I really enjoyed this piece by Krugman. Not only did it read easily but it presented ideas that I had not considered in much depth before this. Specifically, I enjoyed how Krugman dealt with the idea of formal models. Personally, this has been a big point of contention for me with economics. I have never been one to believe a model in its entirety without first completely understanding the limitations and the assumptions of that model. Yet, at the same time I cannot deny Krugman’s assertation that models are the basis of all rational and helpful thought. However, I found that Krugman failed to stress the shaky support on which models stand. Models are meant to be adapted, to be proven wrong and to be changed as we learn more and are better able to represent the world around us. Of course, a model is a fantastic starting place, at the same time, a model should be constantly tested and overthrown if found to be incapable of accurately explaining the world around us. Often, I find that when mathematical modelling is stressed in economics, there is an implication that no fault can be found. Even with exhaustive regression analysis, we must admit that economics is not a perfect science. Fault is found everywhere and anywhere, and we must be careful to draw conclusions. I think Krugman did not stress this point enough and may have a little too much faith in mathematics and modelling. He does admit that he himself should, “not to let important ideas slip by just because they haven't been formulated your way.” I think this is the most important point of the entire paper. Using rich insights from other areas of research and the world around us will certainly help us re-evaluate our economic models that seem cemented into textbooks but may not actually be accurate.
My favorite line in this article is "models are maps rather than reality". In every class I have had with Professor Casey, this concept is consistently emphasized. Especially in ECON 100, we are taught to believe these textbook models as facts, instead of merely a starting point to understanding a more complex story. Most of our classes are not spent modeling, but rather discussing how assumptions/behaviors may differ from the model. We are taught to approach scenarios with a healthy dose of skepticism… often, the answer is 'it depends'. As this article notes, development theory was halted for a period of time due to the lack of modeling capabilities for economies of scale. Delays are likely happening to this day. There are important aspects of development that have no formal model. Are we currently delaying development in other countries because we do not yet have a model that incorporates Sen's capabilities?
I both agree with much of the author's viewpoint on building a model. In today’s world it seems that everyone is result-driven, so coming up with methodology as Hirschman did, without a formal model, is overlooked without a second thought. However, as the author makes note, just because the methodology is presented without a model does not mean it should be disregarded or that line of thought should be dead-ended. Perhaps the researcher can present their methodology and fellow economists can work on producing a model for the methodology. No one person has all the answers and if papers and research are passed upon because they do not have a formal model, then economics, in general, may be missing out on great ideas. Moreover, it is very interesting to see that in the high development theory, labor is the only resource that can fluctuate. Both human and physical capital is neglected. Looking at more recent history, I cannot wrap my mind around that idea considering today’s view of development economics. Improving education to increase human capital and increasing physical capital are two staples in modern development economics.
I liked Krugman's article, and thought that our recent class discussions have related well to it. Krugman points out that the development models created in the 1900s stemmed from mathematics and formulas more so than based on a methodology. From the articles that we have discussed in class, a common theme in discussion has been the idea that the models and solutions to development look great on paper but are questionably effective in real life. The question often arises: will this concept/model actually work? Krugman notes how the theories developed in the 1900s are great and smart ideas, but that they would run into trouble and criticism because of the difficulty of applying mathematics to them. With helping to promote development, there are so many different variables to take into account that it is difficult to even come up with an equation. I liked how Krugman says that it is ok to have thought concepts that aren't necessarily supported by definite formulas because I think that in instances of development, the formulas sometimes miss out on the real-life/human application. The math created effects real people who live their own lives, and while the math is helpful in understanding, it cannot solely be relied on to solve the worlds problems.
As Didi also stated, one of the lines that really spoke to me was that, “models are maps rather than reality”. Often times models fall short in isolating variables to prove causation rather than just correlation, and thus casual links are very hard to prove. This is because in many cases RCT are either not possible or unethical and thus we rely on trying to observe sufficient information or using natural/quasi experiments when collecting data to form basis for models. The problem with this is in a regression analysis, beta can become biased due to a correlation between the epsilon or error term and the variable(s) being measured. Thus, models while they can be good maps as Krugman said, are almost never without flaw relying on assumptions in order to hold true.
A prominent example often brought up in my Economics of Education class is the model for the casual effect of education on earnings. It is difficult to create a model that is able to calculate solely the difference between wage with schooling and wage without. This is for a number of reasons. One of the most prominent ones is that students who obtain higher education levels may be systematically different from those who don’t (ie higher ability or productivity). Those systematic differences, regardless of education level, are likely to impact earnings. Thus, these differences are likely to inflate the wage gap between those with education and those without education. So in order to attempt to isolate the casual impact of education on earnings economists try to observe sufficient information about student’s backgrounds to try to control for the non-random sorting. They also try to find natural experiments where a policy enacted creates some type of natural sorting. To determine the causal impact of education on earnings a mincer regression is often used. The issues with this is that it assumes a linear relationship between education and log earnings. This is often not the case as one year of college would not carry the same relative weight as four years, where then a Bachelors degree is earned. The second issues is that beta may have bias, as mentioned above due to correlation between variables and error terms. The model often assumes that there is no correlation, which can be problematic.
The most interesting piece of the article for me is how we “lose” information that we once knew when our new technology doesn’t explain it. I’d never quite thought it about it in those terms. Krugman argues that in the economic world, ideas without a backing from a model become this information that falls through the cracks. I do think that it is important to create models, and that it is perfectly fine for models to simplify and make assumptions about economics. However, I find Krugman to be a bit extreme view on the importance of models. Krugman poses this question to economists who don’t use models, "Are you sure that you really have such deep insights that you are better off turning your back on the cumulative discourse among generally intelligent people that is modern economics?” I think this question is a bit harsh and close-minded. This article does give hope that ideas that were once pushed to the back burner can eventually be reformulated and actually used with the help of economic models. Lastly, I would fire the editor of this piece simply because of the phrase "can be gotten started".
It was good to hear Kruger refer to the field of economics as a social science- akin to sociology, psychology, anthropology or linguistics. In my experience, economists tend to disassociate themselves from such fields and align themselves with mathematicians or statisticians. In my opinion, economics is uniquely poised to bridge both mathematics and social science. Economics is its own field; it applies high level calculus and statistical analysis to advise good action. This is different than sociology or psychology, which use models or statistics that already exist to prove more abstract theories. I would argue that through regression and modelling, economics creates the statistics and models that are the backbone of other social sciences. In my opinion, economics at its best will balance math and social analysis. Kruger’s article is a good reminder that we have not always and must strive to keep this balance.
The Krugman paper provided additional insight to the history of development economics and how it has changed over time. He refers to the concept as “high development strategy” which is essentially the idea that development happens as a positive feedback loop, but it needs an initial “push” to break out of a low level trap. The idea of the “big push” reminded me of the institutional barriers paper, which provides more insight into how complex these “traps” can be. This requires complex solutions, and Krugman mentioned ideas like coordinated investment and government intervention as means to jumpstart the “self reinforcing growth” of a developing economy. Krugman also mentioned the idea of development of ignorance and the changes in the field of economics, which makes sense in the context of chapters three and four that timelined different theories and models. The interaction with policy, poverty, and real people instead of theoretical models separates development economics from traditional economics, and I agree with Krugman that the conclusions in development economics can be just as valid even if they aren’t supported by traditional technical evidence.
I found this article to be quite thought-provoking. For one, it is quite interesting to think about the idea that modernization breeds modernization. What Krugman means here is that if modernization can get started on a sufficiently large scale, then it will be self-sustaining. However, an economy can also get caught stuck in a position where it never truly gets going, and is instead left behind in the
"traditional" world. This theory made me think of the first Industrial Revolution and how this era developed so rapidly due to technological innovations that took place in many parts of Europe and the United States. Playing off the concept of modernization breeding modernization, it is likely that the Industrial Revolution would not have developed so quickly if it wasn't for Europeans and Americans bouncing their ideas off one another in order to develop their products. I was also interested in the idea that models are maps rather than reality. This goes to show that models are merely a starting point in efforts to explain the way the world works. In reality, models are able to explain the way things happen conditional on all other things being equal--which is more than often not the case.
Krugman’s explanation of the high development theory and its evolution since the mid 1900s clarified much of my confusion regarding the connection between the traditional and modern sectors. According to the high development theory, the modern sector is more productive than the traditional sector and pays higher wages than the traditional sector as long as the market is large enough and enough labor is available. However, without modernization on a sufficiently large scale, the modern sector will not take off and will not be self-sustaining, thus prolonging a poverty trap. This theory advocates for a Big Push and makes a powerful case for government activism in order to allow the modern sector, and its heightened productivity, to take off.
Although his explanation was clear, I would be interested to hear more about Krugman’s opinion of the theory. Would he agree with the theory’s call for government activism? If so, what forms of government activism are most effective to successfully introduce the Big Push? I would also be interested to hear more about the history behind the high development theory by hearing which countries successfully align with the theory. This depiction would further illustrate the story of high development, rather than leaving it in the abstract through models and analogies.
This article reminded me perfectly of all the economics elective courses I have taken on my (very pov & env heavy) track for my economics major. Through introduction to sustainable development, economics of social issues, poverty & food insecurity, environment and natural resource economics, plus more, theories have continually driven so many of our conversations. Every now and then we do put pen to paper and model out a relationship we are talking about, but those models even are extremely flexible. I’m immediately reminded of our midterm exam last semester for ECON 255. So many people’s answers were different, yet they were all right for the most part. While the models do in fact help solidify the ideas and principles being discussed and are necessary, I appreciate their fluidity throughout ideas and thought processes. The mapmaking analogy really helped solidify to me how nicely my trip on the economics track has been. I may not be the strongest economist among my peers, but I understand what I need to, and I can now say that I understand economic modeling more than I ever did before.
I thought that this paper was a nice compliment to what we have been discussing in class in regard to the problems that arise with making assumptions and translating models into the real world. I think the fact that Krugman acknowledged and respected Hirschman’s work made his argument stronger and made it more likely for his readers to be willing to open their ears. Krugman stated that Hirchman’s “intellectual strategy” was “understandable but wrong response” to economic development, but he thought that “the very brilliance and persuasiveness of the book made it all the more destructive”. The acknowledgement of the positive aspect of Hirschman, I think, made Krugman’s argument stronger.
I thought the discussion of the mapmaking was really intriguing. It would make sense for an increase in knowledge to correlate with increase in accuracy of the maps, but as discussed in the paper, this was not a true assumption. The increase in accuracy and knowledge available to make maps “raised the standard for what was considered valid data” which led to a lag in the art of mapmaking. This advancement ultimately led to better maps in the long run, but it wasn’t a linear process. I think this analogy made it easier to understand what happened with economics from the 1940s to 1970s. I also think that this is a good example of the potential to be overconfident and almost blind with knowledge and the danger of not being able to look for or realize one’s flaws or mistakes.
I think that the most important thing I took away from this paper was to be careful with models. As shown with Fultz’s dish pan, you cannot assume the model tells the whole story. For example, Lewis’s surplus labor concept disregarded economies of scale so theorists could have “something they could model using available tools”. Also, with Rosenstein Rodan, economies of scale and dualism are a key component to making the model work, yet it is difficult to know if these assumptions hold true in reality. Both readers of economics and economists themselves should be aware that it is easy to make “untrue simplifications” when using models. Instead, when using models, people should be “self conscious — to be aware that your models are maps rather than reality” and to be "self aware" rather than "sleepwalkers".
In light of our conversations this summer, like several of my peers I was excited to see Krugman write about economics as a social science. I think the interdisciplinary aspect of economics, and its status as a social science is essential to understanding the ways that our economic models are somewhat limiting in what they can tell us- the math only gets us so far, in despite of the fact that it becomes so important when attempting to explain things. I really appreciated Krugman’s examples related to meteorology, a “hard science” in order to show how models and methodology are often created. I found the following quote to be especially impactful: “The cycle of knowledge lost before it can be regained seems to be an inevitable part of formal model-building.” As Krugman explains, this is certainly at least partially what happened in terms of high development theory. I wonder what would have happened not only to the field of development economics, but also to the entire world and developing countries if there had not been this period of “intellectual waste” as it relates to development economics. If development economic models had been created in the 1950’s as opposed to the 1990’s, would we have been able to make greater progress in developing countries if we had been able to fully understand economically what was happening there? Would the world be pushed 40 years ahead in terms of development? As Krugman explains, there is nothing that can be done, but it would be interesting to know what the state of developing countries would be and how the field of development economics could have changed.
What stuck out to me most from this essay were Krugman’s references to maps and models outside of economics and then his comparison of these maps and models to the discipline of economics and its evolution of knowledge and use of models. I was particularly fascinated by his discussion of the ‘evolution of ignorance’ and the evolving European map of Africa. I found Krugman’s closing statement, that ‘a temporary evolution of ignorance’ may be the inevitable price of progress, particularly intriguing. He asserts that during a period in which technique improves and standards of rigor and logic rise, there will most likely be some loss of knowledge and an unwillingness to confront concepts that ‘the new technical rigor’ cannot yet reach. He compares the evolution of European maps of Africa from the 15th to 19th centuries to the discipline of economics between the 1940s and 1970s. This comparison certainly helped clarify his point about the evolution of economics and the discipline of development economics more specifically, and it even seemed logical and probable to me at first, but by the end of the essay when he restated this idea in his conclusion I found I didn’t believe it—and didn’t want to believe it. Maybe it was his use of the word ‘inevitable,’ or maybe ‘ignorance,’ which is kind of a buzzword in our society today, but I find it hard to trust in the truth of Krugman’s assertion. While I cannot deny that progress is never linear and is increasingly complicated in a complex and rapidly changing world, it is difficult to believe that our society today can and ought to knowingly accept stretches of ignorance, even if we know (or think we know) that ultimately it is likely to lead to better models in the long run. Such ignorance would likely have tremendous human impacts—on individuals, cultures, minority populations—which it appears Krugman does not really consider.
Overall, I do not believe in the inevitability of ignorance as the price of progress, nor that such ignorance would facilitate the production of the best models in the long run. Krugman states that ignorance arises at least in part because improved technique raises the standard for what is considered valid data, thus limiting what is included in the model. However, one of the main reasons I am hopeful we can avoid ignorance is that our society today is becoming increasingly able and willing to consider and dig deeper into complex quantifiable data as well as listen to and seek to understand qualitative data and diverse voices. Finally, I think that to make the best models for the world we live in we cannot afford periods of ignorance, as we increasingly benefit from filling in holes in our knowledge and understanding through improved communication and greater opportunities to engage with a variety of perspectives as well as facts and hard data from a growing number of sources.
The concept of “valid data” is what struck me the most in this paper. As someone who takes great interest in statistics, I constantly am evaluating the collection and quality of data used in studies. When it comes to more qualitative data, I often find myself mentally invalidating studies where people have to rate on Likert scales or other relational measures. However, this data should still be taken seriously if collected properly even if it is not the most precise measures. It has been my own struggle with the “social science” aspect of economics. Like the high development theorists, I am more likely to trust a theory that I can see the mathematics behind. During my time studying economics, I have learned that there is often a bigger picture that has yes to be formalize din such a way, but it can still be accepted as valid. I appreciate that in economics as pointed out in this paper, that sometimes getting “very close” is in fact close enough. From a policy point of view, one does not need the exact amount needed to fix a problem but rather the ballpark as a starting point for resource allocation. In math, it is crucial to cover all your bases and leave no stone uncovered. The importance and beauty of economics is the importance of understanding the human experience and not just the numbers while figuring out how to quantify that experience.
I found this reading to be highly enjoyable and very engaging. Krugman writes very persuasively about the role of models, their benefits and their limitations. He illustrates the importance and the usefulness of simplistic models, and his warnings about mistaking these maps for reality rings true, I especially liked that the author described the success of Murphy et al. in illustrating the Big Push model that was first formulated by Rosenstein as a consequence of them “daring to be silly,” i.e. of them going against popular methodologies, or of taking an all-or-nothing attitude toward economic modeling. By working with the tools and procedures that have been established, we can hope to effect the formulation of better models and a wider selection of tools. The trend that Krugman suggests by which ignorance must be experienced along with knowledge is another intriguing aspect of this read. It essentially describes the age-old process for learning anything new: trial and error. And, like Krugman urges, it would be a mistake to disregard simplification of reality for fear that we may lose the facts, because simplification of reality is the only method by which we can ultimately utilize those facts. After that, hopefully we can build back into our theories and our understanding of economics any truths that have been lost along the way, and end up with more knowledge than with which we first began.
I thought Krugman made several insightful analogies in his discussion surrounding formal models in economics. Specifically, relating his map-making analogy to how economists overlooked certain non-formalized areas of economics was interesting. I’m surprised that high development theory was largely ignored even though nobody knew how to model it yet. Economists at the time must have known that perfect competition was no more than a simplifying assumption. If it were an assumption that held true 100% of the time, it would make sense to ignore the high development theory based on increasing returns to scale. Since this is not the case, it surprised me that economists would overlook something simply because of its lack of formalization. Even more recently, it’s pretty clear that other than the Lewis two sector model that didn’t necessitate economies of scale, the Big Push idea didn’t gain traction until it was formalized. Although Krugman tries to strike a balance between models and looking more broadly at the complexity of the world, he clearly states that he believes models are necessary in economics. This is of course true: as in Krugman’s analogy with the dish-pan, economic models are extremely useful in simplifying the world to a point that is understandable given our constraints on time, money, and intellect. However, I think economists should place serious effort at looking beyond the theoretical models (more so than listlessly looking for “the folk wisdom on clouds” as Krugman phrased it). In actuality, I think this is occurring. For example, the field of behavioral economics questions whether the assumptions of rational behavior as they relate to utility and profit maximization are even accurate. I suppose in the end I more or less agree with Krugman that it is important to focus on both modelling as well as paying attention to ideas that go beyond the classic models, but I would place even more emphasis on the latter.
I thought this article was incredibly insightful in regard to the growing quantification of economics. I have had many people tell me that economics is evolving into a science, based on simplifications and/or assumptions that may not be true. As an observer and student of economics, it seems to me that in order to defend the legitimacy of their ideas, economists have turned to number-crunching and complex formulations to motivate their research. However, like Krugman points out in Hirschman’s “Big Push” model, high development economics could have transformed at a more rapid rate and may have had a substantially greater impact on low-income countries had the criteria for legitimacy been lower. Through Murphy et al.’s publication and model, Hirschman’s ideas soon reigned comprehensible and spawned thousands of papers extending his ideas. This goes to show that although the formulation of social science is helpful to reproduce results, the subjective and convenient importance placed on assumptions like perfect competition prevent the entire picture from being understood. Personally, this paper and its implied criticism of over-formulation of economics reminded me of my own frustrations with classical models in Econ 100. To me perfect competition is nonsense, and yet this principle is treated as a law from physics and for a long time indisputable. Overall, the paper was extremely fascinating and made me wonder where we could be as a society had economists diverged from convention.
I really enjoyed the author's discussion of the limitations of formal models, and regardless of those limitations, the necessities for those models. It feels rare that we as students get to challenge the concepts that are presented to us in classes, at least until the college level of education. However, Krugman makes a wonderful point in presenting these formal models as a starting point that isn't wholly reflective of the real world; as a couple of my peers have already mentioned, these models are meant to be built upon and adapted as we learn and are able to improve them.
One thing that stuck out to me in particular was Krugman's argument that "...the most vociferous critics of economic models are often politically motivated. They have very strong ideas about what they want to believe; their convictions are essentially driven by values rather than analysis, but when an analysis threatens those beliefs they prefer to attack its assumptions rather than examine the basis for their own beliefs." I understand the point that Krugman is trying to make in that political motivations can often contaminate the results of academic research, but I also feel that our beliefs (to be clear, I don't mean political beliefs per se, but in more of a gut-level or moral sense) can guide us to see flaws within the model. For example, if our findings point to a typically racist ideology, such as the argument that black people commit more crime, I think that on a gut level (as humans striving to be antiracist) we can reject that idea and see where the model might have gone wrong (because we know, for instance, that policing is concentrated in black neighborhoods, and that black people are more likely to be convicted of crimes).
This reading did not only help me better understand the high development theory, but also gave me a stronger insight into the history of development economics and economics as a whole. Previously, I had not delved into the history of the field nor its evolution over time. To hear that modeling - which I had always believed to be the cornerstone of economics - only became a significant component of the field as recently as the 1950s is fascinating to me.
In my introductory economics class, I remember questioning how a model could explain such a complex situation in such simple terms. How could something so simple be accurate? Surely, there are more factors involved? How can one tell which situations this model applies to? After reading this article, I can look back on my previous curiosity and answer these questions for my past self. Even though such simplifications are unrealistic, simplifying a complex phenomenon allows economists to analyze it at a level that they can handle. From here, they can generate a model that might explain the phenomenon, giving us a better understanding of something so complex.
I found the African map comparison to be particularly interesting. This really helped me understand what exactly happened with high development economics in the 1950s. When the field of economics shifted towards a focus on modeling, high development economics got temporarily left behind; unable to model economies of scale, they simply could not keep up with mainstream economics.
The aspect of this essay that I found the most interesting was how the fact that economics is a social science complicates the way the models are perceived. This is due to assumptions made within the models, which draw the ire of observers. He drew attention to the success of Murphy, which he credits to him "daring to be silly". Silly, being a deviation from the field's standard. It was interesting to think about the adverse impacts a discipline can have by leaders in the field having tunnel vision, and going against anything that doesn't conform to their view on what economics should be. By providing a simplification with his model, Murphy was able to advance the field.
It's interesting to think of the potential of developmental economics, both if the lag discussed in the paper didn't occur and the future of the field. Now that the ball is rolling, we might see significant advancements and research that might improve policy all over the world. Despite what the paper noted, that strong economic and good policy are far less related than we want, the potential exists of positive change as a result of work in the field.
I believe Krugman did an exceptional job in presenting the details to the story of development economics. I am especially interested in his discussion of how high development economics became a niche field which was largely ignored but still provides an interesting lenses through which to think about the world. The use of metaphor and model are useful tools in an ever-growing data driven world. Models in their definition are oversimplifications of reality which give greater insight and understanding, and Krugman does a wonderful job of framing the field of economics and how Hirschman’s works fit into the greater puzzle. Mathematics and statistics are of great importance when assessing certain hypotheses. However, a strong underlying theoretical framework goes a long way for assessing the problem. While econometrics teaches us to seek to capture every possible endogenous variable applicable to a certain research question, a broader approach can often bring fruitful discussion and a greater understanding. The Big Push idea is also quite captivating. While the model discussed in the paper only used labor as an input variable, the conclusions that for many developing markets the adoption of more effective means of production reminded me of our class discussion on why certain subsistent markets might not adopt new technology. Not only might they not have the capital needed to adopt the more expensive means of the production but there might also be an inherent risk factor to the transition to a new method of production.
This piece by Krugman is ultimately a critique of the shortcomings of models. Reading this paper, I was reminded of the conversation we had in class, in which Professor Casey explained that some analysts fail to answer even the simplest questions regarding what happens when a variable is changed in a model, because instead of explaining what happens in real life due to the change in the variable, they basically just answer, "When this variable in the model changes, the variable changes." (._.) This phenomenon is the loss in knowledge that occurs, the "narrowing of vision imposed by the limitations of one's framework and tools, a narrowing that can only be ended [with tools that] transcend those limitations." Of course, I knew that economic models (especially the simple ones we learn in school) are full of flaws since the world isn't as simple as a couple of lines and two axes, but I was disheartened to learn that even in advanced fields of studies and professions, it is difficult to create a completely accurate model that doesn't omit any aspect of reality: "The only exact model of the global weather system is that system itself. Any model of that system is therefore to some degree a falsification..." I came to realize that this is why the observer's judgment and compromise are so important, and why it is so important for us to understand the process of what is happening in real life that the model represents. Without this skill, one will only be able to observe what happens in the graph itself, rather than explain how different variables are affecting real people and movement.
While reading “The Rise and Fall of Development Economics” by Paul Krugman, the statement, “High development theory rested critically on the assumption of economies of scale, but nobody knew how to put these scale economies into formal models,” stood out to me as economic models rely heavily on assumptions. I am immediately reminded of our recent discussion of the Lewis two-sector model where, “Lewis assumed that the level of wages in the urban industrial sector was constant,” alongside using surplus labor (specifically mentioned later in this paper), and two other basic assumptions as the foundation of his development model. Krugman acknowledges the blindspots these accepted truths create, he insists models are still, “basically right.” The entirety of the paper focuses on the controversy that, “ any model of that system is therefore to some degree a falsification: it leaves out some (many) aspects of reality.” Difference between theory and application is a problem beyond the realm of economics: physics and meteorology were detailed as two strong examples. Krugman then emphasizes social science is no exception to this duality.
Krugman's paper explains one of the biggest issues that I have taken with economics since beginning the study of it 3 years ago. I constantly question how applicable something can be when it is based on silly assumptions. Krugman offers a simple explanation for this by saying that if everything had to be based on the full complexity of situations, then we would never have anything to go off of. I found his metaphor of the dish pan meteorological study to be particularly useful in helping me to resolve this bias. I thought that his idea of ignorance was very interesting, and very accurate. He says that as we gain more and more insight into something, then we develop this sense of feeling like our complex understanding must underlie anything which is valid. He uses the example of cartography here. As we gained better techniques, we began to ignore the information (though sometimes ridiculous information) by less reliable sources, and generated maps that would have benifited from these second hand insights. With economics we see the same thing, and it was this that really stuck out to me in the paper. As more and more complex economic models were developed in the mid 20th century, we began ignored those silly assumptions that would have actually generated a great deal of insights. in his conclusion, he poses the question of what if we would have been able to put aside the notion that everything must cocnform with our complex understanding? I think like im sure he does, that this is just the natural progression of things, especially when dealing with such a complex issue as development.
I really enjoyed this piece by Krugman. Not only did it read easily but it presented ideas that I had not considered in much depth before this. Specifically, I enjoyed how Krugman dealt with the idea of formal models. Personally, this has been a big point of contention for me with economics. I have never been one to believe a model in its entirety without first completely understanding the limitations and the assumptions of that model. Yet, at the same time I cannot deny Krugman’s assertation that models are the basis of all rational and helpful thought. However, I found that Krugman failed to stress the shaky support on which models stand. Models are meant to be adapted, to be proven wrong and to be changed as we learn more and are better able to represent the world around us. Of course, a model is a fantastic starting place, at the same time, a model should be constantly tested and overthrown if found to be incapable of accurately explaining the world around us. Often, I find that when mathematical modelling is stressed in economics, there is an implication that no fault can be found. Even with exhaustive regression analysis, we must admit that economics is not a perfect science. Fault is found everywhere and anywhere, and we must be careful to draw conclusions. I think Krugman did not stress this point enough and may have a little too much faith in mathematics and modelling. He does admit that he himself should, “not to let important ideas slip by just because they haven't been formulated your way.” I think this is the most important point of the entire paper. Using rich insights from other areas of research and the world around us will certainly help us re-evaluate our economic models that seem cemented into textbooks but may not actually be accurate.
Posted by: Jack Parham | 09/10/2020 at 12:05 PM
My favorite line in this article is "models are maps rather than reality". In every class I have had with Professor Casey, this concept is consistently emphasized. Especially in ECON 100, we are taught to believe these textbook models as facts, instead of merely a starting point to understanding a more complex story. Most of our classes are not spent modeling, but rather discussing how assumptions/behaviors may differ from the model. We are taught to approach scenarios with a healthy dose of skepticism… often, the answer is 'it depends'. As this article notes, development theory was halted for a period of time due to the lack of modeling capabilities for economies of scale. Delays are likely happening to this day. There are important aspects of development that have no formal model. Are we currently delaying development in other countries because we do not yet have a model that incorporates Sen's capabilities?
Posted by: Didi Pace | 09/10/2020 at 01:36 PM
I both agree with much of the author's viewpoint on building a model. In today’s world it seems that everyone is result-driven, so coming up with methodology as Hirschman did, without a formal model, is overlooked without a second thought. However, as the author makes note, just because the methodology is presented without a model does not mean it should be disregarded or that line of thought should be dead-ended. Perhaps the researcher can present their methodology and fellow economists can work on producing a model for the methodology. No one person has all the answers and if papers and research are passed upon because they do not have a formal model, then economics, in general, may be missing out on great ideas. Moreover, it is very interesting to see that in the high development theory, labor is the only resource that can fluctuate. Both human and physical capital is neglected. Looking at more recent history, I cannot wrap my mind around that idea considering today’s view of development economics. Improving education to increase human capital and increasing physical capital are two staples in modern development economics.
Posted by: Austin Lee | 09/10/2020 at 02:11 PM
I liked Krugman's article, and thought that our recent class discussions have related well to it. Krugman points out that the development models created in the 1900s stemmed from mathematics and formulas more so than based on a methodology. From the articles that we have discussed in class, a common theme in discussion has been the idea that the models and solutions to development look great on paper but are questionably effective in real life. The question often arises: will this concept/model actually work? Krugman notes how the theories developed in the 1900s are great and smart ideas, but that they would run into trouble and criticism because of the difficulty of applying mathematics to them. With helping to promote development, there are so many different variables to take into account that it is difficult to even come up with an equation. I liked how Krugman says that it is ok to have thought concepts that aren't necessarily supported by definite formulas because I think that in instances of development, the formulas sometimes miss out on the real-life/human application. The math created effects real people who live their own lives, and while the math is helpful in understanding, it cannot solely be relied on to solve the worlds problems.
Posted by: Gus Wise | 09/10/2020 at 03:42 PM
As Didi also stated, one of the lines that really spoke to me was that, “models are maps rather than reality”. Often times models fall short in isolating variables to prove causation rather than just correlation, and thus casual links are very hard to prove. This is because in many cases RCT are either not possible or unethical and thus we rely on trying to observe sufficient information or using natural/quasi experiments when collecting data to form basis for models. The problem with this is in a regression analysis, beta can become biased due to a correlation between the epsilon or error term and the variable(s) being measured. Thus, models while they can be good maps as Krugman said, are almost never without flaw relying on assumptions in order to hold true.
A prominent example often brought up in my Economics of Education class is the model for the casual effect of education on earnings. It is difficult to create a model that is able to calculate solely the difference between wage with schooling and wage without. This is for a number of reasons. One of the most prominent ones is that students who obtain higher education levels may be systematically different from those who don’t (ie higher ability or productivity). Those systematic differences, regardless of education level, are likely to impact earnings. Thus, these differences are likely to inflate the wage gap between those with education and those without education. So in order to attempt to isolate the casual impact of education on earnings economists try to observe sufficient information about student’s backgrounds to try to control for the non-random sorting. They also try to find natural experiments where a policy enacted creates some type of natural sorting. To determine the causal impact of education on earnings a mincer regression is often used. The issues with this is that it assumes a linear relationship between education and log earnings. This is often not the case as one year of college would not carry the same relative weight as four years, where then a Bachelors degree is earned. The second issues is that beta may have bias, as mentioned above due to correlation between variables and error terms. The model often assumes that there is no correlation, which can be problematic.
Posted by: Sydney Goldstein | 09/10/2020 at 03:47 PM
The most interesting piece of the article for me is how we “lose” information that we once knew when our new technology doesn’t explain it. I’d never quite thought it about it in those terms. Krugman argues that in the economic world, ideas without a backing from a model become this information that falls through the cracks. I do think that it is important to create models, and that it is perfectly fine for models to simplify and make assumptions about economics. However, I find Krugman to be a bit extreme view on the importance of models. Krugman poses this question to economists who don’t use models, "Are you sure that you really have such deep insights that you are better off turning your back on the cumulative discourse among generally intelligent people that is modern economics?” I think this question is a bit harsh and close-minded. This article does give hope that ideas that were once pushed to the back burner can eventually be reformulated and actually used with the help of economic models. Lastly, I would fire the editor of this piece simply because of the phrase "can be gotten started".
Posted by: Frances McIntosh | 09/10/2020 at 04:33 PM
It was good to hear Kruger refer to the field of economics as a social science- akin to sociology, psychology, anthropology or linguistics. In my experience, economists tend to disassociate themselves from such fields and align themselves with mathematicians or statisticians. In my opinion, economics is uniquely poised to bridge both mathematics and social science. Economics is its own field; it applies high level calculus and statistical analysis to advise good action. This is different than sociology or psychology, which use models or statistics that already exist to prove more abstract theories. I would argue that through regression and modelling, economics creates the statistics and models that are the backbone of other social sciences. In my opinion, economics at its best will balance math and social analysis. Kruger’s article is a good reminder that we have not always and must strive to keep this balance.
Posted by: Mercer Peek | 09/10/2020 at 04:39 PM
The Krugman paper provided additional insight to the history of development economics and how it has changed over time. He refers to the concept as “high development strategy” which is essentially the idea that development happens as a positive feedback loop, but it needs an initial “push” to break out of a low level trap. The idea of the “big push” reminded me of the institutional barriers paper, which provides more insight into how complex these “traps” can be. This requires complex solutions, and Krugman mentioned ideas like coordinated investment and government intervention as means to jumpstart the “self reinforcing growth” of a developing economy. Krugman also mentioned the idea of development of ignorance and the changes in the field of economics, which makes sense in the context of chapters three and four that timelined different theories and models. The interaction with policy, poverty, and real people instead of theoretical models separates development economics from traditional economics, and I agree with Krugman that the conclusions in development economics can be just as valid even if they aren’t supported by traditional technical evidence.
Posted by: Adelaide Burton | 09/10/2020 at 04:50 PM
I found this article to be quite thought-provoking. For one, it is quite interesting to think about the idea that modernization breeds modernization. What Krugman means here is that if modernization can get started on a sufficiently large scale, then it will be self-sustaining. However, an economy can also get caught stuck in a position where it never truly gets going, and is instead left behind in the
"traditional" world. This theory made me think of the first Industrial Revolution and how this era developed so rapidly due to technological innovations that took place in many parts of Europe and the United States. Playing off the concept of modernization breeding modernization, it is likely that the Industrial Revolution would not have developed so quickly if it wasn't for Europeans and Americans bouncing their ideas off one another in order to develop their products. I was also interested in the idea that models are maps rather than reality. This goes to show that models are merely a starting point in efforts to explain the way the world works. In reality, models are able to explain the way things happen conditional on all other things being equal--which is more than often not the case.
Posted by: Mason Shuffler | 09/10/2020 at 05:29 PM
Krugman’s explanation of the high development theory and its evolution since the mid 1900s clarified much of my confusion regarding the connection between the traditional and modern sectors. According to the high development theory, the modern sector is more productive than the traditional sector and pays higher wages than the traditional sector as long as the market is large enough and enough labor is available. However, without modernization on a sufficiently large scale, the modern sector will not take off and will not be self-sustaining, thus prolonging a poverty trap. This theory advocates for a Big Push and makes a powerful case for government activism in order to allow the modern sector, and its heightened productivity, to take off.
Although his explanation was clear, I would be interested to hear more about Krugman’s opinion of the theory. Would he agree with the theory’s call for government activism? If so, what forms of government activism are most effective to successfully introduce the Big Push? I would also be interested to hear more about the history behind the high development theory by hearing which countries successfully align with the theory. This depiction would further illustrate the story of high development, rather than leaving it in the abstract through models and analogies.
Posted by: Julia Foxen | 09/10/2020 at 05:55 PM
This article reminded me perfectly of all the economics elective courses I have taken on my (very pov & env heavy) track for my economics major. Through introduction to sustainable development, economics of social issues, poverty & food insecurity, environment and natural resource economics, plus more, theories have continually driven so many of our conversations. Every now and then we do put pen to paper and model out a relationship we are talking about, but those models even are extremely flexible. I’m immediately reminded of our midterm exam last semester for ECON 255. So many people’s answers were different, yet they were all right for the most part. While the models do in fact help solidify the ideas and principles being discussed and are necessary, I appreciate their fluidity throughout ideas and thought processes. The mapmaking analogy really helped solidify to me how nicely my trip on the economics track has been. I may not be the strongest economist among my peers, but I understand what I need to, and I can now say that I understand economic modeling more than I ever did before.
Posted by: Bridget Bartley | 09/10/2020 at 05:59 PM
I thought that this paper was a nice compliment to what we have been discussing in class in regard to the problems that arise with making assumptions and translating models into the real world. I think the fact that Krugman acknowledged and respected Hirschman’s work made his argument stronger and made it more likely for his readers to be willing to open their ears. Krugman stated that Hirchman’s “intellectual strategy” was “understandable but wrong response” to economic development, but he thought that “the very brilliance and persuasiveness of the book made it all the more destructive”. The acknowledgement of the positive aspect of Hirschman, I think, made Krugman’s argument stronger.
I thought the discussion of the mapmaking was really intriguing. It would make sense for an increase in knowledge to correlate with increase in accuracy of the maps, but as discussed in the paper, this was not a true assumption. The increase in accuracy and knowledge available to make maps “raised the standard for what was considered valid data” which led to a lag in the art of mapmaking. This advancement ultimately led to better maps in the long run, but it wasn’t a linear process. I think this analogy made it easier to understand what happened with economics from the 1940s to 1970s. I also think that this is a good example of the potential to be overconfident and almost blind with knowledge and the danger of not being able to look for or realize one’s flaws or mistakes.
I think that the most important thing I took away from this paper was to be careful with models. As shown with Fultz’s dish pan, you cannot assume the model tells the whole story. For example, Lewis’s surplus labor concept disregarded economies of scale so theorists could have “something they could model using available tools”. Also, with Rosenstein Rodan, economies of scale and dualism are a key component to making the model work, yet it is difficult to know if these assumptions hold true in reality. Both readers of economics and economists themselves should be aware that it is easy to make “untrue simplifications” when using models. Instead, when using models, people should be “self conscious — to be aware that your models are maps rather than reality” and to be "self aware" rather than "sleepwalkers".
Posted by: Christina Cavallo | 09/10/2020 at 06:01 PM
In light of our conversations this summer, like several of my peers I was excited to see Krugman write about economics as a social science. I think the interdisciplinary aspect of economics, and its status as a social science is essential to understanding the ways that our economic models are somewhat limiting in what they can tell us- the math only gets us so far, in despite of the fact that it becomes so important when attempting to explain things. I really appreciated Krugman’s examples related to meteorology, a “hard science” in order to show how models and methodology are often created. I found the following quote to be especially impactful: “The cycle of knowledge lost before it can be regained seems to be an inevitable part of formal model-building.” As Krugman explains, this is certainly at least partially what happened in terms of high development theory. I wonder what would have happened not only to the field of development economics, but also to the entire world and developing countries if there had not been this period of “intellectual waste” as it relates to development economics. If development economic models had been created in the 1950’s as opposed to the 1990’s, would we have been able to make greater progress in developing countries if we had been able to fully understand economically what was happening there? Would the world be pushed 40 years ahead in terms of development? As Krugman explains, there is nothing that can be done, but it would be interesting to know what the state of developing countries would be and how the field of development economics could have changed.
Posted by: Olivia Indelicato | 09/10/2020 at 06:08 PM
What stuck out to me most from this essay were Krugman’s references to maps and models outside of economics and then his comparison of these maps and models to the discipline of economics and its evolution of knowledge and use of models. I was particularly fascinated by his discussion of the ‘evolution of ignorance’ and the evolving European map of Africa. I found Krugman’s closing statement, that ‘a temporary evolution of ignorance’ may be the inevitable price of progress, particularly intriguing. He asserts that during a period in which technique improves and standards of rigor and logic rise, there will most likely be some loss of knowledge and an unwillingness to confront concepts that ‘the new technical rigor’ cannot yet reach. He compares the evolution of European maps of Africa from the 15th to 19th centuries to the discipline of economics between the 1940s and 1970s. This comparison certainly helped clarify his point about the evolution of economics and the discipline of development economics more specifically, and it even seemed logical and probable to me at first, but by the end of the essay when he restated this idea in his conclusion I found I didn’t believe it—and didn’t want to believe it. Maybe it was his use of the word ‘inevitable,’ or maybe ‘ignorance,’ which is kind of a buzzword in our society today, but I find it hard to trust in the truth of Krugman’s assertion. While I cannot deny that progress is never linear and is increasingly complicated in a complex and rapidly changing world, it is difficult to believe that our society today can and ought to knowingly accept stretches of ignorance, even if we know (or think we know) that ultimately it is likely to lead to better models in the long run. Such ignorance would likely have tremendous human impacts—on individuals, cultures, minority populations—which it appears Krugman does not really consider.
Overall, I do not believe in the inevitability of ignorance as the price of progress, nor that such ignorance would facilitate the production of the best models in the long run. Krugman states that ignorance arises at least in part because improved technique raises the standard for what is considered valid data, thus limiting what is included in the model. However, one of the main reasons I am hopeful we can avoid ignorance is that our society today is becoming increasingly able and willing to consider and dig deeper into complex quantifiable data as well as listen to and seek to understand qualitative data and diverse voices. Finally, I think that to make the best models for the world we live in we cannot afford periods of ignorance, as we increasingly benefit from filling in holes in our knowledge and understanding through improved communication and greater opportunities to engage with a variety of perspectives as well as facts and hard data from a growing number of sources.
Posted by: Sarah Hollen | 09/10/2020 at 06:45 PM
The concept of “valid data” is what struck me the most in this paper. As someone who takes great interest in statistics, I constantly am evaluating the collection and quality of data used in studies. When it comes to more qualitative data, I often find myself mentally invalidating studies where people have to rate on Likert scales or other relational measures. However, this data should still be taken seriously if collected properly even if it is not the most precise measures. It has been my own struggle with the “social science” aspect of economics. Like the high development theorists, I am more likely to trust a theory that I can see the mathematics behind. During my time studying economics, I have learned that there is often a bigger picture that has yes to be formalize din such a way, but it can still be accepted as valid. I appreciate that in economics as pointed out in this paper, that sometimes getting “very close” is in fact close enough. From a policy point of view, one does not need the exact amount needed to fix a problem but rather the ballpark as a starting point for resource allocation. In math, it is crucial to cover all your bases and leave no stone uncovered. The importance and beauty of economics is the importance of understanding the human experience and not just the numbers while figuring out how to quantify that experience.
Posted by: Emma | 09/10/2020 at 07:03 PM
I found this reading to be highly enjoyable and very engaging. Krugman writes very persuasively about the role of models, their benefits and their limitations. He illustrates the importance and the usefulness of simplistic models, and his warnings about mistaking these maps for reality rings true, I especially liked that the author described the success of Murphy et al. in illustrating the Big Push model that was first formulated by Rosenstein as a consequence of them “daring to be silly,” i.e. of them going against popular methodologies, or of taking an all-or-nothing attitude toward economic modeling. By working with the tools and procedures that have been established, we can hope to effect the formulation of better models and a wider selection of tools. The trend that Krugman suggests by which ignorance must be experienced along with knowledge is another intriguing aspect of this read. It essentially describes the age-old process for learning anything new: trial and error. And, like Krugman urges, it would be a mistake to disregard simplification of reality for fear that we may lose the facts, because simplification of reality is the only method by which we can ultimately utilize those facts. After that, hopefully we can build back into our theories and our understanding of economics any truths that have been lost along the way, and end up with more knowledge than with which we first began.
Posted by: Katie Timmerman | 09/10/2020 at 07:17 PM
I thought Krugman made several insightful analogies in his discussion surrounding formal models in economics. Specifically, relating his map-making analogy to how economists overlooked certain non-formalized areas of economics was interesting. I’m surprised that high development theory was largely ignored even though nobody knew how to model it yet. Economists at the time must have known that perfect competition was no more than a simplifying assumption. If it were an assumption that held true 100% of the time, it would make sense to ignore the high development theory based on increasing returns to scale. Since this is not the case, it surprised me that economists would overlook something simply because of its lack of formalization. Even more recently, it’s pretty clear that other than the Lewis two sector model that didn’t necessitate economies of scale, the Big Push idea didn’t gain traction until it was formalized. Although Krugman tries to strike a balance between models and looking more broadly at the complexity of the world, he clearly states that he believes models are necessary in economics. This is of course true: as in Krugman’s analogy with the dish-pan, economic models are extremely useful in simplifying the world to a point that is understandable given our constraints on time, money, and intellect. However, I think economists should place serious effort at looking beyond the theoretical models (more so than listlessly looking for “the folk wisdom on clouds” as Krugman phrased it). In actuality, I think this is occurring. For example, the field of behavioral economics questions whether the assumptions of rational behavior as they relate to utility and profit maximization are even accurate. I suppose in the end I more or less agree with Krugman that it is important to focus on both modelling as well as paying attention to ideas that go beyond the classic models, but I would place even more emphasis on the latter.
Posted by: Danny Lynch | 09/10/2020 at 07:36 PM
I thought this article was incredibly insightful in regard to the growing quantification of economics. I have had many people tell me that economics is evolving into a science, based on simplifications and/or assumptions that may not be true. As an observer and student of economics, it seems to me that in order to defend the legitimacy of their ideas, economists have turned to number-crunching and complex formulations to motivate their research. However, like Krugman points out in Hirschman’s “Big Push” model, high development economics could have transformed at a more rapid rate and may have had a substantially greater impact on low-income countries had the criteria for legitimacy been lower. Through Murphy et al.’s publication and model, Hirschman’s ideas soon reigned comprehensible and spawned thousands of papers extending his ideas. This goes to show that although the formulation of social science is helpful to reproduce results, the subjective and convenient importance placed on assumptions like perfect competition prevent the entire picture from being understood. Personally, this paper and its implied criticism of over-formulation of economics reminded me of my own frustrations with classical models in Econ 100. To me perfect competition is nonsense, and yet this principle is treated as a law from physics and for a long time indisputable. Overall, the paper was extremely fascinating and made me wonder where we could be as a society had economists diverged from convention.
Posted by: Stelifanie | 09/10/2020 at 07:39 PM
I really enjoyed the author's discussion of the limitations of formal models, and regardless of those limitations, the necessities for those models. It feels rare that we as students get to challenge the concepts that are presented to us in classes, at least until the college level of education. However, Krugman makes a wonderful point in presenting these formal models as a starting point that isn't wholly reflective of the real world; as a couple of my peers have already mentioned, these models are meant to be built upon and adapted as we learn and are able to improve them.
One thing that stuck out to me in particular was Krugman's argument that "...the most vociferous critics of economic models are often politically motivated. They have very strong ideas about what they want to believe; their convictions are essentially driven by values rather than analysis, but when an analysis threatens those beliefs they prefer to attack its assumptions rather than examine the basis for their own beliefs." I understand the point that Krugman is trying to make in that political motivations can often contaminate the results of academic research, but I also feel that our beliefs (to be clear, I don't mean political beliefs per se, but in more of a gut-level or moral sense) can guide us to see flaws within the model. For example, if our findings point to a typically racist ideology, such as the argument that black people commit more crime, I think that on a gut level (as humans striving to be antiracist) we can reject that idea and see where the model might have gone wrong (because we know, for instance, that policing is concentrated in black neighborhoods, and that black people are more likely to be convicted of crimes).
Posted by: Joey Dickinson | 09/10/2020 at 07:42 PM
This reading did not only help me better understand the high development theory, but also gave me a stronger insight into the history of development economics and economics as a whole. Previously, I had not delved into the history of the field nor its evolution over time. To hear that modeling - which I had always believed to be the cornerstone of economics - only became a significant component of the field as recently as the 1950s is fascinating to me.
In my introductory economics class, I remember questioning how a model could explain such a complex situation in such simple terms. How could something so simple be accurate? Surely, there are more factors involved? How can one tell which situations this model applies to? After reading this article, I can look back on my previous curiosity and answer these questions for my past self. Even though such simplifications are unrealistic, simplifying a complex phenomenon allows economists to analyze it at a level that they can handle. From here, they can generate a model that might explain the phenomenon, giving us a better understanding of something so complex.
I found the African map comparison to be particularly interesting. This really helped me understand what exactly happened with high development economics in the 1950s. When the field of economics shifted towards a focus on modeling, high development economics got temporarily left behind; unable to model economies of scale, they simply could not keep up with mainstream economics.
Posted by: Ben Graham | 09/10/2020 at 07:50 PM
The aspect of this essay that I found the most interesting was how the fact that economics is a social science complicates the way the models are perceived. This is due to assumptions made within the models, which draw the ire of observers. He drew attention to the success of Murphy, which he credits to him "daring to be silly". Silly, being a deviation from the field's standard. It was interesting to think about the adverse impacts a discipline can have by leaders in the field having tunnel vision, and going against anything that doesn't conform to their view on what economics should be. By providing a simplification with his model, Murphy was able to advance the field.
It's interesting to think of the potential of developmental economics, both if the lag discussed in the paper didn't occur and the future of the field. Now that the ball is rolling, we might see significant advancements and research that might improve policy all over the world. Despite what the paper noted, that strong economic and good policy are far less related than we want, the potential exists of positive change as a result of work in the field.
Posted by: Matthew Todd | 09/10/2020 at 07:51 PM
I believe Krugman did an exceptional job in presenting the details to the story of development economics. I am especially interested in his discussion of how high development economics became a niche field which was largely ignored but still provides an interesting lenses through which to think about the world. The use of metaphor and model are useful tools in an ever-growing data driven world. Models in their definition are oversimplifications of reality which give greater insight and understanding, and Krugman does a wonderful job of framing the field of economics and how Hirschman’s works fit into the greater puzzle. Mathematics and statistics are of great importance when assessing certain hypotheses. However, a strong underlying theoretical framework goes a long way for assessing the problem. While econometrics teaches us to seek to capture every possible endogenous variable applicable to a certain research question, a broader approach can often bring fruitful discussion and a greater understanding. The Big Push idea is also quite captivating. While the model discussed in the paper only used labor as an input variable, the conclusions that for many developing markets the adoption of more effective means of production reminded me of our class discussion on why certain subsistent markets might not adopt new technology. Not only might they not have the capital needed to adopt the more expensive means of the production but there might also be an inherent risk factor to the transition to a new method of production.
Posted by: John Lavette | 09/10/2020 at 07:53 PM
This piece by Krugman is ultimately a critique of the shortcomings of models. Reading this paper, I was reminded of the conversation we had in class, in which Professor Casey explained that some analysts fail to answer even the simplest questions regarding what happens when a variable is changed in a model, because instead of explaining what happens in real life due to the change in the variable, they basically just answer, "When this variable in the model changes, the variable changes." (._.) This phenomenon is the loss in knowledge that occurs, the "narrowing of vision imposed by the limitations of one's framework and tools, a narrowing that can only be ended [with tools that] transcend those limitations." Of course, I knew that economic models (especially the simple ones we learn in school) are full of flaws since the world isn't as simple as a couple of lines and two axes, but I was disheartened to learn that even in advanced fields of studies and professions, it is difficult to create a completely accurate model that doesn't omit any aspect of reality: "The only exact model of the global weather system is that system itself. Any model of that system is therefore to some degree a falsification..." I came to realize that this is why the observer's judgment and compromise are so important, and why it is so important for us to understand the process of what is happening in real life that the model represents. Without this skill, one will only be able to observe what happens in the graph itself, rather than explain how different variables are affecting real people and movement.
Posted by: YoungJae | 09/10/2020 at 07:55 PM
While reading “The Rise and Fall of Development Economics” by Paul Krugman, the statement, “High development theory rested critically on the assumption of economies of scale, but nobody knew how to put these scale economies into formal models,” stood out to me as economic models rely heavily on assumptions. I am immediately reminded of our recent discussion of the Lewis two-sector model where, “Lewis assumed that the level of wages in the urban industrial sector was constant,” alongside using surplus labor (specifically mentioned later in this paper), and two other basic assumptions as the foundation of his development model. Krugman acknowledges the blindspots these accepted truths create, he insists models are still, “basically right.” The entirety of the paper focuses on the controversy that, “ any model of that system is therefore to some degree a falsification: it leaves out some (many) aspects of reality.” Difference between theory and application is a problem beyond the realm of economics: physics and meteorology were detailed as two strong examples. Krugman then emphasizes social science is no exception to this duality.
Posted by: abrahamr22 | 09/10/2020 at 07:56 PM
Krugman's paper explains one of the biggest issues that I have taken with economics since beginning the study of it 3 years ago. I constantly question how applicable something can be when it is based on silly assumptions. Krugman offers a simple explanation for this by saying that if everything had to be based on the full complexity of situations, then we would never have anything to go off of. I found his metaphor of the dish pan meteorological study to be particularly useful in helping me to resolve this bias. I thought that his idea of ignorance was very interesting, and very accurate. He says that as we gain more and more insight into something, then we develop this sense of feeling like our complex understanding must underlie anything which is valid. He uses the example of cartography here. As we gained better techniques, we began to ignore the information (though sometimes ridiculous information) by less reliable sources, and generated maps that would have benifited from these second hand insights. With economics we see the same thing, and it was this that really stuck out to me in the paper. As more and more complex economic models were developed in the mid 20th century, we began ignored those silly assumptions that would have actually generated a great deal of insights. in his conclusion, he poses the question of what if we would have been able to put aside the notion that everything must cocnform with our complex understanding? I think like im sure he does, that this is just the natural progression of things, especially when dealing with such a complex issue as development.
Posted by: Andrew Frailer | 09/10/2020 at 07:59 PM