Rachel Glennerster is a professor of economics at the University of Chicago, which is more-or-less the dictionary definition of an academic economist. But earlier in her career, she worked as a policy economist at the UK Treasury, the IMF, and the the UK Foreign, Commonwealth & Development Office, which focuses on topics of international economic cooperation and foreign aid. When it comes to distinctions between academic and policy economists, she knows whereof she speaks. In a blog post back in 2014, she summarized the distinctions with this table.
There are some economists who can move fluidly between the academic and the policy world. But the table helps to explain why many don’t make the jump very well, or prefer not to try.
The thing I like most about the field of economics is that it is still mostly people who are open to empirically grounded discussions of problems in which you’ll acknowledge what the facts are and alternative approaches to modalities of making the facts different.
Then the other thing … I don’t really teach undergraduates very often, but I was invited to give the opening lecture to a development economics course of undergraduates. My take was that economics is the social science of love. It’s the truly loving social science, and what I meant—and they were, of course, like, “What? Economics and love? That’s crazy.” But think about what economists do. We take individuals—objective functions are objective functions. We don’t start with any premise about what would be good for society or good for X or good for Y.
But I think economists, when they’re doing it right, they start from, what is it that people want to accomplish with their lives? Okay. Let’s think about what the actual outcomes are. Let’s think about modalities at the society, political, market level that would facilitate individuals achieving their objectives more or less. And what could be a better description of love than “I’m going to take—what you want is what I want for you, and I’m going to help you achieve that.” Economics is the loving social science, is my take on what economists do best.
I speak from personal experience when I say that perhaps the professional advantage of economists is not greatest in discussions and definitions of love. But what I like about Pritchett’s comment is that so many noneconomists I meet think about the subject in terms of topics like how to make money in the stock market or how to run a profitable business. Pritchett’s definition has its limitations. But at least it captures the idea of economics as subject that focuses on what people want for themselves, and thus on the importance of people having freedom to make choices about work, skill acquisition, and types of consumption.
For those who would like a little more love-talk from economists, in a post some years back about “Is Altruism a Scarce Resource that Needs Conserving?“, I discuss the line of argument among some economists that by allowing a substantial portion of the world’s material needs to be met by the actions of self-interested producers and consumers, we can save our scarce resources of love and altruism for where they are needed.
My tradition on this blog is to take a break (mostly!) from current events in the later part of August. Instead, I pre-schedule daily posts based on things I read during the year about three of my preoccupations: economics, academia, and writing.
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For economists, “widgets” are the example of a hypothetical product you use when you don’t want to get specific. Another common hypothetical product is “leets,” which is “steel” spelled backward. But where did the terminology of widgets first appear, and how did it work its way over to economics?
According to the Oxford English dictionary, the etymology of “widgets” is unclear. It’s sometimes thought to be a spin-off of “gadgets,” but there don’t seem to be examples to support this claim. Instead, the origin of “widgets” is usually credited to the playwrights George S. Kaufman and Marc Connelly in their 1924 play, Beggar on Horseback.
The play revolves around Neil McRae, who is a poor and unknown composer of classical music, working odd jobs to get by. There is a wealthy industrialist named Mr. Cady, with a beautiful daughter named Gladys. Will Neil give up his classical music dreams, marry the boss’s daughter, and work at the factory? In the play, the factory makes “widgets.” Here’s some dialogue from the play between Neil, Mr. Cady, and a secretary named Miss You:
CADY: Why, Neil!
NEIL: Here I am—at work!
CADY: Yes, sir! Business! Big business!
NEIL: Yes. Big business. What business are we in?
CADY: Widgets. We’re in the widget business.
NEIL: The widget business?
CADY: Yes, sir! I suppose I’m the biggest manufacturer in the world of overhead and underground A-erial widgets. Miss You!
MISS YOU: Yes, sir.
CADY: Let’s hear what our business was during the first six months of the fiscal year. [To Neil.] The annual report.
MISS YOU [Reading.]: “The turnover in the widget industry last year was greater than ever. If placed alongside the Woolworth Building it would stretch to the moon. The operating expenses alone would furnish every man, woman and child in the United States, China and similar places with enough to last for eighteen and one-half years, if laid end to end.”
CADY: How’s that?
NEIL: It’s wonderful!
CADY: And wait for September 17th!
NEIL: Why?
CADY: That’s to be National Widget Week! The whole country!
NEIL: That’s fine, but what I came up about …
CADY: Never mind that now—we’ve got more important things. Conferences, mostly.
The terminology of widget seems to have caught hold fairly soon. I was especially struck by this short 1939 movie by the General Motors Department of Public Relations. It’s called “Round and Round,” and as you will see, it’s an attempt to describe a circular flow in the economy. It’s about a factory that uses skilled labor and machines to make widgets. As the video explains: “A widget might be a radio, a refrigerator, a musical instrument, or a motor car. A widget, you know, is just a symbol for any manufactured product that people use.” The factory sells widgets to farmers, coal miners, steel manufacturers, and others. In turn, they use the widgets to produce the inputs needed by the widget manufacturer to make more widgets.
In 1969, the Guinness company decided to take the widget out of the hypothetical, and to make and patent an actual product that has come to be called a “widget.” The company filed a patent application in Ireland for an “Improved Method of and Means of Dispensing Carbonated Liquids from Containers.”As explained here, the widget is a small plastic ball with a hole in it that sits inside a can of beer. When the beer is put under pressure, there is nitrogenated beer under pressure inside this hole. When the can is popped open, this extra dose of nitrogenated beer combines with the rest of the beer in the can to produce a foamy head on the beer as it is poured.
Modern software programmers have also tried to commandeer the terminology of widgets for their own. For example, the Techopedia webpage defines widgets in this way:
Widget is a broad term that can refer to either any GUI (graphical user interface) element or a tiny application that can display information and/or interact with the user. A widget can be as rudimentary as a button, scroll bar, label, dialog box or check box; or it can be something slightly more sophisticated like a search box, tiny map, clock, visitor counter or unit converter. … The term widget is understood to include both the graphical portion, with which the user interacts, and the code responsible for the widget’s functionality.
This seems a long way from National Widget Week as conceived by Kaufman and Connelly back in 1924! But economists have by and large shrugged off the attempts by beer companies and software firms to appropriate their single most prominent hypothetical example. Instead, economics lecturers stick with the meaning of “widget” as defined by the General Motors Public Relations Department.
From the abstract of the academic paper, the authors write:
In this survey, we argue that the economic analysis of fertility has entered a new era. First-generation models of fertility choice were designed to account for two empirical regularities that, in the past, held both across countries and across families in a given country: a negative relationship between income and fertility, and another negative relationship between women’s labor force participation and fertility. The economics of fertility has entered a new era because these stylized facts no longer universally hold. In high-income countries, the income-fertility relationship has flattened and in some cases reversed, and the cross-country relationship between women’s labor force participation and fertility is now positive.
A couple of pictures may help, here. It used to be that countries with higher incomes had lower fertility rates, but among high-income countries, this pattern no longer holds. Here’s a figure taken from the VoxEU overview. The top panel shows that within the group of high-income countries in 1980, countries with higher per capita GDP had lower fertility, but by 2000, countries in this group with higher per capita income had higher fertility.
What about the relationship between women’s fertility and the labor force participation rate of women? Here’s the parallel figure. It shows that in 1980, within the group of high-income countries, those with higher fertility tended to have lower labor market participation for women; by 2000, the countries with higher fertility tended to have higher labor force participation for women.
The previous theories of fertility were based on some intuitively plausible claims. As incomes went up in a given country, the opportunity costs of having a child went up, so women would be more likely to enter the labor force and fertility would decline. But now it appears that as incomes rise in a given country, women are likely to have more children and also to spend more time in the labor force. Instead of higher incomes being less compatible with children and with being in the workforce, they are apparently becoming more compatible. The authors write:
We highlight a number of factors that have blunted the forces emphasized by the first generation of economic models of fertility. For example, in high-income countries, child labor has disappeared and education for most children continues past childhood into the adult years. These changes imply that the tradeoff inherent in quantity-quality models between sending children to school versus having more resources to raise a larger family has lost salience. Similarly, models based on women’s opportunity cost of time posit that raising more children requires mothers to spend less time working in the market. While this tradeoff still exists today, it has weakened as alternative forms of childcare have become more prominent. When childcare is provided by someone other than the mother—whether a hired nanny, a government-run kindergarten, or the child’s father—the cost of children is no longer linked as directly to the mother’s opportunity cost of time.
To explain why the empirical relationship between women’s labor force participation and fertility has not just flattened, but entirely reverted, research has taken directions that go beyond the first-generation models. A general theme in this new literature is that the compatibility of family and career has become a key determinant of fertility in high-income economies. Where the two are easy to combine, many women have both a career and multiple children, resulting in high fertility and high female labor force participation. When career and family goals are in conflict, fewer women work and fewer babies are born. We point out four factors that help mothers combine a career with a larger family: the availability of public child care and other supportive family policies; greater contributions from fathers in providing childcare; social norms in favor of working mothers; and flexible labor markets.
It is far too early to discern whether these kinds of shifts will alter global pattern of lower birthrates. But it does suggest that those who would prefer rising birthrates should focus on policies and norms that make it easier for women to work; conversely, those who prefer lower birthrates might prefer policies and norms that increase the tradeoffs for women of entering the workplace.
“Globotics” is the name that Richard Baldwin gave to the combination of globalization and robotics in service jobs. In his essay “Globotics and macroeconomics: Globalisation and automation of the service sector” (presented at the ECB Forum on Central Banking 2022, June 27-29, where videos of presentations and comments are included), he argues that you can’t understand the likely future of globalization without it.
Baldwin argued that the global economy is in the throes of a third “unbundling” of globalization, which is a phrase he uses to describe the driving force behind a shift in what is traded across global borders. In his telling, the first “unbundling” “happened when steam power and Pax Britannica radically lowered the cost of moving goods,” and the unfolding process of reduced physical transportation costs over the decades drove the rise of globalization from the 19th century up to the 1960s or 1970s (with interruptions for world wars, the Great Depression, and other events).
The second “unbundling” kicked in around 1990. It wasn’t about transportation costs, but rather about information and communication technology (ICT) and how it affected firms in big high-income countries like the G7 group (the United States, Canada, Great Britain, France, Germany, Italy, Japan). In particular, it wasn’t about how economies of some countries were able to produce goods at different prices, which is the standard intro-level theory of trade, but rather how a combination of higher-technology manufacturing firms in high-income countries could coordinate their production chain with lower-wage labor in other countries. Baldwin writes:
Globalisation changed dramatically around 1990 when it entered its offshoring-expansion phase, or what I have called the “second unbundling” to contrast it with the first unbundling (Baldwin 2006). This was triggered by the ICT revolution which relaxed the second separation cost – communication and coordination costs. ICT made it feasible for G7 firms to fragment highly complex industrial processes into production stages, and then spatially unbundle some of them to low-wage nations. Think of this as the offshoring-expansion phase of globalisation where G7 manufacturing firms seized low-hanging opportunities for combining their advanced manufacturing knowhow with foreign low-wage labour in factories set up abroad. As the offshored process had to continue to operate as if it were still bundled, we can think of this as factories crossing borders, not just goods. Trade boomed again.
This third “unbundling,” now underway, is about how the newest versions of interconnected information and communications technology, which one might just call the digital economy, are connecting services industries. Indeed, although the rise in international trade in goods has more-or-less levelled off since about 2008, international trade in services has continued to rise and is becoming an ever-larger share of international trade.
What exactly are these “other commercial services”?
The OCS category consists of a few big items and many small items. Some are easily recognisable. Among the bigger categories are Financial Services (9%), and payments for intellectual property rights. The category Telecommunications, Computer, and Information Services accounts for 11% of the total; much of this is made up of computer services related to software, but a large share is tossed into the category ‘Other computer services other than cloud computing’ (this is typical of the lack of precision in trade statistics). The largest sub-category (23%) is ‘Other Business Services’. This includes a broad array of services. Some – like Architectural, Financial, Engineering, R&D, Advertising and Marketing, and Professional and Management Consulting services – are easily associated with sectors and jobs. Others, like Operating Lease Services, and ‘Other Business Services, not elsewhere included’ are difficult to map into jobs and sectors in the domestic economy.
In my own mind, it’s perhaps useful to think of the third “unbundling” in terms of working from home. If your job is one that can be entirely done by someone working from home, by a telecommuter, then it can be done by someone outside the country. As one of many examples, the K-12 and higher education systems just spent a year delivering their services on-line. Baldwin writes:
Note that the arbitrage here is direct wage competition among service sector workers, and wage differences are probably the largest unexploited arbitrage left in today’s world. Taking Colombia as an example of a middle-income emerging market, a recent study matched the US’s occupation classifications with those of Colombia to compare wage rates (Baldwin, Cardenaz, and Fernandez 2021). Focusing only on the occupations that Dingel and Neiman (2020) have classified as teleworkable in the US, the study found that the wages in the US were on average 1500% higher in the US than in Colombia. Plainly low wages are not the only source of competitiveness in services but with wage gaps being that large, it is likely that the digitech-driven globalisation of the service sector will have an impact on prices in advanced economies.
Some of the arbitrage is done via online freelancing platforms like Upwork, Freelancer, and Zhubajie (these are like eBay but for services). Wage comparisons based on worker-level data scrapped from such online freelancing platforms confirm the presence of enormous wage gaps, although the size varies greatly according to the data selection criteria. Data from a number of the largest freelance platforms reported in ILO (2021) indicate that average hourly earnings paid in a typical week for those engaged in online work is US$4.9, with the majority of workers (66%) earning less than the average. While $4.90 an hour seems like a low wage in Europe, it corresponds to full-time equivalent salary of about $10,000 per year – a salary which is considered comfortably middle-class in most countries.
Baldwin argues that the low-hanging fruit in this area is “intermediate services.” For example, it might be hard for a variety of regulatory reasons for a US firm to hire accountants directly from a company based in India or Brazil or Indonesia. But it’s pretty easy for US-based accounting firm to hire those accountants from other places, and to coordinate their work when delivering accounting services to US-based firms. Moreover, for a lot of emerging market economies, providing services can be pretty straightforward.
[E]xport capacity in emerging markets is not as great a limiting factor in services as it is in goods since every nation has a workforce that is already producing intermediate-service tasks. All emerging market economies have bookkeepers, forensic accountants, CV screeners, administrative assistants, online client help staff, graphic designers, copyeditors, personal assistants, travel agents, software engineers, lawyers who can check contracts, financial analysts who can write reports, etc. There is no need to develop whole new sectors, build factories, or develop farms or mines.
The term “slobalization” is used to describe the level of globalization slowing down. When it come to trade in goods, slobilization applies. But Baldwin’s analysis implies that the world economy may already be seeing the roots of a substantial rise in globalization that will happen via the services sector.
Distressed places, which have low employment to-population ratios (employment rates), are a big problem in America. Consider local labor markets: multicounty areas that contain most commuting flows, such as metro areas or rural commuting zones. About two-fifths of all Americans live in local labor markets whose employment rate for prime-age workers (ages 25–54) is more than 5 percentage points below full employment. For neighborhoods, about one-fifth of all Americans live in census tracts whose prime-age employment rate is more than 5 percentage points below their local labor market’s average. These low employment rates are linked to major social problems: substance abuse, crime, and family stress.
Helping distressed local labor markets requires different policies than helping distressed neighborhoods. In a distressed local labor market, job creation will raise employment rates, with plausibly half of the jobs going to local nonemployed residents. Local job creation is most cost-effectively accomplished by providing businesses with “customized services” such as infrastructure, customized job training, and business advice programs—including manufacturing extension services. Such customized services have less than one-third the cost-per-job-created of business tax incentives.
In contrast, in a distressed neighborhood, more neighborhood jobs will not much help the neighborhood’s residents, as most neighborhood jobs are not held by residents. Residents of distressed neighborhoods can best be helped by services to increase job access, including better transportation, job training, and child care.
Bartik offers a fleshed-out proposal in the longer paper. I’d emphasize four points here:
First, it’s important to remember that the gains from getting people back to work are partly the present and future gains to the income of workers. But the broader social gains also include stronger families, a better network of informal job connections, a decline in state-level spending on Medicaid and welfare payments, reduced drug use and crime, and other benefits.
Second, while Bartik’s proposals are admittedly expensive, they are also affordable: “Total annual costs for all states would come to $30 billion annually—$21 billion for local labor markets and $9 billion for neighborhoods. Tis $30 billion cost is affordable, as it is less than 3 percent of overall state taxes. Many states could cover the required costs by replacing their business tax incentives.”
Third, notice that Bartik is suggesting the practicality of state-level initiatives here. States have been called the “laboratories of democracy,” where policy ideas can be tried out and evaluated. These proposals don’t require the yet another argument over federal spending and taxes or the ability to get a 60-vote supermajority in the US Senate. They just require some states (maybe yours?) to give it a try.
Finally, the proposals do require states to focus on distressed areas, not on tax breaks for companies. Bartik describes his proposed policy as a set of block grants that would be spent across a state based on the employment rate. He points to funding for K-12 schools as a parallel: in many states, funds are from the state on a per-student basis and then spent by school districts under broad guidelines. In this case, funds would be allocated based on the employment rate and all areas would receive some payments–but those with especially low employment rates would receive more. He writes:
But state geographic targeting is politically difficult. At the state level, ostensibly targeted programs often allocate most aid to nondistressed places, and initially targeted programs are then extended statewide. The political problem is partly that most state targeting formulas are arbitrary “price subsidies”: for example, this would include job-creation credits that are higher dollar amounts per job in distressed places. Because the variation in such subsidies has no obvious relationship with need, it is easy to rationalize extending generous subsidies to favored projects in nondistressed places.
In contrast, the state block grants proposed here use targeting formulas directly tied to the number of persons in each area needing jobs. For each distressed neighborhood or local labor market, the formula calculates how many jobs the place is short of full employment by, and then it funds filling some percentage of that employment rate “gap.”
Such needs-based targeting formulas have been successful for other policy areas in making geographic targeting politically feasible. For example, tying state aid for K–12 schools to the number of students eligible for free or reduced-price lunch has been done by many states, resulting in significant targeting of funds to needier school districts.
The block grants also combine targeting with universalism. Most local labor markets would be eligible for some level of block grant, as would most local governments for neighborhood grants. The targeting is accomplished by making higher per-capita grants to places where more people need jobs. Because “everyone” gets something, the block grants have a stronger political constituency.
For some additional posts about research on place-based policies, see:
I have been the Managing Editor of the Journal of Economic Perspectives since the first issue in Summer 1987. The JEP is published by the American Economic Association, which decided about a decade ago–to my delight–that the journal would be freely available on-line, from the current issue all the way back to the first issue. You can download individual articles or entire issues, and it is available in various e-reader formats, too. Here, I’ll start with the Table of Contents for the just-released Summer 2022 issue, which in the Taylor household is known as issue #141. Below that are abstracts and direct links for all of the papers. I will probably blog more specifically about some of the papers in the few weeks, as well.
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Symposium on Intangible Capital
“Intangible Capital and Modern Economies,” by Carol Corrado, Jonathan Haskel, Cecilia Jona-Lasinio and Massimiliano Iommi
The production of goods and services is central to understanding economies. The textbook description of a firm, typically in agriculture or manufacturing, focuses on its physical “tangible” capital (machines), labor (workers), and the state of “know-how.” Yet real-world firms, such as Apple, Microsoft, and Google, have almost no physical capital. Instead, their main capital assets are “intangible”: software, data, design, reputation, supply-chain expertise, and R&D. We discuss investment in these knowledge-based types of capital: How to measure it; how it affects macroeconomic data on investment, rates of return, and GDP; and how it relates to growth theory and practical growth accounting. We present estimates of productivity in the US and European economies in recent decades including intangibles and discuss why, despite relatively rapid growth in intangible capital and what seems to be a modern technological revolution, productivity growth has slowed since the global financial crisis.
“The Economics of Intangible Capital,” by Nicolas Crouzet, Janice C. Eberly, Andrea L. Eisfeldt and Dimitris Papanikolaou
Intangible assets are a large and growing part of firms’ capital stocks. Intangibles are accumulated via investment–foregoing consumption today for output in the future—but they lack a physical presence. Rather than stopping with this “lack,” we instead focus on the positive properties of intangibles. Specifically, intangibles must be stored, so characteristics of the storage medium have important implications for their value and use. These properties include non-rivalry, allowing the intangible to be used simultaneously in different production streams, and limited excludability, which prevents the firm from capturing all the benefits or rents from the intangible. We develop these ideas in a simple way to illustrate how outcomes such as scalability and distribution of ownership follow. We discuss how intangibles can help to understand important trends in macroeconomics and finance, including productivity, factor shares, inequality, investment and valuation, rents and market power, and firm financing.
“Marketing Investment and Intangible Brand Capital,” by Bart J. Bronnenberg, Jean-Pierre Dubé and Chad Syverson
US companies invested over $500 billion in 2021 in intangible brand capital, over 2% of GDP. During the past decade, US companies have also been growing their internal marketing capabilities, an often overlooked source of human capital. We discuss the private and social benefits of these intangible brand capital stocks. While the private returns to companies are fairly clear, the academic literature has been divided over the social benefits and costs of advertising and promotion, the two key investment vehicles. We also discuss the implications of brand capital for measured productivity.
“Four Facts about Human Capital,” by David J. Deming
This paper synthesizes what economists have learned about human capital since Becker (1962) into four stylized facts. First, human capital explains at least one-third of the variation in labor earnings within countries and at least half of the variation across countries. Second, human capital investments have high economic returns throughout childhood and young adulthood. Third, we know how to build foundational skills such as literacy and numeracy, and resources are often the main constraint. Fourth, higher-order skills such as problem-solving and teamwork are increasingly valuable, and the technology for producing these skills is not well understood. We know that investment in education works and that skills matter for earnings, but we do not always know why.
“Measuring Human Capital,” by Katharine G. Abraham and Justine Mallatt
We review the existing literature on the measurement of human capital. Broadly speaking, economists have proposed three approaches to constructing human capital measures—the indicator approach, the cost approach, and the income approach. Studies employing the indicator approach have used single measures such as average years of schooling or indexes of multiple measures. The cost approach values human capital investments based on spending. The income approach values human capital investments by looking forward to the increment to expected future earnings they produce. The latter two approaches have the significant advantage of consistency with national income accounting practices and measures of other types of capital. Measures based on the income approach typically yield far larger estimates of the value of human capital than measures based on the cost approach. We outline possible reasons for this discrepancy and show how changes in assumptions can reconcile estimates based on the two approaches.
“Expected and Realized Inflation in Historical Perspective,” by Carola Binder and Rupal Kamdar
This paper provides historical context for the relationship between expected and realized inflation. We begin with a discussion of early theoretical thought about how inflation expectations are formed. Then, we discuss survey- and asset-based measures of inflation expectations and assess their empirical relationship with realized inflation. Expected and realized inflation are strongly correlated over long samples, but over short samples the correlations can weaken. Lastly, to better understand the subtleties of the interaction between expected and realized inflation over short-lived but important events, we provide a narrative account of the relationship during the Great Depression of the 1930s, the Great Inflation of the 1970s, the Great Recession of 2008–2009, and the recent COVID-19 pandemic. These episodes offer compelling evidence of the importance of expectations and policy regime changes in inflation dynamics.
“The Subjective Inflation Expectations of Households and Firms: Measurement, Determinants, and Implications,” by Michael Weber, Francesco D’Acunto, Yuriy Gorodnichenko and Olivier Coibion
Households’ and firms’ subjective inflation expectations play a central role in macroeconomic and intertemporal microeconomic models. We discuss how subjective inflation expectations are measured, the patterns they display, their determinants, and how they shape households’ and firms’ economic choices in the data and help us make sense of the observed heterogeneous reactions to business-cycle shocks and policy interventions. We conclude by highlighting the relevant open questions and why tackling them is important for academic research and policymaking.
“Blending Theory and Data: A Space Odyssey,” by Dave Donaldson
This article describes methods used in the field of spatial economics that combine insights from economic theory and evidence from data in order to answer counterfactual questions. I outline a general framework that emphasizes three elements: a specific question to be answered, a set of empirical relationships that can be identified from exogeneity assumptions, and a theoretical model that is used to extrapolate from such empirical relationships to the answer that is required. I then illustrate the application of these elements via a series of twelve examples drawn from the fields of international, regional, and urban economics. These applications are chosen to illustrate the various techniques that researchers use to minimize the theoretical assumptions that are needed to traverse the distance between identified empirical patterns and the questions that need to be answered.
“Principles for Combining Descriptive and Model-Based Analysis in Applied Microeconomics Research,” by Neale Mahoney
In this article, I offer guidance on how to combine descriptive and model-based empirical analysis within a paper. Drawing on examples from three recently published applied microeconomics papers, I argue that it is important to create a tight link between the descriptive analysis and the bottom-line deliverable of the model-based analysis, and I try to distill some lessons or principles for doing so. I also offer some thoughts on when a paper should start with descriptive analysis and then proceed to model-based analysis and when alternative structures may be desirable.
“Overreaction and Diagnostic Expectations in Macroeconomics,” by Pedro Bordalo, Nicola Gennaioli and Andrei Shleifer
We present the case for the centrality of overreaction in expectations for addressing important challenges in finance and macroeconomics. First, non-rational expectations by market participants can be measured and modeled in ways that address some of the key challenges posed by the rational expectations revolution, most importantly the idea that economic agents are forward-looking. Second, belief overreaction can account for many long-standing empirical puzzles in macro and finance, which emphasize the extreme volatility and boom-bust dynamics of key time series, such as stock prices, credit, and investment. Third, overreaction relies on psychology and is disciplined by survey data on expectations. This suggests that relaxing the assumption of rational expectations is a promising strategy, helps theory and evidence go together, and promises a unified view of a great deal of data.
“Retrospectives: On the Evolution of the Rules versus Discretion Debate in Monetary Policy,” by Harris Dellas and George S. Tavlas
Episodes of macroeconomic upheaval associated with monetary policy failure have provided the stage for important debates on rules versus discretion. We discuss the main features, results, commonalities, and differences in the debates that emerged after three such episodes. The modern debate was born during the Great Inflation of the 1970s and focused on both rules versus discretion and the properties of alternative rules. The middle debate originated with Henry Simons and the Chicago School during the Great Depression in the 1930s and focuses on policy uncertainty. The earliest systematic debate involved the Currency and Banking Schools in Britain in the 1820s, but, in spite the views of many of its participants and doctrinal historians, it seems to have primarily been about the degree of activism under a single rule—that of the gold standard.
As often happens when civilizations collide, politicians and economists each find the other more than a little odd. There are, in fact, important differences between the two groups, not just in their goals and incentives, but in areas as fundamental as the ways they think and talk. For example:
Logic. You might think logic is logic. However, I characterize the logic economists use as Aristotelian logic—that is, the classical system of logic based on syllogisms, corollaries, and deductive reasoning. Politicians often don’t use that logic. They use instead what I call political logic—what will work best with the voters or with other politicians with whom they are negotiating.
Language. Logic-based as it is, the language that we economists use in speaking and writing is often dry—sometimes barely intelligible to laypeople. In contrast, the language that politicians like to speak and write in is often vivid—and in clear English. But because it is so full of spin, economists tend to tune out when we hear it.
Calculation. … Economists use arithmetic and, when necessary, calculus. Economic equations can be complex, but they are straightforward in that they follow the rules most of us learned in math class. But political arithmetic is different—it’s weighted by influence. … Imagine a policy that generates $1 million each for 10 people, and costs 10 million people $2 each. Making the simple economic calculations, we can quickly see the policy leads to $10 million of gains and $20 million of losses. So we conclude it’s probably a bad idea, unless there’s some good reason to do it despite the loss of wealth. But if you apply political calculus to the same numbers, … [t]he 10 people that it helps so much will pay rapt attention and may even show their gratitude with political donations. Meanwhile, the 10 million people who lose two bucks apiece are probably not even going to notice it. The policy, therefore, has political merit.
Intelligence. Academic economists prize traditional intelligence as captured by things such as high IQ, good ideas, and the ability to express those ideas. Success in academic economics does not typically rely on people skills. Successful politicians, on the other hand, depend much more on their social and emotional intelligence. …
Objectives. Economists who engage in policy are generally trying to maximize social welfare. Politicians, of course, are trying to maximize their prospects for election or reelection, which may not be correlated with social welfare.
Policy evaluation. The aspect of a policy that matters for economists is the substance of the policy: Is it really good for society? What matters in the political world are, naturally, the politics and the message involved in the policy. Does it sound good? Needless to say, what is good and what sounds good are not always aligned.
Concerns. Economists’ main concern is efficiency: we talk about it, think about it, dream about it. But efficiency doesn’t much interest politicians, who are far more concerned about fairness, or perceived fairness, which is a broad concept encompassing income distribution but also much more.
