Sunday, June 16, 2019

Mortality Rate of Children Over the Last Two Millennia

The global mortality rate of infants and youth up to the age of 15, based on an average of many studies, was almost one-half (46.2%) for the two millennia up to about 1900. By 1950, it was 27%. By 2017, it had fallen to 4.6%,.

The global infant mortality rate for children under the age of 1, again based on the average of many studies, was more than one-quarter (26.9%) for the two millennia up to about 1900. By 1950, it was 16%. By 2017, it had fallen to 2.9%

Here's a figure showing the patterns from Max Roser at the "Our World in Data" website (June 11, 2019). Of course, you will need to expand the version here, or go to the other website, to see the details.

I'll put off all the arguments over reasons why this happened and what it means for public policy for another day. It's Father's Day today, I just want to take a few minutes and marvel at this fundamental change in what it means to be a parent in the 21st century, especially in a high-income country. My children were much less likely to die.

Saturday, June 15, 2019

Marijuana Policy: Choosing Between Disastrous or Unpalatable

Slowly and with considerable uncertainty, the United States is altering its marijuana laws. Mark A. R. Kleiman offers an overview of the state of play and the likely tradeoffs in "The Public-Health Case for Legalizing Marijuana" (National Affairs, Spring 2019).  He writes:
John Kenneth Galbraith once said that politics consists in choosing between the disastrous and the unpalatable. The case of cannabis, an illicit market with sales of almost $50 billion per year, and half a million annual arrests, is fairly disastrous and unlikely to get better. The unpalatable solution is clear: Congress should proceed at once to legalize the sale of cannabis — at least in states that choose to make it legal under state law — for recreational as well as "medical" use. ...

First, as a practical matter, cannabis prohibition is no longer enforceable. The black market is too large to successfully repress. The choice we now face is not whether to make cannabis available, but whether its production and use should be legal and overt or illegal and at least somewhat covert. Second, because cannabis is compact and therefore easy to smuggle, a state-by-state solution is unworkable in the long run. States with tighter restrictions or higher taxes on marijuana will be flooded with products from states with looser restrictions and lower taxes. The serious question is not whether to legalize cannabis, but how.
Kleiman offers an overview of the legal status of marijuana, and also makes some key points about the evolution of the market.

Marijuana is a cheap high, even at the current illegal price, and legalization is likely to make it cheaper.
Cannabis, even as an illegal drug, is a remarkably cost-effective intoxicant, far cheaper than alcohol. For example, in New York City, where cannabis is still illegal, a gram of fairly high-potency material (say, 15% THC by weight) goes for about $10. A user can therefore obtain 150 milligrams of THC for $10, paying about 7 cents per milligram. Getting stoned generally requires around 10 milligrams of THC to reach the user's bloodstream, but the smoking process isn't very efficient; about half the THC in the plant gets burned up in the smoking process or is exhaled before it has been absorbed by the lungs. So a user would need about 20 milligrams of THC in plant material to get stoned, or a little less than $1.50 worth. For a user without an established tolerance, intoxication typically lasts about three hours. That works out to about 50 cents per stoned hour. ... So it costs a typical man drinking beer about $4 to get drunk — typically for a couple of hours — and staying drunk costs an additional $1 per hour. That's at least double the price per hour stoned offered by the illicit cannabis market.
For a number of users, marijuana use has adverse health effects.
Over the past quarter-century, the population of "current" (past-month) users has more than doubled (to 22 million) and the fraction of those users who report daily or near-daily use has more than tripled (to about 35%). Those daily or near-daily users account for about 80% of the total cannabis consumed. Between a third and a half of them report the symptoms of Cannabis Use Disorder: They're using more, or more frequently, than they intend to; they've tried to cut back or quit and failed; cannabis use is interfering with their other interests and responsibilities; and it's causing conflict with people they care about. ... Frequent users report using about 1.5 grams (equivalent to three or four joints) per day of use. With increasing prevalence, increasing frequency, and increasing potency, the total amount of THC consumed has likely increased about sixfold since the early 1970s.
A "state's rights" approach isn't likely to work well for marijuana.

Cannabis is simply too easy to smuggle across state lines. If cannabis is cheap anywhere, it will be available and fairly cheap everywhere. The same would be true if states were to adopt starkly different tax or regulatory policies, as these would likely generate large price differences in their respective legal cannabis markets.  ...
Even a very small difference would be more than enough to support a large illicit market, as the state and local taxation of tobacco has proven. New York State has fairly heavy tobacco taxes, and New York City adds a substantial local tax. Virginia, by contrast, taxes tobacco much more lightly. The result is that a pack of cigarettes that retails for under $5 in Virginia sells for $13 in New York City — a difference of $8 per pack. Due to this price gap in the legal tobacco market, more than half of all cigarettes sold in New York City are contraband: mostly genuine brand-name products purchased in bulk in Virginia and driven 250 miles to New York. There, they are resold for about $9 per pack by many of the same retailers who sell full-priced, legal cigarettes — mostly convenience stores in low-income neighborhoods. ... 
The same would be true for product regulation: If Massachusetts allows the sale of the solid concentrates used for the dangerous practice of "dabbing" (flash-vaporizing a hefty chunk of concentrate with a blowtorch in order to inhale a huge dose all at once), then for New York to try to forbid it would be a virtual invitation to smuggle. The states with the lowest taxes and the loosest regulations would wind up effectively dictating policy to the rest of the country.
What might be some general directions for federal-level marijuana legislation?
What would a public-health-friendly legalization program look like? The goals of such a policy would be the elimination or near-elimination of the illicit market and its replacement with a licit market delivering product of certified purity and known chemical composition, while minimizing the growth in heavy or hazardous use and use by minors. Its means would include taxation or minimum unit pricing (to prevent the otherwise inevitable collapse of cannabis prices); product regulation; and limits on marketing to prevent the cannabis industry from promoting the misuse of its product the way alcohol sellers encourage heavy drinking. ... Retail sales clerks — so-called "bud-tenders," now paid the minimum wage plus a sales commission, and thus given strong incentives to encourage overconsumption — could also be licensed, required to have extensive training in pharmacology and in preventing and recognizing Cannabis Use Disorder, and bound to a fiduciary duty to give advice in the interests of the consumer rather than with the goal of maximizing sales. ...Consumers could also be required, before being allowed to purchase cannabis, to pass a simple test showing they're aware of the risks and of basic precautions. More radically, they could be required to establish for themselves (and the stores could be required to enforce) a weekly or monthly purchase quota, as a nudge toward temperance. ... All of this will have to be done in the face of fierce opposition from the for-profit cannabis industry, if there is one.

For a previous post on the evolution of marijuana laws and markets, see "Canada Legalizes Marijuana: What's Up in Colorado and Oregon?" (October 22, 2018).

Friday, June 14, 2019

The "Right" and "Wrong" Kind of Artificial Intelligence for Labor Markets

Sometimes technology replaces existing jobs. Sometimes it create new jobs. Sometimes it does both at the same time. This raises an intriguing question: Do we need to view the effects of technology on jobs as a sort of tornado blowing through the labor market? Or could we come to understand why some technologies have bigger effects on creating jobs, or supplementing existing jobs, than on replacing job--and maybe even give greater encouragement to those kinds of technologies?

Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb tackle the issue of how artificial intelligence technologies can have differing effects on jobs in "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction" (Journal of Economic Perspectives, Spring 2019, 33 (2): 31-50). Perhaps someday "artificial intelligence" will be indistinguishable from human intelligence. But the authors argue that at present, most of the developments in AI are really about "machine learning," which involves using computing power to make more accurate predictions from data. They write (citations omitted):
The majority of recent achievements in artificial intelligence are the result of advances in machine learning, a branch of computational statistics. ... Machine learning does not represent an increase in artificial general intelligence of the kind that could substitute machines for all aspects of human cognition, but rather one particular aspect of intelligence: prediction. We define prediction in the statistical sense of using existing data to fill in missing information. As deep-learning pioneer Geoffrey Hinton said, “Take any old problem where you have to predict something and you have a lot of data, and deep learning is probably going to make it work better than the existing techniques.”
The authors are using "prediction" in a very broad sense: "As an input into decision-making under uncertainty, prediction is essential to many occupations, including service industries: teachers decide how to educate students, managers decide who to recruit and reward, and janitors decide how to deal with a given mess." Here are a few examples from their paper, some fairly well-known, others less so. 

AI and Brain Surgery
For example, ODS Medical developed a way of transforming brain surgery for cancer patients. Previously, a surgeon would remove a tumor and surrounding tissue based on previous imaging (say, an MRI scan). However, to be certain all cancerous tissue is removed, surgeons frequently end up removing more brain matter than necessary. The ODS Medical device, which resembles a connected pen-like camera, uses artificial intelligence to predict whether an area of brain tissue has cancer cells or not. Thus, while the operation is taking place, the surgeon can obtain an immediate recommendation as to whether a particular area should be removed. By predicting with more than 90 percent accuracy whether a cell is cancerous, the device enables the surgeon to reduce both type I errors (removing noncancerous tissue) and type II errors (leaving cancerous tissue). The effect is to augment the labor of brain surgeons. Put simply, given a prediction, human decision-makers can in some cases make more nuanced and improved choices. 
AI and Tax Law
Blue J Legal’s artificial intelligence scans tax law and decisions to provide firms with predictions of their tax liability. As one example, tax law is often ambiguous on how income should be classified. At one extreme, if someone trades securities multiple times per day and holds securities for a short time period, then the profits are likely to be classified as business income. In contrast, if trades are rare and assets are held for decades, then profits are likely to be classified by the courts as capital gains. Currently, a lawyer who takes on a case collects the specific facts, conducts research on past judicial decisions in similar cases, and makes predictions about the case at hand. Blue J Legal uses machine learning to predict the outcome of new fact scenarios in tax and employment law cases. In addition to a prediction, the software provides a “case finder” that identifies the most relevant cases that help generate the prediction.
AI and Office Cleaning
A&K Robotics takes existing, human-operated cleaning devices, retrofits them with sensors and a motor, and then trains a machine learning-based model using human operator data so the machine can eventually be operated autonomously. Artificial intelligence enables prediction of the correct path for the cleaning robot to take and also can adjust for unexpected surprises that appear in that path. Given these predictions, it is possible to prespecify what the cleaning robot should do in a wide range of predicted scenarios, and so the decisions and actions can be automated. If successful, the human operators will no longer be necessary. The company emphasizes how this will increase workplace productivity, reduce workplace injuries, and reduce costs.
AI and Bail Decisions
Judges make decisions about whether to grant bail and thus to allow the temporary release of an accused person awaiting trial, sometimes on the condition that a sum of money is lodged to guarantee their appearance in court. Kleinberg, Lakkaraju, Leskovec, Ludwig, and Mullainathan (2018) study the predictions that inform this decision ... Judges will continue to weigh the relative costs of errors, and in fact the US legal system requires human judges to decide. But artificial intelligence could enhance the productivity of judges. The main social gains here may not be in hours saved for judges as a group, but rather from the improvement in prediction accuracy. Police arrest more than 10 million people per year in the United States. Based on AIs trained on a large historical dataset to predict decisions and outcomes, the authors report simulations that show enhanced prediction quality could enable crime reductions up to 24.7 percent with no change in jailing rates or jailing rate reductions up to 41.9 percent with no increase in crime rates. In other words, if judicial output were measured in a quality-adjusted way, output and hence labor productivity could rise significantly. 
AI and Drug Discovery
A company called Atomwise uses artificial intelligence to enhance the drug discovery process. Traditionally, identifying molecules that could most efficiently bind with proteins for a given therapeutic target was largely based on educated guesses and, given the number of potential combinations, it was highly inefficient. Downstream experiments to test whether a molecule could be of use in a treatment often had to deal with a number of poor-quality candidate molecules. Atomwise automates the task of predicting which molecules have the most potential for exploration. Their software classifies foundational building blocks of organic chemistry and predicts the outcomes of real-world physical experiments. This makes the decision of which molecules to test more efficient. This increased efficiency, specifically enabling lower cost and higher accuracy decisions on which molecules to test, increases the returns to the downstream lab testing procedure that is conducted by humans. As a consequence, the demand for labor to conduct such testing is likely to increase. Furthermore, higher yield due to better prediction of which chemicals might work increases the number of humans needed in the downstream tasks of bringing these chemicals to market. In other words, automated prediction in drug discovery is leading to increased use of already-existing complementary tasks, performed by humans in downstream occupations.
Some of these examples fit the mental model that robots driven by AI are going to replace human workers. Other suggest that AI will make existing workers more productive. It has become common, when looking at effects of technology on labor markets, to focus on the idea that a given job  has a bunch of tasks. A new technology replace most or all of the tasks a certain job, that job may be eliminated. It the technology creates the need for a bunch of new tasks, brand-new job categories may be created. Or often, a new technology may just cause a job to evolve, by replacing some tasks and creating a need for other tasks to be carried out. 

These differing pathways suggest that it might be able to differentiate, at least to some extent, between uses of artificial intelligence that are especially likely to be efficiency-enhancing for existing workers and job-creating for others, and uses of artificial intelligence that are more likely to be job-replacing in a way that saves a little money for employers but doesn't have large efficiency gains. 

For example, an article in Axios described a discussion with James Manyika, director of the McKinsey Global Institute. Manyika notes that in doing AI research: "If your goal is human-level capability, you're increasing the probability that you're doing substitutive work ... If you were trying to solve this as an economic problem, you'd want to develop AI algorithms or machines that are as different from humans as possible." Manyika suggests a few examples of AI-based research that are less likely to replace human workers, because they don't mimic human capabilities: "augmented reality," "AI systems that can predict how proteins are folded, or how to route trucks better," and "robots that can see around corners, or register sounds outside our hearing range."

Daron Acemoglu and Pascual Restrepo tackle this question in a short nontechnical essay "The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand" (IZA Discussion Paper No. 12292, April 2019)
"Most AI researchers and economists studying its consequences view it as a way of automating yet more tasks. No doubt, AI has this capability, and most of its applications to date have been of this mold: e.g., image recognition, speech recognition, translation, accounting, recommendation systems, and customer support. But we do not need to accept that this as the primary way that AI can be and indeed ought to be used. ...
It is possible that the ecosystem around the most creative clusters in the United States, such as Silicon Valley, excessively rewards automation and pays insu¢ cient attention to other uses of frontier technologies. This may be partly because of the values and interests of leading researchers (consider for example the ethos of companies like Tesla that have ceaselessly tried to automate everything). It is also partly because the prevailing business model and vision of the large tech companies, which are the source of most of the resources going into AI, have focused on automation and removing the (fallible) human element from the production process. ...

All in all, even though we currently lack definitive evidence that research and corporate resources today are being directed towards the "wrong" kind of AI, the market for innovation gives no compelling reason to expect an efficient balance between different types of AI. If at this critical juncture insufficient attention is devoted to inventing and creating demand for, rather than just replacing, labor, that would be the "wrong" kind of AI from the social and economic point of view.
As one example, Acemoglu and Restrepo point out that individualized classroom teaching, enabled by AI, will not eliminate the need for teachers--and may even increase it. As they write: "Educational applications of AI would necessitate new, more flexible skills from teachers (beyond what is available and what is being invested in now), and they would need additional resources to hire more teachers to work with these new AI technologies (after all, that is the point of the new technology, to create new tasks and additional demand for teachers)." AI enabled-tools could go well beyond feeding students multiple-choice questions with continually adjusting levels of difficulty, and  provide a kind of feedback that is just different from what any classroom teacher can provide. 

Thursday, June 13, 2019

Some Snapshots of the Global Energy Situation

"Global primary energy grew by 2.9% in 2018 – the fastest growth seen since 2010. This occurred despite a backdrop of modest GDP growth and strengthening energy prices. At the same time, carbon emissions from energy use grew by 2.0%, again the fastest expansion for many years, with emissions increasing by around 0.6 gigatonnes. That’s roughly equivalent to the carbon emissions associated with increasing the number of passenger cars on the planet by a third." Spencer Dale offers these and other insights in his introduction to the the 2019 BP Statistical Review of World Energy. It's one of those books of charts and tables I try to check each year just to keep my personal perceptions of economic patterns connected to actual statistics.  Here are a few figures that jumped out at me. 

One main drive of the rise in world energy use is economic growth in emerging market countries. The horizontal axis of this figure shows average energy use per person. The vertical axis shows the cumulative share of total world population. The yellow line shows the pattern for 1978, while the green line shows four decades later in 2018. 


From the caption under the figure: "In 2018, 81% of the global population lived in countries where average energy demand per capita was less than 100 GJ/head, two percentage points more than 20 years ago. However, the share of the global population consuming less than 75 GJ/head declined from 76% in 1998 to 57% last year. Average energy demand per capita in China increased from 17 GJ/head in 1978 to 97 GJ/head in 2018." The figure constructed from national-level data on average energy consumption. Thus, the big jump the blue line at right about 100 GJ/head is the population of China. Overall, the shift from the yellow to the blue line shows how energy consumption is rising in emerging market economies. 

The sources of global energy consumption are also shifting. Oil consumption as shown by the green line is falling as a share of global energy consumption. (Just to be clear, total consumption of oil-produced energy rose in 2018, but it's rising more slowly than overall energy consumption, so the share of the total declined.) Coal remains pretty much the same share of global energy consumption as it has been for the last 30 years. Natural gas has risen. Hydro power is about the same. Nuclear energy is about the same as the last 20 years. Renewables like wind and solar are up, but still only about 5% of total energy consumption. 
High oil prices, reducing the quantity demanded, are part of the economic picture as to why the share of energy produced by oil has declined. The figure shows oil prices back to the Pennsylvania oil boom of the 1860s, with the light-green line showing prices adjusted for inflation. Oil prices have been volatile since the 1970s, but they seem at present to be rising to the middle of their range over the last 4-5 decades. 

What about renewable energy and carbon emissions? From Spencer Dale's overview: 
Renewable energy appears to be coming of age, but to repeat a point I made last year, despite the increasing penetration of renewable power, the fuel mix in the global power system remains depressingly flat, with the shares of both non-fossil fuels (36%) and coal (38%) in 2018 unchanged from their levels 20 years ago. This persistence in the fuel mix highlights a point that the International Energy Agency (IEA) and others have stressed recently; namely that a shift towards greater electrification helps as a pathway to a lower carbon energy system only if it goes hand-in-hand with a decarbonization of the power sector. Electrification without decarbonizing power is of little use. ... On the supply side, the growth in power generation was led by renewable energy, which grew by 14.5%, contributing around a third of the growth; followed by coal (3.0%) and natural gas (3.9%). China continued to lead the way in renewables growth, accounting for 45% of the global growth in renewable power generation, more than the entire OECD combined.
Here's is a figure showing how electricity is generated around the world. Coal still leads the way, by far. Natural gas is on the rise, while oil is dropping. "Renewables," which is led by wind, but also includes solar and smaller categories like geothermal and biomass, is on the rise, but still under 10%.

Finally, here's a table showing carbon emissions in 2018. This is a trimmed-down version of a bigger table in the text. It mainly shows carbon emissions by region. (CIS is "Commonwealth of Independent States," which refers to the remnants of what was once the Soviet Union.) Notice that the Asia-Pacific region accounts for half of all global carbon emissions, with China alone accounting for more than one-quarter of global carbon emissions. Also, the average annual growth rate of carbon emissions was negative for North America and for Europe from 2007-2017, but rising during that time frame in Asia Pacific, as well as Africa, the Middle East, and South/Central America. As I've written before, a meaningful approach to limiting or reducing global carbon emissions will need to include North American and Europe, but our participation won't be nearly enough.

Tuesday, June 11, 2019

Where Will America Find Caregivers as its Elderly Population Rises?

As we look ahead two or three decades into the future, we know several demographic facts with an extremely high degree of confidence. We know that that the number of elderly people in the population will be rising, and as a result, the demand for long-term care services will rise substantially. We also know that the birthrate has been falling, and so this generation of the eventually-will-be-elderly has had fewer children than the previous generation.

Put these two demographic facts together with a current social pattern: a large share of the care received by elderly adults with disabilities has been unpaid care provided by their children. But that arrangement will not be sustainable, at least not in the same way, moving forward. Three recent essays written for the Peter G. Peterson Foundation as part of its "US 2050: Research Projects" lay out some dimensions of the problem.

For example, here's an overview of the coming patterns from Stipica Mudrazija in "Work-Related Opportunity Costs of Providing Unpaid Family Care" (citations and references to tables omitted):  
Currently, there are almost 13 million caregivers aged 20-64 providing care to 10 million older adults with limitations in daily activities. In addition to the adult children of care recipients (71%), unpaid working-age caregivers to this population include spouses (5%), other family members (17%), and nonrelatives (7%). Overall, these caregivers account for 6.7 percent of the population aged 20-64, but the provision of caregiving is highly unequally  distributed by age as the majority of caregivers are aged 50-64, and adults in this age group are more than three times as likely to be caregivers than those aged 20-49. Accounting for future population aging and trends in physical disability and adjusting for compositional changes of the future population, the number of caregivers needed to keep the current prevalence of unpaid caregiving constant would have to almost double. This implies that the proportion of unpaid family caregivers to older adults would have to increase by more than a half to 6.1 percent for adults aged 20-49 and 19.2 percent for those aged 50-64. 
Thus, one potential future is that about one-fifth of adults from ages 50-64 become unpaid caregivers to the elderly. Of course, this pathway has tradeoffs. Caregivers typically spend less time in paid work: 
Using data from the National Study of Caregiving, the author finds that caregivers are about 9 percentage points less likely to be employed than those that do not provide care. In addition, employed caregivers work 2.1 fewer hours per week than their non-caregiver peers.  The current annual work-related opportunity cost of unpaid care in the United States is about $67 billion, but these costs will more than double by 2050. 
Mudrazija points out that in the past, analyses have suggested "that the economic benefits of unpaid family care in terms of savings to government programs outweigh work opportunity costs." But this pattern seems to be shifting: "Therefore, future discussions of the role of unpaid family care should recognize that this is a finite and increasingly expensive resource."

Gal Wettstein and Alice Zulkarnain focus on the question, "Will Fewer Children Boost Demand for Formal Caregiving?"  They note: "Today, 25 percent of all caregivers of elderly are adult children. However, while the parents of the Baby Boom generation had three children per household on average, the Boomers themselves only have two." People with fewer children are more likely to end up in nursing homes--probably in part because they lack access to unpaid care and support from children. "The authors estimate that, among people over age 50, having one fewer child increases the probability of having spent a night in a nursing home in the last two years by 1.7 percentage points—a magnitude comparable to the effect of having poor self-reported health, or of being ten years older."

Put these factors together, and the demand for paid care for the elderly is likely to skyrocket: "They extrapolate this finding to 2050, and estimate that the decline in fertility of the Baby Boom generation will increase formal care demand per person by an extra 8.6 percent. Combined with the expected tripling of the population over age 85, the authors estimate that formal care demand will increase by about 326 percent relative to the current formal care demand."

Kristin Butcher and Tara Watson raise the issue of "Immigration and Tomorrow’s Elderly."  They find:
"[A]lthough the majority of the population age 80 and up has some type of disability or difficulty, fewer than 10 percent of individuals in their 80s live in an institution. This suggests either that they are getting help that keeps them out of institutions, or that there is an unmet need for such help. The authors identify eight key occupations that may help elderly individuals age in place, such as nursing aides and housekeepers, and predict that these occupations as a share of the overall workforce will increase from 8.4 percent to 12 percent in 2050. Further, they find that immigrants are disproportionately represented in these occupations. Assuming that the ratios of immigrants to total number of workers are fixed within occupations, the authors estimate that 42 million foreign-born workers would be required to maintain current immigrant representation in these fields. This is significantly more than the 30 million immigrants that are projected to be working in the U.S. in 2050." 
To put their point in  my own words, the US has been leaning on its immigrant workforce to provide paid caregiving to the elderly. As the number of elderly who need paid caregiving rises sharply, if unpaid care doesn't double in quantity to fill the gap,  higher levels of immigration is one way of increasing the workforce of caregivers.

For some previous posts on the coming challenges of providing long-term care, along with some international perspective, see:
Also, some readers may be interested in digging further into the "US 2050: Research Projects" from the Peterson Foundation.  There are 31 papers on topics including population trends, early investments in children, employment and adult workers, caregiving (the focus of this post), retirement, and politics. 

Friday, June 7, 2019

The Global Paper Industry: Still on the Rise

Paper is an old industry, dating back to 100 BC in China. For several decades now, there have been predictions that paper would decline, as businesses converted to the "paperless office" and as people moved to reading online rather than on dead tree. How is that transition going? The short answer is "only OK." For a longer answer, the Environmental Paper Network offers a review in The State of the Global Paper Industry, subtitled "Shifting Seas: New Challenges and Opportunities for Forests, People and the Climate" (April 2018).

The report notes (footnotes omitted):
Paper use increases year on year and has quadrupled over the= past 50 years. In 2014, global paper production hit 400 million tonnes per year for the first time ... More than half of this paper is consumed in China (106 million tonnes), the USA (71 million tonnes), and Japan (27 million tonnes), with a further quarter in Europe (92 million tonnes). The entire continent of Africa accounts for just 2% of global paper use, consuming a mere 8 million tonnes per year. Oceania and Latin America between them account for around 8%.
Here's a figure and a table showing total paper consumption by region over time Notice that paper consumption in North America has been falling. Paper consumption is near-zero in Africa and not much higher in Latin America and Oceania. It's rising fast in Asia, which is in large part a China effect. 
 

Here's a figure showing per capita consumption of paper, with North America still leading the way. 
Why has the demise of paper been so slow to arrive? As shown in the table above, one main reason is the growth of paper production in China. The report notes: "China alone, with its rapid build-up of capacity over the past two decades, has taken over as the leading paper producer, providing more than 25% of the world’s paper.The USA, long the global leader in paper production, moved to second place in 2009." This pattern raises the possibility that paper consumption is also likely to rise substantially if and when economic growth proceeds in other parts of the world 

The other reason is that most consumption of paper products isn't about newspapers, reports, and other reading material. It's packaging. Here's a pie chart showing the breakdown of uses of paper in a recent year, and a figure showing the change over time. 

A main concern for the Environmental Paper Network is that paper production is often an environmentally dirty industry. The report notes: 
The pulp and paper manufacturing industry is one of the world’s biggest polluters and must evolve to employ best available technologies and new innovations to clean up its act. The sector is not only the fifth largest consumer of energy, accounting for 4% of all the world’s energy use, but the process of paper uses more water to produce one ton of product than perhaps any other industry. On average 10 litres of water are required to make one A4 sheet of paper – in some cases, it’s as high as 20 litres. The chemically intensive nature of the paper pulping and bleaching process is far from clean. The toxic chemicals used often end up being discharged as effluent into waterways where they pollute rivers, harm eco-systems, bio-accumulate and eventually enter the food chain. Besides carbon emissions, pulp and paper mills also release air pollutants in the form of fine particulate matter (PM2.5), nitrogen and sulphur oxides which can also affect public health. While the industry has made some progress in recent years to operate more sustainably, it has been slow to adopt advances in technology that can deliver higher energy savings and water reductions whilst promoting less toxic production methods.
The report is also careful to note that reductions in paper use don't always provide environmental benefits. Using paper towels in a public restroom is probably more environmentally friendly than a hot-air dryer.  Even the move from paper to digital communication can be tricky to convert into environmental pluses and minuses. The report notes: "Life-cycle assessments of some commodities, for example of books, have compared the energy or climate change costs of paper and electronic alternatives, drawing conclusions about how many e-books need to be read on an e-reader before the unit energy costs are less than the paper option. Few of such studies adequately address the full life-cycle impacts of digital devices, including all the minerals used in their production and post-disposal impacts." 

Thus, the challenge in thinking about environmental effects of the paper industry is to focus on uses where the social benefits of paper are relatively low. The report uses the economics language of "utility" to discuss this subject, although the term isn't quite used in the textbook economics sense:
Some paper applications have considerable social benefits, and therefore high utility. Other applications have either no social benefits, a highly limited lifespan or much more durable alternatives (or more than one of these). They are therefore deemed to be low utility. In surveys of opinion of the utility of different paper applications, the results have assigned high utility to such items as legal papers, passports, money, medical records, toilet paper and books, and low utility to unread magazines, unwanted direct mail (junk mail), excessive packaging and throwaway cups. Reducing use in paper applications that are high volume and low utility can make a big impact, while not causing disadvantage. Excessive packaging, therefore, is an example of a good place to look for efficiencies. Reducing use of paper napkins, on the other hand, being low utility but also relatively low volume, will make less impact, while reducing the use of books, which are fairly high volume but also high utility, could be unpopular and limit the sharing of information by people that have no access to digital devices.
Measuring overall recycling in an economy is hard, and the statistics aren't great. But here's one set of estimates on the extent of paper recycling. 
Paper has its uses, and some of those uses seem likely to persist and even to rise with global economic growth. The challenge, as usual, is to strike a cleaner balance between economic benefits and environmental costs. 

Thursday, June 6, 2019

Interview with William “Sandy” Darity Jr.: Inequality, Race, Stratification, and More

Douglas Clement interviews William "Sandy" Darity Jr. in The Region magazine from the Federal Reserve Bank of Minneapolis (June 3, 2019). As the subtitle reads: "His recent focus has been on reparations for African Americans, but his scholarship spans decades and ranges from imperialism to psychology, from “price-specie flow” to rational expectations." Here are a few points that caught my eye, but the entire interview is worth reading.

Wealth Inequality by Race
The racial wealth gap is customarily measured at the median for households to bypass the problems that are created by large outliers. At the median, when we’re taking the middle households, the most recent data from the Survey of Consumer Finances (SCF) for 2016, I believe, places the white household median at $171,000 and the black household median at $17,600. So, essentially, at the median, blacks have 1 cent in wealth to every 10 cents held by whites. [The SCF 2016] probably has the most conservative estimate of the gap. If, for example, you use the Survey of Income and Program Participation from 2014, which I believe is the most recent year that it’s been taken, the ratio is closer to 1 cent for blacks per 13 cents for whites at the median.
In work that our research group has done for the National Asset Scorecard for Communities of Color, we attempted to get data about individual metropolitan areas throughout the United States, where it might be possible to look at the wealth position of very specific national origin groups. All of our cities have much lower estimates of black median wealth than the national statistics. The number of cities that we’ve studied is substantial but hardly comprehensive. It’s been Boston, Los Angeles, Washington, D.C., Miami, Tulsa, and Baltimore. ...  That’s what we found. $8 is the median [wealth of black households in Boston]. In Miami, it’s $11. I’m not sure how the national statistics get as high as $17 thousand; it’s not really consistent with what we’re finding. So I’m just not sure. There’s something odd. We consistently found, across all of these cities, much, much lower estimates of median black household wealth than you see in the national data. ...
I’m absolutely convinced that the primary factor determining household wealth is the transmission of resources across generations. The conventional view of how you accumulate wealth is through fastidious and deliberate acts of personal saving. I would argue that the capacity to engage in some significant amount of personal saving is really contingent on already having a significant endowment, an endowment that’s independent of what you generate through your own labor.
That being the case, I think that there’s actually some superb research that’s recently come out that supports the importance of what I’d like to call intergenerational transmission effects, rather than intergenerational transfers. I think these effects go beyond inheritances and gifts. I think it includes the sheer economic security that young people can experience being in homes where there is this cushion of wealth. It provides a lack of stress and a greater sense of what your possibilities are in life. ... The sociologists Pfeffer and Killewald have done very, very powerful work on the relationship between grandparents’ and parents’ wealth and the wealth of the youngest generation when it’s of adult age. The connection or the correspondence between which households have higher levels of wealth across three generations is pretty strong.
Then there’s the work of two economists who are with the Fed, Feiveson and Sabelhaus. Their work shows that at least 26 percent of the net worth of a person in the current generation is determined by their parents’ wealth. At least 26 percent. And that’s their lower bound. ...  And if your family’s wealthy enough, you come out of college or university without any educational debt. That can be a springboard to making it easier for you to accumulate your own level of wealth.
What's a "baby bond"?
Baby bonds are not really a bond. They’re really a trust account for each newborn infant. It would be different from other types of programs like seed accounts or child savings accounts because no contribution would be expected from parents, whether they’re rich or poor. The amount of the trust account would vary with the wealth position of the child’s family. It would vary on a graduated basis, so we wouldn’t have any kinds of notch effects. That’s basically the idea.

In most of the versions of the proposal that we’ve advanced, we’ve said the federal government is essentially providing a publicly funded trust account to every newborn child, so it’s a birthright endowment. We would guarantee a 1 percent real rate of interest until the account can be accessed by the child when they reach young adulthood. There’s some debate among us about what that young adulthood date should be.
What is "stratification economics"?
Stratification economics is an approach that emphasizes relative position rather than absolute position. What’s relevant to relative position are two considerations: one, a person’s perception of how the social group or groups to which they belong have standing vis-à-vis other groups that could be conceived of as being rival groups.
Now, it’s an interesting issue as to who constitutes groups that are viewed as rivalrous or oppositional in some sense. But the first thing that individuals value is a superior position for the groups with which they identify. The second thing that they value is a superior position relative to other members of their own group. ... There are two sets of comparisons that are going on: an across-group comparison and a within-group comparison. This kind of frame as the cornerstone for the analysis comes out of, in part, the old work of Thorstein Veblen and also out of research on happiness. The latter increasingly shows that people have a greater degree of happiness if they think that they’re better off than whoever constitutes their comparison group rather than simply being better off; so it’s comparative position that comes into play.
Conventional economics doesn’t start with an analysis that’s anchored on relative position, as opposed to absolute position; so I think that’s the fundamental shift in stratification economics. But also important to stratification economics is the notion that people have group affiliations or group identifications. People feel like they’re part of a team. There can be varying degrees of attachment but, in some sense, people think of themselves as being part of a team, and they want their team to win. That’s somewhat different from conventional economics.