation is less extreme in people than in many other mammals, the same is true for people.
00:42:10.980 I mean, when you say they have high variance compared to what?
00:42:14.660 Well, high variance compared to women, for example.
00:42:17.520 The variability in eventual reproductive success is lower for women than for men or husband.
00:42:22.240 Now, you say sexual access to women, and I think that's exactly the right level to be looking at in contemporary societies.
00:42:28.920 But the reason why that matters is because ancestrally that translated into differential reproduction.
00:42:35.860 In a moderate environment in which, you know, contraceptive technology is available, especially to women, then that correlation may be broken down.
00:42:45.000 But the motives to seek sexual opportunity remain relevant.
00:42:52.960 So one of the things that I wanted to talk to you about, too, is that you made a comment in your book about Adrian Rains.
00:43:00.600 And Adrian Rains has written a book recently about the biological predictors of criminality.
00:43:05.140 And you make a strong case that, in some sense, the turning to violence that's characteristic of men in uncertain situations is rational because it actually legitimately drives status increase, and that produces a variety of positive effects.
00:43:24.920 So in some sense, it's a rational response to a radically uncertain environment where competition is high.
00:43:30.200 Now, Rains would say, and the biological type researchers, they look more at the individual level and conclude that it's individuals who have various forms of prefrontal damage or characterological issues associated with antisocial personality disorder that are more likely to engage in violent acts.
00:43:50.260 And you can track that, I mean, Richard Tromley has done some of this work in Quebec, you can track the emergence of aggression at an individual level all the way back to children at two years of age, because it turns out that children who are two are the most violent children, particularly the boys, but mostly a subset of boys who kick, fight, hit, and bite, and steal at two.
00:44:13.840 Most of them, most of them, most of whom are socialized by the age of four, but a subset of whom are not socialized, and then they become, they're more likely to become the lifetime offenders.
00:44:24.100 And so what I'm wondering is maybe you can reconcile the difference between the two research streams like this.
00:44:30.840 So imagine that as the economic gradient increases and the dominance hierarchy becomes steeper and steeper, the men who are prone to be violent, like it's the disagreeable men that start to be violent first.
00:44:45.280 Maybe the ones that have an impulse control problem or that are characterologically, like the violent two-year-olds, that are characterologically predisposed to be violent.
00:44:54.220 It seems to me that those would be the ones that, you know, as the pressure increases, those men who are more prone to violence for other reasons are going to be the people who react with violence first.
00:45:04.820 Do you think that's a reasonable hypothesis?
00:45:07.320 Yeah, no, I think that's a very reasonable hypothesis.
00:45:09.220 And I mean, my objection to Adrian Rayne's book was that I think he vast, you know, there's definitely evidence that many kinds of violent criminal offenders have got something wrong with their brains.
00:45:23.380 Adrian Rayne wants to extrapolate to the conclusion that violent criminals, and indeed criminals in general, have got something broken about their brains.
00:45:31.620 And it's like criminality is pathological.
00:45:34.900 Well, criminality is not pathological.
00:45:37.220 People steal for cost-benefit-related reasons.
00:45:44.260 The crime is a, if you like, God help us, social construction in the sense that certain behaviors are criminalized by a larger social group in order to deter them, because self-interested individuals would otherwise pursue them.
00:46:03.120 You know, how do you make people stop exploiting others, stealing from others, by criminalizing those activities and imposing penalties?
00:46:12.680 And, you know, there's a rational choice stream of theorizing within criminology that other people like Adrian Rayne just dismiss out of head.
00:46:22.140 No, no, no, no, criminal offenses are pathological.
00:46:25.780 Yeah.
00:46:26.120 And I think that's silly.
00:46:27.560 Well, it seems unnecessary, you know, because it isn't that difficult to make a marriage between the two issues.
00:46:33.340 Like, one of the best predictors, you know, I do research on individual differences in personality, and the best personality predictor of incarceration is low agreeableness.
00:46:42.760 That's one of the dimensions on which men and women differ the most.
00:46:46.440 And so, as you become more disagreeable, you become more self-oriented, I would say, and that can push past the point where you're so self-interested that you're willing to prey on others.
00:46:58.760 And so, those are the guys that, as well as the guys who lack impulse control, those are the guys, the first guys to turn to violence, let's say, when the socioeconomic conditions become sufficiently unstable so that a conscientious approach is not tenable.
00:47:13.920 Yeah, and the marriage between that kind of thinking and thinking about the relevance of inequality is that there's guys at the top who are like the violent people you described.
00:47:26.580 There's people doing very well who are very happy to exploit others, but the costs of individual violent action are high enough, and the opportunities to exploit other people through financial means, through your lawyers, through whatever tactics are available to, you know, well-heeled bullies are safe enough that they opt to behave in those directions.
00:47:51.080 Right, because their long-term future is relatively stable, and so that long-term planning and regulation of behavior actually play an important economic role.
00:48:01.940 And, you know, and then in the case of somebody like Donald Trump, I mean, he looks like somebody who's suffering a little bit of an impulse control problem, especially sort of during the night when he wakes up and his Twitter account is too close at hand.
00:48:16.580 But he's rich enough to bully people in other ways that actually hands-on violence, although, come to think of it, the famous remark that he made during the campaign about women suggests perhaps that, you know, depends on your definition of hands-on violence, I guess that qualifies.
00:48:36.260 Okay, so there's a very large body of research that indicates that alcohol is a major contributor to criminality, too, especially with regards to men, and so about 50% of people who are murdered have a decent blood alcohol level, and about 50% of murderers, and I think that's partly, that stat is equal, equalizes, I think, because much violence among men is exactly the sort that you described, where it's a status dispute, and it's more or less a toss-up who's going to come out as a winner.
00:49:05.900 But then, I guess, what's happening with alcohol, perhaps, is that because it's a disinhibitor, because it reduces anxiety, and anxiety is one of the suppressors of aggressive behavior, that men who are already on the edge, let's say, because of the unstable environment and the steep dominance hierarchy are also more likely to lose control when they're drinking.
00:49:25.040 Right. And maybe that's also fueled, this is something, too, that I'm curious about. I mean, you can think about it as a rational calculation, but I'm also curious about the degree to which it's fueled by emergent negative emotions.
00:49:37.460 So, it's easy for people who are in steep dominance hierarchies to regard the system as unfair and to become resentful and angry about it, as perhaps they should be.
00:49:49.420 I'm not suggesting that that's necessarily an irrational response, but it seems that if the anger is simmering underneath the surface, that it's waiting, in some sense, for an opportunity to break free, and alcohol in a bar or at home perhaps provides that root.
00:50:07.460 Yeah. What you say makes eminent sense to me. I mean, it's probably worth injecting a bit of a caution about the word rationality, generally.
00:50:15.980 When one talks about rationality in crime, but perhaps especially in confrontational violence, the point is not that the person is making good and carefully weighed decisions.
00:50:28.100 I mean, I think, you know, emotions are the handmaiden of what I would call ecological rationality.
00:50:33.080 They help you know how you should feel about certain things and how you should react to them.
00:50:39.260 And the rationality claim is more a claim of this person gets riled up, resents X, and he should.
00:50:48.960 There's good reason to get riled up and resent X.
00:50:51.680 But the fact that alcohol perhaps disinhibits so that, you know, the truly rational balance between inhibitory and aggressive emotions is altered.
00:51:04.540 The idea that alcohol interferes with cognitive processes to the point that people start making stupid decisions when they're drunk, decide to get behind the wheel or whatever.
00:51:17.220 I think this plays very heavily into the reason why so many homicides tend to happen in contexts like to drunks insulting each other or, you know, people who are somewhat under the influence of alcohol insulting each other rather than, you know, if you have more mental wherewithal at the moment,
00:51:39.580 you probably have better capacities to defuse dangerous situations through, you know, ways that don't entail losing face by being articulate.
00:51:55.940 Great, exactly.
00:51:57.540 That's right.
00:51:57.980 You have other tools at your disposal rather than immediate recourse to your fists.
00:52:02.520 Thank you.
00:52:03.060 Yes.
00:52:03.340 So, if I remember correctly, too, in your Chicago studies, this is one of the things that I found particularly fascinating, was you tracked the consequences of killing someone in Chicago.
00:52:15.760 And the consequences were something of the following sort.
00:52:18.980 Well, first of all, you were likely to be charged with something like second-degree murder.
00:52:23.140 It would be difficult for the police to find people to testify against you.
00:52:26.860 And if they did, generally what they would say is that it was a two-way altercation.
00:52:32.660 And so, in many cases, you could plead self-defense.
00:52:36.720 Often it didn't go before a jury because the perpetrator plea bargained it down to manslaughter.
00:52:42.980 The sentence was something on the order of a couple of years, and people were generally out of prison in 18 months with a substantial boost in their social capital
00:52:52.800 because now they were like dangerous sons of bitches not to be messed with, and that was quite clear.
00:52:58.280 And also, perhaps, also improved, so to speak, by their sojourn in prison.
00:53:03.120 Have I got that right?
00:53:05.440 Except for one detail.
00:53:07.020 Well, actually, in our Chicago studies, we didn't have as good follow-up information as what you're talking about.
00:53:12.840 This was an earlier piece of research in the city of Detroit that led to most of those findings.
00:53:18.920 But, yeah, exactly.
00:53:22.360 Hardly, it's interesting.
00:53:24.660 We had a single-year sample of cases in Detroit, and there were, I think, 590 homicides in Detroit in that one year, 1972,
00:53:34.400 at which time Detroit did have the highest homicide rate in the U.S.
00:53:37.280 because a large majority of these are mail-mail disputes of some sort, status disputes usually, but sometimes robberies.
00:53:47.420 And just as you said, witnesses are unlikely to come forward, and the prosecutors are stretched.
00:53:54.000 They don't have the resources that they would need to pursue every case.
00:53:59.060 And so many cases were dismissed, I mean, not even prosecuted, never mind plea bargain,
00:54:06.040 something like approximately half of all mail-mail macho dispute homicides in Detroit that were solved
00:54:11.520 were not prosecuted on the expectation that there was a plausible self-defense argument that might, you know, win with the jury.
00:54:19.640 Then of the half that were prosecuted, almost all of them, yeah, were plea bargained down to manslaughter,
00:54:25.780 and the majority of them got a conviction.
00:54:27.960 It's right, it's three years, 50% of time off for good behavior.
00:54:31.680 If you behave nicely, you go to Jackson State Prison in Michigan.
00:54:35.820 Eighteen months later, you're back out on the streets of Detroit.
00:54:38.920 And Margot, in particular, was very interested in the question of whether killing in these contexts
00:54:43.980 might even actually ultimately pay off for guys.
00:54:47.340 I tend to the view that actually killing is overstepping the bounds of utility.
00:54:55.940 That's reassuring.
00:54:56.940 That deadly threats are very self-interested and effectual, but that actually following through on them
00:55:05.100 is maybe, you know, the non-functional tip of the iceberg.
00:55:08.960 But I honestly don't know that that's true in these kind of cases for exactly this reason,
00:55:13.320 that guys get some social capital out of having done it.
00:55:17.220 Well, hypothetically, among the Yanomamo, the tribes in South America, I believe, or Central, I think it's South America.
00:55:25.900 South America, yeah.
00:55:26.600 Yeah, the more warlike men have a much higher reproduction rate, the ones who've killed more.
00:55:33.660 Now, I don't know, obviously, it isn't necessarily the case that that's directly translatable.
00:55:37.460 But there is some utility in being a successful warrior.
00:55:41.620 That's actually one of the reasons that I think that capitalism, so to speak, is underappreciated.
00:55:47.860 Because I'm speaking in a very specific sense, is that there are disagreeable and warlike men.
00:55:55.500 And some of them are very powerful in many ways, not only physically, but intellectually and characterologically and with great ambition.
00:56:05.760 And the thing about capitalism is that it enables them to wage war in a manner that's not deadly.
00:56:12.620 And to become successful that way and to channel their intense competitive energy into something that, well, I think is often for a social good.
00:56:24.740 Now, it depends on how disagreeable the person is and how selfish they are, of course.
00:56:28.680 But people like that also tend to get punished in their cooperative interactions with other people.
00:56:34.660 Yeah, I mean, I partly agree.
00:56:36.720 But I also feel that the often toward the social good is a bit hopeful.
00:56:40.500 I mean, to the degree that people are successful in a fairly unrestrained capitalist competition.
00:56:48.740 It's usually at the expense of large numbers of people at the bottom.
00:56:52.800 But it depends how unrestrained that capitalist competition is.
00:56:57.320 I was thinking of social good as in better than war.
00:57:01.380 Yeah, better than war for sure.
00:57:03.260 Better than war for sure.
00:57:04.320 And sometimes the way you succeed is by producing goods that actually make people's lives better.
00:57:11.600 No quarrel with that.
00:57:12.700 So now, I also wanted to ask you, in the last couple of chapters of your book, you turned to what I would regard as more political issues.
00:57:21.300 And so I'm very interested in inequality because we'll recapitulate for a minute.
00:57:28.800 So your work and the work of other people seems to indicate that as inequality increases and dominance hierarchies get steeper, not only do young men get more violent and so society becomes less stable, but there's also detrimental impacts on things like population health.
00:57:44.260 And that was documented quite nicely in the spirit level.
00:57:48.260 And so I'm going to address a couple of criticisms of the research, and then I want to ask you, I want to have a discussion about your more prescriptive views, if that's okay.
00:58:00.920 So the first issue, someone just emailed me this a while back when I was talking about inequality, and they said, well, what about places like China, where the rates of inequality are starting to skyrocket quite substantially,
00:58:13.700 and have been for, you know, several years, maybe several decades, yet the homicide rate doesn't seem to be budging much.
00:58:22.240 And so I thought, well, that was interesting.
00:58:24.640 Maybe there's something different about East Asian communities.
00:58:27.580 They tend to have very low crime rates to begin with, like places like Japan, for example, have very low crime rates.
00:58:33.920 And so I'm wondering if what you think about that, is that a reasonable criticism, and how would you address it?
00:58:39.220 Fair enough.
00:58:41.500 Well, I don't think we can characterize, you know, Orientals as less violent than Occidentals or anything like that.
00:58:50.480 I think, you know, history tells us otherwise, that there's been a lot of severe and dangerous violence in Japan in history and in China in history.
00:59:02.140 I don't know how good data we have on Chinese homicide rates, but what I've seen is that they have been going up a bit lately.
00:59:08.780 But still, the point that inequality has been skyrocketing.
00:59:12.700 I mean, partly, there's an interesting question about time lags and the effects on people.
00:59:18.760 You know, how soon is an increased inequality effect going to play out as nasty interpersonal behavior?
00:59:27.120 And, you know, people respond to inequality as a result of their lifetime experiences.
00:59:34.440 You know, you were talking about young kids, very young children, already being predictable in the extent to which they're willing to, you know, use violent tactics against other people.
00:59:45.180 And that, you know, assaying three- and four-year-olds could give you some surprisingly good prediction of how they'll behave as adults.
00:59:51.160 It's not inconceivable that the effects of inequality even are influencing people's development prenatally.
01:00:00.160 And so, you know, the uterine environments that they experience as a function of inequitable environments and the stresses and fraught social comparisons and so on that happen in those environments could be influencing them at all life stages.
01:00:12.760 So I don't think we have any strong basis for expecting rapid change in inequality to be accompanied in the short term by rapid change in violence.
01:00:22.860 That said, there, you know, it's certainly the case that there's other things that matter.
01:00:28.900 And government controls are one.
01:00:31.940 I think strong governments that monopolize the legitimate use of violence can keep a lid on violence for a long time.
01:00:43.440 I, you know, I would question whether they can keep a lid on it indefinitely, but they can keep a lid on it for a long time.
01:00:50.640 If you execute all charged murderers, I presume that that would keep the incidence of murder down, and not only because those people could be recidivists.
01:01:06.780 Right.
01:01:07.080 So there's an element potentially of authoritarian control.
01:01:10.860 Yeah, I think so.
01:01:12.280 And then the other element that I think is particularly interesting is the time lag argument.
01:01:16.280 I mean, you don't know over what period of time precisely inequality has its pernicious effects, and maybe it's not even the span of one lifetime.
01:01:27.080 Do you have any data on that that would help answer the question?
01:01:33.020 Well, I did make reference in my book, Killing the Competition, to one sociological study that was looking at effects of inequality on mortality generally.
01:01:44.120 And the notion that inequality affects mortality generally is mediated by what you were talking about, about health effects, the idea that, you know, stresses and fraught social comparisons produce greater vulnerability to stress-related diseases.
01:01:59.580 And, in fact, many diseases, most diseases maybe even are stress-related in their ultimate impacts on people.
01:02:06.420 So there's this one sociological study by a guy named Zheng in Ohio State, which sought effects of economic inequality on mortality in general, and came to the conclusion that the effects were lagged, that the maximum impact on current mortality was inequality seven years ago, which sounds kind of funny.
01:02:28.540 But he had analyses which seemed to show, and I'm a bit wary about the legitimacy of these analyses, but they seemed to me to show, they seemed to show to him that inequality of a few years ago affects the chance that you'll die now, net of the effects of, you know, age and sex and other predictors of mortality.
01:02:51.660 And that there's sort of a cumulative consequences of many years of past inequality.
01:02:58.660 So seven years ago was the worst, but six and eight also mattered additively.
01:03:03.500 Five years ago and nine years ago also mattered additively.
01:03:07.160 Ten years ago also mattered.
01:03:08.800 So that how bad the inequality was in your past seems to affect your likelihood of dying now.
01:03:14.700 The effects of violence haven't been looked at.
01:03:18.740 It's hard to figure out how you could get a decent enough data set to do that right, but I don't think it's impossible.
01:03:26.480 Okay, so with regards to health effects, so I'm going to lay out an account of them, and you can tell me what you think about this.
01:03:33.640 All right, so your brain is always trying to calculate to some degree how good things are going for you, and that's an extraordinarily difficult calculation because life is uncertain and ultimately uncertain, and it's difficult to predict the future except perhaps by using the past as a marker.
01:03:53.340 And so what seems to happen is that our nervous systems are always interested in how prepared we should be for emergency at any given moment.
01:04:03.040 And as far as I can tell, there are a number of ways that we calibrate that.
01:04:07.300 One is baseline levels of trait neuroticism.
01:04:11.260 So that's sensitivity to anxiety and uncertainty and emotional pain.
01:04:14.820 And so you seem to be born, roughly speaking, at your average level of neuroticism, which can vary substantially between people.
01:04:24.920 It can be also adjusted at puberty, and then the environment can move you in one direction or another.
01:04:30.380 So, for example, if you have a highly anxious child and you encourage them to go out and explore, then you can move them towards the normal range.
01:04:38.820 Jerry Kagan has demonstrated that quite nicely.
01:04:40.940 Okay, so the first estimate of how worried you should be about the future is like genetic roll of the dice.
01:04:48.500 Some people will be born extraordinarily worried, roughly speaking, and some people will be born hardly worried at all, and then that can be modified by the particulars of the social environment.
01:04:59.880 Right.
01:05:00.120 So then the next thing that seems to me to be part of the calculation is comparison.
01:05:05.440 How well are you doing compared to others?
01:05:08.160 Yeah.
01:05:08.280 And that seems to be adjusted by mechanisms that associate perceived social status with serotonin, serotonergic activity, such that as you move up a dominance hierarchy, your serotonin levels rise so that your impulsivity, which would be partly sensitivity to immediate reward, declines.
01:05:29.720 And so does your sensitivity to negative emotion, whereas if you plummet down to the bottom of a hierarchy, you start to become more reward-seeking and also more anxious.
01:05:41.240 And the reason for that, more anxious, is because the bottom of the dominance hierarchy actually is a more dangerous place to be because you don't have access to, you don't have reliable access, as reliable, to shelter or food or mating resources or health care.
01:05:56.640 And you even see this in birds, you know, so if a flu sweeps through an avian population, it's the bedraggled birds at the bottom of the dominance hierarchy that die first.
01:06:07.040 And so then, one more thing, and then tell me what you think about this, is that the other thing that seems to happen is that as you plummet down the dominance hierarchy and your mind settles into a more depressed and anxious state, the levels of cortisol that you produce chronically rise.
01:06:25.040 And cortisol is a good hormone for activating you, but in high doses, high continual doses, it starts to produce brain damage, particularly in the hippocampus.
01:06:36.440 And it also suppresses immunological function, which makes you more susceptible to infectious diseases.
01:06:43.320 So that seems to be approximately the process.
01:06:45.640 And so it's no wonder that people are trying to flee away from the bottom of the dominance hierarchy.
01:06:50.180 Does that seem reasonable?
01:06:51.120 Yes, give me a moment.
01:06:53.980 I've got to cough and blow my nose.
01:06:55.580 Okay.
01:07:04.320 Hay fever season in southern Ontario.
01:07:09.440 Okay.
01:07:10.800 Yeah, I wish I were a better behavioral endocrinologist and knew a bit more, was more expert in some of the processes that you're talking about.
01:07:19.960 But a lot of that makes sense to me.
01:07:22.320 The fraught social comparisons, I mean, the evidence certainly is that it's more stressful to be low ranking than high ranking.
01:07:31.360 We've had a little myth that, oh, being a very high rank puts all this burden of decision making on you, and that's terribly stressful.
01:07:40.080 It makes you vulnerable to heart attack and blah, blah, blah.
01:07:42.160 And the data say the opposite.
01:07:44.260 The data say that's not true.
01:07:45.460 The more power and status and, if you like, decision making authority you have, the less vulnerable you seem to be to stress related diseases.
01:07:54.520 So, you know, a lot of what you're saying makes evident sense to me.
01:07:58.240 And the developmental story that you're telling, I mean, I think it's right that people, I don't know how important the throw of the genetic dice is.
01:08:10.540 I think it's an extremely interesting puzzle evolutionarily, why there's as much heritable genetic variability in seemingly important domains as there is.
01:08:21.180 And I'm not convinced anybody has, you know, really understands what modulates, how much variability there is.
01:08:27.220 But in any case, that things are adjustable in response to what you encounter and in response to social status, perceived social status, in response to social comparisons.
01:08:40.780 Makes evident sense to me.
01:08:42.400 And again, I don't know enough about the punitive damaging effects of excessively prolonged exposure to, say, high cortisol levels to be sure whether there isn't still some adaptation, some actual functionality to the response to long-term exposure lurking beneath the seeming breakdown of the system.
01:09:10.200 Because it just seems to me that sort of a Darwinian, non-evolutionary social scientists and psychiatrists and psychologists have been too quick to assume pathology when they see states of affairs that do indeed have damaging consequences, but may in some nevertheless have some utility.
01:09:32.940 I wish I knew a little more about that.
01:09:34.800 Well, I think both the low serotonin and the high cortisol levels are interesting in that regard, because what does happen is the combination of those two things makes you, A, more impulsive, and B, more prepared for emergency action.
01:09:49.100 Both of those things are very useful in an uncertain environment.
01:09:52.660 Yes, indeed.
01:09:53.000 The detrimental consequences seem to occur as a consequence of prolonged overload, is that because your body is utilizing, imagine that what your body is doing is utilizing more units of resource per moment of time, because of the necessity for preparation for unexpected events, and that can become physiologically exhausting in the long run.
01:10:14.640 So I think it does, it seems to me that those biochemical effects do underlie the sort of adaptive responses that you describe, except that, you know, too much is too much.
01:10:28.760 And if it's hard to live at the bottom, what that means is you age faster, and you don't live as long, and you also have higher susceptibility to disease.
01:10:36.640 And maybe in some sense that's the price you pay for the adaptive impulsivity that's also necessary to give you a chance to shoot back up the hierarchy, if that's the sort of thing that you're looking for.
01:10:47.280 Yeah, no, and I can't help thinking about sort of the evolutionary theories of senescence and bodily repair that were pioneered by Sir Peter Medowar back in the 50s and developed more by George Williams.
01:11:05.800 The idea that many, many things involve some sort of tradeoff between expenditure for expenditure of energy, of accumulative resources, of capacity, in the pursuit of something now, at the expense of reduced capacity to be successful later.
01:11:28.580 And so, you know, one reason why these chronic states may have long-term damaging effects is because selection against being in these chronic states has not been strong, because those who were in them for a long time didn't historically tend to live very long anyway.
01:11:47.760 And they're being, if you like, motivated or prepared to engage in high-risk activities that at least have some chance of short-term payoff, which is more or less what you said, actually.
01:11:59.740 Well, and, you know, you talked about this, let's call it a misbegotten idea that there's stress at the top of the dominance hierarchy, just like there is stress at the bottom, and the stress at the top is responsibility and decision-making and all of that.
01:12:12.760 And, you know, I do believe that there's truth in that, but there's an important, another important biological element that needs to be considered.
01:12:20.580 And so, there's plenty of work done in the domains of clinical psychology, and some of this is psychophysiological and neurophysiological, for that matter, showing that a stress of an equivalent magnitude has fewer negative effects if it's taken on voluntarily.
01:12:39.900 So, because what happens, what happens is that if you voluntarily engage in the stressful activity, your approach systems are activated rather than your defense systems.
01:12:51.280 And the approach systems are associated with positive emotion, and with much, whereas the negative emotions are associated with this defensive posturing that includes preparation for emergency, and that's much more physiologically damaging.
01:13:04.780 And so, whether something, whether you pick up a load voluntarily or have it thrust upon you, seems to make a big difference to how heavy it is.
01:13:13.740 And that's a very interesting piece of, set of research studies as far as I'm concerned.
01:13:20.320 It's quite fascinating that that can be the case.
01:13:23.500 Yeah.
01:13:23.600 Okay, so let me ask you another question.
01:13:26.620 Let's get down to, we might say, brass tacks here.
01:13:29.500 So, we can make a case that inequality destabilizes societies and cranks up the male-on-male homicide rate.
01:13:38.180 And the destabilization occurs because young men become more and more unpredictable and violent.
01:13:43.660 And so, you could make a conservative case, as well as a liberal case, for not having a society that takes inequality to an extreme, because conservatives, at least in principle, should be concerned with the maintenance of social stability over the long run.
01:13:59.740 So, but, but, okay, and so then you might make a case for income redistribution, but that gets very, very troublesome, because it's not that easy to redistribute income.
01:14:08.800 And, and, and that's what I want to talk to you about.
01:14:11.800 So, you know, we're in a situation, of course, where the top 1% of the population controls a substantial proportion of the economic resources.
01:14:22.560 And the top 1% of that top 1% controls the bulk of that.
01:14:27.080 Now, I looked into that quite deeply, and that, that distribution is, it's not a normal distribution of money.
01:14:34.880 It's a Pareto distribution of money.
01:14:36.680 But the weird thing about Pareto distributions, and so that's a distribution where many, many people end up with zero, and, you know, just a few people end up with a lot, is that a Pareto distribution characterizes zero-sum games that are played out to their conclusion.
01:14:51.960 So, like Monopoly, everybody starts in the middle, but then random trading produces an eventual Pareto-shaped distribution where lots of people start to stack up on the loser side.
01:15:02.600 One person accelerates towards victory until finally everyone's at zero except one person.
01:15:07.840 So, it's the logical outcome of a random trading game.
01:15:11.060 So, that's the first thing that's interesting about the Pareto distribution.
01:15:13.960 The second thing that's interesting is that Pareto distributions, they, Pareto distributions emerge in every domain of creative human production, not just the distribution of money.
01:15:27.260 So, for example, we did an analysis of the, of creative achievement across the lifespan using a, using an instrument called the Creative Achievement Questionnaire.
01:15:37.940 So, what it did was assess people's levels of competence across 13 potential domains of creative activity.
01:15:45.320 And so, we were looking at production rather than creative thinking per se, right?
01:15:49.140 Right.
01:15:49.320 Although those two things are related and quite tightly.
01:15:51.780 We wanted to know who actually accomplished things in the world.
01:15:55.640 And so, for musical ability, for example, the zero score would be, I have no training or, or talent in this area.
01:16:04.400 And the maximum score would be, you know, my, my, my, my original compositions have been played for international audiences.
01:16:12.840 And so, we've now administered that to hunt, to hundreds of people.
01:16:16.040 And the median score is zero across all 13 domains.
01:16:19.860 It's a very, very, uh, precise Pareto distribution with a few people who are the outliers producing the overwhelming majority of the goods.
01:16:28.480 And you also, and that, there's a, there's also a law that de Sola Price, uh, came up with back in the 1960s, governing the output of scientific papers.
01:16:39.380 And he found that the square root of the number of people operating within an academic domain produced half the papers that were published in that domain.
01:16:48.480 Right.
01:16:48.500 So, so that's not so bad if there's 10 researchers, because then three of them are producing half the papers.
01:16:54.680 But if there's a thousand researchers operating in a domain, then 30 of them are producing half the papers.
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01:18:18.380 Okay, so and then one more complication, and then I'm going to let you have at this.
01:18:26.080 So I've been looking for, now you can think that the Pareto distribution, which by the way characterizes the distribution of wealth in every known society,
01:18:33.640 although the degree to which the distribution is skewed differs.
01:18:37.680 You can say that the Pareto distribution is a consequence of the final playing out of a random trading game, but then here's the complication.
01:18:47.420 This is something that's been, you know, bothering me for years.
01:18:50.760 There are predictors of long-term life success in relatively stable societies, and the best predictors are in this order.
01:19:01.420 The first predictor is IQ.
01:19:03.280 The second predictor is trait conscientiousness, and it's about half as powerful as IQ.
01:19:08.320 And the third predictor is low neuroticism, and it's about half as powerful as conscientiousness.
01:19:13.580 So if you get a good measure of IQ and a good measure of conscientiousness, then you can predict about 25% of the variance in performance,
01:19:22.440 especially across managerial, administrative, and academic domains.
01:19:26.780 And then with regards to entrepreneurial performance, you can use IQ and trait openness, which is the creativity measure.
01:19:34.220 So there are powerful individual differences that are driving differential performance and also driving this Pareto distribution.
01:19:43.080 And so it's not merely a random game, although how these people managed to make it into not a random game is beyond me.
01:19:50.120 But there is evidence that our society does hierarchically arrange itself, at least to some degree, by ability and competence.
01:19:58.560 And so then the question is, how do you factor that into the equation when you're thinking about practical, let's call them income?
01:20:07.640 I don't think it's so much income redistribution, is that it's an attempt by society to stop too many people from stacking up at zero,
01:20:14.900 and therefore logically turning to violence and that sort of thing as an alternative.
01:20:20.460 Well, as well as an attempt to just improve the level of justice in society.
01:20:28.980 The idea, you know, I mean, especially if there's an element, a strong element of randomness and who ends up where,
01:20:34.660 then there's something unjust about large numbers being stuck down at the zero.
01:20:38.940 But, you know, you say, how is it possible to redistribute?
01:20:43.600 But countries vary in the extent to which they do this.
01:20:45.960 They vary in the extent to which they tax inheritance.
01:20:49.380 They vary in the extent to which they tax large incomes.
01:20:54.360 They vary in the extent to which they provide education and health care, try and provide it relatively universally,
01:21:02.840 try and make opportunity relatively universal.
01:21:05.360 They vary in these things.
01:21:06.380 And, you know, some of the happiest countries in the world, and I think the most productive countries in the world,
01:21:15.160 the Nordic countries, Japan, have been relatively equitable because they rig this game more than some other countries, if you like.
01:21:24.060 So, you know, you say that what stacks up at the top tend to be the most competent and creative people,
01:21:31.020 and to imply that to some degree we have a meritocracy.
01:21:34.380 And to some degree we do have a meritocracy.
01:21:36.820 But, you know, the four wall-balled mariners have as much wealth as the hundred million poorest Americans put together.
01:21:45.340 And they did nothing to earn it.
01:21:49.360 You could say, well, they're high-quality people because they got half their genes from Sam Walton and he did something to earn it.
01:21:57.100 That seems like a pretty weak argument for why they should control that much wealth.
01:22:01.980 If inheritance were more severely taxed in order to provide public goods for everybody, would the society be worse off?
01:22:09.900 Would flattening out that curve of accomplishment actually reduce productivity?
01:22:16.460 You know, I think there's some evidence, I wish I could pull it to the forefront of my mind,
01:22:21.440 about the utility of distributing grant money more or less equitably in certain sciences.
01:22:30.040 The amount of science you get for your buck is better when you give lots and lots of people relatively small grants
01:22:36.020 than when you give a small number of people relatively large grants.
01:22:38.960 Yeah, well, that's interesting because I've worked in the grant system in the U.S. and in Canada.
01:22:44.080 And the grant system in the U.S. is more of the give-a-few-people-a-huge-amount-of-money variety.
01:22:49.600 And in Canada it's distributed more equitably.
01:22:53.060 And I must say that I vastly prefer the Canadian system.
01:22:57.480 I agree with you.
01:22:58.560 And I think the Canadian system has been moving regrettably in the direction of the American.
01:23:02.760 I mean, it partly depends on the field of science, of course.
01:23:04.840 If you need a bloody Hadron Collider, then you need millions and millions of dollars.
01:23:08.860 If you're a psychologist like you or me, things seem to work better in many ways
01:23:16.020 when you fund a higher proportion of grants with a lower variance in the amount awarded.
01:23:22.700 When I first came to McMaster, there was exactly – oh, no, I shouldn't say when I first came.
01:23:29.100 By, let's say, the late 80s and early 90s, essentially everybody in the department had a research grant.
01:23:37.280 For me, the NSERC, the Natural Sciences and Engineering Research Council of Canada,
01:23:42.360 or SHERC, the Social Sciences and Humanities Research Council of Canada,
01:23:45.700 usually the former in our particular department.
01:23:47.720 Everybody in the department had an active research lab.
01:23:51.120 Everybody created the opportunity for two or three students to do a bachelor's thesis in their lab each year.
01:23:57.060 Then when things get more variable, people start – people who are being productive,
01:24:01.100 who are getting out a scientific paper or two, doing decent work, making a contribution to knowledge,
01:24:06.140 when they start being denied these grants, you know,
01:24:09.360 when you refuse two or three times in the competition and say, well, the hell with it, you know.
01:24:14.560 I mean, I've got tenure, I've got a good pension lined up.
01:24:18.360 I think I'll become a real estate speculator.
01:24:20.480 The opportunities for the dissemination of research opportunity to a larger number of students,
01:24:29.240 SHERC, I think it's been a disaster in certain areas.
01:24:34.540 You know, an area where I was raised, animal behavior studies.
01:24:37.260 If you just look at either the number of papers in top-ranked journals by country according to how they allocate their funds
01:24:47.540 or how much money is allocated to it, the money allocated to it is the less strong predictor you'd expect,
01:24:53.380 and the equitability is a stronger predictor.
01:24:55.600 Sweden and Canada used to, last time I looked, both rank far above the United States
01:25:01.620 in numbers of papers per capita getting into top-quality research journals in animal behavior.
01:25:09.360 You know, okay, it's one little anecdote in a way,
01:25:11.700 but I would be very surprised if there isn't some generality to this phenomenon.
01:25:16.320 Well, I wonder, though, to play devil's advocate,
01:25:20.380 the thing about distributing research funds more equitably
01:25:23.360 is that you are distributing them among a population
01:25:26.300 that's already been extraordinarily highly selected for capability.
01:25:30.080 And so it seems counterproductive because it's,
01:25:33.200 for all the flaws of the university system, which are manifold,
01:25:36.980 it is still extraordinarily difficult to become a professor.
01:25:39.980 It's a multi-tiered selection system.
01:25:43.160 And so the people who do become professors are, on average,
01:25:47.440 very intelligent and, on average, very hardworking.
01:25:50.660 And we know that because we know what the predictors are of success in academia
01:25:54.680 and its intelligence and conscientiousness, unsurprisingly,
01:25:58.000 although creativity seems to play almost zero role.
01:26:01.100 Really?
01:26:01.520 Well, the thing, yeah, but that's partly because, you know,
01:26:04.360 science is an algorithmic game, right?
01:26:06.340 And just beetling away at it busily is a very, very powerful mechanism.
01:26:11.100 So I'm not the least bit cynical about that.
01:26:13.620 I mean, the reason that science works is because it's, in some sense,
01:26:16.860 it has the aspect of factory production.
01:26:19.200 It can be distributed.
01:26:20.420 Anyone can learn to do it.
01:26:22.080 And you get a long ways by nibbling at the edges.
01:26:25.100 You know, it's continual slow progress when millions of people are doing it
01:26:29.520 is progress that's plenty rapid.
01:26:31.960 So, okay, so there are definitely situations in which denying people resources
01:26:37.120 seems to be completely counterproductive, and that would be one of them.
01:26:40.800 So, now, the other question, though, is, I would say,
01:26:45.540 and also, that's the thing, there's also an effective means of funneling resources
01:26:50.380 to, let's say, a wide range of professors.
01:26:52.660 It actually works.
01:26:54.620 The problem, one of the problems with general income redistribution is,
01:26:58.120 as far as I can tell, is that we don't really know how to do it very well.
01:27:01.940 And one of the, I mean, look, here's an example.
01:27:04.400 You can tell me what you think about this.
01:27:05.820 So, I used to work, I used to live in northern Alberta
01:27:09.060 when the oil, sporadic oil booms were going on.
01:27:12.160 And my observation was that if you wanted to make money in Alberta
01:27:16.960 when an oil boom was going on, you didn't go out and work on the rigs.
01:27:20.800 Although, if you did that, you could make a tremendous amount of money.
01:27:24.180 Now, it was all young men who did that, pretty much, say,
01:27:26.820 between the ages of 16 and 25, something like that.
01:27:29.520 And they were making fantastic amounts of money.
01:27:32.540 But they, almost all of them came out of it with nothing to show for it.
01:27:36.800 Because they would work for two weeks and then go into town
01:27:40.000 and just have a blowout party for four days and spend everything they got
01:27:43.820 and buy expensive cars and wreck them and so forth.
01:27:46.620 So, it was reckless behavior that I think was akin, in some sense,
01:27:51.680 to the steep dominance hierarchy, violence, and that sort of thing
01:27:57.360 for status-seeking that you're describing.
01:28:00.620 The people who really made money were the bartenders, right?
01:28:04.560 Because they absorbed all the excess profits
01:28:08.160 and actually, generally speaking or comparatively speaking,
01:28:12.800 were able to utilize the money properly.
01:28:15.160 Now, the point I'm making is that an oil boom is a very effective way
01:28:20.000 of distributing wealth down the economic ladder.
01:28:22.800 But it didn't necessarily seem to me to be a very effective one
01:28:26.120 because it didn't, because the money flowed back up to the top 1%
01:28:30.560 damn near as fast as you could shovel it downwards.
01:28:33.120 And that's the thing about that damn Pareto distribution,
01:28:36.560 is that it seems, there are people, there's a group,
01:28:41.360 there's a scientific subfield called econophysicists,
01:28:45.800 and they actually modeled the distribution of money in an economy
01:28:49.940 using the same equations that modeled the distribution of a gas into a vacuum.
01:28:56.080 So, there's something that's natural law-like about this.
01:29:00.320 The economists call it the Matthew Principle, right?
01:29:02.760 To those who have everything, more will be given,
01:29:04.880 and from those who have nothing, everything will be taken.
01:29:07.740 And I don't think that we've done a good job of grappling
01:29:10.720 with the actual complexity of this.
01:29:12.600 And we tend to split up into politically opposed,
01:29:15.820 what would you call, camps,
01:29:17.780 and argue about the solution to inequality.
01:29:21.060 And the left-wing solution is something like,
01:29:24.240 you know, distribute the money, take it from the rich,
01:29:26.500 especially the undeserving rich,
01:29:28.740 if you can identify them and give it to the poor.
01:29:31.680 And the conservatives say,
01:29:32.920 well, no, the poor should bootstrap themselves up
01:29:36.460 and maybe be provided with more opportunity,
01:29:38.320 and that might equalize things.
01:29:39.820 But it isn't clear to me that we're actually grappling
01:29:42.420 with the magnitude of the problem.
01:29:44.860 No, it isn't clear to me either.
01:29:45.960 But what you say about equalizing opportunity, for example,
01:29:49.520 is in a sense distributing the resources,
01:29:51.660 because one way you equalize opportunity
01:29:53.720 is by having universal high-quality health care
01:29:57.280 that's paid for by some sort of government revenue,
01:30:01.320 some taxes picked up somewhere.
01:30:04.880 Free education and universal access to education
01:30:10.740 is certainly another.
01:30:12.140 And it's, you know, it's another way that, in effect,
01:30:15.080 you create a more egalitarian society.
01:30:17.500 So, I mean, there are certain domains,
01:30:19.500 certainly education and health care,
01:30:20.820 or maybe some others that are not springing to mind.
01:30:23.320 Well, I suppose the improvement
01:30:24.280 of various sorts of infrastructure
01:30:26.040 that, you know, make it easier to get from point A to point B,
01:30:31.240 you know, publicly subsidized trends,
01:30:34.440 things like that can certainly be contributors as well.
01:30:37.000 Now, that's equalizing in its own right.
01:30:38.640 You're not taking it from anybody
01:30:39.840 and giving it specifically to anybody else.
01:30:41.920 Then there's things like a guaranteed minimum income.
01:30:44.880 And at first it sounds like a crazy idea,
01:30:46.980 the idea that, you know, you should just,
01:30:48.580 we should take government-accrued resources,
01:30:53.100 which come from some sort of taxation,
01:30:54.740 and we should just make sure everybody has 15,000 bucks a year to start
01:30:59.540 or something like that.
01:31:01.360 It sounds kind of wacky because the standard argument against it
01:31:05.660 from the right has been that it will undermine incentives
01:31:08.220 and nobody will produce bugger all if, you know,
01:31:10.960 if we could all be welfare queens.
01:31:12.560 But we'll want to be welfare queens, mostly.
01:31:15.860 And where this stuff has been tried,
01:31:18.300 my understanding is that it's been surprisingly successful,
01:31:22.440 that there was an experiment in Manitoba
01:31:24.380 where a minimum income was tried for a while where?
01:31:30.260 Gosh.
01:31:31.460 Yeah, I remember that.
01:31:32.720 I think Finland's about to try it.
01:31:35.300 Finland's about to try it.
01:31:37.100 Manitoba has tried it.
01:31:38.560 It was an NDP government, I think,
01:31:40.300 which then would be replaced by a conservative
01:31:42.220 or nominally liberal government
01:31:45.560 that sort of canned the results.
01:31:48.780 But the results came to light later
01:31:50.860 and showed that, for example,
01:31:52.580 the number of people who chose not to work
01:31:54.500 did not go up under this.
01:31:56.800 And that it had various beneficial effects.
01:31:59.600 I think it remains to be seen.
01:32:00.760 But I think even the idea of putting money
01:32:03.460 in the hands of everybody
01:32:05.040 from the great collective wealth
01:32:07.580 that has accumulated
01:32:08.900 could be socially beneficial,
01:32:11.740 could be economically beneficial,
01:32:13.180 could be environmentally beneficial.
01:32:15.240 I wonder...
01:32:17.100 And that certainly in the domains
01:32:19.120 like education and health care,
01:32:20.860 that's, in effect,
01:32:22.400 a kind of redistribution
01:32:23.540 that seems easy to effectuate.
01:32:27.040 I mean, not easy to effectuate
01:32:30.800 in terms of convincing people politically
01:32:32.880 or overcoming the propaganda against it.
01:32:36.600 But we don't have...
01:32:38.860 Well, a whole bunch of...
01:32:39.560 Obviously, a whole bunch of our wealth
01:32:41.920 is embodied in the infrastructure.
01:32:45.780 Yes.
01:32:45.880 I really noticed this, for example,
01:32:47.180 when I lived in Montreal.
01:32:48.860 Because Montreal is a great city.
01:32:51.220 And one of the things
01:32:52.620 that distinguishes Montreal
01:32:53.860 from most cities that I've lived in,
01:32:55.700 especially Western cities,
01:32:58.000 is that people live in the city.
01:33:01.480 They don't live in their houses.
01:33:03.120 Yes.
01:33:03.540 And the fact that the city
01:33:05.340 is extraordinarily livable,
01:33:06.820 so you can walk everywhere,
01:33:08.440 there's always something to do
01:33:09.900 that's exciting.
01:33:10.960 There's a tremendously active street life.
01:33:13.080 It means that there's access
01:33:14.980 to infrastructure
01:33:16.420 and social capital-related wealth
01:33:19.060 just distributed everywhere.
01:33:21.340 And that's a lovely thing.
01:33:22.660 So, Kaz, I'm kind of looking
01:33:25.600 for solutions
01:33:26.280 to the Pareto distribution problem
01:33:28.140 that conservatives
01:33:28.880 and liberals alike
01:33:29.880 could agree upon.
01:33:31.780 And so, some of those you outlined
01:33:33.300 improve the infrastructure
01:33:35.160 of our society
01:33:36.000 because those are public goods
01:33:37.600 that benefit everyone,
01:33:39.480 that also improve productivity.
01:33:41.040 There seems to be no downside
01:33:42.160 to that at all.
01:33:42.920 It also raises employment,
01:33:45.160 improve the quality of education
01:33:46.700 right from day one,
01:33:49.120 which is something that I think
01:33:50.100 we do a very bad job of.
01:33:52.900 And then the issue with healthcare,
01:33:55.360 it's my understanding
01:33:56.300 that the Canadian healthcare system
01:33:58.040 for it, and it has flaws,
01:34:00.660 because it's, of course,
01:34:02.440 dealing with an impossible problem,
01:34:04.660 still uses much less of its capital
01:34:08.520 on maintaining itself
01:34:10.080 and, for example,
01:34:11.600 having to maintain an infrastructure
01:34:14.100 that collects money.
01:34:15.580 I know that the hospitals
01:34:16.960 in the U.S.
01:34:17.760 spend something,
01:34:19.020 some substantial proportion
01:34:20.320 of their revenue,
01:34:21.340 I can't remember precisely,
01:34:22.980 but it's between 17% and 30%,
01:34:24.840 if I remember correctly,
01:34:26.260 just gathering the money
01:34:28.440 for their services,
01:34:29.760 which seems to be
01:34:30.580 a rather counterproductive use
01:34:31.940 of their resources.
01:34:32.680 And I wonder how much
01:34:34.380 is spent on billboards
01:34:35.700 advertising their hospitals, too.
01:34:37.660 If you drive the interstate highways
01:34:39.360 of the U.S.,
01:34:39.980 it's astonishing
01:34:40.760 how much information
01:34:43.980 about, you know,
01:34:45.340 come to such and such
01:34:46.560 where we have the best cancer
01:34:48.140 doctors, etc.
01:34:49.540 And it isn't,
01:34:51.120 well,
01:34:51.380 and Americans pay a lot
01:34:52.860 for their health care.
01:34:54.360 They do, indeed.
01:34:55.360 They do, indeed.
01:34:55.920 I spent three years there
01:34:57.260 recently,
01:34:58.060 and we paid a lot
01:35:00.680 for health care coverage
01:35:02.180 that turned out not,
01:35:03.300 in fact,
01:35:03.780 to be all that
01:35:05.180 thorough a coverage.
01:35:06.840 Right.
01:35:07.200 Well,
01:35:07.360 and when I lived in the States,
01:35:08.460 too,
01:35:08.600 and I had decent coverage,
01:35:09.960 I was teaching in Boston there,
01:35:12.200 I had a pretty good program,
01:35:13.360 but it wasn't,
01:35:14.320 I wouldn't say
01:35:15.000 it was manifestly different
01:35:16.460 from my Canadian experience,
01:35:18.340 which has been mixed,
01:35:19.620 but, of course,
01:35:20.860 it is very important
01:35:21.840 to note that
01:35:22.520 making people healthy
01:35:24.820 is impossible
01:35:25.520 because everybody
01:35:26.380 gets sick
01:35:27.100 and ages and dies,
01:35:28.260 and so it's an impossible task,
01:35:29.900 and it also indicates to me
01:35:31.680 that that's perhaps
01:35:32.740 one of the reasons
01:35:33.360 why it doesn't fit
01:35:34.260 so nicely
01:35:35.220 into a free market model
01:35:37.440 because the free market
01:35:38.920 assumes that there's not
01:35:40.000 infinite demand
01:35:40.920 for something,
01:35:41.600 and there is actually
01:35:42.460 near infinite demand
01:35:43.620 for health care,
01:35:44.620 especially if you're dying.
01:35:46.440 There's that,
01:35:46.980 and there's also just,
01:35:47.980 you know,
01:35:48.340 it's an impossible problem
01:35:50.040 because of an aging population,
01:35:51.740 it's an impossible problem
01:35:53.180 because,
01:35:54.540 you know,
01:35:55.160 governments have,
01:35:56.240 one of the determinants
01:35:57.940 of the costs
01:35:59.100 of the health care system
01:36:00.080 is how many MDs
01:36:00.960 you've got out there
01:36:01.600 billing it,
01:36:02.480 and governments
01:36:03.300 have a tendency
01:36:05.160 to want to respond
01:36:06.440 to this
01:36:06.840 by restricting
01:36:08.140 the number
01:36:09.060 of new medics
01:36:10.100 so as to restrict
01:36:13.000 the number
01:36:13.460 of people billing,
01:36:14.360 but this is
01:36:14.920 not much of a solution
01:36:16.340 when you have
01:36:16.920 large numbers of people
01:36:17.740 trying to find
01:36:18.360 a family doctor
01:36:19.220 unsuccessfully.
01:36:20.880 Okay,
01:36:21.400 so there is
01:36:21.960 some meritocratic
01:36:24.540 structure to our society
01:36:26.140 insofar as IQ,
01:36:27.880 conscientiousness,
01:36:28.700 and openness
01:36:29.200 predict long-term
01:36:30.660 life success,
01:36:31.300 and that's a good thing
01:36:32.300 because that's an indicator
01:36:33.420 of health
01:36:34.660 in a society.
01:36:35.360 I would say
01:36:36.160 I would say
01:36:36.180 it's if your society
01:36:38.240 is set up
01:36:39.720 to allow people
01:36:43.500 who are intelligent
01:36:44.420 and conscientious
01:36:45.480 nearer the pinnacles
01:36:47.140 of power structures,
01:36:48.060 that's a good thing
01:36:48.860 for everyone.
01:36:49.680 Now,
01:36:49.980 you could still have
01:36:50.640 an argument
01:36:51.000 about how steep
01:36:51.860 that gradient
01:36:52.500 should be,
01:36:53.900 but then
01:36:54.440 with regards
01:36:55.240 to the guaranteed
01:36:56.940 annual income issue,
01:36:59.060 I'm also concerned
01:37:00.720 that the importance
01:37:01.780 of individual differences
01:37:02.900 there are not
01:37:04.040 being considered.
01:37:05.060 So,
01:37:05.280 for example,
01:37:06.800 I don't know
01:37:07.540 what people
01:37:08.040 who are extremely
01:37:08.740 low in conscientiousness
01:37:10.020 would do
01:37:10.520 with an annual income
01:37:11.820 because they're not
01:37:13.440 inclined to work
01:37:14.320 and it isn't obvious
01:37:15.300 to me that providing
01:37:16.260 them with an easy
01:37:16.940 way out
01:37:17.460 is the answer
01:37:18.760 because providing
01:37:20.080 unconscientious people
01:37:21.280 with an easy way out
01:37:22.200 seems to be actually
01:37:23.100 quite counterproductive
01:37:24.240 and conscientiousness
01:37:25.500 is a decent predictor
01:37:27.240 of long-term success.
01:37:28.820 We also don't know
01:37:29.540 to what degree
01:37:30.280 necessity
01:37:31.100 is a motivator,
01:37:32.220 which is,
01:37:32.700 of course,
01:37:32.940 the conservative argument.
01:37:35.800 and we also don't know
01:37:37.300 how homogenous
01:37:38.200 and small
01:37:39.520 a society
01:37:40.060 has to be
01:37:40.620 before income
01:37:41.280 redistribution programs
01:37:42.460 will actually
01:37:43.000 be successful.
01:37:44.380 It seems easier
01:37:45.060 to implement them
01:37:46.240 in relatively
01:37:46.920 homogenous societies
01:37:48.040 like the Scandinavian countries
01:37:49.440 or Japan,
01:37:50.900 which is where
01:37:51.420 they tend to have been
01:37:52.500 implemented
01:37:53.620 with more success.
01:37:54.980 So that's a complicated
01:37:56.280 phenomenon as well.
01:37:58.160 And then
01:37:58.520 the other thing
01:37:59.520 that's really going
01:38:00.500 to come up on
01:38:01.100 as hard
01:38:01.500 in the next
01:38:01.940 ten years,
01:38:03.020 I would say
01:38:03.520 this is how
01:38:04.620 it looks to me
01:38:05.280 is that
01:38:05.680 I think
01:38:06.920 computational devices
01:38:08.300 are a multiplier
01:38:09.300 of intelligence
01:38:10.400 and conscientiousness
01:38:11.620 because
01:38:12.740 if you're smart
01:38:14.260 and you know
01:38:14.920 how to use a computer
01:38:15.880 and you're diligent
01:38:17.300 so as a conscientious
01:38:18.860 person would be
01:38:20.020 then you're
01:38:20.980 much more deadly
01:38:22.260 than you would be
01:38:23.040 without your computer
01:38:24.020 because it multiplies
01:38:25.440 your...
01:38:25.700 and there's a huge
01:38:26.420 difference between
01:38:27.160 someone who really
01:38:28.020 knows how to use
01:38:28.680 a computer
01:38:29.100 including knowing
01:38:29.840 how to program it
01:38:30.800 and someone
01:38:32.260 who's literate
01:38:33.320 enough to use
01:38:33.920 their iPad
01:38:34.380 to do a Google search
01:38:36.220 and so I think
01:38:37.320 one of the things
01:38:38.000 that's also driving
01:38:39.000 inequality
01:38:39.640 particularly in societies
01:38:41.100 like the United States
01:38:42.180 is that
01:38:42.680 increasingly people
01:38:44.280 who are smart
01:38:44.920 and conscientious
01:38:45.740 can do a tremendous
01:38:46.900 amount of work
01:38:47.820 without having
01:38:48.320 to hire anyone.
01:38:50.020 So we have
01:38:50.380 these tiny companies
01:38:51.320 that employ
01:38:52.120 almost no one
01:38:52.960 that gather
01:38:53.760 massive resources
01:38:54.920 to themselves
01:38:55.660 and that's
01:38:56.900 going to be
01:38:57.160 a problem
01:38:57.620 well here's
01:38:58.640 a good example
01:38:59.260 here's one thing
01:39:00.020 that's coming
01:39:00.520 so you know
01:39:02.060 the Tesla guys
01:39:02.960 are working pretty
01:39:03.600 hard on autonomous
01:39:04.500 vehicles
01:39:05.000 and they're making
01:39:06.180 a lot of progress
01:39:06.880 and they're not
01:39:07.360 the only ones
01:39:08.020 obviously
01:39:08.500 but you know
01:39:09.360 the biggest employer
01:39:10.260 for males
01:39:10.860 in North America
01:39:11.620 is as driver
01:39:12.560 I didn't know that
01:39:15.300 yeah yeah yeah
01:39:16.340 it's the biggest
01:39:16.980 single employment
01:39:17.740 category
01:39:18.300 so you know
01:39:19.960 we're increasingly
01:39:22.840 eradicating
01:39:24.080 the possibility
01:39:24.880 for people
01:39:25.800 who are on
01:39:26.420 the lower end
01:39:27.100 of the intelligence
01:39:27.840 distribution
01:39:28.500 and the lower end
01:39:29.580 of the conscientiousness
01:39:30.640 distribution
01:39:31.100 to find
01:39:32.480 a place
01:39:33.520 in society
01:39:34.180 and it's possible
01:39:35.120 that providing
01:39:35.720 them with
01:39:36.140 minimal resources
01:39:37.660 to survive
01:39:38.360 might be sufficient
01:39:39.200 to solve that problem
01:39:40.100 but I doubt it
01:39:40.880 because as they say
01:39:42.160 you know man does not
01:39:43.160 live on bread alone
01:39:44.220 and it seems to me
01:39:45.340 that people need
01:39:46.200 that quest
01:39:47.340 the degree to which
01:39:48.480 people need
01:39:49.200 to find a productive
01:39:50.680 and credible place
01:39:52.260 in a functional society
01:39:53.420 is something
01:39:53.840 that we haven't yet
01:39:54.720 we don't know
01:39:55.720 the parameters
01:39:56.380 of that
01:39:56.940 no no I
01:39:58.200 I don't disagree
01:39:59.640 I mean the
01:40:00.280 of course
01:40:02.540 the loss
01:40:03.380 of decently
01:40:04.540 paid work
01:40:05.860 antedates
01:40:07.780 the major
01:40:09.580 computer revolution
01:40:10.500 to some extent
01:40:11.380 or at least
01:40:11.800 the
01:40:12.080 you know
01:40:14.220 modern electronic
01:40:15.840 device
01:40:16.300 and your phone
01:40:17.840 can do everything
01:40:18.540 revolution
01:40:19.060 and you know
01:40:20.700 I live in Hamilton
01:40:21.500 Ontario
01:40:22.020 where
01:40:22.560 formerly a lunch
01:40:25.380 bucket town
01:40:26.060 with an enormous
01:40:27.380 number of people
01:40:28.540 working in
01:40:29.640 decently paid
01:40:31.380 working class
01:40:33.600 jobs
01:40:34.180 and those jobs
01:40:35.260 have been evaporating
01:40:36.180 and
01:40:37.140 if drivers
01:40:39.360 evaporate
01:40:40.260 I mean
01:40:40.720 work is going
01:40:42.540 to
01:40:42.920 change
01:40:44.180 work opportunities
01:40:44.920 are going to change
01:40:45.960 and I take your point
01:40:47.340 that people need
01:40:48.180 something that they
01:40:49.900 can think of
01:40:50.620 as useful
01:40:52.060 work
01:40:52.640 useful work
01:40:55.080 you know
01:40:55.700 it's interesting
01:40:56.120 we're talking
01:40:56.600 we're two males
01:40:57.300 talking about this
01:40:58.280 and we're probably
01:40:58.820 thinking from a
01:40:59.480 somewhat male
01:41:00.040 perspective
01:41:00.520 there's a lot
01:41:01.020 of useful
01:41:01.540 work
01:41:02.040 that is
01:41:02.760 minimally
01:41:03.840 or not at all
01:41:04.500 compensated
01:41:05.640 than predominantly
01:41:06.480 female domains
01:41:07.760 daycare
01:41:09.420 kinds of things
01:41:10.480 various so-called
01:41:11.600 charitable
01:41:12.000 activities
01:41:12.620 and so on
01:41:13.240 and
01:41:14.060 you know
01:41:15.580 the idea
01:41:16.240 that people
01:41:17.020 need something
01:41:17.840 to occupy
01:41:18.780 their time
01:41:19.540 with
01:41:19.860 that feels
01:41:20.660 worthwhile
01:41:21.300 that enters
01:41:22.080 them into
01:41:22.400 a social arena
01:41:23.220 where they
01:41:23.640 engage with
01:41:24.200 other people
01:41:24.840 that they
01:41:25.580 come home
01:41:25.900 satisfied
01:41:26.400 that they've
01:41:26.840 done something
01:41:27.360 useful
01:41:27.820 and they also
01:41:28.960 you know
01:41:29.780 have a chicken
01:41:30.420 in every pot
01:41:31.120 besides
01:41:31.640 I mean
01:41:32.400 if work
01:41:32.880 opportunities
01:41:33.420 shrink
01:41:34.100 and if the
01:41:35.140 next Mark
01:41:35.760 Zuckerberg
01:41:36.420 can employ
01:41:37.200 a hundred
01:41:37.580 people
01:41:38.000 to pull in
01:41:38.900 tens of
01:41:40.220 billions
01:41:40.500 of dollars
01:41:41.120 then
01:41:42.100 where's that
01:41:43.460 going to
01:41:43.680 come from
01:41:44.160 it may
01:41:44.740 come from
01:41:45.360 various
01:41:46.360 sorts of
01:41:47.660 unpaid
01:41:48.560 work
01:41:49.060 with a
01:41:49.680 guaranteed
01:41:50.040 income
01:41:50.560 that
01:41:50.940 you know
01:41:51.920 enables that
01:41:52.460 work to
01:41:52.940 be unpaid
01:41:53.740 and still
01:41:54.200 be fulfilling
01:41:54.800 I don't
01:41:55.780 know
01:41:55.980 I don't
01:41:56.660 know
01:41:56.960 well that's
01:41:57.520 a good
01:41:57.720 thing to
01:41:58.120 think about
01:41:58.540 I mean
01:41:58.840 maybe
01:41:59.120 people will
01:42:00.260 learn
01:42:00.740 how to
01:42:01.620 go out
01:42:02.020 into the
01:42:02.380 community
01:42:02.760 and spontaneously
01:42:03.600 do useful
01:42:04.520 things
01:42:04.940 although
01:42:05.160 I can tell
01:42:06.080 you that
01:42:06.520 my experience
01:42:07.780 trying to
01:42:08.320 find gainful
01:42:09.340 let's call
01:42:10.460 it
01:42:10.620 volunteer
01:42:11.240 employments
01:42:11.940 for people
01:42:12.420 who are
01:42:12.740 on the
01:42:13.060 lower end
01:42:13.620 of the
01:42:13.900 ability
01:42:14.260 distribution
01:42:14.900 has been
01:42:15.400 absolutely
01:42:16.400 it's
01:42:18.660 difficult
01:42:19.340 beyond
01:42:19.860 imagination
01:42:20.600 because it
01:42:21.460 turns out
01:42:21.920 that finding
01:42:22.360 a volunteer
01:42:22.860 position
01:42:23.340 is actually
01:42:23.940 no less
01:42:24.820 difficult
01:42:25.220 than finding
01:42:25.760 a job
01:42:26.280 for example
01:42:26.940 you have
01:42:27.340 to go
01:42:27.580 through a
01:42:27.920 relatively
01:42:28.280 complicated
01:42:28.920 process
01:42:29.400 of police
01:42:29.980 screening
01:42:30.500 for most
01:42:31.300 jobs
01:42:31.700 and you
01:42:31.960 have to
01:42:32.260 produce
01:42:32.560 a resume
01:42:33.020 and you
01:42:33.320 have to
01:42:33.640 be able
01:42:33.820 to work
01:42:34.140 in an
01:42:34.400 office
01:42:34.660 environment
01:42:35.160 and you
01:42:35.940 know
01:42:36.040 you need
01:42:36.460 to have
01:42:36.800 all the
01:42:37.120 abilities
01:42:37.520 that you
01:42:37.940 would have
01:42:38.540 if you
01:42:39.120 were actually
01:42:39.620 having a
01:42:40.080 real job
01:42:40.700 and so
01:42:41.740 that makes
01:42:42.620 things complicated
01:42:43.480 as well
01:42:44.080 so
01:42:44.940 yeah
01:42:45.740 I want to
01:42:46.460 come back
01:42:46.860 also to
01:42:47.300 what you
01:42:47.560 were saying
01:42:48.000 about
01:42:48.380 the predictive
01:42:49.840 power of
01:42:50.580 IQ and
01:42:51.420 conscientiousness
01:42:52.420 which I don't
01:42:53.000 dispute
01:42:53.480 and I'm
01:42:54.240 also not
01:42:54.680 one of
01:42:54.940 these people
01:42:55.360 who suffers
01:42:55.880 under the
01:42:56.340 delusion
01:42:56.680 that these
01:42:57.180 things are
01:42:58.200 totally
01:42:58.800 open
01:42:59.080 socially
01:42:59.720 determined
01:43:00.300 close
01:43:00.820 quotes
01:43:01.180 I mean
01:43:01.460 I
01:43:01.620 understand
01:43:02.220 and believe
01:43:03.420 that they
01:43:04.160 have high
01:43:04.600 heritability
01:43:05.200 and identifiable
01:43:06.160 genetic
01:43:06.800 sources in
01:43:08.440 that variability
01:43:09.120 and so on
01:43:09.820 but you know
01:43:10.620 the standard
01:43:11.200 old joke
01:43:11.880 used to be
01:43:12.500 you can tell
01:43:12.960 me because
01:43:13.260 you know
01:43:13.480 more about
01:43:13.840 personality
01:43:14.280 psychology
01:43:14.840 than I do
01:43:15.340 the standard
01:43:15.720 old joke
01:43:16.120 used to be
01:43:16.420 that everything
01:43:16.900 is 50%
01:43:17.740 heritable
01:43:18.120 that pretty
01:43:20.100 much anything
01:43:20.640 that you
01:43:20.980 can measure
01:43:21.980 as a trait
01:43:22.720 that has
01:43:23.100 any stability
01:43:23.700 within the
01:43:24.120 lifetime
01:43:24.400 also turns
01:43:25.100 out to
01:43:25.720 have a
01:43:26.000 heritability
01:43:26.520 somewhere near
01:43:27.180 0.5
01:43:27.840 but there's
01:43:30.600 the other
01:43:30.900 0.5
01:43:31.660 you know
01:43:33.000 some people
01:43:34.420 have low
01:43:35.400 IQs because
01:43:36.380 they were
01:43:37.020 exposed to
01:43:37.700 too much
01:43:38.120 lead
01:43:38.440 infancy
01:43:39.100 you know
01:43:40.920 I believe
01:43:42.920 that conscientiousness
01:43:44.360 can probably
01:43:45.320 be
01:43:45.880 well I
01:43:47.620 I believe
01:43:47.900 you suggested
01:43:48.800 earlier that
01:43:49.460 we know
01:43:49.780 something about
01:43:50.360 this already
01:43:50.940 about developmental
01:43:51.700 determinants
01:43:52.620 of shifts
01:43:54.320 in conscientiousness
01:43:55.960 and so
01:43:57.700 you know
01:43:58.660 we have to
01:43:59.380 caution ourselves
01:44:00.260 against talking
01:44:01.000 about these
01:44:01.500 individual
01:44:01.920 difference factors
01:44:02.920 as if
01:44:03.560 they are
01:44:04.400 mutable
01:44:04.880 attributes
01:44:05.420 of individuals
01:44:06.080 that are
01:44:06.540 going to
01:44:07.400 undermine
01:44:08.780 any sort
01:44:10.080 of progressive
01:44:11.200 improvement
01:44:11.780 of circumstances
01:44:13.720 for people
01:44:14.240 or are
01:44:15.080 going to
01:44:16.040 create
01:44:16.520 bad
01:44:18.200 byproducts
01:44:19.300 of attempts
01:44:19.880 to produce
01:44:20.420 social justice
01:44:21.220 it's just
01:44:21.660 going to
01:44:22.040 you know
01:44:22.260 you're going
01:44:22.720 to leave
01:44:23.020 your dumb
01:44:24.260 unconscious
01:44:24.800 people out
01:44:25.720 there being
01:44:26.120 parasites
01:44:26.700 or something
01:44:27.280 well you know
01:44:28.260 there is of
01:44:30.180 course
01:44:30.480 decent evidence
01:44:31.660 that there are
01:44:33.180 sociocultural
01:44:34.120 effects on
01:44:34.820 IQ
01:44:35.120 I mean
01:44:35.600 the Flynn
01:44:36.560 effect
01:44:36.940 which is
01:44:37.660 named after
01:44:38.920 the man
01:44:39.360 who described
01:44:40.460 the phenomena
01:44:42.680 indicates that
01:44:44.000 the average
01:44:44.900 IQ has
01:44:45.620 been
01:44:45.960 increasing
01:44:47.080 quite
01:44:47.420 substantially
01:44:48.000 over the
01:44:48.420 last hundred
01:44:48.840 years
01:44:49.160 and the
01:44:49.480 reason for
01:44:50.040 that
01:44:50.360 no one
01:44:51.760 knows for
01:44:52.240 sure
01:44:52.520 but one of
01:44:53.320 the putative
01:44:53.800 reasons for
01:44:54.400 that is that
01:44:55.140 we've lifted
01:44:56.540 the bottom
01:44:57.200 out of
01:44:57.660 catastrophe
01:44:58.420 so there
01:44:59.500 aren't people
01:45:00.020 whose IQs
01:45:00.780 are stunted
01:45:01.480 by
01:45:01.940 exposure to
01:45:04.300 zero information
01:45:05.380 during critical
01:45:06.220 developmental periods
01:45:07.180 and who
01:45:07.720 didn't get
01:45:08.180 enough to
01:45:08.600 eat
01:45:08.900 yeah I was
01:45:10.040 going to say
01:45:10.400 severe malnutrition
01:45:11.440 never by
01:45:11.880 zero information
01:45:12.660 yeah
01:45:12.980 yeah exactly
01:45:13.780 exactly
01:45:14.240 so we've
01:45:14.800 we've wiped
01:45:15.580 out in
01:45:16.380 many ways
01:45:17.120 we've wiped
01:45:17.740 out the
01:45:18.280 worst effects
01:45:19.160 of privation
01:45:20.060 and that's
01:45:20.960 increasingly
01:45:21.620 true as
01:45:22.460 well on
01:45:23.040 on the
01:45:23.460 worldwide
01:45:23.860 scale
01:45:25.220 worldwide
01:45:25.940 stage
01:45:27.500 you know
01:45:28.100 there's about
01:45:28.540 150,000
01:45:29.620 people a day
01:45:30.320 right now
01:45:30.840 being lifted
01:45:31.400 out of
01:45:31.880 absolute
01:45:32.520 poverty
01:45:32.980 by UN
01:45:33.560 standards
01:45:34.140 the fastest
01:45:34.880 improvement
01:45:35.420 in the history
01:45:36.000 of the world
01:45:36.380 by a huge
01:45:36.940 margin
01:45:37.300 and also
01:45:38.080 about
01:45:38.360 300,000
01:45:39.140 people a day
01:45:40.200 being hooked
01:45:40.680 up to the
01:45:41.100 electrical grid
01:45:41.840 so we are
01:45:42.800 making some
01:45:43.400 progress
01:45:43.880 removing
01:45:44.740 the absolute
01:45:45.820 privation
01:45:46.520 problem
01:45:46.980 which is a
01:45:47.480 non-trivial
01:45:47.960 problem
01:45:48.500 the problem
01:45:49.880 with most
01:45:50.460 of the
01:45:50.780 attempts
01:45:51.120 to raise
01:45:51.660 IQ
01:45:52.080 is that
01:45:53.060 they don't
01:45:53.440 change the
01:45:53.940 variance
01:45:54.480 in IQ
01:45:54.980 they tend
01:45:55.460 to raise
01:45:55.940 the average
01:45:56.400 IQ
01:45:56.700 across the
01:45:57.240 population
01:45:57.780 and that
01:45:58.540 leaves
01:45:58.920 the
01:45:59.180 inequality
01:45:59.820 IQ
01:46:00.860 inequality
01:46:01.420 problem
01:46:01.840 basically
01:46:02.280 untouched
01:46:02.860 so there
01:46:03.520 have been
01:46:04.060 studies
01:46:04.800 trying to
01:46:05.320 estimate
01:46:05.900 how much
01:46:06.980 socioeconomic
01:46:07.880 pressure
01:46:09.260 let's say
01:46:09.800 you have to
01:46:10.340 place on
01:46:10.940 an individual
01:46:11.480 to raise
01:46:12.740 their IQ
01:46:13.360 lowering
01:46:14.420 it's easy
01:46:15.060 right
01:46:16.000 because making
01:46:16.740 something worse
01:46:17.400 is always easier
01:46:18.200 than making
01:46:18.660 something better
01:46:19.300 but if I
01:46:20.300 remember
01:46:20.680 correctly
01:46:21.220 if you
01:46:22.280 take
01:46:22.600 an
01:46:23.200 identical
01:46:23.700 twin
01:46:24.120 who's
01:46:24.440 adopted
01:46:24.860 out at
01:46:25.320 birth
01:46:25.720 in order
01:46:26.500 to produce
01:46:27.040 a 15
01:46:27.660 point
01:46:28.060 increase
01:46:28.900 in IQ
01:46:29.340 compared
01:46:29.780 to the
01:46:30.120 other
01:46:30.340 twin
01:46:30.740 which is
01:46:32.060 a one
01:46:32.340 standard
01:46:32.680 deviation
01:46:33.200 increase
01:46:33.860 and about
01:46:34.220 the same
01:46:34.640 as the
01:46:34.940 average
01:46:35.240 difference
01:46:35.660 between
01:46:35.980 a university
01:46:36.560 student
01:46:37.140 in an
01:46:39.040 average
01:46:39.520 state
01:46:39.800 college
01:46:40.140 and an
01:46:40.440 average
01:46:40.740 high
01:46:41.000 school
01:46:41.200 student
01:46:41.620 you have
01:46:42.420 to move
01:46:43.060 the one
01:46:44.080 twin
01:46:44.460 from the
01:46:45.060 fifth
01:46:45.340 percentile
01:46:46.120 of socioeconomic
01:46:47.000 status
01:46:47.400 to the
01:46:47.800 95th
01:46:48.560 percentile
01:46:49.220 so you
01:46:50.080 need about
01:46:50.480 a three
01:46:50.840 standard
01:46:51.280 deviation
01:46:51.840 improvement
01:46:52.440 in socioeconomic
01:46:53.540 conditions
01:46:54.340 to produce
01:46:54.860 a one
01:46:55.260 standard
01:46:55.660 deviation
01:46:56.180 improvement
01:46:56.720 in IQ
01:46:57.120 so it
01:46:58.440 looks like
01:46:58.880 it can be
01:46:59.300 done
01:46:59.540 but it's
01:47:00.040 but it's
01:47:00.760 expensive
01:47:01.260 you know
01:47:02.280 and
01:47:02.640 I see
01:47:03.320 what you're
01:47:03.900 saying
01:47:04.100 I'm kind
01:47:04.480 of surprised
01:47:05.240 actually
01:47:07.760 I mean
01:47:08.080 given
01:47:08.300 you know
01:47:08.920 we just
01:47:09.220 mentioned
01:47:09.560 malnutrition
01:47:10.260 is one
01:47:10.700 possible
01:47:11.160 source
01:47:11.560 of low
01:47:11.900 IQ
01:47:12.280 one
01:47:12.860 possible
01:47:13.180 developmental
01:47:13.640 source
01:47:14.040 I'm
01:47:14.240 kind
01:47:14.420 of surprised
01:47:15.000 that
01:47:15.360 to the
01:47:16.280 degree
01:47:16.500 that the
01:47:16.840 flit
01:47:17.060 effect
01:47:17.420 might be
01:47:17.920 due to
01:47:18.320 things like
01:47:18.960 a reduction
01:47:19.380 of the
01:47:19.680 number
01:47:19.900 of people
01:47:20.240 exposed
01:47:20.660 to severe
01:47:21.140 malnutrition
01:47:21.820 that it
01:47:22.940 wouldn't have
01:47:23.400 also
01:47:23.720 simultaneously
01:47:24.680 truncated
01:47:25.520 the variance
01:47:26.160 a little
01:47:26.480 bit
01:47:26.740 that seems
01:47:28.160 slightly
01:47:28.480 surprising
01:47:28.700 let me
01:47:30.840 restate
01:47:31.340 that
01:47:31.560 it has
01:47:32.240 truncated
01:47:32.900 the variance
01:47:34.480 although
01:47:34.740 the data
01:47:35.480 on that
01:47:35.860 isn't clear
01:47:36.800 isn't as
01:47:37.520 clear
01:47:37.820 but I do
01:47:39.940 believe that
01:47:40.560 it's a
01:47:40.880 reasonable
01:47:41.240 inference
01:47:41.660 to make
01:47:42.040 that the
01:47:42.340 variance
01:47:42.620 has been
01:47:42.900 truncated
01:47:43.260 it's also
01:47:43.660 hidden to
01:47:44.120 some degree
01:47:44.560 because
01:47:44.900 the
01:47:45.620 IQ
01:47:45.900 tests
01:47:46.240 are always
01:47:46.620 re-normed
01:47:47.200 to keep
01:47:47.500 the variance
01:47:48.000 at a
01:47:48.320 standard
01:47:48.620 15
01:47:49.100 points
01:47:49.580 so it
01:47:50.940 makes it
01:47:51.340 difficult
01:47:51.680 to look
01:47:52.640 retrospectively
01:47:53.560 and see
01:47:53.900 what's
01:47:54.160 happened
01:47:54.420 to the
01:47:54.700 variance
01:47:55.160 so
01:47:56.020 but
01:47:56.900 the other
01:47:58.100 problem
01:47:58.480 too
01:47:58.700 is that
01:47:59.060 you know
01:47:59.260 you get
01:47:59.600 these stories
01:48:00.140 now and
01:48:00.520 then about
01:48:00.840 these
01:48:01.040 companies
01:48:01.360 that come
01:48:01.740 out with
01:48:02.140 claims that
01:48:02.720 their brain
01:48:03.240 exercises
01:48:03.800 can improve
01:48:04.500 IQ
01:48:04.800 and the
01:48:05.540 literature
01:48:05.840 on that
01:48:06.320 is damn
01:48:06.800 dismal
01:48:07.360 I can tell
01:48:07.880 you
01:48:08.040 it's that
01:48:08.420 the holy
01:48:09.340 grail
01:48:09.720 is to
01:48:10.040 produce
01:48:10.400 cognitive
01:48:10.860 exercises
01:48:11.500 that produce
01:48:12.160 a legitimate
01:48:13.080 impact on
01:48:13.920 fluid intelligence
01:48:14.700 and like
01:48:15.880 there has been
01:48:16.440 a lot of
01:48:16.840 work done
01:48:17.340 on that
01:48:17.700 and the
01:48:18.480 answer so
01:48:19.060 far is that
01:48:19.720 it doesn't
01:48:20.320 work
01:48:20.640 so what
01:48:21.400 about the
01:48:21.720 video gaming
01:48:23.440 I mean I
01:48:24.060 know there
01:48:24.380 has been
01:48:24.680 this suggestion
01:48:25.400 that playing
01:48:25.960 video games
01:48:26.600 actually improves
01:48:27.740 at least
01:48:28.400 some aspects
01:48:29.520 of intelligence
01:48:30.180 yeah well
01:48:30.500 there's a
01:48:30.860 couple of
01:48:31.220 studies that
01:48:31.720 indicated that
01:48:32.420 video games
01:48:32.980 might improve
01:48:33.520 spatial intelligence
01:48:34.440 but here's
01:48:35.280 the problem
01:48:35.880 and I think
01:48:37.060 this is
01:48:37.480 a critical
01:48:39.120 problem
01:48:39.900 perhaps an
01:48:40.600 insoluble one
01:48:41.400 at least no one
01:48:42.080 solved it
01:48:42.540 is that
01:48:42.880 what you get
01:48:43.940 is that if you
01:48:44.500 exercise yourself
01:48:46.460 substantially on a
01:48:47.420 given game
01:48:47.880 you can radically
01:48:48.640 accelerate your
01:48:49.440 performance in the
01:48:50.160 game
01:48:50.400 so you can get
01:48:51.280 much better at
01:48:52.000 those specific
01:48:52.580 skills
01:48:52.940 sure
01:48:53.260 but you don't
01:48:53.820 get generalization
01:48:54.960 across cognitive
01:48:55.920 sets
01:48:56.440 which is what
01:48:57.300 you're really
01:48:57.640 hoping for
01:48:58.180 because
01:48:58.420 yeah I thought
01:48:58.840 I thought that
01:48:59.360 was the claim
01:48:59.960 from some of
01:49:00.500 these
01:49:00.620 yeah well
01:49:00.880 they have shown
01:49:01.820 some increases
01:49:02.640 in spatial IQ
01:49:03.640 but there's not
01:49:04.340 very many studies
01:49:05.220 and I would say
01:49:06.100 they're far
01:49:06.960 overbalanced
01:49:07.900 by the other
01:49:08.740 side of the
01:49:09.820 research equation
01:49:10.940 which continually
01:49:12.160 says
01:49:12.640 and I've looked
01:49:13.360 at this because
01:49:13.840 I'm really interested
01:49:14.920 in the improvement
01:49:16.340 of IQ
01:49:16.820 I mean that's
01:49:17.580 that's the holy
01:49:18.240 grail in some sense
01:49:19.360 and that
01:49:19.780 and
01:49:20.680 and
01:49:21.160 the
01:49:22.240 the
01:49:23.380 overwhelming
01:49:24.720 preponderance of
01:49:25.720 evidence suggests
01:49:26.620 that you don't get
01:49:27.380 generalization outside
01:49:28.540 the narrow domain
01:49:29.840 now why that is
01:49:31.300 and even
01:49:31.920 this it's even worse
01:49:32.920 because you might say
01:49:33.820 well
01:49:34.020 imagine that you could
01:49:35.420 take five different
01:49:36.720 domains of intelligence
01:49:37.840 still associated
01:49:39.140 tightly with
01:49:39.740 with G
01:49:40.420 and you have people
01:49:41.840 practice
01:49:42.480 routines in all
01:49:44.120 five dimensions
01:49:45.020 maybe you'd get
01:49:46.220 generalization
01:49:46.960 under those
01:49:47.460 circumstances
01:49:48.080 and the
01:49:49.040 the
01:49:49.400 the results
01:49:50.920 of the research
01:49:51.560 attempting that
01:49:52.260 indicate that
01:49:52.940 no as soon as
01:49:53.820 you move away
01:49:54.320 from those specific
01:49:55.120 practice instances
01:49:56.040 you don't get
01:49:57.020 generalization
01:49:57.880 so
01:49:58.680 I guess in some ways
01:50:01.420 some of this is to be
01:50:03.180 expected from the
01:50:04.020 consideration that
01:50:04.920 everything is an
01:50:05.460 allocation problem
01:50:06.520 within
01:50:06.840 within the
01:50:07.780 body and
01:50:08.800 brain
01:50:09.240 that
01:50:10.740 you know by a
01:50:11.940 large
01:50:12.400 an improvement
01:50:14.820 in one domain
01:50:15.740 tends to be bought
01:50:17.240 at the expense of
01:50:18.160 something else
01:50:19.040 you know
01:50:20.060 you
01:50:20.240 you
01:50:21.580 just that
01:50:22.860 right
01:50:23.740 and then
01:50:24.580 conscientiousness
01:50:25.480 I can tell you some
01:50:26.240 research we've done
01:50:27.200 that's cool
01:50:27.820 although we haven't
01:50:29.140 been able to
01:50:29.660 demonstrate that it's
01:50:30.460 actually improved
01:50:31.140 conscientiousness
01:50:31.980 the first thing to
01:50:33.100 note about
01:50:33.480 conscientiousness is
01:50:34.520 that no one
01:50:35.000 understands it at all
01:50:36.080 especially the
01:50:37.240 industriousness element
01:50:38.220 there's no
01:50:38.960 plausible
01:50:39.840 biological
01:50:40.840 psychological
01:50:41.800 neurophysiological
01:50:43.200 or animal models
01:50:44.460 for conscientiousness
01:50:45.500 all we've got is
01:50:46.840 self-reports
01:50:47.520 we can't even find
01:50:48.520 tasks that
01:50:49.340 conscientious people
01:50:50.340 do better
01:50:50.860 it's
01:50:51.640 it's
01:50:51.820 unbelievable
01:50:52.340 but
01:50:53.500 self-reports
01:50:55.440 really
01:50:55.780 well you can get
01:50:57.340 reports from
01:50:57.920 teachers and parents
01:50:58.820 and so forth
01:50:59.400 but it's all
01:50:59.920 human report
01:51:00.760 okay
01:51:01.460 it's the only way
01:51:01.880 we can measure it
01:51:02.720 and we
01:51:03.320 like in my lab
01:51:04.240 we probably tried
01:51:04.920 200 tasks
01:51:06.060 trying to find
01:51:06.960 something that
01:51:07.460 conscientiousness
01:51:08.240 conscientious people
01:51:09.160 do better
01:51:09.680 no luck
01:51:10.780 we can derive it
01:51:12.280 from linguistic
01:51:12.920 analysis of
01:51:13.760 verbal output
01:51:14.400 now to some
01:51:15.040 degree but
01:51:15.500 that's
01:51:15.920 still that's
01:51:16.980 not a task
01:51:17.740 you know
01:51:18.140 yeah yeah yeah
01:51:18.920 so
01:51:19.420 now we
01:51:20.820 we produced a
01:51:21.620 series of programs
01:51:22.520 called the
01:51:23.660 self-authoring suite
01:51:24.560 and one of them
01:51:25.300 the future authoring
01:51:26.500 program
01:51:26.960 it's a writing
01:51:27.560 program that helps
01:51:28.400 people lay out
01:51:29.960 their plans for the
01:51:30.940 future
01:51:31.320 in detail
01:51:32.920 so they have to
01:51:33.720 consider their
01:51:34.560 their intimate
01:51:37.400 relationships
01:51:38.040 their career goals
01:51:39.080 their educational goals
01:51:40.220 their use of time
01:51:41.040 outside work
01:51:41.820 their plans to
01:51:42.620 maintain mental
01:51:43.300 and physical health
01:51:44.200 their use of drugs
01:51:45.160 and alcohol
01:51:45.740 they have to write
01:51:46.780 for 15 minutes
01:51:47.740 about what kind of life
01:51:48.700 they'd like to have
01:51:49.480 if they were taking
01:51:50.360 care of themselves
01:51:51.180 three to five years
01:51:52.400 in the future
01:51:52.960 and then to write
01:51:54.200 for the same amount
01:51:54.980 of time about how
01:51:55.760 terrible their life
01:51:56.580 could be if all their
01:51:57.380 bad habits took
01:51:58.740 control
01:51:59.240 okay
01:51:59.980 and then they have
01:52:00.700 to turn the positive
01:52:01.500 vision into an
01:52:02.300 implementable plan
01:52:03.280 we've managed to
01:52:04.540 improve their college
01:52:05.700 grades by about 20%
01:52:07.340 and drop out their
01:52:08.260 drop drop their dropout
01:52:09.440 rate by about 25%
01:52:10.840 over about 10,000
01:52:12.560 students now
01:52:13.380 but you know
01:52:14.440 we tried to see if
01:52:15.460 that was mediated by
01:52:16.500 an improvement
01:52:17.000 in conscientiousness
01:52:18.060 and there was no
01:52:18.720 evidence for that
01:52:19.540 what it was mediated
01:52:21.240 by was number of words
01:52:23.500 written during the exercise
01:52:24.980 so it turns out that
01:52:26.380 thinking more about
01:52:27.400 your future helps
01:52:28.840 the more you think
01:52:29.760 about it the more it helps
01:52:30.760 and maybe that
01:52:31.800 you know maybe that
01:52:32.800 would translate into
01:52:33.620 an improvement in
01:52:34.360 conscientiousness across
01:52:35.400 time but
01:52:36.040 there haven't been
01:52:38.380 any credible studies
01:52:39.700 that I know of
01:52:40.600 indicating that there
01:52:41.840 are exercises that
01:52:42.960 can be done
01:52:43.540 to improve
01:52:44.680 conscientiousness
01:52:45.880 so that's also
01:52:46.640 you know
01:52:47.520 troublesome
01:52:48.460 and worrisome
01:52:49.560 because that would
01:52:50.120 be a nice thing
01:52:50.680 to be able to do
01:52:51.500 yeah
01:52:52.040 I mean
01:52:53.000 I haven't thought
01:52:54.720 much about
01:52:55.200 and I don't know
01:52:55.720 much about the
01:52:56.240 literature on
01:52:56.900 conscientiousness
01:52:57.760 as a trait
01:52:58.460 but the word
01:52:59.400 seems to
01:53:00.080 connote to me
01:53:01.300 as an ordinary
01:53:01.900 English speaker
01:53:02.720 has a strong
01:53:03.740 social element
01:53:04.520 to it as well
01:53:05.260 it's kind of like
01:53:05.920 a conscientious person
01:53:06.940 is somebody
01:53:07.740 who doesn't forget
01:53:09.000 his obligations
01:53:09.760 your index
01:53:11.680 of conscientiousness
01:53:12.780 in a university
01:53:13.520 professor is
01:53:14.380 you know
01:53:15.220 somebody has
01:53:16.180 asked you
01:53:16.700 to write them
01:53:17.400 a reference
01:53:17.980 letter for
01:53:18.560 getting into
01:53:19.020 graduate school
01:53:19.740 or whatever
01:53:20.220 do you actually
01:53:20.960 prioritize and
01:53:21.760 get the damn
01:53:22.320 thing done
01:53:22.840 on time
01:53:23.260 or is there
01:53:23.620 some risk
01:53:24.140 that you'll
01:53:24.460 just forget
01:53:25.000 about it
01:53:25.340 and shove
01:53:25.680 it to them
01:53:26.020 somewhere else
01:53:27.020 I imagine
01:53:29.140 conscientiousness
01:53:30.040 as having a
01:53:30.600 strong element
01:53:31.360 of attentiveness
01:53:32.960 to social
01:53:34.020 obligation
01:53:34.700 and to the
01:53:35.280 well-being
01:53:35.620 of others
01:53:36.200 as it is
01:53:37.560 defined in the
01:53:38.240 personality literature
01:53:39.100 does it have
01:53:39.640 any of that
01:53:40.100 well I would
01:53:40.780 say not so much
01:53:41.960 attention to the
01:53:42.760 well-being of
01:53:43.340 others because
01:53:43.740 that's more
01:53:44.220 trait agreeableness
01:53:45.320 that's more
01:53:46.120 the maternal
01:53:46.580 dimension but
01:53:47.220 there's definitely
01:53:48.280 a massive effect
01:53:49.940 of social obligation
01:53:51.060 which is part of
01:53:52.140 the reason why
01:53:52.880 conservatives tend
01:53:54.380 to be higher in
01:53:55.040 conscientiousness than
01:53:55.980 liberals but it's
01:53:57.100 not well-being of
01:53:58.260 others it's duty
01:53:59.180 and so the
01:54:01.640 conscientious types
01:54:03.180 form and maintain
01:54:05.060 social contracts
01:54:06.220 they implement
01:54:08.180 their plans
01:54:09.020 and they seem
01:54:10.720 to feel
01:54:11.240 shame
01:54:12.000 and self-contempt
01:54:13.240 when they
01:54:14.120 fail to live up
01:54:15.760 to their social
01:54:16.280 obligations
01:54:16.860 so that's another
01:54:18.000 thing that's
01:54:18.480 interesting about
01:54:19.140 the income
01:54:19.540 redistribution idea
01:54:20.620 because it's
01:54:21.860 conceivable to me
01:54:22.920 that conscientious
01:54:23.820 people would hate
01:54:24.800 that
01:54:25.220 because
01:54:26.080 conscientious people
01:54:27.460 do very badly
01:54:28.320 for example if
01:54:29.140 they're laid off
01:54:30.400 from work
01:54:30.940 even if it's
01:54:31.600 not their fault
01:54:32.340 they still take
01:54:33.320 themselves apart
01:54:34.220 for their failure
01:54:35.180 and so
01:54:37.420 conscientious people
01:54:38.780 in particular
01:54:39.640 seem to find
01:54:40.560 inactivity
01:54:41.400 without productivity
01:54:42.880 highly aversive
01:54:45.080 and aversive
01:54:46.080 enough to really
01:54:46.620 cause them
01:54:47.080 major health
01:54:48.140 problems
01:54:48.660 so
01:54:49.560 well
01:54:50.780 yeah
01:54:51.020 that brings us
01:54:52.120 back to what
01:54:52.740 we were discussing
01:54:53.340 a little while
01:54:53.940 ago
01:54:54.120 the problem
01:54:54.680 of
01:54:55.860 ensuring
01:54:59.260 that
01:54:59.840 large numbers
01:55:01.640 of people
01:55:01.940 have access
01:55:02.500 to meaningful
01:55:03.100 work
01:55:03.800 in an age
01:55:04.880 in which it is
01:55:05.500 more and more
01:55:06.000 the case
01:55:06.540 that
01:55:06.920 big components
01:55:09.000 of the economy
01:55:09.860 are booming
01:55:11.960 away with
01:55:12.560 very few
01:55:13.060 employees
01:55:13.700 and that's
01:55:15.120 going to
01:55:15.480 continue
01:55:15.920 that's probably
01:55:17.160 going to escalate
01:55:18.680 or back to
01:55:20.420 that same
01:55:20.860 topic
01:55:21.260 to some extent
01:55:21.960 yes
01:55:22.280 and again
01:55:24.200 I mean I think
01:55:24.940 I think
01:55:25.840 inequality
01:55:26.860 of opportunity
01:55:29.420 is sort of
01:55:30.460 the bottom
01:55:32.000 or bedrock
01:55:32.820 of inequality
01:55:33.620 having its
01:55:35.420 impacts upon us
01:55:36.940 and it's
01:55:38.000 certainly the
01:55:38.640 bottom of bedrock
01:55:39.440 of
01:55:39.720 why we should
01:55:43.020 care about it
01:55:43.760 on moral
01:55:44.360 and social
01:55:44.860 justice grounds
01:55:45.840 it's like
01:55:46.880 why should
01:55:48.460 people
01:55:49.240 who
01:55:49.660 why should
01:55:52.000 one's birthright
01:55:52.980 affect the
01:55:53.880 opportunities
01:55:54.400 available to one
01:55:55.360 and
01:55:56.200 well it's also
01:55:57.780 a social
01:55:58.260 catastrophe
01:55:58.860 because
01:55:59.400 hypothetically
01:56:00.720 you want to
01:56:01.340 set up a
01:56:01.720 society
01:56:02.120 so that
01:56:02.640 whatever
01:56:03.780 someone has
01:56:04.720 to offer
01:56:05.400 is
01:56:06.160 maximally
01:56:07.480 offerable
01:56:08.080 to the
01:56:08.420 community
01:56:08.820 because otherwise
01:56:09.440 the community
01:56:09.920 loses
01:56:10.460 yes
01:56:11.260 and that seems
01:56:11.920 to be
01:56:12.200 I mean
01:56:12.500 I think
01:56:13.040 one of the
01:56:13.500 great examples
01:56:14.160 of that
01:56:14.540 although I don't
01:56:15.080 think this
01:56:15.480 accounts for
01:56:16.120 all of it
01:56:16.520 is that
01:56:16.900 the relationship
01:56:17.980 between the
01:56:18.620 provision of
01:56:19.180 women's rights
01:56:19.880 by countries
01:56:20.620 and their
01:56:21.000 economic productivity
01:56:22.020 is staggeringly
01:56:22.980 high
01:56:23.420 so I think
01:56:24.800 that also has
01:56:25.520 to do with
01:56:26.000 openness in
01:56:27.520 general
01:56:28.160 to transformation
01:56:29.360 and change
01:56:30.360 with the provision
01:56:32.000 of women's rights
01:56:32.820 being an index
01:56:33.680 of that
01:56:34.040 but nonetheless
01:56:34.620 it's a great
01:56:35.800 predictor of
01:56:36.440 eventual economic
01:56:37.300 success
01:56:37.980 well so
01:56:38.800 partly an index
01:56:39.940 and partly
01:56:40.380 perhaps a
01:56:41.060 more direct
01:56:42.080 effect
01:56:42.680 that after all
01:56:43.480 slightly over
01:56:44.460 half the
01:56:44.840 population
01:56:45.400 maybe
01:56:47.100 their talents
01:56:48.320 are better
01:56:49.540 utilized
01:56:50.300 right
01:56:52.220 well that's
01:56:52.860 certainly what
01:56:53.300 we would hope
01:56:53.900 and I think
01:56:54.860 as you said
01:56:55.920 I think the
01:56:56.360 evidence at least
01:56:57.040 suggests that
01:56:58.040 so okay
01:56:59.080 so let me
01:57:00.040 recapitulate
01:57:00.720 because we should
01:57:01.460 probably fold
01:57:02.620 this up
01:57:03.080 and so
01:57:04.360 as far as
01:57:06.880 I'm concerned
01:57:07.540 your work
01:57:08.200 was revolutionary
01:57:08.900 because it
01:57:09.580 undermined
01:57:10.180 the general
01:57:11.500 proposition
01:57:12.080 that the
01:57:12.560 fundamental cause
01:57:13.380 of crime
01:57:14.280 and violent
01:57:15.080 crime in particular
01:57:15.860 was poverty
01:57:16.580 instead you
01:57:17.960 flipped it
01:57:19.100 on edge
01:57:20.480 so to speak
01:57:21.100 and made
01:57:21.920 the claim
01:57:22.860 well substantiated
01:57:24.440 by the research
01:57:25.280 that it's
01:57:26.060 relative poverty
01:57:27.300 that drives
01:57:28.800 violent crime
01:57:30.120 because of
01:57:30.560 status seeking
01:57:31.280 primarily among
01:57:32.640 young men
01:57:33.180 and although
01:57:34.760 there are
01:57:35.240 effects of
01:57:36.020 absolute
01:57:36.780 privation
01:57:37.660 and that would
01:57:38.360 be the
01:57:38.640 poverty effect
01:57:39.380 the effects
01:57:40.980 of relative
01:57:41.980 deprivation of
01:57:43.380 status are
01:57:44.180 much more
01:57:45.100 let's say
01:57:45.900 especially in
01:57:46.840 our societies
01:57:47.440 much more
01:57:48.120 socially significant
01:57:49.140 and that the
01:57:50.320 status competition
01:57:51.060 itself is driven
01:57:52.060 at least in part
01:57:52.940 by the desire
01:57:54.680 of men to
01:57:55.380 attain status
01:57:56.300 to obtain
01:57:57.620 access to
01:57:58.700 women
01:57:59.460 roughly speaking
01:58:00.560 and it's partly
01:58:01.420 because women
01:58:01.880 outsource the
01:58:02.700 problem of
01:58:03.280 mate selection
01:58:04.380 to the male
01:58:05.600 competition domain
01:58:06.760 right so the
01:58:07.520 males compete
01:58:08.100 the women
01:58:08.460 peel off the
01:58:09.180 top
01:58:09.460 it's like a
01:58:10.080 market solution
01:58:10.820 in some sense
01:58:11.580 and then
01:58:13.180 having pointed
01:58:14.900 out that
01:58:15.580 inequality
01:58:17.040 not only drives
01:58:18.220 male homicide
01:58:18.840 but also
01:58:19.500 tends to
01:58:20.080 destabilize
01:58:20.760 societies
01:58:21.320 there is an
01:58:22.740 impetus for
01:58:23.360 people to
01:58:23.820 consider how
01:58:24.660 we can stop
01:58:25.680 the winner
01:58:26.940 from taking
01:58:27.760 all
01:58:28.260 without becoming
01:58:29.420 unduly
01:58:29.880 authoritarian
01:58:30.380 about it
01:58:31.080 or
01:58:31.320 impeding
01:58:33.340 individual
01:58:33.940 productivity
01:58:34.580 given the
01:58:35.600 fact that
01:58:36.000 there is
01:58:36.340 individual
01:58:36.800 variation
01:58:37.460 in the
01:58:38.860 elements that
01:58:39.600 actually produce
01:58:40.520 productivity
01:58:41.240 that's our
01:58:41.760 set of social
01:58:42.600 problems
01:58:43.100 exacerbated by
01:58:44.320 the fact that
01:58:45.140 we're going to be
01:58:45.820 wiping out
01:58:46.340 employment for
01:58:47.100 huge categories
01:58:47.960 particularly of
01:58:49.360 men over the
01:58:50.320 next 15 years
01:58:51.920 let me in
01:58:53.860 this context
01:58:54.620 just make a
01:58:55.960 point that I
01:58:56.940 spent most of
01:58:57.760 a chapter of
01:58:58.400 my book on
01:58:58.960 and that is
01:58:59.480 that the
01:59:00.860 notion that
01:59:01.960 inequality is
01:59:03.180 somehow the
01:59:04.840 engine of
01:59:05.860 productivity
01:59:07.120 has been pretty
01:59:08.640 much rejected
01:59:09.480 by economists
01:59:10.540 themselves in
01:59:11.480 recent years
01:59:12.160 they've come to
01:59:12.660 the conclusion
01:59:13.200 that relatively
01:59:14.480 equitable places
01:59:15.400 actually have
01:59:16.480 more economic
01:59:18.720 productivity in
01:59:20.140 the ensuing
01:59:20.860 period of time
01:59:21.680 than those that
01:59:22.220 start out more
01:59:22.880 inequitable
01:59:23.560 and there's a
01:59:24.000 lot of reasons
01:59:24.680 for that
01:59:25.200 the one that I
01:59:26.560 think is most
01:59:27.020 striking that I
01:59:27.980 would commend
01:59:28.440 people to look
01:59:29.080 into is the
01:59:30.580 concept of
01:59:31.420 useless if you
01:59:33.380 like or wasteful
01:59:34.300 expenditure on
01:59:35.240 guard labor
01:59:36.020 and relatively
01:59:36.680 unequal societies
01:59:37.740 and guard labor
01:59:38.560 is a term coined
01:59:39.480 by economists
01:59:40.800 Sam Bowles and
01:59:41.680 Arjun Jayadev
01:59:42.680 and what they've
01:59:44.000 shown is that
01:59:44.820 the number of
01:59:45.640 people who are
01:59:46.060 employed in just
01:59:47.160 jobs like being
01:59:47.980 security guards
01:59:49.220 goes up as
01:59:50.800 inequality goes up
01:59:51.660 it's no great
01:59:52.180 surprise when you
01:59:52.860 think about it
01:59:53.580 but you can
01:59:54.700 define guard labor
01:59:55.620 more broadly or
01:59:56.460 more narrowly and
01:59:57.280 the general result
01:59:58.140 is that a large
02:00:00.160 proportion of people
02:00:00.900 are engaged in
02:00:01.600 work that is in a
02:00:02.320 sense nonproductive
02:00:03.320 it's just trying to
02:00:04.220 prevent people from
02:00:05.100 usurping the
02:00:05.760 property of other
02:00:06.460 people and that
02:00:08.380 this is a very
02:00:09.940 wasteful consequence
02:00:11.100 of extreme inequality
02:00:12.340 and economic waste
02:00:14.800 that's reduced in
02:00:15.660 relatively equitable
02:00:16.540 societies and there
02:00:17.940 are others
02:00:18.560 right so as the
02:00:20.040 society becomes
02:00:21.940 more unequal
02:00:22.740 it tilts towards
02:00:24.180 authoritarianism at
02:00:25.380 multiple levels of
02:00:26.480 organization
02:00:27.180 it's also
02:00:28.000 counter
02:00:28.380 sorry I was just
02:00:30.200 going to say
02:00:30.460 and towards
02:00:30.960 exactly and it's
02:00:32.520 counterproductive even
02:00:33.460 from the point of
02:00:34.220 view of simple
02:00:35.860 you know economic
02:00:37.260 criteria of GDP
02:00:38.620 and so on
02:00:39.400 that inequality
02:00:41.760 gets in the way
02:00:42.640 of that for a
02:00:43.080 bunch of reasons
02:00:43.760 another really
02:00:44.420 interesting one
02:00:45.140 that Bowles has
02:00:46.520 articulated in a
02:00:49.260 recent book
02:00:49.880 Bowles B-O-W-L-E-S
02:00:51.960 Sam Bowles if
02:00:52.600 you want to look
02:00:53.120 them up
02:00:53.420 I think his book
02:00:55.400 was called
02:00:55.940 The New Politics
02:00:58.080 of An Equality
02:00:58.720 of Redistribution
02:00:59.600 and I liked it a
02:01:00.400 lot
02:01:00.620 anyway one thing
02:01:02.540 that he's shown
02:01:03.320 that I thought
02:01:03.820 was very interesting
02:01:04.680 and had never
02:01:05.140 entered my head
02:01:05.820 before I read
02:01:06.460 him was that
02:01:07.620 the actual
02:01:08.740 quality of goods
02:01:10.180 in a society
02:01:11.140 can be damaged
02:01:12.240 by severe inequality
02:01:13.660 when rich
02:01:16.700 individuals
02:01:17.440 and rich firms
02:01:18.360 have the capacity
02:01:19.320 to keep innovators
02:01:21.460 and small
02:01:22.080 companies
02:01:23.920 from establishing
02:01:25.160 themselves
02:01:25.640 you mentioned
02:01:26.120 before about
02:01:26.820 the differences
02:01:28.080 in entrepreneurial
02:01:28.960 undertakings
02:01:30.100 and where
02:01:31.820 large numbers
02:01:33.560 of people
02:01:33.820 with worthy
02:01:34.280 small business
02:01:35.060 plans can't
02:01:36.040 capitalize them
02:01:36.940 properly
02:01:37.300 and can't get
02:01:37.800 off the ground
02:01:38.320 you've actually
02:01:38.780 got the phenomenon
02:01:39.880 of people
02:01:41.500 with lots
02:01:42.260 of wealth
02:01:42.880 and shoddy
02:01:43.740 products
02:01:44.300 can drive
02:01:44.840 people
02:01:45.140 with better
02:01:45.660 quality
02:01:46.220 products
02:01:46.900 who are
02:01:47.120 trying to
02:01:47.420 get started
02:01:47.900 at the bottom
02:01:48.480 out of the market
02:01:49.500 with negative
02:01:50.920 results
02:01:51.480 for just
02:01:52.040 the consumers
02:01:53.200 of the society
02:01:54.360 with having
02:01:55.100 people stack
02:01:55.900 up at zero
02:01:56.680 zero turns out
02:01:57.900 to be a very
02:01:58.420 very difficult
02:01:58.980 place to get
02:01:59.700 out of
02:02:00.080 because you
02:02:00.820 can't leverage
02:02:01.680 yourself out
02:02:02.440 of it
02:02:02.960 it's also
02:02:03.620 in those
02:02:04.040 really unequal
02:02:04.720 societies too
02:02:05.560 like say
02:02:06.220 Central American
02:02:06.960 societies
02:02:07.520 it also
02:02:08.420 becomes
02:02:08.860 increasingly
02:02:09.420 unpleasant
02:02:09.980 for the people
02:02:10.600 who are wealthy
02:02:11.240 because they're
02:02:11.760 only wealthy
02:02:12.300 in a very
02:02:12.800 narrowly defined
02:02:13.920 way
02:02:14.340 because they
02:02:15.100 can't go
02:02:15.580 outside
02:02:16.140 they can't
02:02:16.860 let their
02:02:17.220 children
02:02:17.600 go out
02:02:18.400 into public
02:02:19.360 because they'll
02:02:19.840 get kidnapped
02:02:20.360 I mean
02:02:20.860 the societies
02:02:21.720 get pretty ugly
02:02:22.460 when the fences
02:02:23.080 have to be
02:02:23.660 really high
02:02:24.300 and so
02:02:26.220 yeah
02:02:29.140 among the rich
02:02:31.420 countries of the world
02:02:33.880 those problems
02:02:34.520 are not absent
02:02:35.380 I mean
02:02:35.800 they're certainly
02:02:36.240 worse in the US
02:02:37.200 than they are
02:02:37.760 in Canada
02:02:38.320 or most of
02:02:38.920 Western Europe
02:02:39.540 yeah
02:02:41.960 well
02:02:43.280 all right
02:02:43.740 that was really
02:02:44.400 good
02:02:44.680 I'm very happy
02:02:45.820 that you agreed
02:02:46.580 to do a podcast
02:02:47.240 with me
02:02:47.820 and I mean
02:02:48.720 I found your work
02:02:49.840 well
02:02:50.360 I definitely
02:02:51.620 regard you
02:02:52.420 as one of the
02:02:53.380 people who's been
02:02:54.160 highly influential
02:02:55.100 on my thinking
02:02:55.820 I mean
02:02:56.180 I think that work
02:02:56.900 on relative poverty
02:02:57.820 is just
02:02:58.300 and the effect
02:02:59.720 size
02:03:00.020 is the work
02:03:00.520 you guys did
02:03:01.080 in Chicago
02:03:01.600 your work
02:03:02.920 on indicating
02:03:04.000 the adaptive
02:03:05.420 utility
02:03:06.400 of uncertainty
02:03:08.260 related
02:03:08.920 dominance
02:03:09.400 challenges
02:03:09.960 in unequal societies
02:03:11.400 all of that
02:03:11.980 is brilliant
02:03:12.440 I think
02:03:12.920 and nicely
02:03:13.780 biologically predicated
02:03:15.120 and the science
02:03:15.860 is done
02:03:16.260 extremely soundly
02:03:17.380 and it has
02:03:17.800 remarkable policy
02:03:19.020 implications
02:03:19.640 and you know
02:03:20.580 and it changes
02:03:21.180 the view
02:03:21.780 around crime
02:03:23.340 and wealth
02:03:25.340 and in a very
02:03:26.220 important way
02:03:26.920 to tilt it over
02:03:27.740 towards the inequality
02:03:28.780 side
02:03:29.180 I think it's
02:03:30.280 and it fits so nicely
02:03:31.380 in with the dominance
02:03:32.240 hierarchy literature
02:03:33.000 and all of that
02:03:33.660 it's really profound
02:03:35.100 stuff as far as
02:03:35.920 I'm concerned
02:03:36.380 so I'm really glad
02:03:37.580 you had a chance
02:03:38.380 to share it
02:03:38.940 with everyone
02:03:39.500 thank you very much
02:03:41.460 I appreciate
02:03:42.320 those kind words
02:03:43.480 flattery will get
02:03:45.580 you everywhere
02:03:46.180 yeah well the thing
02:03:48.000 is the best kind
02:03:49.000 of flattery
02:03:49.540 is truth
02:03:50.080 so and
02:03:52.060 I would certainly
02:03:53.360 recommend that people
02:03:54.260 take a look
02:03:54.740 at your book
02:03:55.220 if they're interested
02:03:56.180 in what we've been
02:03:57.080 discussing
02:03:57.520 again that's
02:03:58.300 killing the competition
02:03:59.260 which is
02:04:00.320 it's very readable
02:04:01.280 I would say
02:04:01.900 it provides a lovely
02:04:03.140 argument with regards
02:04:04.540 to inequality
02:04:05.240 addresses the major
02:04:06.760 criticisms I think
02:04:07.720 very effectively
02:04:08.480 and starts to
02:04:10.080 lay out
02:04:10.860 what is going to be
02:04:11.720 an increasingly
02:04:12.300 necessary public
02:04:13.380 discussion about
02:04:14.280 how civilized
02:04:16.060 societies can
02:04:17.000 ensure that
02:04:19.580 they don't collapse
02:04:20.460 into two extreme
02:04:21.480 distribution
02:04:22.560 into two extreme
02:04:26.460 distributions of wealth
02:04:27.520 or other resources
02:04:28.280 it's a real danger
02:04:29.240 it's a conscious
02:04:29.920 constant danger
02:04:31.700 needs to be
02:04:32.560 thought through
02:04:33.100 and addressed
02:04:33.500 very intelligently
02:04:34.380 so thanks again
02:04:36.680 hopefully
02:04:37.060 maybe we'll get
02:04:37.660 another chance
02:04:38.120 to talk
02:04:38.560 hopefully a couple
02:04:39.260 hundred thousand
02:04:39.780 people will watch
02:04:40.460 this
02:04:40.720 that would be good
02:04:41.340 that would be great
02:04:42.680 thanks a lot
02:04:43.720 you bet
02:04:44.440 signing off
02:04:45.780 signing off
02:04:46.920 okay
02:04:47.640 thank you
02:04:48.320 bye now
02:04:49.360 thank you
02:04:54.160 for listening
02:04:54.600 to the
02:04:55.000 Jordan B.
02:04:55.600 Peterson podcast
02:04:56.640 to support
02:05:00.140 these podcasts
02:05:01.000 you can donate
02:05:01.600 to Dr.
02:05:02.160 Peterson's
02:05:02.700 Patreon account
02:05:03.580 the link to
02:05:04.440 which can be
02:05:04.960 found in the
02:05:05.460 description of
02:05:06.160 this episode
02:05:06.740 Dr.
02:05:09.640 Peterson's
02:05:10.280 self-development
02:05:11.220 programs can be
02:05:12.480 found at
02:05:12.940 self-authoring
02:05:13.780 dot com
02:05:14.480 thank you