I thought the article was interesting and I found the discussion of the models and more generally epistemological discussion more interesting than the particularities of development economics.
While I thought that the author had a point about models being of crucial importance because they are the only way of understanding reality by simplifying it to a great extent, I thought that he still put too much emphasis on them and them being the only valid tool. I think the criticism towards this overreliance on models was presented as a strawman and does not acknowledge that overreliance on models can justify some policies that dramatically impact people’s lives. Unlike weather models which allow us to plan out our days better, economic models lead to changes in the material conditions of people. Thus having that empty map middle because of reluctance to accept certain types of data, could hurt people, and that is I think an important difference.
I believe that one has to be humble in one's approach to knowledge, sometimes a model is the best we have, sometimes the model can reveal some truths while stories and metaphors can deepen them, and sometimes metaphors might conceal an underlying mechanism that could be modeled. Yet this dogmatic approach to economics and overreliance on models I believe hurts the discipline and the public, as all dogmatism does.
I did find the fact that models might reveal a simpler truth, as with the case of high development theory, that it is not cultural barriers, irrationality, or something like, that was causing the poverty trap, but rather some market principle.
The point about modern methods of production being potentially more productive than traditional ones stuck out to me. While the author was referring to large scale economies, he used the example of twenty thousand unemployed workers being placed into a shoe factory, and how the workers individually did not yield a high return. The solution that similar investments must be made into multiple industries does argue towards greater productivity, but I argue that modern methods of production rely not just on the quantity of labor present in a given industry, but the quality and job satisfaction they face.
In several ways, development requires multiple avenues of effort to yield success. A lot of the achievement is attributed to the human capital generated. In my social networks and power capital class, we discussed the power between weak and strengthened social ties. This topic relates to the article because it adds social dimensions of viewing productivity based on how people see, think, and feel in labor settings. While development is virtuous and circular driven by external economies, the people in each respective country can contribute positively if their role in society and relation to others are properly analyzed.
I additionally thought the evolution of ignorance section with the wrong perception of African maps was a strong metaphor to allude to economic models. This comparison illustrated that people continuously make with what they have, and do not often fact check. Human beings make macro-economic, social, and political decisions about their perceptions of the world, many times blind to reality. In this case, the use of economic modeling and its progression towards being an accurate representation of a world situation highlights how they are a tool to supplement general understanding and knowledge.
I found the evolution of ignorance section of the paper super interesting because it relates closely with the work I am studying in my Management and Organizational Behavior class. In that class, we recently discussed bias that shows up in all humans decision making. Two Ideas that fit with the evolution of ignorance section are the Confirmation Bias and similarly the Dunning-Krueger Effect. Confirmation Bias is when you quickly accept an answer that you may have previously believed without looking at all of the relevant information. Economists like to model and theorize about ideas that they already have a good grasp of. Because of this, we may not use all the relevant information to solve a more complex problem.
I find it interesting that while pointing out many problems with Models, the author still ends by saying he encourages even the most technical models. Krugman is right however I think some parts of developmental economics can get so complex and unique, that no model may fit it. No two situations are the same, so drawing from models works, but important to remember the model is not the real world.
I thought this paper was building further on topics we have discussed in pervious classes, such as the ignorance associated with models, large-investments and coordinated efforts to support economic growth, drawing labor from the traditional sector to the modern sector, etc. Market failures, which prevent economies from progressing, can be lessened with investments in industry and infrastructure, because growth in one sector can promote growth in other sectors through forward and backward linkages. It must be profitable for each individual to produce. Clearly this all depends on certain externalities, risk vs, reward, etc.
I thought the paper did a good job arguing the importance of models to illustrate concepts, but also being aware of their simplifications and sensitivity. I agree with Krugman that models are “maps rather than reality.”
I am interested on what caused the shift away from high development theory in the 70’s and what brought it back. It must just be a sign of the times, which could be said about all economic theories.
I found the article to be somewhat confusing. I found the part about The Evolution of Ignorance to be interesting and I thought the example of the maps was great. What confuses me is the writer towards the end of the article basically says "What if the model in the figure was developed in 1955 instead of 1989". I am curious how this is different from the map example. For me the map example meant it takes time for ideas to build on one another to develop better understandings of a certain topic or idea. Did it take until 1989 to develop the model because economies of scale did not fit into the traditional models? If so then could the model have been developed in 1955?
I think this authors analysis of formal modeling as a means to explain economic theory was nuanced and interesting. I like the the author has admitted that there limitations and shortcomings to models, but that despite these things there is still something that can usually be taken away. I really like this idea that models are just metaphors used for comparison and analysis rather than an outright explanation. A metaphor in and of itself is a way representing an idea by comparison, we think of them because it allows us to think of something concrete on different terms and can often times shed new light on the way we think of things.
The author admits that the model is unrealistic in its assumption but gives us a better understanding for development theory. I agree that the model is helpful as it gives us a visual of what these relationships could potentially look like, but it is not necessarily an explanation for specific cases.
I did not fully understand the shift away from earlier economic theories and why the author viewed this to be such a loss. This paper seemed like a clear rebuttal to me but I didn't feel like I had a full understanding of his attitude.
Krugman’s “The Fall and Rise of Development Economics” pulls from multiple economists and theories to demonstrate that it is possible to tell development-style stories in the form of rigorous models and that models that allow for highly simplified settings are crucial to the evolution of high development logic. Ideas surrounding development economics were initially shaped by themes of industrialization and import-substitution in the 1950s, and did not reemerge until the 1980s, when a combination approach to neoclassical economics and the reincorporation of technology and real-world market functioning materialized.
A key idea Krugman facilitates through his piece is that we must adopt the intellectual attitude that Hirshman rejected, which includes a willingness to do “violence to the richness and complexity of the real world in order to produce controlled, silly models that illustrate key concepts.” While Krugman recognizes the limitations of models, he also illustrates that the temporary “evolution of ignorance is the price of progress,” which is an inevitable component of making sense of the world’s complexity (Krugman). For example, Krugam observes the limitations of models as “blindspots,” which are created when economists emphasize modeling what they formally know how to model. That said, Krugan’s piece demonstrates the nuanced nature of models, in that their helpfulness is not negated by the fact that their system may have some degree of falsification. Rather, models are maps that help us make sense of our reality.
Aside from the importance of recognizing the nuanced nature of economic modeling, Krugman emphasizes how Rosenstein Rodan’s Big Push story is essential to the high development model. He states that if many industries make investments that move workers from the traditional to the modern sector, that this becomes a profitable scenario assuming economies of scale and dualism. However, I am left wondering more about forward and backward linkages. While I recognize that forward linkages involve the ability of an industry to “reduce the costs of potential downstream users of its products and push them over the threshold of profitability” (Hirshman), I am left wondering how such linkages relate to economies of scale and modeling (or a lack thereof).
Finally, I appreciate how Krugman equates the mapping of Africa demonstrating European ignorance to the development of formal economic theory. Counterintuitively, improved economic technique actually led to some loss in knowledge much like the mapping of Africa. To explain, between the 1940s-1970s, “a rise in the standards of rigor and logic led to a much improved level of understanding of some things, but also led for a time to an unwillingness to confront those areas the new technical regor could not yet reach” (Krugman).
From my understanding of this paper, it is a criticism not just of development economics and its models, but one of the subject as a whole. With rigid and well-structured models that either conclude an economy to be completely perfect or completely imperfect, they don’t leave space for those in the middle - which most of the world falls under. And I agree with the author’s reasoning because when complicated economic news cannot be explained through the models I am learning in class and the case suddenly becomes an exception, one can’t help but realize that these perfect models aren’t depicting much of the reality at all.
I always thought that basic economic models were simplified to be easily comprehended by an average person but the reality is that these simplifications are just metaphors that don’t accurately explain developing economies. These economies are shaped by more than 5 or 6 variables and for the most part our graphical models do fail to understand this. However, I think this is also why there are more papers and theories emerging that explain developing economies and their economics through niche data now.
While reading this paper, I noticed Krugman's unique style of writing and how the discussion of the failure of the high development theory is tragic and sometimes confusing. To elaborate, I picked out a quote from the "METAPHORS AND MODELS" section: "You make a set of clearly untrue simplifications [...] to something you can handle; dictated [...] by guesses about what is important [...] And the end result, if the model is a good one, is an improved insight into why the vastly more complex real system behaves the way it does."
Reading this quote reminded me of the same way we perceive most of the models we look at in class. We joke and make fun about how the models are just simply models. Reading this article has added on to how I look at how we view economic models, because it is unfortunate that when sensible ideas can not be modeled, they are lost, but when we can model economic concepts, we can not forget that they are "just models" and can change significantly depending on the real-world.
Krugman uses this article to critique development economics, especially with regard to the high development theorists and the "evolution of ignorance." The high development theorists disregarded the reality of quick industrialization and technology research, development, and deployment. Countries that incentivized agricultural workers to move to the city and seek jobs in the modern sector without sufficiently scaled modernization efforts, the economies floundered. High unemployment in the cities and low productivity in the traditional sector resulted in a nasty trap that might've been worse than remaining at status quo for these countries. Krugman also critiques the high developmental arguments in the evolution of ignorance, citing how economics was not yet equipped to formally model oligopolistic economies, which is how most developing countries functioned at the time––capital owners were few, rich, and faced competition from their peers in modernization and mechanization efforts. Other key points missed by high developmental idealists included forgetting that previous economists had already developed formal theories for circular causation and linkages. However, Myrdal and Hirschman refused to formally model their ideas and missed an opportunity for generalizability and adoption of their developmental theories into mainstream economic thinking. I was interested in the conclusion of this article when considering why developmental economics stagnated for so long due to disregard for older, yet applicable ideas left behind from previous thinkers. However, I do not fully understand the reason for asking about the "could've beens" rather than celebrating the resurgence of the field and simply leaving the conclusion as a warning to not disregard current thinking in economics for future use in yet undeveloped models.
I want to discuss the effectiveness of the article The Fall and Rise of Development Economics. The intro to this article is essential because of the precise language Krugman uses to hook the reader. Krugman states “I am a great admirer of The Strategy of Economic Development” but also says “I do not think it was helpful to development economics” even going as far as to say that due to the book's persuasiveness it made it destructive. Although Krugman does not agree with all of Albert Hirschman's ideas he wants to make it clear that Hirschman should not be viewed as a villain but instead a tragic hero. The author also gained credibility as he acknowledges the shortcomings of models. He states that models are important to understand ideas but they can create blind spots. This ties back into one of the first topics we covered in our class as we acknowledged that graphs are just mere cartoons due to their assumption that all variables remain constant. Although after reading this article my final thought was the importance of planning for the future in development Economics. This field of study is constantly changing and evolving as models and economies become more complex and advanced. While the study of the past is beneficial it should be used to plan for the future and economists need to be prepared for their theories and policies to change.
This paper made me think about how we use models in economics, social sciences, and science as a whole. One specific excerpt that intrigued me was comparing economic models to early European maps of Africa. Krugman points out that as there was a greater push for precision, there was a loss of information that was not considered as precise. While models are very useful to help simplify complex systems like the "Big Push," I wonder if these simplifications sometimes cause economists to miss key insights, especially in situations where cultural and institutional factors play a role in economic development. Krugman argues that models are necessary for progress, but can too much focus on formalism actually steer policymakers in the wrong direction by glossing over the messy realities of real-world development? Ultimately, I side with this position. Without models, it would be nearly impossible to make sense of complex systems. However, when forming models, it is crucial to recognize the assumptions taking place and how those assumptions cause the model to differ from the real world. With these thoughts in mind, how do scientists in other fields strike the balance between making things simple enough to understand and keeping them accurate enough to reflect the richness of what’s actually happening?
One thing that struck me was the comparison to physics. In some ways, physics is the perfect science because it always has a clear and specific answer which can be derived from a specific formula. Importantly, physics has models and formulas which are repeatable. There are no exogenous variables like in economics models. In other words, physics is predictable and repeatable, economics is less so. This is an important distinction because in the development of the theories surrounding these disciplines, physicists could conduct specific experiments and get specific results while economists could not. Physicist also had the benefit of good data, especially around the time that development economics was in its infancy.
For me, this paper was more about the validity of economic models and theories than development economics itself. Theories are the beginning of economic thought, but that leaves out testing those theories with real world. Human nature itself is really critical in economic reality and is the main difference between theory and realty. Testing theory against the data is the only way to make sure that theory does not end up in exile.
I found this article's discussion on the effect of modeling very interesting.
Most specifically, the idea that incredibly important knowledge in a scientific context can be lost just because it is difficult to model was striking to me. The Africa example, where the interior of the continent was actually more accurately mapped BEFORE accurate cartographic techniques were adapted (for a few hundred years) was striking. I'm now wondering, what other important ideas were pushed to the side because of their difficulty in being modeled, specifically in the field of development economics? Don't get me wrong, modeling is incredibly important, and having accurate results is what any field of the sciences strives for, but I'm wondering now if there are other overlooked ideas that might have a massive role in explaining certain trends in countries that have been ignored for a long time.
Also, I feel that some people (unfortunately highly important people) are being taught models wrong. All models rest on a set of simplifying assumptions that may or may not be true given a specific country's circumstances (in our case). Additionally, more complex factors that are harder to model may be left out of models. However, many leaders and important individuals in development have come to see economic models as a guide for unconditional success, rather than tools for development (if certain conditions are met). This was the case (as I learned in my Postcolonial African History class last year) in a place like Zimbabwe, where factories were invested in, but the government made NO attempt to invest in human capital (the model they were following suggested that increasing capital and savings alone would lead to economic growth and success in their country) and so productivity still floundered. If models were more widely explained understood as flawed, conditional representations of reality, I think important figures and leaders would make better choices in harnessing them.
I very much admired Krugman's analysis of the Big Push model. I think his main two critics are important of all of economic learning: are we understating the difficulty of holding these assumptions and is a model backed by mathematical analysis. This seems very similar to the idea that many models are cartoons. However, giving supporters of Big Push economics the benefit of the doubt, many economic models depend on a lot of things. Nonetheless, it is difficult to provide much credit to a model with so little analytical evidence.
I think Krugman explores a really interesting discourse throughout The Fall and Rise of Development. The part that I probably most enjoyed the most was his discussion about the perceived ignorance of economics and its transcendence over time. It’s not a perspective I had ever heard before (although a sentiment I’m familiar with), especially in the way that he connects it with the evolution of European maps of Africa. In my society and natural resources class, we often discuss the humanitarian consequences of development and colonialism as it has impacted the world but usually without in-depth investigation of data. In my econ courses, I usually see the opposite, it’s all data, regression, and endless correlation with little investigation into the surrounding conditions. This is where development economics seems to be the interface between the two and why Krugman’s examination of the erasure of African maps to the ignorant trajectory of development economics seems so nuanced to me. An initial burst of information, endless unexplored areas and opportunities, that is slowly cut down to a trickle as time continues. Standards are determined. Divides are set.
It’s a pattern I wouldn’t have expected the two to share but that I also don’t find all that unique. In fact, this is an idea we play with a lot in S&N resources: when does advancement become a hinderance to progress? While we were able to more accurately map these new lands, it was at the cost of erasing that which was determined to be “of no importance”. While the ability to formally model these scenarios allowed for increases in data accuracy, it was at the cost of a true understanding of the issues. It’s an interesting, and I’d argue, strong comparison.
The part of the paper that stood out to me the most was Krugman's analogy between the evolution of European maps of Africa and the trajectory of development economics. The shift towards more rigorous modeling that ironically led to some loss of knowledge and an obviously flawed model (the "darkest Africa") reminded me of the ongoing reliance on GDP as the primary measure of economic success and social well-being. Measures of GDP are appealing because it's a quantifiable metric that is fairly precise and without much room for disagreement. However, it ignores aspects like well-being, inequality, and environmental sustainability—critical factors that make up the true "landscape" of an economy.
I also found myself thinking about how these oversimplifications affect public opinion, just as how Europeans' lack of knowledge of Africa led them to them viewing African civilizations as primitive and homogenous. GDP is clearly a flawed and incomplete measure of development and overall well-being, and I would think that few development economists would argue against that. However, an average person may assume that a country having a significant rise in GDP like Botswana (referred to as the "African miracle" in last week reading) means that standard of living and wealth is rising for all, yet this is not always the case. While oversimplifications are valuable and models are the foundation of economic analysis, rigid thinking can cause us to overlook solutions to disparities in today's world, as well as impede the development of new models that are messier but also more insightful.
In Krugman’s article, “The Fall and Rise of Development Economics”, he mentions the importance of surrounding economic policy on market behavior while focusing on current developmental changes around the world. I find this very insightful because the paper builds off of what we talked about in class this past Tuesday. In the Lewis Two Sector Model, he discusses the four big assumptions, which includes an assumption of surplus in agriculture labor, full employment in modern sectors, how there are diminishing returns in firms but they are still profitable, and finally how profits are then reinvested back into the modern sector. Although development countries do have a majority of their population in agriculture, there are some farms where they are more profitable in comparison to others. If profitable farms start to leave the traditional sector, then it could lead to shortage in food or possibly famine. Furthermore, the idea that there is full employment in modern sectors is rarely the case. In most developing countries, there is an unemployment of about 30% maybe 40%. Although all countries are different in their problems, you can’t base your economic policy on the Lewis Two Sector Model assuming ceteris paribus, which is what Krugman highlights in his article.
I was particularly interested in the comparison of development economics in the 1950s-70s and the cartographical history of Africa. It was incredibly surprising to discover that a paper as influential as Rosenstein Rodan’s would not have gotten published in the 1950s because of the lack of formal models. One thing that has been a common theme in the science, social science, and even philosophy classes I’ve taken is the notion of “standing on the shoulders of giants”. This is essentially using ideas from previous research as a jumping-off-point for new research, which has led to many massive scientific breakthroughs. I think the reason why it is so shocking to me that economists would not publish more abstract studies is that it inhibits future economists’ ability to use those ideas and create more research from them. Abstract studies can beget mathematical models, but those models cannot be created if the research and ideas are not out in the world.
Throughout Krugman's article, I was balancing between agreeing and disagreeing with one of his main assumptions: the model's simplicity justifies avoiding important complexities of reality. Krugman himself pointed out that restrictive assumptions led to a few decades of ignorance in the development theory, and I think the same holds for the conclusion of his article. Was the high development theory destined to wait so long for its time to shine? Krugman leans towards "yes" but heavily relies on his analysis of precedence in other disciplines rather than a proven mechanism. Could there be a way of gradually formalizing high development theory reaching a complete model way earlier? After all, it was mostly the rejection from the formal mainstream that led Hirshman to give up on formal modeling altogether. I think the issue lies in the fact that many economists fall into the trap of spending their whole careers in comfort zones worked out over the years, which makes them immune to novelty and unable to bend their way of thinking to understand different approaches. Arthur Lewis was a rare example of a person who successfully tried (by giving up on the increasing returns assumption) but a two-sided willingness to understand would bring much better results. Maybe if economists were less resistant to adopting different approaches, the whole science would gain a new breadth.
Krugman's article felt a little bit like discussing economics with my grandfather. Both provide strong context by boiling complicated economic reality down to its most base elements. I think Krugman's approach is fascinating, though, because he invites the reader to learn these simple models, challenge them, and add nuance. In other words, his models were all about economies of scale, and his approach to teaching them was "at scale," too: he encouraged students to take a "macro" view of development economics so they could later analyze the "micro" factors that skewed his models, rather than narrow down his models to small-scale economies or only specific situations.
I wish there was a Krugman in behavioral economics. Honestly, it would be nice if every discipline had a Krugman - someone who could take incredibly complex models, transfer them in a simpler format, and inspire their students to take steps beyond the basic model to learn more about what flaws alter the original and why. In behavioral econ, every model I've analyzed has been very narrowly applicable. The field itself offers such cool intersections between neuro and political science, but (from some of the papers I've read) it feels like there's such a strong emphasis on intersectionality that you lose out on the ability to learn the 'basics' first.
Krugman's models must be taken not as the "closer" for a discussion, but rather a strong starting point for continuing research.
Traditions, common practices and/or a "that's just how we do things" mentality seems to have created the temporary losses of knowledge the article mentions, like the unknowns at the center of Africa or the loss of solid development ideas from Economics for decades. The author, understandable, struggles to conclude with actionable advice. However, in a collaborative field, I think it's worth understanding that it doesn't much matter if one individual fixates on understanding the world through mathematics, or another through models, or yet another through metaphor. Individuals can have their own biases, and have their biases checked and balanced by others. The problems of loss of knowledge arose when all the economists all at once rejected the models in favor of mathematics, or when all of the weather scientists ignored the clouds.
It isn't individual bias, but a collective bias, that led these scientific disciplines to ignore good evidence and ideas.
It makes sense that individuals in a field will follow the lead of breath through techniques or of thought leaders, and I don't know how to make the discipline of Economics open to more diversity of ideas or techniques, especially when every school with an economics major wants to teach to the standard of the time, and few entering the field will have the cajones to challenge the traditions in place. And those atop the field, who write influential papers or approve what gets into which journals, won't turn back on what brought them to success. I don't know how to have scientific fields be rigorous without falling into the trap of choosing tradition over other, better ways to find truth.
I thought the article was interesting and I found the discussion of the models and more generally epistemological discussion more interesting than the particularities of development economics.
While I thought that the author had a point about models being of crucial importance because they are the only way of understanding reality by simplifying it to a great extent, I thought that he still put too much emphasis on them and them being the only valid tool. I think the criticism towards this overreliance on models was presented as a strawman and does not acknowledge that overreliance on models can justify some policies that dramatically impact people’s lives. Unlike weather models which allow us to plan out our days better, economic models lead to changes in the material conditions of people. Thus having that empty map middle because of reluctance to accept certain types of data, could hurt people, and that is I think an important difference.
I believe that one has to be humble in one's approach to knowledge, sometimes a model is the best we have, sometimes the model can reveal some truths while stories and metaphors can deepen them, and sometimes metaphors might conceal an underlying mechanism that could be modeled. Yet this dogmatic approach to economics and overreliance on models I believe hurts the discipline and the public, as all dogmatism does.
I did find the fact that models might reveal a simpler truth, as with the case of high development theory, that it is not cultural barriers, irrationality, or something like, that was causing the poverty trap, but rather some market principle.
Posted by: Ignas | 09/22/2024 at 03:45 PM
The point about modern methods of production being potentially more productive than traditional ones stuck out to me. While the author was referring to large scale economies, he used the example of twenty thousand unemployed workers being placed into a shoe factory, and how the workers individually did not yield a high return. The solution that similar investments must be made into multiple industries does argue towards greater productivity, but I argue that modern methods of production rely not just on the quantity of labor present in a given industry, but the quality and job satisfaction they face.
In several ways, development requires multiple avenues of effort to yield success. A lot of the achievement is attributed to the human capital generated. In my social networks and power capital class, we discussed the power between weak and strengthened social ties. This topic relates to the article because it adds social dimensions of viewing productivity based on how people see, think, and feel in labor settings. While development is virtuous and circular driven by external economies, the people in each respective country can contribute positively if their role in society and relation to others are properly analyzed.
I additionally thought the evolution of ignorance section with the wrong perception of African maps was a strong metaphor to allude to economic models. This comparison illustrated that people continuously make with what they have, and do not often fact check. Human beings make macro-economic, social, and political decisions about their perceptions of the world, many times blind to reality. In this case, the use of economic modeling and its progression towards being an accurate representation of a world situation highlights how they are a tool to supplement general understanding and knowledge.
Posted by: Jaeya | 09/23/2024 at 09:54 PM
I found the evolution of ignorance section of the paper super interesting because it relates closely with the work I am studying in my Management and Organizational Behavior class. In that class, we recently discussed bias that shows up in all humans decision making. Two Ideas that fit with the evolution of ignorance section are the Confirmation Bias and similarly the Dunning-Krueger Effect. Confirmation Bias is when you quickly accept an answer that you may have previously believed without looking at all of the relevant information. Economists like to model and theorize about ideas that they already have a good grasp of. Because of this, we may not use all the relevant information to solve a more complex problem.
I find it interesting that while pointing out many problems with Models, the author still ends by saying he encourages even the most technical models. Krugman is right however I think some parts of developmental economics can get so complex and unique, that no model may fit it. No two situations are the same, so drawing from models works, but important to remember the model is not the real world.
Posted by: Colin Ryan | 09/24/2024 at 06:20 PM
I thought this paper was building further on topics we have discussed in pervious classes, such as the ignorance associated with models, large-investments and coordinated efforts to support economic growth, drawing labor from the traditional sector to the modern sector, etc. Market failures, which prevent economies from progressing, can be lessened with investments in industry and infrastructure, because growth in one sector can promote growth in other sectors through forward and backward linkages. It must be profitable for each individual to produce. Clearly this all depends on certain externalities, risk vs, reward, etc.
I thought the paper did a good job arguing the importance of models to illustrate concepts, but also being aware of their simplifications and sensitivity. I agree with Krugman that models are “maps rather than reality.”
I am interested on what caused the shift away from high development theory in the 70’s and what brought it back. It must just be a sign of the times, which could be said about all economic theories.
Posted by: WD | 09/25/2024 at 08:49 AM
I found the article to be somewhat confusing. I found the part about The Evolution of Ignorance to be interesting and I thought the example of the maps was great. What confuses me is the writer towards the end of the article basically says "What if the model in the figure was developed in 1955 instead of 1989". I am curious how this is different from the map example. For me the map example meant it takes time for ideas to build on one another to develop better understandings of a certain topic or idea. Did it take until 1989 to develop the model because economies of scale did not fit into the traditional models? If so then could the model have been developed in 1955?
Posted by: Henry | 09/25/2024 at 03:38 PM
I think this authors analysis of formal modeling as a means to explain economic theory was nuanced and interesting. I like the the author has admitted that there limitations and shortcomings to models, but that despite these things there is still something that can usually be taken away. I really like this idea that models are just metaphors used for comparison and analysis rather than an outright explanation. A metaphor in and of itself is a way representing an idea by comparison, we think of them because it allows us to think of something concrete on different terms and can often times shed new light on the way we think of things.
The author admits that the model is unrealistic in its assumption but gives us a better understanding for development theory. I agree that the model is helpful as it gives us a visual of what these relationships could potentially look like, but it is not necessarily an explanation for specific cases.
I did not fully understand the shift away from earlier economic theories and why the author viewed this to be such a loss. This paper seemed like a clear rebuttal to me but I didn't feel like I had a full understanding of his attitude.
Posted by: Katherine | 09/25/2024 at 04:17 PM
Krugman’s “The Fall and Rise of Development Economics” pulls from multiple economists and theories to demonstrate that it is possible to tell development-style stories in the form of rigorous models and that models that allow for highly simplified settings are crucial to the evolution of high development logic. Ideas surrounding development economics were initially shaped by themes of industrialization and import-substitution in the 1950s, and did not reemerge until the 1980s, when a combination approach to neoclassical economics and the reincorporation of technology and real-world market functioning materialized.
A key idea Krugman facilitates through his piece is that we must adopt the intellectual attitude that Hirshman rejected, which includes a willingness to do “violence to the richness and complexity of the real world in order to produce controlled, silly models that illustrate key concepts.” While Krugman recognizes the limitations of models, he also illustrates that the temporary “evolution of ignorance is the price of progress,” which is an inevitable component of making sense of the world’s complexity (Krugman). For example, Krugam observes the limitations of models as “blindspots,” which are created when economists emphasize modeling what they formally know how to model. That said, Krugan’s piece demonstrates the nuanced nature of models, in that their helpfulness is not negated by the fact that their system may have some degree of falsification. Rather, models are maps that help us make sense of our reality.
Aside from the importance of recognizing the nuanced nature of economic modeling, Krugman emphasizes how Rosenstein Rodan’s Big Push story is essential to the high development model. He states that if many industries make investments that move workers from the traditional to the modern sector, that this becomes a profitable scenario assuming economies of scale and dualism. However, I am left wondering more about forward and backward linkages. While I recognize that forward linkages involve the ability of an industry to “reduce the costs of potential downstream users of its products and push them over the threshold of profitability” (Hirshman), I am left wondering how such linkages relate to economies of scale and modeling (or a lack thereof).
Finally, I appreciate how Krugman equates the mapping of Africa demonstrating European ignorance to the development of formal economic theory. Counterintuitively, improved economic technique actually led to some loss in knowledge much like the mapping of Africa. To explain, between the 1940s-1970s, “a rise in the standards of rigor and logic led to a much improved level of understanding of some things, but also led for a time to an unwillingness to confront those areas the new technical regor could not yet reach” (Krugman).
Posted by: Sofia Iuteri | 09/25/2024 at 04:41 PM
From my understanding of this paper, it is a criticism not just of development economics and its models, but one of the subject as a whole. With rigid and well-structured models that either conclude an economy to be completely perfect or completely imperfect, they don’t leave space for those in the middle - which most of the world falls under. And I agree with the author’s reasoning because when complicated economic news cannot be explained through the models I am learning in class and the case suddenly becomes an exception, one can’t help but realize that these perfect models aren’t depicting much of the reality at all.
I always thought that basic economic models were simplified to be easily comprehended by an average person but the reality is that these simplifications are just metaphors that don’t accurately explain developing economies. These economies are shaped by more than 5 or 6 variables and for the most part our graphical models do fail to understand this. However, I think this is also why there are more papers and theories emerging that explain developing economies and their economics through niche data now.
Posted by: Archita | 09/25/2024 at 05:09 PM
While reading this paper, I noticed Krugman's unique style of writing and how the discussion of the failure of the high development theory is tragic and sometimes confusing. To elaborate, I picked out a quote from the "METAPHORS AND MODELS" section: "You make a set of clearly untrue simplifications [...] to something you can handle; dictated [...] by guesses about what is important [...] And the end result, if the model is a good one, is an improved insight into why the vastly more complex real system behaves the way it does."
Reading this quote reminded me of the same way we perceive most of the models we look at in class. We joke and make fun about how the models are just simply models. Reading this article has added on to how I look at how we view economic models, because it is unfortunate that when sensible ideas can not be modeled, they are lost, but when we can model economic concepts, we can not forget that they are "just models" and can change significantly depending on the real-world.
Posted by: Brandon Lee | 09/25/2024 at 05:39 PM
Krugman uses this article to critique development economics, especially with regard to the high development theorists and the "evolution of ignorance." The high development theorists disregarded the reality of quick industrialization and technology research, development, and deployment. Countries that incentivized agricultural workers to move to the city and seek jobs in the modern sector without sufficiently scaled modernization efforts, the economies floundered. High unemployment in the cities and low productivity in the traditional sector resulted in a nasty trap that might've been worse than remaining at status quo for these countries. Krugman also critiques the high developmental arguments in the evolution of ignorance, citing how economics was not yet equipped to formally model oligopolistic economies, which is how most developing countries functioned at the time––capital owners were few, rich, and faced competition from their peers in modernization and mechanization efforts. Other key points missed by high developmental idealists included forgetting that previous economists had already developed formal theories for circular causation and linkages. However, Myrdal and Hirschman refused to formally model their ideas and missed an opportunity for generalizability and adoption of their developmental theories into mainstream economic thinking. I was interested in the conclusion of this article when considering why developmental economics stagnated for so long due to disregard for older, yet applicable ideas left behind from previous thinkers. However, I do not fully understand the reason for asking about the "could've beens" rather than celebrating the resurgence of the field and simply leaving the conclusion as a warning to not disregard current thinking in economics for future use in yet undeveloped models.
Posted by: Porter | 09/25/2024 at 05:52 PM
I want to discuss the effectiveness of the article The Fall and Rise of Development Economics. The intro to this article is essential because of the precise language Krugman uses to hook the reader. Krugman states “I am a great admirer of The Strategy of Economic Development” but also says “I do not think it was helpful to development economics” even going as far as to say that due to the book's persuasiveness it made it destructive. Although Krugman does not agree with all of Albert Hirschman's ideas he wants to make it clear that Hirschman should not be viewed as a villain but instead a tragic hero. The author also gained credibility as he acknowledges the shortcomings of models. He states that models are important to understand ideas but they can create blind spots. This ties back into one of the first topics we covered in our class as we acknowledged that graphs are just mere cartoons due to their assumption that all variables remain constant. Although after reading this article my final thought was the importance of planning for the future in development Economics. This field of study is constantly changing and evolving as models and economies become more complex and advanced. While the study of the past is beneficial it should be used to plan for the future and economists need to be prepared for their theories and policies to change.
Posted by: Harry G | 09/25/2024 at 06:49 PM
This paper made me think about how we use models in economics, social sciences, and science as a whole. One specific excerpt that intrigued me was comparing economic models to early European maps of Africa. Krugman points out that as there was a greater push for precision, there was a loss of information that was not considered as precise. While models are very useful to help simplify complex systems like the "Big Push," I wonder if these simplifications sometimes cause economists to miss key insights, especially in situations where cultural and institutional factors play a role in economic development. Krugman argues that models are necessary for progress, but can too much focus on formalism actually steer policymakers in the wrong direction by glossing over the messy realities of real-world development? Ultimately, I side with this position. Without models, it would be nearly impossible to make sense of complex systems. However, when forming models, it is crucial to recognize the assumptions taking place and how those assumptions cause the model to differ from the real world. With these thoughts in mind, how do scientists in other fields strike the balance between making things simple enough to understand and keeping them accurate enough to reflect the richness of what’s actually happening?
Posted by: John Santowski | 09/25/2024 at 07:39 PM
One thing that struck me was the comparison to physics. In some ways, physics is the perfect science because it always has a clear and specific answer which can be derived from a specific formula. Importantly, physics has models and formulas which are repeatable. There are no exogenous variables like in economics models. In other words, physics is predictable and repeatable, economics is less so. This is an important distinction because in the development of the theories surrounding these disciplines, physicists could conduct specific experiments and get specific results while economists could not. Physicist also had the benefit of good data, especially around the time that development economics was in its infancy.
For me, this paper was more about the validity of economic models and theories than development economics itself. Theories are the beginning of economic thought, but that leaves out testing those theories with real world. Human nature itself is really critical in economic reality and is the main difference between theory and realty. Testing theory against the data is the only way to make sure that theory does not end up in exile.
Posted by: Ryman Smith | 09/25/2024 at 07:52 PM
I found this article's discussion on the effect of modeling very interesting.
Most specifically, the idea that incredibly important knowledge in a scientific context can be lost just because it is difficult to model was striking to me. The Africa example, where the interior of the continent was actually more accurately mapped BEFORE accurate cartographic techniques were adapted (for a few hundred years) was striking. I'm now wondering, what other important ideas were pushed to the side because of their difficulty in being modeled, specifically in the field of development economics? Don't get me wrong, modeling is incredibly important, and having accurate results is what any field of the sciences strives for, but I'm wondering now if there are other overlooked ideas that might have a massive role in explaining certain trends in countries that have been ignored for a long time.
Also, I feel that some people (unfortunately highly important people) are being taught models wrong. All models rest on a set of simplifying assumptions that may or may not be true given a specific country's circumstances (in our case). Additionally, more complex factors that are harder to model may be left out of models. However, many leaders and important individuals in development have come to see economic models as a guide for unconditional success, rather than tools for development (if certain conditions are met). This was the case (as I learned in my Postcolonial African History class last year) in a place like Zimbabwe, where factories were invested in, but the government made NO attempt to invest in human capital (the model they were following suggested that increasing capital and savings alone would lead to economic growth and success in their country) and so productivity still floundered. If models were more widely explained understood as flawed, conditional representations of reality, I think important figures and leaders would make better choices in harnessing them.
Posted by: Ethan Babb | 09/25/2024 at 08:00 PM
I very much admired Krugman's analysis of the Big Push model. I think his main two critics are important of all of economic learning: are we understating the difficulty of holding these assumptions and is a model backed by mathematical analysis. This seems very similar to the idea that many models are cartoons. However, giving supporters of Big Push economics the benefit of the doubt, many economic models depend on a lot of things. Nonetheless, it is difficult to provide much credit to a model with so little analytical evidence.
Posted by: Ben_Sundell | 09/25/2024 at 08:01 PM
I think Krugman explores a really interesting discourse throughout The Fall and Rise of Development. The part that I probably most enjoyed the most was his discussion about the perceived ignorance of economics and its transcendence over time. It’s not a perspective I had ever heard before (although a sentiment I’m familiar with), especially in the way that he connects it with the evolution of European maps of Africa. In my society and natural resources class, we often discuss the humanitarian consequences of development and colonialism as it has impacted the world but usually without in-depth investigation of data. In my econ courses, I usually see the opposite, it’s all data, regression, and endless correlation with little investigation into the surrounding conditions. This is where development economics seems to be the interface between the two and why Krugman’s examination of the erasure of African maps to the ignorant trajectory of development economics seems so nuanced to me. An initial burst of information, endless unexplored areas and opportunities, that is slowly cut down to a trickle as time continues. Standards are determined. Divides are set.
It’s a pattern I wouldn’t have expected the two to share but that I also don’t find all that unique. In fact, this is an idea we play with a lot in S&N resources: when does advancement become a hinderance to progress? While we were able to more accurately map these new lands, it was at the cost of erasing that which was determined to be “of no importance”. While the ability to formally model these scenarios allowed for increases in data accuracy, it was at the cost of a true understanding of the issues. It’s an interesting, and I’d argue, strong comparison.
Posted by: Kylie Sheridan | 09/25/2024 at 08:37 PM
The part of the paper that stood out to me the most was Krugman's analogy between the evolution of European maps of Africa and the trajectory of development economics. The shift towards more rigorous modeling that ironically led to some loss of knowledge and an obviously flawed model (the "darkest Africa") reminded me of the ongoing reliance on GDP as the primary measure of economic success and social well-being. Measures of GDP are appealing because it's a quantifiable metric that is fairly precise and without much room for disagreement. However, it ignores aspects like well-being, inequality, and environmental sustainability—critical factors that make up the true "landscape" of an economy.
I also found myself thinking about how these oversimplifications affect public opinion, just as how Europeans' lack of knowledge of Africa led them to them viewing African civilizations as primitive and homogenous. GDP is clearly a flawed and incomplete measure of development and overall well-being, and I would think that few development economists would argue against that. However, an average person may assume that a country having a significant rise in GDP like Botswana (referred to as the "African miracle" in last week reading) means that standard of living and wealth is rising for all, yet this is not always the case. While oversimplifications are valuable and models are the foundation of economic analysis, rigid thinking can cause us to overlook solutions to disparities in today's world, as well as impede the development of new models that are messier but also more insightful.
Posted by: Evan Daigle | 09/25/2024 at 08:43 PM
In Krugman’s article, “The Fall and Rise of Development Economics”, he mentions the importance of surrounding economic policy on market behavior while focusing on current developmental changes around the world. I find this very insightful because the paper builds off of what we talked about in class this past Tuesday. In the Lewis Two Sector Model, he discusses the four big assumptions, which includes an assumption of surplus in agriculture labor, full employment in modern sectors, how there are diminishing returns in firms but they are still profitable, and finally how profits are then reinvested back into the modern sector. Although development countries do have a majority of their population in agriculture, there are some farms where they are more profitable in comparison to others. If profitable farms start to leave the traditional sector, then it could lead to shortage in food or possibly famine. Furthermore, the idea that there is full employment in modern sectors is rarely the case. In most developing countries, there is an unemployment of about 30% maybe 40%. Although all countries are different in their problems, you can’t base your economic policy on the Lewis Two Sector Model assuming ceteris paribus, which is what Krugman highlights in his article.
Posted by: afanney | 09/25/2024 at 09:00 PM
I was particularly interested in the comparison of development economics in the 1950s-70s and the cartographical history of Africa. It was incredibly surprising to discover that a paper as influential as Rosenstein Rodan’s would not have gotten published in the 1950s because of the lack of formal models. One thing that has been a common theme in the science, social science, and even philosophy classes I’ve taken is the notion of “standing on the shoulders of giants”. This is essentially using ideas from previous research as a jumping-off-point for new research, which has led to many massive scientific breakthroughs. I think the reason why it is so shocking to me that economists would not publish more abstract studies is that it inhibits future economists’ ability to use those ideas and create more research from them. Abstract studies can beget mathematical models, but those models cannot be created if the research and ideas are not out in the world.
Posted by: Dara Bage | 09/25/2024 at 09:22 PM
Throughout Krugman's article, I was balancing between agreeing and disagreeing with one of his main assumptions: the model's simplicity justifies avoiding important complexities of reality. Krugman himself pointed out that restrictive assumptions led to a few decades of ignorance in the development theory, and I think the same holds for the conclusion of his article. Was the high development theory destined to wait so long for its time to shine? Krugman leans towards "yes" but heavily relies on his analysis of precedence in other disciplines rather than a proven mechanism. Could there be a way of gradually formalizing high development theory reaching a complete model way earlier? After all, it was mostly the rejection from the formal mainstream that led Hirshman to give up on formal modeling altogether. I think the issue lies in the fact that many economists fall into the trap of spending their whole careers in comfort zones worked out over the years, which makes them immune to novelty and unable to bend their way of thinking to understand different approaches. Arthur Lewis was a rare example of a person who successfully tried (by giving up on the increasing returns assumption) but a two-sided willingness to understand would bring much better results. Maybe if economists were less resistant to adopting different approaches, the whole science would gain a new breadth.
Posted by: Eryk Chojnacki | 09/25/2024 at 09:39 PM
Krugman's article felt a little bit like discussing economics with my grandfather. Both provide strong context by boiling complicated economic reality down to its most base elements. I think Krugman's approach is fascinating, though, because he invites the reader to learn these simple models, challenge them, and add nuance. In other words, his models were all about economies of scale, and his approach to teaching them was "at scale," too: he encouraged students to take a "macro" view of development economics so they could later analyze the "micro" factors that skewed his models, rather than narrow down his models to small-scale economies or only specific situations.
I wish there was a Krugman in behavioral economics. Honestly, it would be nice if every discipline had a Krugman - someone who could take incredibly complex models, transfer them in a simpler format, and inspire their students to take steps beyond the basic model to learn more about what flaws alter the original and why. In behavioral econ, every model I've analyzed has been very narrowly applicable. The field itself offers such cool intersections between neuro and political science, but (from some of the papers I've read) it feels like there's such a strong emphasis on intersectionality that you lose out on the ability to learn the 'basics' first.
Krugman's models must be taken not as the "closer" for a discussion, but rather a strong starting point for continuing research.
Posted by: Grace Rustay | 09/26/2024 at 12:15 AM
Traditions, common practices and/or a "that's just how we do things" mentality seems to have created the temporary losses of knowledge the article mentions, like the unknowns at the center of Africa or the loss of solid development ideas from Economics for decades. The author, understandable, struggles to conclude with actionable advice. However, in a collaborative field, I think it's worth understanding that it doesn't much matter if one individual fixates on understanding the world through mathematics, or another through models, or yet another through metaphor. Individuals can have their own biases, and have their biases checked and balanced by others. The problems of loss of knowledge arose when all the economists all at once rejected the models in favor of mathematics, or when all of the weather scientists ignored the clouds.
It isn't individual bias, but a collective bias, that led these scientific disciplines to ignore good evidence and ideas.
It makes sense that individuals in a field will follow the lead of breath through techniques or of thought leaders, and I don't know how to make the discipline of Economics open to more diversity of ideas or techniques, especially when every school with an economics major wants to teach to the standard of the time, and few entering the field will have the cajones to challenge the traditions in place. And those atop the field, who write influential papers or approve what gets into which journals, won't turn back on what brought them to success. I don't know how to have scientific fields be rigorous without falling into the trap of choosing tradition over other, better ways to find truth.
Posted by: _jteer | 09/26/2024 at 12:29 AM