This paper reminded me a lot of Professor Silwal's lecture on FDI and the important factors that influence FDI. The key things that influence FDI that I took away from that lecture were the quality of labor, institutions, and economic status of the country that is being invested in. I think these factors are equally important in the discussion of this paper concerning capital flows and interest rates in countries. Essentially, much of it has to do with whether or not borrowing or lending is discouraged or encouraged based on the country's traits. Specifically, the paper discusses much about the spreads for East Asia and Latin America and the effects of the US interest rate and treasury yields. The latter has many implications for the supply and demand of investing for the developing countries. It's always very interesting to think about the impacts of developed nations upon developing countries, and this paper also made me think of the legacy of colonialism. The word fails to come to mind but there is a term we learned early in the year that described developing nations' dependency on the developed. This was listed as a potential reason why developing countries struggle to emerge from their low status. However, this paper made me wonder if there are not ways to manipulate capital flows and such so that a developing country may increase its FDI and its overall development.

It is actually really interesting and mind blowing that a 4 degrees Celsius difference can have such a great impact on the world. Obviously, those hit most by the change in climate would be the developing countries because of the negative repercussions such as heat waves, drought, and floods. Clearly, everyone will be impacted by the change in climate, and considering the severity of the repercussions, perhaps more policy should be implemented concerning emission pathways and such. The issue is clear that a 4 degree Celsius increase in global temperatures coupled with other issues such as social, economic, and population stresses can be a major problem facing the entire world. Poverty and inequality will be even more stratified and adaptability may not be a plausible coping mechanism. Basically, this is a BIG DEAL!!

As a chemical engineering major who is interested in going to graduate school for regenerative medicine and tissue engineering, the introduction grasped my attention right away with the mention of the genetic polymorphism sickle cell trait and its relationship with malaria. Economically speaking, I think this development of sickle cell trait in the human genome and its corresponding fatalities can also be considered a consequence of malaria. Not only this but the method of transmission of malaria and its relationship with temperature was also very interesting. Previously, I was not aware of the mechanism of malaria transfer, but the introduction is very thorough in prepping the reader with background information. Now delving more into the economic side of things, I found the annual GDP difference between malarious countries those without intensive malaria to be quite astonishing. I feel like it's an accepted conjecture for the most part that malaria has a detrimental effect on the economy, however, putting actual numbers to the statement really puts it in perspective. Also, the 'quanity-quality' trade off of children was something I hadn't really considered (by being raising the fertility rate due to the infant mortality rate, each child gets less invested in it).

Rodrik's paper conveniently discusses China after a relatively quick but thorough overview of China's growth strategies in class on Tuesday. Rodrik mentions that China has achieved its economic growth through unconventional methods, yet that growth has been sustained relatively well over the years and continues. In class on Tuesday, we discussed China's use of the Lewis Theory of Development, and the two major assumptions necessary for this theory to hold: the marginal product of labor in the agricultural sector is zero and there is no unemployment in the manufacturing sector. Instead of using the theory and hoping these two assumptions would occur naturally, China implemented policy to make sure these things occurred, and in part, this helped them achieve their desired economic growth. This unorthodox way of using policy to make sure assumptions from certain models or theories of economic growth hold went hand in hand with what Rodrik was discussing. Not only this, but Rodrik also mentions that unorthodox principles and methods for economic growth were used widely throughout East Asia with relative success (the major examples being South Korea and Taiwan). Adhering to the Washington Consensus doesn't necessarily automatically mean economic growth, and this was exemplified best by the floundering Latin American countries. Compared to their East Asian counterparts, their economic growth was not well sustained, if at all. However, East Asia's deviation from the Washington Consensus and adoption of unusual methods of growth was very successful. Rodrik points out that topography, culture, geography, and other things of this nature are often important when considering economic growth tactics, and the juxtaposition of East Asia and Latin America provided supportive evidence of this claim.

One of the things I found to be exceptionally interesting was the distribution of consumption by the poor. Despite how malnourished/weak the impoverished are, they tend to still spend what little money they have on alcohol, tobacco, and other similar items. I found this interesting because once again this ordeal is a testament to the irrational behaviors of people, and this type of information is crucial to making effective policy to aid the poor in their situation. Not only this, but spending on festivals is also an interesting use of money. Perhaps this attests to the strong cultural connection that the impoverished have. This leads to further questions such as: do the poor have a stronger sense of community than their counterparts? Is there any way to actually measure this and could this have any policy implications? Similarly, I find it surprising that the levels of self-reported unhappiness are not typically incredibly low for the poor as a whole. I understand the stress and anxiety accompanied with being impoverished are an incredible onus, but I would have thought that their levels of self-reported unhappiness would be lower because of this.

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.