Wednesday, January 31, 2018

Winter 2018 Journal of Economic Perspectives

I was hired back in 1986 to be the Managing Editor for a new academic economics journal, at the time unnamed, but which soon launched as the Journal of Economic Perspectives. The JEP is published by the American Economic Association, which back in 2011 decided--to my delight--that it would be freely available on-line, from the current issue back to the first issue. Here, I'll start with Table of Contents for the just-released Winter 2018 issue, which in the Taylor household is known as issue #123. Below that are abstracts and direct links for all of the papers. I will blog more specifically about some of the papers in the next week or two, as well.

Symposium: Housing

"The Economic Implications of Housing Supply," by Edward Glaeser and Joseph Gyourko
In this essay, we review the basic economics of housing supply and the functioning of US housing markets to better understand the distribution of home prices, household wealth, and the spatial distribution of people across markets. We employ a cost-based approach to gauge whether a housing market is delivering appropriately priced units. Specifically, we investigate whether market prices (roughly) equal the costs of producing the housing unit. If so, the market is well-functioning in the sense that it efficiently delivers housing units at their production cost. The gap between price and production cost can be understood as a regulatory tax. The available evidence suggests, but does not definitively prove, that the implicit tax on development created by housing regulations is higher in many areas than any reasonable negative externalities associated with new construction. We discuss two main effects of developments in housing prices: on patterns of household wealth and on the incentives for relocation to high-wage, high-productivity areas. Finally, we turn to policy implications.
Full-Text Access | Supplementary Materials

"Homeownership and the American Dream," by Laurie S. Goodman and Christopher Mayer
For decades, it was taken as a given that an increased homeownership rate was a desirable goal. But after the financial crises and Great Recession, in which roughly eight million homes were foreclosed on and about $7 trillion in home equity was erased, economists and policymakers are re-evaluating the role of homeownership in the American Dream. Many question whether the American Dream should really include homeownership or instead focus more on other aspects of upward mobility, and most acknowledge that homeownership is not for everyone. We take a detailed look at US homeownership from three different perspectives: 1) an international perspective, comparing US homeownership rates with those of other nations; 2) a demographic perspective, examining the correlation between changes in the US homeownership rate between 1985 and 2015 and factors like age, race/ethnicity, education, family status, and income; 3) and, a financial benefits perspective, using national data since 2002 to calculate the internal rate of return to homeownership compared to alternative investments. Our overall conclusion: homeownership is a valuable institution. While two decades of policies in the 1990s and early 2000s may have put too much faith in the benefits of homeownership, the pendulum seems to have swung too far the other way, and many now may have too little faith in homeownership as part of the American Dream.
Full-Text Access | Supplementary Materials

"Sand Castles before the Tide? Affordable Housing in Expensive Cities," Gabriel Metcalf
This article focuses on cities with unprecedented economic success and a seemingly permanent crisis of affordable housing. In the expensive cities, policymakers expend great amounts of energy trying to bring down housing costs with subsidies for affordable housing and sometimes with rent control. But these efforts are undermined by planning decisions that make housing for most people vastly more expensive than it has to be by restricting the supply of new units even in the face of growing demand. I begin by describing current housing policy in the expensive metro areas of the United States. I then show how this combination of policies affecting housing, despite internal contradictions, makes sense from the perspective of the political coalitions that can form in a setting of fragmented local jurisdictions, local control over land use policies, and homeowner control over local government. Finally, I propose some more effective approaches to housing policy. My view is that the effects of the formal affordable housing policies of expensive cities are quite small in their impact when compared to the size of the problem—like sand castles before the tide. I will argue that we can do more, potentially much more, to create subsidized affordable housing in high-cost American cities. But more fundamentally, we will need to rethink the broader set of exclusionary land use policies that are the primary reason that housing in these cities has become so expensive. We cannot solve the problem unless we fix the housing market itself.
Full-Text Access | Supplementary Materials

Symposium: Friedman's Natural Rate Hypothesis after 50 Years

"Friedman's Presidential Address in the Evolution of Macroeconomic Thought," by N. Gregory Mankiw and Ricardo Reis
Milton Friedman's presidential address, "The Role of Monetary Policy," which was delivered 50 years ago in December 1967 and published in the March 1968 issue of the American Economic Review, is unusual in the outsized role it has played. What explains the huge influence of this work, merely 17 pages in length? One factor is that Friedman addresses an important topic. Another is that it is written in simple, clear prose, making it an ideal addition to the reading lists of many courses. But what distinguishes Friedman's address is that it invites readers to reorient their thinking in a fundamental way. It was an invitation that, after hearing the arguments, many readers chose to accept. Indeed, it is no exaggeration to view Friedman's 1967 AEA presidential address as marking a turning point in the history of macroeconomic research. Our goal here is to assess this contribution, with the benefit of a half-century of hindsight. We discuss where macroeconomics was before the address, what insights Friedman offered, where researchers and central bankers stand today on these issues, and (most speculatively) where we may be heading in the future.
Full-Text Access | Supplementary Materials

"Should We Reject the Natural Rate Hypothesis?" by Olivier Blanchard
Fifty years ago, Milton Friedman articulated the natural rate hypothesis. It was composed of two sub-hypotheses: First, the natural rate of unemployment is independent of monetary policy. Second, there is no long-run trade-off between the deviation of unemployment from the natural rate and inflation. Both propositions have been challenged. The paper reviews the arguments and the macro and micro evidence against each. It concludes that, in each case, the evidence is suggestive, but not conclusive. Policymakers should keep the natural rate hypothesis as their null hypothesis, but keep an open mind and put some weight on the alternatives.
Full-Text Access | Supplementary Materials

"Short-Run and Long-Run Effects of Milton Friedman's Presidential Address," by Robert E. Hall and Thomas J. Sargent
The centerpiece of Milton Friedman's (1968) presidential address to the American Economic Association, delivered in Washington, DC, on December 29, 1967, was the striking proposition that monetary policy has no longer-run effects on the real economy. Friedman focused on two real measures, the unemployment rate and the real interest rate, but the message was broader—in the longer run, monetary policy controls only the price level. We call this the monetary-policy invariance hypothesis. By 1968, macroeconomics had adopted the basic Phillips curve as the favored model of correlations between inflation and unemployment, and Friedman used the Phillips curve in the exposition of the invariance hypothesis. Friedman's presidential address was commonly interpreted as a recommendation to add a previously omitted variable, the rate of inflation anticipated by the public, to the right-hand side of what then became an augmented Phillips curve. We believe that Friedman's main message, the invariance hypothesis about long-term outcomes, has prevailed over the last half-century based on the broad sweep of evidence from many economies over many years. Subsequent research has not been kind to the Phillips curve, but we will argue that Friedman's exposition of the invariance hypothesis in terms of a 1960s-style Phillips curve is incidental to his main message.
Full-Text Access | Supplementary Materials


"Exchange-Traded Funds 101 for Economists," by Martin Lettau and Ananth Madhavan
Exchange-traded funds (ETFs) represent one of the most important financial innovations in decades. An ETF is an investment vehicle, with a specific architecture that typically seeks to track the performance of a specific index. The first US-listed ETF, the SPDR, was launched by State Street in January 1993 and seeks to track the S&P 500 index. It is still today the largest ETF by far, with assets of $178 billion. Following the introduction of the SPDR, new ETFs were launched tracking broad domestic and international indices, and more specialized sector, region, or country indexes. In recent years, ETFs have grown substantially in assets, diversity, and market significance, including substantial increases in assets in bond ETFs and so-called "smart beta" funds that track certain investment strategies often used by actively traded mutual funds and hedge funds. In this paper, we begin by describing the structure and organization of exchange-traded funds, contrasting them with mutual funds, which are close relatives of exchange-traded funds, describing the differences in how ETFs operate and their potential advantages in terms of liquidity, lower expenses, tax efficiency, and transparency. We then turn to concerns over whether the rise in ETFs may raise unexpected risks for investors or greater instability in financial markets. While concerns over financial fragility are worth serious consideration, some of the common concerns are overstated, and for others, a number of rules and practices are already in place that offer a substantial margin of safety.
Full-Text Access | Supplementary Materials

"Frictions or Mental Gaps: What's Behind the Information We (Don't) Use and When Do We Care?" by Benjamin Handel and Joshua Schwartzstein
Consumers suffer significant losses from not acting on available information. These losses stem from frictions such as search costs, switching costs, and rational inattention, as well as what we call mental gaps resulting from wrong priors/worldviews, or relevant features of a problem not being top of mind. Most research studying such losses does not empirically distinguish between these mechanisms. Instead, we show that most highly cited papers in this area presume one mechanism underlies consumer choices and assume away other potential explanations, or collapse many mechanisms together. We discuss the empirical difficulties that arise in distinguishing between different mechanisms, and some promising approaches for making progress in doing so. We also assess when it is more or less important for researchers to distinguish between these mechanisms. Approaches that seek to identify true value from demand, without specifying mechanisms behind this wedge, are most useful when researchers are interested in evaluating allocation policies that strongly steer consumers towards better options with regulation, traditional policy instruments, and defaults. On the other hand, understanding the precise mechanisms underlying consumer losses is essential to predicting the impact of mechanism policies aimed primarily at reducing specific frictions or mental gaps without otherwise steering consumers. We make the case that papers engaging with these questions empirically should be clear about whether their analyses distinguish between mechanisms behind poorly informed choices, and what that implies for the questions they can answer. We present examples from several empirical contexts to highlight these distinctions.
Full-Text Access | Supplementary Materials

"Do Economists Swing for the Fences after Tenure?" by Jonathan Brogaard, Joseph Engelberg and Edward Van Wesep
Using a sample of all academics who pass through top 50 economics and finance departments from 1996 through 2014, we study whether the granting of tenure leads faculty to pursue riskier ideas. We use the extreme tails of ex-post citations as our measure of risk and find that both the number of publications and the portion consisting of "home runs" peak at tenure and fall steadily for a decade thereafter. Similar patterns hold for faculty at elite (top 10) institutions and for faculty who take differing time to tenure. We find the opposite pattern among poorly cited publications: their numbers rise post-tenure.
Full-Text Access | Supplementary Materials

"Retrospectives: Cost-Push and Demand-Pull Inflation: Milton Friedman and the "Cruel Dilemma," by Johannes A. Schwarzer
This paper addresses two conflicting views in the 1950s and 1960s about the inflation-unemployment tradeoff as given by the Phillips curve. Many economists at this time emphasized the issue of a seemingly unavoidable inflationary pressure at or even below full employment. In contrast, Milton Friedman was convinced that full employment and price stability are not conflicting policy objectives. This dividing line between the two camps ultimately rested on fundamentally different views about the inflationary process: For economists of the 1950s and 1960s cost-push forces are responsible for the apparent conflict between price stability and full employment. On the other hand, Friedman, who regarded inflation to be an exclusively monetary phenomenon, rejected the notion of ongoing inflationary cost-push pressures at full employment. Besides his emphasis on the full adjustment of inflation expectations, this rejection of cost-push theories of inflation, which implied a decoupling of the two previously perceived incompatible policy objectives, was the other important element in Friedman's attack on the Phillips curve tradeoff in his 1967 presidential address to the American Economic Association.
Full-Text Access | Supplementary Materials

"Recommendations for Further Reading," by Timothy Taylor
Full-Text Access | Supplementary Materials

"Using JEP Articles as Course Readings? Tell Us About It!"
Full-Text Access | Supplementary Materials

Tuesday, January 30, 2018

The Rising Importance of Soft Skills

What skills are most important for an employee to succeed at Google? Back in 2013, the company undertook Project Oxygen to answer that question.  Cathy N. Davidson described the result in the Washington Post last month ("The surprising thing Google learned about its employees — and what it means for today’s students," December 20, 2017).  She writes:
"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:

Friday, January 26, 2018

Why Has US Regional Convergence Declined?

In the decades after World War II and up into the 1980s, the US economy experienced regional convergence: that is, the economies and incomes in poorer regions (like the US South) tended to grow more quickly than the economies of richer regions (like the US North). But in the 1980s, this pattern of regional convergence slowed down.

A couple of recent research papers have investigated the shift. Peter Ganong and Daniel W. Shoag have published "Why Has Regional Income Convergence in the U.S. Declined?" in the Journal of Urban Economics (November 2017,  pp. 76-90). The paper isn't freely available online, but some readers will have access through library subscriptions, and there is a July 2016 version freely available as a Hutchins Center working paper. Elisa Giannone adds some aditional pieces to the puzzle in "Skilled-Biased Technical Change and Regional Convergence" (January 4, 2017), written as part of her doctoral dissertation.

 Both papers are tackling the same basic fact pattern, although with different data sources. Thus, Ganong and Shoag write:
"The convergence of per-capita incomes across US states from 1880 to 1980 is one of the most striking patterns in macroeconomics. For over a century, incomes across states converged at a rate of 1.8% per year. Over the past thirty years, this relationship has weakened dramatically, as shown in Figure 1.1 The convergence rate from 1990 to 2010 was less than half the historical norm, and in the period leading up to the Great Recession there was virtually no convergence at all."
Here's a figure to illustrate the pattern. In the left-hand panel, the horizontal axis measures the per capita income of states in 1940. The vertical axis shows the growth rate of state per capita income from 1940-1960. The downward-sloping line shows that the lower-income states in 1940 tended to have faster growth in the two decades that followed. The right-hand panel does the same exercise, but this time starting in 1990 and running through 2010. The downward-sloping but much flatter line in the right hand panel shows that while the lower income states in 1990 did grow a bit more quickly in the next two decades, the rate of convergence had become much slower.
A similar pattern shows up with what the authors call "directed migration," or movement of people from lower-income to higher income states: that is, the amount of such movement has declined substantially in the last few decades. 

In her paper, Giannone is using city-level data, rather than state-level data, and she writes: "Between 1940 and 1980 the wage gap between poorer U.S. cities and richer ones was shrinking at an annual rate of roughly 1.4%. After 1980, however, there was no further regional convergence overall."

A number of economic models predict convergence between regions. After all, people from lower-wage regions have an incentive to move to higher-wage regions, and some of them will do so. Conversely, firms have some incentive to invest in plant and equipment where the labor force and land are cheaper, and some of them will do so. Over time, these patterns should lead to a degree of convergence. What has changed?

To explain the slower convergence between regions, Ganong and Shoag offer an explanation based in patterns of migration and housing prices. They argue that the rise of housing prices in a number of high-income areas of the US had discouraged migration by lower-wage workers who don't already live there. They argue that the US economy has shifted from a converging labor market across states, with higher levels of migration between states, to a market where we are sorting into two group: those with higher incomes who live in areas with higher housing prices, and those with lower incomes who live in areas with lower housing prices.

Giannone offers a a different explanation based on "skill-biased technical change," which is the lingo for when a certain kind technological progress tends to help those with higher skills (and wages) more than those with lower skills (and wages). Her evidence suggests that the decline in convergence only happened for college-educated workers, while wages for workers with less education continued to converge. Moreover, her evidence shows that in a certain number of cities, the premium paid for  high-skill workers increased dramatically--even as more high-skill workers migrated to those cities. Her argument is that "agglomeration effects," which refer to the notion that a lot of skilled workers in close proximity may generate an extremely high wage urban economy (for example, think Silicon Valley).
These explanations based on technological change and housing markets are potentially complementary, and they do not exhaust the possibilities for why mobility across regions, and thus economic convergence, has declined. For example, last month I posted about David Shleicher's argument that ("Why More Americans Seem Stuck in Place," December 7, 2017). He attributes much of the issue to state and local laws affecting housing, jobs, and personal finance. For example, he wrote:
"[S]tate and local (and a few federal) laws and policies have created substantial barriers to interstate mobility, particularly for lower-income Americans. Land-use laws and occupational licensing regimes limit entry into local and state labor markets. Differing eligibility standards for public benefits, public employee pensions, homeownership tax subsidies, state and local tax laws, and even basic property law doctrines inhibit exit from low-opportunity states and cities. Building codes, mobile home bans, location-based subsidies, legal constraints on knocking down houses, and the problematic structure of Chapter 9 municipal bankruptcy all limit the capacity of failing cities to shrink gracefully, directly reducing exit among some populations and increasing the economic and social costs of entry limits elsewhere."  
Many countries have longstanding divergences in income between certain areas or regions: north and south, east and west, coastal and inland, rural and urban. The power of economic convergence can reduce such differences over time, but only in very rare occasions does it eliminate or overturn such differences. However, mobility across regions isn't just about economic convergence. It's also about a whether people in all areas have a sense of opportunity; whether people in the lower-income areas know a few people who moved elsewhere and enjoyed it; whether people from higher-income areas have friends with family and personal connections in lower-income areas. Those who move across regions are a kind of social bungee-cord, so that the distance between geographic areas isn't just measured by mileage on a map, but is bridged by human connections as well.

Thursday, January 25, 2018

A Puzzle: Why Do Retail Chains Charge Uniform Prices Across Stores?

Imagine yourself as the profit-seeking owner of a chain of retail stores. Would you charge the same (or nearly the same) price across all the stores? Or would you vary prices according to average income level of consumers who use that store, or according to whether the local economy was  robust or shaky, or according to whether the store had geographically nearby competitors?

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.

Wednesday, January 24, 2018

What's Wrong with Macro? A Symposium from the Oxford Review of Economic Policy

Macroconomists were notorious for their disagreements before 2007. Such wrangling only increased with the carnage of the Great Financial Crisis and its aftermath. The Oxford Review of Economic Policy  has now devoted a special double issue (Spring-Summer 2018) to a symposium on the topic of "Rebuilding macroeconomic theory." Lots of big names (to economists!) are featured, and at least for now, all the papers are freely available and ungated.  

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.

Tuesday, January 23, 2018

Bitcoin and Illegal Activity

One of the main attractions of bitcoin is its anonymity, which is worth the most to those who are carrying out questionable or illegal transactions. But how much of bitcoin used is tied to illegal activity? Sean Foley, Jonathan R. Karlsen,  and Tālis J. Putniņš tackle this question in their January 2018 working paper, "Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?" available at the SSRN website.

I've tended in the past to view bitcoin and other digital cryptocurrencies as a fascinating sideshow: that is, a combination of the deeply interesting blockchain technology, but at a relatively small scale. The authors point out that the scale has been rising substantially (footnotes omitted).
"Cryptocurrencies have grown rapidly in price, popularity, and mainstream adoption. The total market capitalization of bitcoin alone exceeds $250 billion as at January 2018, with a further $400 billion in over 1,000 other cryptocurrencies. The numerous online cryptocurrency exchanges and markets have daily dollar volume of around $50 billion. Over 170 “cryptofunds” have emerged (hedge funds that invest solely in cryptocurrencies), attracting around $2.3 billion in assets under management. Recently, bitcoin futures have commenced trading on the CME [Chicago Mercantile Exchange] and CBOE [Chicago Board Options Exchange], catering to institutional demand for trading and hedging bitcoin. What was once a fringe asset is quickly maturing."
I'm not sure where the dividing line is, but as cryptocurrencies seem headed toward exceeding $1 trillion in total value, greater attention will need to be paid. The focus of these authors is on links from bitcoin to illegal activity. Their research uses a key fact about the technology of bitcoin: the transactions carried out by bitcoin are all publicly available and observable, but the names of the participants are not. For example, you can observe that accounts A, B, and C all made a simultaneous payment to accounts D and E--but you don't know who those parties are. The authors write:

"We extract the complete record of bitcoin transactions from the public bitcoin blockchain, from the first block on January 3, 2009, to the end of April 2017. For each transaction, we collect the transaction ID, sender and recipient address, timestamp, block ID, transaction fee, and transaction amount. ... The data that make up the bitcoin blockchain reveal “addresses” (identifiers for parcels of bitcoin) but not the “users” (individuals) that control those addresses. A user typically controls several addresses. ... Our sample has a total of approximately 106 million bitcoin users, who collectively conduct approximately 606 million transactions, transferring around $1.9 trillion."

However, there are cases where the anonymity of blockchain has been broken, or at least dented. For example, law enforcement may expose who actually controls a certain address. Or certain addresses may be escrow accounts for firms operating on the "dark web." Or one can look at darknet forums, where anonymous parties may in some cases reveal their bitcoin address--for example, because they are complaining that they never received what they paid for.

Once you have a list of bitcoin addresses linked to anonymous activity, you can then track the transactions to and from those addresses. By looking at the patterns that emerge, you can build up a "cluster" of accounts and transactions that seem likely to be illegal. The authors compare the size of this cluster to the total number of bitcoin transactions. They write:
"We find that illegal activity accounts for a substantial proportion of the users and trading activity in bitcoin. For example, approximately one-quarter of all users (25%) and close to one-half of bitcoin transactions (44%) are associated with illegal activity. Furthermore, approximately one-fifth (20%) of the total dollar value of transactions and approximately one-half of bitcoin holdings (51%) through time are associated with illegal activity. Our estimates suggest that in the most recent part of our sample (April 2017), there are an estimated 24 million bitcoin market participants that use bitcoin primarily for illegal purposes. These users annually conduct around 36 million transactions, with a value of around $72 billion, and collectively hold around $8 billion worth of bitcoin.
"To give these numbers some context, a report to the US White House Office of National Drug Control Policy estimates that drug users in the United States in 2010 spend in the order of $100 billion annually on illicit drugs.5 Using different methods, the size of the European market for illegal drugs is estimated to be at least €24 billion per year. While comparisons between such estimates and ours are imprecise for a number of reasons (and the illegal activity captured by our estimates is broader than just illegal drugs), they do provide a sense that the scale of the illegal activity involving bitcoin is not only meaningful as a proportion of bitcoin activity, but also in absolute dollar terms."
As the authors note, these amounts are large enough to suggest that cryptocurrencies have the potential to shift how black markets operate. Many bitcoin accounts make only a single transaction, and then are never active again. And unsurprisingly, we are also seeing  "the emergence of alternative cryptocurrencies that are more opaque and better at concealing a user’s activity (e.g., Dash, Monero, and ZCash)." In the past, I have tended to believe that if law enforcement really wanted to break the anonymity of a cryptocurrency account, and devoted sufficient time and energy to a combination of old-fashioned and cyber-police work, it could do so. But the technology for anonymity keeps moving ahead.

For a recent, readable, and fairly short overview of bitcoin and the underlying blockchain technology, see "A Short Introduction to the World of Cryptocurrencies," by Aleksander Berentsen and Fabian Schär, in the Federal Reserve Bank of St. Louis Review (First Quarter 2018, pp. 1-16) For previous discussions of Bitcoin and blockchain technology on this blog, see:

Monday, January 22, 2018

Textiles: Your Clothes are Pollutants

"[T]he way we design, produce, and use clothes has drawbacks that are becoming increasingly clear. The textiles system operates in an almost completely linear way: large amounts of non-renewable resources are extracted to produce clothes that are often used for only a short time, after which the materials are mostly sent to landfill or incinerated. More than USD 500 billion of value is lost every year due to clothing underutilisation and the lack of recycling. Furthermore, this take-make-dispose model has numerous negative environmental and societal impacts. For instance, total greenhouse gas emissions from textiles production, at 1.2 billion tonnes annually, are more than those of all international flights and maritime shipping combined. Hazardous substances affect the health of both textile workers and wearers of clothes, and they escape into the environment. When washed, some garments release plastic microfibres, of which around half a million tonnes every year contribute to ocean pollution – 16 times more than plastic microbeads from cosmetics. Trends point to these negative impacts rising inexorably ..."

This, from the Executive Summary, is one of many jolting statements from A new textiles economy: Redesigning fashion’s future, published by the Ellen MacArthur Foundation in November 2017. The report seeks to envision a "circular" model of textile production: "In a new textiles economy, clothes, textiles, and fibres are kept at their highest value during use and re-enter the economy afterwards,
never ending up as waste."

Sales in the textile industry are growing rapidly as world incomes rise, and as people expand their wardrobes and wear each fewer times.

A few comments from the report on the environmental consequences, which are rising, too (footnotes omitted throughout):
"Large amounts of nonrenewable resources are extracted to produce clothes that are often used for only a short period,4 after which the materials are largely lost to landfill or incineration. It is estimated that more than half of fast fashion produced is disposed of in under a year. ..."
"Worldwide, clothing utilisation – the average number of times a garment is worn before it ceases to be used – has decreased by 36% compared to 15 years ago. While many low-income countries have a relatively high rate of clothing utilisation, elsewhere rates are much lower. In the US, for example, clothes are only worn for around a quarter of the global average. The same pattern is emerging in China, where clothing utilisation has decreased by 70% over the last 15 years. Globally, customers miss out on USD 460 billion of value each year by throwing away clothes that they could continue to wear, and  some garments are estimated to be discarded after just seven to ten wears. ..."

"Less than 1% of material used to produce clothing is recycled into new clothing, representing a loss of more than USD 100 billion worth of materials each year. As well as significant value losses, high costs are associated with disposal: for example, the estimated cost to the UK economy of landfilling
clothing and household textiles each year is approximately GBP 82 million (USD 108 million). Across the industry, only 13% of the total material input is in some way recycled  after clothing use ..." 
"The textiles industry relies mostly on non-renewable resources – 98 million tonnes in total per year – including oil to produce synthetic fibres, fertilisers to grow cotton, and chemicals to produce, dye, and finish fibres and textiles. Textiles production (including cotton farming) also uses around 93 billion cubic metres of water annually, contributing to problems in some water-scarce regions. ... [I]t is recognised that textile production discharges high volumes of water containing hazardous chemicals into the environment. As an example, 20% of industrial water pollution globally is attributable to the dyeing and treatment of textiles. ..."
Much of the report is given over to discussion of four broad areas in changes could be made, with numerous examples of what is happening in each area: "1. Phase out substances of concern and microfibre release; 2. Transform the way clothes are designed, sold, and used to break free from their increasingly disposable nature; 3. Radically improve recycling by transforming clothing design, collection, and reprocessing; 4. Make effective use of resources and move to renewable inputs."

I found especially interesting the vision in which clothes are designed for greater durability, combined with business models in which consumers rent a far greater share of their clothing. Once you start thinking along these lines, market segments where this approach might work well (aided by the ability of clothing providers to know your measurements in advance) become apparent. Small children? Maternity wear? That ski outfit you only wear a few times a year? As the report notes:
"Subscription models allow customers to pay a flat monthly service fee to have a fixed number of garments on loan at any one time. These models can provide an attractive offering for customers desiring frequent changes of outfit, as well as an appealing business case for retailers. ... Subscription models are already disrupting the market, with brands such as Le Tote, Gwynnie Bee, Kleiderei, and YCloset. This demonstrates that there is a willingness to pay monthly subscriptions for clothing, with YCloset in China securing a USD 20 million investment to scale up in March 2017. Another successful model is Rent the Runway, initially set up for online short-term rental of clothing for occasion wear and high-end luxury garments, which expanded to include a monthly rental subscription model in 2016. ... YCloset is riding the wave of popularity for sharing economy services in China, gaining customers in over 100 Chinese cities since their app launched in 2015. They target mid-market urban customers who want to access variety and a fresh look, but who lack the budget to buy midrange or luxury clothing. ..."
"The Danish company Vigga, established in 2014, allows parents to access igh-quality baby clothing for a fraction of the cost of buying new, with bundles of 20 appropriately sized baby clothing items provided at a time through a subscription service. By increasing durability, centralising washing and quality control, and streamlining operations through RFID (Radio Frequency Identification) tagging, on average Vigga circulates their baby clothes to five families before they are visibly used and go into
recycling, and they are working on increasing this number. Similar services have emerged in other countries, for example Tale Me in Belgium. Subscription services have also been introduced for pregnant women through companies such as Borrow For Your Bump, attempting to better address a woman’s needs for maternity wear. ..."
"Houdini Sportswear has offered customers the option to rent their outdoor sports shells since 2013. This creates an attractive financial model for both the brand and the customer, who can afford high-quality performance sportswear for one weekend or week for 10–25% of its retail price, rather than buying a cheaper, low-quality version or needing to store the garment for the rest of the year. At the same time, Houdini achieves higher overall margins by combining rental and resale. ..."
The report especially appealed to me for a couple of reasons. One is that I tend to think of textiles as an important but rather sleepy industry, gradually being transformed by robots and automated production. The report persuaded me that the opportunities for innovation in textiles are far greater than I had imagined. Also, it seems to me that even among those who drive hybrid cars and recycle religiously, the environment effects of clothing choices are often not much considered. This report should help to open up a new dimension of environmental awareness.

For a different angle on these issues, see "Quandaries of Global Trade in Secondhand Clothing" (May 22, 2015).

Friday, January 19, 2018

Attributing Economic Outcomes to Presidents: Year One of Trump

The US economy has performed well on a wide variety of measures in the year since President Trump was inaugurated on January 20, 2017.  The unemployment rate was 4.8% in January 2017 and 4.1% in December 2017. The unemployment rate among black Americans has fallen to its lowest level in the 45 years that regular statistics have been kept. The most recent estimates of GDP growth (which are preliminary and subject to later revision) show GDP growth of 3.1% in the second quarter of 2017 and 3.2% in the third quarter. Stock market indexes like the S&P 500 have risen dramatically.

The new year has brought a wave of economic good-news stories in the business press.  Business investment spending seem to be on the rise. By one measure, US manufacturing in 2017 has its best year since 2004. News sources not known to be overly friendly to the Trump administration, like the New York Times, are reporting stories like "The Trump Effect: Business, Anticipating Less Regulation, Loosens Purse Strings." US carbon emissions declined in 2017, which the US Energy Information Administration attributes in substantial part to fewer days of hot weather than in 2016--thus reducing the need for heavy use of air conditioning.

How much credit does President Trump deserve for this showing? Trump's critics quickly point out that an enormous economy like the United States has considerable momentum. For example,  Paul Krugman says that Trump gets "essentially zero" credit for US economic performance in 2017.  I agree that the effect of presidents--and especially newly elected presidents--on the economy is often overrated. But the observation that a US president has only a modest effect on the economy during a first year of office often includes a heavy dose of partisan bias.

Maybe I missed it, but when Trump had been elected and was headed toward taking office in January 2017, I didn't hear a lot of his critics say: "Well, the US economy is really set up for a strong year in 2017, but when it happens, Trump won't deserve any credit." Instead, there were predictions of grave difficulties ahead. Moreover, if the US economy had headed south early in 2017, with rising unemployment, sluggish output, stagnant investment, and a falling stock market, I strongly suspect that Trump critics like Krugman would place the blame on Trump's election. And in that case, it would be Republicans and Trump sympathizers arguing that the new president had inherited an unexpectedly poor situation and should receive essentially zero blame.

It's interesting to reflect back on previous presidencies, and apply the standard of "for the first year a president is in office, what happens (for good or bad) is largely what they inherited."  For example, the Great Recession ended in June 2009, six months into President Obama's first term. By this standard, the exit from the Great Recession should be credited to the economy and policies inherited from the previous Bush administration. The 2001 recession arrived in March of that year, just two months after President George W. Bush had assumed office. By this standard, that recession should be attributed to the economy and policies inherited from the Clinton administration. The incoming Clinton administration in 1993 inherited an economy with a falling unemployment rate, which by this standard should be attributed to the outgoing Bush administration.

I'm sympathetic to the argument that the first year of any presidential administration is essentially inherited, but this only sharpens the question of why the US economy performed so well in 2017. I'd suggest three possibilities.

First, the election campaign of 2016 seemed to involve all the candidates talking down the economy. But the national unemployment rate had fallen to 5,0% in September 2015, and has stayed at or below that level since then.  Here are the quarterly rates of real GDP growth since 2000.  The annualized growth rates of slightly more than 3% in Q2 and Q3 of 2017 are just fine, but they don't really stand out from a number of previous quarters since about 2010.  Overall, the US economy has considerable forward momentum at this point. It has continued to grow despite a succession of interest rate increases, and despite some terrible weather-related events in 2017. Growth across the main sectors of the economy has been quite balanced, rather than tilted toward a sector like housing or high-tech in a way that can lead to instability.
Second, although Americans like to think of our economy as an outpost remote from the rest of the world, we are in fact tied into the global economy in a number of ways. The World Bank argues that 10 years after the Great Recession, the global economy is at last again producing at its full potential. When an economic pattern happens across a number of nations at the same time, it's wise to suspect that there is a common underlying force that goes beyond national policies. For example, the fact that income inequality has been rising across many nations of the world, not just the US, suggests that the reasons for that increase are deeper economic patterns that affect many countries, not specific national actions. Similarly, with unemployment rates falling and stock markets rising in many countries around the world during 2017, it suggests that the reasons for that increase are patterns that cross many countries, not specific national actions.  My own sense is that many firms and banks had been holding their breath for a few years, waiting to be sure that the carnage of the Great Recession is behind them--and now they are stepping up.

Third, even with the US domestic momentum and full-potential output of the world economy taken into account, it does feel to me as if the Trump presidency was in some way an inflection point. The Trump administration's two main economic policy changes in 2017 involve a much more hands-off regulatory environment and the recently released tax bill. On the merits of these policies, I've expressed some concerns about both. Regulatory reform can be a positive step for an economy, and the UK and Canada have shown some ways to carry it out, but reform that does more to sort out regulations that justify their costs from those that don't is one thing, while just blocking and negating regulations willy-nilly is something else. The recent tax reform bill has many moving parts, but to me, the crucial question is the extent to which lower tax rates and other changes that benefit corporations pay off in a demonstrable surge in investment and wages. There are a bevy of recent anecdotes of companies announcing such changes, but in the next year or two, it will be interesting to check the follow-through on those promises--or whether most firms just cash the tax breaks, pay big bonuses to executives, and continue their investment and and wage-paying along much the same trajectory.

But whatever the merits of  these changes, it's not possible that their effects would have been directly felt early in 2017. Instead, firms would need to be reacting to the expectation of an improved business climate in the future. Like a lot of economists, I mistrust using "business climate" or "business confidence" as an explanation.  I'd prefer to be able to trace back "business confidence" to specific measurable parts of the economy, and to focus on those instead. But just because something is hard to measure doesn't mean it isn't real. It seems at least plausible that firms in a number of industries felt that the US business climate was not supportive, and interpreted Trump's election as a sign that polies more likely to support profit-seeking firms were on their way.

In thinking about business confidence, it may also be that some of Trump's important policy steps were the ones not taken. There were concerns that Trump might trigger a trade war. But while he has been hostile to additional trade agreements (as were the main Democratic party contenders for President in 2016), he did little to add impediments to trade in 2017.  Similarly, there was concern that Trump might replace Federal Reserve chair Janet Yellen with someone who didn't have the necessary trust and connections in financial markets, but the selection of Jerome Powell seemed to calm those concerns.

There's an old line commonly attributed to John Naisbitt (I don't have a citation) that "leadership involves finding a parade and getting in front of it." Politicians often excel at this kind of leadership, and in that spirit,  I don't blame President Trump for claiming excessive credit for the good news of the US economy in 2017. When it comes to economic outcomes, presidents are a bit like the coaches of professional sports teams--that is, they often get an outsized share of the credit for success and the blame for failure.

Thursday, January 18, 2018

The Global Output Gap Has Closed: What Next?

A decade after the global financial crisis circa 2008, the global economy has finally recovered. Tthe Global Economics Prospects 2018 report just published by the World Bank, subtitled "Broad-Based Upturn, but for How Long?" tells the story. 
"The global financial crisis tipped the global economy into a deep recession that affected first the advanced economies but spread—especially with the subsequent collapse of commodity prices—to emerging market and developing economies (EMDEs). Recoveries have been slow, but by 2018 the global economy is expected to return to its potential for the first time in a decade as the global output gap is expected to be closed. This in turn could mean a continued withdrawal by advanced economies of the extraordinary policy accommodation that was provided during the crisis, with important spillovers to EMDEs through trade and financial linkages. ...

"A broad-based cyclical global recovery is underway, aided by a rebound in investment and trade, against the backdrop of benign financing conditions, generally accommodative policies, improved confidence, and the dissipating impact of the earlier commodity price collapse. Global growth is expected to be sustained over the next couple of years—and even accelerate somewhat in emerging market and developing economies (EMDEs) thanks to a rebound in commodity exporters. Although near-term growth could surprise on the upside, the global outlook is still subject to substantial downside risks, including the possibility of financial stress, increased protectionism, and rising geopolitical tensions. Particularly worrying are longer-term risks and challenges associated with subdued productivity and potential growth. With output gaps closing or closed in many countries, supporting aggregate demand with the use of cyclical policies is becoming less of a priority. Focus should now turn to the structural policies needed to boost longer-term productivity and living standards. A combination of improvements in education and health systems; high-quality investment; and labor market, governance, and business climate reforms could yield substantial long-run growth dividends and thus contribute to poverty reduction." 
The report offers considerable detail across countries and regions, for the reader who wants to delve further. But at a time when the global economy is again, at long last, producing near its potential output, it's worth emphasizing that the formula for long-run growth is fairly clear: gains in education and human capital, gains in capital investment, and research and development for gains in technology, all interacting in an economic environment flexible enough to offer meaningful incentives for innovation. The report puts it this way:
"Global productivity growth has slowed over the past two decades. Some of the underlying drivers of this slowdown may fade over time, such as policy uncertainty and crisis legacies. Others, however, are likely to persist: the decline in labor force growth and population aging; a levelling-off of productivity-enhancing innovations in information and communication technologies; and maturing global supply chains. Policies to address these persistent factors include better education for improved learning in aging populations and initiatives to stimulate investment in physical capital and research and development. Other measures, such as regulatory reform and trade liberalization, could raise productivity by reducing informality and increasing competition."
It's also worth remembering that one reason for the current health of the American economy is that  US economic growth is being bolstered and supported by growth from the rest of the world. 

Wednesday, January 17, 2018

Snapshots of Economic Inequality Around the World

Compiling data on economic inequality from countries all around the world is a hefty task, which has been shouldered by a group of more than 100 researchers around the world who contribute to the efforts of the World Inequality Lab and the World Wealth and Income Database. The World Inequality Report 2018, written and coordinated by Facundo Alvaredo, Lucas Chancel, Thomas Piketty, Emmanuel Saez, Gabriel Zucman, provides an overview of their findings. Here are a few of the figures that jumped out at me.

This figure shows the share of income going to the top 10% of the income distribution in a number in some prominent countries and regions. Inequality in the US-Canada area (blue line) is clearly rising, but so is inquality across all of these areas. In particular, economic development in China and India has made some parts of those economies much better-off than others, so inequality his on the rise. The rise of inequality in Russia during the 1990s is also apparent.

This is a similar graph, but with a different set of comparison regions. The blue line for the US-Canada area remains the same. But as you can see, inequality in the Middle East, sub-Saharan Africa, and Brazil have long been above US-Canada levels, and by this measure, India has now passed the US level of inequality.
This figure is known as the "elephant graph," because if you squint a little, you can imagine that the bump on the left is the top edge would trace out the elephant's head, and then the upward movement on the right would be the top edge of the elephant's trunk. As the text explains: "On the horizontal axis, the world population is divided into a hundred groups of equal population size and sorted in ascending order from left to right, according to each group's income level. The Top 1% group is divided into ten groups, the richest of these groups is also divided into ten groups, and the very top group is again divided into ten groups of equal population size. The vertical axis shows the total income growth of an average individual in each group between 1980 and 2016."

For example, the figure shows that an adult who was in the 20th percentile of the world income distribution in 2016 had an income that was about 120% higher than an adult who was in the 20th percentile of the world income distribution in 1980. The "head" of the elephant shows that the gains to those in the 20-40th percentiles of the world income distribution were substantial. The drop in the middle shows that gains were smaller for those from the 50th-80th percentiles of the world income distribution. And on the far right, the top percentile is divided up into smaller slices. The gains for the top percentile were substantial, but comparable to those in the 20th-40th percentile. However, the gains the top 0.01% and the 0.001% were substantially larger. Of course, these groups at the very top are also for much smaller groups, and thus are  harder to measure, and probably also involve more turnover year-to-year.

Underlying these overall patterns are some shifts in regional economic patterns that are fairly well-known, but remain striking. For example, this figure looks at average incomes in Africa and across Asia, and  how they compare to the average world income. In 1950, Africa was well ahead of Asia relative to average world income, but that pattern has dramatically reversed.
As a similar exercise, China lagged far behind Latin America relative to world income back in 1950. But Latin America has underperformed the world economy, and China has outperformed it, and China appears to be on its way to outstripping Latin America in average incomes in the next few years.
This volume is a rich resource, with lots of information on inequality of incomes by country and by region,  inequality of wealth, shifts in public wealth, and other topics. The policy discussion is relatively brief (better education, progressive taxation, rethinking labor institutions), but that was  fine with me. The fundamental point of this exercise is to generate a common fact base, and then let the policy discussion build upon it.

Monday, January 15, 2018

Some Economics for Martin Luther King Day

On November 2, 1983, President Ronald Reagan signed a law establishing a federal holiday for the birthday of Martin Luther King Jr., to be celebrated each year on the third Monday in January. As the legislation that passed Congress said: "such holiday should serve as a time for Americans to reflect on the principles of racial equality and nonviolent social change espoused by Martin Luther King, Jr.." Of course, the case for racial equality stands fundamentally upon principles of justice, not economics. But here are a few economics-related thoughts for the day from the archives:

1) Inequalities of race and gender impose large economic costs on society as a whole, because one consequence of discrimination is that it hinders people in developing and using their talents. In "Equal Opportunity and Economic Growth" (August 20, 2012), I wrote:

A half-century ago, white men dominated the high-skilled occupations in the U.S. economy, while women and minority groups were often barely seen. Unless one holds the antediluvian belief that, say, 95% of all the people who are well-suited to become doctors or lawyers are white men, this situation was an obvious misallocation of social talents. Thus, one might predict that as other groups had more equal opportunities to participate, it would provide a boost to economic growth. Pete Klenow reports the results of some calculations about these connections in "The Allocation of Talent and U.S. Economic Growth," a Policy Brief for the Stanford Institute for Economic Policy Research.

Here's a table that illustrates some of the movement to greater equality of opportunity in the U.S. economy. White men are no longer 85% and more of the managers, doctors, and lawyers, as they were back in 1960. High skill occupation is defined in the table as "lawyers, doctors, engineers, scientists, architects, mathematicians and executives/managers." The share of white men working in these fields is up by about one-fourth. But the share of white women working in these occupations has more than tripled; of black men, more than quadrupled; of black women, more than octupled.

Moreover, wage gaps for those working in the same occupations have diminished as well. "Over the same time frame, wage gaps within occupations narrowed. Whereas working white women earned 58% less on average than white men in the same occupations in 1960, by 2008 they earned 26% less. Black men earned 38% less than white men in the typical occupation in 1960, but had closed the gap to 15% by 2008. For black women the gap fell from 88% in 1960 to 31% in 2008."

Much can be said about the causes behind these changes, but here, I want to focus on the effect on economic growth. For the purposes of developing a back-of-the-envelope estimate, Klenow builds up a model with some of these assumptions: "Each person possesses general ability (common to
all occupations) and ability specific to each occupation (and independent across occupations). All groups (men, women, blacks, whites) have the same distribution of abilities. Each young person knows how much discrimination they would face in any occupation, and the resulting wage they would get in each occupation. When young, people choose an occupation and decide how
much to augment their natural ability by investing in human capital specific to their chosen

With this framework, Klenow can then estimate how much of U.S. growth over the last 50 years or so can be traced to greater equality of opportunity, which encouraged many in women and minority groups who had the underlying ability to view it as worthwhile to make a greater investment in human capital.

"How much of overall growth in income per worker between 1960 and 2008 in the U.S. can be explained by women and African Americans investing more in human capital and working more in high-skill occupations? Our answer is 15% to 20% ... White men arguably lost around 5% of their earnings, as a result, because they moved into lower skilled occupations than they otherwise would have. But their losses were swamped by the income gains reaped by women and blacks."

At least to me, it is remarkable to consider that 1/6 or 1/5 of total U.S. growth in income per worker may be due to greater economic opportunity. In short, reducing discriminatory barriers isn't just about justice and fairness to individuals; it's also about a stronger U.S. economy that makes better use of the underlying talents of all its members.

2) The black-white wage gap--and the share of the gap that is "unexplained"-- is rising, not falling. Here's part of what I wrote about in "Breaking Down the Black-White Wage Gap (September 6, 2017):

Mary C. Daly, Bart Hobijn, and Joseph H. Pedtke set the stage for a more insightful discussion in their short essay, "Disappointing Facts about the Black-White Wage Gap," written as an "Economic Letter" for the Federal Reserve Bank of San Francisco (September 5, 2017, 2017-26). Here are a couple of figures showing the black-white wage gap, and then seeking to explain what share of that gap is associated with differences in state of residence, education, part-time work, industry/occupation, and age. The first figure shows the wage gap for black and white men; the second for black and white women.

Here are some thoughts on these patterns:

1) The black-white wage gap is considerably larger for men (about 25%) than for women (about 15%). Also, the wage gaps seem to have risen since the 1980s.

2) The three biggest factors associated with the wage gap seem to be education level, industry/occupation, and "unexplained."

3) The "unexplained" share is rising over time time. As the authors explain: "Perhaps more troubling is the fact that the growth in this unexplained portion accounts for almost all of the growth in the gaps over time. For example, in 1979 about 8 percentage points of the earnings gap for men was unexplained by readily measurable factors, accounting for over a third of the gap. By 2016, this portion had risen to almost 13 percentage points, just under half of the total earnings gap. A similar pattern holds for black women, who saw the gaps between their wages and those of their white counterparts more than triple over this time to 18 percentage points in 2016, largely due to factors outside of our model. This implies that factors that are harder to measure—such as discrimination, differences in school quality, or differences in career opportunities—are likely to be playing a role in the persistence and widening of these gaps over time." The authors also cite this more detailed research paper with similar findings.

4) In looking at the black-white wage gap for women, it's quite striking that this gap was relatively small back in the 1980s, at only about 5%, and that observable factors like education and industry/occupation explained more than 100% of the wage gap at the time. But as the black-white wage gap for women increased starting in the 1990s, an "unexplained" gap opens up.

5) It is tempting to treat the "unexplained" category as an imperfect but meaningful measure of racial discrimination, but it's wise to be quite cautious about such an interpretation. On one side, the "unexplained" category may overstate discrimination, because it doesn't include other possible variables that affect wages (for example, one could include previous years of lifetime work experience, or length of tenure at a current job, scores on standardized tests, or many other variables). In addition, the variables that are included like level of education are being measured in broad terms, and so it is possible that, say, a blacks and whites with a college education are not the same in their skills and background. On the other side, the "unexplained" category could easily understate the level of discrimination. After all, education levels and industry/occupation outcomes don't happen in a vacuum, but are a result of the income, education, and jobs of family members. For this reason, noting that a wage gap is associated with some different in education or industry/occupation may reflect aspects of social discrimination. The kinds of calculations presented here are useful, but they don't offer final answers.

In short, the black-white wage gap is rising, not falling. The wage gap is also less associated with basic measures like level of education or industry/occupation than it was before. I can hypothesize a number of explanations for this pattern, but none of my hypotheses are cheerful ones.


3) The patterns in which speeding tickets are given for those just a little over the speed limit can  reveal discrimination. I discuss some evidence on this point in "Leniency in Speeding Tickets: Bunching Evidence of Police Bias" (April 5, 2017):

Imagine for a moment the distribution of speed for drivers who are breaking the speed limit. One would expect that a fairly large number of drivers break the speed limit by a small amount, and then a decreasing number of drivers break the speed limit by larger amounts.

But here's the actual distribution of amount over the speed limit on the roughly 1 million tickets given by about 1,300 officers of the Florida Highway Patrol between 2005 and 2015. The graph is fromFelipe Goncalves and Steven Mello, "A Few Bad Apples? Racial Bias in Policing," Princeton University Industrial Relations Section Working Paper #608, March 6, 2017. The left-hand picture shows the distribution of the amount over the speed limit on the speeding ticket given to whites; the right-hand picture shows the distribution the amount over the speed limit on the speeding tickets given to blacks and Hispanics.

Some observations:

1) Very few tickets are given to those driving only a few miles per hour over the speed limit. Then there is an enormous spike in those given tickets for being about 9 mph over the speed limit. There are also smaller spikes at some higher levels. In Florida, the fine for being 10 mph over the limit is substantially higher (at least $50, depending on the county) compared to the fine for being 9 mph over the limit.

2) The jump at 9 mph is sometimes called a "bunching indicator" and it can be a revealing approach in a number of contexts. For example, if being above or below a certain test score makes you eligible for a certain program or job, and one observes bunching at the relevant test score, it's evidence that the test scores are being manipulated. If being above or below a certain income level affects your eligibility for a certain program, or whether you owe a certain tax, and there is bunching at that income level, it's a sign that income is being manipulated. Real-world data is never completely smooth, and always has some bumps. But the spikes in the figure above are telling you something.

3) Goncalves and Mello note that the spike at 9 mph is higher for whites than for blacks and Hispanics. This suggests the likelihood that whites are more likely to catch a break from an officer and get the 9 mph ticket. The research in the paper investigates this hypothesis in some detail ...

In the big picture, one of the reminders from this research is that bias and discrimination doesn't always involve doing something negative. In the modern United States, my suspicion is that some of the most prevalent and hardest-to-spot biases just involve not cutting someone an equal break, or not being quite as willing to offer an opportunity that would otherwise have been offered.


4) Many of the communities that suffer the most from crime are also the communities where the law-abiding and the law-breakers both experience a heavy law enforcement presence, and where large numbers of young men end up being incarcerated. Here are some slices of my discussion from "Inequalities of Crime Victimization and Criminal Justice" (May 20, 2016):

And law-abiding people in some communities, many of them predominantly low-income and African-American, can end up facing an emotionally crucifying choice. One one side, crime rates in their community are high, which is a terrible and sometimes tragic and fatal burden on everyday life. On the other side, they are watching a large share of their community, mainly men, becoming involved with the criminal justice system through fines, probation, fines, or incarceration. Although those who are convicted of crimes are the ones who officially bear the costs, in fact the costs when someone needs to pay fines, or can't earn much or any income, or can only be visited by making a trip to a correctional facility are also shared with families, mothers, and children. Magnus Lofstrom and Steven Raphael explore these questions of "Crime, the Criminal Justice System, and Socioeconomic Inequality" in the Spring 2016 issue of the Journal of Economic Perspectives. ...

It's well-known that rates of violent and property crime have fallen substantially in the US in the last 25 years or so. What is less well-recognized is that the biggest reductions in crime have happened in the often predominantly low-income and African-American communities that were most plagued by crime. Loftrom and Raphael look at crime rates across cities with lower and higher rates of poverty in 1990 and 2008:
"However, the inequality between cities with the highest and lower poverty rates narrows considerably over this 18-year period. Here we observe a narrowing of both the ratio of crime rates as well as the absolute difference. Expressed as a ratio, the 1990 violent crime rate among the cities in the top poverty decile was 15.8 times the rate for the cities in the lowest poverty decile. By 2008, the ratio falls to 11.9. When expressed in levels, in 1990 the violent crime rate in the cities in the upper decile for poverty rates exceeds the violent crime rate in cities in the lowest decile for poverty rates by 1,860 incidents per 100,000. By 2008, the absolute difference in violent crime rates shrinks to 941 per 100,000. We see comparable narrowing in the differences between poorer and less-poor cities in property crime rates. ... "
It remains true that one of the common penalties for being poor in the United States is that you are more likely to live in a neighborhood with a much higher crime rate. But as overall rates of crime have fallen, the inequality of greater vulnerability to crime has diminished.

On the other side of the crime-and-punishment ledger, low-income and African-American men are more likely to end up in the criminal justice system. Lofstrom and Raphael give sources and studies for the statistics: "[N]nearly one-third of black males born in 2001 will serve prison time at some point in their lives. The comparable figure for Hispanic men is 17 percent ... [F]or African-American men born between 1965 and 1969, 20.5 percent had been to prison by 1999. The comparable figures were 30.2 percent for black men without a college degree and approximately 59 percent for black men without a high school degree."

I'm not someone who sympathizes with or romanticizes those who commit crimes. But economics is about tradeoffs, and imposing costs on those who commit crimes has tradeoffs for the rest of society, too. For example, the cost to taxpayers is on the order of $350 billion per year, which in 2010 broke down as "$113 billion on police, $81 billion on corrections, $76 billion in expenditure by various federal agencies, and $84 billion devoted to combating drug trafficking." The question of whether those costs should be higher or lower, or reallocated between these categories, is a worthy one for economists. ... Lofstrom and Raphael conclude:
"Many of the same low-income predominantly African American communities have disproportionately experienced both the welcome reduction in inequality for crime victims and the less-welcome rise in inequality due to changes in criminal justice sanctioning. While it is tempting to consider whether these two changes in inequality can be weighed and balanced against each other, it seems to us that this temptation should be resisted on both theoretical and practical grounds. On theoretical grounds, the case for reducing inequality of any type is always rooted in claims about fairness and justice. In some situations, several different claims about inequality can be combined into a single scale—for example, when such claims can be monetized or measured in terms of income. But the inequality of the suffering of crime victims is fundamentally different from the inequality of disproportionate criminal justice sanctioning, and cannot be compared on the same scale. In practical terms, while higher rates of incarceration and other criminal justice sanctions may have had some effect in reducing crime back in the 1970s and through the 1980s, there is little evidence to believe that the higher rates have caused the reduction in crime in the last two decades. Thus, it is reasonable to pursue multiple policy goals, both seeking additional reductions in crime and in the continuing inequality of crime victimization and simultaneously seeking to reduce inequality of criminal justice sanctioning. If such policies are carried out sensibly, both kinds of inequality can be reduced without a meaningful tradeoff arising between them."

5) An "audit study" of housing discrimination involves finding pairs of people, giving them similar characteristics (job history, income, married/unmarried, parents/not parents) and sending them off to buy or rent a place to live. In "Audit Studies and Housing Discrimination" (September 21, 2016), I wrote in part:

Cityscape magazine, published by the US Department of Housing and Urban Development three times per year, has a nine-paper symposium on "Housing Discrimination Today" in the third issue of 2015. The lead article by Sun Jung Oh and John Yinger asks: "What Have We Learned From Paired Testing in Housing Markets?" (17: 3, pp. 15-59). ...

There have been four large national-level paired testing studies of housing discrimination in the US in the last 40 years. "The largest paired-testing studies in the United States are the Housing Market Practices Survey (HMPS) in 1977 and the three Housing Discrimination Studies (HDS1989, HDS2000, and HDS2012) sponsored by the U.S. Department of Housing and Urban Development (HUD)." Each of the studies were spread over several dozen cities. The first three involved about 3,000-4,000 tests; the 2012 study involved more than 8,000 tests. The appendix also lists another 21 studies done in recent decades.

Overall, the findings from the 2012 study find ongoing discrimination against blacks in rental and sales markets for housing. For Hispanics, there appears to be discrimination in rental markets, but not in sales markets. Here's a chart summarizing a number of findings, which also gives a sense of the kind of information collected in these studies.

However, the extent of housing discrimination in 2012 has diminished from previous national-level studies. Oh and Yinger write (citations omitted): "In 1977, Black homeseekers were frequently denied access to advertised units that were available to equally qualified White homeseekers. For instance, one in three Black renters and one in every five Black homebuyers were told that there were no homes available in 1977. In 2012, however, minority renters or homebuyers who called to inquire about advertised homes or apartments were rarely denied appointments that their White counterparts were able to make.