Verena Halsmayer on Managing Growth in Miniature: Solow’s Model as an Artifact

Summary

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Podcast Summary: Verena Halsmayer on Managing Growth in Miniature: Solow’s Model as an Artifact

Podcast: New Books Network – Peoples and Things
Host: Lee Vinsel
Guest: Verena Halsmayer
Date: November 24, 2025

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Episode Overview

This episode explores Verena Halsmayer's award-winning book, Managing Growth in Miniature: Solow’s Model as an Artifact. Halsmayer, a postdoctoral scholar at the University of Vienna, discusses the iconic Solow growth model and its role not just as a theory but as a practical tool and a product of its scientific and institutional milieu. The conversation delves into how economic reasoning, especially around growth and technological change, emerged, stabilized, and spread through the work of Robert Solow and his peers. Halsmayer emphasizes the cultural, material, and practical dimensions of economic modeling and traces the afterlives and ironies of the Solow model in policy, teaching, and development.

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Key Discussion Points and Insights

The Book’s Intent and Approach

• Understanding Economic Reasoning as Practice

  • Halsmayer aims “to better understand economic reasoning,” focusing not just on economy theories or ideas, but on how these are rooted in “historically specific and culturally framed practice that is done with specific tools” ([04:43]).
  • She brings a historian of science perspective, considering modeling and measurement as practices, not simply abstract ideas.

• Importance of the Solow Model

  • The Solow growth model is credited with explaining economic growth and the importance of technological progress. However, Halsmayer interrogates what "explaining growth" and "technological progress" actually mean in the context of economic reasoning ([08:49]).
  • The Solow model formalized a certain style of economic reasoning—mathematical modeling—that became dominant between the 1930s and 1960s ([06:21]).

What Is the Solow Growth Model?

• Model Features

  • Presents a “self-sustaining economic equilibrium” via a simple mathematical formula—a frictionless world assuming full employment, perfect competition, and perfect foresight ([08:49]).
  • Key finding: population growth and capital investment alone can't account for growth rates; the residual is attributed to technological change ([08:49–13:16]).

• The “Solow Residual”

  • When the model is deployed empirically, the gap between input and output growth becomes the “Solow residual” or “Total Factor Productivity” (TFP)—labeled technological progress, though it captures everything not explained by the model, including “social, political, cultural factors, and measurement errors” ([14:52–15:43]).
  • Memorable moment: “Technology was just the label put on the residual, on the rest.” — Verena Halsmayer ([15:06])

Models as Tools and Practices

• Method and Theoretical Framing

  • Halsmayer draws from philosophers and historians such as Mary Morgan, Marcel Boumans, and Harold Moss, looking at models as artifacts and practices ([17:12]).
  • Her approach moves beyond “doctrine” to investigate "specific intellectual cultures, social worlds...research and teaching, institutional and material conditions" ([24:37], [25:41]).

• Teaching and Disciplinary Norms

  • The Solow model is not just a research tool but a teaching device: “if you want to be an economist, this is how you do it” ([27:00]).
  • Robert Solow at MIT was seen as “the teacher,” emphasizing collegiality and open workshop culture ([27:20]).

Performativity, Policy, and the Life of Models

• Performativity vs. Interventionist Knowledge

  • While related to performativity literature (i.e., models shaping reality), Halsmayer is more focused on “how the problem of growth was constituted through the model,” and the model’s unintended afterlives ([29:37]).
  • Models can develop a life of their own, sometimes leading to regret among their creators ([31:01]).

• Interventionist Knowledge

  • Halsmayer co-edited a special issue on “interventionist knowledge,” examining how social science tools informed post-war policy across states, businesses, and organizations ([32:00–33:43]).
  • One of the ironies: models or knowledge devices are often used in ways “other than intended, led to different outcomes than expected, or was simply ignored” ([33:43]).

The Book's Structure and Solo’s Place in the Story

• Halsmayer intentionally avoids a hero biography of Solow, focusing sparsely on him as a person ([34:17–35:50]).
• Tracking the model’s place among “parallel constructions”—Solow’s became dominant, but she resists simplifying into hero/villain narratives ([35:45], [38:09]).

The Model’s Flexibility and Political Afterlives

• Interpretive Ambiguity

  • The Solow model’s very abstractness and simplicity made it widely adaptable—and ambiguous. It “could be interpreted in a variety of different ways,” making it a vehicle for both state-driven and market-driven policy ([49:13–53:50]).
  • Quote: “A model is ambiguous enough that you can kind of project your uses and desires...and use it for your ends.” — Lee Vinsel ([53:27])

• Policy Device and Knowledge Infrastructures

  • The model became embedded in macroeconomic forecasting, planning models, and policy evaluation even as the details changed ([54:36–57:41]).
  • “It’s not only about the acceptance of a specific theory...but really about the hegemony of a specific kind of problematization through the tools and infrastructures of knowledge.” — Verena Halsmayer ([57:41])

• Political Ironies

  • Salo’s model is sometimes directly associated with neoliberalism, but in fact, its politics are “far from obvious”—it has served many different projects and worldviews ([67:53–69:07]).
  • Halsmayer’s focus: “It’s the politics of the knowledge” ([68:21]).

The Model and Technology Studies

• Computerization and Modeling
  • Halsmayer addresses the crossover between economics and computing—Solow’s is a “sweet, sweet exercise in comparison” to computationally more complex input-output models ([62:18–63:21]).
  • She traces empirical labor: transforming raw industrial data into usable model form ([63:52–64:42]).
  • Shift: From rich, discursive documentation of empirical problems (e.g., NBER) to the dominant style of mathematical abstraction with little methodological transparency ([64:44]).

Current and Future Work

• From Economic Planning to Alternative Planning
  • Halsmayer is now researching “alternative economic planning,” specifically the Lucas Plan (1975–82), an industrial worker initiative for socially useful production in the UK ([74:23–76:34]).
  • Focus: How knowledge, calculation, and participatory planning technologies moved across domains, aiming to show that alternative forms of organizing production could also be economically viable.

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Notable Quotes & Memorable Moments

• On Economic Models as Practice:
  “I think what is most important to me was to not think of economists’ knowledge as a set of theories or ideas, but rather as a practice, like historically specific and culturally framed practice that is done with specific tools.” — Verena Halsmayer ([04:43])

• On the Solow Residual:
  “Technology was just the label put on the residual, on the rest.” — Verena Halsmayer ([15:06])

• On Teaching the Model:
  “If you want to be an economist, this is how you do it.” — Verena Halsmayer ([27:00])

• On Interpretive Flexibility:
  “A model is ambiguous enough that you can kind of project your uses and desires...and use it for your ends.” — Lee Vinsel ([53:27])

• On Political Complexity of the Model:
  “It is one additional small-scale storyline of market thinking that maybe tones down a little the break of the 1970s in that it focuses on the tenacity and resilience of governmental knowledge infrastructures… the sources of neoliberal governance are not only the ideas of the big names…” — Verena Halsmayer ([69:07])

• On Ongoing Measurement Debates:
  “You can see the computer age everywhere but in the productivity statistics.” — (After Solow’s famous quip, cited at [72:27])

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Important Timestamps

• [04:43] — Verena summarizes the book’s purpose and approach to economic reasoning.
• [08:49–13:16] — Explaining the Solow Model and Solow Residual.
• [14:52–15:43] — What “technology” actually means in the model.
• [17:12] — Influence of historians of science and philosophy, rubric of “models as artifacts.”
• [27:00] — The Solow model’s role in teaching economics.
• [29:37–33:43] — Models, performativity, and interventionist knowledge.
• [34:17–35:50] — Decision not to make the book a biography of Solow.
• [49:13–53:50] — Ambiguity of the model, “carrier” for divergent political could-be’s.
• [57:41] — The persistence of knowledge infrastructures and models.
• [67:53–69:07] — Ironies, politics, and the model’s adoption.
• [74:23–76:34] — Halsmayer introduces her research into the Lucas Plan and alternative planning.

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Final Thoughts

This episode offers a richly layered exploration of how economic thinking is shaped, taught, and institutionalized, using the Solow growth model as a lens. Halsmayer’s work complicates the triumphalist or villainous readings of economics’ “great men” and their models by highlighting the practices, ambiguities, and unforeseen consequences in the long afterlife of a paradigm. The conversation is interspersed with personal and professional anecdotes, careful attention to the materiality of knowledge, and a humility regarding the ongoing limits of economic measurement—a perfect listen for anyone interested in economics, science studies, or the politics of expertise.

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Transcript

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[A]

It's so good to see you again. Thanks so much for having me, Lee.

[B]

So Managing Growth and Miniature is a really neat book. If you're talking about it with strangers. And I know it's like what kind of stranger? Whatever. If you're talking about it with strangers, what do you tell them it's about and what were you trying to do with it?

[A]

That's like a really mean question. But I know that you ask it the question that has haunted me for ages because I never know what to say now that the book is out. I would say that my goal was actually quite basic in a sense, so I just tried to better understand economic reasoning. So that is how economists think and by extension how dominant thinking about the economy works and how it shapes policymaking economic life more generally. I think what is most important to me was to not think of economists knowledge as a set of theories or ideas, but rather as a practice, like historically specific and culturally framed practice that is done with specific tools of what economists treat and use as tools. So basically is a history of science approach to the history of economics. So modeling and measuring as a practice, what do economists do when they model and measure? How do they relate to what they do with and talk about whatever it is they want to find out about. Yeah.

[B]

So this is great. I mean you just in that you gave us what we're going to unpack the whole time. So that was beautiful. And so why, I mean I want to ask why did you pick this model of solos to as like your object for what, you know, to do with this approach?

[A]

But of course in the beginning it was like more like an accident. So yeah, I, I did learn about the model in my study. So I studied a long time ago economics in Vienna and it was very mathematical economics, mostly game theory, general equilibrium theory. But I also learned about Solo's model, but just like in half a session and was like, yeah, this old model. So. But when I stumbled across it again when I did my master's in history, history of science, it was very intriguing to me how even though today it's not like a, how do you say, a top notch model, it still structures the very way of thinking about growth, about development and different other fields. So it intrigued me that it seemed to be one of the major agents of the formalization of the discipline of economics between, I don't know, in the book I look at time between the 30s and the 60s. So a process of formalization meaning that economics turned into a self declared modeling science. And it mostly presented its knowledge in the form of a few small scale mathematical models which were kind of an epistemic norm. So the right proper way of making economic knowledge.

[B]

Look, there's lots of reasons I wanted to have you on to talk about your book, including just I like your work and how you do things. But there's also, I mean, the reason, you know, like a kind of, like a very quick reason into why I wanted to have you on is that if we think about economic growth and technology, Salo's work in the 50s really kind of sets up a whole movement that leads eventually down to Robert Gordon's the Rise and Fall of American Growth, which many of my listeners will know about and is very kind of important to my thinking about things. And so, yeah, I mean, just give. So maybe just in a kind of brief way, just talk about what the model is and why technology kind of ends up being a part of it, even though what technology means in it is kind of complicated.

[A]

Okay, so most commonly I would say the model is credited with explaining economic growth and illuminating the importance of technological progress and innovation. And it was actually one of the goals when I started this to just very basic, investigate what does that mean? What does explaining growth and illuminating the importance of technological progress mean in the context of this kind of reasoning? When you look at this model, it features a self sustaining economic equilibrium. So it is expressed in the form of a linear differential equation. And just in a nutshell, it's about growth, growth of output, of national income. It's the same in this model, determined by output and saving. So labor is given outside the model and capital always adapts flexibly. So we have output, we have capital and labor, the both the two inputs and both of them in this equilibrium are fully employed. So we have the assumption there is full employment, we have the assumption of perfect compet repetition and we have the assumption of perfect foresight. So every future development is known in the present. And then the basic. So this is a very curious world, right? I mean there's no questions of power, there is no land, no energy, there is no environmental depletion. It's just a smooth, very smoothly working mathematical world. There's only one good. There is no uncertainty, no risk. It's really this frictionless cosmos. And what the model then it was published in 1956 in this dominant version by Robert Solow. And the argument of the paper is then population growth and capital investment are not enough to increase this equilibrium growth rate. It can only be changed through additional factors such as technological change. So the thing is, and this was really fascinating for me that contemporary economists, they said this is not about a new idea of growth or it's not about like there were already works talking about the importance of technological progress for growth. That was nothing new. But what was new was the form of the model in the sense that it provided a very neat and simple like for those who could deal with the mathematics mechanism to think about growth in this very specific mathematical world. So to me it seemed then that this model equipped the post war vocabulary of growth, development and productivity with a very essential efficient image of a manageable mechanism that would lead toward an ever more prosperous future like manageable growth.

[B]

Would they have used the term parsimony in the sense that the model's as simple as possible, that there's a kind of beauty of the expression that it's boils down this big thing to a very small thing.

[A]

Yeah, it was like, I would say it was parsimonious model world. I don't know, like, I don't recall that the actors expressed, expressed it this way. Like they talked about like Amadya Sen in 1970 spoke about the beautiful simplicity of the model and other economists pointed to the ingenious simplicity and usefulness. So it was simple and useful. Yeah. And you asked about technology, what is technology in this model? So yeah, yeah, in this model it is something that is outside the model, like it can, it's not in the model.

[B]

Yeah.

[A]

But one of the things that contemporary economists appreciated in the model and what made it so, so new was that it was useful as an instrument of measurement. And this was then a paper that solow published in 1957. And so here it was. About the interesting bit here is in the 1956 model, there was no empirical study. So the results merely derived from the mathematical. From the constructed mathematical model. So from this fictitious world, and neither the model nor its assumptions were tested. And when the model was applied to empirically, data, it was all about using this mathematical model to interpret data sets. So Solow made existing data fit the model's categories. So there was labor and capital, the inputs, as I said before. So he measured input growth and then output growth, and then he compared the two. So there was a certain rest. So the difference between these two growth trends, this was the rest, the residual. It came to be known as the solo residual, or tfp.

[B]

Yeah, Total factor productivity.

[A]

Exactly. And the thing is that this residual captured everything that made output growth exceed input growth. So everything that was not captured by the model, including all kinds of social, political, cultural factors, but also simple measurement errors.

[B]

Right, right.

[A]

So that's pretty funny. And actually, Solow, in this paper, is very frank about that. And also later, he discouraged the use of what by then had become the solo residual. But indeed, apparently it was a very efficient instrument for measurement compared to different kind of indexes that were there before. And it seemed to be more straightforward than other techniques. Yeah. So here technology was just the label put. Technological progress, the label that was put on the residual, on the rest.

[B]

Yeah, I remember reading.

[A]

Sorry, no, you go for it.

[B]

Go.

[A]

You could also see it as a, you know, as an indicator of the mismatch of the model to the data.

[B]

Right, right. Yeah. Well, I remember reading, I could dig it up, but in an essay I found really helpful getting my head around this stuff years ago, that someone just pointed out that it came to be called technical change, but it captured all kinds of stuff, including business model change, changes in practices that aren't actually changes in machines, and stuff like that. So, I mean, I think that I'm pretty convinced technology, the machines themselves play a role, but it's just so capacious in the way we're talking about. So what I would like to do before we kind of jump into the kind of guts of the book is kind of pull back now for a second and kind of talk about your approach and background, because I see you. You already kind of mentioned the history of science, and I think I see you as part of this wider movement of people kind of applying history of science tools to economic thinking. Is that fair? Is that a fair description? Yeah. And so, I mean, where were you trained? Who are thinkers you were looking to who were doing similar things? Yeah.

[A]

So when I started off, it was specifically the work of Mary Morgan, of Marcel Baumans, of Harold Moss, who looked at models as artifacts and modeling as a practice, as a style of reasoning. This is more like, I don't know, they talk differently about their approaches. Somewhere in between history and philosophy of economics, philosophy of scientific practice and economic methodology. And as the project developed, I think I got more into the history, science side of things. So I started to look more specifically at this style of reasoning in a specific historical realization in the post war us. Yeah. And so I think that maybe institutional, this is where institutional factors, more material factors and cultures of doing research came in. So these, but these were very important for me because they provided a language or a way of thinking for me. But then also like when I. So I used to study economics, as I said, and then I did history, I studied history then and I did my PhD in History of science. So. And then there I encountered various history of science literatures, also sts and sociology of knowledge.

[B]

Yeah, that makes sense. And so your book just won this major award and what was the organization that it won it from?

[A]

That's the European association for the History of Economic Thought. So it's one of the big associations within the field of the history of economics or the history of economic thought. And like I always very much appreciated like the history of economics. It's a very specific community. It's not too big, I guess, and it's always on the brink of being marginalized. Lots of scholars are economists, but working on the history of economic thought or the history of economic ideas. And it's also quite diverse in the sense of intellectual backgrounds and questions people ask, things they're interested in. But what I very much appreciated is that I always found people in this community that very much supported my projects. And this was like at a time when I started my PhD, even before that, even though I was not their PhD student, even though I was not, like they were not responsible or obligated in any sense. But I was invited to workshops. People read my stuff, they gave feedback, they helped me out. When it all seemed very difficult and marginal, maybe this was even most helpful. They sent me their work to get my feedback so that I really made me feel like I could contribute something. So it is a small but very fine community. I thought.

[B]

Yeah, yeah, that's nice. And you know, how many. So you know, as you said, a lot of folks in those spaces are economists by training. Sometimes they have like, are in economics departments as a kind of historian of economic thought in there. But how many are. Is, you know, like, what is there a lot of. Are there a lot of people influenced by kind of history of science and STSC thinking or is it more kind of more mainstream econ?

[A]

It's difficult to say. So there is part of the community is SDSC history of science and also there is history of economics being done within history departments and within history of science without engaging with the history of economics community intensely. So the field is quite big then, in the end. Yeah. And of course, like, if I think the approach of my book is different than like if you look at the solo solo economist or Solow's model, solo's growth theory as a theory or in terms of a history of ideas. But I don't think it's. It's too far apart actually. Like, I think it's how to say, intelligible. And it's different approaches to look at partly the same materials. And it requires different readings of the same sources, maybe.

[B]

Yeah, these different approaches.

[A]

Yeah. And then complement them with other sources then.

[B]

Yeah, yeah, I saw that in your book. I mean, there's a variety of ways of coming at the same thing, in a sense, in the different chapters. There's a couple more things I want to get into to kind of touch on before we kind of hop into the book itself. And I'm always battling with myself about how to order things, but I think what I'm going to do is. So we have this mutual friend, Eric Hounchel, who also does history of social science and econ and things. And Eric and I, one thing that he told you, I have this hobby horse. I just recorded an introduction for a different episode about my beautiful collection of hobby horses, which is to say minor obsessions. And one of them, or you could call them soapboxes, I guess one of my soapboxes are hobby horses about kind of intellectual history as kind of idealist in a way of kind of focusing on getting stuck at the level of ideas. And one of the things I see you doing is in this is kind of pretty far, like trying to push back or not push back on that, because that might not be your hobby horse, but you are kind of taking this kind of, we could call it materialist approach in focusing on tools, including models, models as tools and then practices. So I wanted to you to talk a bit about that part of your approach, like how you think about what you're doing there by focusing on tools and practices.

[A]

Yeah, I think originally, I think it was just an attempt to get away from being centered on ideas while not casting them aside entirely.

[B]

No, that's the opposite mistake. You don't want to cast ideas aside entirely either. That would, that would be the wrong move. Yeah.

[A]

Yeah. And it's like that. You look at economic theories not as a set of doctrines or ideas, but as arising from intellectual activity, from specific practices, everyday practices of working economists and their analytical toolkits. So the Subaru Share the Love Event is on from November 20 to January 2. For 18 years, Subaru and its retailers have supported over 2,700 local charities through the Share the Love event. When you purchase or lease a new vehicle during the 2025 Subaru Share the Love Event, Subaru and its retailers will make a minimum $300 donation to charity. Visit subaru.comshare to learn more.

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[B]

His book on models.

[A]

On the Feynman diagram.

[B]

Yeah, the diagram.

[A]

Right.

[B]

And how that kind of travels around. Yeah. And I saw something very similar in your book in that teaching came up. Sometimes it's like these diagrams, these models are useful for teaching. It's not just about. And that kind of work of the life of these professors basically. Right. That they're not just producing papers, they also have to explain it to grad students and bringing them up.

[A]

Yeah. And the model is like in a way it, you know, it it not. It was not only how to think about growth. Like it was a, I think a very useful teaching device for that like, but also how to think as an economist. So if you want to be an economist, this is how you do.

[B]

Yeah.

[A]

And, and, and it's also that the, the constructor of, of this neoclassical growth model, Robert Solo, he had like, in this, this group of MIT economists, he was the one who had like was labeled the teacher.

[B]

Okay.

[A]

And, and who had shows of grad students and was very also like also pushed the importance of teaching and having this colloquial workshop Atmosphere, open door policies. Like it was also a style and the culture of imparting technical knowledge.

[B]

Huh. Yeah, I remember this. He didn't die that many years ago, right? I mean.

[A]

Oh, he just passed away and I forgot. But it's a few months ago.

[B]

Okay. Yeah. Really recently. Yeah. I mean I remember this kind of collegiality and teacherliness being kind of highlighted in the profiles of him and the.

[A]

Maybe it was a. At the end of 2023.

[B]

I think that sounds right to me. Yeah. So, I mean, one way. So we think about models as a kind of work that are caught up in these kind of practices of teaching and thinking and how economists do things. I think one place that stse people's heads would go is towards this kind of notion of performativity that was big. And Michel Calonne just died within the last week, I think. May he rest in peace while we're recording this. And you also see it in Donald McKenzie's it's an Engine, Not a Camera about computer systems and models that these models, these economic pictures that are coming out of these models aren't just pictures or some kind of theory. They're also doing kinds of work. But it seems to me that your approach is a little different than that performativity based one, am I right?

[A]

It is a bit different, but I would say it fits very well together. So what was in the first place, what I started off doing was to look at how this model was part of a specific proposal, problematization of growth. So the prop. How the. How the problem of growth was constituted through the model and different other technologies of measurement and. And modeling around that model and. But what. What I found out or what I didn't realize, I think when I started off, and this fits very well with the performativity literature is the. The active potential of such like the stimulating effects also like on the desk of the constructors, like when they worked with the model, but also later on when the model developed a life of its own and traveled to different places and often to the frustration and even regret of the economists who built the models. And oftentimes they. They uttered regret when they saw how their models stabilized.

[B]

Yeah. Well, the one other way I wanted to ask you about this kind of practices and tools traveling thing is that. So you have this special issue with Eric Hounchel, who I already mentioned that I have an essay in and it's coming out and by the time this episode is published it will be. The special issue will be in Science and Context and it's on the theme of interventionist knowledge. And I see the. So first of all, maybe tell us what interventionist knowledge is, but I also see ways in which Salo's model kind of fits that paradigm of like, it travels around, it ends up getting talked about in all these policy documents that I've read for my innovation work. And so it starts showing up in people trying to shape the world.

[A]

Right?

[B]

Yeah, yeah. So why don't you tell us a bit about.

[A]

Yeah, yeah. So the special issue. Yeah, I'm very happy that it finally.

[B]

Did take a while.

[A]

Yeah, it took a while, but it was very wonderful collaborative project with Eric. So basically it deals with the expanding post 45 endeavor to found interventionist action on the social sciences. So not only interventionist action on behalf of the state, but also businesses, labor organizations, banks, international organizations. So, and the issue that our contributors who did a great work, examined this trend through a collection of case studies between the 1950s and the 1990s. So they focus on the tools and devices of social science that were central to intervening. So it's again, like this attempt to get away from being centered on ideas. And so the case studies, they deal with the continuities and practices, modes and forms of interventionist knowledge during these decades. Decades. And they also focus on the ironies these practices, modes, and forms elicit. That is, the ways in which interventionist knowledge was used in different ways than intended, led to different outcomes than expected, or was simply ignored. Like, all these twists and turns in fabricating and using interventionist knowledge, which was never straightforward or full of contingencies, as you know. Yes.

[B]

Yeah. No, no. And I think I want to return to this notion of ironies later because I think it is a part of your. Kind of baked into the way you think about things. Okay. So I think that kind of gives listeners a kind of picture of how you do things and what you've been up to in a kind of broader way. So now, I mean, this is kind of like. I could ask this question in a number of ways, but one of them is you wrote a book about this, Sallow's model. But it's. It seems like you tried pretty hard to not make it a solo biography.

[A]

Right.

[B]

Which is kind of interesting.

[A]

I think as a person. He only comes up as a person. Like, I don't know.

[B]

Yeah, it's like in chapter four or something like that.

[A]

He's like.

[B]

He's like actually enters as a human being. And so, I mean. Okay, so I wanted to first ask you about that decision. Just talk about that Decision first and then I'll ask you a follow up question.

[A]

Yeah, so I think that I simply was not interested in writing a biography of Solow. And it's really about the model and this mode of reasoning. But of course giving that I look at measurement of growth and productivity at the nbr. I look at input output models and measurement at the Harvard Economic Economic Research Project, which is related to economic planning and computers. So it's, it's about these various forms of thinking and, and then I put the solar model in relation to these efforts. And the thing is, the thing is that there were, as happens often in the history of science, there were parallel constructions. But solar as well is the one that, that got the, that dominant and I think so histories of economics are always a danger of telling either hero or villain stories.

[B]

Yes, yes, we have a lot of both of them. Yeah.

[A]

So even though like, I thought it was important to have Solo in because a lot of like, he's very, I think he's also famous for his quips. I'm sure you know, some of them. And some of them give I think a good idea of what modeling is to economists. So it's also, give me, give us an example. Like you talked about it. Like, this is not one of the famous quips, but it was, he talked about like modeling as engineering in the design sense, which helped me a lot. This was very much at the beginning. I did an interview with him and it helped me a lot to, you know, like, not to tell how he thinks it is, but to use the way he talked about modeling in different settings as a way to better understand economists model talk as part of their work and performance as economists. And so it's not about taking these statements at face value, but also to compare. Like, what do economists say in published papers, how do they write about models in personal letters, how do they talk about them in interviews and so on and so forth. And then you also, you find all kinds of model talk. And like Solo, you could, and he is quoted a lot. Like you can quote him about how great and important exact models are, but you can also find him quoted for pointing at the dangers of models being deceivingly exact or worrying about the practice of modeling created in some cunning way a substitute for reality itself. Then in letters you find statements where they bemoaned that their younger colleagues were too taken with technique and didn't care too much about the practical and political meaning of what they were doing. Yeah, and this are these different level, levels of model talk that I found Very interesting, but not in a way of I want to understand what Robert Solo, like, thought. And then to say there's an early Solo and a late.

[B]

Oh, yeah, yeah, yeah, I can imagine that book. I can imagine that book. Yeah, I don't want to read that book, actually, now that I think about it. But so, I mean, just because I.

[A]

Didn'T want to write it.

[B]

So just because we're on a podcast and not writing a book, tell us a bit about this guy. Who is this guy and where does he fit in 20th century American economics?

[A]

That's actually a difficult question for me. So Solo, I will leave out the biographical stuff that I know. I didn't put it in the book, so I don't want to talk about it here either. So he did his PhD at Harvard, and at a time where there was still this interdisciplinary flavor of the social sciences, he actually did read the Abetsluzen von Marienthal, like a famous study, social study in German, just as an example for how broad the education and interests.

[B]

Yeah. So this is a moment when like, maybe the journal Econometrica has already been founded. Is that in the 20s or something? But it's not so dominant that they're not still studying like, the German Historical School and these other ways of thinking about the economy, the Wavelin and this kind of qualitative stuff. Right. There's, it's, it's very broad in that sense.

[A]

Yeah, it's very broad. There is also a good part of the curriculum is history of economic economic thought. There is a. Courses in Marxist and socialist economics. So it's quite diverse. And at some point, Solow gets more and more interested in the more technical fields within economics. Yeah.

[B]

And he's, he's becoming more interested in that technical side of things as that is taking hold in the field too. Right. I mean, is that fair?

[A]

Yeah, it's, it's getting bigger. But then, then, like, it really takes off when Solo is already a professor and he's like part of this transformation of American economics to, to a modeling science where MIT and MIT's Department of Economics plays a big role. Beatrice Cherrier has written about it, a good article about it, and studied this in detail.

[B]

So just to kind of restate that he ends up at MIT after World War II or when does he land.

[A]

There in World War II is like, in contrast to many of those social science, computer mathematics, engineer intellectuals, he's in the war as a soldier and he only starts studying after that. I don't remember. Like, he finishes his bachelor before or after that. But he went on to study economics only after the war.

[B]

Okay.

[A]

And he starts at MIT, if I remember correctly, in 1950. Okay. Yeah.

[B]

So he was a. He was a kind of young scholar or thinker when he writes this first contribution paper in 56.

[A]

Yeah, exactly. In 56. He's still pretty. Pretty young. Like, he's at MIT with Paul Samuelson, who became a lifelong friend and. Yeah, and then he publishes the paper and the model, which will then take his name.

[B]

Right. Even the places he doesn't want it to go.

[A]

Even the places he doesn't want it to go. Yeah.

[B]

Okay, so that's helpful. And so where do you start us off in chapter one? I mean, when you take us into the kind of body of the story, where do you start and why?

[A]

So chapter one is really about the model and about this paper. So it's very much about. It's like one of those classic texts, maybe, that gets rarely read. So I thought it would be important to really understand what is he doing there. And then once I traced what this model is and does, then I. I go back in time to the 1930s and 1940s to measurements of growth and productivity, and I go into input output stuff, modeling and planning. And then I come back in chapter four to Solow and the way he and other economists at MIT present the model as a contribution to the theory of economic growth. So there I contrasted with other kinds of mathematical economics at the time to understand the diversity of models and of understandings of models at the time. So there's a lot of ambiguity in their understandings of what a model is and does and should do, but also like the ambiguity in the model itself. This episode is brought to you by.

[B]

Oops.

[A]

I've got a box of Cheez It Crackers staring at me, and I just wanted that irresistible Cheesy Crunch. Sorry, that was a total snackcident. Mmm. What was I supposed to be talking about? So salty. So crunchy. So cheesy. Whoops. Lost my train of thought. I've heard of Brain Freeze, but Brain cheese?

[B]

Mmm.

[A]

I'll just have one more cheese at Cracker, and then I'll get back to it.

[B]

Hit pause on whatever you're listening to and hit play on your next adventure this fall.

[A]

Get double points on every qualified stay. Life's a trip. Make the most of it at best Western. Visit bestwestern.com for complete terms and conditions.

[B]

Yeah, no, that. It was very helpful for me because I got onto this stuff. Well, I mean, first of all, I should have Made clear earlier. Maybe I shouldn't. This gets very incestuous, but. But Erik Hountschel's father, David Hounschel, is my doctoral advisor. That's how I met Eric in the first place. And David had me read. I don't know if you ever bumped into this thing. There's Nathan Rosenberg, this economist, but a kind of historically interested economist for sure. Fascinating guy on the economics of technology. I think it's like around 70 or so. He puts together a. Basically a kind of edited. It could be like a textbook on the economics of technology. I can't remember the title off the top of my head, but I have it in a very prominent place in my office. And it has. I'm pretty sure it has Salow's 57 article Technical Change in the Aggregate Production Function in it. So, I mean, David had kind of reared me in that stuff. Right. It was part of my training that this was going on in the late 50s. And like, this is like it comes out in the innovation delusion. All my stuff on innovation. People don't realize that I was like trained into this way of thinking, like very early on. But it was only later, once I discovered Gordon and these other people writing about the economics of technology, that I really went. Went back and read that 57 article closely. And I think for me, I just assumed you totally have disabused me of this notion. But I just saw it as a kind of ex nilo thing that it was like a total invention. I think one of the things I really like about your chapter is that it shows. It's like it's fitting into these much. There's all kinds of growth accounting and other people have models of this stuff. It's something that people are doing at that moment. Right. And he fits into a much broader thing. And then again, I think you kind of said it earlier, but it was like the. You know, the beauty or parsimony maybe that allowed it to travel beyond in ways that those other ones.

[A]

Yeah, yeah, I think it was. It was two things or let's say maybe three. What I basically do in these. So the first chapter is about the model. And then there are three chapters embedding the model within different kinds of growth knowledge at the time. So I thought that. And I think that it is possible like to describe the model as one further step in. In a process of constituting the problem of growth and problemizing, problematizing in. In a specific way. So in a way I would say that it's like a further leap in Both abstraction. So it was more abstract in a way than. Than. Than the measurements at the nbr, for instance.

[B]

Okay.

[A]

But also concretization in the sense, like as we talked about before, that you have this small mathematical model and you can, you know, how do you say, turn the levers like push buttons and do things and then it does something. And in this sense, it was a further step in the objectification of the economy and like presenting it as a separate realm. Yeah. And as I said before, it was seen. It was used as a very efficient mechanism on the one hand, so it formatted data in way that economists deemed productive for further inquiry. But also, I think another aspect of its wide uses, like it was really successful in the sense that people adapted it, extended it.

[B]

Right.

[A]

Was also that it was not only seen as an efficient tool, but also that there was a certain ambiguity to it in the sense that it could be interpreted in a variety of different ways. And this is. This picks up on this, on the ironies, maybe, that we talked about before. Like, for these MIT mathematical economic economists, such models were part of a knowledge apparatus that would allow for more effective policy designs. So it belongs under this interventionist economics under the labor of Keynes. So people have written about it, like Yan Zhiro or also Timothy Scheng, like MIT economists who stylized themselves as middle of the road economists between libertarian and radical economists. And for them, the moral of the model like this also, like, they talked in terms of parables and models, like simple stories. You can draw from such a model. Like, if government took the right steps, then the model in the long run would come into its own. So there would be a world of equilibrium growth if the government took the right steps. So it was all about fine tuning, about using markets like underwriting businesses. The state is a neutral R D. R D, Yeah. Major.

[B]

Yeah. Yeah.

[A]

In order to bring about growth.

[B]

Yes.

[A]

Well, it was, in a sense, the model depicted a future world, a world to be established. And so it was not about the real world being a world, being a world of perfect markets, or they would work best without government regulations or organized labor. So this would be the model in this context of MIT economists in the mixed economy of the 1960s. But the thing is, the model itself, of course, it left open how such a world could be achieved. And so it could be interpreted in really different ways and in some strands, or at least this is what Solo complained about. The model was taken too literally. It was taken as an image of the world as it was. And I think this is how it could also turn into a symbol of market fundamentalism, particularly in the field of development, because its meaning was quite ambivalent.

[B]

Ah, right, yeah, I see what you're saying. And of course, this is the moment when kind of development economics is of course obsessed with growth and you end up with these kind of. Of ridiculous assumptions about how whatever poor country might turn into a technological powerhouse or whatever.

[A]

Right, yeah, yeah, yeah, yeah. And like. And now from a perspective of how to look at a model as an artifact, or as an artifact that gets treated and used as a tool, it's nice to see how a model is able to transgress the boundaries of different theoretical schools, as it was used by neoclassical economics for real business cycle theory and in other fields. So it turned into an unchallenged routine. But both the model, like even if the basic structure remained constant, like both the model and the notion of growth changed. Yeah, So I think this is one of the.

[B]

I like that. I like that reading. And I think it's. God, I have this part of my brain that I've tried to beat into non existence that read a lot of French theory when I was a young person. I haven't talked to you about this side of my life, but a lot of Lacan. Oh my God. The only thing I regret in life is all the Lacan I read.

[A]

I wouldn't, I wouldn't.

[B]

And even, you know, even where Zizek was using it, like in the 90s before he went off the rail rails. But it's just like. I think part of what you're saying is that the model is ambiguous enough that it's kind of empty in a way. There's parts of it that are empty enough that you can kind of project your uses and desires and all these things into it and kind of use it for your ends. Right. That's part of what you're saying here.

[A]

Is that would be one part. Yes. So it could become a carrier of. Of more market fundamentalist views that Solo was comfortable with and an exemplar for the belief in the omnipotence of markets. But then there is also still another side, like when you look at it as a. Like, not as a set of ideas or theories to trace how the model turns into a part of what, for instance, Daniel Hirschman or Elizabeth Pop Berman have called policy devices. So this links to the performativity literature, but not in the sense that we look at market devices, but policy devices or the epistemic infrastructures of policy making. And here, of course, you said it before in terms of R and D measurements, but also more generally like the neoclassical growth model just, just took part and further pushed the trend to subject policy to an economic evaluation. And this is maybe still quite known. What I found, what I found really interested is that when I started to trace a few ways the model went, there were less invisible, less visible ways. For instance, in economic planning, like as part of these large scale macro econometric forecasting and planning models, which were some kind of of decision support systems in the field of macroeconomic planning. So it was still a very technical notion of planning. It was basically the organization and intellectual foundation for policy making. So it was not about larger political programs for planning. And also here, even if you couldn't see, these models had integrated a neoclassical growth model to talk about the long run and to say which kind of policies should be chosen if the goal was to have this and that growth rate by the year 1980, something like that. It was also again about imagining possible future worlds and to play the what happens if game and tracing ways how to achieve them through macroeconomic management. So this was another way the model got implemented in policy making. And the thing that ties in with the special issue that you mentioned before is that the model infrastructures that evolved from the interventionism of the 1950s and 60s, they remain rather stable. So I mean of course this is something where case studies have to be made. But this technique of forecasting translates well into the 1970s and way beyond. So either alongside newer techniques such as DSGE models, but also if these macroeconomic forecasting models were adapted and became more detailed, the foundational architecture still remained relatively stable. So this would be like one argument how it's not only about the acceptance of a specific theory or the adoption of a specific technique, but really about the hegemony of a specific kind of problematization through the tools and infrastructures of knowledge.

[B]

One of the. So you might be interested in my episode with my colleague, my Virginia Tech colleague, Matt Wisnowski that came out recently and his book Every American An Innovator, because I think that. So he has a chapter on American public policy, federal government stuff around innovation. And there's this, this kind of famous. In. In famous. Oh my God, put that in quotation marks. Famous amount of like innovation nerds. Innovation studies nerds. There's this famous study called the. What's sometimes called the Charpie Report, which came out in 1967 and I can't remember the things like actual title but it was a report that came out of the Department of Commerce and it was one of the like earliest, most important documents around kind of innovation. Thinking about how one of the fascinating things that I wrote, maybe I write about this in Innovation Delusion, was that Salow's work is footnoted in that report in 67. But basically what it's saying is that I'll make it simple and cute, but that Salo and other economists had realized that there's this residual thing that's like driving growth, that's explaining most of growth. Right. And that it is innovation basically. And then the question of the report is how to get more of it. Right. And you know, and then it goes into R and D and education, human capital, all this kind of stuff. Right. I mean they don't use the term human capital, I think, but it's there. It's there. That's what they're talking about. And so, yeah, I just think that's another kind of fascinating place this kind of plays out. And it's, you know, and it's, you know, sure, there's neoliberalism versions down the road. We'll talk about that in a second. But, but that it's very much caught up in this kind of Keynesian, broadly construed, it's active state, you know, like vision during this period is like kind of how it becomes used by these folks.

[A]

Yeah, yeah, that was, that was something that was difficult for Solo then for him and for his colleagues it was, you know, growth and technology were framed as state driven. And then in like business cycle theory, for instance, growth and technology were just to be brought about by the workings of the markets. Like innovation. Yeah, but still like the intellectual tools and the epistemic problems were there. So it's, it's a different tradition of, of market thinking that ties in nicely then with developments in the 70s and 80s.

[B]

Yeah, yeah. And so one other, I do want to kind of talk to you about politics in a second, but one other thing that I mean, because this is a technology studies podcast, we should probably try to start touch on that. This is also a moment when social science is being computerized and you have a nice chapter on kind of like how this model is able to be brought into that world or connected to that practice. Right. Is that a fair way to put it?

[A]

Yeah.

[B]

So what kind of interested you there?

[A]

So for me, what was interesting was. The parallel developments of different forms of mathematical economics and different styles of modeling. Like all the complexity talk that was happening as well. And here this model was kind of a comparative foil. Everybody insisted on its simplicity because it was really a simple model and not one of those complex computer models where you don't really know what happens. It's clean and clear cut, but it was still very close communities. And also I didn't do that as much as I probably should have, but the way they talk about a model as an artifact, as engineering in the design sense, it's very akin to a lot of computer thinking and reasoning at the time.

[B]

Yeah, no, I thought it was nice, it was a nice treatment. And it gets into like, you know, in your practices and tools, it gets into the guts of. There's work there of building tables and.

[A]

Yeah.

[B]

And organizing information. You got to do a bunch of pre work. You know, this is kind of like historians of computing like to emphasize.

[A]

When you look at the work done at the Harvard Economic Research Project in input output analysis under Leontief, there is so much, there is so much work in, you know, they called up factories and talked to production engineers, ask them about input and outputs and, and then to turn that into things that you could work with and fit into table and then to calculate these tables and make them fit the computer. So this was just a lot of work and it took ages. And there a model like Solos was a sweet, sweet exercise in comparison.

[B]

Right.

[A]

But what, what is obvious is. Oh, what became obvious to me is when you look at, for instance at the nber, their growth and productivity measurements, these are really like, these are books. There are several volumes and they have appendices. Appendices, yeah. Where they describe how they actually got to the data, what they did to get them, how they.

[B]

Oh, nice.

[A]

Reshape them. What would have been different if they had, if they had done something different? Yeah, like, I mean, it's super tough to read. It's not like the most exciting read, but it was, it was more or less all there. So they gave a pretty good account of what needs to happen in order to get at these clean numbers. And what are the difficulties with these numbers which you just can take at face value. Similarly with input output models during they're not as pronounced. And then with the, with the neoclassical growth model, you just have this mathematical world and then you use it to interpret pre existing data. You don't talk about the data anymore. And this becomes the dominant style of economic measurement.

[B]

Oh, that's interesting, huh?

[A]

Yeah. So it's, it's kind of, you know, getting rid of discussing the intricacies of empirical work.

[B]

And do they talk about it in terms of testing like physicists? Do they imagine that they're building Mathematical models or equations that can then be banged up against empirical reality to see if you're right. Is that part of their image?

[A]

What happens is that in the case of this model, that you can use different formulations of the production function and then show which one fits best to the data.

[B]

Yeah, so it's best.

[A]

It's not like a test in the. In the common sense. Yeah, yeah.

[B]

And best fit is. Is still kind of. I can't remember. What is it? Oh, God. It has to do with inference. It's best fit. Yeah. Yeah. I mean, is this still very common in, like, business school? Kind of more positivist research in business schools are still doing. Talking about best fit a lot as a kind of. I'm sorry that I'm just having a brain fart at the moment. My business school friends are going to be ashamed of me. Okay, so. And I do have business school friends. You know, I know it disappoints people, but. So. Okay, so I want to talk to you about. Second, about politics and ironies. And so I thought you put it pretty well that in the history of economic thought, we have heroes and villains, and we have a lot written in that mode, both of those modes. And who the heroes and villains are, of course, depends on where you sit in the world. Like, if you're a member of the libertarian Mercatus center at George Mason University, then Hayek and Friedman and all these characters are like, they're the best. But if you're, I don't know, Morawski or Melinda Cooper or any number of other folks, then, like, you know, it's the neoliberal thought collective, and there's that whole literature. But I see you as kind of, you know, I see you. You have a chapter or a chapter, not a section on politics in your introduction that I thought was really nice. And it starts off with this kind of very bold quotation, just saying, like, that Salo's model is neoliberalism. You know, it's just like a declaration. I can't remember who you're quoting. But then. Then you kind of deconstruct that and pull it apart and say, you know, Salo's politics are not that obvious. He's kind of. He's kind of complicated and doesn't fit anywhere neatly. And it also reminds me of kind of like Eric's work on Paul Lazarusfeld, the sociologist, also not somewhere you can just kind of pin down. And so I wanted to talk to you about that, about the kind of attention to. And we can call it ironies that's what, you know, I think that's how we thought about it in the interventionist knowledge special issue. But yeah, I mean, I think you hear what I'm saying?

[A]

So, yeah, I think here it's again really important to me. It's not about solo.

[B]

Yeah, okay, right.

[A]

I think Eric is really also interested like in Paula's fellow politics. Maybe I'm wrong, maybe we are wrong. But, but I think that like he's more also like, in addition he is more interested in the politics of the people. And I think for me what is really interesting is it's the politics of, of the knowledge.

[B]

Like where what we were saying before about where it gets adopted and that people can read it into different political programs. Is that what you're saying? Okay, yeah. Okay.

[A]

So also in the sense of, you know, what does specific kind of knowledge include and exclude, what kind of seeing what kinds of action the specific forms of do, specific forms of knowledge allow and forbid, which epistemic forms are accepted as real, as appropriate, true or useful, and which are not. So all these questions are like more like more at the center for me. But this is the quotation that you talked about. It's by the Ethiopian. It was the Ethiopian prime Minister.

[B]

That's right.

[A]

And he development policies and the phantasmatic belief in markets. And this was like one of these instances where I just was interested in how did the model end up there and how did it support specific development under, quote, policies. So it is. Yeah, it is. It is one additional small scale storyline of market thinking that maybe tones down a little the break of the 1970s in that it focuses on the tenacity and resilience of governmental knowledge infrastructures. And in the sense that it just, you know, the sources of neoliberal governance are not only the ideas of the big names like Hayek or mises, but also US interventionism of the 50s and 60s and the various styles of reasoning that the economists that I worked on established. Yeah, but here also, like Amy Offner or Melinda Cooper, Timothy Schenck, these are all scholars that work on this kind of understanding of what happened in those decades.

[B]

Yeah, well, I mean, I think that neoliberalism, I mean the kind of free market, you can see how this can slot into that view. But it's also amazing how much Salo's model and picture kind of continues to today and all these other kind of ways of thinking about economies. And I'm just like the Mariana Mazzucato entrepreneurial state picture. Right. I mean, I think, you know, it's very, I don't, I can't remember if she cites Salo in that book, but it's very much a part of that kind of very active state vision. And so this. And I mean that's kind of the amazing thing about this, you know, the model created in 1956 is that it's, it continues right up today. Including. I can't remember the name of it. I just read a book. Oh, it's gonna kill me that I'm not remembering this, but it was, it's a, it's a new book about. Oh, this is gonna kill me. Including this very interesting Diane Coyle, I think is in it.

[A]

The gdp. Gdp?

[B]

No, it's not her new GDP book. It's an edited volume she's in that just came out that's all about productivity and.

[A]

Okay, I need to write this down.

[B]

Yeah, yeah, I just read it. It's a bunch of economists looking at the kind of the productivity paradox. Oh, is this what.

[A]

Another quip of solos? Right?

[B]

Which, which was what? Oh yeah, exactly. Yeah, well, I mean that's. Oh, I'm glad we got that on the record for the, for the podcast. I mean that's another reason, you know, that's so important is this statement that, you know, in 88 or whatever that computers are everyone but. But in the growth, the, the, the productivity statistics.

[A]

Yeah, you can see the computer age everywhere, but.

[B]

Yeah, but in the computer. Yeah, exactly. So I can't find it. I'll try to remember to put it in the show notes if I can. But yeah, it's not her new. It's a whole volume dedicated to kind of still thinking through whether we're mismeasuring productivity and all this. And it all goes back to Salo in a way.

[A]

Interesting.

[B]

It lives on. That's the amazing thing. It's like a zombie, right? Maybe I just like, it just, just keeps going. So yeah, I wanted to use that kind of politics question, I guess to set up just like what are you working on now and how you feel like this project thinking, doing all this work on that project, kind of set you up for where you're headed next and what you're doing.

[A]

Yeah. So actually I did get stuck on something that I worked on with the book at the end when I looked at economic planning, which in the context of a book refers to. Yeah, just the infrastructures of policy making. So I continued looking at how so called theoretical models got adapted into computerized model infrastructures for political decision making in the 70s and 80s, but also now I started in parallel to look at other kinds of planning, like in businesses, in projects of so called development. And this is what I'm working on right now, alternative economic planning. So okay, this is really interesting to me and that I'm really in the middle of it. So I'm looking at the Lucas plan. I don't know if you've ever heard of it.

[B]

No. Tell me what that is.

[A]

So it was an alternative company plan. So alternative to the plans of management of Lucas Aerospace, which was a British multinational. So we are in 1975 and 1976 and the workers organized and their combine presented this alternative plan. And this plan suggested a restructuring of production. So they want. Wanted to move from defense related production to socially useful, what they called socially useful production. And all of this was embedded in broader suggestions for economic reorganization. So in this initiative, like an industrial alternative movement, as Alfred Som Retle called it, it went on from 1976-82. So and I have started to look into the ways, how they organize their knowledge. So how they organized because it was very important. Like the plan emphasized, this is a collective knowledge project. So it was not only about a different kind of democratic planning in the face of de industrialization and unemployment, but it was also about collective knowledge and participatory decision making. So it was both a new social and epistemic form and also about alternative bookkeeping, alternative planning in the sense of they did use input output analysis to show that this reorganization of production would also be economically viable. Right, There you go.

[B]

That's very interesting.

[A]

How do these technologies move to different places? And how did they. Like they had to show that it is economically viable even though it was part of like. Also like the utopian core of the project was that eventually the Lucas plan could motivate like a change in the mode of production as a whole.

[B]

But you have to show it's not going to tank the economy to a certain extent.

[A]

Yeah. So this is what I'm looking at. How this calculative technologies to different places.

[B]

That's cool. That's very cool.

[A]

Yeah, I'm pretty excited about this project right now.

[B]

So that's great. And so just say a word about like what is. Are you. Are there archives? I mean, is there a certain. Yeah, where do you go?

[A]

There are actually like people active in the, in the initiative back then. They put together an archive and they also put it online so you can. And they are also like very open to discussing. And so I was happy to talk to Phyllis Asquith and also to Hillary Wainwright, who was engaged with the Lucas plan. And so I'm. Yeah. And I'm following up on several. That's great. Storylines that go from there.

[B]

That's great.

[A]

Yeah.

[B]

Well, very cool. The book. I did find the book. It is called the Measure of Economies. Subtitle Measuring Productivity in an Age of Technological Change, edited by Reinsdorf and Shiner. So, yeah. And they don't know. They don't. They have no clear idea. I mean, after, you know, like, you can just, like, you read the whole book and it's like, we still don't know is the last page, basically. So that's. That figures. Thank you, Verena.

[A]

Technology. You're like.

[B]

Yeah, exactly. I don't. I don't. I mean, I think it's actually. Yeah. I think these. These problems are real and it's hard to account for this stuff, you know, and so, yeah, I take. I know. I take Solo and the productivity paradox and all this stuff very seriously in a way that I think a lot of historians just don't. Right. They're just like. Well, I don't. That's their problem. It's a measurement problem, and it's just their problem. I'm like, I don't think. Actually, these are pretty deep questions and I think it's really hard to account for this stuff, like technological change and its influence on things. So, yeah. Reyna, I knew this would be wonderful to talk to you about this stuff and fascinating. And so thank you so much for coming on.

[A]

Thank you so much for inviting me. This was really great. Thank you.

[B]

I hope you enjoyed this episode of our podcast. You can reach us with questions, comments and [e-mail] or by following me on Twitter @stsnews or on YouTube @peoplesthings. Our podcast is distributed by the New Books Network, the leading platform for academic podcasts. So that you can find us wherever you get your podcasts. Peoples and Things, like most things in this world, depends on the work of many people. I want to thank my brother, Jake Vincl for writing the music for the show. I want to thank my buddy Juliana Castro for designing the logos for the podcast. You can check out her [e-mail] Joe Fort is the producer for the podcast and Mandy Lam is the production assistant. This podcast and other Peoples and Things programming are produced in affiliation with Virginia Tech Publishing and supported by the center for Humanities and the University Libraries at Virginia Tech. For information about other podcasts from Virginia Tech Publishing visitors, visit Publishing Vt Edu. For the entire Peoples and Things team I am Lee Vincel. And most importantly, I want to thank you for listening. Thanks.

[A]

Sam.