« ECON 280 for Friday | Main | ECON 280 Outline for Monday and Wednesday »



Emma Richardson

I really enjoyed this piece. Several points were made in this paper that I found powerful: models are silly and unrealistic in many ways, models are fundamentally needed for economics ideas to be taken seriously, people think of the world in models (and thus there is no alternative for them), and that assumptions in models for science are more accepted than assumptions in models for hard sciences.

As a part-time student of economics, I have always struggled with the crazy assumptions that are made in models. There is pretty much never a situation with perfect competition or a logical consumer; anything you do to reduce the world to two axes is utterly ridiculous. I appreciated that Krugman empathized with Hirschman regarding the silliness of models and felt that this stance added to the paper’s value.

Another point that I had not previously considered with much effort was the importance in economics for something to be modeled. Despite being a senior with many econ classes under my belt, I had never given much thought to the ability of each econ concept to be modeled. Taking a step back now, I am able to see why concern about an unmodeled idea might arise.

The point made about humans thinking of the world in models really hit home for me. It is so obvious that I had previously never considered this point (much like my attitude towards models used in economics). It would be ridiculous for humans to try to understand the world in its whole form when it is so much more easily digestible in bits and pieces. This once again reaffirms why so much importance was given to models in the 1960’s and 1970’s.

The last point that really stood out to me was the one regarding differences in acceptance of assumptions for models in social and hard sciences. As Krugman described, “when it comes to physical science, few people have problems with this idea. When we turn to social science, however, the whole issue of modeling begins to raise people’s hackles.” For some reason, we are willing to accept the flat dish meteorology experiment, but would never accept the equivalent for social sciences. It’s a provocative comparison, and certainly deserves a greater deal of study.

Megan Philips

Krugman’s “The Fall and Rise of Development Economics” really helped digest the varying theories and models we read about in Chapter 3 of our textbook, some of which he touches on in the article. While reading the different backgrounds, setups, and especially the criticisms of the development theories, I found it hard to understand how all of the theories were building on one another. Krugman's description of the importance of models in their most basic forms was a nice step-back approach to tackling the models we have been learning. I failed to think about models in this way while reading the text, which highlighted more of the incorrect assumptions or outcomes of the work than the benefits of developing these ideas. When it comes down to it, we use models for the basic structure and patterns that they can indicate might be at work on a more complicated level in real life, which means that sometimes the theories with less plausible assumptions can lead to deeper understandings later on. On a different note, I had never thought about why people often approach physical science and social science models so differently. As Krugman highlights, there are emotional values attached with studying social humanity, which can explain the extensive detail and criticism that often come with the economic models, like those we have discussed in class and in the readings, that physical science does not receive to the same extent. It's really interesting to think about how this shift in topic can really change a field of study. I know that I will think about and look at these theories differently after reading his insight on why development economics has changed over time and how we receive and study it.

Michael Adams

I found Krugman's article to be both interesting and informative. His use of the African map and weather metaphors helped me to wrap my mind around the somewhat abstract idea of the evolution of understanding in economics following the widespread adaptation of models. While Albert Hirschman had some great insights, it took the later development of more sophisticated models to reaffirm his findings. In the meantime, some of the understanding into development economics that Hirschman provided was lost. Krugman made a good case for the emergence of rigorous models while acknowledging that models are just that, models.
On a related note, reading Krugman's discussion of the limitations of any model, no matter how well specified, reminding me of our earlier discussion of neoclassical economics and its relationship to development economics. In Economics 100 we studied the assumptions inherent in this model and discussed how they are not always accurate. Sometimes, however, when considering the behavior predicted by the neoclassical framework we forget these fundamental assumptions that may not necessarily hold true. This is important in the field of development economics as we consider the policy choices suggested by the neoclassical model, and perhaps implemented with success in Western countries, may been ill-suited to developing economies.

Lee Bernstein

When reading about the Big Push Theory, there are clearly many assumptions that are being relied on and possible faults within the model. There is one that stood out to me that applies to both this theory as well as some other development economic theories, like the Lewis Two Sector Model that was discussed in class. Krugman writes that “Obviously, there are three possible outcomes. If the wage premium w-1 is low, the economy always "industrializes"; if it is high, it never industrializes; and if it takes on an intermediate value, there are both low- and high-level equilibria” (Krugman 10). While maybe yes, over a long and stretched out period of time this will definitely start to happen. The part that I am focused on is the assumption that people will automatically move to where wages are higher in order to get economic gain. Are people automatically looking to just get more money, or will they be attached to their cultural and traditional ways of life? I wonder if people who are in more rural and traditional sectors are less likely to move because they just do not want to leave or uproot their family, or give up what they know, to go participate in something that they may not have the education or skills for. If this is the case, it seems to me unlikely that many of these models will hold true, because it is difficult to jump start the industrialization and growth of the modern economy if no one is moving to the urban areas and taking the jobs.

Elly Cosgrove

The first thing I noted about Krugman’s work is his use of anecdotes to explain concepts to the reader—which I really enjoyed. For example, he talks about “the evolution of ignorance” and the evolution of European maps of the African continent from the 15th to the 19th century. He talks about how the art of mapmaking improved over time making the coast of the African continent almost identical to maps today. However, the interior became an empty mystery that did not include any sort of information including rivers and cities. Krugman’s point with this anecdote is that improved cartography ultimately led to better maps, but there was a period of lost knowledge. Krugman relates this to economics between 1940 and the 70s. An increase in “standards and rigor” had improved the knowledge and understanding in some areas, but also caused economists to not confront or tackle other areas new knowledge could not explain or understand. Krugman talks here about the shift away of high development theory and the inability of economists at the time to put economies of scale into a model.

Krugman later mentions a meteorological theorist at the University of Chicago who studied the complexities of weather. He designed something to simulate global weather patterns using a dish pan, turning table, and heating and cooling elements. While this did not capture all the elements of the Earth, it created a simplified model that provided some insight into weather patterns. Krugman relates this to the social sciences in that economists between the 40s and 70s moved towards extremely complex ideas, arguments, theories and models that did not provide understandable insight into the developing world. Krugman encourages simple modeling in the social sciences as a starting point that can then be tested and manipulated. Krugman then provides an example of what he feels is an ideal model, which is a formal treatment of the classic model of high development theory, or Rosenstein-Rodan’s Big Push. Krugman says, “Our paper-and-pencil dish-pan -- our model economy -- consists of a set of assumptions about the supply of resources; technology; demand; and market structure.”

Overall, Krugman argues that high development theorists at the time were not able to express their ideas in tight models. He argues that high development theory does in fact make sense and we now adopt the intellectual attitude that Hirschman rejected in his work The Strategy of Economic Development, which is “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.”


Hirschman’s article pertaining to the historical path of development economics proved to be an excellent follow up to our discussions from this past week. His discussion of various different theories and models, many of which that we have touched on this week, as an informal timeline for the history of development economics helped me understand the topic, but more importantly, uncovered difficulties within the subject that extend to general economic thinking as a whole. His two points that resonated most following this reading were his discussion of African Maps as a mirror for historical economic thinking and his analysis of models as a whole within economics.
Hirschman’s comparison of economic thinking to the evolution of African map making helped illustrate the fundamental issue of validity within theories. Although the development models initially gained traction within the world of economics, as “the standards of rigor and logic” improved, theories that could be proven by this so called logic were accepted and many other ideas that failed to fit this validity mold fizzled out of relevancy. These events reveal human characteristics about trust and beliefs. This rise and fall of development economics also reflects the idea that being early is the same thing as being wrong. Whether or not the ideas surrounding development economics were correct upon their inception, if the general economic body couldn’t grasp the concepts within their current framework and understanding, the ideas could not be accepted. Hirschman’s commentary on models similarly stuck with me. The reason I enjoyed this part of the paper is because it reflects some of my thoughts surrounding certain aspects of economics. Although models can be extremely effective in helping understand trends within societies, the oversimplification necessary to make them accurate makes them far less applicable than many people believe them to be. The simplifications made to create effective models by no means mirror the complexities of the real world. So long as people keep that within their head when studying and applying models, no severe issues should arise.

Maddie Geno

I was really intrigued by Krugman's comparison of models in social sciences and models in physical sciences, and I'm not entirely sure if I agree with his criticism towards those who take issue with social science modeling. Krugman states, "When it comes to physical science, few people have problems with this idea. When we turn to social science, however, the whole issue of modeling begins to raise people's hackles." Perhaps many economists and other scientists object to extreme simplification in social modeling because of how irrational and complex human behavior truly is in comparison to weather patterns. With the extreme complexity and unpredictability that comes with human behavior, how could an economist simplify human decisions down to a single graph, model, or dishpan. We learned in Adam Smith’s “The Theory of Moral Sentiments” that people are truly irrational and often act in opposition to their own well-being for a multitude of reasons. With this irrational and unpredictable behavior, it seems unlikely that social science models could simplify human behavior down to its most basic form and still explain the patterns that occur in reality. I don’t mean to negate the success of many models in allowing us a better understanding of the way our world works; I just don’t think Krugman should be so quick to hail the success of models as they rarely if ever succeed in capturing the big picture.

Turner Banwell

I enjoyed this reading. We deal with economic models daily, so it is encouraging to hear Krugman’s perspective on the limitations of any type of model –economic or not – and it is refreshing to read his perspective on how useful and effective they are. One paragraph particularly stood out to me: “the important point is that any kind of model of a complex system – a physical model, a computer simulation, or a pencil-and-paper mathematical representation – amounts to pretty much the same kind of procedure. You make a set of clearly untrue simplifications to get the system down to something you can handle.” He later notes that if one succeeds at crafting a good model, then the end result is a better insight into why a complex system acts the way it does.

The primary reason this struck me is because, as economists, we heavily depend on assumptions to use our models to better understand why things might happen. However, I will admit it can be frustrating when these assumptions are more unrealistic in a real world scenario than plausible. Take the Lewis Two-Sector model we discussed on Monday as an example. If the two core assumptions the model hold – the cost for the migration of labor from the traditional to modern sector is zero and all profits are reinvested domestically) – then great, the model will graphically illustrate how the developing country will enjoy economic growth. But the reality is, as seen through the many Latin American countries that attempted this approach, the only way these assumptions hold is if there are actual policies in place to assure that they will. I think this serves as a good example for all models in general. If the assumptions hold then models provide a reasonable explanation for why something might happen. The reality, unfortunately, is that many times the assumptions we use are unrealistic and do not. Regardless, assumptions are critical for any model to adequate serve its purpose – something I’ve come to learn and accept is essential to economics.

Cordelia Peters

I loved reading this article, especially following our discussions this week about different models. Each model that we discussed had certain key assumptions; it seemed as if these assumptions were pivotal to the outcomes of the model, but also were the primary weaknesses of the model. I found each model's oversimplification of the world to be unsettling and made me wonder how useful they really are. With the exception of China and other countries where the government enforces policies which can make the assumption of the models true, the models do not accurately depict many societies.

My key takeaway from this article was that while that much knowledge is lost when we develop a model, there are no alternatives. Instead, we must be aware of the model's shortcomings and focus on its essential point.

The anecdote about the European depiction of Africa was really interesting. It described how improved techniques in map making led to loss of information about Africa. In my global politics class, we recently read an article about how the rise of mapping technologies led to the emergence of the state. We also explored how Europe and the US are often depicted disproportionally larger than South America and Africa; the map reflects western feelings of superiority. Also, there is no rational reason behind why Europe is always drawn above Africa-it could be the other way around. Maps are widely distributed and the way that they are drawn effects the teaching of students in school and the perceptions of younger generations. This relates to Krugman's article because the models that we use influence the way that we perceive certain institutions, countries, economic policies etc. A model is easy to teach, and something that is simplified and reproducible is more likely to endure (just like maps where Europe is drawn larger). In this way, simplified models, however flawed they may be, continue to play a big role in economic policy and research.

Nick Anders

One of the things I liked about Krugman’s paper was his view on the role that models play in the world. He argues that for all areas, the only perfect model that exists is the system itself, such as the global weather system. All models that try to replicate this system, no matter how “good” they might be, are incomplete and have “some degree of falsification”, as many aspects of reality are left out of consideration. On top of that, as models become more sophisticated and accurate, there is a level of ignorance that comes as well. He gave the example of African cartography and explained how as maps of the African coastline became more precise, the interior of the continent was left essentially blank, leaving out rivers, mountains, etc. His point here was that as knowledge about economics grew in many areas, some areas were neglected due to a lack of understanding and explanation. Krugman argues that “a rise in the standards of rigor and logic led to a much improved level of understanding of some things, but also led to an unwillingness to confront those areas the new technical rigor could not yet reach.”
Furthermore, I liked how Krugman was very straightforward regarding the vast number of assumptions needed for models to work and how rarely all of them are met. Going back to some of our earlier classes, I recall talking about some models and the assumptions that went along with them. However, some of the assumptions seemed a little farfetched and being in a situation where all of them were met was highly unlikely. Krugman recognizes this and highlights the fact that models are designed to be predictors. He suggests that the important thing to remember is that “models are maps rather than reality.”
Finally, Krugman raised a question at the end of his paper that had struck me earlier while reading his work: Where would not only high development theory, but also economics as a whole be if we were able to avoid a large level of ignorance between the 1940’s and 1970’s? One can only speculate what could have been, but Krugman makes an interesting point regarding this, saying that ignorance is the cost of progress. In order to make strides in understanding the complexity of the world we live in, sacrifices are going to have to be made; ignorance is inevitable. This view raises another question: If development economics had not been pushed to the side, would something else have been and if so, what would it have been?

Lucas Longo

I really liked Krugman’s discussion of models, a subject that I have never felt like I fully grasped. His analogies between meteorology and economics I thought were especially helpful in getting his point across in regard to the benefits of models and why we use them. I had never really thought of modelling as making “a set of clearly untrue simplifications to get the system down to something you can handle”, but once I thought about it that’s really what it boils down to. I liked how he then compared the use of perfectly competitive markets to Fultz’s dish pan model, which helped to shed light on the thought processes of pre-1970s economists. Additionally, I found it funny that there is pushback to the use of models when it comes to the social sciences, as I’m skeptical as to how simplifying human society in such models threatens one’s dignity.

Another point I found worth noting was Krugman’s staunch defense of models; he states bluntly that “there is no alternative to models”. I thought one of the strongest arguments for models that he presented was how models can show what might be true. This, Krugman asserts, engenders further research, whereas a verbal argument presents something flatly as true, which can create a false confidence in its validity. I also liked how Krugman qualified his argument when he noted the issue models have in that they can cause a narrowing of focus. Sometimes, therefore, it is important to look at issues more broadly, as exemplified by the Norwegian scientists in 1919.

Maggie Phipps

I appreciated this reading because it’s the first text I’ve come across that has acknowledged some of the downfalls and confusion surrounding models. I wish I had read it early on in my Econ 100 course. Many of the complaints about models that were recognized in this paper were the same criticisms that my classmates had about the models we were learning about in the introductory course. I noticed that students who were struggling with basic models, such as supply and demand curves, monopoly curves, or the PPF model, just couldn’t accept the simplifications that these models required. The section in this paper called “Metaphors and Models” would have really helped with this sort of confusion. I really liked how Krugman was able to explain the process of modeling in one sentence: “You make a set of clearly untrue simplifications to get the system down to something you can handle; those simplifications are dictated partly by guesses about what is important, partly by the modeling techniques available.” Krugman also explains that for every model, we gain something, but also lose something. While we do have to make simplifications, and thus “lose” certain factors to create a model, Krugman explains that we gain insight on why the complex system behaves the way it does.
Of course, if we’re losing more than we’re gaining, the model isn’t worthwhile. Other complaints that Krugman noted were that models can be politically motivated, or even insult our intelligence with their simplicity. I definitely agree with this criticism that models can be politically motivated, but I don’t see that as a flaw with models. Rather, the person creating the model and demonstrating arguments with political bias should correct themselves. I do not agree that models insult our intelligence. Fultz’s dishpan model was extremely simplified, but it told us something about the global weather pattern. If a model can tell us something, it’s only strengthening our intelligence. I agree with Krugman’s praise for models and their ability to help us understand complex systems through necessary simplification.

Tanner Smith

The most interesting paragraph of this article to me came under the section of "The Evolution of Ignorance," where it states that 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 rigor could not yet reach. Areas of inquiry that had been filled in, however imperfectly, became blanks." This part struck me because of my studies into the baseball analytics revolution, and the similarities that I saw. In many of the initial baseball analytics studies things that could be immediately quantified, such as a skill for getting on-base or slugging, were highly valued, while other things that could not as easily be quantified, such as defense, batted ball quality, and baserunning, were dismissed. It was clear then, as it is today, that defensive ability is valuable, but back then we did not have the ability to quantify it, so they allowed it, as Krugman calls it, become a "blank," or "a dark area." This shows me that in cases where we have a clearly relevant variable, but do not know how to quantify it, we should not dismiss it out of hand; instead we should try to develop new methods to do our best to quantify our blanks. When the methodology to quantify defense was developed in baseball analytics, even though it is still far from perfect, it was acknowledged in those circles once again that defense was and still is valuable. The value of models and analytics cannot be disputed, but the hubris that occasionally comes with it can misdirect well-intentioned analysis. Within economic modeling there needs to be an acknowledgment of the limitations of models, but that does not mean that the models need to be thrown out or completely dismissed. Gathering information or doing the math is the easy part; the hard part is using and interpreting that data in responsible ways and discovering the capabilities/limitations of that type of analysis. Within that hard part comes in asking the right questions: why do we weigh things the way that we do, and is there a better way to do it? What are we potentially missing? These type of questions lead to good analysis. The math is not the problem here; human limitations and biases that come in interpreting and using data are.

Claire McCutcheon

The casual tone of this piece and colloquial language helped shed light on some of the more complex ideas of economic modeling. Like other commenters, one of my biggest resistances against economics is the reliance on assumptions, but Krugman makes a good argument for the importance of boiling down the complexity that is human behavior to a manageable state, and using it to gain insight. It is quite funny that people will fight economic modeling, but welcome it in physical science, even I am guilty of such (call me a diet economics major). Economic modeling is a necessary evil if we want ourselves to any understanding at all.

This article brought to mind our conversation last week about modern sector versus traditional sector enlargement/enrichment. While we discussed the tactfulness of traditional sector enrichment in countries who have a largely agricultural economy, and then transitioning into modern sector (China example), Krugman emphasizes the importance of the size of the modern sector market. In order for modernization to be self-sustaining it has to be adopted at a large scale, or an economy can be “caught in a trap.”Rodan spoke to the danger of industry isolation, something I feel plays a large role in the struggle to development. West Virginia, for example, spent decades as a coal dependent state, and is now struggling to support its residents in a post-coal reality. Diversification outside natural resources is the current focus for West Virginian politicians, with nods to marijuana and tourism. While one might not see West Virginia as a traditional “developing economy”, I would argue it is a perfect example of the danger of industry isolation.


In "The Fall and Rise of Development Theory", Krugman makes the argument that development theory was frowned upon and disregarded for thirty years because in many ways it didn't fit with the evolving economics of the 1950s. What Krugman calls "high development theory" is the idea that modernization breeds modernization and that weakness breads more weakness. This is not a complicated concept. When certain countries find all the crucial components for growth in place they can find themselves growing exponentially. On the other side, weakness follows a similarly sloping trend line, just in the opposite. This simple concept is explained by Rodan's Big Push ideology, which argues that it takes a coordinated big push forward for an economy to get on the right track towards a developed state.

The problem with these simple concepts is that economists who were too focused on complicated equations and were more mathematically driven were unable to accept the conclusions of high development economists. Krugman goes on to criticize these economists but showing why it is acceptable to use models and shortcuts to make broad observations about the state of developing countries. I agree with his arguments here especially, in that there is no need to make economics an overcomplicated science. Economics can be so useful to policy makers and if they understand basic modeling, while understanding the limitations and assumptions made in the models, they can apply the science of economics in a powerful way.

Charlie Bovard

This piece by Krugman does an excellent job drawing upon the struggles of high development economics modeling in the mid-20th century. I felt like his comparison to meteorology was helpful in understanding how economists got so bogged down by incorporating complex elements like economies of scale into their models that they didn't broaden their view of the models and address the larger issue: market structure. I found it frustrating in my microeconomics class my freshman year that our basic models were based on a perfectly competitive market that is almost non-existent in the real world, and I think Krugman makes a good point that there is no general theory dealing with the most common market structure we face, oligopolies. He makes an interesting point on the relationships between modeling and methodology, which makes me think about how there's a constant cycle of searching for model and studying historical analyses in order to develop new models. His quote "It is said that those who can, do, while those who cannot, discuss methodology" makes me wonder how helpful methodology is to developing models for economic growth, because of his implications that referring to methodology is a poor outlet to rely on when dealing with economics.

Andrew Zandomenego

In a study that relies so heavily on modeling, it’s surprising to learn about the history of development economics and its struggles in finding a solid model to outline the discipline’s economic theories. Yet, development economics seems unique in the sense that, as Professor Casey mentioned on Wednesday, there is way more that needs to be understood about an underdeveloped nation than its general economic values (e.g. GDP, unemployment). This is not to say that models are not important in the study, because they certainly are in demonstrating how different approaches towards resolving underdeveloped situations work in a simple economy. But, more important is the holistic approach of evaluating all pieces of the underdeveloped nation, since they are all unique in different ways, in an effort to produce a course of action.
What I mean to say is that I understand why the production of a useful model was difficult in development economics. It is simply an area of the whole economy where assumptions can be all over the place. For instance, I noticed this when we talked about modern-sector enrichment (even though I wouldn’t call it a strict model). This is a case where outcomes on income are unclear because we just aren’t exactly sure how different nations (and cultures) will respond to different policies or investment alterations. Nevertheless, The Big Push takes an interesting approach, and I particularly like the distinction between modern and traditional sectors as we have discussed extensively in class. I wonder if the lag in model development could have been due to the slow adoption of “technology” up until the 1970’s. Maybe when computers and intelligence came into play, the modern sector really became the modern sector and technology finally came into serious consideration as a large economic factor in development.

Katrina Lewis

Krugman makes two important points in “The Fall and Rise of Development Economics” that I think everyone studying economics should understand. The piece is especially applicable for our class since he illustrates his broader assertions using economic development models, but I would argue that this piece has value outside of the specific field of development economics. I think his piece would help first-time econ students especially because he explains the complexities of using models in a readable, digestible way through his use of anecdotes.

My main takeaways from the piece were that (1) models are unrealistic in many ways but (2) they are nonetheless necessary in the field of economics. Krugman does not hide that he thinks models are “silly” in the sense that they are often based on “clearly untrue simplifications.” He notes that economists have to make assumptions when designing models because, as he explains through the anecdote about Dave Fultz, “the unrealism of Fultz's model world was dictated by what he was able to or could be bothered to build -- in effect, by the limitations of his modeling technique.” I think the fact that he acknowledges the shortcomings of models makes his ability to advocate for their importance far greater because if he only addressed the pro-model side, it would be easy to undermine his argument by pointing out the deficiencies of models.

Krugman goes on to state that shortcomings aside, models are still necessary in the field of economics because they ensure that findings are “codified in a reproducible -- and teachable -- form.” Moreover, so long as more insight is gained than is lost, a model is doing its job. He importantly defines what makes for a good model because his definition gets at the commonly held belief that a good model has to represent reality. Rather, “it is a good model if it succeeds in explaining or rationalizing some of what you see in the world in a way that you might not have expected.”

Tom Kellogg

Krugman describes the economic background of that has spawned much of development economic theory through the 1950s to 1970s. He describes high development theory as “modernization [breeding] modernization,” and concludes that some countries, typically those lagging behind the rest of the world, are not included in this modernization cycle and “remain stuck in a low level trap”. Krugman, like the article on institutional barriers we read, concludes that government activism is a great way of breaking out of this trap. Krugman also points to the modern sector as being “more productive than traditional,” but concludes that adoption of modernization in developing countries needs to be started on a large scale, so as to make it self sustaining. Krugman is also pretty bearish on many of the formal economic models used during the 50s, stating that they were traditional based on large economies, and the inherent assumptions of those models failed to hold up in developing, smaller economies. Krugman’s model favors modern sector development for developing economies, stating that as long as the advantage of modern production is sufficiently large, then it is favored. However, he goes on to emphasize that it is important to look at the profitability of individual entrepreneurs, concluding that individual entrepreneurs will continue to produce, but they must take into account the paying of a premium wage (premium over wages in the traditional sector) which is determined by the ‘decisions of all the other entrepreneurs” as well. He sees that development economists created their models fully ignoring many of the factors that spur development in the modern sector (they framed their models in terms of highly developed countries, undeveloped countries). I value Krugman’s model as an effective way of demonstrating how employment of a labor force in the modern sector definitely creates a greater output than traditional sector employment. Furthermore, I appreciate that he understands that the assumptions behind these models may fall through in developing economies.

Maddox Wilkinson

I very much enjoyed reading this article for its insights and clarifications into economic models and how they have evolved. After reading this, it is much more clear to me our discussions in class relating to why these economists were thinking the way they had been. With only a perfectly competitive markets model to work with, it makes sense why there was so much change throughout the years as that model evolved and new ones emerged. I particularly enjoyed Krugman’s explanation of how sometimes technological advancements can lead to ignorance. I agree with what he is saying, however I believe it is not so much the technological advancements, but the accuracy they provide that can cause the ignorance he describes. Advancements that allow us to be more accurate cause an obsession with that accuracy that can drive out sound ideas on the bases of them not being backed by current modeling techniques. This paper really puts into perspective how some “truths” that economists have claimed throughout the decades haven’t held. This piece at least for me really helped me see just how limited economic modeling can be and how heavily assumptions are relied upon. At the same time though, without these assumptions we would have no holding models
Krugman also stresses another crucial point in that without modeling, no matter how good your theory may be, it will eventually be forgotten and pushed to the peripherals of economic theory. This paper serves to remind that economic theory should always be revisited, no matter how long it has “gathered dust in the economics attic.”

Heeth Varnedoe

Krugman’s discussion of the “Fall and Rise of Development Economics” provides powerful insights into the nature of economic modeling and development theory. As a student of economics, models can often be frustrating, particularly the assumptions that are imbedded in every model. However, like Krugman’s dish pan example illustrates, there are real insights that can be gained from a seeming oversimplification of reality. While this paper primarily deals with the history of development economics and its almost half-century dive into obscurity within economic literature, I think that it highlights one of the most fundamental challenges pertaining to development and development theory: human behavior and markets are often unpredictable and do not always exhibit the behavior that theory dictates. There are countless examples pertaining to international development where a model would predict a certain behavior or outcome, but we observe the opposite or an unforeseen outcome. For example, many people might assume that providing people with mosquito nets for free would produce better health outcomes and make people more productive in the economy. However, there have been instances in which such a service was provided and people simply did not use the nets or used them for purposes other than personal protection from mosquitoes. The unpredictability of human behavior is perhaps one of the greatest challenges for identifying effective development mechanisms. For this reason, I find the work of Esther Duflo to be extremely fascinating, as she has pioneered the method of Randomized Controlled Tests to help policymakers glean real insights about human behavior and make better informed development decisions.


I think it is interesting to see how this article mirrors some of the concepts we have mentioned in class. Particularly, Krugman mentions how development policy sometimes does not truly reflect what the models are expressing, that basic assumptions are not being held constant to see the intended effects. Krugman cites that even in various aspects of science, some disciplines require models with extreme precision while others are based off of intuition and, quite frankly, little specific detail.

Furthermore, I found it really interesting and unique how he compared the evolution of development economics to mapmaking in Africa. And although his point is a little confusing at first and I believe could use a bit more explanation, I think it is a valid point. Essentially saying that the rise in “smarts” and “rigor” led to advancements and model creation, but failed to tackle the gaps that allowed these models to ring true. In my opinion, he is talking about the assumptions that are necessary and how exactly to construct policy to force it true. Which circles back to what I commented above about how we are constantly discussing that the key to these models is to force underlying assumptions to hold true.

On other note, I like his brief discussion about how development economics has lagged in accumulating a heavier mathematical basis. I don’t necessarily view this as a “bad” thing for development economics, primarily because there are so many qualitative factors that cannot be fully and accurately measured with values. Culture and history are two glaring examples that often play into the development discussion that don’t particularly have a unit value attached to them.


The quote from the reading that resonated most with me is that “...we are all builders and purveyors of unrealistic simplifications. Some of us are self-aware... Others…. are sleepwalkers: they unconsciously use metaphors as models.” This abridged quote resonated with me so well because my greatest frustration in studying economics is the drawing of conclusions (especially policy decisions) using models that hold too many unrealistic assumptions to be true. I believe that we can learn a lot from models and that models are valuable, but not without recognizing the shortcomings of those models. Krugman makes several great comparisons between physical science and social science that really exemplifies the double standard within the sciences. With basic natural science modeling, few would negate that the results are applicable to real life, but there’s always the understanding that laboratory conditions are not the same as actual real life conditions. It seems that in economic modeling, that understanding of the model’s shortcomings are never mentioned. When utilizing a economic modeling, it seems that everyone must following the model perfectly or completely dismiss it for a lack of realism. Few onlookers of economic research seem willing to accept the advantages and shortcomings of economic modeling simultaneously— despite the fact that they have so much to offer if considered in reasonable light.

Another idea that Krugman brought up that I hadn’t considered was how improvements in technology or modeling lead to short run deficits in areas that haven’t been reconsidered with the new modeling strategy. Paired with the analogy of the maps of Africa, I think it’s important to remember that economics is still a developing social science. New discoveries and more accurate models are being found and developed. I subconsciously consider the natural sciences to be the only sciences subject to change— perhaps because our modeling seems so concrete and definitive. Alternatively, there is so much that we don’t know about the natural world that it seems intuitive that we discover and develop new things. The Krugman reading has really encouraged me to remember that economic research and economic theory development are not completed processes, but rather, ongoing projects.

Paul Callahan

Krugman's commentary was very interesting and his discussion on a wide range of topics shined a light on some important problems economics has and continues to face. His comparison of map making from the 15th to 20th centuries to economics between the 1940s and 1970s was particularly fascinating. There was a period in time where oversight and even ignorance led to a empty gap in knowledge.
Krugman's continuing commentary on the problems of high development theory and its shortcomings was interesting. From the article we can see that economic modeling has been a problem for decades and that it is necessary to make key assumptions in order to generate any meaningful work. The formality with which many economists seem to operate limits them in their ability to visualize markets and problems. With the science example about meteorological theorist David Fultz we see it's clear that simplifications are necessary in order to wrap our heads around what we are seeing the only problem is that it seems harder for people to want to apply this to the social sciences. What seems evident is that over time we have been able to better understand the power of economic modeling and its restrictions but we have learned how to handle them in order to produce more meaningful work. It is clear that fast developing social standards have been an impetus of change for economic theory.

Sam Boxley

Krugman’s piece on the fall and rise of development economics and the role that economic modeling has played in the field’s trajectory offers a great change of pace from the ordinary economic paper. Krugman offers insight into the history of not only development economics but economic modeling as a whole, providing us with a pragmatic take on the function of economic models and theory that I feel like we seldom see in economics. People often get so caught up in the intricacies and specifics that they fail to see the greater point, which Krugman so satisfyingly reminds us of: Its just a model. There is no way to appropriately boil down the complexities of human behavior and thinking into a series of theories and graphs, but from dumbing down different phenomena through the process of assumptions, theories, and modeling we are able to extract what is truly important. It enables us to grasp difficult concepts because, as Krugman says, “You make a set of clearly untrue simplifications to get the system down to something you can handle; those simplifications are dictated partly by guesses about what is important, partly by modeling techniques available.” The purpose of models are not to act as some sort of end all be all solution to a given problem, but rather to give us a starting point to build off of.
Krugman’s comparison of early mappings of Africa to the path that development economics has taken through time was thoroughly enjoyable and made the main points he was trying to put forth both relatable and easy to digest. It helps to explain why the works of the likes of Albert Hirschman, once a prominent development economist, have faded into insignificance. The idea that “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 area the new technical rigor could not yet reach” is something that can be applied not only to social sciences, but many other facets of life as well. We often reach a certain point where we demand such an unequivocal validity to ideas and theories that anything that cannot be proven in such a way is not only deemed unsubstantiated, but irrelevant and useless. Its a very potent comparison that I think sheds light on some of the shortcomings associated with advancements in all professional fields, but as Krugman says, is often “an inevitable part of what happens when we try to make sense of the world’s complexity.”

The comments to this entry are closed.