We’ve spent much of this course discussing the issues in the environment and how they relate to the economy. We’ve talked about potential policy implications and the economic theory behind them. These readings introduced race as a factor in considering the damages done by pollution on public health. In light of everything we’ve discussed, I’m wondering if a policy aimed to address racism and redlining would be more feasible than a policy aimed to address climate change. In thinking about the most high-risk populations, I wonder if addressing the race bit would be a way to take care of some “low-hanging fruit” as far as pollution and public health go. With this, there are many things to consider including policy framing, how likely it is to get passed, and how it would be implemented. Overall, I’m curious if a policy to address elevated health issues in minority populations would be a sufficient way to begin addressing pollution and climate change overall.

One thing I really enjoyed about this paper was the pure focus on fiscal implications. I think sometimes the narrative that gets pushed by environmental-focused policies tends to reflect more of the social cost mitigation and how expenditures now will be beneficial in the future. As rational as it may seem to want to save the world from climate change and global warming, people just aren’t willing to give up money now for something they might not even notice in the future. I appreciated that this paper addressed those concerns from a different angle by bluntly estimating how much different cap-and-trade programs will cost the individual consumer. The authors’ conclusions affirmed what we discussed in class Tuesday, and the reporting on statistics helped make it even more approachable. The decision to report the percentage of income that a proposal may impact is a key one, as it helps put the hard value in perspective for those unfamiliar with a certain tax bracket. Overall, strictly speaking about the impacts on individual’s income negates arguments against a cap-and-trade system that say we can’t understand how this will affect people. I also felt like some of their estimates may have been slightly inflated. Because the implementation is likely to occur over time, it’s reasonable to predict that companies will become more efficient and innovate, further driving down costs.

I think these readings were interesting and helpful in providing an additional angle to the environmental issues we’re facing right now. When considering the conclusions/policy implications of these paper in combination with the more environmentally geared papers, it almost makes the whole issue seem even more impossible. Not impossible in the sense that these problems cannot be fixed, but impossible in that it seems like there is nothing we can say or show to make change. I’m sure these findings, or similar findings, have been presented to policy makers in the past so the sentiment just feels like, “What more evidence do they need that the best thing to do is to address these issues head on?” Talking about global warming and natural disasters hasn’t seemed to help, talking about health issues hasn’t seemed to help, so it feels like the issues are just so ingrained in our society and cannot be fixed without essentially turning the whole thing upside down. I am taking an American National Government class right now, and we’ve spent a lot of time talking about legislation and how difficult it is to make change so perhaps my worries are stemming from that class as well. I suppose we talked about it today (albeit morbidly) and said that we have to wait for some people to die so that we can make change. While I feel like the situation is somewhat dismal now, I do see hope in that we know of ways we can address it and that it is not truly impossible.

I thought that the solutions for rising CO2 presented in the Harvard magazine were very interesting and I had not considered many of them. The one that stood out the most was injecting CO2 deep into the ocean. I did not even know that this was possible, let alone helpful when considering the amounts of CO2 in the world. I wonder, if this solution were to be implemented, would it act as more of a mitigation or reset? Would it be used to take carbon dioxide out of the atmosphere at the same rate that it is put in to balance it down and make sure levels are not increasing or would it be used along with abatement to decrease carbon dioxide in the atmosphere overall? Overall, though, I tend to think of these readings as somewhat depressing, especially when I consider the publication date. Clearly, we have options and the technology to pursue these options. The fact that we haven’t already done so just makes the introductions to these problems more overwhelming because there’s kind of a precedent set that nothing will be done. What would it take for these solutions to be pursued on a large scale and for the world to finally address these issues in a real way?

This reading was interesting to me because of the depth it goes into and how interconnected economics becomes with ecology for this particular issue. I took Dr. Humston’s Ecological Modeling and Conservation Strategies course last spring term, and throughout the reading I was constantly thinking of how I would model things in Vensim. With this modelling in mind, I felt like I was thinking about the empirical model through a different lens. Clearly this problem has many ecological/population biology issues, but it is also an economics problem. I’m curious about what fields tend to deal with this issue the most. In particular, in the US, what group/office has the most influence on fishery management policies? Is it primarily economists, ecologists, other related fields, or teams made up of all of these types of professions? The reading also mentioned issues with fishery management on a global scale, so my question becomes even more difficult – who is it up to?

This reading make me think about questions more along the lines of “next steps.” Qualitatively and quantitatively, it makes sense that China can have a large effect on Brazil’s rainforest through various avenues. If we assume that these exploitative, environmentally damaging effects are bad, we are still left with the question of what to do. Is it on China to decrease their demand and consumption or is it on Brazil to stop supplying China through decreasing their rainforest stock? It’s hard to imagine a world where either one of those avenues are utilized. As we discussed in class, a tax could end up impacting small farmers negatively, but a subsidy seems like it would have to be large enough to where farmers would turn down money from China. But, what if demand from China is more inelastic than predicted, and it becomes a sort of cat-and-mouse game where Brazil (or another environmentally conscious firm) must subsidize these farmers more and more as China keeps trying to throw money at the farmers? Is there a point at which China may decrease their demands, switch to another product, or just turn to their own land to exploit? Perhaps I am imagining a situation that could never happen, but I feel like the international trade seems to exacerbate the problem from a more institutional side that boils down to a “who’s in charge?” sort of scenario.

Throughout reading these two papers, I kept thinking about the story we learned in Development last semester where Americans were asked if they thought the US should give less money to developing countries and how much. In my mind, that story highlights the absurdity of the discrepancy between what people think the US should give and what they actually give, but the story also highlights the lack of a sort of “anchoring” number that Casey and Schuhmann (2018) discuss in their findings of people’s WTP. In both cases, respondents stated higher values without an anchoring effect. I’m curious about if this is always the case – if people tend to think of higher values in WTP scenarios when they’re not given an anchoring number. Also, if this is commonly observed in WTP surveys for valuations of the environment, is there a way to account for this? Finally, I’m curious about if/how policymakers take this data into consideration when making decisions about environmental conservation issues. Clearly, people seem to be willing to pay a higher amount than they currently do (at the time of the study), but I’d imagine people would be reluctant to actually raise an entrance or exit fee out of fear of backlash.

Reading Solow’s lecture on sustainability along with Quiggin’s piece gave me a sort feeling of doom. Both outline the ideals of sustainability, and say that we can get there, but it would require a deviation from our current habits as a society. Quiggin in particular says that the “ultimate barriers to achieving a good life for all… are in our beliefs, values and social institutions.” I just wonder how this could be addressed. How do we change our beliefs, values, and institutions to promote sustainability? As Solow points out, we would need to forego current consumption. However, how do we make the shift to decide that future consumption is more valuable than our current consumption? I don’t know if the answer is truly in changing our values, and I wonder if there is something concrete that can be done to promote this shift.

As several people have already mentioned, this paper was a little bit tougher to follow, but after rereading some sections a few times, I think I understand why what the authors found was so interesting. In class, we use a lot of models and we all understand that it’s what theory predicts, but there is evidence that certain trends will be followed in the real world. In this paper, the authors ran a different statistical analysis than what is commonly use to help find data to support different theories. I think it’s important to recognize any shortcomings with particular research/analytical methods and try to fully investigate a problem before drawing any conclusions. Ordinary least squares regression is arguably the most common regression analysis used for Economics-related data, but many of its assumptions typically don’t hold up in the real-world and this isn’t always addressed. I liked that this paper reinvestigated a topic but from a new lens with a different statistical analysis – maximum likelihood. Through this, the authors were able to draw different conclusions than before and I appreciated their use of multiple types of analyses to analyze their data.

This reading emphasized a lot of the topics we’ve already covered throughout the semester. Based on the different measurement methods, I am curious about how previous studies have tried to measure the social benefits of education (or anything). The authors mentioned that ideally, the social benefits should include non-monetary benefits of education, but that limited empirical evidence on the social benefits make this impossible. The authors also mentioned that estimation of returns to education has been popular subject in research and that contributions have grown. I’m sure that more details would have been somewhat irrelevant in the context of this paper, but I’m curious about how/in what ways people have made progress in attempting to estimate the social benefits of education. The paper points out that it’s difficult to estimate things like “the number of lives saved because of improved sanitation conditions followed by a woman because she has received more education.” I believe there are ways to estimate measures like this, and I wonder what approaches other researchers have taken in order to make these estimations. Furthermore, the authors note the creation of databases with estimates of the return to schooling and I wonder how accurate they are in measurement and how they compare with each other.

I really enjoyed this paper and thought it reaffirmed many of the things we’ve been learning in class. Duflo recommends taking policy actions that favor women at the expense of men. Similar to many other policies, fighting for policies that favor women at the expense of men would be a consistently uphill battle. Duflo recognizes this and says that the way to promote these policies would be to outline the potential benefits of instituting a policy like this. However, it will still be difficult to promote policies without using any hard evidence. This reveals a circular process in how these policies would be put into effect. People won’t want to institute policies without any hard evidence, but to get hard evidence, we need to put the policies into practice. This sort of loop reminds me of the Big Push theory and makes me wonder if a similar concept could be applied in women-promoting policies. Furthermore, because men have consistently been seen as a higher gender in history and may cultures and religions, instituting a policy that promotes women at the expense of men will be even more difficult. Duflo does a good job outlining these obstacles and makes a strong case for taking policy actions that favor women. As she outlines, promoting women will eventually help the economy as a whole, making everyone better off.

One of the things I found fascinating from a development/growth standpoint was the emphasis on the effects of global warming on health. Most people are familiar with some of the broader implications of climate change like rising water levels, rising temperatures, and lower crop yields, but I appreciated the sections that mentioned the direct implications for human health. Specific to Europe and Central Asia, the report discusses that rising heat can increase temperature-related events and cause higher incidences of disease transmittance. In regard to the Middle East and North Africa, the report mentions the heat-related illnesses and other disease transmittance. However, what I found most interesting about the paragraph here was the mention of undermining labor productivity. In class, we’ve spent a lot of time discussing investments in human capital to increase productivity, and I’ve never considered that climate change could have such a strong impact against the productivity of human capital. From this I wonder if, using a Solow growth model, we would model the decreased productivity from climate change as a shift in the output or more of a rotation. It’s clear that climate change is a pressing issue that will affect everyone on Earth, but I enjoyed how this report called more attention to the less-glaring issues that will arise from climate change.

One of the things I found interesting were lagging countries that had seemingly been in an economically advantageous state but hit various obstacles that kept them trapped. In the paper, it is noted that Ghana had a very diverse and robust manufacturing sector in 1970 following the launch of state-owned import substitution industries. This seemed like a great set-up for the country to be able to thrive, but non-selective industrialization policies behind high barriers of protection kept them from developing “adequate industrial capabilities and infrastructure.” Kenya also seemed to follow a similar track. After gaining independence, Kenya experienced rapid growth until the 1970s when it slowed down due to oil crises. The paper notes that these crises were especially bad for Kenya due to the high dependence on imported petroleum. In both Ghana and Kenya, there seemed to be a positive trend until the 1970s, when both economies stopped expanding at a high level, and have since been trapped. Ghana’s slow was caused by non-selective industrialization policies whereas Kenya’s was caused by an over-dependence on oil. This contrast, while not perfectly opposite, outlines the careful balance that needs to be pursued in developing economies.

One of the main concepts Krugman emphasizes throughout the article is how formalizing and modeling concepts tends to leave out important information. In economics classes, we’ve always discussed that models are used to simplify real-world things to more clearly understand the interactions between variables. Krugman illustrates several cases where the blind spots created in simplifying/formalizing thoughts have been left virtually untouched and disregarded in such a blatant way. Reading the examples about Africa and storms, I could almost understand why previous thoughts and beliefs were written off as merely folklore or myths, but it was more difficult for me to read that essentially the same thing had happened in economics. While I agree that it is important in economics to present arguments and theories in a more rigorous and formalized way using models and equations, it is interesting that such a large part of development economics was ignored for so long. Krugman addresses this by explaining that it may not have been possible for economists at the time to address the role of increasing returns and circular causation because of their attraction to the possibilities of perfect competition and constant returns. Still, I wondered why these topics were essentially abandoned for the sake of such powerful assumptions.

One of the things I found intriguing about this paper were the strong correlations between many of the environmental and sustainability-related goals and biology. In a biology course I took last year, we discussed the currently unknown but widely hypothesized carrying-capacity (the maximum number of organisms an environment can sustainably support) for humans. Harvard sociobiologist Edward O. Wilson estimates this number to be between 9 billion and 10 billion (https://www.livescience.com/16493-people-planet-earth-support.html) , the same number Sachs mentions the current population trajectory is on. In biology, this number is impacted by fertility (population growth) and food availability (a major limiting factor). Sachs touches on both of these. From the reading on Wednesday, we learned that many low-income populations tend to have high fertility rates, an especially troubling fact when considered in conjugation with the forecasted carrying-capacity. We can even consider many of these extreme poverty, high fertility populations to be relatively independent (separate populations, in biological terms). Viewing them as independent is even more frightening because, while low-income populations are major contributors to the high fertility rates, they will likely bear the most drastic repercussions. As Sachs mentioned, these “high-fertility settings should be empowered to adopt rapid and voluntary reductions of fertility to benefit themselves, their children, and the local and global economy and environment”. While I agree that it is an important point, I almost wish he had emphasized it even more. Sachs’ description of the main limiting factor – food availability – also paints a somewhat dire picture. He describes that “many of the key yield-raising technologies of the green revolution have run their course”. So, even though the human population is growing, it’s unlikely that food production and availability will increase rapidly enough to support the growing population. However, even if it is possible to increase food production, it will likely come at a cost to environmental and social resources. Natural habitats, climate change, decreased biodiversity, and more are all direct outcomes of increased grain production. Furthermore, if food does truly become a limiting factor, increases in the price of food will push more people into hunger.