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There have been some experiments along these lines in recent decades, but the idea is now gaining substantial interest,  James Benford, Jonathan D. Ostry, and Robert Shiller have edited a 14-paper collection of papers on Sovereign GDP-Linked Bonds: Rationale and Design (March 2018, Centre for  Economic Policy Research, available with free registraton here).

For a taste of the arguments, here are a few thoughts from the opening essay: “Overcoming the obstacles to adoption of GDP-linked debt,” by Eduardo Borensztein, Maurice Obstfeld, and Jonathan D. Ostry.  They provide an overview of issues like: Would borrowers have to pay higher interest rates for GDP-linked borrowing? Or would the reduced risk of default counterbalance other risks? What measure of GDP would be used as part of such a debt contract? They write:

“Elevated sovereign debt levels have become a cause for concern for countries across the world. From 2007 to 2016, gross debt levels shot up in advanced economies � from 24 to 89% of GDP in Ireland, from 35 to 99% of GDP in Spain, and from 68 to 128% of GDP in Portugal, for example. The increase was generally more moderate in emerging economies, from 36 to 47% of GDP on average, but the upward trend continues. …

“GDP-linked bonds tie the value of debt service to the evolution of GDP and thus keep it better aligned with the overall health of the economy. As public sector revenues are closely related to economic performance, linking debt service to economic growth acts as an automatic stabiliser for debt sustainability. ..  While most efforts to reform the international financial architecture over the past 15 years have aimed at facilitating defaults, for example through a sovereign debt restructuring framework (SDRM), the design of a sovereign debt structure that is less prone in the first place to defaults and their associated costs  would be a more straightforward policy initiative. GDP-linked debt is an attractive instrument for this purpose because it can ensure that debt stays in step with the growth of the economy in the long run and can create fiscal space for countercyclical policies during recessions. …

“The first lesson is to ensure that the payout structure of the instrument reflects the state of the economy and is free from complexities or delays that can make payments stray from their link to the economic situation. To date, GDP-linked debt has been issued primarily in the context of debt restructuring operations, from the Brady bond exchanges that began in 1989 to the more recent cases of Greece and Ukraine. …  This feature, however, gave rise to structures that were not ideal from the point of view of debt risk management. For example, some specifications provided for large payments if GDP crossed certain arbitrary thresholds or were a function of the distance to GDP from those thresholds. In addition, some payout formulas were sensitive to the exchange rate, failed to take inflation into account, or were affected by revisions of population or national account statistics. All these mechanisms resulted in payments that were  disconnected from the business cycle and the state of public finances, detracting from the value of these GDP-linked instruments for risk management (see Borensztein 2016).

“The second lesson is that the specification of the payout formula can strengthen the integrity of the instruments. GDP statistics are supplied by the sovereign, and there is no realistic alternative to this arrangement. This fact is often held up as an obstacle to wide market acceptance of the instruments. However, the misgivings seem to have been exaggerated, as under-reporting of GDP growth is not a politically attractive idea for a policymaker whose success will be judged on the strength of economic performance. … 

“[T]he main source of reluctance regarding the use of GDP-linked debt, or insurance instruments more generally, may not stem from markets but from policymakers. Politicians tend to have relatively short horizons, and would not find debt instruments attractive that offer insurance benefits in the medium to long run but are costlier in the short run, as they include an insurance premium driven by the domestic economy�s correlation with the global business cycle. In addition, if the instruments are not well understood, they may be perceived as a bad choice if the economy does well for some time. The value of insurance may come to be appreciated only years later, when the country hits a slowdown or a recession, but by then the politician may be out of office. While this problem is not ever likely to go away completely, multilateral institutions might be able to help by providing studies on the desirability of instruments for managing country risk, and how to support their market development, in analogy to work done earlier in the millennium promoting emerging markets� domestic-currency sovereign debt markets.”

To shed light on this controversy, it would be useful to to have a measure of financial conditions in the US economy that doesn’t involve one specific interest rate, but instead looks at actual factors like whether credit is relatively available or not, whether leverage is high or low, and whether those who provide loans are able to raise money with relatively low risk. Fortunately, the Federal Reserve Bank of Chicago has been putting together a National Financial Conditions Index based on exactly these components. Here’s a figure of the data going back to the 1970s.

This figure needs a little interpreting. Zero is when financial conditions are average. Positive numbers reflect when financial conditions are tight or difficult. For example, you can see that in the middle of the Great Recession, there is an upward spike showing that financial conditions were a mess and it was hard to raise capital or get a loan at that time. Several previous recessions show a similar spike. On the other side, negative numbers mean that financial conditions are fairly easy by historical standards to finance and receive loans.

As the Chicago Fed explains: “The National Financial Conditions Index (NFCI) and adjusted NFCI (ANFCI) are each constructed to have an average value of zero and a standard deviation of one over a sample period extending back to 1971. Positive values of the NFCI have been historically associated with tighter-than-average financial conditions, while negative values have been historically associated with looser-than-average financial conditions.”

The interesting thing about our present time is that although the Fed has been raising its target interest rate since early 2016, financial conditions haven’t gotten tighter. Instead the National Financial Conditions Index is lower now than it was back in early 2016; indeed, this measure is at its lowest level in about 25 years. At least for the last two years, any concerns that a higher federal funds interest rate would choke off finance and lending have been misplaced. Instead, having the Fed move the federal funds rate back close to its historically typical levels seems to have helped in convincing financial  markets that the crisis was past and normality was returning, so it was a good time to provide finance or to borrow.

The National Financial Conditions Index can also be broken down into three parts: leverage, risk, and credit. The Chicago Fed explains: “The three subindexes of the NFCI (risk, credit and leverage) allow for a more detailed examination of the movements in the NFCI. Like the NFCI, each is constructed to have an average value of zero and a standard deviation of one over a sample period extending back to 1973. The risk subindex captures volatility and funding risk in the financial sector; the credit subindex is composed of measures of credit conditions; and the leverage subindex consists of debt and equity measures. Increasing risk, tighter credit conditions and declining leverage are consistent with increases in the NFCI. Therefore, positives values for each subindex have been historically associated with a tighter�than�average corresponding aspect of financial conditions, while negative values indicate the opposite.”

Here’s a figure showing the breakdown of the three components. Although the three lines do tend to rise and fall together, it seems clear that the blue line–showing the extent of leverage or borrowing–plays an especially large role in the fluctuations over the last 25 years. But right now, all three parts of the index are comfortably down in the negative numbers.

Patterns can turn, of course. Perhaps if the Federal Reserve increases the federal funds rate at its next scheduled meeting (March 20-21), financial conditions will worsen in some substantial way. But at least for now, the Federal Reserve raising interest rates back from the near-zero rates that had prevailed for seven years is having the (somewhat paradoxical) effect of being accompanied by  looser financial conditions. And concerns over raising those rates at least a little further seem overblown.

Imagine that with the arrival of driverless trucks, the trucking industry splits into two parts: long-distance driverless trucks, which operate almost mostly on highways and large roads between a network of “transfer hubs,” and short-distance trucks with human drivers, which take the trucks from transfer hubs to local addresses. As the Uber authors point out:

“The biggest technical hurdles for self-driving trucks are driving on tight and crowded city streets, backing into complex loading docks, and navigating through busy facilities. At each of the local haul pick ups and drop offs, there will need to be loading and unloading. These maneuvers require skills that will be hard for self-driving trucks to match for a long time. By taking on the long haul portion of driving, self-driving trucks can ease some of the burden of increasing demand, while also creating an opportunity for drivers to shift into local haul jobs that keep them closer to home.”

The crucial part of the scenario is that most trucks, given their human drivers, are now on the road for only about one-third of every day. However, the long-distance driverless trucks could be on the road two-thirds or more of every day. As a result, the costs of long-distance shipping would drop substantially, which in turn would give firms and consumers an incentive to expand the quantity of what they ship by truck. In one simulation that has 1 million driverless long-distance trucks on the road, the result is an additional 1.4 million drivers needed for shorter-haul local trucking.

For a great many truckers, short-hauling offers a better lifestyle, in part because you can sleep in your own bed every night. It’s not clear how wages might adjust in response to these kinds of changes. Wages for long-haul truckers might fall,  because competing with driverless technology on those routes would be tough, but the shift in wages for short-haul truckers depends on other ways in which the industry might evolve. The Uber folks are trying to crowd-source the economic analysis here by putting their models and data up on a GitHub site, so if this kind of analysis floats your boat (or you want to assign it as a student project), you have an option here.

Just to be clear, I’m not endorsing the scenario that 10 years from now, there will be a million autonomous trucks on US highways and even more truckers in the short-haul business. But I am endorsing the broader point that a simple “technology replaces jobs” story–even one as seemingly straightforward as how autonomous trucks will affect the number of truck drivers–is always more complex and sometimes even counterintuitive to how it may appear at first glance.

“Sergey Brin and Larry Page, both brilliant computer scientists, founded their company on the conviction that only technologists can understand technology. Google originally set its hiring algorithms to sort for computer science students with top grades from elite science universities. In 2013, Google decided to test its hiring hypothesis by crunching every bit and byte of hiring, firing, and promotion data accumulated since the company�s incorporation in 1998. Project Oxygen shocked everyone by concluding that, among the eight most important qualities of Google�s top employees, STEM expertise comes in dead last. The seven top characteristics of success at Google are all soft skills: being a good coach; communicating and listening well; possessing insights into others (including others different values and points of view); having empathy toward and being supportive of one�s colleagues; being a good critical thinker and problem solver; and being able to make connections across complex ideas.”

Well, Google is a big company. Perhaps the soft skills matter for a lot of its employees. But for the A-level invention teams, surely the technical skills count for more? Last spring, Google tested that hypothesis with Project Aristotle. Davidson reports the results:

“Project Aristotle, a study released by Google this past spring, further supports the importance of soft skills even in high-tech environments. Project Aristotle analyzes data on inventive and productive teams. Google takes pride in its A-teams, assembled with top scientists, each with the most specialized knowledge and able to throw down one cutting-edge idea after another. Its data analysis revealed, however, that the company�s most important and productive new ideas come from B-teams comprised of employees who don�t always have to be the smartest people in the room. Project Aristotle shows that the best teams at Google exhibit a range of soft skills: equality, generosity, curiosity toward the ideas of your teammates, empathy, and emotional intelligence. And topping the list: emotional safety. No bullying. To succeed, each and every team member must feel confident speaking up and making mistakes. They must know they are being heard.”

Well, maybe the importance of soft skills is for some reason more pronounced at Google, or at a certain kind of high-tech company, than for the economy as a whole? Davidson notes: “A recent survey of 260 employers by the nonprofit National Association of Colleges and Employers, which includes both small firms and behemoths like Chevron and IBM, also ranks communication skills in the top three most-sought after qualities by job recruiters. They prize both an ability to communicate with one�s workers and an aptitude for conveying the company�s product and mission outside the organization.”

The evidence for the rising importance of soft skills goes beyond the anecdotal. David J. Deming provides an overview of economic research on this topic in “The Value of Soft Skills in the Labor Market” (NBER Reporter, 2017 Number 4). Deming cites evidence that for the US economy as a whole, the number of STEM (science, technology, engineering, mathematics) jobs rose rapidly from 1980 to 2000, but has declined since then. Moreover, the labor market returns to higher levels of cognitive skill have declined, too. Deming writes (footnotes omitted):

“While cognitive skills are still important predictors of labor market success, their importance has declined since 2000. An important recent paper finds significantly smaller labor market returns to cognitive skills in the early and mid-2000s, compared with the late 1980s and early 1990s. It compares the returns to cognitive skills across the 1979 and 1997 waves of the National Longitudinal Survey of Youth (NLSY) � the same survey that was used to document the importance of cognitive skills in several influential early papers. In a 2017 study, I replicate this finding and also show that returns to soft skills increased between the 1979 and 1997 NLSY waves. Moreover, recent findings suggest that employment and wage growth for managerial, professional, and technical occupations stalled considerably after 2000, which the researchers argue represents a `great reversal’ in the demand for cognitive skills.

“The slow overall growth of high-skilled jobs in the 2000s is driven by a decline in science, technology, engineering, and math (STEM) occupations. STEM jobs shrank as a share of all U.S. employment between 2000 and 2012, after growing strongly between 1980 and 2000. This relative decline of STEM jobs preceded the Great Recession. In contrast, between 2000 and 2012 non-STEM professional occupations such as managers, nurses, physicians, and finance and business support occupations grew at a faster rate than during the previous decade. The common thread among these non-STEM professional jobs is that they require strong analytical skills and significant interpersonal interaction. We are not witnessing an end to the importance of cognitive skills � rather, strong cognitive skills are increasingly a necessary � but not a sufficient � condition for obtaining a good, high-paying job. You also need to have social skills.

“Between 1980 and 2012, social skill-intensive occupations grew by nearly 12 percentage points as a share of all U.S. jobs. Wages also grew more rapidly for social skill-intensive occupations than for other occupations over this period.” 

Here’s a figure from one of Deming’s papers. The pattern is that wages for jobs that are “high-social low-math” rose at about the same rates as jobs that are “high-social, high math.” The jobs with slower wage growth are those that are “low-social,  high-math,” or “low-social, low math.”

Again, the underlying message here is not that tech skills don’t matter. Any high-income economy needs a substantial number of workers out on the on the bleeding edge of technology. But most workers in any economy are going to be involves in using and applying technology.
When it comes to use and application across a variety of contexts, soft skills and social skills start to become quite important.

Those who want some additional follow-up on this issue might begin with:

• “The Economics of Noncognitive Skills” (October 14, 2016)
• “The Importance of Social and Emotional Learning” (May 31, 2017)

In their working paper on “Uniform Pricing in US Retail Chains,” Stefano DellaVigna and Matthew Gentzkow argue that most retail chains do in fact charge the same (or nearly the same) prices across stores, but that profits would be higher if they did varied prices instead (Stanford Institute for Economic Policy Research Working Paper No. 17-042, November 14, 2017). Obviously, this finding poses a puzzle. They describe the data on 73 retail chains in this way:

In this paper, we show that most large US food, drugstore, and mass merchandise chains in fact set uniform or nearly-uniform prices across their stores. …  Our analysis is based on store-level scanner data for 9,415 food stores, 9,977 drugstores, and 3,288 mass merchandise stores from the Nielsen-Kilts retail panel. … Our first set of results documents the extent of uniform pricing. While we observe no cases in which the measured prices are the same for all products across stores, the variation in prices within chains is small in absolute terms and far smaller than the variation between stores in different chains. This is true despite the fact that consumer demographics and levels of competition vary signifi cantly within chains: consumer income per capita ranges from $22,700 at the average 10th percentile store to $40,900 at the average 90th-percentile store, and the number of competing stores within 10 kilometers varies from 0.6 at the 10th-percentile store to 8.3 at the 90th-percentile store. Prices are highly similar within chains even if we focus on store pairs that face very different income levels, or that are in geographically separated markets. 

In one calculation, if the stores raised prices in stores where the average buyer had higher incomes, they could increase profits by 7%. Why don’t stores do this? Indeed, they point to other evidence on European retailers, and on the sale of certain-brand-name products in US markets, which suggests that this pattern of strangely uniform prices is widespread. In the style of any good detective story, there are a list of suspects and clues.

For example, advertising might create a situation where a certain price is publicized to a wide area.  However, the chains in this study advertise mostly on a city-by-city basis, and it would not be difficult for them to vary their advertising across cities.

Another possible explanation is that chain stores don’t want to get into a price war with their competitors. With uniform prices, they are signalling to their competitors that they won’t be locally flexible in their price choices. However, the evidence shows the same uniformity both when chains are facing lots of competitors, and when they are not, which suggests that worries about avoiding a price war aren’t the main issue.

Perhaps varying prices across stores would appear unfair to consumers, and thus damage the brand name of the store. But not many consumers are going to comparison-shop across stores in widely separate areas. And if prices are  higher for stores in high-income areas than in low-income areas, it’s hard to imagine that lots of consumers would view this as violation of some ethical rule.

The explanation these authors find most likely involves managerial decision-making costs: that is, figuring out how to set varying prices across stores, and how to adjust those prices over time, is a substantial task. Unless the payoff in terms of higher profits is large, the inertia of uniform pricing becomes attractive. The authors find some suggestive evidence that store-level price flexibility does seem higher, and seems to be increasing, in settings where the profit potential is larger, which they ascribe in part to the ability of improved information technology to keep track of varying prices across stores.

The authors forthrightly note that “none of this evidence is de finitive,” which means that the phenomenon of what seem to be overly uniform prices is a good talker for courses in microeconomics and business schools, and an interesting research topic.

In addition the pattern of overly uniform prices is more than just an intellectual puzzle, as the authors point out. For example, less-uniform prices might mean lower prices for areas with low-income consumers, while “redistributing” the higher prices to higher-income consumers, and in this way reduce inequality. Less-uniform prices would mean bigger price cuts when a local economy has a shaky time, which in turn could help that local economy recover. On the other side, less-uniform prices would presumably mean higher prices for those in remote areas, with less geographic competition.

In an introductory essay, David Vines and Samuel Wills isolate some of the common theme in their introductory essay: “Four main changes to the core model are recommended: to emphasize financial frictions, to place a limit on the operation of rational expectations, to include heterogeneous agents, and to devise more appropriate microfoundations.” However, I found myself most struck by an essay by Ricardo Reis which dares to pose the question “Is something really wrong with macroeconomics?”
Here are a couple of the points that stuck with me from that essay.

One common complaint about macroeconomics is that most models did not forecast the Great Recession, and indeed, that macroeconomic forecasts in general are often incorrect. Reis points out that most macroeconomic research is not about forecasting, which he illustrates by referring to recent issues of a leading research journals in macroeconomics and to the PhD topics of young researchers in macroeconomics. With regard to the specific sub-topic of forecasting, Reis offers a provocative analogy to the state of medical knowledge:

“Imagine going to your doctor and asking her to forecast whether you will be alive 2 years from now. That would sound like a preposterous request to the physician, but perhaps having some actuarial mortality tables in her head, she would tell you the probability of death for someone of your age. For all but the older readers of this article, this will be well below 50 per cent. Yet, 1 year later, you have a heart attack and die. Should there be outrage at the state of medicine for missing the forecast, with such deadly consequences?

“One defence by the medical profession would be to say that their job is not to predict time of death. They are driven to understand what causes diseases, how to prevent them, how to treat them, and altogether how to lower the chances of mortality while trading this off against life quality and satisfaction. Shocks are by definition unexpected, they cannot be predicted. In fact, in practice, most doctors would refuse to answer the question in the first place, or they would shield any forecast with a blank statement that anything can happen. This argument applies, word for word, to economics
once the word �disease� is replaced by the words �financial crisis�. …

“Too many people all over the world are today being unexpectedly diagnosed with cancer, undergo enormously painful treatment, and recover to live for many more years. This is rightly hailed as a triumph of modern oncology, even if so much more remains to be done. After suffering the worst shock in many decades, the global economy�s problems were diagnosed by economists, who designed policies to respond to them, and in the end we had a painful recession but no melt-down. Some, somehow, conclude that economics is at fault. …

“Currently, the major and almost single public funder for economic research in the United States is the National Science Foundation. Its 2015 budget for the whole of social, behavioural, and economic sciences was $276m. The part attributed to its social  and economic sciences group was $98m. The main public funder of health studies in the United States is the National Institute of Health (NIH), but there are many more, including several substantial private funders. The NIH�s budget for 2015 was $29 billion. Its National Institute of Allergy and Infectious Diseases alone received $4.2 billion in funding. A very conservative estimate is that society invests at least 40 times more trying to study infectious diseases, including forecasting the next flu season or the next viral outbreak, than it does in economics. More likely, the ratio of public investment to science devoted to predicting and preventing the next disease is two or even three orders of magnitude larger than the budget of science dedicated to predicting and preventing economics crises. There is no simple way to compare the output per unit of funding across different fields, but relative to its meagre funding, the performance of economics forecasting is perhaps not so bad.”

Another complaint about modern macroeconomics is that doesn’t seem to offer clear guidance for policy. Reis points out that macroeocnomics is not the only area of economics with this issue: for example, there is considerable dispute among economists about topics like minimum wages or what tax rates to levy on those with high incomes, too. But perhaps even more to the point, economists often have little control over economic policy–except, in recent years, for central banks. Reis observes:

“In deciding the size of the budget deficit, or whether a fiscal stimulus or austerity package is adopted, macroeconomists will often be heard by the press or policy-makers, but almost never play a decisive role in any of the decisions that are made. Most macroeconomists support countercyclical fiscal policy, where public deficits rise in recessions, both in order to smooth tax rates over time a nd to provide some stimulus to aggregate demand. Looking at fiscal policy across the OECD countries over the last 30 years, it is hard to see too much of this advice being taken. Rather, policy is best described as deficits almost all the time, which does not match normative macroeconomics. Moreover, in popular decisions, like the vote in the United Kingdom to leave the European Union, macroeconomic considerations seemed  to play a very small role in the choices of voters. Critics that blame the underperformance of the economy on economists vastly overstate the influence that economists actually have on economic policy.

“One area where macroeconomists have perhaps more of an influence is in monetary policy. Central banks hire more PhD economists than any other policy institution, and in the United States, the current and past chair of the Federal Reserve are distinguished academic macroeconomists, as have been several members of the Federal Open Market Committee (FOMC) over the years. … Looking at the major changes in the monetary policy landscape of the last few decades�central bank independence, inflation targeting, financial stability�they all followed long academic literatures. Even individual policies, like increasing transparency,the saturation of the market for reserves, forward guidance, and balance-sheet policy, were adopted following academic arguments and debates.” 

Reis points out that central banks around the world were tasked with the job of keeping inflation low, and they have largely done so. Moreover, the response of central banks to the Great Financial Crisis was heavily shaped by macroeconomic research:

“Macroeconomists did not prevent the crises, but following the collapse of Lehman or the Greek default, news reports were dominated by non-economists claiming that capitalism was about to end and all that we knew was no longer valid, while economists used their analytical tools to make sense of events and suggest policies. In the United States in 2007�8, the Federal Reserve, led by the certified academic macroeconomist Ben Bernanke, acted swiftly and decisively. In terms of its conventional instruments, the Federal Reserve cut interest rates as far as it could and announced it would keep them low for a very long time. Moreover, it saturated the market for reserves by paying interest on reserves, and it expanded its balance sheet in order to affect interest rates at many horizons. Finally, it adopted a series of unconventional policies, intervening in financial markets to prevent shortages of liquidity. Some of these decisions are more controversial than others, and some were more grounded in macroeconomic research than others. But overall, facing an adverse shock that seems to have been as serious as the one behind the Great Depression, monetary policy responded, and the economy recovered. While the recession was deep, it was nowhere as devastating as a depression. The economic profession had spent decades studying the Great Depression, and documenting the policy mistakes that contributed to its severity; these mistakes were all avoided in 2008�10.”

Macroeconomics is a juicy target for controversy, and many of the essays in this volume hit their mark. But it’s also true that, shocking though this may sound, macroeconomics isn’t magic, either. Economics is a developed analytical structure for thinking about issues and potential tradeoffs, not a cookbook full of easy answers. Reis makes a strong case that macroeconomics has its fair share of success stories, and also its fair share of open questions–just like a lot of other policy-relevant academic research.

Here’s a listing of the articles in the special issue of the Oxford Review of Economic Policy, with links to the individual articles.  Again, all of the articles appear to be ungated and freely available, at least for now.