ECON 280 reading for next Thursday

http://web.mit.edu/krugman/www/dishpan.html

 

I will also post a short reading and video to guide our discussion on Tuesday.

Comments

[Daniel Rodriguez-Segura]

Aside from describing the evolution of the discipline, I thought that the Krugman article dealt in a very interesting way with the politics of academia and the repercussions that this might have on the expansion of our body of knowledge. The dichotomy presented between the “inevitable” moving towards more (mathematical/formal) models and the more abstract concepts, like those presented by Hirschman, is not at all portrayed as constructive competition between two schools of thought, but as a vicious process that pushed one of the two out to irrelevance. This seems to have resulted in little progress in the discipline, along with lost potential of researches that perhaps had correct ideas but “could not quite express in fully worked-out models”. Before reading this, I had not fully thought of how academia could push researchers to its periphery just because they were not using the standard frame or were challenging the status quo, and this article really put some flesh to the concept by giving a fine example of the possible consequences. It actually reminded me of a quote I heard Noam Chomsky say once, referring to most academics as a “herd of independent minds”. This is not to fully disagree with Krugman: I think that standard, mainstream models can certainly expand our knowledge, but perhaps Krugman does not acknowledge how much more could have been achieved had the core academics not behaved (perhaps “rationally”?) as this “herd”. Yet, Krugman’s recommendation of continuing to work with models without discarding other ideas (potentially even disciplines) because of the way they are approached seems to be an enriching compromise between the two views in academia, and one that I am glad to continually see in my undergraduate Economics classes.

[Austin Gilbert]

While reading about Hirschman’s development theory and his experience publishing and explaining his ideas, I was shocked to see how easily and quickly other economists bypassed his work because he failed to condense his claim into a formal model. Also, it was interesting to see how intensely economists sought perfection between the 1940s and 1970s, as Krugman points out, “areas of inquiry that had been filled in, however imperfectly, became blanks.” This stood out to me later when Krugman described the degree of difficulty in creating models, as their restrictive qualities often prevented the construction of a truly perfect model. Though presenting a clear, simple model creates a more attractive product, the process of simplifying can risk excluding significant details. Therefore, I support Hirschman’s verbal style of presentation and agree with his decision to take a stand against mainstream pressures. Additionally, I appreciated the author’s concluding remarks addressing intellectual progress, as he recommends remaining open minded when faced with ideas conveyed without the formal structure that one prefers.

[Mitchell Brister]

The Krugman paper draws a clear line in describing the history of Development Economics between the High Development Theory, a time of simpler models and a more loose form of economics, and the more mathematical and empirical period that came after. Both, as the paper describes, have their pros and cons. The powerful takeaway from this piece that can apply to almost any aspect of life, is that one way doesn’t have to be right over another. That instead of a black and white mindset, the positives of each should be taken into account when addressing an economic problem. The aggregate supply and demand curves are an extremely rudimentary and simple model used to explain an extremely complicated economy, yet the general gist and trends seen in the model are extremely effective in understanding the economy. Mathematical models are always going to be better when they can be implemented, but just because a model isn’t as empirical and precise as another doesn’t mean it should be discounted. This paper did a great job of using an example from development economics to prove this point.

[Riley Stout]

This paper clearly demonstrates that mathematical models for economic analyses are useful, but do not always explain the entire situation and may leave some important details out. Krugman states that any model of a system has “some degree of falsification: it leaves out some aspects of reality,” and they are many examples of models presented in this paper that are not directly related to economic models. Krugman uses examples about the mapping of Africa and a dish pan filled with water to simulate global weather patterns. Both of these examples included assumptions that simplified each model, however, these assumptions left out factors that clearly affect the final result and/or outcome. These two models are then compared to most economic models between 1820 and 1970 which were perfectly competitive markets. It was very interesting to see two different models getting compared to perfectly competitive markets in economics. Krugman also writes that losing knowledge before it is regained “seems to be an inevitable part of formal model-building,” and this is idea can be applied to essentially every model in any discipline.

[Kasey Cannon]

"Model-building, especially in its early stages, involves the evolution of ignorance as well as knowledge." When reading this sentence, it reminded me of several economics courses I have taken here at W&L. On several occasions, professors have started off a class by saying, "So you know that assumption that you made in your previous class, well that is completely wrong and we will be making a new assumption in this class." This paper also made me wonder if there are any other alternatives to the simple modeling in economics, but as the author points out, "the problem is that there is no alternative to models." As demonstrated in the history of development economics, creating controlled, "silly" models is a very easy way for people to grasp the complexity of the real world. That being said, I agree with Austin's point about remaining open minded and supporting Hirschman's verbal style of presentation.

[Rachel Stone]

Although Krugman illustrates the Big Push model, he notes that high development theory temporarily disappeared due to the lack of a strong economic model. He makes the argument in his conclusion that good ideas shouldn't be bypassed because they haven't been formatted in the most frequently understood way. "Look for the folk wisdom on clouds," he says. In understanding development economics, one ultimately wants to aid nations that are trying to develop and advance. This notion brings to mind an article that I read by Jared Diamond called 'What Makes Countries Rich or Poor?'. In it he brings up many possible ideas to try to explain why some nations fail while others succeed. These include concepts such as good institutions (with regards to public policy, the market, etc.), the reversal of fortunes, and simply the luck of natural resources. While geography isn't something that can be changed, the idea behind "the reversal of fortunes" is something that economists could capitalize upon. Diamond describes the reversal of fortunes as such: "Among non-European countries colonized by Europeans during the last five hundred years, those that were initially richer and more advanced tend paradoxically to be poorer today." History shows that incentives to encourage people to do the work themselves and rewarding them for doing so enables them to prosper in the future. Again, this example cannot necessarily be explained through an economic model. However, as the study of the high development theory starts to gain traction, pulling from those who have already fleshed out potential reasons for the disparity between countries can provide good evidence in support of the economist's conclusions.

[Jack Masterson]

The first thing I thought of when I read the article was how it related in some ways to the article we read for class on Tuesday called "Behavioural development economics: A new approach to policy interventions." Much of that article talked about how previous versions of the World Development Report had ignored these three main principles because they were not traditional economic models saying, "Economists have long known that people are rarely as coherent, unbiased, foresighted, selfish, or fixed in their preferences as standard economic models make them out to be." Even though the three principles mentioned are all intuitive they were not included in policy decisions because they did not fit into conventional economic modes. This reminded me exactly of what Krugman mentions in his paper about how Hirschman and his high development theory were not taken seriously or even considered in academic circles for a long time because they "were having hard time expressing their ideas in the kind of tightly specified models that were increasingly becoming the unique language of discourse of economic analysis." While I understand Krugman's criticisms of Hirschman and his decision to break from mainstream economics I don't think that it detracts from the ideas he had. I really liked the idea that it would take economies of scale for the modernization to produce modernization and I am not really sure how a underdeveloped country would be able to modernize on a large scale all at once.

[Caroline Sanders]

Great minds think alike because Jack and I are definitely on the same page. While reading Krugman, I couldn’t help but wonder if the field of behavioral development economics is facing challenges similar to those of high development theory when it comes to mathematical modeling and acceptance in mainstream economic theory. If you think back to the article we read for Tuesday, the authors present a model of human decision-making (automatic, social, based on mental models) that is indeed the opposite of the assumption of rationality that pervades economic models. However, the authors assert that this more complex approach is important to the formation of effective policy because it better resembles how people actually think. We don’t necessarily need a mathematical model to understand or believe that this theory makes sense because we experience it almost on a daily basis. For example, you probably feel richer with a one-dollar bill in your pocket as opposed to four quarters – why? The feeling’s not rational, but it may be as simple as that’s just the way our brains work. Krugman discusses some of the politics of academia (as Daniel put it) that explain why some theories don’t pass muster with mainstream thinking and are therefore rejected. However, with new insights that challenge an assumption as fundamental as rational decision-making, why haven’t behavioral economic concepts made larger headway into how economists model, and more importantly, teach basic principles like choice and utility? Krugman writes that creating effective models requires “a willingness to do violence to the richness and complexity of the real world.” However, it does not excuse completely disregarding concepts that enrich our understanding, but don’t neatly fit the model.

[Benjamin Bayles]

As I read I was increasingly impressed by the difficulties that arose in making accurate models. Krugman implies the models have progressed greatly over time in their usefulness and popularity claiming “In the 1940s and even in the 1950s it was still possible for an economist to publish a paper that made persuasive points verbally, without tying up all the loose ends… such a paper would have been unpublishable any time after 1970,” yet even models today have “some degree a falsification.” This caused me to wonder, as we become increasingly stringent on the requirements of good economic modeling, if the models we use today will carry any weight to the economists of the future. The allusion to mapmaking seemed to be an appropriate corollary. Just as “the improvement in the art of mapmaking raised the standard for what was considered valid data,” will the standard for economic data raise to the point where today’s research is no longer “valid?”

[sara.cook]

I found Krugman’s dishpan and map of Africa analogies to be insightful and beneficial to my understanding of models and rise and fall of development economics. The dishpan analogy (for me) highlighted the difference of physical and social sciences. In physical sciences, modeling is more fact-based; however, in social sciences – as Krugman pointed out – there is a lot more bantering in creating “good” models. I think this stems from the idea that society is always changing. It is rare to model two exactly identical economic situations, in which the outcomes are also identical. With that being said I thought Krugman put too much emphasis on the importance of model building in social sciences and may have left out parts of the history of development economics. I would have liked to have heard more about Hirschman’s verbal style in the paper. Models are crucial for economics; however, they do not show the whole picture and I think a balance between verbal presentation and modeling is most effective.

[Austin Tamayo]

The article by Krugman gave an insightful and interesting look into the history of development economics and its accompanying theories. What specifically peaked my interest, and what has already been discussed briefly by my classmates, was the comparison of model-usage in both social and physical sciences. Krugman discusses that models were utilized to describe high complexity systems, and that successful models were used to explain or rationalize something that was previously unknown. Although models are useful in explaining certain phenomena found within the world, Krugman also argues that models should not be the end all be all for validation of academic theories, especially within the subject of development economics. He urges others to “not to let important ideas slip by just because they haven't been formulated your way”, but rather, be open to ideas and theories that can not be necessarily explained through models. Models, Krugman argues, can not accurately describe the entire complex system, but instead must be created with “compromise and judgement”.

[Rachana Ghimire]

This article really highlighted some of the thoughts that I have grappled with throughout my economics classes. As Kasey pointed out, we make a lot of assumptions in our classes to make things simple, but then as we progress into other classes, we make assumptions that seem to contradict the assumptions we made originally, or we learn that the assumptions are simply are not true. As the author put it, “You make a set of clearly untrue simplifications to get the system down to something you can handle.” Though the real world is much more complicated than what our model entails, the thought is that the model can at least tell us something about the bigger picture. It’s reassuring the article points out that economics is not the only subject that does this, though economics gets the most critique. The example of the dish-pan in the natural sciences was an interesting one, and something that I had not really considered before. The author makes a valid point when stating that simple assumptions in physical sciences do not get as much resistance as I feel the social sciences get. However, Sara makes a valid point when stating that people and society are always changing – so maybe it is justified that we are more hesitant about accepting simplified models. I personally have had the thought “but this isn’t actually what happens” many times when looking at simplified models, but which is worse – having no model to explain something or having a simplified model that could have some wrong assumptions but still paints a general picture of what could be happening? I would argue that having a simplified model that has some wrong assumptions at least gets us started to have a discussion or a debate in order to move in the right direction and create a model that is more accurate.

[Sarah Rachal]

As a science major, I have a strong appreciation for accurate, well-controlled models. However, I was quickly swayed by Krugman’s argument for more relaxed restrictions on modelling in the case of development economics. While many of Hirschman’s ideas sound logical, especially the importance of backward linkages and forward linkages in developing industries, it is difficult to accept their validity without a basic model. It is unfortunate that these intriguing ideas were laid aside for so long because of Hirschman’s choice to leave mainstream economics.

While Krugman’s model does make sense and provides some validity to Rodan’s Big Push idea, one must still question whether it would hold true given the many complex factors affecting a developing economy. Considering the difficulty of modelling so many inputs, the next logical step would be to conduct a randomized trial on the impacts of a Big Push program. I was able to find an interesting article about Jeffrey Sachs, a Columbia economist who set up a “millennium village project” in rural African villages. The investments in these villages followed a Big Push format but were more focused on human capital than physical capital. The article concluded that these interventions did not significantly impact household income and that the unrandomized trial was not particularly valid. However, I would be fascinated to find data on a more industrially-focused Big Push in a larger region, which would be more indicative of the macroeconomic effects of these interventions. http://www.economist.com/node/21541001

[Hugh Gooding]

Krugman touches on an aspect of economics that I have always had a hard time dealing with even as an economics major. It is the idea that we must use simple and almost methodological approaches to discuss complex issues. It has always bothered me that we must make so many assumptions and keep so many variables constant (ceteris paribus) when dealing with complex issues, because there is simply no way to account for everything. And it was comforting to read in Krugman's paper that I have not been the only one thinking about this, and that this issue has proven to be very problematic in high development theory especially. However, I think Krugman makes a good point that puts me at ease when he notes that a model is not meant for the purpose of solving a complex issue immediately but instead, "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." In saying this, I believe Krugman to be saying that models show us the smaller picture. We simply need to be patient and not expect to solve the most complex problems in one sitting. Instead, models are intended to provide us with clues and answers to smaller problems of a larger issue.

[Jonathan Jetmundsen]

I think this paper is a great way to develop a competent framework when evaluating or creating models. Economic models can seem frustrating to depend on because they require important assumptions to be made and often the results can be generalized. Krugman makes an important point though emphasizing the difference between economics and sciences (like physics). When someone is studying physics, they are looking for an exact equation to explain the way forces in nature work. The problem is that people take this same approach to economics expecting an exact explanation for how economic forces work. Although sometimes economic models seem inadequate to explain major topics, as some people have already mentioned, some of the most complex parts of economics can be effectively explained by very simple models. I also liked Krugman's map analogy, describing how advances in mapping of Africa helped more accurately determine Africa's boundaries, but at the same time overrode previous beliefs about the interior, some which were true and some which weren't. Over time some of the old beliefs were incorporated back in to maps as they were proven true. The move in economic modeling towards focused, mathematical based models has overridden older, less specific theories such as high development. It will be very interesting to see how as economic modeling progresses, which older ideas are brought back and how they are able to fit in with modern theories.

[Jacqueline Carson]

The history of economics is a subject that I have never given much thought to, but this article certainly made me want to think about it more in depth in relation to other fields and not just development economics now. I never realized that the process of synthesizing a new field in a subject could be so difficult. Upon reading this article, I couldn't help but think of how conformity truly rules our society and impedes innovation.

It seems that Krugman is frustrated with the conformity within economics making the field closed minded to tools other than models. He advocates for the "folk wisdom on clouds" in the concluding section and laments the "what could have been" of development economics. Because of this, like Kasey, I questioned whether or not there were any other alternatives to economics other than using formal models but then Krugman immediately followed up to my thought with "there is no alternatives to models." It seems to me that Krugman is falling into his own trap of being too close minded. Although he acknowledges the use of more fluid contraptions to explain development economics (and economics in general), he never seems optimistic about the prospects of letting go of the model as the core norm. I would argue that if he took a more optimistic approach to a paradigm shift for economics, innovation and understanding of the field could potentially grow dramatically. With the continued mindset of solely model usage in economics, nothing will change and the future "development economics" will continue to die out unless they conform.

[Alena Hamrick]

My favorite statement through the entirety of the Krugman paper is the very last: "A temporary evolution of ignorance may be the price of progress, an inevitable part of what happens when we try to make sense of the world's complexity." In context, Krugman is referring to the deviation from simple models in economics to other "metaphors" that claim to be more complex and analyze the the world more accurately. Of course, Krugman counters and says that these "metaphors" are not as good at predicting things as simple models. This sentence was most interesting to me because the issue of modeling is very much so interdisciplinary. The very issues that Krugman discusses concerning economics also applies to the hard sciences as well. Krugman uses physics as an example of a know-all type science, but in fact, that's far from the truth in itself because we can't possibly know all the things going on in a hydrogen atom because of the uncertainty principle. However, there exists (perhaps slightly more than simple) a model called Schrodinger's equation which can indeed predict the probabilities of where electrons in a certain atom will be. I am digressing, but ultimately, to me, this paper reinforced the importance of models and simple, analytical thinking to describe complex forms. We as human beings are incredibly complex and unpredictable (sometimes) as shown in both the TED talk from Tuesday and the small article on behavioral development. Modeling our decisions or our society-- arguably a major part of the economics discipline-- is a crucial underpinning towards understanding the mechanisms within our world much better, and just because we must resort to a simpler method of analysis, such as models, does not justify a negative stigma associated with this type of analysis.

[Sarah Schaffer]

Similar to several other students, this article reminded me of the ideas brought forth in the “Behavioral Development Economics” article we read for Tuesday. The idea that economists are stuck in the “model mindset,” assuming that humans self-interested even when they know that not to be true, made this article even more interesting to me. Creating models is natural in order to understand behavior and predict future behavior, but social science models seem to be the most contested – simply because humans are so wildly unpredictable and society changes. When reading this article, I appreciated Hirschman’s attempt at explaining behavior through evocating linkages instead of creating models because it had the potential to incorporate more of human nature. But what drew me back in was Krugman’s statement that the best thing to do is to not pretend to stop thinking in simplified models all the time, but instead “be self-conscious – be aware that your models are maps rather than reality”. This allows room to grow, interpret, and to take other factors into consideration, while still having a basic model.

[George Park]

I pretty much just want to echo what everyone else on this blog has been saying. Like Hugh, I often find myself frustrated with how much things get simplified in economics. In life things aren't just magically held ceteris paribus for you. However, the author did a great job illustrating the importance of simplified models. Bringing up Dave Fultz, a scientist whose work is widely accepted even though he essentially modeled the earth with a flat dish, really put things into perspective for me.

I think I saw Jacqueline talk about this too as I skimmed through the comments, but I didn't really like the note that the author ended on. He advises people to look for good ideas from people who don't write formal models, but then kind of just concluded that models are 100% necessary and that the "intellectual waste"that occurs from strict adherence to models is inevitable. It just seemed contradictory and overly pessimistic. Maybe there is a happy middle ground through which we can continue to rely on models but lessen the amount of intellectual waste that occurs in the process.

[Davis Turner]

I found this article stimulating as it pertained to the practices of standardization and innovation in development. Both innovation and standardization do not always result in a linear progression. Standardization has the ability to create a consistent model for people to follow, but in its attempt to create uniformity caste aside views and customs not accepted by majority. Innovation aims to create a new idea, product, system, and etc. Krugman’s example of the mapping of Africa highlights the blinding effect of innovation. The hubristic nature of a new model or system rejects its predecessor and or cast a shadow. The new standard disregards the simplicity or the distinctness of the old method creating gaps. Ultimately the implementation of innovation or standardization points in the direction of surplus maximization. The positive externalities of these innovations/standardizations in the long run fuel the closing of gaps. Dualism suggests modern markets perform better than traditional markets given the proper scale. Upon consideration, developmental theories such as ISI (import, substitutions, and industrializations) in Central America failed to immediately capitalize on the positive externalities of innovation and standardization. ISI lacked scale, capital, infrastructure, and the inability to timely fill in the gaps of its new system ultimately leading to ISI’s failure.

[Emily Rollo]

While reading this article, I found myself recalling what Professor Goldsmith repeats several times a class in my Econ 211 course. He jokes about the economists who were afraid to model what complicated situations would look like and how they always wanted to keep it easy and simple. Krugman stresses the same idea throughout this article. He makes it clear that the problems of economics and social science are due to the fact that we do not know “how to deal with complex systems.” I believe that this idea on the economic development struggle relates to a point made by Dr. Doty. He explains that women and men need to step outside their original, straightforward roles in order for the economy and the rest of society to flourish. The woman’s role becomes more complex. She no longer will be solely the caretaker, but instead she will be a source of power. There will not be such a clear distinction between men and women in the household. This new household model is no longer a simple system. It is something that society needs to adapt to, but traditional economists ignore this complexity, just as Krugman states. Overall, I think that the overlaying theme of all economic development aspects deal with the idea that our models are too simple. In order to advance, we must accept that by nature, systems are becoming complex and that it is for the better.

[Ali Coy]

Like most people have already noted, while reading “The Fall and Rise of Development Economics,” the power of models, due to their ability to demonstrate economic concepts, was very apparent. I was surprised to read how the high development theory was disregarded from the late 1950’s to the 1970’s because of the inability to appropriately create a model for this theory. However, my surprise was how this period of time was when economists were focusing on “tightly specified models” instead of “controlled, silly models that illustrate key concepts.” The word “silly” stuck out to me as I read this word a few times throughout the paper and therefore, became intrigued by the author’s word choice. At the end of the paper, the author states that Murphy and al were successful in creating an economic model to convey the high development theory “by daring to be silly.” I think this last statement is important to remember as in some circumstances, it can be necessary to take a minute to stand back, revert, and be “silly.” By doing so, it can sometimes answer our questions and lead us to something even greater. Also, I think this idea is very relevant in our lives because many of us can overthink due to external stress and sometimes simplifying and thinking foolishly can be the best remedy.

[Ali Norton]

“In some ways the problems of economics and of social sciences in general are a part of a broader methodological problem that afflicts many fields: how to deal with complex systems.” This excerpt identifies both the goal of and problem with economics: the practice seeks to “explain” the complex. When considering the issues of explaining something complex through a simplified and concise model, my mind jumped to Dr. Doty’s TED talk and the relationship, or “externality” that altered the course of his life. The life-altering situation Dr. Doty encountered was not a monetary donation or educational opportunity- Doty’s gift was the receipt of compassion. While maybe you could argue that Doty went to the magic shop seeking human interaction, the relationship he formed as a result of that seemingly coincidental decision could not have been predicted. That is, the externality that altered his life and changed his productivity as a human being was unpredictable and rooted in human emotion.
In considering the Krugman piece, I find it crucial to take into account the author’s point that humans (and perhaps economists) are “builders and purveyors of unrealistic simplifications.” In this aim to understand, we simplify and this process is representative of human nature. In economics, we form concise models to explain complex systems. Socially, we utilize stereotypes to explain what we do not understand. When considering economic issues of development, I see it natural to be attentive to issues at the level of an individual human being. As Dr. Doty’s story portrays, human emotion and the exhibition of compassion, an unpredictable phenomenon, can begin to alleviate poverty, thus exemplifying the ways in which we cannot explain an entire system through a singular model.

[Andrew Head]

Krugman's point about early development economists being pushed to the side early on due to a lack of traditional models struck me as ironic given the volume of model weaknesses Krugman would go on to describe in the article. That being said, he ultimately argues that they are the best available option in illustrating a given economic concept, and for this reason should continue to be heavily utilized in the social sciences. I think economic models will only continue to improve over time for two primary reasons. First, as neuroeconomics, and more broadly, behavioral economics continue to develop, economists will be able to identify more patterns and themes in "irrational" human thought which can hopefully be incorporated into mathematical models. Second, as technology improves and more data can be gathered about economic agents and their decisions, economists will be able to more thoroughly work through assumptions of the traditional models that guide economic thought today.

[Buck Armstrong]

While reading the Krugman article, there was one major theme that kept popping up in my mind. This thought was my personal opinion about Econ and how it's models are not always taking all factors into account. I think that belief comes from being a business administration major and also having a limited experience in Econ outside of Econ 101. Nonetheless, the moment in Krugman's piece where I started to really think about my opinion is when he referenced his old friend who loves Africa and collected historic maps. He used this anecdote to show how thoughts and theories evolve over time which is a main theme in his piece. This little story reminded me of the article we read for Tuesday in which the World Bank discussed how the current economic models neglect to take in new theories about how humans think into account. Up until recently economic models always stated that humans are rationale people with their self-Interest at heart. However the World Bank report stated that there are new theories about how humans think such as automatically thinking or thinking socially. This world bank report fits in with Krugman's article because he talks about how Hirschman's theories evolved. While, the World Bank report also noted that the new theories about how humans think hasn't been accounted in the models yet, but they are cognizant about how the future models will be. While prior to this class I had a fixed thought about Econ and how it's too archaic and doesn't take all factors into account, now I know it has always been changing and that the future economic models will be even more comprehensive in the future. I was so wrong about Econ and I'm looking forward to learn more.