DISCLAIMER: This webinar transcript was loosely edited and there may be inaccuracies. 

Eliana LaFerrara: Hello, everyone, Welcome to Dev talks. I am Eliana LaFerrara, and I’m a professor of public policy at the Harvard Kennedy School. It is a great pleasure today to be welcoming Dr. Joe Henrich from Harvard University for a session that will largely revolve around his book, titled the weirdest people in the world how the West became psychologically peculiar and particularly prosperous.

Joseph Henrich: Thanks to the Growth Lab for having me and for being flexible on the format. So in the the initial there was going to be more discussion. But one of my goals here, and and why I’m. So excited to be talking to a diverse group with policy interests and interest in economic development is that I come from the field of cultural evolution, which is a a kind of newly emerging discipline, and I think it can provide a valuable framework. But most of you are probably unfamiliar with cultural evolution. So I wanted to do a little bit of groundwork and kind of lay some of that out, so i’ll be drawing on both books, and we’ll spend some time on the weirdest people in the world. But this book really provides the framework for thinking about culture and cultural evolution one.

Now, I thought, since the group is maybe half economists, I would make this my outcome variable and just start thinking about the things that influence economic growth, and my kind of ah prelude or preamble is going to be based on a new paper where we just try to establish a simple and robust connection between family structures around the world, traditional family structures and economic growth. So i’m, drawing here on a a paper with Duma and Barani, Rod Jonathan Beecham and Jonathan Shilts.

And we’re going to argue that we should think about kinship norms as kin based institutions so cult fully transmitted social norms that shape things like marriage, the organization of family. Some societies have clans, kindreds, different marriage systems, and different forms of customary inheritance.

I’ll be talking about this more, But the key dimension we want to try to measure is kinship intensity. And this is actually a concept from anthropology. So we’re going to combine an economic problem with an idea from anthropology, and see if we can look for a link here.

Ultimately I’ll be arguing that economic growth is generated when there’s a fit between informal institutions, like kinship and the psychology, that they produce the kinds of social networks they produce in larger scale impersonal institutions, things like democracy or the organization of business firms.

So, as a as an outcome measure as an initial outcome measure, we’re going to use this nighttime satellite luminosity which has been widely used. Now it’s. Ah, you know, it gives us a fine-grained pixel level measure of economic prosperity that correlates strongly with other measures of economic growth and prosperity, and in the paper I’m. Referring to, we also use regional Gdp measures from genioli, twenty, fourteen, which gives us over one thousand five hundred different regions in eighty four countries, and we perform a bunch of analyses linking these two in the paper.

Now, kinship intensity, As I mentioned many of you. If You’re from societies that are Western, educated, industrialized, rich, and democratic. A lot of this talk about kinship may seem strange because those societies are at the extreme end of low kinship intensity where the family has been suppressed and doesn’t play an important role in distribution. Consumption networks, all kinds of things like that.

So we’re going to measure kinship intensity by looking at the presence of Clans cousin. Marriage rules about post marital residents where you live. After you get married you live near uh the bride’s family, or the grooms family. Uh, do you have polygeny? And what kind of inheritance system do you have patrilineal matrilineal inheritance by Testament.

So we’re going to measure this kinship intensity from this anthropological data source composed in the nineteen sixty S. Um. Which is going to has a whole bunch of information about kinship, and when I first went to graduate school I was like, Why are anthropologists so obsessed with kinship?

and then I went to do research in the South Pacific, and i’m like Oh, because the people are obsessed with kinship. And so they write about what the people obsess about. And So there’s lots of data in the ethnographic atlas on that the year which most of these observations refer to as one thousand nine hundred. So it’s going to precede our satellite observations, our nighttime luminosity measures, which are two thousand and ten we’re; also going to be drawing this kinship intensity Measure from a a paper by Jonathan Schultz, myself, and others. Actually, it’s the same Co. Authors uh that we published in science. So in that sense we’re going to tie our hands right. We have to use the exact same measure that we use in the science paper, so we can’t kind of fudge around with the index at all.

Ah, and we’re also going to take data from genetics. So my colleague, David Reich, who’s over in the department of Human Evolutionary biology, has put together the largest data set of human genetic data, and these kinship practices leave an imprint in the genome, which is one way to kind of assess that what these anthropologists report is actually affecting things because it’s going to shape the the lengths of runs of homozygosity, both polygeny and cousin marriage and other kinds of endogamous marriage preferences should do that. So we’re going to use that, and we’re just going to substitute this in sometimes for that.

Okay, So a standard issue economic regression table. Hopefully, at least half of you are familiar with these. I’ll just walk you through some of the key insights. So we’re trying to predict this pixel level luminosity. So the logarithm of pixel level luminosity we’re using this kinship intensity index from the anthropologists in the nineteen sixty s that we’ve used to assign ethnic groups all around the world a measure of of kinship intensity.

And so, by putting this here and noticing that this is about one, this coefficient. Here we can interpret this as a as a prosperity per capita, so it’s luminosity per capita.

So that’s that, And then this is our coefficient here. So a one standard deviation increase in kinship, intensity across societies results in a forty percent decrease in luminosity. So if you buy that as a measure of economic prosperity. There’s a relationship there now. We estimate a whole bunch of different specifications, different models We always. We put in country fixed effects. So we’re comparing only ethnic groups in the same country.

We can’t. We control for a whole bunch of the standard geographic controls political hierarchy and malaria index stuff from other other work that could be leading us astray.

And then we take that out. We use the exact same model, and then we stick in our f value, and we’re able to show that that gives the same result. And then we do the same thing with our satellite luminosity. We take that out same specification plug in the G, and only regional stuff.

You have to make a few adjustments. But you get basically the same. Answer. So that’s that’s suggesting that There’s this robust linkage there.

There’s some details about how we match the ethnographic Atlas that i’m going to pass over for our purposes here. 

Now the other thing we did that helps us, maybe think that this is. There’s really something to this: this ancestral kinship intensity affecting contemporary economic outcomes.

We do what’s called a spatial regression, discontinuity, analysis. So we look at the borders between our ethnic groups, and we only focus on groups within countries, So there’s no country level differences here, and we compare, and we look at what happens when we move from a a population that has a low kinship intensity so small, maybe monogamous nuclear families to a group with clans or something like that, some intensive group.

And what we find that you can see pictured here is that there’s a drop in ah in luminosity. So you have less economic prosperity as you move from groups with hot, with low kinship, potentially to high intensity. So that’s consistent with the other results.

We can, you know, do the full analysis. And that’s the basic answer. We get these coefficients here are just the Ki when you cross that uh the effect there.

Okay. So that suggests that there’s a linkage here. Now, why is that the case?

Lots of economists and others have argued that. Well, it has to do with differences in innovation, specialization, political institutions, trade. And so there’s a causal relationship there. Potentially. I leave that to other people. I’m. Interested in how these affect this.

So in the weirdest people in the world, I make the case that different kinship norms create an environment that leads to different ways of thinking about the world, and then that leads to more or less innovation, specialization, different functioning, political institutions, and more or less trade.

These can also be seen in social networks. So these have a huge and direct effect on your social networks. And so these two co-evolve.

Where’s my arrow? Okay, there it is now. I’m also going to think about where kinship norms come from.

So there’s a lot of reason to think ecology matters, and that’ll pop up at one point, but I’m. Mostly interested in the historical effect of religions. Religions have been very opinionated about marriage in the family, especially as you move through time over the last few millennium, so we’ll get into the Catholic Church. But other religions also have opinion, so we can think about how different religious traditions may have affected things through kinship. Mars.

One of the challenges. If you look across the social science, I think, is how people think about culture.

When I first started in anthropology. Culture felt like this sort of murky thing, that sort of looms almost like a haze around people. It’s hard to get, you know my my background is aerospace engineering that I was an engineer for a while before going into this business, and so I want things to be concrete and measurable.

So I found this work by. Ah, yeah, I ended up working closely with Rob Boyd and Pete Richardson, and also there’s other work by two biologists, cavali, sports, and feldman. And they said, Well, culture is this stuff that resides in individuals heads, and it gets transmitted from one person to another.

Why, you’re growing up, but also why are an adult? So it’s about how you learn from people. So you can build models of cultural evolution by understanding how people learn from each other and then cobbling up thinking, Okay, people interact, They learn, they do stuff, they interact again. And how can that get us to explain sociological phenomena, and what we know from a lot of research now is people automatically and unconsciously learn all sorts of things. So they learn motivations, valences, ideas, beliefs, and values. They also learn decision, making biases and heuristics and the kinds of things that directly affect people’s decision making.

So culture Here is information stored in people’s brains that got there via social learning. And so with that definition, technologies fall into this. Languages fall into this fertility, preferences, or any traits that influence fertility. I’m just throwing those out there, because often some people try to carve those out of culture.

So anything we socially learn from others we acquire while growing up. Of course, institutions are shaped strongly by social wars.

So now, where the evolution comes in, and why, I think that’s really important is, How do we decide how people learn from each other?

Well, I mean, when I talk to economic theorists who think about this, they’ll just make an assumption or or assume people are rational. What we do in the cultural evolution field is, we recognize that we’re a kind of ape, and that natural selection has shaped our minds to be good at learning from others.

So, more than any other species, we’re awesome at imitation. We’re awesome at inferring the underlying ah motivations and strategies that others have, and we copy those all the time. And outside of conscious awareness we’re particularly tuned into copying some kinds of people and not other kinds. So we use cues of shared dialect or prestige to figure out who to pay attention to.

So we can use evolutionary theory and build formal models that tell us what this should look like. And then, of course, we have other features of our evolved psychology that are going to affect behavior, and this is going to be really important for kinship.

Now, the product of this cultural evolution creates things like complex tools, rituals, and practices, but also social norms at institutions as well as languages. And this is the world in which kids are evolved in.

And so the argument that’s in the secret of our success is, we have these big brains, and we have a great deal of plasticity, because we need to learn how to navigate the world built by cultural evolution. And of course, cultural evolution has built lots of different worlds, and it’s been doing it for hundreds of thousands of years, so our minds have evolved to figure out how to navigate these worlds and some things we know. We know that the illusions that people see across societies vary. So if you read a look at a textbook on cognitive psychology, you might see something like the molar allusion which has the arrows in or the arrows out well, and if you’re in Chicago you’ll you have to see. One line has to be twenty five percent longer than the other line before people perceive them in Chicago as the same length. If you study hunter-gatherers, they don’t see the illusion so basic. Visual perception. Depends. On the world you grow up on. Self. Regulation is affected by religious devotion. People who engage in daily religious devotion have greater self regulation. It’s cultural practice, same thing with the others. I could go into secret of our success gets into something.

all right, and the last thing i’ll mention is that we’re at the stage, and the evolution of the social sciences where we have to get rid of the the dualism, the you know, culture. On one hand, biology, on the other hand, when we grow up in these worlds it changes our brains. So you can take genetically identical twins. Raise them in two different societies. They end up with different brains.

So that’s just that’s just a fact.

Okay. And then my interest is actually, I think this has been going on for a really long time. So i’m interested in how this has shaped features of human psychology.

All right.

Okay. And i’ll just make a comment on the culture and institutions question. Just because this seems to be an ongoing issue in economics from the perspective that I’ve tried to lay out here There’s no Is it institutions, or is it culture? Because institutions are a product of cultural evolution?

So people learn from each other, and this gives rise to social norms. What are social norms?

So they are. Ah! You acquire both the behavior. Don’t, eat pig, and the rules for judging other pig eaters or bad news. And so then you get a social norm that prevents some group from eating pig. It’s it’s enforced. It’s self-reinforcing, and you can build game, theoretic models it’ll remain stable that sort of thing, so that gives rise to norms collections of norms that govern a domain or institution.

So marriage societies have had marriage institutions, for as long as we can figure, and marriage is governed by where the where the couple lives after they get married. Who pays bride price or dowry? There’s all these rules surrounding marriage. It’s an institution so formal institutions. The only difference is there you can write stuff down, and then future generations can interpret it all right now. Kin based institutions, i’m going to argue are special as institutions go, because we have an evolved psychology that we share with other animals to preferentially be altruistic towards those who are genetically related to. So those who have a probability of sharing the same altruistic genes.

It’s quite a bit of research on that. But kinship systems extend that so lots of societies will call cousins, brothers or sisters, and you’re supposed to treat that person like us like a classicatory sibling. You’re also not supposed to have sex with them, if you call them sister, and you’re a male, or and vice versa mit ctl and um. We also have a pair Bonding instinct, and marriage builds on that so like gorillas, who also have a pair of bonding instinct. We form long-term emotional bonds for the purposes of child rearing and protection. And then marriage formalizes that reinforces it et cetera, one hundred and fifty, and then finally incest aversion. Like other animals, we have to avoid inbreeding with close relatives. So we have an in a diversion that we develop towards siblings. People will grow up in the same household with, but that can be extended and created an incest taboos, and apply to in-laws apply to cousins other other members of the group,

and even hunter-gatherers in the Kalahari would, if you asked him about sex with a cousin they’d be like. Well, that’s like having sex with my sister, and they’ll they’ll actually make the link, and everybody knows what that sounds like. The sibling of what that feels like.

So Anyway, these structure human social networks, and they’re highly variable across human societies. It’s good to ask why. And that’s one of the things I want to do.

And then, finally, one of the arguments and the weirdest people in the world is that the emergence of pre-modern States were really built on the logic of kin, based institutions so lower strata and upper strata. Think about kings and queens, and marrying cousins and lineages, and all that stuff, and of course, the lower strat. It worked as a productive unit. They’d re, you know, arranged marriage, that kind of thing, and it was really this thin layer of, and in personal institutions sandwiched between two versions of complex kinship that made pre modern states work mit ctl. And and it’s hard to get to modern states. If you have all this complex kinship operating in the background. So part of this is an argument about where modern states can come from one hundred and fifty.

Okay, Now, all of this talk about kinship and clans and stuff may seem unusual to at least some of you. Ah, because you come from these weird societies, and just to give you a sense of how  unusual it is. These are a set of kinship traits that vary around the world and bilateral descent. So tracing descent through Mom and Dad. Most societies Haven’t had that they’ve had something else. Patrilineal descent, matrilineal descent. So that’s rare cousin marriage seventy five percent of societies have some form of cousin marriage. In some places it’s preferred monogamous marriage.

Eighty five percent of human societies have allowed high status elite males to take additional lives. Nuclear families rarer yet ninety two percent of societies. Haven’t had nuclear families. They’re organized in some kind of extended family and near local residents. So that’s when the bride and the groom lives separate from either the bride or the groom’s family, and that’s even more rare if we look at the world as presented in the ethnographic Atlas.

Most societies have none of these weird traits. So fifty, one, fifty point, one percent of societies have zero. If we look down at other societies that have five. They’re either European descent societies that appear in the ethnographic Atlas, or they’re places where usually Spanish missionaries have arrived early.

So I actually found there’s this island of Sebu, which has weirdly five of these, all five of these traits. So I went and did research on it, and I went to Antonio Pig fed his journal of Magellan’s Chronicles, and he records polygeny like crazy and cousin marriage, and all that stuff in Sabb. But then the Spanish Dominican missionaries got there, and by the time the anthropologist wrote stuff down, the Spanish missionaries had done their bit, kay, So families are a great place to start, because they’re where children are born into. So they’re the first institution kids encounter, and they’re historically the oldest institution. So everything we know is that some societies only have kin based institutions.

All right. So where did they come from? A common assumption is that these come from wealth? People get rich, and then they start living in monogamous nuclear families. That’s what everybody wants to do. Right?

Um. But there’s good reason to believe in. Historians and anthropologists have long argued that it was actually the action of the Roman Catholic Church, one branch of Christianity that dissolved the complex kinship networks of Europe into monogamous nuclear families,

And one fun piece of evidence is that you know all of us speak English. We know the term for affine, so that’s the anthropological term. But if you where does the in-law come in? Sister-in-law, or brother-in-law or father-in-law, it comes from in canon law so every time you say sister-in-law you’re channeling the Catholic church’s taboo on sex with your sister-in-law or sex with your brother-in-law, because that was supposed to remind you treat her like a sister you can’t marry her.

Important, because even in the Bible there were forms of marriage, where, if your husband died, you’d marry his brother so like Leverett marriage so oral marriage very common across societies. Church does away with it.

You’re also, maybe familiar with the common thing in marriage ceremonies. Oftentimes the priest or preacher will say something like if any one here can show just cause why this couple should not be lawfully joined together in holy matrimony, let them speak now or forever to hold their peace. That comes from the caroling gene. Empire’s effort to root out incest meaning marriage with cousins, and you get everyone together, and you say, Ok, are these two related in any way? Does anybody know how they might be related? And that’s essentially what that is?

Ah! So eventually the church begins in late antiquity bands first cousin marriage, but it eventually goes out to six cousins, and then it contracts a bit in twelve, fifteen. Ah! Down to third cousins, but still pretty broad incest taboos, Remember, most societies have cousin marriage. Seventy five percent of them.

Polygamy also is banned. Europeans had polygamy like crazy secondary wives all that kind of stuff before the church.

Um no arranged marriages so very common cross-culturally the church required bride to the the Christian marriages still have. The bride has to say I do. At one point Most societies don’t ask the bride her opinion. Ah, but so the church forces that, and the priest is supposed to be checking. Right? Okay, you’re good with this right?

And the church also discourages corporate ownership. So they wanted people to be able to give the church land, and if the uncles can get a hold of the land after their brother dies, the church can’t get their hands on it, so they they try to confine ownership to the individual and prevent collateral inheritance.

So the a great book to read about this is Jack Goody’s development of the church and family and marriage in Europe.

Oh, gosh! What happened to this slide? Um. Okay. So this is just the same thing I was showing you before, and i’m going to focus on this link and this link, all right. So the first thing that I may need to persuade you of is that there’s a lot of psychological variation around the world, and if you didn’t know about this, you’d be forgiven. Because if you take a course in social psychology, there’s a social psychology text which presents it. As if this is how people think, when actually it to how weird people think uh. So just to give you a sense. This is a measure of individualism around the world. Ninety-six percent of psychology studies are with European descent populations, the Us. Primarily. So they’re studying some of the most psychologically unusual populations around the world. Similar things with differences between trust in your out group versus trust in your in group.

This affects beliefs and preferences. So here i’m thinking of economists. But if you’re a cognitive scientist, it also affects attention, memory, perception, reasoning, decision, styles, and and a challenge. I have yet to find a decision-making heuristic, prevented by the found by the behavioral economist that doesn’t vary across societies. There’s either no evidence or I can show you there’s a variation.

And I’m going to focus on individualism in personal trust conformity and and patients which, at least to an intuitive first approximation, might affect economic outcomes.

Okay, Now, one thing I have a caveat, which is that people from societies tend to think traits that promote success in their society are good. Ah! And so they tend to negatively balance things. And some people think you know. Why are you saying bad things about these people calling them conformists?

You know it’s thinking of a more conformist society. But in those societies being a conformist, is good, so um conformity is well-studied. So if you show Americans parents with their child behaving in a conformist way in a Nonconformist way. The American parents like the non-conformist They’ll be like that Kid’s smart, you know, we we like that kid. Whereas if you go to other societies and show a conformist and a non-conformist child you know same videos, they pick the other kid Mit.

Okay, Now, I want to give you a sense of why our psychology would vary. So i’m going to think about an intensive tin based society and a weak kinship society.

So in an intensive kinship society you get your relationships at birth, and a lot of life is about figuring out how to navigate those relationships live up to your responsibilities.

You can form to the roles and obligations of this society might be. Why, conformity is good? Because you’re trying to. You know not shame anybody Now, in a weak kinship. Society, you don’t have very many family connections. You have to find your ah mates and friends and partners and all that kind of thing by cultivating a set of attributes that make you interesting to other people. So maybe you want to be creative or trustworthy or honest mit seek. Others based on attributes and mutual interests, seek new relationships based on existing network connections. So in lots of places you’re not worried about whether someone has a trustworthy disposition.

You want to have a lot of social connections to them, because if you do, you know, they’ll behave in a business relationship, say, and and other kinds of relationships. It’s the linkage between ah people. So trust is based on embedded embeddedness and trust, based on dispositions.

Ah, these these tend to be shame oriented. So you’re trying to not violate social norms. If you do, other members of your family could experience shame so social standards here. Here. Guilt is often your personal standards, so I might feel guilty for not going to the gym, because i’m trying to stay in shape. But my neighbor doesn’t care about that. And if they find out my brother’s not going to experience shame. But i’m feeling guilt, something like that.

And here your identity is based on relationships and network. And here it’s that set of cultivated traits on the scientist. I’m a kayaker, that sort of thing.

Okay. Now, empirically, the case we set out for ourselves in the science paper was we needed to connect the church to kinship. We need to be able to connect kinship to weird psychology, and if both of these hold we should also be able to do this.

So. Ah! We have the kinship, intensity, index, which I told you about. We also got a lot of data on cousin marriage. We can do that within Europe based on dispensation requests. And then we also have measures from this biologist in bittles, and then we have measures of the duration of the church’s marriage and family program which we can calculate globally or within different European regions based on the diffusion of bishoprics.

I’ll say more about that in a second. So this just gives you a sense of cousin marriage around the world. One in ten marriages even today, is between cousins, the pashtun in in Afghanistan, fifty, two percent cousin marriage. That’s the Taliban as a pashtun organization.

So I want to avoid doing. I can show you I can show you seventeen different plots like this for seventeen different psychological variables cross nationally.

But let me summarize all that, and it summarizes nicely by this plot more centuries under the church for a population less cousin marriage. The same would be true if I put kinship intensity as measured from the ethnographic atlas, so that link holds cross nationally, more cousin marriage, less individualism and impersonal psychology. So people are less individualists more relationally oriented. And then, finally, we can go this way, and in all of our analysis we always do. The Eastern Church, which had a kind of a soft, less enthusiastic version of the marriage and family program that the Western Church imposed to dismantle the family structures in Western Europe. So you often see a weak but positive correlation with the Eastern Church, and then the strong result with the Western Church. So with the crossnet we see lots of variation cross nationally it roughly seems to pattern how we think it might pattern.

But we want to dig in and see if we can get closer to you know, build more confidence in that. There’s really a relationship here.

So my collaborator, Jonathan Shilts, created a database of the diffusion of Catholic bishoprics through Europe. So we have a Gps location and a year, and so we can use that to assign a dosage to each place, and this is actually spiraling through time. So it just started. You’ll see certain places turn gray.

That’s because that part of the of Europe was under a political power unfriendly to the Bishop of Rome. So the Pope in Rome you’ll see. Southern Spain deal is so Sicily. Well, Sicily’s under Islamic powers.

Okay. So you get this variation in dosages. We have the Carolingian Empire here, which which may come up later, and we have also the the Iron Curtain, both of which we have to deal with in the analysis.

But just ah! To to measure psychology. We use questions from the European Social Survey. Um, I won’t. Go into the detail, but they asked about conformity and obedience, individualism, independence, impersonal fairness, and in personal trust. So we get variation around through hundreds of thousands of Europeans. Ah, contemporary, and we see if we can explain the variation, and what we find is that regions. So we have four hundred and forty, two European regions that had more exposure to the Church mit ctl, and have greater impersonal fairness, greater impersonal trust, less conformity and obedience, and greater individualism independence. So we’re only comparing Europeans and Europeans. Here we’re just doing it within the same country. And so we’re comparing different regions of the same country two.

We can hold individual demographics, so sex, age, h squared income and education are important ones. They don’t seem to do much work at all.

Geography, climate, the influence of Roman roads, initial prosperity. We also put in medieval universities, monasteries, and the Carolingian Empire. One of one of the most persuasive analyses for me was when we threw out Western Europe, and we just do this analysis on Eastern Europe, and we find we can explain this variation in these aspects of psychology within Eastern Europe.

Okay, Um. We also managed to get some cousin marriage data. And so this is the percentage of cousin marriage on a log scale, and that’s conformity. So more cousin marriage in Italy, Spain, France, and Turkey, we get greater conformity and obedience, less impersonal trust

and less individualism, independence, and less impersonal fairness. So interestingly, Turkey, which actually has a very different history right from places like France, they fall right where they should be on the plot. Once we know the rates of cousin marriage, so one of the differences could be this shift in kinship.

Um, i’m actually going to just really short-circuit this because i’m looking at the clock. But we did this trick, which is a fantastic trick from economics, where we look only at second-generation immigrants. So these are people who grew up their whole lives in Europe, but their parents come from somewhere else, and we can actually tag them to an ethnic group.

We tagged that individual who grew up entirely in Europe with a kinship intensity, or a cousin marriage rate from where their parents came from, and then we can predict aspects of their psychology and control for their income and all that sort of thing all right.

Okay, Now, one of the things I wanted to explain in the weirdest people in the world was that industrial revolution simple topic, because it shouldn’t take long. Ah! And i’m interested in this idea of the collective brain which I develop in the secret of our success. And there the idea is pretty simple. It’s that most innovations are recombinations of different ideas. So the things that should feed into that you want larger populations. You want more cognitively diverse populations, and you want more free flow amongst individuals. So we can get together and swap ideas.

So that’s the basic idea of the collective brain, and I apply it to you, fluid social interactions among cognitively diverse individuals. And I look at the institutions, and I also look at the psychological relationships between innovation and and those psychological traits, but something like the journeyman phase. And an apprenticeship means You’ve apprenticed under a master, and you’re probably he’s probably not your father, which is unlike many places or another kin kinfolk, and then you have to go to somewhere far away. It’s. Like a post-doc, go somewhere far away and hang out with a bunch of other people who apprentice under somebody. It seems like a perfect environment for creating recombinations of diverse ideas one hundred and fifty.

But in elsewhere. If you look at India, you read the literature on apprenticeship. There you look at China. Their clans wanted to keep secrets, or there were regional specializations. Nobody wanted to share information.

And then, finally, the psychological factors that’ll create this recombination. Of course, the crucial here is is tolerance.

Okay. So to try to put this to the test. Uh, I started working with Slava Savinsky, who’s in the crowd, and Jonathan Schultz, uh former postdoc in my lab, and we took the patent database from uh one thousand nine hundred and eighty to two thousand and fourteen, and that gives us a Let’s see. This gives us a measure of patents across Europe, and the idea is, is, we think, that you know, the social networks which allow people to swap. Ideas are going to promote more patent and greater individual, is a nonconformity and impersonal trust. Those same measures that I just told you vary around Europe should lead to more patenting. So does all this psychology stuff I’m talking about on this survey, which you know just could be a bunch of survey answers, but as a cash out and more patents, and we also have the same measure. So again, our hands are tied right. We already tied our hands in the science paper.

So these are the new patent database that Slava put together. You can see It’s these very small nuts, three regions in Europe, so over one thousand four hundred regions, and then we have our measures of ah trust, fairness, individualism, and conformity. And the basic question is, is, Can we explain the patents using those as a first step, and what we find is that we can take each of these pieces of psychology, and each one of them will explain patents in a regression holding the country constant and and other stuff. So a standard deviation increase in weirdness, or in in any of these four traits, will lead to between a point two, seven, and point four, three increase in patents. So that’s a pretty good increase in patents and patent. Here is my measure of innovation. There’s lots of interesting arguments about how good a measure that is. But it does seem to have some some value if we do a principal components on these four pieces of psychology and get a single dimension. We get over half a standard deviation increase in patenting across these different regions. So just a quick look at the standard economics table. So this is each of the four, and the weird psychology with the principal component um country fixed effects, so only comparing people in the same country all the standard battery of of geographic controls and things like that, so that suggests that innovation might be related to psychology. We can do the same thing with our nuclear families. They still vary across Europe, and and we have a measure of that. Or we have this measure of Facebook friends. So what percentage of Facebook friends are over one hundred miles away, And we use that to explain the patents. So, just to give you an example of the kind of result a one standard deviation increase in far away Facebook trends increases. Ah! By a quarter of a standard deviation. Patents per capita across these different regions. So this network thing seems to hold, and we think they’re co-evolving with the psychology right? So so those are correlated. But can we go from the church. So this is our medieval church exposure. Bin scatter plot. So each of those dots is a combination of of dots and patents per capita. So it’s a partial regression plot, more church exposure, more patents per capita.

Okay, and just the the long arm of history here. So patents per capita is the thing we’re trying to explain. And medieval church exposure is Ah is we? Can. We can still pick up that long arm, and i’m making the case that this is due to its effect on psychology and families.

This holds even when we use small administrative regions. So these are subnational regions. So we’re really only comparing people that leave which in some small part of Germany rather than all of Germany and all of France, those kinds of things.

Okay, um. I took this out, but somehow I must have not saved that version of the Powerpoint. Ah, so this I just find it amazing, and i’ll just mention it here because it’s the slide is here, but we have our centuries under the medieval church, and that predicts patents.

But then Jonathan Shilts had gone through the records. You know some historians had helped them a lot, and he found that at these different Bishop Bricks they took attendance when they had a big meeting, and we know all the issues. They talked about. The church keeps good written records at the meeting, and we know who attended. So we have an attendance list.

So Jonathan was able to put together a data set that has, whether the bishops in different parts of Europe actually attended the meetings where incest legislation was discussed, and that picks up some additional variation in patents. So the idea is, the Bishop goes to the meeting. He gets fired up about stamping out incest  mit Ctl, and people believed it was causing all kinds of problems, right? So people were marrying their cousins, and God was angry. And so he was sending plagues and ah, economic hardship and things like that. So it’s like a public health campaign. So we had to get rid of the incest one.

Okay, Now this. Uh i’m always nervous when I present Iv regressions in front of economists.

So here here the idea is, we think that there’s a case that can be made that medieval church. The Church is kind of expanding ideosyncratically wherever it sees a a chance to to kind of get a foothold, and wars are one in law, sometimes by a single battle due to luck. So maybe this is exogenous. But so we use medieval church exposure to predict aspects of psychology which it does nicely. I already do that from the science paper, and then we use the predicted values uh to predict patents per capita, and it comes out. It comes out very nicely.

Maybe there was the ah, maybe this fits the required restriction. Um, exclusion, restriction. Maybe it doesn’t, but we did that, and we can show the same thing if we do it this way, where we’re looking at the social network data, the Facebook friends or the um nuclear family households, so that seems to make the connection there and then. Finally, I’ll just very briefly touch on this.

There’s a bunch more analyses in this paper, but One of the things we did was that Carolingian Empire. The historians tell us that the the Carolingians So this is Charlemagne, eight hundred Ce. He. He teams up with the Church and the Popes, and they really try to impose the marriage and family program within the boundaries of the Carolingian Empire.

And if that story holds, we should be able to find that there is a drop off in patents when you cross the border of the Carolingian Empire. So if we can do it for the full sample, but we can also do it for different parts of the Carolingian Empire around Europe, and we get the same result in each piece of Europe.

Um. And again, you know, keeping country constant, at least for those first.

Okay, All right. So that is the basic picture. So there. I’m. Making the case that events that happened with how the Church transformed the family led to differences in psychology and social structure that have implications for innovation today

in the weirdest people in the world. I try to look at different places as well. So kinship intensity varies a lot within China, and even among high Han Chinese and researchers. I’m. Here I’m. Drawing mostly on Thomas Talhelm, but but others have argued that Patty Rice, agriculture, and also environmental risk based on monsoons has affects the presence of clans, and these lineages that spread after one thousand Ce.

So Paddy rice leads to greater kinship intensity, and Thomas has shown by collecting data all around China that that’s associated with ah, greater analytic thinking, more individualistic self-concept and less in group loyalty. So it fits the same pattern of psychological variation. If you just look at Han Chinese within China, and it’s based here in kinship variation, but not due to the church due to ecological factors that affect kinship, intensity since the book. Ah, Augustine Bergeron, an economist who who graduated from Harvard economics. He has looked at the way in which he’s looked at the democratic Republic of Congo, and he has people living in the same city of Conanga,

and he traces them back to their natal villages, and then he asks how close those Natal villages are to historical Christian missions, and the closer the Natal villages are to the historical Christian missions, the lower the kinship, intensity of the individuals, and the higher they are on moral universalism and lower on in group loyalty. So to see the same pattern just analyzing populations within the Drc. Where here it’s through Christian missions.

And then, finally, this recent paper by the folks at the University of British Columbia, Goshen colleagues.

What they do is they look at State law. So European populations are expanding across the Us. And some of them are isolated. Their cousin marriage crops back up because the Protestantism didn’t embody the same taboos as the Church. Uh cousin. Marriage spreads a bit, States imposed laws that banned cousin marriage, and he’s able to show that that reduces the amount of cousin marriage, and then that leads to a greater income and urbanization in the longer run. So that’s the ghost paper.

Okay, so ah, final take-home messages is that i’m making the case that we can think about culture systematically. It’s something We can measure shapes, economic and political outcomes.

What I actually presented is not a story of historical, of historical persistence, but is actually a story of historical change. So the Church gets these different peculiar views about marriage in the family, and they then use that to T, or that ends up, leaving them to transform the families of Europe, and that change, then leads to different cultural evolutionary trajectories and economic growth.

Um, Okay, Crucially, here is the idea that institutions and psychology co-evolve. So our minds have all that plasticity because we’re a cultural species, and we’re responding and and learning ways to better navigate the cultural worlds that we confront, and it affects lots of features of our our psychology.

And then, lastly, ah! You know the the place where we’re coming from takes evolution very seriously, so you can’t get kin based institutions correct. Unless you realize we have pair bonding, We have kin based altruism, and we have incest avoiders which gives incest taboos their power psychologically. And it also means that kinship institutions will always reassert themselves. So they’re somehow more natural than anonymous and personal institutions where you have to be nice to strangers and things like that one.

DISCLAIMER: This webinar transcript was loosely edited and there may be inaccuracies.

Farmer: It’s great to be here. The work I’m going to talk about today grew out of a visit in two thousand and eight by the Director of the National Renewable Energy Lab, Dan Arvizu, who came to SantaFe Institute to ask us if we could help them think out of the box, and what were they missing? And during the course of that visit, and a workshop, it became clear to me that the costliness, and the correct path to dealing with climate change really depended on not what technologies cost now, but what technologies will cost twenty years from now. So that set me on a path of trying to understand, to try to produce reliable answers to that question. And so that’s what this talk is about.

Now, just to provide a little context, energy makes about seventy five percent of emissions. So, if we can make the green energy transition, we’ve made most of the transition we need to make to deal with climate change, or at least to stop increasing Greenhouse gases in the atmosphere.

And the punchline that I’m going to deliver is that we can, and I’ll come back and discuss what “can” means, make green energy transition quickly in a profit, and I’ll throw out a scenario at least a possible scenario for doing that discuss a little bit about the possible roadblocks to making that happen. Now, most of the story is in this plot that I’m showing here, which is giving the history of the evolution of the global energy landscape over the last one hundred and forty years.

And so, we see in the panel on the left, where we update on the x-axis, and cost measured in standard units on the Y-axis we see the price of various forms of making energy through time. For example, here’s the prices of coal prices of oil, coal fired electricity. And so, you see they’re kind of bouncing around, and one of the striking things about this is while they wiggle, there’s no overall trend. In fact, I remember at this meeting in two thousand and nine. Dan saying, well, solar energy prices are coming down. And Dan saying, well, how do you know that coal prices aren’t going to do that, too? And that actually ended up with a paper that James wrote on the cost of coal-fired electricity, showing that yes, it did actually come down for a while. But then it went back up, decomposing that and finding the causes. And you know the main cause is that ultimately cold, for electricity is going to money about coal, which, as you can see from here and comes down in mathematical tests, is essentially flat over time.

These be a very different week than these. So, you see solar energy, wind batteries, electrolyzers. And so those have been coming down quite systematically through time.

And at present we’re in a peculiar state where we have many different sources of energy that are all competing around roughly the same cost. So, you can see it’s very unusual. This over here shows history of energy, so you can see traditional biomass remaining roughly constant, coal becoming dominant, oil and gas rising through time, nuclear power rising and then flattening out, and then once again solar energy when batteries, electrolyzers all shooting up and race where they’re being deployed at around forty percent per year.

So solar energy has been increasing deployment at forty percent per year. For the last thirty years it’s been dropping a price in ten percent per year for the last thirty years, which is something unique in terms of historical energy supply. So, the question is, what are these going to do? Are they going to just flatten out and remain around the same price level with the other ones? Or are they going to continue to drop? And so that’s what this talk is about, and then putting that together to think about the whole green energy transition. And, by the way, stop me at any point, if you have questions.

So, I’m just going to make a few observations about technological change. So, these are empirical observations, which is that first of all technologies, improve at very different rates, rates that are technology specific.

These rates are highly persistent, and this only becomes clear if you look at granular data, so you can really identify individual technologies and there’s an identified identification question. We can come back to say more about if people are interested. But for now, let’s say there’s a such thing as a technology that you can track the cost of through time.

I’m going to argue we can use this to make predictions in one of several ways. One is to just note the trend. So here we take a span of over twenty years. The price changes in goods ranging from hospital services to television sets all starting at some standardized zero point, and we see that over those twenty years hospital services went up by two hundred and twenty five percent. The quality adjusted price of televisions went down by ninety-five percent. So, in other words, by a fact, television has dropped in price by the fact of twenty over those twenty years hospital services went up by a factor of two or two and a quarter, and so this illustrates the point about diversity. It also illustrates my point about persistence. Hospital services went up every year. Television sets dropped every year. So, these changes were quite persistent through time.

This illustrates the point about heterogeneity from a different point of view. So, we take the price indices for us, goods relevant for us, manufacturing from one thousand nine hundred and fifty, eight to two thousand and eleven. Group them by type, and what you can in this re-plot. Now make a histogram of the average annual growth rate set for the price, and you can see that most things like here is a valve middle that most things sit on, and most things are not too far away from just going along with the herd, the herds moving at the inflation.

But then there’s some set of things. In this case, all computer electronics related things that are dropping in a cost in a much higher rate, suggesting that the heterogeneity of rates of change is a highly skewed distribution with a heavy tail on the positive side. We’ve done some other work that’s not yet published that reinforces this point of view.

It’s not just computers that do this, though they were one of the main things to do. So how do we take advantage of this? Well, we’re going to make use of empirical laws for forecasting technological costs based on historical data. The most famous of these is Moore’s law, so more in one thousand nine hundred and sixty-five made a pronouncement that semiconductor devices increased in density, on chips and identity, increased roughly, became twice as great every two years.

He later on, adjusted the timescale that I always mix up which way the adjustment went. But it’s been a remarkably accurate prediction now over a seventy-year span. There’s another, even older author, who is a very interesting figure. He is the brother of Seoul Wright, who is a famous evolutionary biologist, and Theodore Wright, who was considered one of the founders of political science, but he was the black sheep of the family who went off to World War. I became a flying ace and went to the aviation business, and in one thousand nine hundred and thirty-six he wrote his one academic paper, just mentioning that the cost of producing a specific airplane from a specific factory drops by twenty percent every time the cumulative production of that airplane doubles just an empirical observation.

And so this is an example of that actually, that I think James prepared for me a long time ago, and since then there have been a thousand papers written about Wright’s law looking at all kinds of other technologies, and demonstrating that they did this and the law is also moved from being about a specific item from a specific factory to being about much broader items like solar photovoltaic cells rather than globally, rather than solar and photovoltaic cells produced by a specific factory. And that’s the sense in which I’m going to be using here.

Wright, by the way, went on to become the head of an aircraft manufactured during World War II. And used its own law to successfully forecast the cost of aircraft production during World War II. And I’ll come back and say a little more about.

So, Wright’s law looks like this. If you put it magnetically, it says that X is the cumulative production that the cost of a good or positive technology drops its X to some power omega where omega is technology-specific and omega would be a positive number here for meeting. The cost goes down.

Now, here’s an example with four different technologies, transistors global takes Art describes in ethanol where we plot the total cumulative production on the x-axis and the average unit price and dollars on this axis and notice the plots are in log scale. So, the power law turns into a straight way, and this gives you some feeling abruptly. It’s not perfect. It varies in how well things stick to these curves depending on the technology. That also seems to be technology specific, but the pattern is pretty clear.

Now you might ask, where does Wright’s law come from? So, I would say it’s not very clear. But the best explanation is due to James McNerney and me and Redner and Trance. And so, we built on a model actually by John Muth, who, if for those of you who are economists, is the original, the original architect of rational expectations in economics. So, but he also did in those days non-rational thing, like ass ing that engineers just go dart to the dartboard, and what engineers are capable of doing is picking out which dark foes are better than which, and taking going with the best solution they found. And so, it turns out this gives you something like it also actually makes a prediction about how Omega scales with the complexity of the design of the device. As devices become more complex or less modular, then improvement becomes more difficult because you have to coordinate improvements across more pieces.

But let me just say I don’t think we fully understand Wright’s Law and Wright’s law. Certainly, also it’s a proxy in that cumulative production is presumably just a proxy for level of effort. And so cumulative production is much easier to measure than level of effort.

We took this, and actually several other laws like Moore’s law, and acquired a Library of Technologies, History of Technological Costs through time, and tested this and tested actually several other laws. We tested seven candidate laws, and we did this by, pretending to be in in the past, that, as we pretend to be at a given time in the past. We forecast each future date from that point in the past. If we didn’t know anything about the future, we repeated, after all, past days, and we scored the methods based on their forecasting errors, and we made an assumption that the improvement process is the same for all technologies except for parameters.

We’ll see more about that in a minute, so that allowed us to test our forecast. Now, the data set we tested on is shown here, and, as you can see immediately. It’s a bit of a monthly data set. There were fifty different technologies, chemical processes shown in black here, computer hardware shown in red. Actually, that day we got reward and more of a stop con er goods like beer energy related data sets their genomics.

So, we have several different data sets. The data was acquired by just basically grabbing every data set we could find where we had the cost versus time for a specific technology. And the data was put together by my postdoc, Bayla, Naj. Together with that help of some high school students, we wrote a paper actually testing this testing these different laws, and we showed that basically Wright’s law and Moore’s law came in with roughly a tie in this data set.

And we also did some things to try and understand how large the errors were, but for various reasons we couldn’t properly test this. Now, the side anecdote I can never resist telling is that, well, one of the things we showed was an equivalence between Moore’s law and the Wrights Law, and that if costs are dropping exponentially in time and deployments increasing exponentially in time. It’s easy to show that Wright’s law will haul under those circumstances, and that was the case for these fifty technologies.

This was originally pointed out by a fellow named Sahat Sahel, who wrote several books on technological change in the Eighty’s, and who mysteriously disappeared. One day the hell just vanished, and nobody knows what happened, and Bala became obsessed with Sahal, corresponded with his brother, and then, about two years later, he mysteriously disappeared. So, I’m sorry. I just can’t assist telling this. I was, you know Hope Vale is out there doing something good somewhere.

I’m not planning on mystery. So, I’m not obsessed with this. But anyway, thanks to Bailey’s work that we managed to put this data set.

Now, how did we model technological change? So, we rewrote Wrights law, which you would write just said, there’s a deterministic law that people have historically said, using regressions. We instead turned it into a Time series model. So, we rewrote it as a geometric random walk with drift.

That depends on where the rate of the drift depends on cumulative production of the good. And so, if y now is the logarithm of the cost, so the difference between the amount that the cost drops in, say, a year, is proportional to some constant omega times, the logarithm of the ratio of the accumulative production in here t plus one to your plus a noise term that has some scale.

So, this expression has two free parameters, Omega and S. And we ass e that all technologies follow this process. We take the data, and we fit Omega and pass from the data and on past data. And then we use that to predict future data.

Now, the key thing that we did was to derive expressions. For how accurate the forecasts are that is, let’s ass e the world really does follow this process, and to make things even simpler, Let’s say that this noise process is an Iv normally distributed.

I thought of the time that was going to be heroic assumption, because I ass e that this would be entail for the structure breaks and so on, but at least for the data step that we applied it to. To our surprise, it fit amazingly well, and we were able to derive expression for the normalized errors. That is, if this epsilon here is the absolute value of the squared error, and this K. Hat. Excuse me, that should have been s in the previous slide. I don’t know my slide’s consistent, so that’s the same as big as there, then, the ratio of these scales like this,

The normalizing constant that determines what the error is, has a term that’s proportional, to which is the forecasting horizon end of the future. So, because this is a diffusive process, then there’s a there’s the squared error goes according to scales, according to Tau, and actually, you know.

And then there’s another term that goes according to Tau Square, which has to do with the Estimation error. Where M. Is the number of data points in the data set that you’re fitting this okay. So, in other words, you take some data, and you do your best job to fit the parameters.

Then the error you will find a basis expression where this is a student distribution with M one degrees of freedom, so you can just derive this by from saying that the world follows this kind of process. And so, this clock over here shows what happens. The green line is the T distribution. The

The black line is what happens if we just ass e the simple geometric random walk, and the red line is what happened when we ass ed a random walk with put a little bit of correlation in positive correlation. So, in other words, what we found is to make this work. We had to ass e that the improvement rates are correlated from year to year.

And so, this is the result of making six thousand roughly forecasts for all horizons up to twenty years, and then taking all the data from fifty different technologies and twenty different horizons, and a bunch of different forecasts, and putting it all into one bin, and seeing whether it followed this law that we predicted a prior hold, and I should say, if I don’t, this is with Francois Hollande.

Well, we’ve actually showed this first for Moore’s law, and then there’s a paper with a bunch of authors led by Francois that chose it for my song, because Wrights law was a bit harder to juice. So, what we found is that we could not reject our null hypothesis, which surprised us.

Audience Member: Did you try with opening data and using the datasets to test by making predictions about this?

Doyne Farmer: That’s exactly what we did. So, we went. We did this process here. So, we pretended to be in the past, using only the data we had up to that point in time.

And yeah, and we actually did things like systematically look if we only use the five most recent points. We always saw that you always want to use the most data you can. Processes are surprisingly stationary.

Yes, you’re treating wars wrong and right. It’s all in some sense sort of in people handed way. And there, you know, right conceptually, you’re very, very, very different. Yeah, one of them is really the learning by doing deal.

Yeah, if the factory just takes a break, it because of a labor strike for years. One of them is you’re marching along the other. See that you’re just chilling out for a while. Yeah, I totally agree. And so, this has been debated in the literature which works better. And so, to try and resolve this question Francophone, Diana Greenwald and me.

I wrote a paper where Diana managed to acquire lots of data from World War II, which is a good natural experiment for us, because, unlike these other technologies here, that just, you know, are coming down smoothly over time and are being ramped up exponentially over time in World War II. The Us. Went from the eighteenth-largest military to producing two over three of the military, both sides. By so we got data on the costs of five hundred different types of military equipment. The data was crude. In other respects, we really only had started endpoints.

We did have the whole time series for maybe a hundred and basically what we were able to show by a lot. And let me say the key thing there is that the deployment ramped up enormously, and then it went back down, plunged out as the war was ending, so from that we could see that two things one is, the causality flows from. There is a strong element of causality flowing from cumulative production to cost, that is, and there was also an overall trend. Everything came down during that period.

And so, in other words, there was an exponential trend. But it we ass e the same exponential trend. For all of them there was, but there was also a significant, very significant Wrights law component.

Yeah, we are doing and the exactly right. So that suggests that there is the causality there, and that’s why, in what I’m going to show you in a minute. We’re going to use Wrights law rather than north long.

No, there’s some problems with that, too, that maybe I can come out and talk about it, you know. Then you get these miraculous things. If you don’t continue

Well, I mean you do even better if you have wars law.

So, it’s even more miraculous in that case, and a key clarifying remark in the study. We had to war, too. We ass e the same exogenous trend for all five hundred technologies to make this work for individual technologies. You really have to ass e very different trends for different technology.

And so, the exogenous trend that we saw in World War II Doesn’t really seem to explain more as well. Here, I think Moore’s law is really explained by the fact that lots of technologies happen to have exponentially increasing production exponentially driving across. Let me just emphasize again that different technologies improve at really different rates. Here is coal. Here’s the price of the coal in terms of, you know, lots of electricity you can generate what call. And here’s soarable political takes. And here’s nuclear power.

So, technologies behave really differently. Now we’ll move to talking about climate change. How long will it take for technologies to mitigate climate change? What’s the right investment strategy which technologies should we support, and how much will it cost?

So let me just show what happens when you apply the kind of thing to extension to four key technologies for dealing with climate change. So, we have in the Solar Vaughan able tanks. We’re plotting a logarithm of cost versus the logarithm of an experience. It’s just a shorthand for cumulative production. The same for wind and lithe batteries and p two x electrolyzers p two x means power to something, so that you start with electricity. You make hydrogen, and you use hydrogen and make a fuel like ammonia, or one of several other possible lyrics here for forecasts.

So, the Black Doc should the historical values through time. And if you stare at this for a while, you can see that it works pretty Well, that is, things tend to stay near the center of demand that were forecast, and they never actually straight outside of the ninety five percent confidence maybe one point two and you would expect them to do one time and twenty-Two x electrolyzers. You can see our regions are a lot sooner, because we have less data, and the data is noisy. Remember, there’s two technology-specific parameters one for the rate of improvement and one for the so this is just to give you a flavor for how this might apply to energy.

I also just want to remark that, you know. Now this isn’t so surprising because over the last fifteen years people were very aware that solar energy has dropped to be competitive. But back in two thousand and ten I actually made a forecast nature, and we predicted that it would. Its cost would follow all that of nuclear energy or call. And just to illustrate that at the time, like in two thousand and fourteen. The economist was still saying solar power. It’s by far the most expensive way to reduce carbon emissions. So we were, I was moderately successful in forecasting in contrast, the integrated assessment models and the International Energy Agency have done an extremely bad job of forecasting both deployment and cost of renewable energy, and consistently getting pessimistic about it here’s a plot where we’ve taken the data here plotted it in semi-log scale And so you see a rough, noisy, exponential decline, and you see forecasts made in the past that all go out of the slopes that are quite different from the historical slope. Here we made a histogram of about three thousand different predictions by various integrated assessment models that reported in two thousand and fourteen for the cost, for the rate at which solar would drop between two thousand and ten and two thousand and twenty. And this is the histogram of the forecast the most optimistic ones. This is what actually happened.

And similarly, if you look at integrated assessment models, they typically put in what are called floor costs, they ass e that there’s some cost where that the technology can’t go below. So, in these models they ass e they’ll follow Wrights law for a while, but then they’ll hit these floors and flatten out. They do this because otherwise, and that the answers become unstable. And this just shows historical floor costs compared with historical costs, showing that it just plunged right through the forecast as they then put up. And my forecast.

These models, by the way, also have deployment constraints. They put in constraints in the rate of deployment, and one of the problems with these

The evaluation of the scenario depends strongly on these two things, which have been more or less arbitrary. We’ve never seen any evidence for floor costs, so we don’t put them in to what we’re doing now.

So, we’re going to provide an alternative, just doing something very simple. We’re going to forecast technological costs conditional on the point. So, we’ll throw out some scenarios. We can debate whether they’re plausible or not, and we’re going to make a few representative scenarios, and we’re going to Ass e that sector by sector useful energy demand continues to grow as it has been growing historically at two percent a year. So that is, we ass e that there aren’t any dramatic changes in lifestyle or anything like that, apply the methods I just mentioned, and then we just add up the cost of the technologies needed to make the energy sector work.

We say that our fast transitions in our other and key technologies are solar global tanks with batteries and P. And we are picking these well, we’re picking these in part, because we think they’re good horses to bet on, but also because they have extensive data. There are other things that might may prove to be very important, like just moving power around and transmission lines which we don’t put in there because we don’t have good historical data, or how the cost will.

So, we use Wrights law to extrapolate, but we extrapolate existing deployment terms in this solution. I’ll show you a moment. We phase out fossil fuels over the next twenty-five meters, and we rely heavily on Power Dx fuel for energy storage. So, we ass e that we actually make so much P. Two x fuels that we can run the entire global energy system for a month, even if there’s no sunlight or no wind, which is pretty conservative.

We also use pfix fuels for liquid fuels, for heat, shipping and air transport, and usages like that.

Now, in the fast transition, let me actually show you what we do in all three of the scenarios. We investigated these three four key technologies.

So, we shall hear the historical data for the deployment now with solar energy. So, the average, the annual generation in terrawy hours for each year we put a dashed red line through it, just to show the trend. And then this is our fast transition scenario. This is our slow transition scenario, and this is our business as usual.

So, I’m putting this up here hopefully to convince you that we’re not doing something that’s crazy. We’re extrapolating an existing trend. In fact, we begin falling off of that existing trend by the way, as solar reaches maturity, and starts to flatten out. But at this rate it does solar energy and wind. You can see here become dominant after about ten years, and the cut, and completely displays fossil fuels in about twenty years, twenty, five years.

So, this is the same thing for batteries, showing what we ass e for batteries in these three scenarios and p-tax tools, where we ass e for p-text goals that we do see on the existing historical tread for another decade, noting that this is a much less mature technology than the.

And by the way, this is standard and technology literature to ass e that you have s curves. That is, technologies tend to grow exponentially for a long period, and then they flatten out when they reach saturation. So that’s what we’re doing here. You guys have a question

I’m: a bit of course.

Yeah. Our original business. As usual, we more or less flattened this out to make it a straight line, and the referees complain that we were. We were that that wasn’t realistic. So, we saw that our business-as-usual scenario. It didn’t matter very much.

They’re only price projections for. Yeah. So, we are saying these are assumptions, and we’re then going to predict prices. It’s true that integrated assessment models try and predict both of those in tandem. I think that’s one of their problems. They’re doing something they can’t really do.

And if you were going to do it well, because these end up, depending very sensitively on the assumptions that I mentioned before through time, which is shown down here for the three different scenarios, and so that when we’re doing it for useful energy, which is the energy you actually use, the amount of work that actually comes out vital energy, which is saying one energy would be, How much gasoline we have for our useful energy is, how much, How many joules of work do you actually get out of your car, and this shows the requirements for electricity, for electricity, generation, and storage, and which is then showing that we are talking about ripping the rhythm quite substantially by about a factor of four in order to make this happen.

These show the forecast for some of the key technologies. I should mention that what you can use Wrights, law for oil and gas, and actually because for those for those technologies the omega parameters effectively zero. And so, you just get a random walk if you, if you use Wright’s law, Random Walk turns out to be too diffusive. That is, overspend of time longer than about twenty, ten years. Prices just scatter around too much. There’s some mean reversion, so we add in for fossil fuels. We probabilistic nature of our forecasts and maybe I didn’t stress that enough that that the generalization we make of Wright’s law isn’t just making point forecast. It’s predicting the probability of outcomes at different cost levels at different points in time. And what we tested was that probabilistic prediction. I think these are the one standard deviation and the two standard deviation error bars in the forecast you can see the uncertainty going through time and as we’re also comparing to forecast and existing integrated accessible models are making about what the class will be at points of time, just to illustrate that our predictions are still substantially different than those of the other models.

And so, this is kind of the punchline it’s showing that if this is under the three scenarios, so fast, transitions flow, transition, no transition. These are we get already right here. Fossil fuel expenditures are coming quite negligible. This is a graph of the media expenditures on energy in each of the years shown so two thousand and twenty to two thousand and seventy, and then looking at the non-fossil fuel expenditures versus fossil fuel expenditures. So, the white is non-fossil fuel expenditures, and the shaded is fossil fuel expenditures.

Here we show the net present cost so we ass e a discount rate, and to put all the different years together. For the investments we ah bring all the costs back to the present by discounting them. And this is what happens if you use a two percent discount rate.

And again, you see the fast transition, the slow transition, and the no transition illustrating the uncertainty, but also showing that the fast transition is on average cheaper than either of the other two cases. This shows the mean cost as a function of a discount rate, illustrating that we don’t need to argue about the discount rate the amount we save changes, but the fast transition is the cheapest regardless. And finally, these are mentioned sort of for the vast composition of the time, and compared with the slow transition, or for no transition.

Maybe I’ll just make a remark about technology investment portfolios. We have a paper where we look at investing in technology as a generalized process. And one of the things we showed is that you can’t use intuition from finance. One hundred and one mark. Markowitz’s portfolio theory doesn’t work at all in this domain, because you know it in Markowitz’s portfolio theory, says that if you have a bunch of assets, and if you have a positive return on all the assets, if they’re not perfectly correlated, then you should diversify by scattering your bets across all the assets.

But if all these assets are following Wright’s law, there’s something very different, because to make progress down the learning curve you have to invest in them. And so, if you really knew which one had the best characteristics, you should just pick that one in with all your money.

But if you have uncertainty, then you need to spread your resources across the assets. And so, with a situation. You’re in a technical technology investment case shows you don’t want to put one egg and every basket you don’t want to put all your eggs in one basket you want to decide. You want to put Ah, most of your eggs in a small number of baskets, depending on how much uncertainty you have, and what the situation is, and it also shows that you get a lot of extreme sensitivity in these kinds of portfolios.

No, this is. This is a good time you asked about direct to your capture directly or captured with fossil fuels.

Yeah, and that would be additional. So, you would integrate. Put that in with an additional ranch law thing that you have. Yeah, So I can. Of course, we don’t have enough data to estimate anything.

Yeah, I mean. There is some historical data on direct air capture, and it doesn’t look good. It hasn’t come down. The other thing is direct. Air capture depends on fossil fuels being competitive without direct air capture, we predict that we’re going to reach a point where that’s not going to happen where that will be true fairly soon. So, if fossil fuels aren’t competitive without trick or capture, they certainly are competitive with it, and you know also direct. Your capture has all their kind of characteristics that make me think it’s not going to come down very much, but that’s more of a subjective judgment.

Now, that’s different from asking. The question of, are we going to need to use? Direct our capture to just pull C O. Two out of the air in the future? And that’s another story. And you know here again what we’re predicting. It helps that because we’re predicting electricity should become very cheap, particularly if you don’t need to deal with storage, and so, so, you know, you could easily imagine that the cost of direct air capture is going to come down quite a bit, using solar electricity.

Now, of course, what I’ve been talking about so far relies on extrapolating technological trends. And you know, one would like to go to something more fundamental and understanding this problem. And so, we’ve been thinking about.

Excuse me, different ways of doing that., for one thing, let me just say a little bit about Wright’s law. You know Wright’s law is a crude approximation at best, because if you pick a given technology, say take solar formal tags. Well, it’s composed of different things. There is the cost of building the cell There’s a cost of putting up the frame that’s going to hold it in place. There is the installation cost, the distribution cost, and these things all behave differently. So generally, you want to decompose things as much as you can, and look at the scaling relationships separately.

This actually relates to a paper another paper of mine with James that James’s elite author on where James showed.

If you take industries and you ask what is driving the price index of an industry that most of the improvements in the price. Sixty-five percent of the improvements on average in the price index ministry come from price improvements, and the inputs to that industry.

So, to really do this better. You want to think about the economy on a more fine-grained scale, and you want to think about the interaction of the different technologies with each other. So, we’ve been working on trying to do that, making more disaggregated models, and maybe just lift the discussion up a little bit. We’ve been pursuing a method of sort of modeling climate economics in a distributed way with on one that we have our energy systems model, which, as I, you know, Virgin, I just described to you. We’re like, for example, making this be the regional instead of law, which already gives significant improvements in terms of providing better advice.

Trying to extract information. We have about technological change. For example, coming from analysis of the patent space where we see interesting patterns like,

Go back and look in the fifties nuclear power. If you do a page rank for Patents nuclear power, it sits in the middle and fossil fuel. Sit in the middle. If you do that now? Renewables in the middle and nuclear power and fossil fuels?

We work on green industrial strategies that is thinking about imports and export stuff that overlaps with the work. Ricardo’s group does think about production networks. So, we have models both at the level of industries and of firms going beyond traditional input output of ah models to make dynamic models which we may use up through a comment because it’s sort of a sign day. We’ve been talking about hooking this up with agriculture and land use models more sophisticated models of household. In fact, we have a model of occupational labor, diffusion.

And so, we’re doing work. Now we’re coupling together our forecast for what’s going to happen in the production network when we rewire from fossil fuels to solar power and wind and batteries and electrolyzers.

And then how does that play out in terms of occupational labor, and then thinking also about treaties and government, and how that interacts with all of this. So, we’re basically pursuing independent projects each of these pains, but also starting to think about how to paste them together, at least pairwise or triple-wise to make improved forecasts, and put in more fundamental information.

I think I think this suggests that doing the things that we need to do to support this.

Yeah. So well, that’s an interesting question in that. When you go back and stop start looking at raw materials like things, your things you mine out of the ground. It’s a quite striking, not well appreciated fact that actually James was the first person to introduce me to. James went, looked at the Usages data, and observed that that century of data for more than one hundred different and ah, he fit some time series models to those, and what you see is once you adjust for inflation, They’re all flat except one exception. Ah, industrial diamonds! Now, industrial diamonds aren’t something You might out of the ground they’ve dropped by a factor of one hundred. Everything else. Nothing has changed by more than a factor of ten in price over a century.

Now that suggests what so raw materials? If there are things you’re mining out of the ground. Why? Because they aren’t going to get much cheaper. What gets cheaper are the processes, for you know making them into things, and that’s where That’s where the progress

I mean. The other thing you might have asked about is shortages of essential materials.

My impression, Yeah, there may be some shortages of essential materials, but when you go back through the history of technologies, shortages of essential materials. Whenever there’s a bottle of actuative essential material. Another material is found that provides a workaround, and there are quite a few historical examples of that chlorophyll carbons for refrigeration, and many others.

So, to wrap up, I think. What are we shown? Well, first of all, I think it’s important it we’ve been. It’s been clear for some time that renewables were getting cheaper and cheaper, but people were still viewing the renewable energy transition or the green energy transition getting rid of greenhouse gases as a burden. Who’s going to take on that we’re saying, Actually, no, it’s going to be cheaper than going on the way we are now. So even if you’re a climate denier, you should be willing to j p on board to make this happen. And it also, I think, reframes the whole discussion from having climate change be a burden to have it being an economic opportunity, and the players who get involved. The firms and the countries who get involved are likely to require the expertise to be players in the economy in the future. And so, I think it really reframes the whole discussion.

Now we also, you know, as I said, we were just throwing out the scenarios as assumptions, not as something we’re driving from first principles, so we try to make them plausible by extrapolating from existing trends.! But stuff has to happen to stay on those trend line in particular, I think the biggest one is the grid. We need to expand the grid globally by a factor of four, and that’s a pretty big change. It’s expensive. But if you look at paying ah, you know a grid even. Let’s say the grid cost was five hundred billion per year. I think that’s higher than the number we’d settled on now, but because we’re already paying four trillion a year for energy.

So, if we can reduce energy costs by, say, a factor of two. Then that dwarves the cost of building out the grid. But we have to worry about the political roadblocks to building out grids and doing that stuff. My son works for the Federal Energy Regulation Commission, and he said that there’s enough proposed renewable energy projects.

Sitting in the pipeline. We not approved yet, but having been given to the Commission, if they approved them all, we would more than double the electrical capacity in the Us. They can’t approve them all, because there isn’t a good capacity to put them all on. So, we really have to deal with that

We did look at stranded asset issues, by the way, and we found that that’s not so bad because you replace a gas station every twenty-five years.

We’re turning over our infrastructure all the time. So, we have to do is let the old infrastructure in the spot expire and replace it with infrastructure for renewables, and we, we pretty much get there without spending that many assets we do need to push on storage technologies. We need to stay on that trend for twenty years if we’re going to make it. And. And, as already said, we reframe climate change as an opportunity. And you know I found myself. I was sailing along the North African coast this s mer. So, this is a photo I took from my phone call. This would have been in Morocco, and, you know, dealing with the bizarre police states in Algeria that have to do with you, have a military government that controls the key resource oil and keeps the whole country under an iron fist.

Oh, I think there are going to be substantial geopolitical changes as a result of this transition. Twenty years is pretty fast, and countries like this are going to have serious problems dealing. So, I have a question, you know, but some large-scale Princeton study of a dead zero. Economy said, you have four and X: Yeah, that’s what we’re saying to it. And the course of keeping that I think it’s important to remember, though, that today in Massachusetts, I suspect in Brooklyn it’s not. A better generation is about forty five percent of our electricity. It includes some storage and some,

or back on capacity, or whatever you want to do, a factor in the overall price.

So that makes me wonder whether you just because solar and wind get super cheap balancing, it is maintained. And then it’s sort of like, well question, you know, module cost versus balance of systems.

Yeah, but burning cards are harder to see on the transmission, And the same is true for hydrogen. And right now, the electromagneters are about twenty percent of the cost of one thousand one hundred now so well, and the other cost, and you know the stuff where learning birds are. They happen? But that is obvious that giant storage tanks are going to come down and roast around, and that’s not even including the distribution of that.

And so, I just. I just wonder what you think about that tension. Yeah. Well, so we you know, we agree that the grid is going to be expensive, and we ass e we spend a lot of time researching. You know what the literature says about what it cost to install red new grid capacity, and there’s of course, a difference of whether you’re talking about transmission or distribution, and there’s a difference of whether you’re putting more wires in an existing line, or when you have to build a new line,

but we made pretty conservative assumptions. We ass e there’s no learning that the costs remain the same as they are now. We just scale them up because we’re doing a factor of four more. So, we increase the cost by a factor of four or five, what I think the people you’re talking to Don’t realize is if the other cost comes down.

Then, you know I mean, you know, if you can, you can get an upper bound by just asking What if the generation were free? Right? So yeah. So, we try to do that now, whether I mean we’re very open to seeing whether our numbers are, you know, having people go over our numbers and see whether they are correct or not. So, I agree. The devil’s in those details.

It’s also true that you have to. It’s very tricky. You see, when you get to fossil fuels, there’s no meaningful statement about cost of fossil fuel production, because you have places like Saudi Arabia, where it’s, you know, a few dollars a barrel, and shell oil in Canada, where it’s fifty dollars an hour, and so the price of fossil fuels depends on what the marginal cost is for whatever the marginal supplier is at that time, and that’s why it’s so valid.

That’s why I think one of the big side benefits of renewables is that we’re going to see much less volatile energy costs as a result.

Because there’s no reason to believe we’re going to have that kind of mechanism where we have such a radically different set of costs. You’re moving up and down the marginal price point all the time based on fluctuations in supply and demand that curves going to be a lot. Cloud.

But yeah, sorry I’m trying to remember. I’m killing. I did. Did I address all of the things you brought up a pretty small percentage of the concentrated.

Well, of course, you know, in the in that plot I showed with the different technologies, there are a bunch of lack of chemical processes, and one of the things you see is chemical processes. All tend to behave about the same way. So, it’s one of the more understood classes of technologies, and there are big economies of scale. From things like tank size, we get surface vol e, relation, surface, vol e relationships help.

So, I think electrolyzers will come, I mean, from looking at related technologies. I’ll be very surprised if they don’t come down in price as roughly, we would expect from the industry we have so far.

But time will tell. So, she thanks for everything That’s another way to answer Dan’s question in that, if you know solar, if you become so cheap you can just paint it on the roof of a building, and then the building is collecting solar energy.

Then you have a lot of possibilities for decentralized generation. You still are going to need to deal with storage

now. Electric vehicles already are going to increasingly provide an example of decentralized storage, and the costs we were looking at were caused for

ah industrial installations. There is a shift for a localized, ah decentralized generation, but if the costs come down enough, then that shift won’t matter much.

I would like to understand connection between different technologies. Evolution, of course. Or

Yeah, you really have to go technology by technology. Now, there is a question of what is a technology? you know, there’s twenty or thirty different flavors of solar photovoltaics, and it’s not clear if all of those follow the same scaling, many of them likely to, because there’s somewhat similar processes.

You know, a technology is always evolving through time. So, the technology is always changing.

Nonetheless, what the data suggests is, you can sort of designate something as a technology and look at its costs through time. Now, coal-fire electricity is a good example where up until the eighties people didn’t care too much about sulfur emissions, and then people started caring about it.

So, you might think a joule of energy is a joule of energy. But there’s dirty jewels and clean jewels. There’s intermittent jewels and steady jewels. So, there’s dangerous jewels and Saint Joules. So nuclear power suffered from that problem. So, your designation of the technology can shift, and the cost can shift if the requirements are placing on it, change as they did it with coal and nuclear power.

But this goes back to what I was alluding to with this plot I showed back here for our attempt to try and take a more holistic view of the economy, and use that to think about climate economics, because we really, we would like to be able to track all the technologies comprehensively and think about the ecology of interacting technologies and use that as the framework in which we think about things, rather than using what might be an inappropriate level of aggregation. And that puts noise on what’s happening, because you expect things like distribution scale very differently than technology parts.

So, we’re increasingly moving towards trying to find the right levels of decomposition and then combine things, often splitting things into their inputs. In the paper James wrote a paper on coal. Ah, in I don’t know. When was it? Two thousand and thirteen or fourteen back there somewhere, and what two thousand and eleven where, you know we decompose coal-fired electricity into operating and maintenance. The cost of building a plant, and we saw that those three things really became different and useful to split that.

So, it’s a tricky question that requires a lot of case-by-case judgment.

I don’t even trip from the very back a lot of the investment in these technologies. There’s been, you know, public investments variable of refinement, and we found that that investment makes a difference as the rate of change.

Yeah, so it’s a bit hard to say in a rigorous way. It certainly seemed like there’s in Germany. There were massive subsidies for solar energy that did correspond to a significant drop in price. This is usually called feed in Terrace. And when you think about when you actually go back, if you think about these curves, I’m showing here. Okay, you know, how did solar get solar is very expensive back at this point in time, and its cost keeps dropping well the very first toll ourselves in one thousand nine hundred and fifty-eight, Bangladesh.

Then people started using it, for you know weather stations at the South Pole places where it cost wasn’t a big issue, and then it got more and more in ex uses, so that the essential thing for a technology to roll out like this is that there be a set of niches such that it can keep advancing along, and as it does, its costs can drop, and so it’s important to keep that going, and I think the cost supports the price supports were important in keeping that happening.

But I Don’t haven’t seen a good study that really teases that out, and shows cause and effect, and it’s probably pretty hard to innovation is tell, you know that’s critical, and you know there’s Marian I’m, at Matsicado. But others have done studies, too, showing that public investment and technology has played a major role in seeding these things.

You’re essentially paying for the material and, the input energy in the house of when you,

I think we will evolve in the new technologies. We don’t we’re agnostic about that. But what’s predictable is the future costs what is not predictable, and the solutions that deliver those costs. I once heard of fascinating talk by a guy who was a chip designer. This was at a meeting in Sfi maybe fifteen years ago, and they were talking about Moore’s law. So, you guys all ass e Moore’s law is just inexorable, he just, and he said, having been a chip designer that wasn’t the way it felt to us at all. We felt like we were looking at a brick wall.

We couldn’t see any way to get to the next step, and then somebody would come up with an idea, and we would break through and find a solution. And miraculously that happened over and over again, and Moore’s law has sustained through that whole period.

You know. Some people say Moore’s law is just a self-fulfilling prophecy. That’s clearly not true, because, for one thing, when Moore wrote the law down. Nobody else was aware of it, and it is true that it has been useful for people to plan right, and I mean a great example of this is my friend Emily Ray Smith, who was in better of Ah CGI graphic techniques for Pixar. And he said they basically had it already to go five years before it actually got rolled out.

They didn’t know they just had to wait for Moore’s law. They had all the techniques down, and They waited five years, and sure enough then, and then then toy story was the result.

So, it does allow you to plan, but it’s not like it’s a done deal, or that it makes itself happen

But if the technology is predicted using silicon data that you were saying to keep it going?

The prediction can come from any.

And you know, you see this if you look at, say information technologies where we made a transition from mechanical information devices to vacumtubes, to discrete transistors, to integrated surface.

So, each of them has its own curve, and but the envelope that they followed is quite smooth, despite the fact that you’re leaping from technology and technology.

Why do you think we’ve here? It hasn’t it the grades more as well, because this also involves the technological.

Yeah. So, there’s It’s a mystery. I don’t think we have a good understanding. Now people have speculated that certain properties like modularity matter. Actually, our theory with James has something like modular, and we called design complexity.

And if you go out and test that you do see that? And there’ been a couple of tasks from our theory. Ah, that indeed, as the as the design complexity goes up, the learning rates tend to get worse.

But it’s very noisy, very noisy, and it’s like one of the facts that I think is amazingly strong. Is this one about stuff you mine out of the ground.

But you know, if you look at the way you extract oil now, it’s radically. It’s incredibly sophisticated compared to what was done a century ago.

Now there’s been there. A trade-off between the whale gets harder to find because we pulled so much out. Technology gets better to find it. But it seems very surprising that for one hundred different minerals you discover that those two trade-offs are just balancing each other across the century, you’d expect U-shaped curves that it would drop, and then it would go back up, and that’s not what you. See? So, there’s something else going on there, and you know, nuclear reactors are.

Well, a couple of things. If you actually, if you read an account of what they have to do to clean up a spill in a nuclear reactor. It’s pretty complicated.

And so, they’re complicated pieces of technology. This the French have maybe had a little bit of success in standardizing it so to make a cookie cutter. But it Hasn’t really paid off. French nuclear power is very expensive, too.

There’s a school of people very strongly advocating for making modular nuclear reactors. Small nuclear reactors not going to fly, because they’re fighting a big scaling law. To begin with, there’s a reason why they made them big to start with, and you have to make that back up, and there’s just no way they’re going to do that. But! But there’s a mystery. There’s still a mystery. There’s no way to predict a priori which technologies are going to improve quickly. And which are it’s a big problem. It’s fascinating problem.

Yep, Sorry, he hadn’t said. Had a chance Yet it might be that it’s general. Yeah, I know. I see your arg ent now, and There’s certainly an element of truth there at the same time, you know. If you look at, say the Danish experience with windmills. The Danes were early adopters of windmills and acquired expertise in windmills, and then they ended up becoming designers of windmills and have an industry around one else. As a result of that, this goes back to what Ricardo always stresses about dawn.

So, you get the know-how by being there early on and if the technology.

And so that’s the flip side of the argument for the concern that you raised.

Well, so we did a study of geographical variation in solar and wind prices, and a couple of things strike you. First of all, you might think that countries that have good solar and wind resources would be the one to install the most it’s roughly uncorrelated. It really has to do with politics, and there is a significant geographical variation due to things like how often the sun shines and institutional factors about installation costs in country A versus country. But, roughly speaking, if you look at solar energy the ins in the course of a decade at a ten percent per year improvement rate, you move from the ninety fifth percentile to the fifth percentile in the course of a decade. In other words: If you’re the place to have the most expensive energy in the ninety fifth percentile, then by a decade later, you’ll have the same energy cost that the people that were in the fifth percentile had,

So that roughly gives a scale of the geographic variation versus the temporal variation that said, we are trying to build out a regionalized version of our model to deal with that kind of thing.

Ricardo. Yes, and we said it was correlated with the information rates, or and you know the sunshine for free,

you know.

No. So please the stuff we’re going to do a variable cost right? So, when you work out the cost, the interest. Yeah,

Our purchase agreements out there,

you know, twenty dollars, and they have one hour soldier,

you know. You know the and it doesn’t. Generally, it could be that we didn’t look at interest rate. So that might be. =

No, I don’t yield.

It’s all right.

Yeah, one of the interesting things that people didn’t anticipate. That’s boosted solar.

The rollout has been that people have been willing to offer very low interest rates for solar projects, just because, in contrast a to nuclear power projects, they’ve been very reliable in terms of. If the project gets going, it tends to be delivered. The uncertainties are much smaller than ah in other domains, so Solar has benefited. Certainly, it’s benefited from the fact that we’ve had low interest rates now for two decades,

but it has among energy. It gets low interest rates because it’s reliable with us.

It’s substantial, and you know, I haven’t looked into that in as much detail as I would like, but the studies I’ve seen Don’t suggest that it’s, and they suggest it’s doable, and that the you know there was a review article in Pts. Six eight years ago, comparing the environmental impact of different technologies and solar, even when you take into account the need for rarer minerals and so on. It still gets the best rating relative to the competitors.

But I’m not thinking of investing in my yeah, you, okay? Well, yeah, well, you know, and I think that, by the way, I think part of the reason mining has this property of being so volatile, is because it requires such long-range investments. I grew up in a mining town, and you know, when the price of copper goes up, everybody goes to work, and when the price of copper goes down, they get laid off, but it takes five years to start up even to start up. An existing wine takes years of preparation.

So those big lead lag effects.

Yeah, one question about the fifty-six of investments,

You know the everybody is going to the cable for this

Well, yeah, so we’re not arguing there. There might not be bottlenecks that would not this top half. I think One thing that you know that I reflected on in doing this is that we’re very bad at thinking about exponential rates change in their mark. So, extrapolate this roll out. You hit the other curves up there.

So, we’re really at a critical point. Right now, we’re you know, solar energy is still just a percent or something of global power, but it’s about to burst forward on the stage, and then the question is, how far can it go before it starts any?

I mean my guess is that this won’t be a It’ll be a little b p, but it won’t be a long-term change in the trend. You know one of the reasons why I think renewables are going to have one of the positive side effects or energy security, which, with Ukraine, we see, is pretty important. It means, if you know, maybe, that if you’re in England, you have to pay more for your power than you do if you’re in Arizona. But if you’re willing to pay for it. You can get energy security, regardless of the natural resources you have regarding as we do now.

So, I think that’s going to be a big factor of pushing countries toward it. And so, we may even benefit from this isolationism in that regard, because it’s yet another incentive to go renewable.

Well, so one thing to realize is that renewables come in third when it comes to subsidies. So, fossil fuels get the most subsidies nuclear power against the second, and then and then renewables, even adding them all up. Yeah, so to do this, I mean, when I was talking to my side, I mentioned where it’s. The Federal Regulation Commission said promptly. Don’t quote me on this.

Thank you. Good set of questions. I really enjoyed the discussion. Thank you.

DISCLAIMER: This webinar transcript was loosely edited and there may be inaccuracies. 

Nikita Taniparti: Today I am truly thrilled and excited to welcome our speaker, Professor Leah Boustan, Professor of Economics at Princeton University, where she also serves as the Director of the Industrial Relations Section.

Her research lies with the intersection between economic history and labor economics and her first book, Competition of the Promised Land, Black Migrants and Northern Cities and Labor Markets, examined the impact of the great migration from the rural south of the United States during and after the World War II.

Her recent work including her new book, Streets of Gold: America’s Untold Story of Immigrant Success is co-authored by Ran Abramitzky and is on the mass migration from Europe to the United States in the late nineteenth and early twentieth century.

Professor Boustan is also co-director of the Development of the American Economy Program at the National Bureau of Economic Research, and she also serves as the co-editor at the American Economic Journal of applied research.

She was named in Alfred P Sloan Research fellow in two thousand and twelve, and won the ICA Young Labor Economist award in two thousand and nineteen. And very recently she was also named fellow to the Econometric Society. Very importantly for us, we’re welcoming her back to the Harvard campus, because it’s also the one place that you called home when you were doing your PHD in economics here.

So we’re going to start, we are presenting some context, setting data points to kind of get us on the same page. And then hopefully, we’ll have a conversation with some questions from the audience as well. But we are over to you.

Professor Boustan: I’m really delighted to be able to share some of the findings from my new book Streets of Gold here at the Kennedy School, which, in addition to the Economics department, the Kennedy School is my Alma Mater. I was part of the inequality program here when I was in grad school, in two thousand and two and two thousand and three.

So I wrote this book with Ran, my long-time collaborator, and we were inspired to write the book, because we believe that national conversations about immigration and immigration reform are driven mostly by myths rather than my fact and data.

And so we set about to share some of our findings to be part of this conversation.

One of the myths inspired our title, Streets of Gold. This idea that anyone can come to the U.S. with just a few dollars in their pocket, and they can quickly make it here. But the truth is a lot more complex.

So I think it’s well represented by this quotation. It’s painted on the wall of the Ellis Island Muse, and it’s attributed to unnamed Italian Congress, saying, “I came to America because I had heard that the streets were paved with gold. But when I got here I found out three things: First, the streets were not paved with gold; second, they weren’t paved at all; and third, I was expected to pave them.”

So we ask ourselves, how would American history change if we listen to these millions of unsung migrants. Note that this was an unnamed background we never know is full story. So we use data on millions of such immigrant families, both in the past and today, to rebuild what we know about immigration from the ground up. That’s our goal.

So where do these findings come from? In the historical case, we’re using the U.S. Census records which become a following after seventy two years and has been fully digitized, and we can use these to follow an immigrant as he lands in the Us, and then spends more years in the country over the course of his career. We also can see children living at home with their parents and then follow that child into the labor market.

So you can think of us like curious grandchildren who are using ancestry.com, the genealogy website. And in fact, that’s where we started with our work, and from there we build algorithms to follow more than just a few family members to really scale this up to thousands and then millions.

What I’m showing you here is one of the sense of manuscripts not selected at all at random. This is my grandfather, Matthew Flatt, who’s circled at the bottom, living at home with his parents, who were immigrants to the country, and his seven other brothers and sisters in the one thousand nine hundred and twentys census.

It turns out that my family’s story is very representative of what we find in the broader data. So my great grandfather, the immigrant generation never moved up the occupational ladder. And that’s something that we see very commonly in both the Ellis Island period, and today that the first generation, the immigrants themselves, move up pretty slowly. So where they start ends up determining when there is a their trajectory will be – if they start with a lot of skills, and they come already Seek a Phd, then that determines one path. If they start coming from a country with less opportunity for education, and they’re entering into a low scale of profession, that determines that path.

But the second generation, the children of immigrants rise. And you can see this with my family. The older kids in the family enter into white-collar positions in offices, or retail jobs and stores, and the younger kids, my grandmother and his younger brother enter the professions.

So what i’m hoping to do today is reassess some immigration myths. Is it really true that there’s an unprecedented flood of immigration today, and the answer is, no, we’ve been here before. Did the Ellis Island generation rise quickly with rags to riches and immigrants today are not as successful in moving up? The answer is, No, and we see this from my family. But we also see this when we scale up, and our immigrant families, their children today stuck in a permanent underclass.

And of course the answer again is not so just freely delay the groundwork for discussing these myths

to start off with. Are we in the midst of an unprecedented flood of immigration? We can see here the long history, one hundred and fifty years of immigrant entry into the country, and recently that fourteen percent of the population is foreign born.

If you ask Americans on surveys, they’ll tell you that we’ve never before had such large numbers of migrants. But of course that’s not true. We had fourteen percent of the population foreign born one hundred years ago, and in between we see this immigration valley. This dip which is policy-driven with the border closing in the nineteen twentys, and bottoming out at four percent.

Of course these waves are very different, and I wanted to show you that the earlier wave immigration, one thousand eight hundred are primarily immigrants from Europe. That’s the yellow part of this graph, whereas today immigrants are coming all over the world.

So there are many reasons to expect that there might be a quite different path today for immigrants. The past doesn’t necessarily have to represent, and so, when we end up finding that there is a common immigrant experience. It’s really quite surprising, contrary to some of our own expectations going into the research.

So second myth, did the Ellis Island generation rise from rags to riches? This myth is wrong in two different ways, kind of interesting. First of all, a lot of the Ellis Island generation migrants were not arriving in rags. So what we’re looking at here the zero line would be earning the same amount as the U.S. foreign born.

And if you see above the zero line. That means that you have immigrated from starting more than one, and the black arms are recent arrivals. The white bars are after we follow these migrants for thirty years. You see immigrants from many Western European countries already earning more than the Us.

So this would be the equivalent of the skilled engineers from India, China, Japan, from Germany. Today we also had many immigrants arriving in poverty, and four of arrivals did not move up to riches within one generation. So the black bars that are below the zero line we see There’s some progress by the time you get to thirty years out, but no one is converging to the earnings of the U.S.

Finally, our immigrant families stop at a permanent underclass today. What I’ll show you is what happens to children and immigrants for children of white U.S., for parents who are raised with similar resources, similar finances to our childhood, and I’ll show you patterns for the twenty fifth percentile of the income distribution. But this pattern is the same If you look at the thirty bit from the fiftieth, and what we’re going to find is that the children of immigrants, raised at the same point as the children of the U.S. – born and achieved more economic mobility over their lifetime.

So this is the picture for today. Each one of these dots I know it’s very hard to see the collections, but the point is, each one of these dots were black children’s parents were born in a set of different the purple dot with the honor our children, whose parents are white and we’re in the Us.

And those kids raise a twenty fifth percentile reach around the forty fifth percentile and adult. On average, children of immigrant parents reach the percentile. And you can see the dispersion around that average with with with kids of income parents from some sending countries reaching the sixtieth or sixty fifth percentile, even though they’re raised in China.

Same patterns in the past is our kids from one thousand eight hundred and eighty on the left hand side kids from one thousand nine hundred and ten on the right hand side. The Us dot, which is the purple dot, is always for the bottom of the picture, and we see the children of immigrants rising.

Nikita Taniparti: Thank you, Leah. I actually wanted to start with that graph and the idea of where do these myths come from, and what consequences do these myths have? Right? So we all live in the U.S. Many of us are immigrants. Do we hear the narrative of immigration that’s unreasonable?

And also in a way you’re telling us that that influence is policy where the tension between immigration patterns actually influences the policy debate and the policy decisions in the mid, when you’re actually influencing the ability of migrants to actually enter. So can you tell us a little bit more about some of those patterns and the underlying causes of how those two things talk to eachother.

Professor Boustan: So I believe your questions is where do these myths come from? And starting with the myth of an unprecedented flood of migration. Where do we get this within the lifetime of everyone who is in the country today? What they have experienced is a rise from the bottom of the immigration valley There were people who were kids in the nineteen, sixties, Nineteen, seventies and they looked around and saw four percent of the population foreign born. In many parts of the country that meant very close to zero percent more and more so.

And now they look around when they see fourteen percent more and more depending on where they live, and that seems like a first up to some people a frightening or dramatic change.

So we don’t have anyone who’s really, you know we can from their whole life experience. We’re back back on the one hundred and fifty years, and I think we have a lot of amnesia about how extensive migration was during the Ellis Island period.

We also have a lot of focus on what’s going on in our southern border, and the discussion of the prices as our quarter. The numbers that we regularly seem scary. Two million contact points at the southern border over the course of this year to date were only in September.

Those long-term points does not be individual. Sometimes there’s going to be more than one, or, you know, up to ten, up to fifteen contact points for a single individual, especially   under Covid, under Title Point. But that number seems quite frightening as well and contributes to this sense of crisis. If you think about the other myths, the   the ragged to richest idea that that’s actually very widely shared. I mean, there’s almost nothing that we can agree on in the Us today, and that’s around you.

But what we can agree upon is that the Ellis Island generation was good.

You hear President Trump talking about this when he talks about why, we have more migrants from Norway, and that sort of targeting back to one hundred years ago, saying the miners were better back then.

We also hear President Obama talking about this, not in contrast to immigrants today, but just really holding up and valorizing immigrants of the past. So this it comes from our families, you know, the selective memory of.

I remember my grandfather. I don’t remember my great grandfather. He’s the one who struggled didn’t. You know we never really spoke English. I never moved up the occasional matter. But I remember my grandfather became a doctor, and that’s the stories that we tell. It’s the stories of great success in our own family. We also hear this in high school there are many cases that were pulled out from the records selectively anecdotal cases of people who came with nothing, and eventually became CEOs,

and we often hear the story of history high school as well. We might explain something different when we tell it to each other and what we actually see. So zooming into this wrath to rich a story where you differentiate between It’s not really the first immigrants who come to America who experiences immense and quick ladder of growth, but it’s the their children. What does the American dream mean to us just in general. And then how do we understand the American Dream for immigrants?

Well, I think the use of the term American dream is very contentious. We perceive our book as teaching us that the American dream is not dead. It’s just as alive today as it was one hundred years ago. We can understand it as the idea of moving to the country to provide better opportunities for our children, even if we, as the immigrant generation might have to sacrifice and suffer in order to do so. But other people have very different and interpretations and meetings of what the American training means. So while we do embrace that term in the book, and if you see the cover image that I showed you on the very first slide, It has a very optimistic, a depiction of immigrants sort of in gray scale in the front, looking forward to what might be New York harbor, and seeing a rainbow above the country.

So there’s a very optimistic take that we embrace in the book. At the same time we recognize that the term American dream is very contentious. It means different things to different people. It might be something we can, you know, discuss further. People have a particular reason on that.

Nikita Taniparti: I’ll just kind of go over a quick question. So. Ah, we are looking your book. This book looks at immigration from out of the Us. But a lot of your other. One looks at internal migration. It’s hard to study that, but we also see a lot of mobility happening even in that valley of immigration, the twenties. So how has your other research trying to understand how mobility and economic outcomes were reshaped in America? How many in economic outcomes were reshaped in America. How many in economic outcomes were reshaped in America? How many in economic output structures change? Is it about agglomerating people as well?

Where people went? What are some of the factors at the beginning? What we observed?

Professor Boustan: Well, one thing that’s really interesting about our predators on the children of immigrants is the underlying mechanisms behind them.

You if you chat about this at the dinner party people will tend to say, well, that’s not surprising to me, because I know immigrants work harder, they have a better work ethic, and more persistent, and they care more about education. So people have in their mind a sense of immigrant values that they think would then be their kids. What we found in the data is that there’s another aspect of being an immigrant that matters a lot and can explain everything that we find in the past, and it’s still an important contributing factor today, and that is where immigrants choose to settle.

Immigrants move to the parts of the country that are the most dynamic, and provide the most pathway for upward mobility to anyone.

So what that means is, if you compare an immigrant family to a us-worn family living next door. The children actually don’t do any better in the immigrant house? What   How immigrants do better on average is that they tend to find themselves in those places that provide a ability for all in the past. It’s quite simple.

Immigrants avoided the U.S. South almost completely so at the time that fourteen percent of the country was foreign born. Only two percent of the population in the South was born, and the sound was an animal’s whole region, and cotton growing did not provide good upward mobility for either white or black Americans.

But even if you’re living outside the South immigrants went to particular states and cities that had a lot of upward moment  industrial jobs, and that provided pathways for their kids. So when you think about what is that special about immigrants? It’s that they reveal themselves willing to leave their homes in order to seek opportunity. They’ve already broken family ties. They’ve already left their home country, and once they get to the Us. Then they end up seeking out places that are very dynamic.

But there is a set of American us foreign who also do the same, and those are people who move across state lines. So if we look at us, foreign parents who are living outside of their state of birth.

Their kids look much more similar on average, to the children of immigrants. So really it’s this willingness to move. Crossing borders matters a lot. But even internal migration   matters as well.

Nikita Taniparti: And how do we think about moving for economic goals? But then, social integration, How do you create a sense of home in a new place, whether it’s moving from Texas to Massachusetts or India to Boston.

Professor Boustan: Well, a couple of things there, I mean. First, I didn’t mention anything in the really short presentation about some findings in our book about cultural integration. So we’re economists. We started out by looking at how our commitment is sharing the labor market, and what’s going on with their earnings. But when we talk to people they said,

Well, it’s all very well good, you know. Immigrants move up in earnings, children, immigrants, children, the children, and your parents do well know our immigrants really ever becoming American, and it’s hard to define what that means, but the there’s a whole set of practices that we can observe in data that will give us some clues here.

So we looked at everything that we could that we could measure both in the past from the present, and that includes food immigrants. Mary. What neighborhoods they live in? Are they surrounded by other immigrants in their neighborhood? Or are they living in more integrated areas? Do they learn English, and what names do they give to their children.

And along all those dimensions we see that immigrants do take steps to change the norms and behaviors as they live in the us, and they start. They end up at the end of their life, or even more like the U.S. born than they do than they did when they first arrived.

So that’s how we think about integration, is. It’s a very empirical question. If there’s no values based upon it not better necessarily to choose names that look like the names of he was born. But let’s just watch and observe what immigrants do, and we see that immigrants do start to change their behaviors as they spend time in the Us.

And at the same pace now as they did in the past. So I mean, this is one of those cases where my priors were really changed, I thought, Oh, in the hill this island period there was a lot of pressure from Normandy, and also immigrants were buying for a bureau, which is sort of more similar in cultural dimensions. So immigrants sort of became American very quickly in the past. They’ve jettisoned their whole language, They, you know, told their kids. Let’s only speak English at home, and these days it’s very different. Well, these days. It’s actually not that different in the data.

But there’s another interesting part of this question, which is sort of like underneath the surface, and I just wanted to to mention it. There. There could be trade-offs here where you know, for example, I mentioned immigrants moving out of neighborhoods with high foreign war, and share that some mitigated good, you know, for an immigrants who leave what you know what people would call on-splayed neighborhoods sometimes that comes along with earnings, but it also comes along with cultural loss.

And so we were able to look at this in a really interesting way. With one particular community, we were looking at Jewish immigrants who were moved out of the city of New York, around one thousand nine hundred to cities and towns all around the country through a volunteer self-health program to disperse the Jewish population.

Many of those immigrants did very well, leaving, but also many of those immigrants chose to move back to New York, and the immigrants that chose to move back to New York were different.

Initially, they had more Jewish sounding first aids they had more to resembling last days. So by at least that measure we could see, they were maybe more connected to the whole set of cultural and religious amenities that they would have received in a large immigrant area. And I really want to just make sure that I emphasize that point as well that you know there is going to be an element of loss and trade off when we see immigrants changing their behavior as they spend more time.

And I think that’s a really interesting note of optimism that you present in the book, which is how much people actually integrate. And then you say a point of not not so much. Optimism is segregation of African-american communities or Mexican and hispanic communities.

Are there some lessons that you’ve learned about from a policy perspective. What can you do to facilitate it? A little bit more to a valer to be support convergence of that upward mobility for these left behind communities.

Well, I was thinking about this. You know the connection between my first book and this book here, and you mentioned competition in the Promised Land. One of my main insights in writing that and getting started on the research that led to that book was that African-americans in the us are also an immigrant population. So in one thousand nine hundred around ninety percent of African Americans lived in the South by one thousand nine hundred and seventy. It was more like fifty over fifty, and in order for that rebalancing to happen across regions that represents the movement of millions of people.

And so, If we start to think of African Americans as an immigrant population, we can apply the same lens that we would use in thinking about the various immigrant groups that I showed you on this slide, and in some ways the African-american migration produced very similar results. And In Some ways. It produced very different results.

I would say that the first generation experienced a pretty similar pattern, where, by the meaning of the those black migrants who moved from south to north, doubled their earnings, and that’s the same as the European migrants in the Ellis Island period doubling their earnings by beefing Italy by leaving Norway and moving to the us.

When African-americans arrived in industrial cities in the North, they did not earn as much as the existing black population.

And there was convergence over the course of their lifetime. So it wasn’t complete convergence. But there was movement in earnings for the first generation. What’s really different?

So has been the experience of the second generation, the children of the great black migrants,

and I didn’t realize this how profound this was, until the work of a Laura Drennan or my colleague at Princeton, who was able to look at the second generation of children who were being great, of, of course, or in the early nineteen eighty S. In cities that had high or not as high inflows during the great black migration period. And then the children that were a part of that   second generation.   who lives in high grade black migration areas did not aspire as much upward mobility at all. So that’s where the difference really lies, and you dig below the surface. It’s the boys from that generation, and not the girls that were really experiencing this damper on Hubble mobility.

Now put that in touch with what we’re finding in the dot box that I was showing you. I showed you a dot plot for the sons I called them the children of, but they were the sons of foreign-born parents. We have a similar plot in the boat for the daughters, and we see a very different pattern for  . The daughters of immigrants from the Caribbean from majority black countries than we do for the sons. It’s quite similar to what a lure is found for the second generation of the great black migration. So here i’m starting to see some echoes.

It’s not true for all majority of black countries. We only have five of those sending countries in our data and due to privacy restrictions.

But for Nigeria and Dominican Republic the sons are also doing very well what I’m thinking about. Here are the data for Haiti, Jamaica, and Trinidad, and tobacco there. The sons are the one exception to this role that I told you that the children of immigrants out reform the children of white us for the children from those whose parents came from those countries. The sons are around to connect with the white Us. Form, but they’re not out remarkable.

The daughters are doing spectacularly well. So there is intersection here between race and gender,

and it’s showing up for whether your parents are foreign born, or whether your parents are part of this internal migration wave from south to north and I, you know certainly there’s some suggestive evidence. And all this paper that is incarceration and policing.

How is the earnings of someone who is incarcerated measured in our data?

If you’re incarcerated, you’re still in the dataset. But you’re if you have essentially zero learning, and that’s really pulling down the average, and we were able to take that group out now. I’m not saying we should, because they’re a meaningful part of the average.

But if we were able to take that group out, my guess is that the sons from those three countries I mentioned Jamaica Trend at to Vega and Haiti would be helpful for the Hawaiian restaurant. And so there is this over-policing and incarceration in Caribbean neighborhoods, as well as in the Neighborhood of the initial of great black Migrants, and that is something that primarily affects the sons of outdoors, and that’s one more question that I know the audience of questions, too. But so you’ve told us about how everybody’s trying to point them to the Us.

You sacrifice everything for your children. You really really try to make it, and then people leave and go back home. So migrants return, and I think the folks that both put it at about twenty, five to thirty percent.

Why do people return home? How do we understand what they do when they return home. How can you amplify the positive benefits of return migrants?

So we had just talked about how some people, some Jewish migrants, were sent to cities and towns around the country, and returned back to New York City. Part of what they were attorney for is a sentence of cultural and religious amenities in this large Jewish economy.

So now not to find that find your home country versus the United States. Some people are returning home because of the full of their home country. But they’re not moving to the Us. Necessarily, you know, out of some kind of misunderstanding or mis oppression of what they’ll mind here. I think the term migrants are often very strategic.

They understand that by moving to the Us. Like I said, you can double your earnings. And today, actually, in many cases it’s more than double your earnings.

So doubling your earnings was for the internal migration south to north that didn’t face any mobility barriers as well As for the Ellis Island period, where there were very few migration restrictions today with so many migration restrictions, the return to migration is actually elevated, and you might even trouble or quadruple your money.

So imagine that in the long run you do want to live in your home country. But you know that there’s this opportunity to move to a place, take it for three to five years. Earn a lot, and if you come home you end up deep frogging over some of the people from your from your local area that did not be. You can use the money that you’ve saved up to buy land. You can use it to build a house, you can use it to start a business. You can import some of the knowledge that you have that you’ve ah acc ulated and acquired here in the Us as well back to the whole country.

So we actually have seen this in the case of the Princeton area. We have a large Guatemalan community, and there were there was a a a guy who went to bed through our public schools, Princeton High School, and while he was at Winston High School. He was working at the local pizzeria, and he learned about bring up and pizza, and he brought and imported some ovens from New Jersey back to Guatemala,and he started the first, for out of Korea in his whole country, and it spreads around the country. And so That’s the kind of local knowledge that you might get. And i’m not only talking about, you know the science technology like the high skill you may be able to import a problem of knowledge across. And in many industries. So people come to gay knowledge also to gain savings and intentionally.

No, that’s not everyone. Some people return because of employment shops, health shops, family  , unexpected events when they need to return. But for many people return migration is part of a strategy, and really has always been around twenty five percent of the term migration in Ellis Island period and around twenty, five percent today.

I think we’ll take a few questions, but maybe for honest, you can have a hand the mic to people. And as you’re doing that I would really love about the focus, how it talks between data and these stories. So it will. The average story, not the Elam.  . I don’t think there’s a question over here first, that okay second room a registered name, and you

Hi   My name is Karissa. So i’m wondering if you managed to identify whether the upward economic mobility and information between Tommaker and Joe Taylor, because there’s a notion that migrants are taking American jobs, and i’m wondering whether it’s true.

I mean this is an enormous question, so I could go on at late. I want to make sure we have time for everyone else, but it’s,

I think, very hard to differentiate and put a label on someone as a maker or a jobmaker. Certainly we have this sense of high-level immigrants are creating jobs for others by opening businesses by patenting and invention and science, and that’s one. But we also have for low, skilled immigrants all sorts of ways in which more skilled immigrants are contributing to job creation and expansion of industriesSo I’ll give you a couple examples. One is markets that just simply wouldn’t exist if we didn’t have immigrants here working in those areas. People often say, Well, if if immigrants weren’t here, then the wages would rise, and us foreign would take the job, and we would just continue as always.

I’ll think about how this works in agriculture. For example, in the one thousand nine hundred and sixtys, we had a strong guest worker program in Mexico called the Brassero Program. President Johnson ended it, and his thought was, Well, if we don’t have the restaurant workers, then we’ll have to   agricultural jobs for investment. Kay was around a dollar an hour at the time, and farmers started to raise pay a little one dollars twenty five. Us home workers still did not come to the field, and so farmers decided well at that pay dollar twenty five. It might be worthwhile to be instead to invest in new machinery, to harvest or awesome. They shut down the production of these crops that have to be   tended by hand, and instead, i’ll shift into crops that could be harvested by the machine, so that market the market strawberries, asparagus,   micro greens, avocados, those   you know, free soapy and vegetables that we enjoy today.   dried up, and the types of fruits and vegetables that we had in the late sixtys early seventys for the types that you could easily harvest by machine, so that industry just simply shut down and shifted away from hand-harvested for sessions. Think about what would happen if we didn’t have immigrants entering in hair salons and managing.

I would simply paint our meals at home, and that is what I used to. I mean. I’m like a child in the seventys. We didn’t have   really an iceberg wet and frozen fruit and vegetables. We painted our nails at home, an idea that you would go and like get a manager was just

like something that only the very rich would do. And now it’s an enormous industry that’s available to many people. And so that is the kind of job creation for others that even lower skilled immigrants can provide by creating markets that said they were not there. Think about childcare. Many, many people would want to pull out of the core, pay market work in order to care for their own day full time and with childcare, and we now have an expansion of job opportunities for others, because we thinking the same.

Thank you so much for having me. My name is Carol. Let me see if I must be better so. One of my questions was around   using saving engagement as a key indicator of integration is wrong. There’s sometimes a notion that you parents. Don’t like to get involved in seven, five

But I actually used to work for the city of Boston, and we were at the Civic Academy, for I mean grandson.

We’ve always got applications for it, right? So i’m just really curious to see if you use that as a key indicator of integration, you know, around the country as well.

That’s a great idea. I have not, and thank you, though, is also asking me a little bit about immigrants and patriotism. And I’ve actually thought to myself, Well, it’s really hard for us to measure such a thing. But in the past hand today, today we have really good surveys, surveys of trust and institutions and attitudes towards American ideals and immigrants were higher than the Us. Warrant on all of the development. So immigrants are more patriotic Americans than us four, and J. But I have no idea what this would have looked like in the past. We don’t go back and do attitudinal surveys. But then I was thinking about it in more detail, and I was like, well,

I mean we can look at registering and enlistment for World War, One World War II. We can look at registering as a border, and that’s something that I actually supervised a senior basis for someone who was working on the Cps voter supplement on both Asian, American and Hispanic American voter registration. So there’s a lot that we could have done. We didn’t do by I’m Kate Jenning, who shared the growth up, and I was wondering do you immigrants who come to the Us as children and They look more like first-generation rooms for second generation.

So that is a really good question. The one point. Five generation are included as children of immigrants in our data. And so they’re contributing to the n bers that we see.

So if you break them out of in the data, they sort of look halfway between the the first generation and the second, but they are included in our social mobility N bers There tends to be sort of a rival Age cut off, after which the ah first the one point. Five generation looks more like the first generation, so they do face more barriers, and that tends to be at around like twelve years old, and so it has to do with learning English fluidly. And spending more time in American schools and public schools. And so they you can think of them as sort of in between. So if you’re interested in what the social mobility of the second generation, purely like people who are born in the country. Then you would have to script those folks out, and my guess is that what we would find would look even more startling in terms of we’re mobility that the and but I was wondering if it then in the add to the diversity of the place, for would there be reverse consolidity, et cetera, or some self-sustaining process, that if you look at all into that and the second one is I.

After I read your book. I saw another woman that we reached coming with a little bit of the idea that you know immigrants bring a lot of Ah, I don’t know what to call it main to the trains, and so on. Heard that, and and I mean that has been used by in some papers. By. I think about this and others on on, you know, attitudes towards their ability or marriage, or whatever that that come with immigration. And I can Guest done, thought that all Catholics were drunk, and it imposed restrictions on alcohol and whatever. So I was wondering, What can you say about this concerning the you know values being, you know, the challenge.

Oh,so on the first question. I think you’re really undoubtedly right that if there weren’t immigrants in those places, and those places wouldn’t look as dynamic as they do   in terms of the density of industrial jobs. Let’s say nineteen, ten or twenty. There’s some decision on the parklands where to locate, and they’re from located places where they have access to a strong and robust labor force. So what we’re saying here is morally descriptive, just in a very mechanical sense. Once we account for geography, the advantages that we see for the second generation children dissipate and think of it, just as I described that if you compare the next door, neighbors, we don’t see this immigrant advantage anymore. But there it is.

It’s certainly possible that one of the elements that makes those markets   more advantageous is because they have immigrants in them.

Yes, it could be so. Connor and Michael Stewart had a paper of pnas a couple of years ago, where they tried to understand in the past what makes a market more dynamic? What makes for all remote, and they basically do a horse race between public schooling and industrial jobs. And in the past, at these industrial jobs mattered a lot more and interestingly, the children of immigrants that we’re looking at in the past have fewer years of education than the children of the Us. Born, raised at the same point in the income distribution, despite earning more

So they’re earning more, even though they’re acting at the educational Spanish. So they’re in markets that had a lot of industrial and manufacturing job opportunities. And therefore, if you’re thinking about the opportunity, cost of staying in school for an extra year, i’m losing out

on the possibility of starting work. So people tended to leave school a little bit earlier, and in fact, it was in some of these more rural areas where public schooling really began and took off as sort of the work of.

So, at least historically, these areas were not necessarily the places with good public schooling, and they were, instead places of with really industrial powerhouses, possibly also with immigration, and the diversity of place contributing to that advantage

On the second question about bringing negative traits, whatever the trade might be, that trait disappears very quickly. And so it is not entirely clear from the question or the the work that you’re citing, which trades people have in mind. But whatever it is, they see that immigrants are race around half of the gap between their own behaviors and the behaviors of the Us. Born in the first generation, and by the second generation those gaps are almost entirely closed, if not entirely so. There are newspapers by R Kel, Fernandez, and others that show that there is some long term vestige of the country that your parents came from.

It’s a long-term vestige in terms of you can pick up as a a statistically significant effect of where your parents had been born. But it’s not important in an our spirit sense. It doesn’t explain very much of the difference between behaviors according to where your parents were based. So if you have enough data, you can certainly pick up this association, whether it’s women’s, labor, force, participation, or fertility, behaviors, or what have you? There are some immigrants coming from countries where women are less likely to work where it’s expected that you would have five or six children instead of two children, and you can pick off vestiges of this in the second generation. But it doesn’t explain very much. The overall pattern is really one of.

I think that it’s also interesting because I mean when people talk about we should only allow high-school workers, or and say no it always sense, because a lot of your findings are about it. Actually doesn’t matter, because, you see, it has been all of that kind of converging to this integration of our mobility, and really just positive followers.

I want to. You just say one big thing on that which is, there was a really truly excellent piece, very much engaging with the ideas and streets of gold by Raymond Salam, that just came out in Foreign Policy Magazine, and I don’t know if you guys have great han. He’s the president of the Manhattan Institute.

He’s often on fox news. We don’t agree on everything.

But there’s a lot of scope for overlap and places of agreement where we disagreed was really on this question of how selective immigration should be, and the way I see it, it’s a question of how many slots we have available right now. We have seven hundred thousand visa slots and around a million if you count direct spaces that come in without being part of that quota. So around a million interests a year, sure if we cut that number to one hundred thousand. I don’t think we should, but if we did, and that’s what happened during the one thousand nine hundred and twenty four to closure, Maybe we would want to prioritize the response to the scientists and engineers who contribute a lot in terms of economic growth in the first generation.

But if we have a million slots, or if we have a million, three hundred thousand songs, which is what some think tanks in Dc. Are suggesting, we need to keep up with the demographics and the slowing down in population growth, you know, like adding three hundred and about slots. We’re not talking something incredibly dramatic here. But we’re talking about one point, three million slots. Well, then, let’s think about what the labor market means are currently.

Do we need agricultural workers? Do we need child care? Do we need elder care construction? Do we need other personal services? There are many ways in which immigrants who did not get a chance to even go to high school, or did not get a chance to finish high school in their home country many ways that they contribute to the economy today, and then what we’re adding in streets of gold is to say that their children are not always going to be doing the same job, same occupations as their parents. The children have the opportunity, once they are educated in the Us. And fluent in English, to move up above the Median.

Thank you very much. I have a follow-up question on the discussion that we just had a record also on cultural assignation.

So following what you explained earlier about how my word is to let into more dynamic communities. And how does kind of someone observe over time? I wouldn’t expect that these communities culture also?

Ah simulates more to an international culture in a way right, so that the effect isn’t necessarily only driven from migrants as stimuli to Americans. But the Americans becoming more international in a way, right? And so along these lines.

I wonder if there is some observed pattern of polarization, cultural polarization, where, on the one hand, you have this culturally diverse environment where people, you know, immigrants move to, and then kind of the opposite.

I think that it’s very profound, and that’s undoubtedly going on. We do talk about it around the edges. It’s about about the different contributions that immigrants make it’s, not just economic contributions. It’s also bringing their foods, their music, different aspects of their culture with them, and the opportunity to live in a state like New Jersey, where I live, which is one of the most immigrant densities in the country, and you know to me it’s a really incredible set of opportunities for cultural exchange.

And that’s a preference that I hold, but not everybody. And so there may be continued sorting along those lines geographic sorting, or even if you all live in the same area, there might be my overall sorting where you go to different churches where you go to different restaurants, and I think that that’s like Seems like it’s certainly going on at an anecdotal level. But I think it’s okay.

I think it would be great to get a handle on and measure and  , and and have some ways of getting out of quantitatively the way that we talk about it. And I think that makes sense to me is that assimilation is a two way street. You know that immigrants change to become Americans, but us foreign also change   when immigrants right for course, migrants or minders that are going to be distribution on the basis. But do you see the success? That because it’s lower the barrier of my patient because that area so do you see any success?

So are you talking about refugees? And yeah, they used to display right?

So I mean, this has been really fascinating. There is good work done on refugee population in the Us. And also around the world in the modern data. But no one has ever come back to think about people who would be classified as refugees. One hundred years ago our refugee system was established in one thousand nine hundred and eighty. Before that we had some refugee act that designated particular populations as refugees going back to one thousand nine hundred and forty eight. But before that we had an

There were many immigrants from Europe who arrived due to a flight of persecution. So we wanted to look at that group as well, and we were able to do this, using some world histories of immigrants, who arrived between one thousand eight hundred and ninety, and one thousand nine hundred and fifty; and we found that these immigrants classified as refugees by us, according to the stories that they told for their reason for moving, were ah assimilating more fully into us Society, as measured by their details of their ability to see English.

We had one hour’s speech of these folks like that. When we had a audio tape we could classify their accent. We had a transcript. We could classify their vocabulary, and we found that refugee immigrants were more successful at integrating, and can think about the economic explanations. For this it makes a lot of sense. If you have no expectation that you’ll be able to go home.

You talked about the twenty five percent, which for migrants, then you have a stronger incentive to start investing in learning English, embedding yourself into labor market adwords, social networks.

We also looked at this in today for the modern data with using the new immigrant survey, and we see the same matter.

Other people have done similar work on refugees, modern data and find that refugees might start out with working staff, but they move up very quickly, and so I think all of the evidence is starting to line up that immigrants who feel like they have no home to go back to are those that actually is similar to that.

Thank you. Thank you for the talk, and uh for the very nice Ah, Kevin session, my question is related to Ah, what used to work in the past that may or may not be working today. And I can think of several, just you know, listening to you. One is the role that industrial employment has played in the past.

If the factories now are either in China, or at least in Alabama and rural parts.

Other things also that we go wrong or may have. But we are maybe, uh, just have of housing in these particularly dynamic places, and and so as an immigrant to have access to these places today. And the third thing is maybe the cost of education. 

So we have limited ability to really answer carefully, because our historical data is incredible. We have millions of people exactly where they’re living or their neighbors.  , We know their education level and their industry and their application. The modern data that I showed you comes from the opportunity, insights lab, and when we’re lucky enough to get attitudes from them of how children will raise a different income distribution there later in life according to their parents.

But we do not have the money or data, so we don’t know education. We don’t know occupation. We don’t even know exactly where the schools are living. 

Which is a lot smaller and doesn’t. Have good geography, anyhow, and we found that geography matters today, but not as much as a day of the past, which means a lot of scope for other explanation. Just because we see similar matters of over mobility doesn’t mean that the mechanisms have to be exactly the same in the past. Geography was just overwhelmingly a factor that mattered today. It may not matter as much, but we still see that it does matter, and I think that housing markets are somewhat of a key here.

The most productive areas in the country are now obscenely expensive to move to,

and immigrants seem to have a comparative advantage of moving to these high expense places relative to the Us. Born in part, because they’re willing to live in smaller housing units and double up in part, because they actually send some of their budget back at all through remittances or saving to go home. And so they don’t spend as much of their budget on the high cost of living location, whereas the Us. Foreign are spending their money almost entirely locally.

And so these reasons immigrants are sort of on this edge and getting into the most productive places still today. But I think there’s probably going to be a lot more scope for education mattering in the modern data than it did in the past. And we just we love to be able to work with the micro data. Maybe one day we can, or maybe that will be another source that will allow us to do that.

I think I’m going to actually go ahead, and i’m so sorry to be able to ask the questions. Maybe you didn’t come to the ah right now. If you haven’t read the book, or any of the s work. We’ve focused on one of our characters book around people. So you can be tested on what you learn today, but kind of reframing. As a lot of countries are in political type of ideological models of a very complex multi-passeted thing, or As you were saying, you might see similar trends, but the nineteen essence can be very different.

So there’s a lot of things I think, for us to be into, as there are policymakers of people who work policy papers by doing research.     So I really want to say thank you for starting the conversation here today. And hopefully, we can really about to continue this.

Thank you, Nikita. Thank you to everyone for your questions. I really appreciate it.

DISCLAIMER: This webinar transcript was loosely edited and there may be inaccuracies.

(Mnyupe) Hi guys thanks a lot for coming out to listen to our conversation. It’s really good to be back. The last time I was here physically was in 2019, I took a class actually with the Harvard Kennedy School. I did an infrastructure and markets economy with a really cool lecture and I had a really good time so it’s good to be back.

As Ricardo has said, he together with the Growth Lab essentially the Namibian government sort of retained the services of the Growth Lab to help us think about economic complexity a little bit and this is where this journey started to a large extent as well.

So I started working for the Namibian government in in September 2020. To give you some context, Namibia’s GDP is anywhere between 11 to 12 billion US dollars on a good day our annual budget is about 2.77 billion. What we spend is about 3.3 so you definitely have a deficit of 500 million US dollars or more. I’m giving you all of those numbers to get context of who we are. Countries around two and a half million people the size of the country is about two and a half times Germany’s size so it’s about 824 a thousand square kilometers, being shared by two and a half million people and that gives us the dubious honor of being the second least densely populated country in the world.

Which before, this it was really hard to sort of see the utility of that particular statistics but it’ll start to come to light. So when we started trying to look at increasing the economic complexity of our country this is basically something we were going or we were looking to do with the growth lab. You know, what sort of new exports we could put together currently, maybe a sort of export minerals to a large extent, so you’d think of diamonds uranium some meat some fish so nothing too complex we’re not exporting Nokia cell phones just yet and then at the same time services or tourism right so that was it there may be an economy in a nutshell.

And then we got approached by a big green hydrogen developer I think it was October 2020 and we started looking into this whole green hydrogen thing. I started working with the world bank and they started saying to us, look, you are actually, potentially very uniquely positioned to do something very special in this space. You have some of the best wind resources on the planet we checked that with various tools that we were able to access through the Bloomberg new energy Finance tools and we saw that the capacity factors we could generate in Namibia.

55 percent or more than eight meters per second of wind meant you had a really interesting world-class wind resource especially in this area the solo was pretty good as well easily  north of 30 capacity factors in selected areas it just so happened to be in the very same area so typically Namibia has started to build some renewable energy potential around the country but like small plants right so Namibia produces, or we consume about four and a half terawatt hours of electricity and we import 60 to 70 percent of that.

I know right pretty sad, and we input a lot of that actually from South Africa right here and they produce a lot of that using coal-fired Power um our biggest generation asset is up here in the north in the ruachana area. And it’s Hydro right and then half of our population are certainly a working population work in the agricultural sector so you can imagine you’re very exposed to climate change right if you get hit by a big drought you don’t get enough electricity half of your people unemployed and you have to pay very high prices for emergency power you’re importing from qualified power stations which is that’s the right climate change um so that was sort of our reality and then we tried to turn our reality into this so after doing a lot of work in this area we essentially put together a fairly robust governance structure that I can talk to you to you guys in some detail.

We went out to the world we put out a global um RFP a request for a proposal and we invited private sector developers to Envision a future um that had green hydrogen as a big part of that and we got nine proposals from six developers, and I’ll show you one such project. But essentially, as we embarked on this economic diplomacy, we started actually getting a lot of interest from people who wanted to build projects everywhere.

We started unlocking interesting grants and funding from larger economies such as Germany and the EU that wanted to partner Namibia on this particular journey and this is where we are at the moment we’re envisioning about three hydrogen valleys we’re putting together a green hydrogen strategy and we’re going to launch that at cop27 um, and this is being done by McKinsey and this I think was a big part of the secret Source I think a lot of international players did not expect the government to organize itself as quickly as we did there’s a technical committee full of technocrats that essentially reports to a green hydrogen Council you could see my one of my roles is a green hydrogen commissioner which essentially entails project managing this Vision.

And then of course we’re reporting to cabinet and that’s the out ultimate decision-making body and so once you started creating a certain amount of clarity but also robust sort of governance structures it just engendered a lot more confidence and a lot more people were willing to come and take the risk of developing in Namibia so that got us you know going on a really exciting Journey this is um the project that is envisioned um in the southern part of Namibia actually the developers are online as well so if you get that really cool technical questions for them we can ask them um but essentially this in the beginning this was envisioned as a 9.4 billion US dollar project we’re now looking north of 11 billion closers to 12.

You must remember the size of our GDP when you’re thinking about that number and it essentially entails almost seven gigawatts of renewable energy assets three gigawatts or so of um electricity about 700 megawatt hours of battery 70 kilometers or so of transmission lines some pipelines that are at the industrial site with desalination at the front ammonia synthesis at the back and ammonia storage tanks you liquefy the ammonia, and you then bring that off to a ship this is a floating a floating boy Mooring system get that into a tanker and then that goes off to you to your client at the moment.

Our clients are predominantly envisioned to be German so two weeks ago we were in Germany in Berlin working closely with their Ministry of economic Affairs and climate change and there was a Business Roundtable where essentially, we were introduced to prospective off takers this particular company hyphen is 35 owned by a German company as well called nit rag and so Germany and Namibia are working very close through economic diplomacy trying to make this project um come to bear and I think Dan and I will get into some details around that as well.

Some interesting features that obviously are really important for Namibians full-time jobs about 15000 90 of those should be Namibian employees now you have to know that the town that is to host this project probably has a population of about 30 000 people ums so if you imagine that these full-time employees have two or three um dependents and most of them are coming from not and they don’t live in the in the town you could double the town’s population right and these are some of the challenges I think that we would love to be working very closely with Ricardo’s team to start really thinking about why this project actually means on the ground what are the anthropological issues socio-economic as well of course as techno economic.

But you know for example what does Namibia have to do um with our immigration policy as well Do all of these skills exist um and what would it take to bring in skills but also accommodate them in a manner that they Thrive as well big local content about 30 local content a big part of that is other local companies balance sheets big enough right um I think one of the examples the developer was giving was that the largest develop the largest construction company in Namibia their order book is typically about 70 million US Dollars and um this project might have an order book of about seven billion rights so we’re going to need many of those Namibian construction companies to actually absorb all of these um all of this capital.

I think this is another very interesting one – the idea is that the project will produce about 18 terawatt hours possibly more actually, because the project has been growing in size since then um about four to five of those terawatt hours would actually be curtailed energy and so we’re looking into the possibility of capturing some of that and actually, feeding that into the Namibian grid possibly with a bit more of a dispatchable profile.

And obviously that would require unique Investments strengthening the grid, a new connection battery storage as well possibly green hydrogen re-electrocution units as well and all of that will then obviously determine the price of this electricity relative to the Imports that we get from South Africa. We think they should be cheaper but we’ve commissioned two studies to do that we think this project can easily produce electricity at two cents a kilowatt hour we pay nine to twelve cents a kilowatt hour for our Imports so there’s an interesting opportunity there to get Namibia to be energy independent. At the same time, if this is the first of many projects you might actually be able to become an exporter of electricity let alone the molecules of to Europe.