Month: June 2019

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).

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. 

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. 

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.

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.

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.\”

\”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. 

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.

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:

• \”Can the US Continue to be an Outlier in Long-Term Care?\” (June 28, 2016)
• \”Long-Term Care Services\” (July 8, 2013)
• \”Long-Term Care Insurance in the U.S.\” (November 22, 2011)
• \”Long-Term Care Insurance in International Perspective\” (August 9, 2011)

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. 

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%.

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.

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. 

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.

Japan is the third-largest economy in the world, behind the US and China. It\’e experience seems to foretell some of the key issues facing other high-income economies, like slow productivity growth, rapid aging, and rising government deficits. But in the last few years, it also seems to have recovered to at least a moderate rate of economic growth. What are some of the main patterns and lessons in Japan?  For background, I\’ll draw on the work of theOECD, which just published one of its \”Economic Surveys\” of Japan in April 2019.

Back in the 1980s, a number of popular books and reports published in the US anointed Japan as the future leader of the global economy. A standard claim was that the disorganized competitive market forces of the US economy were unable to keep up with the government-directed cooperative ventures of Japan\’s economy. Then in the early 1990s, Japan\’s economy experienced a meltdown in stock and housing prices, and its economy entered a period of near-zero growth. Here\’s figure comparing Japan\’s in per capita terms to the rest of the OECD countries.  The left-hand set of bars show that when it comes to per capita output, Japan\’s growth was lagging well behind and is now catching up. The right-hand set of bars show how this pattern is linked to an aging population. If one looks only at Japan\’s output relative to its working-age population, it wasn\’t all that far behind from 1997-2012, and has actually been ahead of average OECD growth since 2012.

Japan\’s is facing a situation of a declining population and workforce, and the share of the population that is elderly is on the rise. This rising share of elderly has been driving up government spending on pensions and health care, and together with attempts to stimulate its economy through government spending (much of it on infrastructure), Japan has run up an enormous government debt. In the last few years, it has been aggressively using the Bank of Japan to buy and hold its government debt. Meanwhile, productivity growth has been stagnant. Let\’s say just a bit more about these patterns.

 Here\’s a figure showing Japan\’s total population, broken down by age group. The OECD writes: \”With Japan’s population projected to fall by one-fifth to around 100 million by 2050, many parts of the country are facing depopulation. Efficiency would be increased by expanding the joint provision of local public services, including health and long-term care and infrastructure, across jurisdictions and developing compact cities.\”

Here\’s the change in total population and working-age population from 2000-2018. The working-age population is dropping fast in Japan, near-zero in Germany and Italy. Although it\’s rising in the other countries, the aging of population in these other countries is coming, too.

The combination of slow growth and a declining population has meant ongoing declines in the price of real estate in Japan for most of the last three decades, before stabilizing in the last few years.

Here\’s a figure showing Japan\’s population age 65 and older as a share of the working-age population aged 20-64. The bar shows the level in 2017; the arrow shows where it\’s headed by 2050. Many high-income countries are getting older, but Japan is an extreme case. The OECD writes: \”Half of the children born in Japan in 2007 are expected to live to the age of 107, which has major implications for the labour market. The number of elderly is projected to rise from 50% of the working-age population in 2015 to 79% by 2050 …\”

Supporting the elderly and attempting to stimulate the economy has led to very high levels of government debt in Japan. The OECD writes: \”Twenty-seven consecutive years of budget deficits have driven gross government debt to 226% of GDP in 2018, the highest ever recorded in the OECD area. The government projects that population ageing will boost spending on health and long-term care by 4.7% of GDP by 2060. Measures to ensure the sustainability of Japan’s social insurance programmes, as spending rises and the number of working-age persons falls from 2.0 per elderly to 1.3 by 2050, is a priority.\”

More than 80% of [Japan\’s] firms continue to set mandatory retirement at age 60, even though life expectancy at that age is 26 years, up from 17 in 1970. While workers can continue until age 65, most are re-hired as non-regular workers at significantly lower wages and in jobs that make less use of their skills. The right of firms to set a mandatory retirement age should be abolished to allow more workers to continue their careers, while fully utilising their skills. An end to mandatory retirement requires shifting away from seniority-based wage systems by giving more weight to job category and performance. In addition, the pension eligibility age should be raised above 65, as healthy life expectancy has reached 75. Lengthening careers in the era of 100-year life spans also requires lifelong learning and job-related training to avoid the decline in skill levels among older workers. An end to mandatory retirement would increase firms’ incentives to increase such investment in older workers, which is currently low in Japan. Finally, longer working lives would also be facilitated by better work-life balance for all workers by strictly enforcing the new 360-hour annual limit on overtime hours, imposing adequate penalties on firms that exceed it and introducing a mandatory minimum period of rest between periods of work.

The share of Japanese women in the labor force has risen in recent years, with a push from expanded child care programs. But Japan has long had a \”dual-track\” economy, with one set of workers who have regular work, good pay and benefits, and a career path, and a second track of irregular work, low pay, and little chance for advancement. Women in Japan have often ended up in this second track. The OECD writes:

For the last six years, the Federal Reserve has been doing an annual Survey of Household Economics and Decisionmaking, which is designed to be nationally representative of the 18-and-older US population. The most recent survey was carried out in October and November 2018, and the Federal Reserve published the result in \”Report on the Economic Well-Being of U.S. Households in 2018\” in May 2019. Here, I\’ll offer a few tables from the report that especially caught my eye. What was interesting to me is how the survey answers conveyed both a sense that the US economy is going pretty well, but also that many people felt dissatisfied.

For example, \”Three-quarters of adults in 2018 indicate they are either `living comfortably\’ (34 percent) or `doing okay\’ financially (41 percent), similar to the rate in 2017. The rest are either `just getting by\’ (18 percent) or “finding it difficult to get by\’ (7 percent).\” When people are asked how their financial situation compares to their parents at the same age, a majority says \”better,\” with blacks and Hispanics more likely to say \”better\” than whites.

It\’s also plausible that when some people with irregular or temporary jobs are saying that they want \”more\” work, what they are talking about is a job that is more secure and predictable in its hours. About one-third of respondents report that they have done \”gig work\” in the previous month.

In this survey, gig work covers personal service activities, such as child care, house cleaning, or ride-sharing, as well as goods-related activities, such as selling goods online or renting out property. This definition of gig work includes both online and offline activities, underscoring the fact that most of these activities predate the internet. Many adults who engage in gig work use it to supplement their income, but some rely on it for their main source of income. Finally, these gig activities are often done occasionally and do not take much time, and thus may not fit neatly in a standard concept
of what is considered to be “work.”

A substantial share of people have not current on paying their bills–often because of an unpaid credit card balance.

Even without an unexpected expense, 17 percent of adults expected to forgo payment on some of their bills in the month of the survey. Most frequently, this involves not paying, or making a partial payment on, a credit card bill. Four in 10 of those who are not able to pay all their bills (7 percent of all adults) say that their rent, mortgage, or utility bills will be left at least partially unpaid. Another 12 percent of adults would be unable to pay their current month’s bills if they also had an unexpected
$400 expense that they had to pay. 

The survey also asks about the \”unbanked\” and the \”underbanked\”:

Six percent of adults do not have a checking, savings, or money market account (often referred to as the “unbanked”). Two-fifths of unbanked adults used some form of alternative financial service during 2018—such as a money order, check cashing service, pawn shop loan, auto title loan, payday loan, paycheck advance, or tax refund advance.13 In addition, 16 percent of adults are “underbanked”: they have a bank account but  also used an alternative financial service product. The remaining 77 percent of adults are fully banked, with a bank account and no use of alternative financial products.

Lots of people don\’t feel that they are on track for retirement:

There\’s lots more in the report with breakdowns of income, education, student loans, housing, neighborhoods, and so on. But again, the overall sense is that most people feel fairly good about their status, but also have a list of economic worries and insecurities about not enough work, being up against the edge of regular bills, and a sense that they aren\’t preparing for retirement. Of course, some of these worries are just the stuff of life. Some of the worries might be addressed by helping people learn to manage their money better. But the stresses of these uncertainties and insecurities are also real and meaningful in the lives of many people, and they are likely to respond to politicians who acknowledge these concerns and offer promises to address them.

As a person who attempts to avoid rhetorical excess, except in my personal life, I\’ve hesitated to refer to the US-China trade disputes as a \”trade war.\” But it\’s gone past being a skirmish, a tussle, or a melee. It\’s gone beyond a battle, too. For those trying to gain an overall perspective, a useful starting point is Trade War: The Clash of Economic Systems Threatening Global Prosperity, a readable e-book of 11 essays plus and introduction, edited by Meredith Crowley (VoxEU.org, CEPR Press, May 2019, available with free registration).

Here, I want to pass along some of the arguments as to why the US-China trade war has erupted, and what the costs are likely to be. For economists looking at trade issues, the Trump presidency is certainly part of what\’s happening, but it is also operating against a particular economic and institutional backdrop that is worth noticing.  Thus, here are four reasons that have helped lead to teh US-China trade war.

1) The \”China shock\” of 2001

China entered the World Trade Organization in 2001. As Justin R. Pierce and Peter K. Schott point out in their essay, \”The costs of US trade  liberalisation with China have been acute for some workers,\” the US also adopted \”permanent normal trade relations\” with China at the tail end of the Clinton administration in October 2000. Before this change, tariffs on imports from China were low, but these low tariffs needed to be explicitly re-approved by the president on an annual basis, and Congress had power to override the president\’s decision. With these changes, the low tariff rates on imports from China were locked in.

An extraordinary rise in China\’s exports and trade surplus followed–a rise that was not anticipated by either China (in its official five-year plans) or by the US. Here are a couple of figures based on World Bank data to illustrate. The first one shows that China\’s exports of goods and services as a share of GDP had been rising in the 1980s and 1990s, but then absolutely took off in the early 2000s, rising from about 20% of China\’s GDP in 2001 to 34% of China\’s GDP in 2006. Not coincidentally, China\’s trade surplus had been about 1.3% of GDP in 2001, but spiked up to 10% of China\’s GDP by 2006.

As Pierce and Schott emphasize, the China shock hit manufacturing jobs in certain parts of the US specially hard. They write:

The sharp drop in US manufacturing employment after 2000 differs markedly from the more gradual decline in manufacturing employment that occurred during the prior two decades. Indeed, in the 21 years following the peak of US manufacturing employment in 1979 to just before PNTR [permanent normal trade relations], US manufacturing employment fell by 2.3 million (or 12%). In the next four years, from 2000 to 2003, it fell by 2.9 million (or 17%) – a decline that is roughly as large as that experienced in the four years following the onset of the Great Recession.

The figures above also show that the \”China shock\” dramatically diminished about a decade ago, since the end of the Great Recession. A few years ago, China\’s exports and trade surplus had already fallen back to where they were around 2001. But the legacy of that shock has lived on in the communities most affected.

2) The Dominance of the US Economy has Declined

The United States, together with the countries of western Europe, have long been at the forefront of the push for reducing global barriers to trade. However, the primary source of economic growth in the world economy in the 21st century, and looking ahead for the next several decades, is happening in the \”emerging market\” economies. In \”Understanding trade wars,\” Aaditya Mattoo and Robert W. Staiger write that when a single economy dominates the global economy, it can be in the interest of that large single economy to have a rules-based trading system for all countries. But if that large economy loses its dominance, it may prefer to shift to a \”power-based\” system of negotiating tariffs with specific trading partners. They write:

In 1947, the US was the unquestioned hegemon of the world economy and played a central role in the creation of the GATT (Irwin et al. 2011). Below we describe how it can be in the enlightened self-interest of a sufficiently dominant hegemon to provide support for a rules-based system that limits its ability to exercise power; but as the dominance of the hegemon wanes, this support can erode, precipitating the collapse of the rules-based system until another sufficiently dominant hegemon rises to take its place.

One aspect of this insight is that when China was a much smaller economy, several decades back, it didn\’t matter all that much to the overall US economy whether China\’s exports were up or down, or whether some US technology was ending up in the hands of Chinese firms. Having rules to govern the world trading system mattered more. But now that China\’s economy is close to the that of the United States in size (or larger, depending on which exchange rate is used to do the comparison), the US cares a lot more about these specific issues with China, and the advantages over overall rules matter less.

3) The World Trade Organization Rules Seemed Too Weak 

Part of the reason for tariffs and a trade war is that the dispute resolution procedures in the World Trade Organization seemed so weak. Chad Bown discusses this issue in , \”The 2018 trade war and the end of dispute settlement as we knew it\”: \”The idea that WTO dispute settlement was not well-positioned to tackle a suite of Chinese policies whose economic effect was to act against the spirit – if not the legal letter – of WTO rules …\”

For example, the WTO does have rules against countries using clear-cut industrial subsidies to boost their trade surplus. But if a country is providing subsidies through a mixture of cheap credit from state-owned banks and tweaks in its tax code that have the effect of favoring certain industries, it\’s not clear that the WTO dispute process works well. Luca Rubini digs deeper into the problems of how to measure subsidies and  how to  have rules about them in \”The never-ending story: The puzzle of subsidies.\”

Similarly, the WTO has rule against stealing intellectual property. But if a country has requirements for joint ventures between foreign and domestic firms, and antitrust laws just happen to be enforced more against foreign firms, and the net effect of these changes is a high level of pressure for technology transfer to domestic firms, then it\’s not clear that an appeal to the WTO will work.

In addition, the US was in the process of arguing that the WTO appeals process was too strong, and that it was handing down unfair decisions against the US. This made it difficult to simultaneously argue that the WTO appeals process should be strengthened to address trade issues with China.

4) The Trump Administration Stops Dancing Around Tariffs, and Starts Dancing With Them

Politicians of both parties have been dancing around with tariffs and protectionism for several decades now. For example, if one goes back to about 2010, or the anti-globalization protests of the late 1990s, or the arguments over NAFTA in the early 1990s, it\’s easy find politicians of both parties who have been been happy to express very grave reservations about trade, but then would eventually sign on to a compromise and amendment-laden bill reducing barriers to trade.

The Trump administration stopped equivocating about protectionism, and embraced it. President Trump has stated plainly his views that tariffs are good, that trade wars are easy to win, and that if no trade occurs, the US economy wins big. The president\’s trade advisers have said that countries are unlikely to retaliate against US tariffs, but then have had to support subsidies for industries like agriculture that suffered such retaliation.

What are the costs of US-China trade war? It\’s obviously hard to evaluate costs when the trade war is still going on, and perhaps still escalating. Ralph Ossa, in his essay \”The Costs of Trade War,\” writes that a fully escalated trade war will reduce GDP by about 2% in the US, China, and the EU, and by considerably more in many smaller economies (like Mexico, Canada, or Switzerland).

Other essays point out that the longer-term disruptions of production and trade can be quite important. After all, even if the US and China signed an agreement next week or next month that settled all their agreements and reduced tariffs back to 2017 levels, companies around the world will now be put on notice that any global supply chains are at risk. For years or decades into the future, they will be less willing to rely on flows of supplies, products, and innovation across international borders. After building up these global supply chains for decades, disrupting and shutting them down will not be a costless process.  Several of the papers in this issue take on the topic of trade barriers in an era of global value chains, including the papers by Emily J. Blanchard and btYi Huang, Chen Lin, Sibo Liu and Heiwai Tang.

The results of the rise US tariffs have been utterly predictable, like higher prices for consumers for products like steel and washing machines, and relatively few jobs saved at high cost.   The imposition of tariffs has been followed by higher US trade deficits.  But ultimately, a full-fledged trade war is about more than some tariff hikes. At this point, we aren\’t just arguing over details like whether China\’s rules for technology transfer are unfair (which they are).

Instead, as a society we are grappling with bigger questions about whether the US would be better off if it created considerably more separation for itself from the rest of the global economy. And we are arguing over whether the world economy and political system is better off when international economic linkages are rising or falling. I don\’t expect that the Trump administration will learn any lessons here, although the costs of its trade policies to US consumers and firms may at some point force them to back down. However, I\’m intrigued to see whether anti-Trump forces will respond to future advocates of tariffs and trade wars in the same way they have responded to Trump–or whether they only oppose tariffs and protectionism if they are initiated by the Trump administration.

_________________
Here\’s a full Table of Contents for the book:

Introduction
Meredith A. Crowley

Part 1: The origins of the trade conflict

1 The costs of US trade liberalisation with China have been acute for some workers
Justin R. Pierce and Peter K. Schott

2 The 2018 trade war and the end of dispute settlement as we knew it
Chad P. Bown

3 Understanding trade wars
Aaditya Mattoo and Robert W. Staiger

Part 2: The costs of trade wars

4 The costs of a trade war
Ralph Ossa

5 How exporters respond to tariff changes
Doireann Fitzgerald

6 Trade wars in the GVC era
Emily J. Blanchard

7 Supply chain linkages and financial markets: Evaluating the costs of the US-China trade war
Yi Huang, Chen Lin, Sibo Liu and Heiwai Tang

Part 3: The challenges for the world trading system

8 Misdirection and the trade war malediction of 2018: Scaling the US-China bilateral tariff hikes
Simon J. Evenett and Johannes Fritz

9 The never-ending story: The puzzle of subsidies
Luca Rubini

10 The policy uncertainty aftershocks of trade wars and trade tensions
Kyle Handley and Nuno Limao

11 China\’s rise and the growing doubts over trade multilateralism
Mark Wu

\”Pareidolia\” refers to the common human practice of looking at random outcomes but trying to impose patterns on them. For example, we all know in the logical part of our brain that there are a roughly a kajillion different variables in the world, and so if we look through the possibilities, we will will have a 100% chance of finding some variables that are highly correlated with each other. These correlations will be a matter of pure chance, and they carry no meaning. But when my own brain, and perhaps yours, sees one of these correlations, I can feel my thoughts start searching for a story to explain what looks to my eyes like a connected pattern. 

Here are some examples from Tyler Vigen\’s website, drawn from his 2015 book Spurious Correlations.

Eye-balling these kinds of figures gives you a sense of why these correlations arise. For example, if you have both a right-hand and a left-hand axis, you can set the scales on those figures so that draw the figure so that the starting points and the ending points of the two lines are close to each other–and then the intermediate lines will look fairly common as well.  If comparing to data on a certain statistic in a certain state (divorces in Maine, fishing accidents in Kentucky), your statistical antennae should be warning you that by the time you look through a large group of family or health statistics for each of 50 states, there\’s a reasonable chance of finding whatever pattern you are looking for just by random chance. If you limit the search to relatively short stretches of data like a decade or so, and plug in your computer to sort through the possibilities, finding meaningless correlations isn\’t going to be hard.