Category: Uncategorized

To interpret these figures, it\’s important to know that new patents granted each year have been rising substantially over time, from about 40,000 in 1980 to 110,000 in 2010. Here\’s a figure showing the distribution of patents by US state: the top panel shows the 1980s, and the bottom panel shows the 2000s (that is, 2000-2010). Given that overall patent levels have risen, the figure shows many more states with higher patent levels (shown by the darker color). 

In the 2000s, patent creation was concentrated mostly in three regions:

• Northeast: New York, New Jersey, Delaware, and the New England states
• West Coast: Oregon, Washington, Idaho, and California
• Rust Belt: Minnesota, Illinois, Michigan, Ohio, and Pennsylvania.

Together, these states accounted for about 67 percent of total patents granted in the 2000s. While the East and West Coast states specialized in the computers and electronics sector, the Rust Belt states specialized in the machinery sector. These two sectors were the most innovative, based on the numbers of patents granted. The least innovative states were Mississippi, Arkansas, and Alaska. The rate of patent creation in the most innovative state was 22 times larger than in the least innovative state.

Here\’s a figure looking at patents by industry. Again, be cautious in comparing the top and bottom panels because the total number of patents has risen (as shown in the horizontal axis). But it is striking that in the 1980s, the distribution of patents across industries covered a reasonably wide spectrum. By the 2000s, patent activity had become much more concentrated in the \”Computer and electronic products\” sector.  

It\’s interesting to speculate about why patents have become more concentrated in one sector. Surely part of the reason is just the enormous technological gains made in computers and electronic products. But it\’s also possible that powerful companies in these industries are generating and buying patents as part of a \”patent thicket\” strategy to limit competitors, and it\’s possible that venture capitalists are more willing to support computer and electronics companies because of the possibility of lower costs and faster payoffs in this industry. For the large and diverse US economy, it seems important to have a very wide portfolio of efforts aimed at new technologies and innovation. 

Manaus, a city on the Amazon River of more than 2 million, illustrates the dangers of complacency. During the first wave of the pandemic, Manaus was one of the worst-hit locations in the world. Tests in spring 2020 showed that over 60 percent of the population carried antibodies to SARS-CoV-2. Some policymakers speculated that “herd immunity”—the theory that infection rates fall after large population shares have been infected— had been attained. That belief was a mirage. A resurgence flared less than eight months later, flooding hospitals suffering from shortages of oxygen and other medical supplies. The pandemic’s second wave left more dead than the first. 

Scientists discovered a novel variant in this second wave that went beyond the mutations identified in the United Kingdom and South Africa. This new variant, denominated P.1, has since turned up in the United States, Japan, and Germany. Scientists speculate that a high prevalence of antibodies in the first wave may have helped a more aggressive variant to propagate. The hopes for widespread herd immunity may be dashed by the emergence of more infectious virus variants.

Since the outbreak in Manaus in January 2021, P.1 has now spread throughout Brazil. The variant is much more transmissible than those that had been circulating previously in the country. High transmissibility and the absence of measures and behaviors to stem the dissemination of the virus have led to the worst health system collapse in Brazilian history.

What are some of the lessons that emerge from thinking about the pandemic and its global scope? Here are a few that come up repeatedly in the book. 

1) It seems important to have coordinated collection of genomic data on COVID or other viruses, both within countries and around the world. That\’s how you know if you are dealing with an existing problem or a new one–and if it\’s a new one, you can start the process of getting appropriate tests and vaccinations up and running. 

2) If you want to stop a pandemic early, before you need to do large-scale long-term lockdowns or watch people die while a vaccine is being developed and tested, the alternative involves lots of testing and follow up.  Martin Chorzempa and Tianlei Huang describe this alternative in \”Lessons from East Asia and Pacific on taming the pandemic.\”

Bloomberg News’ COVID Resilience Rankings evaluate success in handling the pandemic while minimizing the impact on business and society. An astounding ten of the top 15 countries and territories are in East Asia and Pacific. Top performers vary enormously in size, wealth, and political institutions, from small, wealthy, democratic islands like Taiwan and New Zealand to large, middle-income countries under one-party rule like mainland China and Vietnam. Core to their exemplary performance was the use of targeted and less costly mitigation measures that do not require an economic freeze. … The experience in East Asia and Pacific varies among countries with diverse cultures, geographies, and political systems, but one thing is clear: rigorous masking requirements, testing, contact tracing, selective quarantines, border closings, and clear public health communication all helped to avoid the overwhelming economic dislocations that occurred in the West. …

One of the most crucial advantages in the early days of a pandemic is testing capacity, which helps identify both individuals to quarantine and where to focus further testing. The contrast between the United States and South Korea, for example, is instructive. Drawing on memories from the MERS outbreak in 2015, South Korean officials pushed for quick approvals of promising tests from multiple manufacturers even before their effectiveness could be rigorously proven. The US Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA) required lengthy processes that limited testing supply, blinding their officials to the pathogen’s spread. By March 2020, South Korea had tested 31 times more people per capita than the United States, allowing it to catch many more cases and nip transmission chains in the bud.

The inability of the US to choose widespread testing and follow-up was in substantial part due to failures of those at the Centers for Disease Control and the Food and Drug Administration: for a discussion, see the article from a year ago in the Washington Post, \”Inside the coronavirus testing failure: Alarm and dismay among the scientists who sought to help.\”

3) Most US vaccination efforts happen as part of regular health care, delivered during regular visits to doctors. We need to learn more about the most effective ways of widespread distribution of a vaccine during a pandemic. 

In many places, including where I live in Minnesota, the primary method of vaccine distribution for the general non-institutionalized population happens in this way. You go online and fill out a form. The state or local government has a priority list and tells you when it\’s your turn. At that point, you make an appointment for where and when in the metro area to show up. 

For whatever reason, fewer doses were being injected into people’s arms each day, on average, than were being shipped to the states. As a result, the backlog of doses that had been shipped but not injected increased rapidly. By the second week of January, this backlog had moved above 15 million doses. … During the first week of March, more than 2.1 million doses were administered on average per day—the fastest daily pace yet, but still not as fast as the stepped-up pace of delivery. As a result, the backlog moved above 25 million doses in the first week of March. … As of late March 2021, the average daily pace of doses administered has increased from 2.2 million to 2.8 million, and the supply of doses to the states and other jurisdictions has stepped up to 3.4 million per day. Because the supply of doses has continued to outrun utilization, the implied backlog of doses in inventory has moved up into the range between 35 million and 40 million.

4) Because COVID spreads around the world and mutates around the world, high-income countries like the United States have a self-interested motive to see that the problem is addressed around the world. Yes, most high-income countries will look to their own populations first. But that can only be seen as a first step. Several of the essays in this book address how to do this, and I discussed a couple of months ago in \”Why High-Income Economies Need to Fight COVID Everywhere\” (February 2, 2021). 

5) In thinking about future pandemics, we need to think in advance about our ability to scale up production for what is needed. Some of this is physical, like the supply chains for personal protective equipment, for tests, and for developing and producing vaccines even more quickly. Some of this is advance planning so that tasks like contact tracing or distribution of tests and vaccines can go much more briskly. For-profit companies are going to be limited in their willingness to commit large-scale resources to future health risks that are uncertain in their source and timing. Along with a number of other people, I was echoing calls for better pandemic preparedness some  years ago. Although some steps were taken, we turned out to be grossly underprepared when the pandemic came. Today\’s politicians should be judged in part by their ongoing actions in response to COVID-19, but perhaps should be judged even more by whether they are putting policies in  place for the next pandemic. 

The title of the report seems unfortunate to me, because the discussion in the report is broader than the what is usually  meant by biodiversity.  Here, I\’ll start with a snippet from Dasgupta\’s preface to the volume, which gives a fuller sense of its purpose. Then I\’ll try to give a flavor of the discussion by cherry-picking a few of the points that struck me. From Dasgupta\’s preface (footnotes omitted): 

Not so long ago, when the world was very different from what it is now, the economic questions that needed urgent response could be studied most productively by excluding Nature from economic models. At the end of the Second World War, absolute poverty was endemic in much of Africa, Asia, and Latin America; and Europe needed reconstruction. It was natural to focus on the accumulation of produced capital (roads, machines, buildings, factories, and ports) and what we today call human capital (health and education). To introduce Nature, or natural capital, into economic models would have been to add unnecessary luggage to the exercise.

Nature entered macroeconomic models of growth and development in the 1970s, but in an inessential form. The thought was that human ingenuity could overcome Nature’s scarcity over time, and ultimately (formally, in the limit) allow humanity to be free of Nature’s constraints … . But the practice of building economic models on the backs of those that had most recently been designed meant that the macroeconomics of growth and development continued to be built without Nature’s appearance as an essential entity in our economic lives. … We may have increasingly queried the absence of Nature from official conceptions of economic possibilities, but the worry has been left for Sundays. On week-days, our thinking has remained as usual. …

[I]n order to judge whether the path of economic development we choose to follow is sustainable, nations need to adopt a system of economic accounts that records an inclusive measure of their wealth. The qualifier ‘inclusive’ says that wealth includes Nature as an asset. The contemporary practice of using Gross Domestic Product (GDP) to judge economic performance is based on a faulty application of economics. GDP is a flow (so many market dollars of output per year), in contrast to inclusive wealth, which is a stock (it is the social worth of the economy’s entire portfolio of assets). Relatedly, GDP does not include the depreciation of assets, for example the degradation of the natural environment (we should remember that ‘G’ in GDP stands for gross output of final goods and services, not output net of depreciation of assets). As a measure of economic activity, GDP is indispensable in short-run macroeconomic analysis and management, but it is wholly unsuitable for appraising investment projects and identifying sustainable development. Nor was GDP intended by economists who fashioned it to be used for those two purposes. An economy could record a high rate of growth of GDP by depreciating its assets, but one would not know that from national statistics. The chapters that follow show that in recent decades eroding natural capital has been precisely the means the world economy has deployed for enjoying what is routinely celebrated as ‘economic growth’. The founding father of economics asked after The Wealth of Nations, not the GDP of nations. …

If, as is nearly certain, our global demand continues to increase for several decades, the biosphere is likely to be damaged sufficiently to make future economic prospects a lot dimmer than we like to imagine today. What intellectuals have interpreted as economic success over the past 70 years may thus have been a down payment for future failure. It would look as though we are living at the best of times and the worst of times.

Thus, the Dasgupta report calls for estimating the impact of humans and economic development on nature, and comparing it to the rate at which the biosphere can regenerate. The thesis is that human impact greatly exceeds the regenerative rate at present, and the challenge is to bring these into balance. If we work under the assumptions that global population is going to rise for some decades to come (even if it tops out and starts declining later in the 21st century) and also that a higher standard of living for billions of people is desirable, then perhaps the key factor is the efficiency with which an economy draws upon nature to provide an improved standard of living for people. The measures of \”efficiency\” and \”standard of living\” should be understood in broad terms, including not just technology, but also institutions and perhaps even how humans choose to define what what will make them feel better off. 

The volume dives deeply into these topics. Here are few samples, from smaller to bigger topics. Let\’s start with \”Trade in Vicuña Fibre in South America’s Andes Region.\” For the uninitiated, a vicuña is a member of the camel family, related to llamas and alpacas, living in South America (again, footnotes and citations omitted throughout). 

The vicuña, a small member of the camelid family, is one of the most valuable and highly prized sources of animal fibre on the international market. Luxury garments made from vicuña fibre are sold in exclusive fashion houses around the world; a scarf can sell for several thousand pounds. Once hunted to near extinction, the vicuña now thrives in the high-elevation puna grasslands of the Andes. The decision to grant usufructuary rights to communities to shear live vicuña and sell vicuña fibre increased their economic incentive to manage the species sustainably and protect it. As a result, vicuña populations have recovered, and between 2007 and 2016, trade increased by 78% (by volume), and the export value in 2016 was approximately US$3.2 million per annum. Vicuña have become an asset to some of the most isolated and poorest Andean rural communities, rather than being seen as a competitor for pasture with domestic livestock, thus reducing illegal killing and motivating communities to carry out anti-poaching and protection measures. Economic returns from vicuña fibre trade, regulated by CITES, have motivated more communities to start management, extending protection across a large area that central governments could not police effectively. Broader benefits to habitats from decreased grazing have also resulted. However, while this is generally seen as a conservation success story, the equitable distribution of benefits remains a challenge, and communities only receive a small share of the final product value. Efforts are being made to find ways to add value to the fibre that benefits communities.

Here\’s a comment about reforestation. A concern expressed in several places is that while there is a temptation to slap a lot of fast-growing trees and plants into the ground, this may turn out to be counterproductive from the standpoint of a diverse and sustainable natural environment.

The IPCC [Intergovernmental Panel on Climate Change] suggests that increasing the total area of the world’s forests, woodlands and woody savannahs could store roughly a quarter of atmospheric carbon necessary to limit global warming to 1.5°C. To do so would mean adding an additional 24 million ha of forest every year until 2030. Many countries are responding with restoration plans, but 45% of all commitments involve planting vast monocultures of trees. Reforestation of Eucalyptus and Acacia trees in plantations only offers a temporary solution to carbon storage, as once the trees are harvested, the carbon is released again by the decomposition of plantation waste and products (predominantly paper and woodchip boards).

Lewis et al. (2019) calculated carbon uptake under four restoration scenarios that were pledged by 43 countries under the Bonn challenge, which seeks to restore 350 million ha of forest by 2030. They found that natural forests were six times better than agroforestry and 40 times better than plantations at storing carbon. Furthermore, these have greater associated biodiversity and ecosystem services. The pledged mix of natural forest restoration, plantation and agroforestry would sequester only a third of the carbon sequestered by a natural forest restoration scenario. The authors recommended four ways to increase the potential for carbon sequestration by forests: increase the proportion of land restored to forests; prioritise natural regeneration in the Tropics; target degraded forests and partly wooded areas for regeneration; and protect natural forests once they are restored.

Finally, here\’s a comment on  the differences between \”White, Black and Green Swans:\”

‘Black swan’ events can take many shapes, from terrorist attacks to disruptive technologies. These events typically fit fat-tailed probability distributions, i.e. they exhibit greater kurtosis than a normal distribution. Unlike other types of risk events which are relatively certain and predictable, such as car accidents and health events (‘white swans’), ‘black swans’ cannot be predicted by relying on backward-looking probabilistic approaches that assume normal distributions.

Some in the finance community have adopted this framework of thinking about risks associated with the biosphere, terming them ‘green swans’ (or environmental black swans). ‘Green swans’ present many features of typical ‘black swans’; in that they are unexpected when they occur by most agents (who regard the past as a good proxy of the future); they feature non-linear propagation; impacts are significant in magnitude and intensity; and they entail large negative externalities at a global level.

However, despite several common features, ‘black swans’ and ‘green swans’ differ in several key aspects. A key difference is their likelihood of occurrence. ‘Green swans’ are either likely or quite certain to occur (e.g. increased droughts, water stress, flooding, and heat waves), but their timing and form of occurrence are uncertain. By contrast, ‘black swans’ do not manifest themselves with high likelihood or quasi-certainty. ‘Black swans’ are severe and unexpected events that can only be rationalised and explained after their occurrence. While for ‘green swans’, the likelihood of occurrence means the case for preventative action, despite prevailing uncertainty regarding the timing and nature of impacts of these events, is strong … 

Other differences include who provides the main explanation for the events and their reversibility. Explanation for ‘black swans’ tend to come from economists and financial analysts, while for ‘green swans’ understanding comes from ecologists and earth scientists. The impacts of ‘green swans’ are, in most cases, irreversible, whereas for ‘black swan’ events –such as typical financial crises – have effects that are persistent, but have the potential to be reversed over time.

For millennia, farming and food supply have depended on access to accurate information. When will the rains come? How large will the yields be? What crops will earn the most money at market? Where are the most likely buyers located? Today, that information is being collected and leveraged at an unprecedented rate through data-driven agricultural business models. In India, farmers can access a data-driven platform that uses satellite imagery, artificial intelligence (AI), and machine learning (ML) to detect crop health remotely and estimate yield ahead of the harvest. Farmers can then share such information with financial institutions to demonstrate their potential profitability, thereby increasing their chance of obtaining a loan. Other data-driven platforms provide real-time crop prices and match sellers with buyers.

Other examples are about helping the government focus on improved and more focused provision of public services: 

The 2015 National Water Supply and Sanitation Survey commissioned by Nigeria’s government gathered data from households, water points, water schemes, and public facilities, including schools and health facilities. These data revealed that 130 million Nigerians (or more than two-thirds of the population at that time) did not meet the standard for sanitation set out by the Millennium Development Goals and that inadequate access to clean water was especially an issue for poor households and in certain geographical areas (map O.2). In response to the findings from the report based on these data, President Muhammadu Buhari declared a state of emergency in the sector and launched the National Action Plan for the Revitalization of Nigeria’s Water, Sanitation and
Hygiene (WASH) Sector.

When governments run up large debts, the interest cost to servicing this debt will be substantial. Money earmarked to make interest payments could be used for other purposes if interest rates were lower. Thus, the fiscal authority has a strong incentive to keep interest rates low.

The United States faced this situation during World War II. Marriner Eccles, who chaired the Federal Reserve at the time, favored financing the war by coupling tax increases with wage and price controls. But, ultimately, he and his colleagues on the FOMC [Federal Open Market Committee] concluded that winning the war was the most important goal, and that providing the government with cheap financing was the most effective way for the Federal Reserve to support that goal. So the U.S. government ran up a substantial amount of debt to fund the war effort in a low interest rate environment, allowing the Treasury to have low debt servicing costs. This approach freed up resources for the war effort and was the right course of action during a crisis as extreme as a major world war.

After the war was over and victory was achieved, the Treasury still had a large stock of debt to manage and still had control over interest rates. The postwar boom in consumption, along with excessively low interest rates, led to a burst of inflation. Without control over interest rates, the Federal Reserve could not enact the appropriate interest rate policies to rein in inflation. As a result, prices increased 41.8 percent from January 1946 to March 1951, or an average of 6.3 percent year over year. This trend, and efforts by then-Chair Thomas McCabe and then-Board member Eccles, ultimately led to the Treasury-Fed Accord of 1951, which restored interest rate policy to the Federal Reserve. The purpose of the accord was to ensure that interest rate policy would be implemented to ensure the proper functioning of the economy, not to make debt financing cheap for the U.S. government.

For comparison, starting in 2007 before the Great Recession, the ratio of federal debt/GDP was 35.2% of GDP. By the end of the Great Recession, federal debt had doubled to 70.3% of GDP. The most recent Congressional Budget Office projections in February, forecast that federal debt will be 92.7% of GDP this year. This should be considered a lower-end estimate, because these estimates were done before the passage of the American Rescue Plan Act signed into law on March 11, 2021, 

Thus, the ratio of federal debt/GDP rose by 65 percentage points in the five years from 1941-1946.  It has now risen (at least) 57 percentage points over the 14 years from 2007-2021. In rough terms, it\’s fair to say that federal borrowing for the Great Recession and the pandemic has been quite similar (relative to the size of the US economy) to federal borrowing to fight World War II. Of course, a major difference is that federal spending dropped precipitously after World War II, while the current projections for federal spending suggest an ongoing rise. 

In extreme situations, including World War II, the Great Recession, and the pandemic recession, the Fed and the rest of the US government has focused on addressing the immediate need. But by definition, emergencies can\’t last forever, Given the current trajectories of spending and taxes, we are on a path where at some point in the medium term, a confrontation between the enormous borrowing of the US Treasury and the Fed control over interest rates seems plausible. 

• This short overview at the Federal Reserve\’s \”history\” website. 
• The articles in this special issue of the Economic Quarterly published by the Federal Reserve Bank of Richmond in 2001, on the 50th anniversary of the Accord. 

The next panel shows that US imports from China have risen as a share of total US trade to about 21% of total US trade in the years before the pandemic–and seems to have rebounded back to that level after a short drop in the trade war. 

Altman and Bastian describe some other patterns of US-China economic interactions as well: 

Beyond trade, trends are mixed across other flows between the US and China. FDI flows in both directions rose from 2018 to 2019, although Chinese FDI into the US remained far below its 2016 peak. According to a recent analysis from the Peterson Institute for International Economics, “despite the rhetoric, US-China financial decoupling is not happening.” On the other hand, Chinese tourism to the US began declining in 2018, after 15 consecutive years of increases. And while it does not (yet) show up in broad patterns of international flows, US-China tensions over key technologies continue to boil, most notably with respect to 5G networking equipment (centered on Huawei) and social media (TikTok, WeChat) …

Of course, the reality of international trade is that saying \”China depends on the US for a substantial share of export sales\” has precisely the same meaning as saying \”the US depends on China for a substantial share of its supplies from imports.\”  Yes, the US could buy more from non-China countries and China could sell more to non-US countries, but changing the address labels on the shipping crates doesn\’t make much difference to the underlying economic forces at work. I\’m reminded of a comment from Lawrence Summers in an interview last spring about US-China relations: 

At the broadest level, we need to craft a relationship with China from the principles of mutual respect and strategic reassurance, with rather less of the feigned affection that there has been in the past. We are not partners. We are not really friends. We are entities that find ourselves on the same small lifeboat in turbulent waters a long way from shore.

Of course, Figlio and Schapiro emphasize that their approach is focused on Northwestern students, who are not a random cross-section of college students. The methods they use may need to be adapted in other higher-education contexts. In addition, this focus on first-quarter teaching of first-year students is an obvious limitation in some ways, but given that the first quarter may also play an outsized role in the adaptation of students to college, it has some strengths, too. In addition, they focus on comparing faculty within departments, so that econ professors are compared to other econ professors, philosophy professors to other philosophy professors, and so on. But with these limitations duly noted, they offer what might be viewed as preliminary findings that are nonetheless worth considering. 

For example, it seems as if their two measures of teaching quality are not correlated: \”That is, teachers who leave scores of majors in their wake appear to be no better or worse at teaching the material needed for future courses than their less inspiring counterparts; teachers who are exceptional at conveying course material are no more likely than others to inspire students to take more courses in the subject area. We would love to see if this result would be replicated at other institutions.\” This result may capture the idea that some teachers are \”charismatic\” in the sense of attracting students to a subject, but that those same teachers don\’t teach in a way that helps student performance in future classes.

They measure the quality of research done by tenured faculty using measures of publications and professional awards, but find: \”Our bottom line is, regardless of our measure of teaching and research quality, there is no apparent relationship between teaching quality and research quality.\” Of course, this doesn\’t mean that top researchers in the tenure-track are worse teachers; just that they aren\’t any better. They cite other research backing up this conclusion as well. 

This finding raises some awkward questions, as Figlio and Schapiro note: 

But what if state legislators take seriously our finding that while top teachers don’t sacrifice research output, it is also the case that top researchers don’t teach exceptionally well? Why have those high-priced scholars in the undergraduate classroom in the first place? Surely it would be more cost-efficient to replace them in the classroom either with untenured, lower-paid professors, or with faculty not on the tenure-line in the first place. That, of course, is what has been happening throughout American higher education for the past several decades, as we discuss in detail in the section that follows. And, of course, there’s the other potentially uncomfortable question that our analysis implies: Should we be concerned about the possibility that the weakest scholars amongst the tenured faculty are no more distinguished in the classroom than are the strongest scholars? Should expectations for teaching excellence be higher for faculty members who are on the margin of tenurability on the basis of their research excellence?

Figlio and Schapiro then extend their analysis to looking at the teaching quality of non-tenure track faculty. Their results here do need to be interpreted with care, given that non-tenure contract faculty at Northwestern often operate with three-year renewable contracts, and most of these faculty in this category are in their second or later contract. They write: 

Thus, our results should be viewed in the context of where non-tenure faculty at a major research university function as designated teachers (both full-time and part-time) with long-term relationships to the university. We find that, on average, tenure-line faculty members do not teach introductory undergraduate courses as well as do their (largely full-time, long-term) contingent faculty counterparts. In other words, our results suggest that on average, first-term freshmen learn more from contingent faculty members than they do from tenure track/tenured faculty. 

When they look more closely at the distribution of these results, they find that the overall average advantage of Northwestern\’s contingent faculty mainly arises because of a certain number of tenured faculty at the bottom tail of the distribution of teachers seem to be terrible at teaching first-year students. As Figlio and Schapiro point out, any contract faculty who were terrible and at the bottom tail of the teaching distribution are likely to be let go–and so they don\’t appear in the data. Thus, the lesson  here would be that institutions should be have greater awareness about the possibility that a small share of tenure-track faculty may be doing a terrible job in intro-level classes–and get those faculty reassigned somewhere else.

This study obviously leaves a lot of questions unanswered. For example, perhaps the skills to be a top teacher in an intro-level class are different than the skills to teach an advanced class. Maybe top researchers do better in teaching advanced classes? Or perhaps top researchers offer other benefits to the university (grant money, public recognition, connectedness to the frontier concepts in a field) that have additional value? But the big step forward here is to jumpstart more serious thinking about how it\’s possible to develop some alternative quantitative measures of teacher quality that don\’t rely on subjective evaluations by other faculty members or on student questionnaires.

One other study I recently ran across along these lines uses data from the unique academic environment of the US Naval Academy, where students are required to take certain courses from randomly assigned faculty. Michael Insler, Alexander F. McQuoid, Ahmed Rahman, and Katherine Smith discuss their findings in \”Fear and Loathing in the Classroom: Why Does Teacher Quality Matter?\” (January 2021, IZA DP No. 14036).  They write: 

Specifically, we use student panel data from the United States Naval Academy (USNA), where freshmen and sophomores must take a set of mandatory sequential courses, which includes courses in the humanities, social sciences, and STEM disciplines. Students cannot directly choose which courses to take nor when to take them. They cannot choose their instructors. They cannot switch instructors at any point. They must take the core sequence regardless of interest or ability.\” In addition: 

Due to unique institutional features, we observe students’ administratively recorded grades at different points during the semester, including a cumulative course grade immediately prior to the final exam, a final exam grade, and an overall course grade, allowing us to separately estimate multiple aspects of faculty value-added. Given that instructors determine the final grades of their students, there are both objective and subjective components of any academic performance measure. For a subset of courses in

our sample, however, final exams are created, administered, and graded by faculty who do not directly influence the final course grade. This enables us to disentangle faculty impacts on objective measures of student learning within a course (grade on final exam) from faculty-specific subjective grading practices (final course grade). Using the objectively determined final exam grade, we measure the direct impact of the instructor on the knowledge learned by the student.

To unpack this just a bit, the researchers can look both at test scores specifically, which can be viewed as \”hard\” measure of what is learned. But when instructors give a grade for a class, the instructor has some ability to add a subjective component in determining the final grade. For example, one can imagine that perhaps a certain student made great progress in improved study skills, or a student had some reason why they underperformed on the final (perhaps relative to earlier scores on classwork), and the professor did not want to overly penalize them. 

One potential concern here is that some faculty might \”teach to the test,\” in a way that makes the test scores of their student look good, but doesn\’t do as much to prepare the students for the follow-up classes. Another potential concern is that when faculty depart from the test scores in giving their final grades, they may be giving students a misleading sense of their skills and preparation in the field–and thus setting those students up for disappointing performance in the follow-up class. Here the finding from Insler, McQuoid, Rahman, and Smith: