PrefBlog

Low returns are finally catching up to American pension plans:

Twenty-year annualized returns for public pensions in the U.S. are poised to decline to 7.47% once fiscal 2016 results are released in coming weeks, according to an estimate from Wilshire Trust Universe Comparison Service, which tracks pension investment returns.

That would be the lowest-ever annual mark recorded by Wilshire, which began tracking the statistic 16 years ago. In 2001, near the height of the dot-com boom, pensions’ 20-year median return was 12.3%, according to Wilshire.
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Weak annual gains for the California Public Employees’ Retirement System and California State Teachers’ Retirement System dropped their 20-year returns below 7.5% investment targets, to 7.03% and 7.1%, respectively. The two funds, known as Calpers and Calstrs, are the largest public pensions in the U.S. by assets and oversee a combined $484 billion for 2.6 million public workers and retirees.
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Ms. Frost’s comments came days before Calpers said that its fiscal 2016 return was 0.6%, the slimmest gain since the 2008-2009 crisis. Calpers has a funding gap of roughly $112 billion, according to the most recent available data. As recently as last year, Calpers Chief Investment Officer Ted Eliopoulos said in an annual letter that the plan was “reassured by our 20-year investment return of 7.76%,” which exceeded the internal target of 7.5%.

Now, “it is a struggle to have a positive return,” Mr. Eliopoulos said in a media call last week.

Good old CalPERS, always good for a laugh.

Meanwhile, a vitriolic attack on Trump by Mary Anastasia O’Grady titled Wharton Grad Trump Fails Economics has some useful information and links:

In the Foreign Affairs magazine essay recently titled “The Truth About Trade,” economist at Dartmouth Douglas Irwin observed that while the technology has “enabled wide productivity and efficiency improvements,” has “also make a lot of blue-collar jobs obsolete. “Mr. Irwin cites a study by the Center for Business and Economic Research at Ball State University, which “found that productivity growth accounted for more than 85 percent of the jobs lost in manufacturing between 2000 and 2010, a period when employment in the sector fell by 5.6 million. “this 85% compares, according to the study, with 13% of job loss associated with trading during the same period. In other words, to bring most jobs back, Mr. Trump should prohibit mechanization. Would Mr. Pence broke the news to farmers Indiana?

In a paper published last summer in the Journal of Economic Perspectives, an economist at MIT David Autor unload automation reason has hit the middle class hard. He observed that to write code for a task, the programmer should be able to “say explicitly ‘rule’ or procedures” required to do so. But the task is understood by man “secretly” is not easy to automate. Mr. Autor call these obstacles “Polanyi Paradox” after the Hungarian-born chemist and economist who observed that “we know more than we know.”

This is the “higher education” and “low-education” work that requires “interpersonal interaction, flexibility, adaptability and problem-solving” -the most difficult to automate records Mr. Autor. Traditional job-secondary education has become the easiest to replace with technology.

The Polanyi Paradox, by the way, was formulated by Michael Polanyi, who was the father of UofT’s John Polanyi. After a lengthy internet search (I hope you’re grateful!), I have found the CBER Ball State study, by Michael J. Hicks and Srikant Devaraj, titled The Myth and the Reality of Manufacturing in America:

Manufacturing has continued to grow, and the sector itself remains a large, important, and growing sector of the U.S. economy. Employment in manufacturing has stagnated for some time, primarily due to growth in productivity of manufacturing production processes.

Three factors have contributed to changes in manufacturing employment in recent years: Productivity, trade, and domestic demand. Overwhelmingly, the largest impact is productivity. Almost 88 percent of job losses in manufacturing in recent years can be attributable to productivity growth, and the long-term changes to manufacturing employment are mostly linked to the productivity of American factories. Growing demand for manufacturing goods in the U.S. has offset some of those job losses, but the effect is modest, accounting for a 1.2 percent increase in jobs beyond what we would expect if consumer demand for domestically manufactured goods was flat.

Exports lead to higher levels of domestic production and employment, while imports reduce domestic production and employment. The difference between these, or net exports, has been negative since 1980, and has contributed to roughly 13.4 percent of job losses in the U.S. in the last decade. Our estimate is almost exactly that reported by the more respected research centers in the nation.

Manufacturing production remains robust. Productivity growth is the largest contributor to job displacement over the past several decades. This leads to a domestic policy consideration.

The paper by David Autor is titled Why Are There Still So Many Jobs? The History and Future of Workplace Automation:

Major newspaper stories offer fresh examples daily of technologies that substitute for human labor in an expanding—although still circumscribed—set of tasks. The offsetting effects of complementarities and rising demand in other areas are, however, far harder to identify as they occur. My own prediction is that employment polarization will not continue indefinitely (as argued in Autor 2013). While some of the tasks in many current middle-skill jobs are susceptible to automation, many middle-skill jobs will continue to demand a mixture of tasks from across the skill spectrum. For example, medical support occupations—radiology technicians, phlebotomists, nurse technicians, and others—are a significant and rapidly growing category of relatively well-remunerated, middle-skill employment. Most of these occupations require mastery of “middle-skill” mathematics, life sciences, and analytical reasoning. They typically require at least two years of postsecondary vocational training, and in some cases a four-year college degree or more. This broad description also fits numerous skilled trade and repair occupations, including plumbers, builders, electricians, heating/ventilating/air-conditioning installers, and automotive technicians. It also fits a number of modern clerical occupations that provide coordination and decision-making functions, rather than simply typing and filing, like a number of jobs in marketing. There are also cases where technology is enabling workers with less esoteric technical mastery to perform additional tasks: for example, the nurse practitioner occupation that increasingly performs diagnosing and prescribing tasks in lieu of physicians.

On another note, there is perennial weeping about affordable housing in the big cities, with “affordable” being a euphemism for “subsidized slum”. Bloomberg’s Patrick Clark has written a piece titled Why It’s So Hard to Build Affordable Housing: It’s Not Affordable:

“If we want to prioritize closing the gap for low-income households, we’re going to need more funding from public subsidy,” said Erika Poethig, director of urban policy initiatives at the Urban Institute, which published an online simulator Tuesday for the purpose of illustrating the challenges to building new affordable housing. Our Denver developer above is fictional, but he’s an illustration of what that simulator churns out: No matter how you slice it, creating the affordable housing needed today probably requires government help.

Playing with the simulator, you quickly learn that there are only a few levers that truly affect a developer’s ability to finance a project. Taking a smaller fee or negotiating a more favorable loan can help at the margins; so can making the project so appealing to residents that no one ever moves out. To really reduce costs or raise revenue, though, there are just these options: Spend less on land, materials, and labor, or bring in more money by raising rents or finding new public financing. But land, materials, and labor can only be cut so much (construction costs are effectively fixed by labor and commodities markets), and raising rents removes the “affordable” from affordable housing.

That leaves subsidies, the biggest of which is the low-income housing tax credit, which Congress funded to the tune of $7 billion last year. Even so, that program is more useful to developers building for higher wage-earners, said Linda McMahon, chief executive of The Real Estate Council, a trade group for Dallas-area real estate companies. “Below 50 percent of area median income, you’re talking about people who can only afford $500 or so in rent, and you really need another layer of subsidy to pay your [commercial] mortgage,” she said.

DBRS has announced publication of a paper titled DBRS: Basel Capital Requirements – What’s Changing?:

The Basel Committee on Banking Supervision (BCBS) has been active in recent months, finalising the minimum capital requirements for market risk (published in January 2016), while also publishing proposed revisions to the standardised approach (December 2015) and the internal model approach for credit (March 2016) and the standardised approach for operational risk (March 2016). These actions are part of the Committee’s efforts to reform global regulatory standards, and reduce the variability of risk-weighted assets (RWAs) across banks and jurisdictions. While DBRS expects that these efforts will improve comparability across the global banking peer group, further transparency would also be valuable in better understanding the risk profile of banks. In particular, DBRS would view positively the standardized disclosure of RWA calculations and components. DBRS also notes that the full implementation of these new requirements is likely to result in a significant amount of operational work for banks, and is expected to lead to a sizeable increase in RWAs.

With full implementation expected to be required from 2019 (the market risk requirements are to be fully implemented from January 2019 and DBRS expects the time period for implementation to be similar for both credit and operational risk requirements once finalised) this will likely add to the already heavy expense burden associated with regulatory compliance, and result in further pressure for those banks that are currently challenged by limited internal capital generation.

Jason Zweig of the Wall Street Journal has some good advice for fixed income investors in a piece titled Investors: Do the Hard Thing, Don’t Do the Easy Thing:

The reach for yield is becoming a reckless lunge.

While high-quality bonds still have their place, too many investors are buying high-risk bonds instead.

Since June 30, according to TrimTabs Investment Research, a firm in Sausalito, Calif., that tracks how money moves in and out of the financial markets, investors have poured $1.2 billion into exchange-traded funds specializing in bonds from emerging-market countries. So far in July, investors have pumped another $2.8 billion into high-yield ETFs holding so-called junk bonds issued by below-investment-grade companies.

Put simply, one out of every 14 dollars invested in those two fund categories arrived in the past three weeks.
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The easy thing is to submit to your worst instincts and reach for riskier investments that pay higher income — for now.

What is hard is to be patient and ornery. As bonds yield less, save more. Remember that you can get higher yield only by buying longer-term or lower-quality bonds — which will also raise your risk. With interest payments so low, long-term bonds are particularly vulnerable to an unexpected rise in rates.

So now it’s official: it’s not what you know, it’s who you know:

Among currently employed workers, those who found their job through a referral from their network had an average weekly salary of $772.20, or roughly $40,000 per year. Those who did not find their job via a referral had an average weekly salary of $725.84, or nearly $38,000 per year. On average, salaries were 6 percent higher if workers found their job through their networks.

Further, their earnings are even more positively skewed. One way to interpret this is that network searchers have more “upside” risk: They can potentially draw a variety of wages, but there are more very high potential outcomes through the network, To quantify this, Kelley’s statistic is 0.6 for network-finders and 0.44 for others, meaning that 80 percent rather than 74 percent of the dispersion between 90th and 10th percentile is accounted for by the top half (from 90th percentile to 50th).

The distribution of wage offers should typically be different from the distribution of wages among employed workers. Not all offers are accepted, and workers at lower wages tend to make more over time through selective job mobility and pay increases on the job. Still, even among the distribution of wage offers, we see a premium associated with those who found jobs through their network. Workers who were searching while unemployed received offers through their networks that averaged 62 percent more than those found through direct contact. Workers searching while employed received network offers that were 12 percent higher, on average.

This is based on a working paper by Marcelo Arbex, Dennis O’Dea, and David Wiczer titled Network Search: Climbing the Job Ladder Faster:

We introduce an irregular network structure into a model of frictional, on-the-job search in which workers find jobs through their network connections or directly from firms. We show that jobs found through network search have wages that stochastically dominate those found through direct contact. Because we consider irregular networks, heterogeneity in the worker’s position within the network leads to heterogeneity in wage and employment dynamics: better connected workers climb the job ladder faster and do not fall off it as far. These workers also pass along higher quality referrals, which benefits their connections. Despite this rich heterogeneity from the network structure, the mean-field approach allows the problem of our workers to be formulated tractably and recursively. We then calibrate and study the wage and employment dynamics coming from our job ladder with network heterogeneity. This quantitative version of our mechanism is consistent with several features of empirical studies on networks and labor markets: jobs found through networks have higher wages and last longer.

The war on markets continued today with the arrest of Mark Johnson, HSBC’s global head of foreign exchange cash trading in London:

The two allegedly conspired to take advantage of inside information about an unidentified company’s plans to sell part of its stake in an Indian subsidiary, according to the complaint. The client was Cairn Energy Plc, which was selling the unit to Vedanta Resources Plc, according to people with knowledge of the transaction. HSBC was hired to trade about $3.5 billion in proceeds of the sale to pounds. Johnson and Scott began buying pounds in the days before the transaction, anticipating that they would cause the price of pounds to spike — a practice known as “ramping” — then execute the transaction, making the pounds they’d bought earlier more valuable, according to the complaint.

Scott and Johnson — his supervisor at the time — told the client the deal should take place at 3 p.m. “so there’s an element of surprise” to get a better rate, according to the complaint, which quoted from recorded phone calls and messages between the two and their client. There was less liquidity at the 3 p.m. fix than the one at 4p.m., making it easier to manipulate, though they told their client they were about the same.

They and other traders they directed ramped the price, sending the pound to its highest in two days at 2:56 p.m. London time. When Scott told Johnson the client was still going ahead with the full transaction despite the spiking price, Johnson said “Ohhhh, f***ing Christmas,” according to the complaint. In the end, HSBC and the men’s internal accounts reaped about $8 million from the front-running, according to Brooklyn U.S. Attorney Robert Capers.

Click for Big

Well, the main thing that sticks out in this story to me is the fact that whoever it was at Cairn Energy who negotiated this deal is a complete idiot. Converting $3.5-billion into pounds in one trade at one specific time? Didn’t it occur to anybody to think, gee, this is kind of a big trade? It also looks as if this idiot who somehow managed to be in charge of $3.5-billion has no idea whether the guys at HSBC are fiduciaries or counterparties – and the idiot had a responsibility to know that.

The second thing to jump out at me is the question of what the authorities suggest HSBC should have done. They were told to convert $3.5-billion at the 3pm fixing, so they did. As they are not as stupid as the moron at Cairn Energy, they laid off their end in pieces. Does anybody care to guess in the comments what the execution price of the trade would have been if the entire order had been placed electronically as a market order at 2:59:59?

The White House has published a study pooh-poohing the theory that student debt has begun to harm the economy:

The White House just released a big report on student debt that contains all the familiar horrors about for-profit schools, indebted dropouts and students defaulting on their loans. But it has an interesting conclusion: That growing stack of $1.3 trillion in student debt is helping, not hurting, the U.S. economy.

That conclusion is sure to rankle the many student advocates and special-interest groups—from real-estate agents to employers seeking new tax breaks for their young workers—that argue student debt is a big “drag” on the economy. (Hillary Clinton and Donald Trump have each decried the rise in student debt.) But the 77-page report from the White House Council of Economic Advisers backs up its claim with numerous charts and studies from economists and academics.

The report itself, titled INVESTING IN HIGHER EDUCATION:
BENEFITS, CHALLENGES, AND THE STATE OF STUDENT DEBT, rebuts my main concern:

The rise in student loan debt has created challenges for some borrowers with lower earnings, but has not been a major factor in the macroeconomy.

• • Despite its steady rise over the past decade, aggregate student loan debt remains small relative to aggregate income. In 2015, total student loan debt was 9 percent of aggregate income, up from 3 percent in 2003. By itself this is considerably smaller than the rise in mortgage debt prior to the crisis and it has also been accompanied by a reduction in other forms of consumer debt.
• • Additional student debt, as an investment in education, is associated with additional income, putting many households in a better position to buy homes or start businesses. By age 26, households with student debt are more likely to buy a house than those that did not attend college. By age 34, college attendees with and without student debt are equally likely to buy a home, and both much more likely than those without a college education. Research studies have found that conditional on a given education, higher student debt explains, at most, a small fraction of the decline in homeownership among younger households.
• • At the same time, the increase in defaults on student loans as well as the increase in high-loan balances for low earners can be real concerns at the individual level, potentially leading to compromised credit and reduced home buying for some individuals.

My problem with the paper is that it concentrates on proving that post-secondary education is still worth-while, even if it involves taking on debt, which isn’t quite the problem I have focussed on. Debt+Degree is better than nothing, sure, but Degree is better than Debt+Degree! And the paper does admit that yes, there is a measureable effect on home ownership rates:

Work by Mezza et al. (2016) tries to identify the causal relationship and finds a larger, negative estimate of student debt on homeownership.31 Using only the variation in student loan debt due to differences in home-state tuition, they estimate that a 10 percent increase in student loan debt leads to a 1 to 2 percentage point decline in homeownership rates for the borrower. Their estimated effect of student loan debt on homeownership is larger than the Cooper and Wang (2014) or Houle and Berger (2015) studies. It is important to note that all of these studies focus on younger households, so it is possible that rising student loans have delayed but not reduced lifetime homeownership. In addition, these studies hold constant the level of education such that they focus only on the impact of debt, not on the education that the debt helped to fund, thereby excluding the positive boost to homeownership from increased education-related earnings.

As discussed on May 31, 2016 there is at least some reason to believe that student debt has harder-to-measure effects than the simple home-ownership binary:

A 2013 report by the think tank Demos found that student debt has a negative effect on income, by making borrowers more risk-averse and discouraging them from moving to another city or taking gambles on new jobs or launching a new business.

This paper, by Robert Hiltonsmith, titled At What Cost? How Student Debt Reduces Lifetime Wealth, was not addressed by the White House researchers.

There is also the underlying problem with student debt, that the ready availability of loans has caused tuition to skyrocket and that this additional revenue for the universities has not led to any meaningful increase in the quality of their product, but merely to an increase in the quantity of their administrators and the amount of marketing frills they offer (such as improved accommodation, meals, football stadiums, etc.).

So, while I appreciate the intervention of the White House in the issue, I do not consider their pronouncement to be the final words on this matter.