Conversable Economist

One possible clean energy agenda is “electrify everything,” with the idea being to focus on carbon-free methods of generating electricity. However, a number of carbon-free methods, like solar and wind (and even hydro-power at some times and places), have the problem that the power generated can ebb and flow. (Nuclear and geothermal power are examples of carbon-free electricity that is not interruptible.) Are there reliable ways of storing sufficient electricity for those times–which can easily be days and might even be weeks– when the sun doesn’t shine and the wind doesn’t blow? The energy storage question may well determine the workability of the “electrify everything” agenda.

One obvious answer is to store electricity in rechargeable lithium-ion batteries, but doing this at sufficient scale will require dramatic improvements in the price and capacity of batteries. Micah S. Ziegler a and Jessika E. Trancik discuss technoloigicl progress in this area in “Re-examining rates of lithium-ion battery technology improvement and cost decline” (Energy and Environmental Science, 2001, issue 4, pp. 1635-1651). They write:

Energy storage technologies have the potential to enable greenhouse gas emissions reductions via electrification of transportation systems and integration of intermittent renewable energy resources into the electricity grid. Lithium-ion technologies offer one possible option, but their costs remain high relative to cost-competitiveness targets, which could hinder these technologies’ broader adoption … However, their deployment is still relatively limited, and their broader adoption will depend on their potential for cost reduction and performance improvement. Understanding this potential can inform critical climate change mitigation strategies, including public policies and technology development efforts. … Here we systematically collect, harmonize, and combine various data series of price, market size, research and development, and performance of lithium-ion technologies. We then develop representative series for these measures, while separating cylindrical cells from all types of cells. For both, we find that the real price of lithium-ion cells, scaled by their energy capacity, has declined by about 97% since their commercial introduction in 1991. We estimate that between 1992 and 2016, real price per energy capacity declined 13% per year for both all types of cells and cylindrical cells, and upon a doubling of cumulative market size, decreased 20% for all types of cells and 24% for cylindrical cells.

For me, several lessons emerge from their essay: 1) Technological progress in rechargeable lithium-ion batteries has been larger than I realized; 2) It’s not nearly enough, as yet, to make heavily reliance on solar and wind energy possible; and 3) We all need to start divide up our thinking about batteries, in the sense that the batteries that power portable electronics may look quite a bit different from, say, batteries installed to store energy for homes, neighborhoods, or factories.

For example, in California there is an enormous lithium-ion battery facility mostly completed, which “will be able to discharge enough electricity to power roughly 300,000 California homes for four hours.” Another facility is planning to use a group of Tesla batteries so that there will be enough electricity storage to power all homes in San Francisco for six hours. Similar facilities are under consideration or actually being built around the world. These kinds of facilities can serve a useful purpose in avoiding electricity outages, and dealing with situations where power demand spikes above supply for a few hours. They can replace the need for backup generating capacity that would otherwise be called on in these situations. But if you want to rely heavily on wind and solar power, and to be very sure that the power won’t go off for an extended time, these sorts of industrial-sized battery facilities are only a start.

However, another issue with batteries is that manufacturing them requires a surge of mining and extensive use of minerals, and then recycling or disposing of the giant industrial-size batteries requires additional efforts. Thus, the debate over storing energy in batteries often drifts into a related debate about other forms of storing electricity.

For example, one approach is pumped-hydropower storage. The basic idea here is to use carbon-free energy to pump water from a lower river or lake so that it is up behind a dam–where it can then run down and generate electricity. There are now 43 of these projects in the US. The International Hydropower Association writes: “Pumped storage hydropower is the world’s largest battery technology, accounting for over 94 per cent of installed energy storage capacity, well ahead of lithium-ion and other battery types.”

What about if you aren’t near hydropower? In that case, I’m reading a little more these days about “gravity-based batteries.” The idea here is that if you can store energy by using carbon-free electricity to pump water up above the dam, so that it will run a turbine when gravity pulls the water back down, why not use the carbon-free electricity to lift up a heavy weight, and then let the weight drive a turbine as it comes back down? The gravity-battery technology is quite immature. But for the sake of argument, imagine the possibility of using an abandoned mineshaft that runs straight down for a few kilometers, and retrofitting the mine as a gravity battery.

Cathleen O’Grady offers an overview of gravity batteries in “Gravity-based batteries try to beat their chemical cousins with winches, weights, and mine shafts” (Science, April 22, 2021). I have no clear idea if gravity batteries will at the end of the day store a meaningful amount of power. But some of the early studies suggest that it could be cost-competitive with industrial-battery technology. Moreover, hoisting up and lowering big weights is a much cleaner environmental proposition than manufacturing and disposal of batteries.

For the sake of completeness, I should add a mention of hydrogen fuel here. If the hydrogen is generated by a non-carbon technology, then it offers yet another way of storing energy for later use. I’ve written about possibilities and issues with hydrogen power before, and won’t repeat it here. But it’s perhaps worth saying that while there’s been a lot of talk about about hydrogen fuel cells as a source of energy for vehicles, my sense is that the current technology may be better-suited for applications that provide heating, cooling, and electricity for homes and buildings.

On one hand, it seems terribly important that global food supply increase dramatically in the next few decades, to feed the hungry around the world as global population rises. On another hand, obesity is a huge and ongoing health problem around the world, even in many countries that do not have high income levels. And on yet a third hand, food production around the world is often a major contributor to environmental degradation, being related to issues including water pollution, deforestation, pesticides that linger in the ecosystem, and release of greenhouse gases.

A desirable path for the future of global food production will take all of these into account. The Credit Suisse Research Institute takes a shot at reconciling them in “The Global Food System: Identifying sustainable solutions” (June 2021). Let’s start with a description of the competing priorities:

About 9% of global population, consisting of about 700 million people, is undernourished.

About 40% of the world population is overweight.

When it come to environmental issues the report notes:

Food production and consumption already contribute well over 20% to global greenhouse gas emissions and account for more than 90% of the world’s freshwater consumption. After reviewing the environmental footprint of all major food groups, we conclude that the current situation is likely to worsen significantly unless action is taken. The likely growth in the world’s population to around ten billion people by 2050 coupled with a further shift in diets, especially across the growing emerging middle class, could increase emissions by a further 46%, while demand for agricultural land could increase by 49%. … [T]he growth in agricultural land seen to date has come at the cost of greater deforestation. Data from Globalforestwatch suggest that annual tree loss cover has increased from around 14 million hectares in 2001 to around 25 million hectares in 2019 … . The FAO indicates that some 420 million hectares of forest has been lost since 1990, which is the same as roughly eight times the size of France or 50% of the USA. Deforestation not only releases stored carbon dioxide, but
also reduces the ability to capture future carbon releases. Furthermore, it contributes to a loss in biodiversity and puts pressure on soil quality, which in turn is seen as contributing to the risk of drought and floods.

What’s the pathway through this maze of concerns? Start with the environmental issue. Here’s a table that tries to compare environment costs of a variety of foods. I’m sure one can quarrel with the details, but the broad nature of the overall rankings is clear. Vegetables and fruits in general have lower environment effects. Meat and dairy, and beef in particular, have the highest environmental effects.

As it turns out, many of the human health issues of obesity are related to consumption of meat and dairy, and more broadly to limited consumption of vegetables and fruits. And when it comes to expanding calorie outputs for a growing world population, it’s probably efficient to do so by expanding non-meat alternatives.

Along with a shift away from meat in general and beef in particular, there are some other useful steps to be taken. One is to take food waste seriously as a policy concern:

[M]ore than 30% of food produced is either lost or wasted. By way of example, around USD 408 billion of food produced in 2019 went unsold or uneaten. The FAO estimates the economic, environmental and social costs associated with food waste at USD 2.6 trillion. Eliminating food waste in the United States and Europe alone would add 10% to the world’s available food supply. Solutions need to focus across the entire supply chain as about 50% of food is lost in the production and handling phase, while 45% is wasted in the distribution and consumption phase.

The agenda for reducing food waste often focuses on issues like improved storage, faster transportation, and recognizing alternative uses that will give a stable shelf-life to food that would otherwise have spoiled. To cite one of a number of examples from the report: “Baldor, a major food processor that makes products like `baby’ carrots (i.e. regular carrots carved into tiny pieces), turns fruit and vegetable scraps into multiple products: some fruit scraps go to juice companies, vegetable scraps go to chefs for use in stocks, a mix of vegetables are dried and crushed into a flour that can be used in place of wheat, and other scraps are used in meal kits that include veggie noodles.”

Another set of options involve bringing the technology revolution to farming. For example, the report suggests that “[p]recision farming through the use of artificial intelligence, drones, autonomous machinery and smart irrigation systems could yield productivity increases of 70% by 2050.”

Another option is “vertical farming,” “which is an indoor approach consisting of controlling all environmental factors such as light, humidity and temperature, with the aim of producing more
food by harvesting crops vertically. This concept enables the cultivation of various crop types
ranging from leafy greens and tomatoes to herbs and flowers, as well as microgreens, and fulfills
environmental, social and economic goals. … According to the Ellen MacArthur Foundation, it is possible that, by 2050, 80% of the food consumed in urban areas could be produced using vertical farming
technologies.” In fact, Netherlands is the world’s #2 exporter of agricultural products by value thanks to its embrace of these kinds of technologies.

In thinking about a shift away from traditional meat, several options have been getting a lot of attention. “[L]ivestock provides just 18% of calories consumed by humans, but takes up close to 80% of global farmland.”

One option is “plant-based meat” products like Beyond Meat and Impossible Foods. “The reason for supporting the growth of plant-based meat is that it uses 72%–99% less water and 47%–99% less land than traditional animal-based meat. In addition, water pollution is substantially lower, whereas GHG emissions are also between 30% and 90% lower. One other aspect worth highlighting is that plant-based meat does not require the use of antibiotics, which is very common with animal-based meat production.”
Another option is “cultivated meat,” which is meat grown directly from cells. “For example, cultivated meat has a feed conversion ratio (kg in per kg out) that is more than seven times higher than that of beef cattle and almost six times higher than that of pork.” Yet another option is the use of fermentation: “Alternative proteins can also be produced through fermentation processes using microorganisms. Traditionally, fermentation has been used to make beer, wine and cheese, and the same process
can be used to improve the flavor of plant ingredients. … Biomass fermentation has the clear advantage of speed. The doubling time of the microorganisms used is hours compared to months or longer for animals.”

When thinking about these alternative products, I always add two thoughts in my own mind. First, it seems likely that the potential productivity gains for these products are high, which suggests that it will be possible to drive down their price dramatically. If a plant-based “hamburger” at the fast-food drive-through was half the price of ground beef, or less than half the price, my guess is that I wouldn’t be alone in being willing to make the shift. Second, as long as we think about these products as meat substitutes, I suspect they will feel unsatisfying. But it’s relatively straightforward to tinker with the taste of plant-based product, and eventually some of these products will be created that aren’t viewed as substitutes for something, but instead their popularity will stand on its own.

Reports like this one often jump pretty quickly from a list of problems to discussions of how government regulation and requirements, and this report is no exception. Many jurisdictions are passing taxes on sugared drinks; is a tax on meat next? I’m agnostic on much of this policy agenda, which is to say that I’ll judge the individual proposals as they come along. I suspect that the pull and push of demand and supply will bring about many of these changes, as the world moves toward feeding a few billion more people at a time of rising environmental concerns. Thus, I tend to see this report as a forecast of where we are already heading.

What paths can high school students take in accumulating hard and soft skills so that they can make the transition to a career and job? The main answer in US society is “go to college.” But for a large share of high school graduates, being told that they now need to attend several more years of classes is not what they want to hear.

Historically, a common alternative career path was that companies hired young workers who had little to offer other than energy and flexibility, and then trained and promoted those workers. Unions often played a role in advocating and supporting this training, too. But in the last few decades, it seems that a lot of companies have exited the job training business. Their general sense is that young adults aren’t likely to stay with the company, so in effect, you are training them for their next employer. Instead, better just to require that new hires already have experience.

The earn-and-learn career path tries to steer between these extremes. Yes, it involves additional learning, because that’s what 21st century jobs are like, but it seeks to have that learning take place more in the workplace than in the classroom. Also, instead of paying to learn, you get paid while learning. On the other side, firms that participate in this kind of training don’t need to take on the entire responsibility and cost of doing so.

Annelies Goger sketches this framework in “Desegregating work and learning through ‘earn-and-learn’ models” (Brookings Institution, December 9, 2020). She points out the gigantic difference in public support for higher education vs. public support for an earn-and-learn approach.

The earn-and-learn programs under the public workforce system—authorized under the Workforce Innovation and Opportunity Act (WIOA)—are underused and hard to scale. Publicly funded job training options are tiny overall compared to investments in traditional public higher education or classroom-based job training. Funding for public higher education was $385 billion in 2017-18, compared to about $14 billion for employment services and training across 43 programs. The net result is that higher education is the main provider of publicly funded training for most Americans, and most of the $14 billion for employment services and training goes to services (most of which isn’t training) for special populations such as veterans and people with disabilities.

This difference in public support is even more stark when you recognize that those with a college education are likely to end up with  higher average incomes during their lives, so that we are doing more to subsidize the training of the relatively high earners of the future than we are to subsidize the training of the middle- and lower-level earners.

What do the earn-and-learn programs look like in practice? Here’s a graphic:

I won’t try to go through these choices one at a time: for present purposes, the salient fact is that they are all small in size. As long-time readers know, I’m a fan of a dramatic expansion of apprenticeships (for example, here, here, here, and here). But as Goger writes:

For example, the U.S. had roughly 238,000 new registered apprentices in 2018. However, if the U.S. had the same share of new apprentices per capita as Germany, we would have 2 million new apprentices per year; if we had the same share as the United Kingdom or Switzerland, that number would be 3 million.

One infuriating aspect of the US health care industry is the high administrative costs. Here’s a description from David Scheinker, Barak D. Richman, Arnold Milstein, and Kevin A. Schulman in “Reducing administrative costs in US health care: Assessing single payer and its alternatives” (Health Services Research, published online March 31, 2021, not yet assigned to an issue). They write (footnotes omitted):

The transaction cost of paying for services with a commercial credit card is approximately 2% of the total cost, whereas Tseng (2018) calculated that it is 14.5% when providers bill insurance companies for physician services. A similar percentage is consumed for hospital billing, and approximately an additional 15% is retained by commercial insurers for claims processing and other costs under the Affordable Care Act.

Health care administrative costs in the United States are higher than in other rich nations. Estimates suggest over $265 billion of annual spending is wasted due to administrative complexity, yet the substantial literature on wasted health care spending offers little discussion of what drives these costs or how to reduce them. … The most common proposal to reduce the transaction costs of paying for health care has been to advocate for a single-payer “Medicare-for- All” model that nationalizes Medicare fee-for-service coverage, with all of that program’s complexity.

There are at least three possibilities for making a meaningful reduction in administrative spending in the US health care sector: a single payer system, a set of rules that would require standardization and simplification of health care contracts, and a national health care automated clearinghouse for payments to health care providers. Let’s mull them over in turn.

Perhaps the main difficulty with seeking to reduce US health care administrative costs by the use of a single-payer system is that, for better or worse, there seems to be approximately zero political momentum for the US to adopt such a system.

However, an additional concern is that single-payer systems come in many different models. Yes, if there is a single payer for all health care expenses who just sends out checks to all health care providers, administrative costs will lower. But given the very high that the US health-care system is about one-fifth of the entire US economy, and rising, even a single-payer system will need to have various controls over what prices are being charged for what services and what services are covered. Most single-payer systems around the world are combined with overall limits on hospital spending, physician fees, and limits on technology. In other words, part of the reason for lower administrative fees in single-payer systems around the world is that the choices available in what services to offer and what to charge have already been tightly limited.

In an essay on “Reducing Administrative Costs in U.S. Health Care,” David M. Cutler acknowledges that a single-payer system would certainly eliminate some administrative costs, like the costs sales and marketing, as well as the profits, of private insurance companies (March 2020, Hamilton Project, . However, if a US single-payer system did not also include limits on fees and available services, administrative costs will remain substantial. Cutler writes (citations omitted for readability)

Perhaps the major issue in single-payer health care is the trade-off between administrative costs and other ways of controlling use. Many single-payer proposals envision lower administrative costs such as those in the Canadian system. Such a system has other important features, however, that are necessary to offset administrative spending reductions. For example, Canada has very tight restrictions on technology acquisition; there are only one-quarter the number of MRIs per capita in Canada as there are in the United States. Similarly, there is a budget for hospitals and a fee schedule for physicians, where fees are set so that a total spending target is met. The ability to do this in the United States is in some doubt … If the United States does not implement the type of budget and technology regulation that other countries have, the impact of single-payer health care on administrative costs is less certain.

Given the political and practical difficulties of tackling health care administrative costs via a single payer system, Scheinker, Richman, Milstein, and Schulman go through a modelling exercise where they try to separate out the main billing and insurance-related (BIR) costs across “three components of BIR costs (fixed costs, per-visit clinical documentation variable costs, and per-visit nonclinical documentation variable costs) associated with five types of visits (primary care, emergency department visits, inpatient stays, ambulatory surgery, and inpatient surgery).” They then consider a list of ways of standardizing and simplifying current billing procedures, together with how these might happen in a single-payer or in a multi-payer health care system. They conclude:  

Our model estimates that national BIR [billing and insurance-related] costs are reduced between 33% and 53% in Medicare-for-All style single-payer models and between 27% and 63% in various multi-payer models. Under a wide range of assumptions and sensitivity analyses, standardizing contracts generates larger savings with less variance than savings from single-payer strategies. … Although moving toward a single-payer system will reduce BIR costs, certain reforms to payer-provider contracts could generate at least as many administrative cost savings without radically reforming the entire health system. BIR costs can be meaningfully reduced without abandoning a multi-payer system.

I lack the expertise in health care billing systems to offer any useful insight into the realism of their claims, but for what it’s worth, all of the authors are affiliated with the Clinical Excellence Research Center at Stanford’s medical school.

In the essay mentioned above, David Cutler proposes that these kinds of changes might be implemented via a national health care automated clearinghouse for payments to health care providers: that is, all health care providers would submit their bills to the clearinghouse, and all insurers would pay the bills as submitted through the clearinghouse. Cutler makes the argument this way:

I propose that Congress establish a clearinghouse for bill submission … More than 6 billion medical claims are filed annually in the United States. While almost all of these claims are filed electronically, the system is not as efficient as it could be. The issue is sometimes posed as the need for a single claim form, but that is not correct. The HIPAA legislation of 1996 required standardized claims forms and that has now been achieved. Nevertheless, the system still has some limitations. To begin, the information required by different payers can be different, even with the same form. For example, one insurer might require special revenue codes for particular specialties that are different from other insurers. In other cases, the physician’s specialty may differ across insurers (medicine vs. gastroenterology, for example), which could necessitate a different set of codes. Or the codes given for claim denial may differ across insurers. And still other insurers are not required to use these standardized forms (e.g., workers’ compensation and auto insurers). In addition, many insurers require attachments to claims, and these attachments are generally not standardized. An attachment might involve a certificate of medical necessity, a discharge summary, or details of a lab report … [O]nly 20 percent of claims attachments are standardized. One of the primary reasons why attachments are not standardized is that a federal standard has not yet been named by the Department of Health and Human Services as required under HIPAA and the ACA …

Cutler goes on to point out opportunities for streamlining issues like getting prior authorization for procedures and for documenting quality control. He suggests that any health care providers involved with government payments could be required to send their bills through the national system, and those who do not wish to submit payments through the system would be charged a modest fee for doing so.

Making these kinds of changes to health care billing and administration, whether it is done through a national clearinghouse or in some other way, isn’t a deep conceptual problem or one involving complex tradeoffs. In a well-functioning political system, it seems to me like the kind of detailed, nuts-and-bolts problem that could be addressed by detailed, nut-and-bolts politicians and administrators beavering away at the task on a bipartisan basis until it’s done.

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 the entire issue, and it is available in various e-reader formats, too. Here, I’ll start with the Table of Contents for the just-released Summer 2021 issue, which in the Taylor household is known as issue #137. 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 next week or two, as well.

_________________________________________________________

Symposium on COVID-19

“Effects of the COVID-19 Recession on the US Labor Market: Occupation, Family, and Gender,” by Stefania Albanesi and Jiyeon Kim

The economic crisis associated with the emergence of the novel corona virus is unlike standard recessions. Demand for workers in high contact and inflexible service occupations has declined while parental supply of labor has been reduced by lack of access to reliable child care and in-person schooling options. This has led to a substantial and persistent drop in employment and labor force participation for women, who are typically less affected by recessions than men. We examine real-time data on employment, unemployment, labor force participation and gross job flows to document the impact of the pandemic by occupation, gender and family status. We also discuss the potential long-term implications of this crisis, including the role of automation in depressing the recovery of employment for the worst hit service occupations.

Full-Text Access | Supplementary Materials

“The Great Unequalizer: Initial Health Effects of COVID-19 in the United States,” by Marcella Alsan, Amitabh Chandra and Kosali Simon

We measure inequities from the COVID-19 pandemic on mortality and hospitalizations in the United States during the early months of the outbreak. We discuss challenges in measuring health outcomes and health inequality, some of which are specific to COVID-19 and others that complicate attribution during most large health shocks. As in past epidemics, preexisting biological and social vulnerabilities profoundly influenced the distribution of disease. In addition to the elderly, Hispanic, Black and Native American communities were disproportionately affected by the virus, particularly when assessed using the years of potential life lost metric. We provide a conceptual framework and initial empirical analysis that seek to shed light on contributors to pandemic-related health inequality, and we suggest areas for future research.

Full-Text Access | Supplementary Materials

“Tracking the Pandemic in Real Time: Administrative Micro Data in Business Cycles Enters the Spotlight,” by Joseph Vavra

In this paper I discuss the increasingly prominent role of administrative micro data in macroeconomics research. This type of data proved important for interpreting the causes and consequences of the Great Recession, and it has played a crucial role in shaping economists’ understanding of the COVID-19 pandemic in near real-time. I discuss a number of specific insights from this research while also illustrating some of the broader opportunities and challenges of working with administrative data.

Full-Text Access | Supplementary Materials

Symposium on the Washington Consensus Revisited

“Some Thoughts on the Washington Consensus and Subsequent Global Development Experience,” by Michael Spence

This paper discusses the Washington Consensus, its origins, and its insights in terms of subsequent development experience in a broad range of countries. I continue to find that when properly interpreted as a guide to the formulation of country-specific development strategies, the Washington Consensus has withstood the test of time quite well. In my view, subsequent experience, especially in Asia, reveals a number of places where a shift in emphasis would be warranted. Finally, I try to identify some misuses of the Washington Consensus and suggest that it was vulnerable to misuse due to the absence of an accompanying and explicit development model.

Full-Text Access | Supplementary Materials

“The Baker Hypothesis: Stabilization, Structural Reforms, and Economic Growth,” by Anusha Chari, Peter Blair Henry and Hector Reyes

In 1985, James A. Baker III’s “Program for Sustained Growth” proposed a set of economic policy reforms including, inflation stabilization, trade liberalization, greater openness to foreign investment, and privatization, that he believed would lead to faster growth in countries then known as the Third World, but now categorized as emerging and developing economies (EMDEs). A country-specific, time-series assessment of the reform process reveals three clear facts. First, in the ten-year period after stabilizing high inflation, the average growth rate of real GDP in EMDEs is 2.6 percentage points higher than in the prior ten-year period. Second, the corresponding growth increase for trade liberalization episodes is 2.66 percentage points. Third, in the decade after opening their capital markets to foreign equity investment, the spread between EMDEs average cost of equity capital and that of the US declines by 240 basis points. The impact of privatization is less straightforward to assess, but taken together, the three central facts of reform provide empirical support for the Baker Hypothesis and suggest a simple neoclassical interpretation of the unprecedented increase in growth that has taken place in EMDEs since the early 1990s.

Full-Text Access | Supplementary Materials

“Washington Consensus in Latin America: From Raw Model to Straw Man,” by Ilan Goldfajn, Lorenza Martínez and Rodrigo O. Valdés

We take stock of three decades of a love-hate relationship between Latin American policies and the Washington Consensus, reviewing its implementation, national debate, and outcomes. Using regional data and case studies of Brazil, Chile, and Mexico, we discuss the various degrees of the Washington Consensus implementation and evaluate performance. We find mixed results: macroeconomic stability is much improved, but economic growth has been heterogeneous and generally disappointing, despite improvement relative to the 1980s. We discuss the risk that the region could revert parts of the Washington Consensus reforms, which are necessary building blocks for a new agenda more focused on social integration, a fairer and just society, and environmentally sustainable growth based on better education.

Full-Text Access | Supplementary Materials

“Washington Consensus Reforms and Lessons for Economic Performance in Sub-Saharan Africa,” by Belinda Archibong, Brahima Coulibaly and Ngozi Okonjo-Iweala

Over three decades after market-oriented structural reforms termed “Washington Consensus” policies were first implemented, we revisit the evidence on policy adoption and the effects of these policies on socio-economic performance in sub-Saharan African countries. We focus on three key ubiquitous reform policies around privatization, fiscal discipline, and trade openness and document significant improvements in economic performance for reformers over the past two decades. Following initial declines in per capita economic growth over the 1980s and 1990s, reform adopters experienced notable increases in per capita real GDP growth in the post–2000 period. We complement aggregate analysis with four country case studies that highlight important lessons for effective reform. Notably, the ability to implement pro-poor policies alongside market-oriented reforms played a central role in successful policy performance.

Full-Text Access | Supplementary Materials

Symposium on Statistical Significance

“Statistical Significance, p-Values, and the Reporting of Uncertainty,” by Guido W. Imbens

The use of statistical significance and p-values has become a matter of substantial controversy in various fields using statistical methods. This has gone as far as some journals banning the use of indicators for statistical significance, or even any reports of p-values, and, in one case, any mention of confidence intervals. I discuss three of the issues that have led to these often-heated debates. First, I argue that in many cases, p-values and indicators of statistical significance do not answer the questions of primary interest. Such questions typically involve making (recommendations on) decisions under uncertainty. In that case, point estimates and measures of uncertainty in the form of confidence intervals or even better, Bayesian intervals, are often more informative summary statistics. In fact, in that case, the presence or absence of statistical significance is essentially irrelevant, and including them in the discussion may confuse the matter at hand. Second, I argue that there are also cases where testing null hypotheses is a natural goal and where p-values are reasonable and appropriate summary statistics. I conclude that banning them in general is counterproductive. Third, I discuss that the overemphasis in empirical work on statistical significance has led to abuse of p-values in the form of p-hacking and publication bias. The use of pre-analysis plans and replication studies, in combination with lowering the emphasis on statistical significance may help address these problems.

Full-Text Access | Supplementary Materials

“Of Forking Paths and Tied Hands: Selective Publication of Findings, and What Economists Should Do about It,” by Maximilian Kasy

A key challenge for interpreting published empirical research is the fact that published findings might be selected by researchers or by journals. Selection might be based on criteria such as significance, consistency with theory, or the surprisingness of findings or their plausibility. Selection leads to biased estimates, reduced coverage of confidence intervals, and distorted posterior beliefs. I review methods for detecting and quantifying selection based on the distribution of p-values, systematic replication studies, and meta-studies. I then discuss the conflicting recommendations regarding selection resulting from alternative objectives, in particular, the validity of inference versus the relevance of findings for decision-makers. Based on this discussion, I consider various reform proposals, such as deemphasizing significance, pre-analysis plans, journals for null results and replication studies, and a functionally differentiated publication system. In conclusion, I argue that we need alternative foundations of statistics that go beyond the single-agent model of decision theory.

Full-Text Access | Supplementary Materials

“Evidence on Research Transparency in Economics,” by Edward Miguel

A decade ago, the term “research transparency” was not on economists’ radar screen, but in a few short years a scholarly movement has emerged to bring new open science practices, tools and norms into the mainstream of our discipline. The goal of this article is to lay out the evidence on the adoption of these approaches—in three specific areas: open data, pre-registration and pre-analysis plans, and journal policies—and, more tentatively, begin to assess their impacts on the quality and credibility of economics research. The evidence to date indicates that economics (and related quantitative social science fields) are in a period of rapid transition toward new transparency-enhancing norms. While solid data on the benefits of these practices in economics is still limited, in part due to their relatively recent adoption, there is growing reason to believe that critics’ worst fears regarding onerous adoption costs have not been realized. Finally, the article presents a set of frontier questions and potential innovations.

Full-Text Access | Supplementary Materials

Articles and Features

“Why Is Growth in Developing Countries So Hard to Measure?” by Noam Angrist, Pinelopi Koujianou Goldberg and Dean Jolliffe

Occasional widely publicized controversies have led to the perception that growth statistics from developing countries are not to be trusted. Based on the comparison of several data sources and analysis of novel IMF audit data, we find no support for the view that growth is on average measured less accurately or manipulated more in developing than in developed countries. While developing countries face many challenges in measuring growth, so do higher-income countries, especially those with complex and sometimes rapidly changing economic structures. However, we find consistently higher dispersion of growth estimates from developing countries, lending support to the view that classical measurement error is more problematic in poorer countries and that a few outliers may have had a disproportionate effect on (mis)measurement perceptions. We identify several measurement challenges that are specific to poorer countries, namely limited statistical capacity, the use of outdated data and methods, the large share of the agricultural sector, the informal economy, and limited price data. We show that growth measurement based on the System of National Accounts (SNA) can be improved if supplemented with information from other data sources (for example, satellite-based data on vegetation yields) that address some of the limitations of SNA.

Full-Text Access | Supplementary Materials

“Retrospectives: James Buchanan: Clubs and Alternative Welfare Economics,” by Alain Marciano

James Buchanan wrote “An Economic Theory of Clubs” and invented clubs to support a form of welfare economics in which there is no social welfare function (SWF) and individual utility functions cannot be “read” by external observers. Clubs were a means to allow the implementation of individualized prices for public goods and services and to allow each individual to pay exactly the amount he wants to pay. He developed this project to answer and counter Paul Samuelson’s analysis of public goods, in which social welfare functions play a crucial role. Buchanan and Samuelson disagreed over the allocation of the costs of the public good to each individual. To Buchanan, it was by relying on individual’s preferences. To Samuelson, by using a SWF. Buchanan’s clubs are thus foreign and incompatible with the traditional Samuelson-style public economics in which they are used.

Full-Text Access | Supplementary Materials

“Recommendations for Further Reading,” by Timothy Taylor

Full-Text Access | Supplementary Materials

If you don’t know about the Odebrecht case, which the the US Department of Justice called “the largest foreign bribery case in history,” Nicolás Campos, Eduardo Engel, Ronald D. Fischer, and Alexander Galetovic tell the story and offer some insights in “The Ways of Corruption in Infrastructure: Lessons from the Odebrecht Case” in the Spring 2021 issue of the Journal of Economic Perspectives (where I work as Managing Editor). They describe the company’s rise and fall this way:

In 2010, the Swiss business school IMD chose Odebrecht, a Brazilian conglomerate, as the world’s best family business. Odebrecht was chosen for the excellent performance of its companies, its continuous growth, and its social and environmental responsibility. Sales had quintupled between 2005 and 2009,
and Odebrecht had become Latin America’s largest engineering and construction company and ranked 18th worldwide among international contractors (Engineering News-Record Magazine 2009).

By 2015, however, Odebrecht chief executive Marcelo Odebrecht had been arrested on corruption charges. Nine months later he was sentenced to more than 19 years in prison. The Odebrecht case, as it came to be known, involved bribe payments in ten countries in Latin America and two countries in Africa. Deltan Dallagnol, lead prosecutor in Brazil, commented (as reported by Pressly 2018): “The Odebrecht case leaves you speechless. This case implicated almost one-third of Brazil’s senators and almost half of all Brazil’s governors. A single company paid bribes to 415 politicians and 26 political parties in Brazil. It makes the Watergate scandal look like a bunch of kids playing in a sandbox.”

These bribery cases mainly involved bidding on large government infrastructure projects. involved US Department of Justice became involved because many of the bribes were paid through US banks. Here are a few of the details that jumped out at me.

1. The sheer brazenness of the bribery was, in its own way, impressive. the Odebrecht company had an actual division devoted to bribery, with standardized reporting and procedures. The JEP authors write:

By 2006, bribery at Odebrecht had become so institutionalized that the company created the Division of Structured Operations (DSO), a stand-alone department dedicated to corruption. According to the plea agreement between the Odebrecht chief executive officer Marcelo Odebrecht and the US Department of Justice, the DSO specialized in buying influence through legal and illegal contributions to political campaigns and also in paying bribes to public officials and politicians. Within the DSO, three full-time executives and four experienced assistants were responsible for paying bribes to foreign accounts. Bribe payments followed a clear organizational flow. A contract manager would deal with potential bribe recipients—public officials and politicians—and reported to the country manager. The country manager could approve small bribes paid with local funds. Larger bribes were vetted by an executive reporting directly to the Odebrecht chief executive officer who often made the final decision.

2. Odebrecht wasn’t a case of huge bribes being paid and huge company profits as a result. In this sense, the pre-existing rules on public bidding did limit the most egregious forms of corruption. Indeed, the authors suggest that the size of the bribes was pretty much equal to the size of additional profits. Thus, this seems like a case of opportunistic bribes paid at key points, but more with the goal of dramatically expanding the size of Odebrecht (as noted above, the company sales quintupled from 2005-2009), rather than increasing its profit margins.

3. The countries where the bribery took place typically had rules to try to ensure fair bidding for public projects. But as it turned out, these rules often had some key vulnerabilities. One was that the rules often had conditions that firms could only bid if the company had certain technical and business qualifications. This sounds reasonable enough–but it meant that when the qualifications had a degree of subjectivity, was sometimes possible to bribe the public officials who were looking at these qualifications, so that they would disqualify some other potential bidders. But perhaps the main vulnerability was that when a company won the project, it could then later re-open negotiations for higher payments, or additional payments for expanding the scale of the work, and so on. Thus, a firm could bid low on a project, but then bribe the re-negotiators later in the process.

The public policy lessons here–applicable around the world–are to make the rules about bidding on public projects as objective as possible, and to have a high degree of openness, including the possibility of putting additional work out for open bids, when contract renegotiations come up.

There isn’t likely to be one magic bullet that addresses all the issues related to carbon emissions in the atmosphere. Instead, you take your partial solutions where you can find them, and call it progress. Could geothermal energy be one of those partial solutions? Eli Dourado offers a useful overview of the state of play in “Harnessing the Heat Beneath our Feet” (PERC Reports, Summer 2021).

Geothermal energy finds ways to harness heat from the earth’s core and turn it into electricity. In some ways, this is old technology. As Dourado writes:

Humans have produced electricity from the earth’s subsurface heat since 1904, when Italians first harnessed geothermal steam at Larderello, in Tuscany. Today, the same site produces enough power for 10 million Italian households, about 10 percent of the world’s geothermal electricity. … Conventional geothermal technology is only deployed at sites where subsurface heat makes itself evident through visible features like hot springs, geysers, and fumaroles. The main geothermal field at Larderello is called Valle del Diavolo—Valley of the Devil—because it contains springs of boiling water.

Thus, the question for geothermal for some time has been whether it was limited to the few locations where the heat was literally bubbling up to the earth’s surface, or whether it was possible, in a substantially larger number of locations, to reach down below the earth’s surface. It turns out that some of the underground drilling technologies used in fracking can also be used, in a different way, to set up geothermal sources of electricity. One option is what Dourado called “conventional geothermal”:

Conventional geothermal wells are technically hydrothermal—they work by extracting steam from a production well. Typically this steam flows upward through hot porous rock, acquiring heat energy along the way, but then gets trapped under impermeable caprock. Placing a production well where the steam is trapped gives it only one way to go—up the well, where, at the surface, it can power a turbine to produce electricity. A second well, called an injection well, is used to put water back into the system, without which the supply of steam would eventually dry up and lose pressure. Producing hydrothermal energy is pretty simple, and it would be very cheap at scale, but it requires this subsurface configuration—hot porous rock topped with impermeable capstone—to work.

The current research is to find ways of generating geothermal electricity in a wider range of subsurface configurations. As one example, there are “closed-loop” designs where the news drilling technologies first go down, and then go horizontally underground. The “working fluids” that are injected to generate steam are recaptured and re-injected. Start-up companies and academic researchers are are trying out different approaches. It turns out that in a US context, most of the sites that might be suitable for geothermal electricity are in the western part of the country, and thus, given the enormous amounts of western land still owned by the federal government, the willingness of the feds to allow development of geothermal resources on federal lands is likely to make a big difference.

Another recent overview of geothermal is “Can Geothermal Power Play a Key Role in the Energy Transition?” by Jim Robbins in Yale Environment 360, an online magazine published by the Yale School of the Environment (December 22, 2020). Robbins offers an example of geothermal energy–albeit for heating rather than electricity–from Boise, Idaho:

A river of hot water flows some 3,000 feet beneath Boise, Idaho. And since 1983 the city has been using that water to directly heat homes, businesses, and institutions, including the four floors of city hall — all told, about a third of the downtown. It’s the largest geothermal heating system in the country. Boise didn’t need to drill to access the resource. The 177-degree Fahrenheit water rises to the surface in a geological fault in the foothills outside of town. It’s a renewable energy dream. Heating the 6 million square feet in the geothermally warmed buildings costs about $1,000 a month for the electricity to pump it. (The total annual cost for depreciation, maintenance, personnel, and repair of the city’s district heating system is about $750,000.)

Of course, most places aren’t located above an accessible river of steaming hot water. But the warmth of geothermal can be used for “district heating” in some cases; in Iceland, which sits on abundant geothermal resources, about 90% of all homes are heated with geothermal energy. In addition, geothermal for electricity is already in wider use in the US than you might think. Robbins notes:

Even though geothermal is barely on the alternative energy radar, the U.S. already produces 3.7 gigawatts (GW) of geothermal electricity, enough to power more than 1 million homes. It’s the world’s leading producer — primarily in central California and western Nevada. California has 43 operating geothermal generating plants, and is about to build two more.

There’s also a 2019 report from the Geothermal Technologies Office at the US Department of Energy. Robbins describes a central finding of the report this way:

And a 2019 U.S. Department of Energy (DOE) report — GeoVision: Harnessing the Heat Beneath Our Feet — refers to the “enormous untapped potential for geothermal.” By overcoming technical and financial barriers, the report says, generating electricity through geothermal methods could increase 26-fold by 2050, providing 8.5 percent of the United States’ electricity, as well as direct heat.

Geothermal certainly isn’t going to address climate change issues all by itself. But unlike intermittent sources of carbon-free energy like solar and wind, geothermal does have one great advantage: It is always on.

In early July, the Gallup Poll carried out an annual survey in which people are asked about their confidence in various institutions. Here are some of the results, as reported at the Gallup website by Jeffrey M. Jones, “In U.S., Black Confidence in Police Recovers From 2020 Low” (July 14, 2021) and by Megan Brenan, “Americans’ Confidence in Major U.S. Institutions Dips” (July 14, 2021).

This figure shows the share of people who express “A great deal/Quite a lot of confidence” in each of these institutions. The overall percentage of approval is on the far right, and the breakdown by white, black, and Hispanic is shown by the dots.

For me, figures like this lead to lots of inner conversations, and I will spare you most of that. But since I’ve been reading a fair amount about policing lately, here are a few thoughts:

1. It was interesting to me that while whites were the group expressing the most confidence for the top few items on the list, Hispanics were expressing the most confidence for many of the items lower on the list. The one institution in which blacks expressed the greatest confidence was “Television News.”
2. The extremely low levels of confidence expressed for Congress, the media, big business and big labor, and other areas is worth some reflection
3. The biggest gap between confidence of whites and blacks appears for the police.
4. The survey asks separately about “The Police” and “The criminal justice system.” The confidence level in the police is far higher for every group than the confidence in the rest of the criminal justice system.
5. Despite a year of intense controversy over the police, they still rank well above many of these other categories in terms of public confidence.
6. A separate figure shows the confidence in police for blacks and whites over time. It’s interesting to note that the confidence of blacks in this area was already declining in the 214-2019 time frame, and that after a very sharp decline in 2020, there has been something of a bounceback in 2021.

Here’s one more figure, this one showing a breakdown of the same categories by political party.

Republicans are vastly more confident in the police, organized religion, the military, and small business. Democrats are vastly more confident in the presidency, newspapers and television news, public schools, and organized labor. The lack of approval for Congress, the criminal justice system, banks, and big business is largely bipartisan.

It’s hard to get data on the investment patterns of the very wealthy. Many surveys are intended to cover the entire population, from to bottom, so they don’t offer many data points for looking at the behavior of the top 1% or the top 0.1%. In addition, any survey about economic facts, like personal wealth, is only as good as the memories and willingness-to-disclose of those taking the survey.

Thus, one of the hot topics in economics research is finding ways to access “administrative” data–which refers to data that was collected for other purposes, but in an appropriately anonymous form can be made available to researchers. For example, researchers looking at income inequality have found ways to used appropriately anonymized income tax data. For wealth data, Cynthia Mei Balloch and Julian Richers found a source of such data to address the question of “Asset Allocation and Returns in the Portfolios of the Wealthy” (presented at the 2021 Conference on Research in Income and Wealth held at the Summer Institute of the National Bureau of Economic Research, July 19-20, 2021). Just in case this isn’t yet broad knowledge in the economics community, I’ll also add that the NBER holds many workshops, methods lectures, and mini-conferences during its Summer Institute, and hours of material of of top research economists presenting their current work is available at the NBER YouTube page).

Here’s how Balloch and Richers describe their data:

[W]e use anonymized portfolio-level data from Addepar, a leading technology provider for the wealth management industry. Addepar provides an advanced financial reporting and analysis software platform for private wealth advisors. These advisors range in scale from single family offices to large wealth management firms with thousands of individual advisors and client portfolios. Advisors use the platform to get a comprehensive picture of asset holdings and returns across different asset and sub-asset classes, ranging from standard equity and fixed income investments to private equity, real estate and collectibles. While individual investors can access their own account data directly, advisors are the primary users of the software. These include family offices, private wealth advisors at banks, and
other advisors. Across 373 managing firms, we observe over 50,000 client portfolios on the platform, each representing an individual household. The range of total holdings ranges from the mid-six
figures to multi-billion dollar portfolios, with an average total size of portfolios of 16.8 million
(median 1.3 million) at the end of 2019. By this time, there are close to 1 trillion in assets
recorded on the platform …

With this data, the authors are observing changes in market values over time; for example, they can see both realized and unrealized capital gains. Because the wealth managers want to know about all aspects of health, this data also includes information on wealth held in private businesses and in real estate. Again, this data is the actual investments of the wealthy, not what the wealthy say when they fill out surveys about their wealth.

What are some of the main patterns that emerge?

One is that as wealth increases, people are more likely to put a larger chunk of their money in “alternative assets,” which is a category that refers to special funds like private equity funds or hedge funds. A second pattern is that as wealth increases, the average rate of return goes up, but so does the level of risk:

Among investors with less than three million in assets under management, the average return is 4.38 percent, while for investors with more than 100 million in wealth, the average return increases to 6.37 percent. This pattern of increasing return is mirrored in the standard deviation of returns, which rises from 13.9 percent among the least wealthy investors to 19.8 percent at the top of the wealth distribution.

Among other kinds of investments–bonds, stocks, mutual funds, exchange-traded funds–the returns on assets for the wealthy are basically the same as they are for everyone else, after adjusting for the risk of each kind of investment. However, the returns that the ultra-wealthy get from hedge funds and private equity funds are substantially higher than the returns that the less-wealthy get from these categories of investments. This pattern suggests that the ultra-wealthy have access to some combination of better money managers and better investment opportunities than the rest of us.

Those familiar with patterns of how college and universities have invested their endowments in the last few decades will recognize this pattern (for discussion, see “Some Snapshots of University Endowments,” July 22, 2019). The big kids like Yale, Harvard, and others crowded into various categories of alternative investments back in the 1990s, and have made outsized returns in doing in the last few decades. Indeed, the enormous endowments that the wealthiest universities have built up are just as much (or even more) due to canny investment teams as they are to big donors. However, universities and colleges with smaller endowments who attempted to follow the same pattern have generally not been as successful.

Of course, the follow-up question is whether there might be a way to give smaller-wealth investors a chance to benefit from these higher-return alternative investments.

A number of financial markets let investors make predictions about the future path of prices and risks: for example, those who buy a stock are thinking that the price will rise. There are also “futures” markets, which allow someone to promised to deliver a certain item at a certain prices at a given date in the future. Some futures markets are based on financial prices like the price of the Standard & Poor’s stock market index; others are based on prices of physical items like oil, gold, wheat, soybeans, and many others.

When economists refer to a “prediction market,” they have in mind a market where instead buying and selling based on expectations of future prices, the buying and selling happens based on expectations of future events. One of the best-known examples is the Iowa Electronic Markets, where you can place small-sized bets on the outcome of elections and related events. Rising or falling prices in this market can then be used as a measure of the likelihood of who will win the election.

But back in the early 2000s, the idea of making more widespread use of prediction markets took a serious public relations hit. Justin Wolfers and Eric Zitzewitz tell the story in their article “Prediction Markets” in the Spring 2004 issue Journal of Economic Perspectives (where I work as Managing Editor). They wrote:

In July 2003, press reports began to surface of a project within the Defense Advanced Research Projects Agency (DARPA), a research think tank within the Department of Defense, to establish a Policy Analysis Market that would allow trading in various forms of geopolitical risk. Proposed contracts were based on indices of economic health, civil stability, military disposition, conflict indicators and potentially even specific events. For example, contracts might have been based on questions like “How fast will the non-oil output of Egypt grow next year?” or “Will the U.S. military withdraw from country A in two years or less?” Moreover, the exchange would have offered combinations of contracts, perhaps combining an economic event and a political event. The concept was to discover whether trading in such contracts could help to predict future events and how connections between events were perceived. However, a political uproar followed. Critics savaged DARPA for proposing “terrorism futures,” and rather than spend political capital defending a tiny program, the proposal was dropped.

After that experience, while prediction markets have continued to exist, they have mainly been in small and limited forms. For example, the Iowa Electronic Markets is limited in size as an experimental market designed specifically for research and teaching purposes. It has become fairly common for big companies like Google and Ford to run “internal” prediction markets, where those inside the company can place bets on issues like whether project deadlines or sales targets will be met; it often turns out that the feedback from the internal market is a useful corrective to the promises from managers that everything is going just fine. The Hollywood Stock Exchange lets you place a bet on what the sales totals of a movie will be in the weeks after it is released. Of course, Americans can also bet on various outcomes using better markets in other countries–or just watch those market to see what messages they are sending.

But now there are some stirrings that US prediction markets never completely went away, and may be ready to rise again. Mary Brooks an Paul Rosenzweig tell the story in “Let’s Bet on the Next Big Policy Crisis—No, Really” (Lawfare blog, July 13, 2021). They point to some new academic experimentation beyond the long-running Iowa Electronic Markets:

More recently, Georgetown University built out its own crowd-forecasting platform—which is not strictly prediction market but rather a way of surveying and pooling expert opinions—specifically for geopolitical futures. Similarly, Metaculus offers a platform for a quasi-prediction market, in which the currency of exchange is prestige points, and anyone can submit a question for inclusion in the market.

They point out that use of internal corporate prediction markets has been rising, and indeed, the there is a market for those with top-secret clearance in the US intelligence community:

[T]here is significant demand for internal corporate prediction markets and crowd-forecasting. Google, Ford, Yahoo, Hewlett-Packard, Eli Lilly and a number of other prominent corporations have operated or continue to operate a corporate market. Some of their questions may delve into geopolitics, but in most cases employees bet on subjects such as whether deadlines will be met, what products will take off and what earnings statements will be. …

For example, in 2010, the intelligence community started a prediction market for top-secret-cleared government employees on its classified networks. From 2011 to 2015, the Intelligence Advanced Research Projects Activity (IARPA)—the intelligence-minded sister of DARPA—ran the Aggregative Contingent Estimation (ACE). ACE was a project designed to “dramatically enhance the accuracy, precision, and timeliness of intelligence forecasts … [by means of] techniques that elicit, weight, and combine the judgments of many intelligence analysts.” Today, IARPA still runs the Hybrid Forecasting Competition, which “develop[s] and test[s] hybrid geopolitical forecasting systems.”

But perhaps most interesting, there is now a company called Kalshi, approved by US government regulators, which will allow trading on the outcome of events. Brooks and Rosenzeweig write:

Just this past week, a prediction market that operates as a true financial exchange opened its digital doors. Kalshi—a San Francisco-based startup currently operating in beta—is the first fully regulated (CFTC-approved) prediction market. Because Kalshi is regulated, more significant amounts of money can be wagered than in many other markets, enabling them to build out a new asset class of events futures. The implications for this are obvious: An asset class like this could serve as an alternative or a supplement to more traditional insurance, allowing companies and individuals to hedge against crop failures, cyberattacks or floods.

It’s of course easy to raise concerns about prediction markets. Will they allow some people to benefit when an unpleasant event occurs? Yes, but so do any number of completely legal investments one can make in other financial markets (like those related to stock prices of insurance companies). Can they be gamed by investors? While a group of investors can certainly drive the price in one direction or another, there is the ultimate question of whether the event actually happens or not. Those who seek to drive the prediction market price to strange places need to be prepared to lose money.

No one says the market prediction is perfect: in sports betting, for example, the favorite doesn’t always win. But prediction markets provide a way of bringing together inputs of information and belief from a wide variety of people, rather than relying on other imperfect methods of prediction like listening to insider experts, outside experts, or polling data. If you think the chance of a future event embodied in the prediction market prices is wrong, that’s fine; are you willing to back your belief with actual cash? If you feel queasy about doing so, perhaps you should reconsider how strongly you hold that belief.