Sunday, April 18, 2021

A Hive of Authentic College Applicants

As someone with a couple of college-age children who have navigated the admissions process at selective colleges, I found myself nodding in agreement with Matt Feeney's essay in the Chronicle of Higher Education, "The Abiding Scandal of College Admissions: The process has become an intrusive and morally presumptuous inquisition of an applicant’s soul" (April 16, 2021). 

A basic fact is that applications at selective colleges are way up, and given a fixed number of slots of students, acceptance rates are way down. For example, the Washington Post just reported: "Columbia's applications were up a stunning 51 percent this year, and Harvard's were up 42 percent. There were also double-digit increases at Brown (27 percent), Dartmouth (33 percent), Princeton (15 percent), the University of Pennsylvania (33 percent) and Yale (33 percent)." Acceptance rates at places like Harvard, Stanford, and Princeton are in the range of 4-5%.

When a school is accepting only one applicant of every 20, or every 10, or every five, you might think that the school would want to be clear with applicants about their low odds--before those applicants invest time, sweat, soul, and money in writing the essays and doing the paperwork. But of course, that's incorrect. Lots of applicants and a low acceptance rate may mean wasted time and enormous disappointment for applicants, but it looks good for the school. 

So instead, selective schools encourage everyone to apply: we were on tours at multiple selective schools that started with hundreds of people in auditoriums where such encouragement was given. We were repeatedly not to worry too much about test scores or high school grades--although even the most casual acquaintance with the facts about who is actually admitted suggests that these measures are pretty important. Instead, the emphasis was, as Feeney points out, on "holistic admissions" and "authentic" application that demonstrates the real specialness of you. 

On one side, saying that it's all about "authenticity" is an encouragement to apply. On the other side, if not accepted based on your authentic self, while others are accepted based on their authentic selves, it will seem pretty clear to an overwhelming majority of applicants that either your authentic self was either presented poorly or judged and found wanting. It's all too reminiscent of what Groucho Marx said about "sincerity," "If you can fake that, you've got it made." 

Moreover, it's clear at selective colleges that the applicant all need to show their special personal authenticity in some very specific ways: grades/test scores, involvement in extracurriculars and the community, ability and willingness to diagnose and write about their own selves, and so on. 

As Feeney points out, as college admissions have become more selective in recent decades, what the admissions people say they are looking at and emphasizing has changed, too. There was a stretch in the 1980s and 1990s where the emphasis was on extracurricular activities and the "well-rounded" applicant After this (quite predictably).after this resulted in an epidemic of extreme resume-padding, "more recently they have come to favor the passionate specialist, otherwise known as the `well-lopsided' applicant." Apparently on the horizon is an admissions online platform that will let you start storing your essays and videos starting in ninth grade. 

(Bad news here for applicants to selective colleges: Multiply the number of applicants by, say, a generously estimated one or two hours to look over every application. The admissions personnel on average don't have much more time than that. The idea that they are going to spend many hours looking over video and text of the best science reports, short stories, choir/band concerns, sports team highlights, and community service projects for every applicant is delusional. At best, they could skim and skip through a few entries for specific applicants.) 

Here's Feeney in the Chronicle of Higher Education:  
The people who made applying to college an elaborate performance, a nervous and years-long exercise in self-construction have now decided that the end result of this elaborate performance must be “the real you.” The tacit directive in all this — “Be authentic for us or we won’t admit you” — puts kids in a tough position. It’s bad that kids have to suffer this torment. It’s also bad that admissions departments actually think that the anxiously curated renderings that appear in applications can in any way be called “authentic.” It’s like watching Meryl Streep portray Margaret Thatcher and thinking: Now that is the real Meryl Streep. ... 

What distinguishes an applicant here is not authenticity, but access to the best advice on how to create the right authenticity effect — cultured parents, costly admissions coaches, able and informed college counselors. ... This points to another dark aspect of all this personalizing, with its imposed subtleties of performance and discernment — the barely hidden class bias. Admissions personnel are generally eager to add their voices to the chorus bewailing the socioeconomic and racial bias in standardized testing, but they’re largely incurious about the class bias in their own softer measures. In practice, that is, what ends up resembling “authenticity” to admissions officers is an uncannily WASPy mix of dispensations better understood as discretion, or, perhaps, good taste. After all, what admissions readers really dislike are the braggarts, and isn’t bragging a vice of the classless, the parvenus and arrivistes? ...

Admissions bureaucrats faced with thousands more applicants than they can accept soon reach a level of arbitrariness. At that point, they launch an inquisition of their applicants’ souls. This makes little sense academically but allows them to stage a powerful, utterly undeserved disciplinary claim on the inner lives of teenagers — that is the abiding scandal of college admissions. ....

Admissions officers have come to see the process they oversee in therapeutic terms. They present the college application as a set of therapeutic prompts, gentle invitations for the applicant to free herself from repression and self-deceit and move toward authentic self-expression and self-knowledge. ...

Setting up a years-long, quasi-therapeutic process in which admissions goads young people into laying bare their vulnerable selves — a process that conceals a high-value transaction in which colleges use their massive leverage to mold those selves to their liking — is reprehensible. It is terrible thing to do. It renders the discovery of true underlying selves absurd. Sometimes, as we’ve seen, admissions people will admit they have this formative leverage over young people. But they fail to show the humility that should attend this admission, the clinician’s awareness that to use this power is to abuse it. Instead, they want even more power. They want to intrude even more deeply into the souls of their applicants. ...
I can easily understand some sensible reasons why colleges want their own admissions department. Sometimes there is a really good fit between the abilities and interests of student and the specific strengths of an institution. Pools of applicants will vary from year to year, and there's some logic in trying to make sure that you admit a class that has a degree of balance in terms of academic interests, nonacademic interests, and geographic and demographic characteristics. 

But with no deep disrespect meant to the admissions personnel at selective colleges and universities, who I think are mostly just doing the best they can, they aren't professors or therapists. So who died and made them the monarchs of defining what is the desirable kind of authenticity, and how a holistic view of that authenticity should be expressed?  Especially the authenticity of 17 year-olds? 

Friday, April 16, 2021

Interview with Esther Duflo: On Experimental Methods and Inequality

Douglas Clement provides an "Esther Duflo interview: Deciding how to share" (For All: Federal Reserve Bank of Minneapolis, Spring 2021).  

On the existence of a tradeoff between growth and inequality:
I think the whole notion of a trade-off is likely a fallacy, for various reasons. First of all, there is no clear link either on theoretical grounds or empirically between higher inequality and more growth. There is no reason why inequality is necessary for growth. And there is no law of economics that says that growth increases inequality either. So I think there is no causality necessarily going in either direction; therefore, there is not necessarily a trade-off. Just as a matter of accounting, growth is equality-enhancing if most of the benefits of growth are going toward the poor. And growth is inequality-enhancing if most of the advantages are going toward the rich. Both are possible. I don’t think there is a systematic pattern either way. ... 

In fact, we don’t seem to have much of a handle on what causes growth anyway, although we might have interesting theoretical narratives on growth. If there is a consensus among macroeconomists, it’s on what should be avoided at all costs, like hyperinflation. But there is not a set of recipes that guarantees growth, and it’s not that these recipes therefore lead to a trade-off. So, I think there is actually no trade-off.
On how evidence from randomized control trials is like a pointillist painting
The idea of the pointillist painting is, imagine a painting by Seurat. It’s literally made of dots, and each of these dots on its own is perfectly nice, but it doesn’t generalize to anything. But if you step back and accumulate all these dots, you see the entire painting of, say, a family on the bank of the Seine having a picnic.

Suppose you’re trying to assemble a jigsaw puzzle of that Seurat painting. Just by looking at the rest of the painting, you sort of know what goes next. You have a prediction about where a given piece fits. You might find that your piece doesn’t fit. It might be wrong. It’s not what you expected. But the frame, the painting, gives you good guidance for what you might expect.

That’s how progress happens. The caricature is that you try one small experiment in one place, and then you can take the result to the entire world. That’s not it. The way it actually works is: Do your small experiment; get some findings that are interesting. They might contradict or confirm the theory that you started from, but they give you fodder for the next experiment, and so on and so forth, until you have an understanding of what might be the entire shape or contour of that problem.
On using the superstar power of economists to save lives
My husband, Abhijit Banerjee, also a Nobel Prize laureate, was asked to be the chairman of the coronavirus response team in West Bengal. ... We knew from previous work ... that stars and celebrities are very influential in conveying these messages, so we were looking for stars to pass along very basic social distancing advice to households in India at a time when it was completely confusing. It finally dawned on us that the best star we had was right on our team! Abhijit Banerjee has been a bit of a household name in West Bengal—where he’s from—since he won the Nobel Prize. ...

Abhijit recorded messages that were sent in two rounds to subscribers with Airtel, a bigger subscriber network. One message was about asking people to be kind to coronavirus patients and not to shun them out of the village, and the other was about how travel during Durga Puja, where people normally come in droves to town and make pilgrimages to makeshift temples. So, potentially, a scene of millions of people crowded together, coming from everywhere and going back. It could have been a coronavirus disaster.

Abhijit worked with others in putting together something that was feasible. You cannot say, “Cancel the holiday.” That’s not really an option. So something that was feasible, but would improve things. And we sent one more round of messages urging people to stay home if they’re older, and if they do go out, visit just one location, and wear a mask.

And quickly after that, Durga Puja happened, and we saw that the attendance was down a significant amount from previous years. So it was much, much, much lower attendance. And we can now see whether there was an uptick of coronavirus and we don’t see that.

So, of course, it was not just his messages. There was also the chief minister went on television to relay the message. But this entire effort to convince people with clear messages about what to do seems to have been very effective. I’m convinced that that saved thousands and thousands of lives ultimately. You don’t get to do that every day.

For an earlier post on the award of the 2019 Nobel Prize in economics to Duflo, Banerjee, and Michael Kremer, see "A Nobel for the Experimental Approach to Global Poverty for Banerjee, Duflo, and Kremer" (October 18, 2019). 

Thursday, April 15, 2021

Pharma R&D: Vaccines and Other Drugs

Surely one of the key lessons of the pandemic is the value of research and development, which in turn means the value of making the investments over time in education and equipment so that researchers are tooled up and ready to go as needed. The Congressional Budget Office has published "Research and Development in the Pharmaceutical Industry" (April 2021), which offers a useful primer in getting up to speed on some of the key trends and issues. Here are five of the main themes as I see them. 

1) Research and development may play a bigger role in pharmaceuticals than in any other industry. 

This figure shows how much different industries spend on R&D as a share of their "net revenues"--that is, revenues minus expenses. A few years back, pharmaceuticals were similar in their "research intensity" to industries like semiconductors and software, but in the last decade or so, pharma has become even more research-intensive. 

Pharma R&D spending has gone way up. The CBO writes: 

In real terms, private investment in drug R&D among member firms of the Pharmaceutical Research and Manufacturers of America (PhRMA), an industry trade association, was about $83 billion in 2019, up from about $5 billion in 1980 and $38 billion in 2000. Although those spending totals do not include spending by many smaller drug companies that do not belong to PhRMA, the trend is broadly representative of R&D spending by the industry as a whole. A survey of all U.S. pharmaceutical R&D spending (including that of smaller firms) by the National Science Foundation (NSF) reveals similar trends.

Let's say that again: in real (that is, adjusted for inflation) dollars, pharma R&D is up by a factor of about 10 from the average in the 1980s and even before the pandemic had more than doubled since 2000. The CBO also points out that the cost of developing a successful new drug can commonly be in the range of $1-$2 billion, once the costs of the drugs that didn't work out are included, and the process of developing a drug so that it's ready to sell can take a decade or more. 

2) There's controversy over the direction of pharma R&D spending. 

Pharma companies will be attracted by producing expensive drugs for big markets. Conversely, the incentive for a drug company to spend $1 billion  and a decade addressing a smaller market or finding a lower-cost alternative to an existing money-maker will not be large. The CBO writes: 

The number of new drugs approved each year has also grown over the past decade. On average, the Food and Drug Administration (FDA) approved 38 new drugs per year from 2010 through 2019 (with a peak of 59 in 2018), which is 60 percent more than the yearly average over the previous decade. Many of the drugs that have been approved in recent years are “specialty drugs.” Specialty drugs generally treat chronic, complex, or rare conditions, and they may also require special handling or monitoring of patients. Many specialty drugs are biologics (large-molecule drugs based on living cell lines), which are costly to develop, hard to imitate, and frequently have high prices. Previously, most drugs were small-molecule drugs based on chemical compounds. Even while they were under patent, those drugs had lower prices than recent specialty drugs have. Information about the kinds of drugs in current clinical trials indicates that much of the industry’s innovative activity is focused on specialty drugs that would provide new cancer therapies and treatments for nervous-system disorders, such as Alzheimer’s disease and Parkinson’s disease.

Here's a figure showing the therapeutic areas where US drug spending has increased the most in the last decade. The big ones at the top are drugs to address cancer, diabetes, and autoimmune diseases. Because these are the big markets, this is also where pharma R&D for future drugs will tend to be focused. 

3) There's controversy over the role of larger and smaller pharma companies. 

The pharma industry has developed a partial division of labor, where smaller companies are more likely to be doing R&D, and larger companies are more likely to be leading the way on the clinical testing needed before the drugs come to market. Thus, a common dynamic is that if a small company has developed a promising drug, either the drug or the entire company may be bought by a larger firm. There's nothing necessarily wrong with this dynamic. It gives successful entrepreneurs a way to start small and then cash out when successful. But it does raise a danger that big pharm companies are buying out the very firms that could, in time, have grown into being their future competitors. There's evidence that in some cases, large pharma companies have bought smaller firms with a new drug that might have competed with existing drugs--and then halted development of the new drug. The CBO writes (footnotes and references to text boxes omitted): 

Although total R&D spending by all drug companies has trended upward, small and large firms generally focus on different R&D activities. Small companies not in PhRMA [the trade association of big pharma companies] devote a greater share of their research to developing and testing new drugs, many of which are ultimately sold to larger firms. By contrast, a greater portion of the R&D spending of larger drug companies (including those in PhRMA) is devoted to conducting clinical trials, developing incremental “line extension” improvements (such as new dosages or delivery systems, or new combinations of two or more existing drugs), and conducting post-approval testing for safety-monitoring or marketing purposes. ...

Small drug companies (those with annual revenues of less than $500 million) now account for more than 70 percent of the nearly 3,000 drugs in phase III clinical trials. They are also responsible for a growing share of drugs already on the market: Since 2009, about one-third of the new drugs approved by the Food and Drug Administration have been developed by pharmaceutical firms with annual revenues of less than $100 million. Large drug companies (those with annual revenues of $1 billion or more) still account for more than half of new drugs approved since 2009 and an even greater share of revenues, but they have only initiated about 20 percent of drugs currently in phase III clinical trials.

4) The government has always played an important role in vaccine markets, with its requirements for who needs to get vaccinated. And of course, the government played a substantial role in developing COVID-19 vaccines with the Warp Speed program. 

Here's the CBO summary of what companies got money for a COVID-19 vaccine, and for what purpose.  Given the costs of COVID-19, this $19 billion probably ranks with the most cost-effective money the US government has ever spent on anything.

5) Research expertise in vaccines, as in many other areas, often can shift from one disease to another, so that what looks like "failure" in producing a vaccine for one disease can build expertise in addressing a different disease. 

For example, it turns out that although the effort to produce an HIV vaccine has not so far been successful, many of of the technologies and skills developed in that search were useful in creating a ?COVID-19 vaccine (for discussions of this point, see here and here). 

Jeffrey E. Harris argues this case in some detail in "The Repeated Setbacks of HIV Vaccine Development Laid the Groundwork for SARS-COV-2 Vaccine" (March 2021, NBER Working Paper 28587). As he points out, before AIDS the common vaccines were "dead" or "live." A "dead" vaccine (like the polio vaccine) treated the infectious organism with heat or chemicals so that it was no longer infectious, but still helped the body to produce an immune response. A "live" vaccine (like the measles vaccine) ran the infectious organism through animals or other treatments to produce a version that produced only a very mild infection--but still caused the body to produce an immune response. 

But neither method worked in trying to produce a vaccine for the highly mutable AIDS virus. Instead, working on an AIDS vaccine forces researchers to think about how a vaccine might attack the molecular structure of AIDS. I won't embarrass myself by trying to summarize the progression of scientific research, but it turns out that a key "spike" protein that had been studies in the HIV research turned out to be the key protein for the mRNA vaccines that are being used against COVID-19. In addition, discussions in the trade press suggest that, in turn, the knowledge gained from the COVID-19 vaccine about mRNA technologies could help lead to a vaccine for malaria, hepatitis C, dengue--and even HIV.  

The broader point is that although private pharma firms clearly have strong incentives to do R&D aimed at large existing drug markets, there is a broad social benefit from having research into many areas of vaccines and other drugs, because you can't know in advance how scientific progress will lead to practical gains. 

Tuesday, April 13, 2021

What Do You Call a Bigger Wave of Debt?

Sometimes you work on a big and worthwhile project, and then find yourself to be overtaken by events. The project remains worthwhile, but it can suddenly feel outdated. Thus, I found myself wincing in sympathy at  Global Waves of Debt: Causes and Consequences, a World Bank report written by M. Ayhan Kose, Peter Nagle, Franziska Ohnsorge, and Naotaka Sugawara and published in March 2021. 

The problem is that the report focuses on four major waves of government debt up through 2018. Of course, when the authors launched into this project they had no way of knowing that the world was on the cusp of a COVID-related surge in government debt starting in 2020.  But the result is that the authors are warning of the potential dangers of a wave of government debt given the debt levels of 2018--but pandemic-related debt wave is now bigger than they would have anticipated. For example, they write: 

The global economy has experienced four waves of broad-based debt accumulation over the past 50 years. In the latest wave, underway since 2010, global debt has grown to an all-time high of 230 percent of gross domestic product (GDP) in 2018. The debt buildup was particularly fast in emerging market and developing economies (EMDEs). Since 2010, total debt in these economies has risen by 54 percentage points of GDP to a historic peak of about 170 percent of GDP in 2018. Following a steep fall during 2000-10, debt has also risen in low-income countries to 67 percent of GDP ($268 billion) in 2018, up from 48 percent of GDP (about $137 billion) in 2010. ...

Before the current wave, EMDEs [emerging market and developing economies] experienced three waves of broad-based debt accumulation. The first wave spanned the 1970s and 1980s, with borrowing primarily accounted for by governments in Latin America and the Caribbean region and in low-income countries, especially in Sub-Saharan Africa. The combination of low real interest rates in much of the 1970s and a rapidly growing syndicated loan market encouraged these governments to borrow heavily.

The first wave culminated in a series of crises in the early 1980s. Debt relief and restructuring were prolonged in the first wave, ending with the introduction of the Brady Plan in the late 1980s for mostly Latin American countries. The Plan provided debt relief through the conversion of syndicated loans into bonds, collateralized with U.S. Treasury securities. For low-income countries, substantial debt relief came in the mid-1990s and early 2000s with the Heavily Indebted Poor Countries initiative and the Multilateral Debt Relief Initiative, spearheaded by the World Bank and the International Monetary Fund.

The second wave ran from 1990 until the early 2000s as financial and capital market liberalization enabled banks and corporations in the East Asia and Pacific region and governments in the Europe and Central Asia region to borrow heavily, particularly in foreign currencies. It ended with a series of crises in these regions in 1997-2001 once investor sentiment turned unfavorable. The third wave was a run-up in private sector borrowing in Europe and Central Asia from European Union headquartered “mega-banks” after regulatory easing. This wave ended when the global financial crisis disrupted bank financing in 2007-09 and tipped several economies in Europe and Central Asia into recessions. ... 

The latest wave of debt accumulation began in 2010 and has already seen the largest, fastest, and most broad-based increase in debt in EMDEs in the past 50 years. The average annual increase in EMDE debt since 2010 of almost 7 percentage points of GDP has been larger by some margin than in each of the previous three waves. In addition, whereas previous waves were largely regional in nature, the fourth wave has been widespread with total debt rising in almost 80 percent of EMDEs and rising by at least 20 percentage points of GDP in just over one-third of these economies. ... 
Since 1970, there have been 519 national episodes of rapid debt accumulation in 100 EMDEs, during which government debt typically rose by 30 percentage points of GDP and private debt by 15 percentage points of GDP. The typical episode lasted about eight years. About half of these episodes were accompanied by financial crises, which were particularly common in the first and second global waves, with severe output losses compared to countries without crises. Crisis countries typically registered larger debt buildups, especially for government debt, and accumulated greater macroeconomic and financial
vulnerabilities than did noncrisis countries.
Although financial crises associated with national debt accumulation episodes were typically triggered by external shocks such as sudden increases in global interest rates, domestic vulnerabilities often amplified the adverse impact of these shocks. Crises were more likely, or the economic distress they caused was more severe, in countries with higher external debt—especially short-term—and lower international reserves.
Of course, pandemic-related debt has increased the previous debt projections. Here are some figures from the IMF Fiscal Monitor published in April 2021. The first panel shows debt/GDP ratios from 2007 to 2021. The yellow lines show interest payments, which so far have been able to remain fairly low thanks to the prevailing low interest rates. The rising debt/GDP ratios in emerging market and developing economies are clear.
The second set of panels shows how debt projections have changes since the pandemic for these three groups of countries. The bars show annual deficit/GDP predictions, pre- and post-pandemic, while the lines show the shift in accumulated debt, pre- and post-pandemic. 
As the authors of the World Bank report above point out in their discussion, rising debt does not automatically bring disaster. The sharp-eyed reader will note that the debt/GDP ratios for advanced economies are higher than those for emerging market and developing economies. There is a general pattern that as an economy develops, the financial sector of that economy also develops in ways that typically lead to a higher debt/GDP ratios. More broadly, the depth of the financial sector and the sophistication of financial regulation will make a big difference. 

On the other side, debt is often referred to as "leverage," because it magnifies the outcome of both positive and negative events for a national economy (or for a company or a household)  . With a higher level of debt, an adverse event can easily become two problems--the adverse event itself and also a debt crisis. It is concerning that this risk was viewed as high for many countries around the world, even before they increased their debt during the pandemic. 

Monday, April 12, 2021

The US Productivity Slowdown After 2005

In the long run, a rising standard of living is all about productivity growth. When the average person in a country produces more per hour worked, then it becomes possible for the average person to consume more per hour worked. Yes, there is a meaningful and necessary role for redistribution to the needy. But the main reason why societies get rich is by redistributing more: rather, societies are able to redistribute more because rising productivity expands the size of the overall pie. 

In the latest issue of the Monthly Labor Review from the US Bureau of Labor Statistics, Shawn Sprague provides an overview in "The U.S. productivity slowdown: an economy-wide and industry-level analysis" (April 2021). In particular, he is focused on the slowdown in US productivity growth since 2005, after a resurgence of productivity growth in the previous decade. Here's a figure showing the longer-run patterns, which have birthed roughly a jillion research papers. 
Notice that total productivity growth is robust in the decades after World War II, from 1948 to 1973. Then there is a productivity slowdown, especially severe in the stagflationary 1970s, but continuing through the 1980s and into the 1990s. There's a productivity surge from 1997 to 2005, commonly attributed to acceleration in the power and deployment of computing and information technology. But just when it seemed as if the economy might be moving back to a higher sustained rate of productivity growth, then starting around 2005, productivity sagged back to the levels of the slowdown in the 1970s and 1980s. 

The figure also shows how economists break down sources of economic growth. First look at how much the quality of the labor force has improved, as measured by education and experience. Then look at how much capital the average worker is using on the job. After calculating how much productivity growth can be explained by those two factors, what is left over is called "multifactor productivity growth." This is often interpreted as changes in technology--broadly understood to include not just new inventions but all the ways that production can be improved. But as the economist Moses Abramowitz said years ago, measuring multifactor productivity growth as what is left over, after accounting for other factors, means that productivity growth is "the measure of our ignorance."

As Sprague points out, variations in multifactor productivity growth are the biggest part of changes in productivity over time. 
The deceleration in MFP growth—the largest contributor to the slowdown—explains 65 percent of the slowdown relative to the speedup period; it also explains 79 percent of the sluggishness relative to the long-term historical average rate. The massive deceleration in MFP growth is also emblematic of a broader phenomenon shown in figure 2. We can see that throughout the historical period since WWII, the majority of the variation in labor productivity growth from one period to the next was from underlying variation in MFP growth, rather than from the other two components.
However, the most recent slowdown in productivity also seems to have something to do with capital investment. Sprague again: 
At the same time, in addition to the notable variation in MFP growth during the recent periods, something unprecedented about these recent periods was the additional contribution from variation in the contribution of capital intensity. The contribution of capital intensity had previously remained within a relatively small range (0.7 percent to 1.0 percent) during the first five decades of post-WWII periods, but then in the 1997–2005 period, the measure nearly doubled, from 0.7 percent up to 1.3 percent, followed by nearly halving to 0.7 percent in the 2005–18 period. ... The contribution of capital intensity accounts for 34 percent of the labor productivity slowdown relative to the speedup period and explains 25 percent of the sluggishness relative to the long-term historical average rate.
What are some possible explanations for the growth slowdown? As Sprague writes: [N]not only has the productivity slowdown been one of the most consequential economic phenomena of the last two decades, but it also represents the most profound economic mystery during this time ..." Sprague does a detailed breakdown of economy-wide factors that may have contributed to the productivity slowdown as well as industry-specific factors. Here, I'll just mention some of the main themes. 

A first set of explanations focus on the Great Recession, and the sluggish recovery afterwards. One can argue, for example, that when the financial sector is in turmoil and an economy is growing slowly, firms have less ability and less incentive to raise capital for productivity gains. This seems plausible, and surely has some truth in it, but it also has some weak spots. For example, the productivity slowdown in the data pretty clearly starts a few years before the Great Recession. Also, one might argue that in difficult times, firms might have more incentive to seek out productivity gains. Finally, it feels like a circular argument to ask "why aren't additional inputs producing output gains as large as before?" and then to answer "because the output gains were not as large as before." 

A second explanation is that productivity gains at the frontier have not actually slowed down: instead, what has slowed down is the rate at which these gains are diffusing to the rest of the economy. From this point of view, the real news is a wider dispersion in productivity growth within industries, as productivity laggards fall farther behind leaders (for discussion, see here and here). At a more detailed level, "not many of the firms that have been innovating have not similarly been able to scale up and hire more employees commensurate with their improved productivity." It could also be that there are certain characteristics of productivity growth leaders--like an ability to apply leading-edge information technology to business processes throughout the company--that are especially hard for productivity laggards to follow. This lack of reallocation in the economy toward high-productivity firms may be related to other prominent issues like a decrease in levels of competition in certain industries or rising inequality. 

A third explanation is that the productivity surge from 1997-2005 should be be viewed as a one-time anomalous event, and what's happening here is a long-term slowdown in the rate of productivity growth. Sprague writes: 
One underlying rationale for this potential story is provided by Joseph A. Tainter. This author offers that, in general, as complexity in a society increases following initial waves of innovation, further innovations become increasingly costly because of diminishing returns. As a result, productivity growth eventually succumbs and recedes below its once torrid pace: “As easier questions are resolved, science moves inevitably to more complex research areas and to larger, costlier organizations,” clarifying that “exponential growth in the size and costliness of science, in fact, is necessary simply to maintain a constant rate of progress.” Nicholas Bloom, Charles I. Jones, John Van Reenen, and Michael Webb offer supporting evidence for this view regarding the United States, asserting that given that the number of researchers has risen exponentially over the last century—increasing by 23 times since 1930—it is apparent that producing innovations has become substantially more costly during this period.
Again, this explanation has some plausibility. But it also feels as if the modern economy does have a substantial number of innovations,  and the puzzle is why they aren't showing up in the productivity statistics.

A fourth set of explanations digs down into which industries showed the biggest falls in productivity after 1995 and which ones showed the biggest rises. Here's an illustrative figure. The industries with the biggest losses are computers/electronics products, along with retail and wholesale trade. 
This selection of industries may feel counterintuitive, but remember that this is a comparison between two time periods. Thus, the figure isn't saying that productivity outright declined in these sectors--only that the gain after 2005 was slower than the gain in the pre-2005 decade. In computers, for example, rate of decline in  prices of microprocessors began to slow down in the mid-2000s. Similarly, retail and wholesale businesses underwent a huge change in the late 1990s and early 2000s that increased their productivity, but then the changes after that time were more modest.  In short, this is the detailed version industry-level version of the argument that the productivity rise from 1997-2005 was a one-time blip.

A final explanation, not really discussed by Sprague, is worth considering as well: Perhaps we are entering an economy where certain kinds of gains in output are not well-reflected in measured GDP gains. For example, imagine that the development of COVID-19 vaccines halts the virus. The social welfare gains from such vaccines are much larger than just the measured gains to GDP. Or imagine that a set of innovations makes it possible to reduce carbon emissions in a way that reduces the risk of climate change. From a social welfare perspective, this avoided risk would be a huge benefit, but it wouldn't necessarily show up in the form of a more rapidly expanding GDP. 

Or consider the range of online activities now available: entertainment, social, health, education, retail, working-away-from-the-office. Add in the services that are available at no direct financial cost, like email, software, shared websites, cloud storage, and so on. It seems plausible to me that the social benefits from this expanding set of options are much greater than how they are measured in GDP terms--for example, by how much I pay for my home internet service or how much ad revenue is taken in by companies like Google and Facebook. 

Again, this thesis has some plausibility. One never wants to fall into the trap of thinking that output as measured by GDP is also a measure of social welfare. It's well-known that GDP measures money spent on health care and money spent on environmental protection, but will have troubles measuring gains in actual health or the environment. GDP will often have a hard time measuring gains in variety and flexibility as well.  

But this set of explanations also raises issues of its own. It suggests that people may be experiencing gains in their standard of living that are not reflected in their paychecks. In contrast, when productivity gains in terms of output per worker slow down, we are talking about output as measured by what is bought and sold in the economy. In short, gains in measured productivity are what can help to produce pay raises. But if these other kinds of gains are meaningful, they can't be used to pay your rent or your taxes.  

Friday, April 9, 2021

Electrify Everything: Some Limitations of Solar and Wind

The "electrify everything" vision supports generating electricity in low-carbon or zero-carbon ways, and then also using electricity to replace other sources of energy like oil, coal, and natural gas--for example, by using cars powered by electricity rather than vehicles rather than by gasoline. It seems to me that some advocates of this vision are (implicitly) hoping that solar and wind power can meet most or all of future energy needs. That's not likely, as discussed in "Clean Firm Power is the Key to California’s Carbon-Free Energy Future" by Jane C.S. Long, Ejeong Baik, Jesse D. Jenkins, Clea Kolster, Kiran Chawla, Arne Olson, Armond Cohen, Michael Colvin, Sally M. Benson, Robert B. Jackson, David G. Victor, and Steven Hamburg (Issue in Science and Technology, March 24, 2021). 

Just to be clear, this group of authors can't be caricatured as naysayers on non-carbon energy. For example, two of the authors are scientists with the Environmental Defense Fund (Long and Hamburg). another is director of the Clean Air Task Force (Cohen), and another is co-director of the Deep Decarbonization Initiative (Victor). Others are researchers and scientists either in academia (including at Stanford and Princeton) or involved in green investment funds. The publication is published by the National Academies of Sciences, Engineering, and Medicine and Arizona State University.

They start with the fact that California has announced that it wants to have zero net emissions of carbon by 2045. This means not only having all electricity generated by non-carbon methods (rather than, say by natural gas or coal), but also following the "electrify everything" agenda so that electricity replaces fossils fuels for transportation, heating homes and buildings, and industrial uses. In short, total electricity output will need to double, and noncarbon energy sources will need to more than double. 

Can solar and wind handle this shift? The authors write: 

Groups from Princeton University, Stanford University, and Energy and Environmental Economics (E3), a San Francisco-based consulting firm, each ran separate models that sought to estimate not only how much electricity would cost under a variety of scenarios, but also the physical implications of building the decarbonized grid. How much new infrastructure would be needed? How fast would the state have to build it? How much land would that infrastructure require? ...  Despite distinct approaches to the calculations, all the models yielded very similar conclusions. The most important of these was that solar and wind can’t do the job alone. ...

Although the costs of solar and wind power are now fully competitive with other sources per kilowatt-hour, their inescapable variability creates reliability problems. Average daily output from today’s California solar and wind infrastructure in the winter declines to about a third of the summer peak. Periodic large-scale weather patterns extending over 1,000 kilometers or more, known as dunkelflaute (the German word for dark doldrums), can also drive wind and solar output to low levels across the region that can last days, or even several months. Average wind and solar outputs also vary from year to year, particularly for wind power.

What can be used to address this problem? One possibility is to store energy in batteries. But as the authors write: "Better batteries play a key role in a carbon-free grid; they provide flexibility on hourly and diurnal time scales, for instance by saving some solar-generated electricity from late afternoon into the evening. But economical batteries cannot provide energy for weeks at a time." They point out that "the largest battery storage facility in the world is being built at Morro Bay ... and will be able to provide power for 4 hours, or 2.4 gigawatt-hours, enough to power 80,000 homes for about a day." But relying on solar and wind, together with battery storage, would require that California alone build hundreds of Morro Bay-sized batter facilities. 

Another possible back-up plan is to build large amounts of extra capacity for solar and wind power. Imagine that during certain times weather prevents solar from working well, and a given solar panel can generate only (say) one-tenth as much power as usual. But 10 times as many solar panels operating at a small fraction of their top power could make up the difference. The issue here is that there would be a huge and costly overcapacity of solar panel installations much of the time. Not only would building thos overcapacity of solar/wind (along with the additional required transmission liness) drive electricity costs up, it may not be physically possible. The authors write that this approach "would require expanding solar capacity at a rate 10 times higher than has ever been done before. There may not be enough people, supplies, or land to do this." For example, investing in solar overcapacity in California means that "more than 6,250 square miles of land would be required—bigger than the combined size of Connecticut and Rhode Island."

Thus, the results of this modelling drive the authors to the need for what they call "clean firm power," by which they mean " carbon-free power sources that can be relied on whenever needed, for as long as they are needed." 

Like what? One theoretical option would be to keep using natural gas for generating electricity, but combined with future generations of carbon capture and storage technology.  Another option is nuclear power. Geothermal energy is option that would work at certain locations in California. In other states, hydroelectric power might play a role. The researchers also mention the possibility of producing hydrogen from noncarbon sources.  

The authors aren't wedded to any particular source of "clean firm power." But their calculations emphasize that even in a solar-friendly state like California, solar is only part of the answer to a low-carbon future and a willingness to rely on "clean firm power" will be needed, too.  

Thursday, April 8, 2021

Dispose of Masks Properly, Or Else

II suppose it was pretty much inevitable that when a few billion disposable masks were distributed around the world in response to the pandemic, they would become a garbage problem, too. 

The first report I saw on this subject was called "Masks on the Beach: The Impact of COVID-19 on Marine Plastic Pollution," by Teale Phelps Bondaroff  and Sam Cooke from a marine conservation nonprofit called OceansAsia (December 2020). They write:

The number of masks entering the environment on a monthly basis as a result of the COVID-19 pandemic is staggering. From a global production projection of 52 billion masks for 2020, we estimate that 1.56 billion masks will enter our oceans in 2020, amounting to between 4,680 and 6,240 metric tonnes of plastic pollution. These masks will take as long as 450 years to break down and all the while serve as a source of micro plastic and negatively impact marine wildlife and ecosystems.
Of course, the plastic in masks (and latex gloves and other personal protection equipment) is only a small proportion of overall plastic waste ending up in oceans.
Plastic production has been steadily increasing, such that in 2018, more than 359 million metric tonnes was produced. Estimates suggest that 3% of this plastic enters our oceans annually, amounting to between 8 to 12 million metric tonnes a year. This plastic does not ‘go away,’ but rather accumulates, breaking up into smaller and smaller pieces. Annually, it is estimated that marine plastic pollution kills 100,000 marine mammals and turtles, over a million seabirds, and even greater numbers of fish, invertebrates, and other marine life. Plastic pollution also profoundly impacts coastal communities, fisheries, and economies. Conservative estimates suggest that it could cost the global economy $13
billion USD per year, and lead to a 1-5% decline in ecosystem services, at a value of between $500 to $2,500 billion USD.
Articles in academic journals are now beginning to emerge that echo this point. For example, Elvis Genbo Xu and Zhiyong Jason Ren have written "Preventing masks from becoming the next plastic problem" in Frontiers of Environmental Science & Engineering (February 28, 2021, vol. 15, article #125). They write (citations omitted):
Face masks help prevent the spread of coronavirus and other diseases, and mass masking is recommended by almost all health groups and countries to control the COVID-19 pandemic. Recent studies estimated an astounding 129 billion face masks being used globally every month (3 million / minute) and most are disposable face masks made from plastic microfibers. ... This puts disposable  masks on a similar scale as plastic bottles, which is estimated to be 43 billion per month. However, different from plastic bottles, ~ 25% of which is recycled, there is no official guidance on mask recycle, making it more likely to be disposed of as solid waste. ... It is imperative to launch coordinated efforts from environmental scientists, medical agencies, and solid waste managing organizations, and the general public to minimize the negative impacts of disposal mask, and eventually prevent it from becoming another too-big-to-handle problem.

As another example, Auke-Florian Hiemstra, Liselotte Rambonnet, Barbara Gravendeel, and Menno Schilthuizen write about "The effects of COVID-19 litter on animal life" in Animal Biology (advance publication on March 22, 2021). They write (again, citations omitted): 

To protect humans against this virus, personal protective equipment (PPE) is being used more frequently. China, for example, increased face mask production by 450% in just one month. It is estimated that we have a monthly use of 129 billion face masks and 65 billion gloves globally. Similar to the usage of other single-use plastic items, this also means an increase of PPE littering our environment. PPE litter, also referred to as COVID-19 litter, mainly consists of single-use (usually latex) gloves and single-use face masks, consisting of rubber strings and mostly polypropylene fabric. Three months after face masks became obligatory in the UK, PPE items were found on 30% of the monitored beaches and at 69% of inland clean-ups by the citizen scientists of the Great British Beach Clean. Even on the uninhabited Soko Islands, Hong Kong, already 70 discarded face masks were found on just a 100-meter stretch of beach. A growing public concern about PPE litter became apparent during March and April 2020, as a Google News search on ‘PPE’ and ‘litter’ showed a sudden increase in news articles. As a response to the increase of COVID-19 litter, many states in the USA have raised the fines for littering PPE, sometimes up to $5500 as in Massachusetts. ... While the percentage of COVID-19-related litter may be small in comparison with packaging litter ... [b]oth masks and gloves pose a risk of entanglement, entrapment and ingestion, which are some of the main environmental impacts of plastic
 pollution ...

It is striking that all the reported findings of entanglement, entrapment, ingestion, and incorporation of PPE into nests so far involved single-use products. Switching to reusables will result in a 95% reduction in waste ...  To minimize the amount of COVID-19 litter and its effect on nature, we urge that, when possible, reusable alternatives are used.

I'll spare you the pictures of fish and wildlife tangled up in plastic masks and gloves, and just say it in words. Wearing a mask when in proximity to others was a reasonable step to take during this past year (as discussed here and here). But disposing of masks properly matters, too.

Wednesday, April 7, 2021

Evolving Patterns of Innovation Across States and Industries

Patents are an imperfect measure of innovation, but they can nonetheless convey the underlying story. 
Jesse LaBelle and Ana Maria Santacreu offer some interesting descriptions of how patent patterns changed between the 1980s and the 2000s in "Geographic Patterns of Innovation Across U.S. States: 1980-2010 (Economic Synopses, Federal Reserve Bank of St. Louis, 2021, #5). 

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). 
The two figures also show a geographic shift in the patterns of innovation. The authors write: 
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. 

Tuesday, April 6, 2021

Policy for the Next Pandemics

After a year of pandemic, one of the last topics I want to think seriously about is a future of pandemics. But with pandemics as with so many other problems, not thinking about it doesn't make it go away. Monica de Bolle, Maurice Obstfeld, and Adam S. Posen have edited a short 12-chapter e-book titled Economic Policy for a Pandemic Age: How the World Must Prepare (Peterson Institute for International Economics, April 2021). The book considers the discomfiting possibilities that COVID-19 may be a chronic pandemic for some time to come and what lessons might be learned for future pandemics. 

Several of the essays warn about the emergence of COVID variants around the world, including the UK, Brazilian, and South African variants that are known, but quite possibly others variants that are not yet known. Chad P. Bown, Monica de Bolle, and Maurice Obstfeld tell the story of the Brazilian city of Manaus in their essay, "The pandemic is not under control anywhere unless it is controlled everywhere."
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. 

I can see the appeal of this approach to a certain kind of administrative mind. There's a master list on a government-run computer, and priorities can be set. But of course, this approach also assumes that you have internet access and are comfortable navigating the government website, that you receive the follow-up messages and respond, and that you have the transportation and flexibility to keep what may be several vaccination appointments. Some people will be a lot better-positioned to jump through these hoops than others: for example, my elderly parents (who live in their own home) would probably not have been vaccinated except for family members who got them registered, followed up, and transported them to the designated location. And of course, this entire process also assumes that you want the vaccine enough to jump through these hoops. Mary E. Lovely and David Xu discuss some of these topics in their essay, "For a fairer fight against pandemics, ensure universal internet access." 

I remember as a small boy when we had a mass vaccination at school (maybe for what was then called "German measles" and now is called "rubella"?). We were marched out of our classrooms, lined up in the hallways, and then paraded by the nurses. That's not a workable model for the general population in 2021. But we need thinking about how to vaccinate many different ways--via workplaces, pharmacies, maybe roving vaccine-mobiles at familiar places like libraries, churches, and so on. 

As David Wilcox points out in his essay, "US vaccine rollout must solve challenges of equity and hesitancy," one result has been a large and growing backlog of available vaccine doses that have not been distributed. Wilcox writes (footnotes and references to figures omitted): 

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. 

Friday, April 2, 2021

Nature as Part of the Stock of Humanity's Wealth

I despair of writing a blog post that captures a sense of The Economics of Biodiversity: The Dasgupta Review (February 2021) The report is 600 pages. It is a UK government-backed report, technically the "Final Report of the Independent Review on the Economics of Biodiversity led by Professor Sir Partha Dasgupta." If you know Dasgupta or his remarkable output of deeply insightful, nuanced, and humane work, you need no further persuasion to take a look. If not, this is a chance to get acquainted. 

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.

Wednesday, March 31, 2021

The Spread in Labor Costs Across the European Union

In a common market, labor costs will look fairly similar across areas. Sure, there will be some places with differing skill levels, different mixes of industry, and different levels of urbanization, thus leading to somewhat higher or lower labor costs. But over time, workers from lower-pay areas will tend to relocate to higher-pay areas and employers in higher-pay areas will tend to relocate to lower-pay areas. Thus, it's interesting that the European Union continues to show large gaps in hourly labor costs. 

Here are some figures just released by Eurostat (March 31, 2021) on labor costs across countries. As you can see, hourly labor costs are up around €40/hour in Denmark, Luxembourg, and Belgium, but €10/hour or below in some countries of eastern Europe like Poland or the Baltic states like Lithuania. (For comparison, a euro is at present worth about $1.17 in US dollars. Norway and Iceland are not part of the European Union, but they are part of a broader grouping called the European Economic Area.)

Another major difference across EU countries is in what share of the labor costs paid by employers represent non-wage costs--that is, payments made by employers directly to the government for pensions and other social programs. In France and Sweden, these non-wage costs are about one-third of total hourly labor costs. It's interesting that in Denmark, commonly thought of as a Scandinavian high social-spending country, non-wage costs are only about 15% of total labor costs--because Denmark chooses not to finance its social spending by loading up the costs on employers to the same extent. 

These differences suggest some of the underlying stresses on the European Union. Given these wage gaps across countries, tensions in high-wage countries about migration from lower-wage countries and competition from firms in lower-wage countries will remain high. The large differences in non-wage costs as part of what employers pay for labor represents some of the dramatic differences across EU countries in levels of social benefits and how those benefits are financed. Proposals for European-wide spending and taxing programs, along with the desire of higher-income EU countries not to pay perpetual subsidies to lower-income countries, run into these realities every day. 

For comparison, here are some recent figures from the US Census Bureau on average employer costs per hour  across the 10 Census "divisions."  Yes, there are substantial differences between, say, the Pacific or New England divisions and the East South Central or West South Central divisions. But the United States is much more of a unified market than the European Union, both in wage levels and in the way non-wage labor costs are structured, and so the gaps are much smaller. 

Tuesday, March 30, 2021

Data and Development

The 2021 World Development Report. one of the annual flagship reports of the World Bank, is focused on the theme of "Data for Better Lives" (released in March 2021). The WDR is one of the flagship reports of the World Bank, and it is always a nice mixture of big-picture overview and specific examples. Here, I'll focus on a few of the themes that occurred to me in reading the report. 

First, there are lots of examples of how improved data can help economic development. For many economists, the first reaction is to think about dissemination of information related to production and markets. As the report notes: 
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.
Other examples are from the private sector, like logistics platforms to help coordinate trucking services.

These platforms (often dubbed “Uber for trucks”) match cargo and shippers with trucks for last-mile transport. In lower-income countries, where the supply of truck drivers is highly fragmented and often informal, sourcing cargo is a challenge, and returning with an empty load contributes to high shipping costs. In China, the empty load rate is 27 percent versus 13 percent in Germany and 10 percent in the United States. Digital freight matching overcomes these challenges by matching cargo to drivers and trucks that are underutilized. The model also uses data insights to optimize routing and provide truckers with integrated services and working capital. Because a significant share of logistics services in lower-income countries leverage informal suppliers, these technologies also represent an opportunity to formalize services. Examples include Blackbuck (India), Cargo X (Brazil), Full Truck Alliance (China), Kobo360 (Ghana, Kenya, Nigeria, Togo, Uganda), and Lori (Kenya, Nigeria, Rwanda, South Sudan, Tanzania, Uganda). In addition to using data for matching, Blackbuck uses various data to set reliable arrival times, drawing on global positioning system (GPS) data and predictions on the length of driver stops. Lori tracks data on costs and revenues per lane, along with data on asset utilization, to help optimize services. Cargo X charts routes to avoid traffic and reduce the risk of cargo robbery. Kobo360 chooses routes to avoid armed bandits based on real-time information shared by drivers. Many of the firms also allow shippers to track their cargo in real time. Data on driver characteristics and behavior have allowed platforms to offer auxiliary services to address the challenges that truck drivers face. For example, some platforms offer financial products to help drivers pay upfront costs, such as tolls, fuel, and tires, as well as targeted insurance products. Kobo360 claims that its drivers increase their monthly earnings by 40 percent and that users save an average of about 7 percent in logistics costs. Lori claims that more than 40 percent of grain moving through Kenya to Uganda now moves through its platform, and that the direct costs of moving bulk grain have been reduced by 17 percent in Uganda.

Some examples combine government efforts with privately-generated data. For example, there are estimates that reducing road mortality by half could save 675,000 lives a year. But how can the the government know where to invest on infrastructure and enforcement efforts?  

Unfortunately, many countries facing these difficult choices have little or no data on road traffic crashes and inadequate capacity to analyze the data they do have. Official data on road traffic crashes capture only 56 percent of fatalities in low- and middle-income countries, on average. Crash reports exist, yet they are buried in piles of paper or collected by private operators instead of being converted into useful data or disseminated to the people who need the information to make policy decisions. In Kenya, where official figures underreport the number of fatalities by a factor of 4.5, the rapid expansion of mobile phones and social media provides an opportunity to leverage commuter reports on traffic conditions as a potential source of data on road traffic crashes. Big data mining, combined with digitization of official paper records, has demonstrated how disparate data can be leveraged to inform urban spatial analysis, planning, and management. Researchers worked in close collaboration with the National Police Service to digitize more than 10,000 situation reports spanning from 2013 to 2020 from the 14 police stations in Nairobi to create the first digital and geolocated administrative dataset of individual crashes in the city. They combined administrative data with data crowdsourced using a software application for mobile devices and short message service (SMS) traffic platform, Ma3Route, which has more than 1.1 million subscribers in Kenya. They analyzed 870,000 transport-related tweets submitted between 2012 and 2020 to identify and geolocate 36,428 crash reports by developing and improving natural language processing and geoparsing algorithms. ... By combining these sources of data, researchers were able to identify the 5 percent of roads ... where 50 percent of the road traffic deaths occur in the city ... This exercise demonstrates that addressing data scarcity can transform an intractable problem into a more
manageable one.
There are lots of other examples in the report. "For remote populations around the world, receiving specialized medical care has been nearly impossible without having to travel miles to urban areas. Today, telehealth clinics and their specialists can monitor and diagnose patients remotely using sensors that collect patient health data and AI that helps analyze such data." Similar points can be made about delivering education services. "DigiCow, pioneered in Kenya, keeps digital health records on cows and matches farmers with qualified veterinary services."

My second main reaction to the report is that, despite the many individual examples of how data can help in economic development, there are substantial gaps in the data infrastructure for developing economies. At the national level, most countries now do a full census about once a decade, which often provide a reasonable population count at that time. But details on the population are often scanty. The report notes: 
Lack of completeness is often less of a problem in census and survey data because they are designed to cover the entire population of interest. For administrative data, the story is different. Civil registration and vital statistics systems (births and deaths) are not complete in any low-income country, compared with completeness in 22 percent of lower-middle-income countries, 51 percent of upper-middle-income countries, and 95 percent of high-income countries. These gaps leave about 1 billion people worldwide
without official proof of identity. More than one-quarter of children overall, and more than half of children in Sub-Saharan Africa, under the age of five are not registered at birth.
As another example of missing data, "Ground-based sensors, deployed in Internet of Things systems, can measure some outcomes, such as air pollution, climatic conditions, and water quality, on a continual basis and at a low cost. However, adoption of these technologies is still too limited to provide timely data at scale, particularly in low-income countries."

In some cases, it's possible to use other data sources to fill in some of the gaps. For example, measuring poverty is often done by carrying out much more detailed household surveys in a few areas, and then using the once-a-decade census data to project this to the country as a whole. The result is a reasonable statistical estimate of the poverty rate for the country as a whole, but not much knowledge about the location of actual poor people across the country. The report notes: 
Estimates of poverty are usually statistically valid for a nation and at some slightly finer level of geographic stratification, but rarely are such household surveys designed to provide the refined profiles of poverty that would allow policies to mitigate poverty to target the village level or lower. Meanwhile, for decades high-resolution poverty maps have been produced by estimating a model of poverty from survey data and then mapping this model onto census data, allowing an estimate of poverty for every household in the census data. A problem with this approach is that census data are available only once a decade (and in many poorer countries even less frequently). Modifications of this approach have replaced population census data with CDR [call detail record, from phones] data or various types of remote sensing data (typically from satellites, but also from drones). This repurposing of CDR or satellite data can provide greater resolution and timelier maps of poverty. For example, using only household survey data the government of Tanzania was able to profile the level of poverty across only 20 regions of the country’s mainland. Once the household survey data were combined with satellite imagery data, it became possible to estimate poverty for each of the country’s 169 districts (map O.3). Combining the two data sources increased the resolution of the poverty picture by eightfold with essentially no loss of precision.
The complimentary problem with lack of data is that is that data infrastructure in many low-income countries is often weak. This is a problem in the obvious way that many people and firms have a hard time accessing available data. But it's also a problem in a less obvious way: people who can't access data also can't contribute to data, and thus can't answer surveys, report on local conditions, offer feedback and advice, or offer access to data on purchase patterns and even (via cell-phone data) on location patterns. As the report notes: 
That said, efforts to move toward universal access face fundamental challenges. First, because of the continual technological innovation in mobile technology service, coverage is a moving target. Whereas in 2018, 92 percent of the world’s population lived within range of a 3G signal (offering speeds of 40 megabytes per second), that share dropped to 80 percent for 4G technology (providing faster speeds of 400 megabytes per second, which are needed for more sophisticated smartphone applications that can promote development). The recent commercial launch of 5G technology (reaching speeds of 1,000 megabytes per second) in a handful of leading-edge markets risks leaving the low-income countries even further behind. ...
The second challenge is that a substantial majority of the 40 percent of the world’s population who do not use data services live within range of a broadband signal. Of people living in low- and middle-income countries who do not access the internet, more than two-thirds stated in a survey that they do not know what the internet is or how to use it, indicating that digital literacy is a major issue.
Affordability is also a factor in low- and middle-income countries, where the cost of an entry-level smartphone represents about 80 percent of monthly income of the bottom 20 percent of households. Relatively high taxes and  duties further contribute to this expense. As costs come down in response to innovation, competitive pressures, and sound government policy, uptake in use of the internet will likely increase. Yet even among those who do use the internet, consumption of data services stands at just 0.2 gigabytes per capita per month, a fraction of what this Report estimates may be needed to perform basic social and economic functions online.
As a third reaction, the report often refers to potential dangers of increasing the role of data in an economy, including invasions of personal privacy and the danger of monopolistic companies using data to exploit consumers. In high-income countries and some middle-income countries, these are certainly important subjects for discussion. But in the context of low-income economies, it seems to me that the challenges of the lack of data are so substantial that worries about problems from widespread data are premature. 

The situation reminds me of Joan Robinson's comment in her 1962 book Economic Philosophy (p. 46 of my Pelican Book edition): "The misery of being exploited by capitalists is nothing compared to the misery of not being exploited at all." In a similar spirit, one might say that the misery of data being misused or monopolized is nothing compared to the misery of data barely being used at all. 

Finally, data is of course not valuable in isolation, but rather because of the ways that it may help people and firms and government to choose different actions. In the examples above, for instance, data can help government understand the location of social needs, or help a farmer adjust agricultural practices, or help a producer ship a products to a buyer, or a provide a method for someone to find work in the gig economy.  Data flows are also a feedback mechanism, both for markets and for government Without data to show the extent of problems, it's harder to hold public officials accountable.  

For some previous posts with additional discussion of government data and academic data, much of it from the context of the US and other high-income countries, see: