The American Prospect #331

Washington ’s Secret Policy Engine

How economic models dominate and distort

Don’t Let Corporations Undermine BUY AMERICA

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A PROSPECT SPECIAL ISSUE

04

Hidden in Plain Sight

The distorting power of macroeconomic policy models By Rakeen Mabud and David Dayen

08 How Models Get the Economy Wrong

Seemingly complex and sophisticated econometric modeling often fails to take into account common sense and observable reality. By Joseph E. Stiglitz

12 How Policymakers Fight a Losing Battle With Models

Reforms are needed to ensure that inaccurate budgetary math doesn’t take precedence over maximizing long-term prosperity. By Elizabeth Warren

14 Six Ways Economic Models Are Killing the Economy

The alleged science doesn’t match up to the real world. By Nick Hanauer

20

Is Economics Self-Correcting?

There are more economists doing useful real-world work. But the closer you get to the pinnacle of the profession, the less has changed. By Robert Kuttner

28 Prisoners of Their Own Device

How Congress underwrites the models that trap American policymaking By Philip Rocco

36 The Beltway’s Favorite Bogus Budget Model

The Penn Wharton Budget Model, bankrolled by finance moguls, is out to grow its power in Washington. By Jarod Facundo

42 How Model-Dependent Policymaking Ignores Race

Despite decades of policies aimed at creating new generations of homeowners, many African Americans grapple with a hostile housing sector. Where did the assumptions go wrong? By Gabrielle Gurley

46 Picking Over a Melting Planet

Government and the private sector rely increasingly on risk-modeling firms that claim they can zero in on exposure to climate change. By Lee Harris

52 The Forgotten Left Economics Tradition

In the Progressive and New Deal eras, there was a markedly different response to rising prices, and a different usage of economic theory. By Meg Jacobs

60 Ripping Off the Invisible Straitjacket

We need better economic models, but we also need Congress to free itself from the self-imposed constraints of modeling on the policymaking process. By Lindsay Owens

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Hidden in Plain

The distorting power of macroeconomic policy models

By Rakeen Mabud and David Dayen

In 2019, as a part of her presidential bid, Sen. Elizabeth Warren (D-MA) released a bold policy proposal: a wealth tax on the richest Americans. The 2 percent annual tax on total assets above $50 million, rising to 6 percent above $1 billion, would have hit plutocrats hard, from Jeff Bezos to Warren Buffett. According to the Warren campaign, the wealth tax was estimated to raise $3.75 trillion over ten years, and perhaps even more critically, decrease the political power of wealthy individuals.

With similar proposals on the table from Sen. Bernie Sanders (I-VT), it seemed like there was a real acknowledgment of the way that tax policy could be leveraged to reshape the power dynamics of the U.S. economy. But with one report, the momentum behind these proposals ground to a halt.

An institution called the Penn Wharton Budget Model (PWBM) conducted an analysis of Sen. Warren’s proposed tax and found that it would raise $1 trillion less than what her campaign had argued. More importantly, according to the model used by Penn Wharton, wealthy individuals would reduce their investment to avoid taxes, which would depress long-run economic growth and even lower wages across the economy.

The PWBM failed to factor in many aspects of Sen. Warren’s proposal, including a heightened tax enforcement regime

and especially the economic benefits of programs that she proposed funding with the tax revenue (universal child care, increased education funding, and student loan forgiveness), which would expand the economy on their own. But the widespread news coverage of the findings, blaring headlines linking the policy to recession, ultimately provided important ammunition for conservatives to push back against the proposed tax and paint it as bad policy. In the fight over Build Back Better, while a minimum tax on corporations did make it into law, wealth taxes were largely sidelined.

Because of the high profile of Warren’s plan and the presidential race, we know about this particular policy, and the pushback that ultimately doomed it. But there are dozens of other ideas that are more quietly shoved aside, or not even contemplated, thanks to a series of obscure gatekeepers who have come to dominate the way we deal with America’s most pressing challenges. They use the language of math and the presumption of certainty to dismiss innovative solutions before they can build a coalition of popular support. They claim to be neutral arbiters reflecting rigid realities about how the world works. But their methods are uncertain, their biases barely concealed, and their ideological goals apparent—if you know where to shine the light.

The key mechanism used to assert this

dominance is the macroeconomic policy model. It’s important to be very precise here. Models can be constructive tools that process the best available evidence and incorporate, to the extent possible, the desires of policymakers and elected representatives. The Environmental Protection Agency’s air quality model that formed the basis for how to fight acid rain was a sterling example of the best approximation of the real-world effects of policy driving to a positive result.

As an economist and a journalist who rely on data and evidence, we are in no way reflexively skeptical of the use of numbers. We would not condemn models per se, any more than we would condemn a calculator or a slide rule. But we also know the old adage that predictions are hard, especially about the future. The bigger the system being modeled, the harder it is for those predictions to represent the truth. That’s particularly true when you’re talking about enormous sectors like health care or the environment, or in the case of macro models, the entire national economy over a span of years or even decades.

We believe there should be more humility about the results of macro policy models when they are used in policy debates, especially given how they frequently serve as political weapons. While they produce potentially useful metrics for assessing policy, models are only as good as the assumptions built into them and the information that gets inputted.

Plain Sight

The specific modelers most listened to in Washington today are a particular set of economists, think tanks, and government agencies. Budgetary scorekeepers at the Congressional Budget Office and the Federal Reserve, and outside practitioners like the Penn Wharton Budget Model , the State Tax Analysis Modeling Program (STAMP), REMI , and the Tax Foundation’s Taxes and Growth Model, hold immense power over what gets considered. They help ingrain narratives into the public consciousness, dictating how policymakers, journalists, and other players understand what is good or bad for the economy. This creates invisible shackles on policymakers, guardrails that cannot be touched, problems that cannot be solved, no matter how ingenious the prescription.

The modelers and the models they use are rarely scrutinized deeply, even by those most closely attuned to the world of policymaking. Yet for all the claims of neutrality and rigor, these modelers are neither dispassionate, comprehensive, or even accurate much of the time.

In this special issue, we shine a bright light on these gatekeepers to try to understand what the models they use are, where they

get their power, and how they acquired so much influence. We will dig into the history of macro models, and try to understand why fixating on the macroeconomy can often lead us down the wrong path. We will better understand the ways that macro models constrain our scope of imagination about what is possible in the policy landscape, and what needs to change in order to make these tools useful.

Molding the Models

As Elizabeth Popp Berman detailed in her book Thinking Like an Economist , over the past several decades an economic style of thinking has taken hold over policy debates in Washington. Prior to this era, major legislation about societal problems could target power dynamics, or uphold universal rights. But starting in the 1960s, this gave way to an approach that sees policy questions through the lens of market dynamics. The best policy, under this framework, is that which finds the most efficient path to solving the problem.

This is how we got housing vouchers over

public housing, cap-and-trade over mandated reductions in pollution, “bending the cost curve” over Medicare for All. The economic style tended to downplay regulation in favor of behavioral nudges. It tended to prefer market forces over public forces. “Economists, and the economic style, are not the primary reason that Democratic policy positions moved away from the high liberalism associated with the KennedyJohnson era and the Great Society,” Berman writes. “But … economists, and the economic style, were the channel through which this change took place in the Democratic Party.”

Modeling became the further channel for economists to adopt this policy approach, as the primary way to quantify gains and weigh them against costs. And structures rose to account for this.

The Congressional Budget Office was established in 1974, with the goal of wrestling expertise out of the executive branch and back toward Congress. In the hands of its first director, Alice Rivlin, CBO became a

go-to forecaster, beyond just presenting the budgetary outlook for particular bills. Its long-term estimates of deficits, interest rates, demographic shifts, expected economic and employment growth, and other economic indicators became one way that the public heard about the state of the nation.

CBO has a point of view, described in the Prospect in 2020 as short-run Keynesian and long-run classical. As Nick Hanauer explains in this issue, CBO deliberately minimizes the long-term economic benefits of public investments, building into the model the assumption that over time, public spending crowds out private spending. And the entire project of CBO, to assess policy solely in terms of its budgetary cost rather than its total effects on national income and quality of life, stresses the politics of deficits and debt over equality and prosperity. That perspective is broadly in line with other practitioners of macro models, both inside and outside of government.

These models reinforce, justify, and calcify a particular theory of change, backed

by the same players that have been trying to embed a conservative, neoliberal ideology in Washington politics for decades. And it’s clear why this has been so successful: In the hands of a politician, an estimate that makes your policy look good or an opponent look bad can be extremely powerful. Even if the modeler makes caveats about ranges of estimates, having one number to use as a cudgel can be seductive. And that number can launch a thousand headlines, without the context or uncertainty that underlies it.

Every time a politician or media figure ties budgetary numbers to a particular policy, it reinforces the macro model’s importance. But when that evidence is rooted in assumptions and ideologies that ultimately damn progressive ideas to the dumpster, these models are only useful for those who agree with them.

Excavating the Assumptions

The economy, of course, is not a simple set of equations that can be neatly resolved

Models hold immense power over what bills get considered.

with some elegant number-crunching. The economy is a complicated, messy system, featuring hundreds of millions of individual actors pushing and pulling in different directions. Boiling progressive policy proposals down to one number belies the complexity of systemic, long-standing crises, and gives policymakers a pass from actually grappling with that complexity and the inevitable trade-offs. Many have tried to map the economy scientifically with precision; many have failed, as you will see in this issue.

Though economists may not want you to believe it, the assumptions they build into their models to make the math work are actually embedding a perspective about the economy. Some of these assumptions can belie common sense. There’s the idea that investments in children or the climate that pay off over the long term are too costly in the short term to justify; or that distributional outcomes by race, gender, wealth, and other salient characteristics are irrelevant to policy considerations. Other assumptions fail to incorporate the last several decades of economic research, such as the idea that public investment and private investment are substitutes rather than complements, or the idea that concentration in goods production or labor markets doesn’t matter for long-run economic outcomes.

That these assumptions are divorced from reality is not just a technical matter. The lack of grounding in reality also means that these models are wrong. The Trump tax

cuts were rolled out with the imprimatur from both CBO and Penn Wharton that they would expand the economy over the next ten years—CBO’s model predicted an increase in GDP of 0.7 percent, while Penn Wharton provided a range from 0.6 to 1.1 percent. Notwithstanding the point that the models couldn’t have foreseen the pandemic and the emergency economic measures taken to counteract it, if you control for that it remains clear that this did not bear out. Despite the macro religion, the tax cuts were simply not expansionary.

The fact that these assumptions are hidden under tangles of math has enormous implications. The lack of transparency means that massive decisions that affect the lives of millions are being justified using a set of equations that only a few people fully understand and control. And yet, when we look under the hood, it becomes clear that the same money and power that governs influence all over Washington has its hand in these technical gatekeepers as well. Ideologies should be challenged in the marketplace of ideas, not buried under a mountain of numbers.

Those who publicize the work of the macro modelers, whether in politics or the press, are only interested in the results, and have no interest in the way the sausage was made. They trust the figures as the product of impartial observers who simply apply the math. They don’t question the methods, or the biases. That’s what gives the models their power; it’s the way they are used, or

abused, by the modern political system that gives them outsized sway over the policies that govern our lives.

Where Do We Go From Here?

In this issue, we dig inside two of the biggest modelers, the CBO and the Penn Wharton Budget Model, exposing their blind spots and in some cases their hidden ideologies. We detail the questionable assumptions built into macro models, and the ways in which economists have come to rely on a process that excuses them from looking at the real world. We give space to progressive economists and policymakers who have had to work under the world that the macro modelers built, constrained by the hidden handcuffs on their ambitions and the illogic that faulty models predict. And we try to sketch out another world, mindful of the boldness of the past but also the necessary perspective of the future, that can give economic analysis its proper place in the policymaking process: as an aid to problem solving, not a roadblock to overcome.

Leon Keyserling, President Harry Truman’s chair of the Council of Economic Advisers who was a major figure in constructing the Fair Deal, once wrote to his immediate predecessor, Edwin Nourse, complaining about the flaccid state of the economic profession. “While we economists have long talked in the refined atmosphere of theoretical underpinnings,” Keyserling said, “we live in a world where prices and wages and profits are being made.” That real world is where policy must emanate from, not through retreating to perfect dioramas of a model economy.

Our economic challenges continue to grow ever larger in scale and scope, in ways that crude algorithms of dubious quality cannot reach. Combating existential threats like climate change and racial injustice will require analyses that account for the benefits that come from shifting our economy fundamentally over the long run, even if they come with significant up-front costs. The models of the future will have to recognize that not everything can be boiled down into a simple cost-benefit analysis.

In other words, if tackling the challenges of the future requires us to rethink the trickle-down, neoliberal approach to policymaking, the models must follow. n

Rakeen Mabud is the chief economist and managing director of research and policy at Groundwork Collaborative.

Though economists may not want you to believe it, the assumptions they build into their models are actually embedding a perspective about the economy.

How Models Get the Economy Wrong

By Joseph E. Stiglitz

Economics is commonly described as the science of scarcity.

We have limited resources, and we have to use them wisely. So there are trade-offs. In the old textbook example, if we want to spend more on guns, we have to spend less on butter. And so, not surprisingly, the question of how much we have to spend, now and in the future, is critical.

There are many things wrong with this seemingly impeccable and simple logic.

The first is that if we are not using our resources fully, then we can have more guns and more butter at the same time. Sometimes, we have people who would like to work but we don’t have jobs to give to them;

other times, as the former head of the Federal Reserve Ben Bernanke once claimed, we have a “saving glut,” with firms and households saving so much the money has nowhere productive to go. In those instances, we don’t have to make any trade-offs. The country, and the world, has frequently been in such a situation. In the Great Depression, 1 out of 4 workers were unemployed; in the Great Recession, more than 1 out of 10. At the height of the pandemic, 1 out of 7.

The second problem with the guns-orbutter logic is that the market economy is often inefficient. Resources are wasted when they are not used as productively or as wisely as they could be. Of course, ensuring that resources are used well is supposed to be a core virtue of the market economy,

as ruthless competition ensures that firms produce what consumers want at the lowest cost possible. But no one living in 21stcentury America should believe that such a myth describes the economy today, marked as it is by mega-monopolies and oligopolies.

Can it possibly be the case that the most efficient use of our limited research resources should be directed toward making an ever-better advertising machine (the business model underlying Facebook and Google) aimed at better exploiting consumers through discriminatory pricing and targeted and often misleading advertising? Would an efficient 21st-century “market machine” be unable to deliver safe baby formula? That’s a simple enough product to get right, and yet last year, the country

Seemingly complex and sophisticated econometric modeling often fails to take into account common sense and observable reality.

faced a massive shortage. And why did the market creep so slowly toward cost-saving renewable energy?

When there are inefficiencies of this kind, the economy can produce more guns and more butter by reducing these distortions. The economy is rife with such “market failures.” Public policy needs to be directed at reducing their magnitude.

Another major weakness in today’s economy results from lack of sufficient public investments. The obvious example is infrastructure: If we don’t invest enough in roads, harbors, and airports, it will cost private firms much more than it should to get their goods to market. Because there has been such severe underfunding in these areas, the returns on these public investments today are far higher than those on the average private investment.

And what matters for economic performance is social returns. When there are market distortions, such as when firms spend money to enhance their market power, they can create large private returns but low or even negative social returns. Arguably, investments in building a better advertising machine to target consumers more precisely may have a negative social return, even if Google and Facebook wind up being among the wealthiest firms on the planet.

Public policies redirecting overall investment toward more socially productive uses increases the size of the economic pie. But the economic returns from public investments in health and education and basic research and technology are even higher than in hard infrastructure, so the scope for increasing the size of the pie is even larger.

There is a third problem in the simplistic

trade-off analysis, which centers on how those trade-offs are calculated. To do this, economists use models. Models are simplifications of reality. They attempt to capture statistically what will happen if we spend more on infrastructure or raise taxes. Underneath the arcane mathematics, though, there are always simplifying assumptions. There is nothing wrong with simplification. The problem is that if the models make the wrong simplification, they will give the wrong answer. And often, the simplification determines the answer. If one assumes that the economy is efficient, then of course one can’t get more guns without giving up butter. But why would one make that assumption in the first place, one might well ask, when

it is obviously wrong? It’s hard to answer that question without impugning motives.

Most of the models that economists currently use ignore the role of market power in today’s economy. And in the gizzards of the models are a variety of other assumptions that affect how the consequences of any policy are calculated, including the macroeconomic consequences that determine the size of the pie and the nature of the tradeoffs. The estimated responses to any policy change are claimed to be the most reliable estimates of what will happen, based on past data, using the “best” models and best statistical techniques. Typically, these estimates are not robust—with large variations in the estimates depending on how they are

done and the sample period over which they are done. The sample period is in fact critical: The current situation may be markedly different from the one in which the studies were conducted. Applying those results to today leads to faulty conclusions.

For instance, if most of the time, in the historical sample, the economy was near full employment, as it was during the late 1990s and the beginning of this century, an increase in government spending would not lead to an increase in GDP. How could it? But both in 2008 and in 2020, government spending had a big effect, with GDP increasing a multiple of the amount of government spending, as standard Keynesian economics had predicted. During these periods, there were underutilized resources, and the underutilization would have been far worse in the absence of government action. The increased aggregate demand resulting from government spending led to a better utilization of resources.

Often, simple reasoning can beat out seemingly complex and sophisticated econometric modeling. In 2017, then-President Donald Trump proposed, and Congress adopted, a massive cut in the corporate income tax. The claim was made, supposedly based on models, that it would massively stimulate investment. It did not. It simply stimulated increases in share buybacks and dividends, funneling money to investors. It was, in effect, a big gift to rich corporations and their shareholders.

I had predicted that investment would not increase by much. Why? The corporate profits tax is a tax on pure profits, on the excess of returns over all the costs of production: labor, the goods that go into production, and capital. Firms make such pure profits, for instance, when they have market power. Some firms have a little market power. But in our economy marked by ever-increasing market power, many have a lot of market power—and thus large profits. The 2017 tax bill allowed firms not only to deduct the cost of their plants and equipment, but even to deduct some of the interest they paid. A basic result of standard economics is that a tax on pure profits does not discourage either investment or employment—and, by the same token, a lowering of such a tax doesn’t encourage investment or employment.

The standard models used by corporate interests to sell the tax cut assumed that the tax was equivalent to a tax on capital,

simply forgetting the fact that expenditures on capital were tax-deductible. (It was, I suspect, not an innocent mistake.) If it were a tax on capital, it would have discouraged capital expenditures. One can easily calculate by how much a tax on capital might discourage investment, and voilà, one has an estimate of how much lowering the corporate income tax will encourage investment. The magic is in the assumptions, which are hard to discover.

A coherent model of the entire economy recognizes that such a corporate tax will lower the value of firms’ equity—and lowering the tax will correspondingly increase the value of equity. If there is a pool of savings to be allocated between holding equity (reflecting the value of after-tax pure profits) and productive capital, then an increase in the value of equity will crowd out real capital accumulation. At least in the medium term, lowering the corporate income tax may actually result in less investment, and reduced GDP

Another critical assumption that goes into the standard modern macro econometric model concerns full employment. This is usually taken to be the level of unemployment below which inflation starts to increase, a number that is referred to as the natural unemployment rate, or NAIRU (non-accelerating inflation rate of unemployment). The idea is simple: If labor markets get too tight, wages start to increase, increasing the rate of inflation.

The problem is that the NAIRU cannot be reliably estimated, as the debate in the aftermath of the pandemic illustrates. Before the pandemic, we had very low levels of unemployment with very little inflation. Some thought that the pandemic had induced a permanent change in the labor market; for example, Larry Summers

believed that undoing the inflation (which he wrongly attributed to excess aggregate demand, but was clearly the result of a series of pandemic-induced supply-side shortages and demand shifts) would require a high level of unemployment for a long period of time. Others thought the pandemic, with its unprecedented levels of job separations (particularly stark in the U.S.), had temporarily shifted the relevant curves, but that eventually matters would normalize. It might take a while; we know, for instance, that quit rates are much higher in the early years of a new job. With a much larger fraction of workers getting new jobs, aggregate quit rates would be expected to be higher. In fact, there is mounting evidence of a normalization of labor markets in just a few years as the pandemic winds down.

A third example of a macro model assumption involves estimating the impact of public investments. I’ve already pointed out that public investments yield very high returns, and even if taxing corporations resulted in less private investment (which it does not), diverting resources from private to public investment would increase the size of the national pie. But because public investment may actually increase the returns to private investment, such investments may crowd in private investment. Typically, such longer-term effects are not included in the budgetary analyses. While there may be uncertainty about the value of these effects, we can say, with some certainty, that they are significant. Assuming them away, as much of the budgetary modeling does, is wrong, and prejudices the policy analysis.

There are a host of other examples. Models build in our views of how the economy and society function. We know that there are

There is a remarkable record of poor forecasts in critical moments, like the 2oo8 financial crisis.

differences in these views, and that predominant views change over time, so we shouldn’t be surprised that models incorporating different views would yield different results. Tragically for our country, the models that have been prevalent for the last quarter-century embed a particular set of views that are increasingly out of touch with the realities of today’s economy.

I’ve mentioned one aspect—the assumption of a competitive economy. More broadly, the “neoclassical economy” presumes profit-maximizing firms interacting with utilitymaximizing individuals in perfectly competitive and efficient markets. But we know that neither firms nor households behave according to that model, and that markets are far from perfect.

These deviations can be of first-order importance. To take but one example: In the perfectmarket model, there is what Arthur Okun called “ The Big Tradeoff .” One can only have more equality at the cost of poorer economic performance. But increasingly, experts recognize that in our economy marked by high levels of imperfections, including rent-seeking from firms with market power, equality and economic performance can be complementary. We pay not only a high price for inequality in terms of social and political divides, but even more narrowly in terms of economic performance. Even establishment institutions like the Organisation for Economic Co-operation and Development and the International Monetary Fund view it this way. Yet, this perspective is still not incorporated into standard macro budget models.

To be fair, the models used in the U.S. are not as bad as they could be. A standard model of the right—dating back to Herbert Hoover and before—entails “expansionary austerity.” This view says that cutting spending, even in a recession, is expan -

sionary, not contractionary! The magic is worked by what Paul Krugman has called the “confidence fairy.” Somehow, the cutbacks inspire such confidence that investors rush in and, in a self-fulfilling prophecy, this not only undoes the effect of the cutbacks but propels growth.

A main problem with this “theory” is that it runs counter to virtually all experience. Hoover’s cutbacks didn’t propel the economy into a new boom, but into an ever-deeper Depression. As did the IMF ’s cutbacks in East Asia, Greece, Spain, Portugal, and Ireland. Investors understood the underlying economics better than the “modelers.” They understood that contractionary policies, like raising interest rates and budget cuts, are … well … contractionary. They understood that when the economy goes into a downturn, sales decrease and bankruptcies increase—and returns to capital decrease. Austerity leads to less investment. Households, worried about the future, husband their resources, so it can even lead to curbing consumption. The knock-on effects of

austerity deepen the downturn. Common sense, once again, triumphs over the model.

There is a remarkable record of poor forecasts in critical moments, like the 2008 financial crisis and the euro crisis, by central banks and the international financial institutions. All of them were based on bad modeling. If the flawed modeling were just an academic exercise, that would be one thing. But policies are based on these models. Educations were interrupted and lives were broken by austerity. Millions lost jobs, homes, and livelihoods.

Flawed models have made us face false choices. It’s time to formulate new ones that accurately reflect the world in which we live. Only then can we make informed decisions that will lead to a healthy and robust economy for all citizens. n

Joseph E. Stiglitz is University Professor at Columbia University, co-recipient of the 2001 Nobel Memorial Prize in economics, and former chairman of President Clinton’s Council of Economic Advisers.

Models assume a level of unemployment below which inflation starts to increase. But it cannot be reliably estimated.

How FightPolicymakers a Losing Battle With Models

By Elizabeth Warren

If you’re wondering why the U.S. has failed so miserably in developing a workable child care and early-childhood education system, consider the role of economic modeling.

In 2021, when the Congressional Budget Office (CBO) released its much-anticipated score for the cost of the child care provisions in the Build Back Better Act, it produced one headline number: $381.5 billion. This was what CBO estimated as the amount of money the government would lay out for child care.

But that budget score badly missed the mark on the net cost of the program. It did not account for any of the savings predicted by reams of academic research on the long-term economic benefits of child care. Nothing about how kids with high-quality early care do better in school, stay out of trouble, and have higher lifetime earnings. Nothing about the increased tax revenues generated by mamas and daddies who could now work full-time. Nothing about the mountains of data that show that when mothers are held out of the workforce in their early years, their lifetime earnings and even their security in retirement are seriously undercut—something universal child care could reverse. And nothing about the impact of higher wages for child care workers—wages that would mean many of those workers would be paying more taxes and wouldn’t need SNAP, Medicaid, housing supplements, and other help offered to the lowest-paid people in the country. In other words, according to CBO, investing in our children and filling a wheelbarrow with $381.5 billion in cash (a big wheelbarrow) and setting it on fire would have exactly the same impact on our national budget and our nation.

To every CEO of a Fortune 500 company or owner of a small neighborhood restaurant, budget scoring like this must sound like a crazy way of doing business. After all, investments don’t just have costs—they also have benefits. That’s why companies invest in things like building factories, converting to green energy, or offering employee benefits, even if they have to book a big cost up front. Those corporate executives don’t take on big-ticket projects out of the goodness of their hearts; they take them on because they want to boost profits, retain workers, and improve the company’s long-term outlook.

Budget rules, by contrast, tilt against investing in people. And there’s a reason for that. Decades ago, Congress decided that CBO cannot account for the indirect or secondary effects a policy change may have on other parts of the budget. Research shows, for example, that federal spending on things like safe housing and nutrition assistance for babies makes people healthier and reduces total health costs. But because of the rules Congress set, CBO cost estimates for these programs cannot assume taxpayers would save any money on health insurance costs or that taxpayers would spend less on Medicaid. Meals on Wheels helps seniors stay out of much more costly nursing homes and saves Medicare and Medicaid billions of dollars, but the federal government says it is nothing but an expense.

These and other self-imposed rules structurally bias the policymaking process away from making commonsense investments that meet families’ needs.

Bad budget modeling, and how members of Congress respond to it, also distorts the way we design policies. Consider again our country’s need for child care. The U.S. is 33rd out of the 37 richest nations in terms of what we

spend on child care, and millions of parents— mostly mamas—are kept out of the workforce because they can’t find safe, affordable child care. The pandemic drove this crisis into the open, fueling a national outcry over the sorry state of care for our youngest children.

When I was invited to deliver one of the keynote speeches at the all-remote 2020 Democratic convention, I spoke from a closed child care center in Springfield, Massachusetts—standing amid the blocks, tiny chairs, and individual cubbies in the room for threeyear-olds. As more people rallied behind the need for universal child care, and as Democrats won both the White House and Congress, I believed this was our moment.

But as I assembled a new, comprehensive bill, the first question I got was the dreaded “How does it score?” The answer hobbled the process from the start. Instead of fighting for good policy, Democrats arbitrarily decided that the child care provisions in the bill would need to cost less than $400 billion. Universal care costs a lot more than that.

So the bill that ultimately moved forward was not based on how much money it would take to make certain every child had access to care. Instead, it was loaded with ways to game the policy design so the CBO score would meet the $400 billion threshold. The bill cut out millions of families that needed care, delayed implementation for years, and let states opt out. Bad budget modeling meant that these decisions weren’t driven by what would maximize our children’s well-being or our nation’s long-term growth. Instead, decisions were driven by the political imperative to produce a smaller score, regardless of what it meant for the workability of the proposed program.

The only number used to evaluate a child

Reforms are needed to ensure that inaccurate budgetary math doesn’t take precedence over maximizing long-term prosperity.

care program was the direct outlay for care. The compromises made to hit a politically palatable CBO number raised questions about who would and wouldn’t benefit from a compromise program, draining away support. As one bill after another passed the 2021-2022 Congress, child care was left behind.

Our current budget models don’t create random errors. They don’t sometimes overstate and sometimes understate costs. They create systematic errors, making many investments look far more expensive than they are. They lead to the routine underfunding of critical programs and enforcement activities, and they distort the policymaking process from start to finish.

For those who want to shrink the government to a size that can be drowned in a bathtub, the current budget-scoring model works great. But for those who live in the

real world and want a country in which all our people have a chance to thrive, bad budget models are choking us.

Reforming these economic models would not be easy. CBO cost estimates generally exclude the potential macroeconomic effects of a proposed policy precisely because, as they explain it, they have too few analysts to crunch the numbers. Worse, say former CBO scorers, the “estimates of macroeconomic effects are highly uncertain.” Translation: It’s hard.

Yes, estimating the costs and benefits of major investments decades out is hard— really hard. Figuring out the right model and the right assumptions is tricky and uncertain, and real life can prove our best estimates wrong. Politics can weave its way into judgment calls. Data are imperfect. But “hard” is no excuse for not trying.

Congress has a responsibility to maximize our people’s long-term prosperity, and

we need economic models that stand a better chance of doing that.

We should start by changing the rules on modeling so that both costs and benefits are accounted for. And because this is hard, we should ensure that the agencies we rely on for modeling have the resources they need to provide a solid understanding of both the likely costs and the likely benefits of any given policy.

We should also ensure that our modelers, who make countless judgment calls in doing their work, reflect a real diversity of perspectives. Consider CBO’s Panel of Economic Advisers. CBO relies on these experts, “selected to represent a variety of perspectives,” to help calibrate their models and gut-check their assumptions. But look at the team: Of the 22 people on last year’s panel, 20 had doctorates in economics; 11 of those 20 went to the same three Ph.D. programs. Recognizing that economic modeling is highly uncertain and highly dependent upon assumptions means that we should strive for diversity among our modelers and be open to different kinds of models when making decisions. CBO shouldn’t be the only game in town. We should also build accountability into the modeling system. Instead of scoring, voting, and moving on, we should assign independent outside teams to collect data on programs that have been adopted to see how far wide of the mark past modeling turned out to be. That information would arm us to improve modeling over time.

Finally, policymakers need to remember that modeling the costs and benefits of major public policies isn’t just about numbers—it’s also about our values. Yes, we need better data and better models, but we also can’t be afraid to make the case for bold investments, even when the (accurate) price tag is high. I believe that with accurate modeling, high-quality child care pretty much pays for itself. But even if not, such care helps us create an America in which everyone has opportunities—and “everyone” includes mamas. An investment in care is also an investment in care workers, treating them with respect for the hard work they do. In other words, just as a CEO would make the case to her board for taking a bet on a big new project, Congress shouldn’t shy away from making the investments the American people and our economy need. n

Elizabeth Warren is the senior Democratic senator from Massachusetts.

Economic Models Are Killing the Economy

The alleged science doesn’t match up to the real world.

By Nick Hanauer

Americans have been hammered for decades with an economic message that amounts to this: When wealthy people like me gain even more wealth through tax cuts, deregulation, and policies that keep wages low, that leads to economic growth and benefits for everyone else in the economy. And equally, that investing in you, raising your wages, forgiving your debt, or helping your family would be bad—for you! This is the trickle-down way of thinking about economic cause and effect, and there can be no doubt that it has substantially contributed to the greatest upward transfer of wealth in the history of the world.

You would think that trying to sell such a disastrous outcome for the broad mass of citizens would be incredibly unpopular. No politician would outright say they want to shrink the middle class, make it harder to get by, or reward hard work less. No politician would outright say that rich people should get richer, while everyone else struggles to make a decent life.

But this message has been hidden under the confusing, technical-sounding, and often impenetrable language of economics. Many academic economists do important work trying to understand and improve the

world. But most citizens’ experience of economics comes from hearing a story—a narrative that rationalizes who gets what and why. The people who benefit from trickledown policy the most have deployed economists to work their magic to tell this story, and explain why there is no alternative to its scientific certitude.

One of the trickle-down economists’ main persuasive tools is the economic model, used to predict and assess the outcome of economic policies and other major economic developments. These existing models exert such great force on the political debate in large part because their predictions are treated by politicians and reporters as neutral, technocratic reality—simple economic facts, produced by experts, that reflect our best understanding of economic cause and effect.

What few understand is that these economic models do not, and never can, fully reflect the extraordinary complexity of human markets. Rather, the point is to create useful abstractions to provide decision-makers with a sense of the budgetary and economic impacts of a given policy proposal. More disturbingly, the assumptions baked into these models completely define what the models predict. If the assumptions are wrong, the models will be wrong too.

And these models are deeply and consistently wrong.

But “wrong” doesn’t capture the true problem. The deeper problem is that these models are all wrong in the very same way, and in the same direction. They are wrong in a way that massively benefits the rich, and massively disadvantages everyone and everything else.

The headlines derived from these models consistently reflect this bias: “Raising Minimum Wage to $15 Would Cost 1.4 Million Jobs, CBO Says,” or “Biden Corporate Tax Hike Could Shrink Economy, Slash U.S. Jobs, Study Shows.”

Models serve less as scientific analysis and more as incantations from the cult of neoliberalism, and if politicians and journalists continue to accept them with the same naïve credulity that they always have, they will hamper the astounding middle-out economic progress that the Biden administration has made toward rebuilding a more equitable, prosperous economy for all.

The problem is that few people take the time to explain what these faulty assumptions are, why they all promote the worldview of the rich and powerful, and why they shouldn’t be treated as science but as a trickle-down fantasyland.

Here are six of the assumptions built into most economic models that are among the most pernicious:

1. Models assume that public investments will “crowd out” private investment, and are by definition less productive than private investments.

What happens to the economy if the federal government spends $1 billion? The normal person would say that it depends what they spend it on, and how the policy is designed.

Not so in most economic models. They assume that any government spending will have less of a return than whatever private businesses spend their money on. Always.

But that’s not all. They say that government spending even comes with a penalty: It automatically causes businesses to spend less, leading to lower overall investment. Always.

Essentially, models assume that every increase in public investment is canceled out by the combination of lower returns and reduction in private investment. Taking this assumption to its logical extreme, there’s almost nothing government should ever invest in. It’s a good thing Eisenhower took office before the neoliberal style of thinking came to dominate Washington, or instead of interstate highways we’d still have dirt roads.

These assumptions aren’t even well hid-

den in models but baked directly into the math. As economist Mark Paul has noted , the Congressional Budget Office model assumes that all public investments are exactly half as productive as private investments. Public investments return 5 percent annually, while the same amount of private investment returns 10 percent.

The first indication that something is amiss here can be sensed in all these round numbers—a flat declaration that public spending is 50 percent less good than private spending. Precisely 50 percent. Every time. Obviously, this is not the result of rigorous data analysis. It’s simply recapitulating the old trickle-down myth that government is by definition wasteful, while private invest-

ment is always maximized for the greatest efficiency and return.

And it’s not even a little bit true. Think about health care. The U.S. government invests billions in basic research each year and is responsible for funding an incredible range of innovations, from m RNA vaccine technology to new antibiotics. Everyone benefits from this publicly funded research, sparking further innovations and benefits— much of it carried out by the private sector.

Then consider how Big Pharma invests its profits: with huge marketing budgets, predatory patent enforcement, $577 billion in stock buybacks over five years (more than was spent on research and development), and a 14 percent increase in executive compensation. It’s a bonanza for those corporations, but it’s the opposite of efficiency—except in the make-believe world constructed by economic models.

The point isn’t that government spending always returns more than private spending, just that the flat assumption that it is always worse by 50 percent simply doesn’t map to reality. We should assess policy by what it proposes to do, not who proposes to do it.

The other idea, that public investment leads to lower private investment, is usually expressed with a fancy term: “crowd out.” It is a bedrock principle of neoliberal economics, and most models simply assume it’s true. The Penn Wharton Budget Model, for example, explicitly holds that government investments reduce the amount of private capital investment. Because the model also assumes that private investment is “productive” and public spending is “unproductive,” this automatically results in any large-scale government investment causing lower growth and lower returns. That informs their budget model’s analyses that the bipartisan infrastructure law will somehow lead to a 0.2 percent decline in productive private capital, that the $2 trillion Build Back Better proposal would reduce GDP by 0.2 percent, and that the COVID relief package would also reduce GDP by a similar amount.

The State Tax Analysis Modeling Program (STAMP) from the Beacon Hill Institute makes an even stranger decision, modeling government simply as a pass-through entity that causes “no indirect or induced effects” whatsoever.

Thankfully, President Biden rejects this nonsense. A central plank of Biden’s middleout approach is to attract private investment in key industries through the strategic use

of public dollars. As Secretary of Commerce Gina Raimondo explained about the implementation of the CHIPS and Science Act, “If we do our job right, the $50 billion of public investment will crowd in $500 billion or more of private investment of additional funding for manufacturing, for research and development, for startups” (emphasis added).

This strategy is already working. According to the Semiconductor Industry Association, the CHIPS and Science Act has already sparked $200 billion in private investment. The Climate Smart Buildings Initiative— created by the Inflation Reduction Act—is expected to attract over $8 billion of private-sector investment for modernizing federal buildings. The Biden administration has allocated $2.8 billion in public funds for investments in battery manufacturing for electric vehicles, which has already leveraged $9 billion in additional private investments. The story is much the same across the Biden administration’s constellation of strategic middle-out investments—public dollars are attracting private dollars, not displacing them, wholly disproving model assumptions in the court of reality.

2. Models assume workers’ wages are a direct reflection of their productivity.

Does Jamie Dimon produce 917 times what the average JPMorgan Chase worker produces? Does the CEO of McDonald’s produce 2,251 times the average cook or cashier? The answer is obviously no.

People are not paid what they are worth. They are paid what they have the power to negotiate. You don’t ask for a raise when the company just laid off an entire division and unemployment is high. If the company just posted a bunch of job openings? That’s a good time. We’d like to think that our hard work and worth to the company determines our salary, but just look around the office.

Most of the time, bargaining power, not the value that worker provides, tells the story.

But the Econ 101 assumptions embedded in these budget models claim that wages are a direct and perfect reflection of a worker’s economic contributions—that every worker is paid exactly what they’re worth.

There’s no discrimination in the alternate universe created by models, so structurally lower pay for women, immigrants, and people of color must necessarily reflect their lower productivity. Separately, Wall Street speculators—who produce pretty much nothing of value for anyone but themselves—are of the highest economic value because they get paid the most. Does anyone really believe that private equity barons are more productive in society than teachers? The models do, because they assume wages perfectly reflect productivity.

If the models correctly understood that power plays the primary role in wages, they would see raising the minimum wage as correcting for the power imbalance of a loose labor market or an exploitative industry. But since the models connect wages with productivity, raising the minimum wage, by definition, lifts a worker’s income above their economic value, and thus should cause substantial job losses. That’s just what the REMI model and the synthetic University of Washington model and the Employment Policies Institute model and the CBO model said. The Baker Institute’s DiamondZodrow model even reached the ludicrous conclusion that a higher minimum wage negatively impacts children’s health, modeling that a 1 percent increase in minimum wage caused a 0.1 percent decrease in their height-for-age, in spite of empirical evidence to the contrary

These predictions occurred throughout the Fight for $15 as minimum wages rose across the country. And if the models were

Models serve less as scientific analysis and more as incantations from the cult of neoliberalism.

right, Seattle and California and New York and Florida would have seen substantial job losses. But guess what? It never happened. On the contrary, businesses, particularly those affected by the minimum wage, like restaurants, boomed. Not once did these increases cause predicted job losses in the real world.

That reflects a simple fact about the economy. When more workers have more money, they will spend at more businesses. And that broad-based consumer demand sparks growth and innovation, which in turn drives productivity. In other words, higher wages are a cause of productivity, not a reflection of it.

3. Models assume that higher taxes on corporations and high-income people reduce growth and investment.

While corporate lobby groups and the zillionaires they represent regularly complain that taxes kill jobs and slow overall economic activity, no such relationship is detectable in the historical record. If anything, the opposite is closer to the truth: When the top marginal tax rate was above 90 percent in the 1950s, overall economic growth was

remarkably strong and broad-based. When the top rate was slashed to 28 percent in Reagan’s trickle-down revolution, inequality exploded, and growth sagged for decades as money was redistributed upward.

But the economic models that control the D.C. policy debate take as absolute truth the trickle-down assumption that people will work less if they are taxed more, and that this effect is very large. Any increase in tax rates on the rich therefore reduces the amount of work performed by the very richest people—and since rich people are in the world of these models the most productive people around, that means a sharp reduction in economic output.

In reality, of course, rich people don’t organize their lives as a tax-avoidance strategy, much less work less if they earn less. The model assumes away any factor that drives people to make a lot of money; ego, to use one example.

A corollary assumption is that high incomes arise in a world of perfect competition, where new products are able to beat out incumbents by force of their genius alone, supply and demand are always in

perfect equilibrium, and monopoly is just the name of a board game. In this world, all taxes and regulations must simply reduce productive corporate spending, and thus reduce economic growth.

The Tax Foundation’s Taxes and Growth model, for example, arbitrarily assumes that all payroll taxes are fully borne by workers, and corporate income taxes are assumed borne 50 percent by capital and 50 percent by workers. Therefore, corporate tax cuts will always “increase the capital stock and expand the whole economy, including wages and employment,” while a payroll tax cut will do nothing for investment and economic expansion. By contrast, a corporate tax increase would harm investment and growth.

It’s no accident that this is entirely untethered from actual human behavior or productivity measures—it’s the point of the model. In the real world, dominant market players regularly take advantage of their position to extract excessive rents and stifle innovation. Anyone who has flown on a commercial jet or tried to buy concert tickets has experienced the wildly imperfect competition that exists in the American economy.

A semiconductor factory being constructed in Arizona. The CHIPS and Science Act has already sparked $200 billion in private investment.

Well-designed public policy can get at these problems by aiming taxes specifically at those places where rents are extracted, making the entire economy more productive. And antitrust enforcement can similarly spark meaningful competition where it may have been eroded by predatory market actors. The only place where this doesn’t make sense is in the middle of an economic model.

4. Models assume that investing in poor people reduces economic activity, and that immigrants are less productive than domestic American workers.

What happens if the government gives people in need financial help?

If you get $50 for food, will you eat more food? If you get $100 for health care, will you go to the doctor more? If you get $100 for rent, will you be able to afford an apartment? And will any of these benefits enhance your personal well-being?

In the world of models, all of that is irrelevant. No matter what the money is for, the result of any federal assistance is that you will work fewer hours. Always.

While the models insist that rich people must be offered higher wages or lower taxes to incentivize them to work more, they hold that more economic support to lower-income people leads to them working less. In other words, rich people require the

promise of even more wealth to motivate them to be productive, but the poor must be threatened with destitution in order to motivate them.

The Baker Institute’s Diamond-Zodrow model makes the explicit assumption that “any increases in government transfers … reduce labor supply as individuals choose to ‘consume’ more leisure because household income level has increased.” The Tax Foundation’s model makes a similar assumption: that people choose between leisure and work, and that such choices are sharply impacted by taxes and transfers. These kinds of assumptions are how the Penn Wharton Budget Model’s analysis of Biden’s Build Back Better proposal could find that “lowering the Medicare age to 60 and making the ACA subsidies more generous lower households’ financial risk, so they save and work less,” and that reducing Medicare drug prices similarly reduces hours worked (and, oddly, reduces household savings as well).

This is nefarious stuff. These models assume negative consequences from the government investing in affordable housing or food security—basic necessities that make it possible for people to participate in society. It’s an Ebenezer Scrooge understanding of the economy: People are poor due to their own laziness, so any dime you flick in their direction just encourages them to do even less.

Most experiments with basic income, child tax credits, and other transfers finds that basic investments enable people to participate more fully in the economy and in their communities. Plus, providing a basic standard of living can also spur future economic gains. Only a sociopath could unequivocally conclude that giving people food always makes them work less. The models are even more direct when it comes to immigrant workers. The Penn Wharton model incorporates a baseline assumption that, as a natural law of economics, immigrants produce less than American-born workers do.

This is stupid and wrong, not to mention racist. While immigrant workers do tend to get paid less than other workers, there is no data whatsoever showing that they produce less. In fact, research even suggests the opposite: that increased immigration is tied to higher employment, higher incomes, and higher productivity.

5. Models work on ten-year budget horizons that force short-term thinking.

By the rules of Congress, the country’s best-known model, the CBO model, is required to estimate budget impacts of policy proposals over a ten-year window. For this reason, Penn Wharton, the Tax Foundation, and others follow suit. That

The ten-year budget window shortchanges investments in children that pay off over their lifetimes.

decade-long window creates the jaw-dropping multitrillion-dollar numbers that make headlines when Congress debates economic policy.

This arbitrary time frame is the worst of both worlds. It’s long enough that the accuracy of the estimates becomes highly questionable, and it’s also far too short to even begin to assess the economic impact of interventions on generational issues like climate change or early-childhood education.

For example, essentially everyone agrees that universal pre-K produces extraordinary benefits to children’s education, and essentially everyone agrees that these investments will create better outcomes for the entirety of a child’s life.

But over ten years, that investment won’t be realized. Children enrolled in pre-K at three years old are still in middle school ten years later—not really time to see much (if any) economic impact, now that child labor is (generally) frowned upon. So universal pre-K is basically worthless to these models, no matter how much economic growth it may create over the next six or seven decades. Investments in children are mostly thought to pay for themselves, except in the case of a budget model.

Some policies have a short enough scope that the impact is realized in the budget window. But the arbitrary ten-year cutoff cannot possibly be appropriate for all policy interventions. It literally shortchanges longterm planning, which is about as wrongheaded as you can get.

6.

Models measure GDP and revenue rather than well-being.

Economic growth is generally a good thing. It brings more people into the econ-

omy, and creates more resources to produce solutions to human problems. This is fundamental to how markets work.

But not every positive human outcome can be measured in terms of growing GDP or increased revenue. As Bobby Kennedy so eloquently pointed out in his March 1968 remarks at the University of Kansas, an exclusive focus on these strictly numerical measures of the economy counts napalm and nuclear weapons as positives, ignores the value of health, education, and community, and “measures everything, in short, except that which makes life worthwhile.”

This measurement gap stubbornly persists in today’s budget models, which have no way to interpret things that generate economic activity but that we might want less of—for example, carbon emissions or water pollution. Viewed as a model input, reduced carbon emissions and pollution could tend to reflect less economic activity—a net negative.

It’s certainly true that the economic value of these reductions is hard to measure solely in terms of dollars. And it might not even be desirable for economic models to attempt to calculate a literal price on mass shootings or the net present value of a climate apocalypse. But to the extent that we continue to let the numerical results of these flawed models drive our economic decision-making, we preclude even the possibility of reducing the factors we want less of in order to build a better society for generations to come.

When we clarify the extent to which the dominant economic models we use to judge policy are rigged against working families and the broader economy, the cause of the extraordinary upward transfer of wealth in

the past four decades becomes much easier to understand. Policymakers in Washington have based their decisions on models that are consistently biased toward the status quo, the rich, and private capital. They often amount to little more than a Mad Libs version of trickle-down economics, their exalted status in media and politics notwithstanding.

The basic structure of these models always remains fixed. Government is too inefficient and public investments are too expensive to make a difference; competition is perfect, market concentration is imaginary, and corporate power should be left alone; discrimination does not exist, all markets are at equilibrium, and wages perfectly correspond to productivity. The adjectives, policies, and numbers may vary a bit, but the story is always the same. Any policy benefiting capital, industry, or the rich is an unalloyed good. Any policy that benefits people directly is inefficient, kills incentives, and will harm the overall economy.

By tilting the playing field to restrict investment, undermine regulation, push down taxes, and lower wages, these economic models are doing their best to kill the economy. They may produce dense economic jargon and elegant mathematics that sounds super-impressive and scientific when it’s quoted on the front page of The New York Times. But on closer inspection, it looks a lot more like a protection racket for the rich and powerful than a social science.

Until we build models that reflect how the economy really grows, our leaders and the media should eye models from mainstream economists with skepticism. Models trying to convey the effects of policy should reflect the basic understanding that when more people have more money, that’s good for business. We need models that understand the basic principle that when the economy grows from the middle out, that’s good for everyone, and when more people participate in the economy, their consumer demand drives job creation and sparks innovation. In other words, our economic models must reflect the world as it really is—not as it was portrayed in the trickle-down Econ 101 classrooms of the 20th century. n

Nick Hanauer is a Seattle-based entrepreneur and venture capitalist, and the founder of Civic Ventures, a public-policy incubator.

Models have no way to interpret things that generate economic activity but that we might want less of—for example, carbon emissions or water pollution.

Is Economics Self-Correcting?

By Robert Kuttner

You can tell two stories about what has happened to the economics profession in this century. In the first story, academic economics has changed, significantly and for the better. Economists are less imprisoned by the unreal assumptions of models and more committed to real-world inquiry. Those with once-heretical views have been welcomed into the profession.

In the second story, change has come mainly around the edges. The heterodox thinkers doing important work are for the most part not in elite economics departments or top economics journals. Economics is still substantially captive to the use of abstruse models and ever more elaborate equations. And the teaching of economics, especially to undergraduates and first-year grad students, is depressingly familiar.

There is an ideological dimension to this conflict. Standard economics is a handy commercial for political conservatism. If markets are efficient by definition, then any state intervention must make things worse. So the market paradigm becomes a one-size-fits-all

cudgel against regulation, progressive taxation, wage regulation, public investment, and the rest of the arsenal to produce a more just society. And if the math is impenetrable to the laity, so much the better. Best to leave these questions to the experts.

Milton Friedman added the claim that market freedom is the essence of liberty. By contrast, job security, the ability to get good health care and education irrespective of private means, or freedom from hidden toxic substances, workplace hazards, and ruined environments, are not really freedoms.

A little autobiography is in order. As a 21-year-old, I explored getting a doctorate in economics. It turned out that the kind of economics I wanted to study was something called political economy—the interplay of power, institution, history, and the question of who gets what. Once, this was the core of economic inquiry, taught at elite schools.

My professors advised me that I was born too late. So I went off to UC Berkeley, to study international political economy as a political scientist. I never did get my doctorate and became an economics journalist.

I’ve been a critic of academic economics

ever since. If the profession is changing, that’s long overdue.

According to the hopeful view, the unreal assumptions of the Chicago school model, which colonized not only mainstream economics but law and political science as well, have been embarrassed by reality and now have far less influence. They included the ideas that markets are invariably efficient; that prices accurately reflect supply and demand, not manipulation or asymmetric information or market power; that labor markets efficiently pay workers what they deserve based on their marginal productivity; that people behave rationally; that all transactions by definition are voluntary and thus power doesn’t matter; that policy makes no constructive difference because economic actors rationally anticipate the impact and alter their behavior accordingly; and that economic concentration doesn’t matter because if prices get too far out of line, new competitors will enter the market.

Events have blown away such assumptions. Even some key Chicagoans such as Michael Jensen, author of the influential

There are more economists doing useful real-world work. But the closer you get to the pinnacle of the profession, the less has changed.

theory that the only duty of a corporation is to “maximize shareholder value,” have recanted in the face of evidence of pervasive share-price manipulation.

After the discrediting of the Chicago model, there has been a profusion of more venturesome theoretical and applied work. Computers have allowed the tabulation of massive amounts of data, which has allowed economists to return to empirical inquiry and be less captive to prior assumptions. The Nobel Prize, and the prestigious John Bates Clark Medal for the best economist under 40, keep being awarded to those with views and research techniques that once might have been shunned. Presidential addresses to the American Economic Association regularly criticize the profession and raise policy questions that defy simplistic modeling.

Two years ago in the Prospect , Harold Meyerson profiled the economics department at Berkeley, which had recruited Emmanuel Saez and Gabriel Zucman, two younger mainstream economists doing pioneering empirical work on income inequality. The economics department worked in

close collaboration with other schools, such as the public-policy school where Robert Reich was based. Berkeley became a national center of eclectic applied work, without sacrificing academic rigor.

One key figure in the makeover of Berkeley economics was David Card, who had been recruited from Princeton in 1997, where he and his colleague, the late Alan Krueger, wrote one of the seminal works that overthrew bad theory with ingenious use of data. In standard economics, raising the minimum wage results in increased unemployment. But in 1992, the adjoining states of New Jersey and Pennsylvania offered Card and Krueger something that rarely occurs in economics, a natural experiment.

New Jersey raised the minimum wage; Pennsylvania did not. Closely tracking 410 fast-food places in the adjoining areas of the states, they found no impact on unemployment. Good data had refuted bad theory. The article was published in the ultra-mainstream American Economic Review (AER) and expanded into a book, Myth and Measurement. Card later won the Nobel.

I asked Card if he thought his experi-

ence was emblematic of a shift in the profession. “You can find a lot more papers in the AER and the QJE [Quarterly Journal of Economics] that are highly empirical,” he told me. Some subfields of economics, especially Card’s area of labor economics and development economics, are far more data-driven today, but others are still “cultish and formalistic.”

Another fine example is David Autor at MIT, a mainstream and empirical economist venturing far afield into realms that would have been unthinkable a generation ago. Autor is also curious about real-world institutions and political feedback loops, a sensibility that was all but driven out of the mainstream profession in the heyday of Chicago.

In 2020, Autor and three co-authors published a pathbreaking research article in the flagship American Economic Review with the startling title “Importing Political Polarization?: The Electoral Consequences of Rising Trade Exposure.” The researchers, using an extensive data set, concluded that areas heavily impacted by free trade and outsourcing populated by the white working class “saw an increasing market

share for the Fox News channel (a rightward shift), stronger ideological polarization in campaign contributions (a polarized shift), and a relative rise in the likelihood of electing a Republican to Congress (a rightward shift).” They also became more likely to elect Republican representatives. Majorityminority areas with these characteristics, by contrast, were more likely to elect liberal instead of moderate Democrats.

In addition to the unorthodox subject matter and research questions, it contained a respectful shout-out to “an emerging political economy literature that connects adverse economic shocks to sharp ideological realignments that cleave along racial and ethnic lines.” Political economy is ordinarily disdained by mainstream economists as something less than real economics. The piece also rebukes standard trade theory, which holds that if trade increases economic efficiency (which it does by definition), then the political consequences are of little interest. In any case, we can always decide to compensate the losers with a formulation splendidly oblivious to the political feedback effects.

Several others whom I interviewed pointed to a new openness. In a 2019 essay on the state of post-neoliberal economics in the Boston Review, three of the leading heterodox economists— Dani Rodrik of Harvard’s Kennedy School, Suresh Naidu of Columbia, and Zucman of Berkeley—wrote, “Economists also often get overly enamored with models that focus on a narrow set of issues and identify first-best solutions … Many policy failures—the excesses of deregulation, hyper-globalization, tax cuts, fiscal austerity—reflect such first-best reasoning.”

But they see significant and hopeful change. “The typical course in microeconomics spends more time on market failures and how to fix them than on the magic of competitive markets. The typical macroeconomics course focuses on how governments can solve problems of unemployment, inflation, and instability rather than on the ‘classical’ model where the economy is self-adjusting.”

As good empiricists, they added, “The share of academic publications that use data and carry out empirical analysis has increased substantially in all subfields and currently exceeds 60 percent in labor economics, development economics, international economics, public finance, and macroeconomics.”

I was beginning to be persuaded. Then I got in touch with Luigi Zingales.

Zingales heads the Stigler Center at the University of Chicago, and criticized the old orthodoxy in two important books, A Capitalism for the People (2012) and Saving Capitalism From the Capitalists (with Raghuram Rajan, 2003) and in his ongoing work at his center, which includes regular media convenings to expose journalists who cover economics to a broader brand of scholarly economic inquiry.

It is a delicious irony that a heretic like Zingales now heads an institute created to honor and carry on the work of George Stigler, who along with Milton Friedman was among the purest and most influential of the Chicago theorists. In 2015, Zingales, a valued member of the business school faculty as well as the economics department, was being courted by other universities. To keep him at Chicago, he was invited to head his own research institute. The Stigler Institute had been relatively inactive, so naming Zingales to direct it seemed like a win-win proposition.

I told Zingales of my working hypothesis that economics was starting to change for the better, at least in some places and subfields.

“You are being far too optimistic, Bob,” he said. But wasn’t Zingales’s own highly visible presence, in the belly of the beast, evidence of the shift?

“The changes have not been all that dramatic,” he said. “Many people who are not members of the club are having more influence elsewhere in the academy, but don’t conflate that with a shift in the core of the profession.” Plenty of economists and noneconomists are tackling the important questions of real-world political economy,

but they are doing that in public-policy schools, business schools, law schools, and in second- and third-tier economics departments and journals.

Most important for public policy, the new thinking has not penetrated the economic models that policymakers rely upon. Decades after the widely accepted Card and Krueger analysis of minimum wages and employment, the Congressional Budget Office still routinely publicizes that wage increases will lead to job loss.

Microeconomics, according to Zingales, is particularly unchanged. But what is microeconomics?

The name implies concrete exploration of different sectors of the economy, but that’s not quite it. Rather, micro is the purest form of traditional economics, the old view of supply and demand determining price, ornamented by a great deal of modeling and algebra, but with too little curiosity about actual practices in industries that are a far cry from perfect markets.

When Paul Samuelson first published his famous textbook in 1948, the idea was to tame the more radical ideas of Keynes into an invented “neoclassical synthesis” that would leave most of the standard paradigm intact while admitting that at the level of the whole economy, prolonged disequilibria could occur, needlessly depressing output and requiring government intervention. The surviving orthodoxy was called microeconomics; the awkward fact of prices failing to equilibrate at the level of the whole economy was called macro. But if supply and demand could fail the macro economy, then something was fatally wrong with the entire model.

This new synthesis de-radicalized Keynes, who taught that prices could be “wrong” not just during anomalous depres-

Computers have allowed the tabulation of massive amounts of data, which has allowed economists to return to empirical inquiry.

sions at the macro level but throughout the economy in normal times, especially in finance and labor. Keynes’s disciple Joan Robinson, a leading theorist of monopsony, aptly termed the so-called neoclassical synthesis “bastard Keynesianism.” It served the profession’s need to preserve most of its model while making some grudging room for the brilliantly embarrassing insights of Keynes, whose broad humanistic and radical work was neutered into a mechanical formula for taming business cycles.

To confirm Zingales’s view that micro remains retrograde, I read through what is still the leading micro text for under -

graduates, now in its tenth edition, written by N. Gregory Mankiw. It is a kind of time capsule of crude Chicago economics, circa 1970. He even repeats the thoroughly debunked canard from a 1975 paper by fellow Chicagoan Sam Peltzman that seat belts cause more auto accidents. Why? Because they give false assurance of safety, and lead drivers to drive more recklessly. It’s a classic case of how Chicagoans go to absurd lengths to contend that government regulation invariably backfires.

What’s not in the textbook is any serious, empirical analysis of the economy, sector by sector. The point is to instruct

undergraduates in irrefutable axioms, using highly selective evidence to prove why they must be true.

Barry Lynn, author of the prophetic 2005 book End of the Line, on the vulnerability of far-flung global supply chains, tells of his more than 20-year quest to find an economist, any economist, interested in studying supply chain fragility. He had no takers. Paul Krugman met with Lynn, sniffed around the subject, and never followed up. Economists “knew” from free-market theory that just-in-time production using inputs from low-wage countries had to be efficient; otherwise corporations wouldn’t rely on it. Lynn’s concepts of systemic fragility, or vulnerability to disruption, or corporate myopia, were outside their field of vision and professional training.

The fragility of supply chains, ironically, lent itself beautifully to empirical analysis. An economist might have tabulated the dependence on distant supply chains, sector by sector, simulated a disruption that would have blocked or delayed x or y percent of critical inputs, and calculated the impact on supply and price industry by industry. That would have been a perfect topic for a microeconomics worthy of the name, which could have had real-world influence on both corporate practice and public policy, perhaps even moderating the economic costs of the supply chain crisis during the pandemic. But there was no interest among economists.

Even when mainstream economists do take on empirical questions, their work is often misleading because of its reliance on prior assumptions, rather than deep empirical inquiry. The Nobel Committee was pleased with itself for awarding the 2018 prize to Yale economist William Nordhaus for his pioneering work on the environment, which applied economic techniques to models of climate change.

But other economists and climate scientists challenged Nordhaus’s projections and the assumptions on which they were based. Nordhaus claimed that three degrees Celsius of warming would reduce global GDP by only 2.1 percent. They faulted him for overstating the economic costs of preventing further warming and understating the benefits, leaving out both technological breakthroughs induced by tighter regulation, and the catastrophic economic losses caused by the interaction of natural systems as the Earth continues to warm.

Luigi Zingales of the University of Chicago thinks economics hasn’t changed dramatically.

A young mainstream economist whom I sought out was Gabriel Chodorow-Reich, another brilliant Ph.D. from the Berkeley department, now teaching at Harvard. Like others whom I interviewed, he made the point that the increasing use of data is evidence that the profession is becoming less theory-bound and more empirical.

But are these data being used to ask the right questions? Chodorow-Reich and two colleagues wrote a paper on the dynamics of the Greek fiscal crisis and subsequent economic collapse. The paper, titled “The Macroeconomics of the Greek Depression,” is available on the Harvard website, awaiting publication in a major journal.

The paper combines a great deal of data with a “rich estimated dynamic general equilibrium” model and an immense amount of algebra, and is technically unimpeachable. Its broad finding is that external demand, government consumption, and fiscal transfers fueled the pre2007 boom, while fiscal contraction was implicated in the collapse, with wages and prices falling precipitously. This is illuminating as far as it goes. What’s missing is the political story of what drove the perverse fiscal policies that in turn deepened the Greek collapse.

I’ve addressed this dimension of the Greek crisis in two of my own books. To make a long story short, George Papandreou, a moderate socialist, won the 2009 election just in time for the crisis. When he took office that October, he learned that the outgoing conservative government had cooked the budget books. The deficit, reported at 3.5 percent of GDP, was actually more like 12 percent. The budget had been falsified with the help of special bonds created by Goldman Sachs, to disguise borrowing as money management.

When Papandreou learned the truth, he dutifully reported it to the European Commission and the European Central Bank. This was the cue for hedge funds to make massive bets against Greek bonds. Instead of taking the other side of those bets and stabilizing the bond market, the ECB and the EC, joined by the IMF and directed by the deficit obsessives from Germany, punitively piled on, making austerity demands in exchange for paltry aid that went mainly to bail out banks and bondholders and not to help the real Greek economy. (Imagine if such demands and policies had been

imposed on FDR’s New Deal during the Great Depression.)

As we talked, it became clear that Chodorow-Reich knew that part of the story as well as I did. In the article, it is mentioned only in passing. I asked him if there was any way of integrating the political-economy story with his elegant technical analysis in one piece that might be accepted in one of the top economics journals. He thought there wasn’t.

In fact, there are mainstream economists who do manage to integrate political and institutional analysis with large data sets, and get published in the top journals, including Chodorow-Reich himself in some of his other work. But as Berkeley economic historian Barry Eichengreen observes, demonstrating virtuosity with models and algebra-heavy methodology is what young scholars at the top economics departments are socialized to do, in order to win tenure, publication, promotion, and acclaim. When they are older, they can take more risks and write bolder articles.

There was once a highly empirical brand of institutional economics known as industrial organization (IO). Economists looked at actual industries, their structures and dynamics. In a sense, the first IO economist was Adam Smith, with his famous analysis of a pin factory. (Smith was far less rigid than many disciples who practice economics in his name.)

You might think that IO would be undergoing a revival today. That’s only partly true. The “new” IO has also become among the most formalist branches of economics— an extreme case of economists competent in abstruse theory and methods talking to each other. Eichengreen says, “Many people

who work in IO are hung up on purity of technique and statistical theory. That can cause you to miss the real story.”

Antitrust economics, a branch of IO, epitomizes the good news/bad news aspect of recent shifts in how economists conceive of their work. In the late 1970s and 1980s, led by Chicagoan Robert Bork, economic concentration was defined out of existence as a problem. The old-school test of oligopoly, the degree to which a few companies dominated a sector, was sidelined in favor of new dogmas like “consumer welfare.” Bork presumed that greater size and market power was likely to produce greater efficiency. If consumer prices didn’t rise (compared to what?), then concentration was not a problem. Other harms from monopoly and monopsony were ignored. So was institutional, empirical inquiry about how monopolies actually used market power. Bork further argued that antitrust enforcement itself could retard the greater efficiency of mergers.

A generation of economists grew up doing research whose purpose was to validate the Chicago view. This pseudo-scholarly work had great (and malicious) influence. Judges were acculturated to buy the Bork view. The FTC and the Justice Department under several presidents of both parties essentially ceased blocking abusive mergers, either because they bought the Bork doctrine themselves or because they feared being overruled by Chicago-influenced courts.

When a new generation rediscovered antitrust and the abuses of ever-worsening concentration, much of the pathbreaking work was done by scholars in the law schools, such as Lina Khan, mostly not in elite economics departments.

John Kwoka of Northeastern University’s economics department, who recently has

Demonstrating virtuosity with models is what young scholars at the top economics departments are socialized to do.

been chief economic adviser to Lina Khan, is one of their leaders. His generation of scholars, he tells me, was criticized for being “a-theoretical.” The Bork generation went to the other extreme: all theory, and proof by deduction. Kwoka, who has always done applied work looking at the actual impact of concentration, says there is still too much abstract formalism in antitrust economics, but also evidence of progress toward a new, empirical happy medium.

For example, the work of Michael

Whinston at MIT ’s Sloan School combines technically sophisticated modeling and econometric work with careful empirical analysis. A 2022 paper in The American Economic Review co-authored by Whinston with Volker Nocke looks at the impact of actual mergers, and benchmarks them against the evolving merger guidelines of the FTC and the Department of Justice. The paper finds that the guidelines have been “too lax” to prevent price increases, to the disadvantage of consumers. The article

even resurrects the long-disparaged Herfindahl-Hirschman index of industry concentration as an important analytical tool.

Kwoka also cites another important empirical research paper titled “Employer Consolidation and Wages: Evidence From Hospitals,” showing how hospital consolidation can depress wages. The authors, Elena Prager and Matt Schmitt, like Whinston, work at schools of management and not in economics departments.

Some leading economics departments, however, are receptive to this new empiricism. Joseph Harrington at Penn Wharton does work that is entirely empirical. One of Harrington’s recent research papers is titled “Collusion in Plain Sight: Firms’ Use of Public Announcements to Restrain Competition,” with extensive data, narrative analysis, and no algebra. His colleague Aviv Nevo, who has written on bargaining models in merger enforcement, is now on leave from his academic job to head the FTC economics bureau. And there are dozens of others working in this spirit.

On the other hand, the leading IO journals still publish the same outmoded formal modeling. Here is an emblematic example from the prestigious RAND Journal of Economics. The article is titled “Mergers and Innovation Portfolios.” Ostensibly, it addresses the important question of how mergers affect investment in innovation. The abstract states: “We show that when the project that is relatively more profitable for the firms appropriates a larger (smaller) fraction of the social surplus, a merger increases (decreases) consumer welfare by reducing investment in the most profitable project and increasing investment in the alternative project. The innovation portfolio effects of mergers may dominate the usual market power effects.”

Aside from the sheer opacity of the prose, there is nothing in the article that displays any study of the impact of an actual merger on actual innovation, or compares investment in innovation before and after a firm was acquired by a larger firm. Rather, the article begins with a massive literature review and then models possible behavior with several pages of algebra. The conclusion of the piece basically repeats the prem-

John Kwoka of Northeastern University is part of a handful of older antitrust economists who fought the Borkian revolution.

ise. The fact that young scholars are trained to show their prowess in this brand of technical work—in a field that cries out for genuine empirical investigation—shows how much of the profession remains unchanged.

Outmoded textbooks intensify the syndrome. When I was contemplating getting an economics degree, I spent a year at the London School of Economics to acquire some technical skills. My graduate-level micro textbook, which is still on my shelf, Price Theory by W.J.L. Ryan (16 shillings), was 95 percent algebra, which manipulated assumptions and inferences. There was just about nothing on the real world. Texts like this beat the institutional curiosity out of many apprentice economists, who then either become professionally invested in the math and the formal proofs, or quit the profession.

David Card explains that bad textbooks survive because the prime market for them is courses taught by adjuncts, who are harried and underpaid, and don’t have time to master new coursework required by newer textbooks. You might call the fallout from the overreliance on adjuncts yet another form of market failure.

“Students are made to walk over the coals of theory, and not until you get past the straight theory do you get to reality,” Kwoka said. This helps explain the self-reinforcing vicious circle. Economists are made to learn long-discredited modeling in order to get their Ph.Ds. And then, the safe way to win promotion and tenure is to publish articles in the same genre.

In 2018, economists James Heckman and Sidharth Moktan published a statistical analysis of the role of the top five economics journals as “filters” in incestuously reinforcing conventional methods and biases. After collecting data on tenure-track faculty hired by the top 35 U.S. economics departments between 1996 and 2010, they found that publication in the top five journals “greatly increase[s] the probability of receiving tenure.” They added: Using this system “creates clientele effects whereby career-oriented authors appeal to the tastes of editors and biases of journals … It raises entry costs for new ideas and persons outside the orbits of the journals and their editors.”

Many younger economists curious about the real world who do manage to get their Ph.D.s eventually leave economics departments to study questions of politi -

cal economy in other venues. Perry Mehrling, author of the important new book Money and Empire , taught economics for 30 years at Barnard College, becoming department chair. But there was little sympathy among colleagues for his brand of historical and institutional work. So in 2018, Mehrling took a post at Boston University’s School of Global Studies, a haven for economists doing political economy. Mehrling is also playing a leading role with one of the most important transformational institutions in the effort to

reclaim a usable economics, the Institute for New Economic Thinking (INET).

INET was founded in October 2009 with an initial grant of $50 million from George Soros. As the financial collapse deepened, Soros had been pressed by financial reform groups, which were hopelessly outspent in the struggle for better regulation, to donate to their cause. Instead, given his somewhat contradictory role as both hedge fund speculator and progressive philanthropist, Soros agreed to put serious long-term money into

Teresa Ghilarducci says, “Heterodox economics is not as heterodox as you might think.”

a project to build an alternative but mainstream economic research network.

The director of INET for its entire existence has been Soros confidant Rob Johnson, a Princeton Ph.D. in economics and expert on finance who served on the staff of the Senate Banking Committee (where I once worked) and also spent several years working for a Soros hedge fund. INET has attracted some of the profession’s leading thinkers. After more than a decade, INET ’s impact has been formidable, sponsoring conferences, underwriting research, and publishing an online journal. INET Oxford is now a major independent research center.

In 2011, Perry Mehrling became one of the leaders of INET ’s Young Scholars Initiative, which introduces a far more eclectic brand of economics to students and junior faculty worldwide. “Academic economics is extremely well defended,” Mehrling says. “So do not attack the citadel. You will fail. You need to go around it. There are a lot of people who want to study economics, but not the way it is taught at elite universities. We can create a parallel universe.”

Mehrling’s intellectual hero is economist Charles Kindleberger, whose classic The World in Depression remains one of the best books on the Great Depression. It was Kindleberger who originated what became known as the theory of hegemonic stability, demonstrating that it took a hegemonic power to stabilize the global monetary system. Britain played that role before 1914 and the U.S. after 1944; Kindleberger attributed the monetary and financial chaos of the interwar period to the absence of a hegemon. Mehrling’s new book adds the insight that monetary hegemony is also a useful engine of empire. It is doubtful that an economist like Kindleberger, whose method was mainly historical, but whose insights were

profound, would be tenured at an elite economics department today.

Others have joined the effort besides INET. The Hewlett Foundation’s Economy and Society Initiative, launched in 2018, will spend upwards of $120 million from Hewlett to underwrite research at six new academic centers and support and convene scholars from economics and other fields. Hewlett, which now has several other foundation partners and new plans for centers in the Global South, makes the connection between the standard economic model and the broader incursions of neoliberalism as a governing philosophy. (The Prospect, along with many other institutions, is a grantee.)

A prominent project of INET ’s called CORE Econ, which stands for Curriculum Open-Access Resources in Economics, has the goal of creating a rigorous and engaging alternative to the way economics is taught. CORE , whose areas of inquiry include “climate change, injustice, innovation and the future of work,” offers online free courses and a free interactive digital textbook, which is giving standard textbooks a run for their money. In Britain, it is already used in two-thirds of university economics departments.

CORE Econ is led by two great figures in dissenting economics, Sam Bowles and Wendy Carlin. Bowles initially taught at Harvard, where he was denied tenure despite a sterling record as teacher and scholar because he was a neo-Marxist. He then went off to UMass Amherst, where he and his colleagues created one of the great programs in radical economics. Carlin, who got her doctorate at Oxford as a Rhodes Scholar, is professor of economics at University College London.

Bowles, now based at the Santa Fe Institute, offers a number of free textbooks. He

counts himself in the optimistic camp on the issue of the degree of change in the economics profession, but shares Mehrling’s view that the change has to come from outside.

In an article published in the Journal of Economic Literature in 2020 titled “What Students Learn in Economics 101: Time for a Change,” Bowles and Carlin surveyed what subjects interest young economics students most. The top ones, everywhere in the world, are inequality, unemployment, poverty, and climate change, subjects relegated to the periphery of macro and micro courses and textbooks. “If you start the book with inequality and climate,” Bowles says, “you’re going to have to rewrite the whole book.”

So, how much has the economics profession changed since its models and methods were overtaken by reality? Some, but not enough. Change is proceeding from the outside in. And one of the most potent forces retarding change is the continuing indoctrination of undergraduates and young assistant professors pursuing tenure.

Teresa Ghilarducci, an economics professor at the New School and a leading researcher on pensions, says, “Heterodox economics is not as heterodox as you might think.” In a forthcoming paper that surveys the field, she and five colleagues write, “Alternative or ‘heterodox’ visions have achieved little traction in most economic departments, and modern economics is isolated from other social sciences and fields of inquiry.”

The conflict about economic modeling is most acute where methodology meets ideology. In a journal article titled “Shrinking Capitalism,” Bowles and Carlin write that the standard market model of supply and demand giving people precisely what they deserve grants “a kind of moral extraterritoriality to economic interactions that suspends ordinary ethical judgments within its compass.”

The efforts of INET and others to transform the profession begin with an appreciation that the formalism in economic modeling, and the presumption that markets are efficient until proven otherwise, serve as pseudo-scientific rationalizations for corporate power and gross inequality. Inequality of wealth has political spillover effects that preclude the reforms that society needs. The project of changing economics goes hand in hand with the project of changing capitalism. n

Economists are made to learn long-discredited modeling, and then the safe way to win promotion and tenure is to publish articles in the same genre.

Prisoners of Their Own Device

Congress

By Philip Rocco

Phil Swagel had good news and bad news for Bernie Sanders.

It was the spring of 2022, and the director of the Congressional Budget Office (CBO) was testifying virtually while recovering from a case of COVID -19. His audience was the Senate Budget Committee, where Sanders served at the time as chair. Yet, as Swagel was the last of the committee’s witnesses that day, that audience had been reduced to Sanders alone. The topic of the testimony was the Vermont democratic socialist’s signature policy proposal: Medicare for All.

The good news, according to Swagel, was that his office had determined that a single-payer health care system would vastly expand the availability and quality of coverage in the United States, while potentially reducing national health expenditures (the total amount that the country and its citizens pay for health care).

The bad news: Enacting single payer, Swagel said, would reduce gross domestic product, the key measure of economic growth, between 1 and 10 percent over the next ten years.

Sanders could have taken the opportunity to grill Swagel on these projections. Instead,

he accentuated the positives. “Are we in agreement,” the chairman asked, “that if everybody in this country were guaranteed comprehensive health care, people would be freer to leave their jobs and do what they really wanted to do?” Swagel agreed. “That is right,” he said, “and that would boost productivity and entrepreneurship.”

Sanders’s Republican colleagues, by contrast, pounced. “Democrats’ math does not add up,” read the press release published by the office of Budget Committee ranking member Lindsey Graham. “When you examine CBO’s analyses on both the creation of a single-payer system and how it would be financed … the economic outlook is bleak.” And so, the endless “war of the estimates” rolled ever onward.

Making policy invariably means trying to predict the future. Every time an idea is proposed, its champions engage in a kind of forecasting, highlighting how their legislation will usher in a glorious new reality. This bill will protect seniors from the ravages of poverty. That one will reduce supply chain bottlenecks, lowering costs for consumers.

Some of these forecasts are relatively easy to make. Tax cuts will reduce government revenues (though don’t tell that to conserva-

tives). Lowering the Medicare eligibility age will increase program enrollment.

Others require answering far harder questions about the relationship between government and the economy. How will federal laws affect the number of hours people work, or the amount of money they spend and save? What about firms’ investments in new technology? Or carbon emissions? Or how many children will be born in a given year, as CBO was recently tasked with estimating?

To answer these questions, policymakers employ experts to construct models of the world, each of which relies on a long chain of assumptions about how governments, people, and firms will interact with one another. Practically speaking, as the British statistician George Box once noted, “all models are wrong, but some are useful.” Even an inaccurate employment forecast might nevertheless provide policymakers some perspective or trend line on how the future labor market might look under different conditions.

A model might cease to be a benign source of information, however, if it systematically ignores important dimensions of public problems. For example, economic models produced by the CBO assume, in the face of significant empirical evidence, that

How

underwrites the models that trap American policymaking

federal investments deliver half the rate of return of private investments. Equally important, the budget office admits that it has no basis for estimating the effects of climate change mitigation efforts on future economic realities. Taken together, progressive economists argue, these assumptions stack the deck against public solutions to major problems.

Importantly, CBO’s economic forecasts are not merely one set of information among many that members of Congress consider. Rather, they shape policy in profound ways, regardless of their accuracy or utility. They become a taken-for-granted component of the budget baseline, a benchmark projection of federal spending and revenues that sets the terms for every congressional debate over fiscal policy. Once computed, the “hard numbers” found in CBO’s baseline tables conceal all the assumptions and uncertainties involved in producing them.

The root of the problem here is not necessarily CBO, however, or even the models its economists build. After all, the CBO is a legislative support agency, created, funded, and directed by Congress. Congress also determines the way that CBO’s numbers are used. Yet while Republicans in Congress have heaped scorn on the CBO when its projections threaten their policy ambitions, and attempted overtly to change the way those projections are computed, the same cannot be said for Democrats. Even progressive congressional leaders—who know well how CBO scores can affect the prospects of legislative success—have largely refrained from calling out questionable assumptions found in its models. Why?

However essential to the craft of legislation, projecting policy outcomes is a notoriously fraught human enterprise. As the authors of a leading graduate-school textbook on the subject bluntly assert, “even veteran policy analysts do not do it very well” and many “often duck it entirely, disguising their omission by a variety of subterfuges.”

To see why, let’s imagine a hypothetical legislative proposal to invest $50 billion in federal grants-in-aid per year over the next ten years—all of which would be financed through federal borrowing. Roughly half of this investment would go to fixing the country’s crumbling transportation infrastructure. The other half would be divided almost evenly between investments in education and R&D.

Given the poor—often perilous—conditions of American highways and railroads, one might assume that this kind of investment would be a boon to the economy. Yet, according to the CBO, this is not the case. At best, the policy would have a negligible positive impact on gross domestic product. To see why, we can consult CBO’s 2016 report on “The Macroeconomic and Budgetary Effects of Federal Investment,” which argues that the average short-run rate of return for federal investment is roughly 5 percent; this is one-half as large as the return on investment in the private sector.

CBO makes this claim for two reasons. First, the average productivity of investments by federal, state, and local government, the report asserts, is 8 percent—three-fourths as high as the average productivity of private investments. Second, each dollar of investment by the federal government increases total public investment by only two-thirds of a dollar. That is because federal spending generally leads to a decrease in state and local spending. The rest, we are told, is simple multiplication: “Two-thirds times three-fourths equals one-half.”

It all sounds simple enough. Yet scratch the surface even slightly, says Rutgers University economist Mark Paul, and CBO’s assumptions turn out to be “very wrong.”

One fundamental flaw, Paul suggests, is CBO’s claim that a dollar of public-sector investment is far less productive than an equivalent level of investment in the private sector. “In fact,” he points out, “the evidence shows that public investment often has a higher economic rate of return than private investment.” The evidence in question is not a cherry-picked set of outliers. It is a widely cited survey of 68 studies published between 1983 and 2008. That review, published by economists Pedro Bom and Jenny Ligthart in the Journal of Economic Surveys, finds that the average rate of return for public

investment is 10 percent in the short run and 16 percent in the long run. That is double the rate the CBO model employs. When Bom and Ligthart narrow the inquiry to examine only “core” public investments—in roads, highways, airports, and utilities—they find that the rate of return is higher still.

To its credit, CBO does mention Bom and Ligthart’s findings in its 2016 report. It dismisses their overall point, however, because “studies examining relatively recent periods” found that the output effects of government spending had “declined over time” and might be far lower than their average estimate. Yet even if returns from government investment have decreased somewhat over time, recent studies—including studies restricted to the U.S. context—still suggest they are far higher than CBO’s baseline. Still, the report concludes that “in CBO’s judgment” its assumption is consistent with the “range of estimates found in the research literature.”

This is not the only questionable assumption found in CBO’s models. As the Roosevelt Institute’s Emily DiVito and Mike Konczal point out, the models routinely assume that deficit-financed federal investment invariably raises interest rates and results in negative economic growth, “independent of the economic trends we’ve seen in the past 20 years.”

We have an actual real-world study to consult here: the Biden administration’s investments in advanced manufacturing. Since the White House declared its intent to onshore critical components like semiconductors, and incentives were approved by Congress, electronics manufacturing has soared. It’s an example of public investment not crowding out private spending, as CBO often assumes, but crowding it in.

Such arguments might not pass muster in an academic journal, but then again, CBO is not really an academic institution. While the office’s estimates may stimulate intel-

Once computed, the “hard numbers” found in CBO’s baseline tables conceal all the assumptions and uncertainties involved in producing them.

lectual debate, that is not their central purpose. They exist as a benchmark Congress can use to compare competing proposals to one another, using the same models and rules. Accuracy is explicitly sacrificed for consistency.

It is often impossible, however, to fashion those rules in a neutral manner. Despite the powerful effect of CBO cost estimates on the chances of passing legislation, the agency’s work remains focused primarily on analyzing the costs of major policy reforms, not their benefits. Yet even where costs are concerned, the office’s lack of subject-matter expertise imperils its usefulness for members of Congress.

The budget office admits, for example, that it “has no basis for estimating” the future savings of climate change mitigation policies “because many of the linkages between climate change and the federal budget require further assessment.” Lack-

ing such information yields conclusions that border on the cruelly absurd. A 2021 CBO report suggests that climate change might amount to a savings for federal programs like Social Security, Medicare, Medicaid, and Supplemental Security Income. The costs of those programs will decline, the report concludes, “to the extent that participants die at younger ages.”

Another target for criticism has been the guidelines CBO relies on to score legislation. For example, CBO does not treat the effects of changes in IRS tax enforcement as a source of “revenue” when analyzing the costs of legislation, even though common sense (and real-world observation) would dictate that investing more in enforcement would uncover more tax cheats.

The Biden administration’s proposed Build Back Better Act increased funding for tax enforcement, ramping up audits of corporations and wealthy individuals who ben-

efited from the gradual defunding of the IRS in the previous decades. CBO estimated that this would bring in more than $100 billion over the next decade. Yet in the midst of the debate over the legislation, CBO announced that it would not count the change as a cost savings, even though it would be reflected in the office’s baseline budget projections once the legislation was enacted.

Yet another limiting principle is the CBO’s adherence to a strict ten-year window for evaluating the benefits of federal investments. To be sure, longer-range forecasts come with far greater levels of uncertainty, greater at any rate than CBO economists are willing to tolerate. Nevertheless, when policy proposals make significant changes to society—such as climate change mitigation measures—their full impact may not be realized for several decades, rendering any benefits invisible in the ten-year window of analysis.

While CBO ’s assumptions are often “wildly out of line with empirical studies,” Paul says, they are still very much in line with what gets taught in graduate macroeconomics courses, which have not yet caught up to the literature. In the midst of this disciplinary chaos, conventional wisdom dies hard—especially inside CBO.

Economists, Paul suggests, need to admit that “we’re not great at forecasting the economic effects of policies.” While he acknowledges that CBO has improved its efforts at transparency in recent years, the budget office “has to do a better job at educating people on the assumptions its models use” and how the models contain a “lot more uncertainty” than CBO reports portray.

To model the effects of singlepayer reform, CBO economists first had to craft multiple stylized versions of the policy, mixing and matching different payment rates, levels of cost sharing, and benefit packages. Using the budget office’s “life cycle” growth model, they then simulated how these policy choices would affect a dizzying array of interactions between households, firms, and the government. Adding to the complexity, the models attempted

A CBO press conference in 2013. The budget agency has elevated status inside Washington. JACQUELYN MARTIN / AP PHOTO

to consider how policy would affect population health and, in turn, life expectancy. With each added layer of complexity comes more choices about assumptions and hence greater potential for error. The more groundbreaking the legislation, the harder it will be to model with any level of certainty.

You can see the inherent difficulty associated with forecasting the long-term daily economic decisions of hundreds of millions of not-entirely-rational humans in CBO’s baseline budget projections. For the entirety of the 2010s, CBO projected long-term interest rates significantly higher than reality, assuming a return to an equilibrium that never happened. This put many policies out of reach, because of the expectation that deficit spending would cost more than it would have. The same problems pop up elsewhere; CBO has an entire section of analysis on its website called “Accuracy of Projections” assessing its failings. This doesn’t stop news outlets from promoting the latest CBO budget analysis with rock-solid sureness.

Therein lies the problem: The assumptions behind CBO models are rarely contested before the public eye. One reason for this is the process by which the budget office’s research is transformed from working papers—replete with caveats, limitations, and acknowledgments of uncertainty—into the individual point estimates that become the official “scores” for major bills and the headlines that typically advertise the legislative “price tag.” The lawmaking process

has little tolerance for footnotes or ranges of possible estimates.

Moreover, members of Congress who consume CBO reports, and under current rules must live under their dictates of how much a policy costs and how much revenue they would have to find to cover it, have few incentives to look “beneath the hood” of the estimates to evaluate their assumptions. A colloquy about output elasticities hardly makes for good political theater. When committee chairmen are given a “mixed bag” report from CBO, they are naturally inclined to salvage the good, polish up the bad, and leave the ugly as a consolation prize for the ranking member.

If what the CBO does is a blend of art and science, members tend to describe the budget office in terms that frequently bend toward the religious. “CBO is God around here,” as Sen. Chuck Grassley (R-IA) put it in 2006, “because policy lives and dies by CBO’s word.” The history of health reform, Sen. Ron Wyden (D-OR) agreed, “is congressmen sending health legislation off to the Congressional Budget Office to die.”

Few institutions in American political life are described with such alpha-and-omega portent. Little wonder that the event that seems to have prompted then-congressional candidate Greg Gianforte’s 2017 body-slamming of a reporter was that reporter asking him about the latest CBO report.

But the CBO is just an organization of 275 staff members, many of whom have Ph.D.s, and most of whom spend their time pro -

jecting how much federal programs cost. It took members of Congress—together with the Beltway press—to make it into a god.

Following impoundment controversies in the Nixon administration in 1974, congressional policy entrepreneurs attempted to design the CBO as an agency that would counterbalance the power of the president in the budgetary process. It would be an alternative source of information that would be loyal to Congress, checking the proclivities of the Office of Management and Budget as well as other executive branch organizations. In that role, the CBO frequently came in for criticism, specifically when it called out the Reagan administration’s argument that a massive tax cut would lead to a budget surplus.

During that first decade, few members of Congress would have called the CBO a “god” on Capitol Hill. Instead, they frequently slashed the office’s budget, creating half a dozen “near-death experiences,” in the words of former director Robert Reischauer. Former Dixiecrat Sen. Russell Long (D-LA) was so incensed about CBO’s projections on tax cuts that he vowed to “find somebody who knows more how to put the answer in the computer so that it comes out the right way.”

But the CBO would soon win an important set of champions. First, the House and Senate Budget Committees—which relied on the office for leverage over congressional appropriators—defended it from its attackers in the Reagan administration and, later,

CBO economic models smooth out future estimates, despite the clear volatility of the past.

protected it from Gingrich-led efforts to bleed Congress of any independent legislative support. During the 1980s, increasingly conservative Democrats came to value the CBO as a tool for critiquing the Reagan administration’s budget-busting policies. The 1984 Democratic platform mentions the deficit over 40 times.

The strongest defense of the budget office came from an increasing elite preoccupation with the federal budget deficit. While the CBO continued to face cuts throughout the 1980s, mounting deficit pressures gave the office a window to influence the policy process in a new way. As former director Rudy Penner recalled , while the CBO could not take policy positions, he felt it was “safe for me to be against deficits.” In interviews with journalists and public speeches, Penner frequently warned about fiscal irresponsibility, which netted the office “a lot of credibility.”

By the early 1990s, congressional efforts to set automatic deficit reduction targets made CBO scores an “obligatory passage point” for legislation, the central standard by which new policy ideas are adjudicated. Following the enactment of “pay-as-you-go” (PAYGO) requirements in the early 1990s, members of Congress began to seek out “under the table guesstimates” of program costs from CBO before legislation was completed. As Robert Reischauer recalls, PAYGO had an almost “psychological” effect on members of Congress. Republicans instrumentally used the CBO’s scores to attack Bill Clinton’s national health reform proposal. Democrats attacked Republican tax cuts by citing projections by the CBO of their deficit effects. Members would soon begin shelving or heavily revising costly proposals so as not to “screw up the PAYGO scorecard and piss off their colleagues.”

As the deficit obsession increased, the CBO also became a go-to source of policy information for journalists. My research

suggests that citations to the budget office in The New York Times are highly correlated with increases in the federal deficit, and the largest share of op-eds mentioning the CBO in The Washington Post between 1975 and 2018 are focused on deficit effects.

The CBO’s transformation into a deficit oracle did not mean congressional leaders always passively accepted scores or reports that they disagreed with. As former CBO director Douglas Holtz-Eakin—who later founded the conservative American Action Forum—recalls, “everyone makes mistakes.” His was rushing out an “undisguised free trade manifesto” on CBO letterhead before the office’s editorial staff could ensure that it was “balanced in the usual CBO fashion.” Fifteen minutes after the release of the letter, he found himself in the office of Sen. Robert Byrd (D-WV), who asked him if he’d like to “run CBO on a dollar a year.”

Holtz-Eakin’s predecessor, Dan Crippen, found out the hard way what happens to a CBO director who stands up to concerted congressional opposition. A former domesticpolicy adviser in the Reagan administration, Crippen was appointed by Republicans to direct the CBO in early 1999. Yet within several years, he became embroiled in a long-simmering controversy over so-called “dynamic scoring”—a method of analyzing the macroeconomic feedback effects of legislation.

Since the early 1990s, congressional Republicans had insisted that economists at the CBO should apply the technique to analyses of major tax-cut legislation, which they believed would give them proof for their long-sought belief that tax cuts pay for themselves. After all, budget analyses produced by the President’s Council of Economic Advisers—Democratic and Republican alike—employed this technique. Historically, however, the House of Representatives had forbidden the CBO from using dynamic scoring. CBO leaders were also averse to

using a technique that was likely to stir up partisan controversies and which they personally believed was—in the words of Robert Reischauer— “ideology, not economics.”

With the appointment of Crippen, Republican leaders thought that they might finally get movement on dynamic scoring. Soon after his appointment, rumors began to surface that Crippen had abandoned the apolitical pretenses of prior directors and was prepared to take a more explicit partisan stand on proposals to reform Medicare and Social Security. Yet when it came to dynamic scoring, Crippen did not budge, much to the chagrin of House Budget Committee chair Jim Nussle. During a particularly tense 2002 closed-door meeting on the Republican budget proposal, Nussle bluntly announced: “CBO sucks, and you can quote me on that.”

Holtz-Eakin, appointed by Republicans at the end of Crippen’s term in 2003, saw things differently from his predecessor. Dynamic scoring was hardly voodoo, as Democrats seemed to believe. Rather, it was just another set of assumptions about how government policy affected the economy. To the former Syracuse University professor, it made a great deal of sense for the CBO to check its own suppositions and admit uncertainty. The CBO should, he thought, help Congress “to look at the world without the policy, look at the world with the policy, and compare all the differences.”

Soon after his arrival, Holtz-Eakin convened a meeting of his staff to inform them of his decision. “We’re going to do dynamic scoring,” he told them. When this invited a litany of objections, he brought the hammer down. “Let me explain this to you,” he said, “if I have to write in every number personally, I will. Or you can do the work. Which way is this going to happen?”

Still, Holtz-Eakin did not believe that dynamic scoring was the “magic elixir” Republicans thought it would be. “They’re not numerate in the Congress, uniformly,” he told an interviewer in 2011, “so my solution to this was they want the forbidden fruit, have an apple and beware.” In any case, his experience told him that it wouldn’t make much of a difference. Yet when CBO’s dynamic scores produced what Holtz-Eakin calls a “big nothing,” Republicans hardly snapped out of it. “What they concluded, instead, was I did it wrong.”

When the House of Representatives is under Republican control, its rules now routinely require the CBO, as well as the Joint

As Sen. Chuck Grassley put it in 2oo6, “CBO is God around here.”

Committee on Taxation (JCT), to provide a dynamic score of major tax changes “to the extent practicable.” In theory, this creates a double standard for how major legislation is evaluated. Dynamic-scoring requirements, after all, do not extend to spending bills. In practice, given the extensive costs and time required to produce valid dynamic scores, the CBO has sometimes found it difficult to provide them, especially when the legislative process moves quickly.

Yet the idea of dynamic scoring is arguably of greater utility to Republicans than the scores themselves. In 2017, for example, the CBO provided only a static score of Republicans’ signature Tax Cuts and Jobs Act, because it was “not practicable for a macroeconomic analysis to incorporate the full effects of all of the provisions in the bill, including interactions between these provisions, within the very short time available between the completion of the bill and the filing of the committee report.”

A dynamic analysis released by the JCT two weeks later also revealed that the tax bill would result in significant deficit increases. In response, congressional Republicans circulated a set of talking points attacking the “substance, timing, and growth assumptions of JCT ’s ‘dynamic’ score,” and highlighting prediction errors in the CBO’s prior analyses. According to Republicans, the JCT ’s dynamic score was insufficiently dynamic, because of its assumptions about how consumers and workers would respond to lower levels of taxation and its assumptions about the pace at which the Federal Reserve would raise interest rates. Doubts about these scores, if anything, enabled swift legislative action. In the final hours before the bill’s passage, Senate Majority Leader Mitch McConnell announced that he was “totally confident that this is a revenue-neutral bill.”

Incidentally, CBO’s eventual score for the Tax Cuts and Jobs Act, with a ten-year cost of $1.5 trillion, ended up being too conservative. Four months later, the budget office revised its update; it would now cost $1.9 trillion.

For all the godlike properties ascribed to the legislative scorekeepers, Republicans

have realized that gods that fail can be ignored. Holtz-Eakin seems to agree: “The CBO can’t stop anything that Congress really wants to do. You can tell them anything and they’ll just go ahead.”

But if scorekeeping is not a binding constraint on policy, Democrats seem not to have received the memo. It is virtually impossible to imagine Chuck Schumer dismissing a CBO score out of hand—even on “priority” legislation. Nor are Democrats as aggressive as Republicans when CBO scores spell trouble for their preferred bills. When the CBO’s analysis revealed that 2017 legislation to “repeal and replace” the Affordable Care Act would cause millions to lose health insurance coverage, Republicans hauled the CBO director before the agency for a series of grueling oversight hearings.

Two members of the House Freedom Caucus introduced amendments—ultimately unsuccessful—to gut the office’s budget.

One reason for this is that, during the Obama years, Democratic leaders were

arguably more enamored with deficit reduction—or at least the appearance of deficit reduction—than their Republican peers. As Robert Saldin highlights in his book When Bad Policy Makes Good Politics, lawmakers obsessed with generating projected “savings” for the Affordable Care Act — making good on President Obama’s commitment to not pass a plan that “adds a dime to the federal deficit” — pursued an ill-fated proposal for a voluntary long-term services and supports program called the CLASS Act.

To anyone who knows anything about how risk pools work, the phrase “voluntary long-term care insurance” will sound titanically stupid. And it is. But in the land of pay-fors, it was a golden opportunity. As designed, the program’s long vesting period meant that CLASS would generate nothing but increased revenue in the first half of CBO’s ten-year legislative scoring window— helping Democrats to make good on their misguided promise of deficit-neutral health reform. But because too few healthy people

Rep. Barbara Lee (D-CA) and Sen. Bernie Sanders (I-VT) have introduced pieces of legislation requiring CBO to perform new sorts of policy analysis.

would sign up for the plans, CLASS would ultimately end up generating an insurance “death spiral” of increased costs and declining participation. Congress repealed the program within a few years of its creation. (And paying for long-term care is still a disaster for most families.)

This is not an isolated case. In the first few months of the COVID -19 pandemic, House Speaker Nancy Pelosi explicitly rejected including automatic stabilizer provisions in relief legislation—which would have allowed the federal government to tie benefit levels to the duration of the crisis rather than arbitrary cutoff dates. Her reasoning? The CBO’s scorekeeping rules for automatic stabilizers would have inflated the total price tag for the legislation beyond Pelosi’s proposed $3 trillion ceiling. “If we put every good idea people wanted in the bill, it would be an $8 trillion bill,” as one staffer put it.

And Democratic leaders’ obsession with a “good score” is reportedly one reason why their 2021 plan to expand Medicare dental, vision, and hearing benefits delayed the phase-in of benefits for eight years. Consider that Medicare Parts A and B were initially implemented in 11 months. In 1972, Congress required the new end-stage renal disease benefits to take hold in one year. Even Medicare Part D and the Children’s Health Insurance Program took less than three years to implement. The Affordable Care Act took four.

Progressives are hardly unaware of how CBO scores—which emphasize the costs, though not the benefits, of major legislation—imperil their chances at reform. The last decade has seen the introduction of a handful of legislative proposals that require the CBO to perform new sorts of policy analysis. The Poverty Impact Trigger Act, intro -

duced by Rep. Barbara Lee (D-CA), requires the CBO to forecast the effects of major legislation on poverty and establishes a Poverty Impact Division of the budget office. Prior to chairing the Senate Budget Committee, Bernie Sanders introduced legislation requiring the CBO to estimate the effects of legislation on carbon emissions.

Still, progressive efforts to reform the CBO have hardly made it past the drafting stage.

Nor have progressives made it a priority to publicly investigate the assumptions underlying CBO’s models. As ranking member on the Senate Budget Committee, Sanders focused his attention on attacking the Trump administration’s budget. When he became the chair of that committee in 2021, Sanders held hearings on the need to expand Social Security and to enact Medicare for All, as well as on the costs of inaction on the climate crisis.

There is a clear political logic here. Oversight hearings rarely persuade the CBO to alter its methods or assumptions. So why waste valuable public attention on the arcane mechanics of macroeconomic models that might be better spent at growing support for a popular, progressive agenda? On the other hand, this means that dubious assumptions about the value of public investment remain submerged in the back of obscure government reports, or in closed-door meetings without public pressure. This only perpetuates CBO’s oracular mystique.

That mystique comes at a cost. Consider the CBO’s February 2021 finding that increasing the minimum wage to $15 an hour would cause 1.4 million Americans to lose their jobs. While major news sources treated the report as the official word on the matter, they ignored that the CBO decided to weight studies with larger job losses more heavily in its

analysis, and chose not to examine a variety of high-quality studies on policy trade-offs included in a systematic review commissioned by the British Treasury. Among other things, CBO included an initial study of the Seattle minimum-wage experiment that found evidence of major job losses, but excluded the authors’ follow-up study, which produced more positive results.

Forecasting errors and debatable assumptions would not be a problem if CBO’s scores were treated as one potentially useful source of information among others. Yet by transforming the office into an oracle, Congress has become a kind of supplicant. CBO scores are now all too easily used as an excuse for legislative inaction rather than a tool for decision-making. The limitations of that tool become more apparent when applied to proposals for more sweeping changes to the economy or society. The bigger an effect a lawmaker wants to have on society, the harder CBO makes it to accomplish.

However hard-won, the CBO’s reputation as an honest broker of hard truths in a polarized age—or, as one Washington Post editorial put it, a “skunk at the congressional picnic”—has had costs of its own. It is far easier to report the office’s point estimates as stylized facts rather than what they are: conditional, assumption-laden projections of the future. “The fact that CBO is just so solidly in Wonkville,” as economist Mark Paul puts it, “makes it harder to go after and also it looks more partisan going after it,” even when its assumptions are off base. “In reality,” Paul says, “how you crunch the numbers is a deeply political question.”

Still, as Republicans demonstrated during the passage of the Tax Cuts and Jobs Act, scorekeeping is only as powerful an obstacle to legislation as its audience allows it to be.

Members of Congress aren’t handcuffed to the current scorekeeping regime, or even to the results of a single commissioned study. These institutions are, at best, trick handcuffs: Congressional coalitions create them and can alter them when they want to. Or, more appropriately, when they receive intense, cross-cutting pressure to do so.

In other words, to build better models, we might need to start with building a better politics. n

Philip Rocco is an associate professor in the Department of Political Science at Marquette University.

The bigger an effect a lawmaker wants to have on society, the harder CBO makes it to accomplish.

The Beltway’s Favorite Bogus Budget Model

The Penn Wharton Budget Model, bankrolled by finance moguls, is out to grow its power in Washington.

By Jarod Facundo

American politics often goes like this. A politician proposes ambitious social policy. She’s smacked immediately with a barrage of familiar questions: How much does it cost? How are you going to pay for that? What’s the impact on the economy? Will this stop people from working? Have you considered how this might hurt business?

Those questions, she is told, can be answered by only the smartest economists, who magically all arrive at the same conclusions. They say: We ran the numbers and it doesn’t add up. The costs are too high, and the impact on jobs and growth uncertain. We’re not advocating for policy—we’re just calling balls and strikes, contributing data and knowledge to the debate. Rinse, repeat.

In Washington, a metric ton of policy shops, think tanks, and interest groups

advocate for their pet issues, on all sides of the political spectrum. The more inoffensive-sounding their names are, the greater your suspicions should be. The institutions that deny having a political agenda the most are typically the ones most invested in ideological outcomes. And that’s especially true of the self-appointed budget scorekeepers.

One of the most influential players in this space can be found just up I-95 from Washington, in America’s original capital city of Philadelphia. There sits the Penn Wharton Budget Model (PWBM), a project of the University of Pennsylvania’s Wharton School. Founded in 2016, PWBM has rapidly risen to the top of the pack of outside budget modelers, which run analyses on various policies and release bitesized summaries of their impacts. You can see its influence across corporate media; its findings are recited as gospel in newspaper headlines, alongside the Congressional Bud-

get Office’s estimates. Combined, they become the prism through which all policy is debated, and lawmakers take notice.

PWBM touts its work as above politics, pointing to instances where Democrats and Republicans alike have disagreed with its findings. That dual-sided criticism gives the organization the ability to posture that it’s a mere truthseeker, not a political animal.

But in a 2020 interview, PWBM ’s faculty director, former Congressional Budget Office economist and George W. Bush administration Treasury Department official Kent Smetters, spoke candidly about the model’s deep involvement in the policymaking process. “Policymakers often come to us before they write bills. It’s very clear when our footprint is on those bills, because we give feedback—usually off the record—about what the impacts would be if they try to achieve something one way versus another.”

What’s more, in recent years the Penn Wharton Budget Model has inserted itself further into the corridors of power. In 2020, the organization developed a highly competitive public-policy education program, designed for congressional staffers and other policy professionals. The Prospect obtained documents from the course’s current iteration, which began last October and runs until early May.

Though Penn Wharton has a website dedicated to the seven-month course, several former government officials and policy experts who spoke to the Prospect were unaware of it. Yet they described the course’s existence as emblematic of how interest groups try to ingratiate themselves on Capitol Hill.

In other words, Penn Wharton consciously and deliberately attempts to set the terms of debate, mainly through heightening fears about deficits, so that any public spending is viewed unfavorably. This helps push policy in a particular direction, one

that aligns with the political and financial elites who support and fund the project.

Announcing the creation of the model in June 2016, the former dean of the Wharton School, Geoff Garrett, said, “We’re harnessing the power of information for policy impact and using our analytics expertise to fuel data-driven decision making.” To assuage concerns over politicking, Garrett’s statement added: “We see an opportunity to make a difference at the intersection of business and policy—to help business, legislators and the public make crucial decisions based on rigorous data rather than ideological debate.”

The model, developed by Smetters and former CBO and Treasury officials, had an interactive component, allowing users to download and test specific policies to see the effect on the budget and the economy. A cute animated video beckoned people to get engaged. But the model’s true impact was always pointed toward Washington.

In 2017, PWBM estimated that the Trump

tax cuts would increase economic growth, albeit modestly, because they would stimulate private investment. For the record, this did not happen. But the analysis did jump-start the budget model’s rise to prominence. Numerous traditional media outlets highlighted it; then-Vox writer Ezra Klein called the organization “the respected Penn Wharton Budget Model,” at a time when it was barely a year old.

Marshall Steinbaum, an economist at the University of Utah, has a particular familiarity with Penn Wharton. He co-authored a 2017 report for the Roosevelt Institute on the effects to the macroeconomy if the United States implemented a universal basic income (UBI) program. Penn Wharton’s Kent Smetters responded, concluding that no matter how the program was funded, it would result in a lower gross domestic product; that’s economist-speak for “it’s not worth it.”

Steinbaum explained in an interview with the Prospect that the model made two assumptions when analyzing UBI: that

increased household income dampens the economy’s labor supply, and that federal budget deficits lead to increasing interest rates. Steinbaum conceded that the Federal Reserve has increased rates lately, but that had nothing to do with budget deficits. “That’s a policy choice,” Steinbaum explained. “Not something that happens automatically.”

The notion that guaranteed income from non-labor activities results in lower labor rate participation, Steinbaum said, is a false rationalization for people who believe the welfare state creates a culture of poverty. In fact, one of the citations for Smetters’s analysis was a paper analyzing the labor market effects of the Alaska Permanent Fund, an income-producing social wealth fund for every resident. But the paper concluded that the cash dividend had no effect on employment. Some people on the fringes of the labor market moved from full-time to parttime work, but the impact was not large enough for the original researchers to reach a definitive conclusion. Yet that extraneous result became the basis for Smetters to argue that a nationwide UBI program would have negative macroeconomic effects.

The example may seem trivial, because it’s not like Congress is on the verge of passing a UBI for every American citizen. But it mat-

ters. Institutions like PWBM and those who rhapsodize about its findings and analyses consider themselves to be the most serious, straight-edged people in the room. Yet if you poke their findings with a stick, they can be just as flimsy as any other pundit’s hot take. These expert conclusions become impenetrable only because of their complex language.

This happens over and over, and across administrations. For example, former President Donald Trump found himself at war with PWBM over his proposed infrastructure plan. Penn Wharton concluded that the $200 billion investment would have no impact on GDP. An independent think tank with actual expertise with transportation, the Eno Center, published a brief deconstructing how Penn Wharton’s analysis was off. But that didn’t matter, because The Washington Post had already run with the blazing headline “The Math in Trump’s Infrastructure Plan Is Off by 98 Percent, UPenn Economists Say,” and the conversation was over.

During the 2020 presidential primaries, Penn Wharton claimed that Sen. Bernie Sanders’s (I-VT) Medicare for All proposal would reduce GDP by 24 percent over 40 years. This was entirely derived from the fact that the plan “lacks a financing mechanism,” leading the budget model to assume

that it would be entirely deficit-financed. A note at the top of the analysis stated that “the long-run impact on GDP varies by as much as 24 percentage points,” or all of the projected loss, “depending on how the plan is financed.” Yet the headlines again did away with the ambiguity, asserting that the model showed that Medicare for All would “decimate” the economy. Potential benefits of the plan, like how workers would no longer be spending out of pocket for insurance premiums, thereby leaving them with additional money that could be circulated through the economy, were not integrated into the analysis.

Along the way, PWBM became a favorite adviser of deficit obsessive Sen. Joe Manchin (D-WV), who used its findings as a pretext to stall the social spending measures of the Build Back Better Act. PWBM projected that the total cost estimate would run higher than what the White House predicted, based on assumptions that the package would be permanently extended. The experience of the enhanced Child Tax Credit, which expired at the end of 2021, shows the extreme uncertainty with such a methodology, but it was enough to collapse negotiations. Most of the social spending was eliminated from the final Inflation Reduction Act (IRA).

The Penn Wharton Budget Model leans into the critique that America’s most pressing policy problems are deficits and debt.

Manchin got a taste of his own medicine later. When the budget model scored the IRA , it claimed that the bill would have little effect on inflation. Manchin’s response that he disagreed with PWBM further bolstered the model’s reputation as a neutral arbiter. Sen. John Cornyn (R-TX) took the opportunity to publish a short press release titled “Manchin Criticizes Budget Model He Once Touted.”

At the core, the Penn Wharton Budget Model is not simply a conservative, or even entirely a Republican, project. It’s a product of the commingling of America’s financial and political elite.

When you trace PWBM’s universe, you find a web of supporters that would make any sane person doubt the model’s supposed nonpartisan stance. Some actors are better than others, but most are cutthroat business executives who have reaped the most from financialization, at the expense of everybody else. They combine with experts from a decade ago who believe that the financial crisis was handled just fine, or that too much was done. Some even had a hand in its creation.

The most benign of the main trio of financial supporters is former Microsoft CEO, and current owner of the Los Angeles Clippers, Steve Ballmer. He’s a public supporter of good data creating good government. His $10 million donation went toward creating USAFacts, a trove of standardized government data—the raw material that fuels the Penn Wharton Budget Model.

The bulk of the rest of PWBM ’s funding comes from John D. Arnold and Marc Rowan, who have donated $6.6 million and $50 million, respectively, through their philanthropic organizations.

In recent years, Arnold has become an enigmatic darling in liberal circles for his work on drug pricing reform. His charity, Arnold Ventures, has spent more than $100 million on the issue, supporting the most respected patient advocacy groups. But as the Prospect has previously reported, Arnold was financing a consultant group that pared down the scope of how far drug pricing reform would go.

Aside from drug pricing, his interests extend into public-employee pensions. A 2017 Governing article titled “The Most Hated Man in Pensionland” detailed Arnold’s support for “pension reform,” that is, privatizing pensions. Matt Taibbi’s 2013 coverage in Rolling Stone detailed how, as Arnold funded the Pew Research Center, the organization started publishing reports on the unsustainable costs of public pension systems, while omitting the role played by the financial crisis and its actors. The conclusions were correct enough for a surface interpretation, but hollow in explaining the underlying reasons.

Before philanthropy, Arnold made a career out of destabilizing oil prices, earning the moniker “the king of natural gas.” He started his career on Enron’s trading desk on the West Coast, becoming indispensable before the company’s collapse. During Enron’s collapse, public pensions lost $1.5 billion through their investments in the company. Thereafter, Arnold rose once again in oil trading through his energyfocused hedge fund. A 2006 Senate investigation placed the blame for rising oil prices squarely on figures like Arnold, for their role in manufacturing conspiracies that the world was running out of oil.

The Penn Wharton Budget Model’s largest supporter is Marc Rowan, CEO of the private equity firm Apollo Global Management. In 2018, Rowan and his wife donated $50 million “to attract and retain worldleading faculty.” As a Penn Wharton alumnus, Rowan said he was “honored” to help “Wharton researchers advance and shape their fields.”

Like many private equity firms, Apollo is known for being ruthless, but it has earned a particularly corrosive reputation. Other private equity firms will try to whitewash their own practices by saying things like “We’re not like Apollo.” Co-founder Leon Black tends to catch the most bullets from the media. But Rowan was another co-founder of the firm; he took over the CEO role from Black in 2021.

If you look at the increasing concentration of hospitals, degradation of quality health care services, decreases in employee wages and benefits, and the shutterings of rural hospitals, Apollo is behind those maneuverings. Numerous Apollo-backed firms, from EP Energy to Phoenix Services to Hexion to Chisholm Oil & Gas, have hit bankruptcy in the past few years. Apollo executives helped invent the practice of winning while losing in bankruptcy, stripping assets out of a dying company and avoiding any legal consequences.

The budget model’s assumptions, which push against higher taxes, public investment, and most other things that anger the rich, fit together nicely with the outlook of a financial services industry tycoon or a billionaire CEO.

Financially supporting a project is not an automatic quid pro quo. But for the Penn Wharton Budget Model, the worldview of its funders is not so different from its list of advisers. External advisers include former House Ways and Means Committee member Rep. Allyson Schwartz (D-PA). After public office, Schwartz spent six years leading the Better Medicare Alliance (BMA), an insurance industry–backed front group where she served as president and CEO. Wendell Potter, the former insurance industry insider, has said that BMA’s “raison d’etre is to widen the federal spigot of taxpayer dollars” for directing public money away from traditional Medicare and toward Medicare Advantage plans.

Though there is one moderate tax economist with PWBM , UC Berkeley’s Alan Auerbach, there’s also Gregory Rosston,

At the core, the Penn Wharton Budget Model is a product of the commingling of America’s financial and political elite.

an economist who studied under Bill Baxter, the “total zealot” who rewrote antitrust merger guidelines under former President Ronald Reagan. Former Obama administration alumni and austerity hawks Peter Orszag and Austan Goolsbee are on the external advisory board as well. To Orszag and Goolsbee’s right, PWBM has former Sen. Judd Gregg (R-NH), the anti-government conservative who withdrew his nomination to become Obama’s commerce secretary over “irresolvable conflicts” on the scope of the 2009 stimulus package.

Meanwhile, Maya MacGuineas, president of the Committee for a Responsible Federal Budget (CRFB), one of the most consistent budget hawk voices in Washington, also serves on the board. CRFB happens to consistently cite the Penn Wharton Budget Model’s findings, as proof of why various pieces of proposed legislation have deleterious effects on the federal budget. CRFBfriendly language about fiscal responsibility and “tough choices,” by the same token, is prominent on Penn Wharton’s frequently asked questions page.

In a 2020 debate with MacGuineas and Larry Summers about federal deficits, Summers, in the nicest way possible, called her economic view of the world idiotic and unsophisticated. “I think Maya’s move to austerity as soon as possible is dangerous and misguided,” Summers said. “I think it’s analytically wrong because it fails to appreciate the big structural changes taking place in our economy.” Summers continued: “If we had the advice that Maya and those like her had consistently recommended from 2010 and onwards, we would have had an even

slower than the slowest-in-history recovery since the 2008 recession.”

MacGuineas is of course well compensated to espouse this worldview. CRFB has for years been funded with the fortune of the late Pete Peterson, the co-founder of private equity giant Blackstone and backer of a number of pro-austerity front groups. Peterson spent nearly half a billion dollars in the late 2000s and early 2010s encouraging deficit reduction, particularly through cuts to earned-benefit programs like Social Security and Medicare.

Step deeper inside the Penn Wharton Budget Model maze, and you find its team of experts. It includes the “internationally recognized expert on entitlement reforms” Jagadeesh Gokhale, a Cato Institute and American Enterprise Institute alumnus.

His professional work has focused on ways to privatize Social Security. Meanwhile, Cathy Taylor, listed as a “nonresident fellow,” is just an outright Republican activist. She’s the author of Red Is the New Black: How Women Can Fashion a More Powerful America , a book alleging that the Republican Party embodies the values women care about more than the Democratic Party. The book is conveniently not mentioned in her bio on the Penn Wharton site.

The Penn Wharton Budget Model’s certificate program is broken down into six separate sessions. Half of those are electives, selected by those taking the class. Meanwhile, the required classes include “Intro to the Economics of Tax and Spending Policies,” “An Insider View of Policymaking in the White House,” and “How Do Economists Predict the Economic Effects of Policies?”

The Prospect was able to see some of the names of the people inside the course. Most of them were congressional staffers, while others worked for other federal agencies or were policy types not affiliated with the government. Sources told the Prospect that the congressional staffers in the course are typically an even split of Democrats and Republicans. However, this latest cohort had more Democratic staffers than Republican ones, with an ideological range across the caucus, from Squad members to the most conservative.

In the program’s introductory course, an anonymous source described to the Prospect that the instructors emphasized how economists are not concerned with “poli-

PWBM’s power is derived from its claims to nonpartisanship and its ability to drive its message through the media.

Slides from the Penn Wharton Budget Model’s certificate program, a sevenmonth course designed for congressional staffers and policymakers

tics”—which for them was a reference to race, inequality, or gender. Penn Wharton’s Caroline Pennartz said in a statement that PWBM “takes an economic view of public policy rather than a political science or sociological view,” but that studies on their site do incorporate race and gender.

From the very beginning, class participants are drilled with the assumption that all taxes create a loss of efficiency, meaning that any dollar spent toward the government is a dollar never distributed in the economy. This frame of thinking leads one to conclude that hypothetically speaking, a flat tax is actually fairer than a progressive tax system, because such dollars could run further outside of government. (Pennartz said that the course adds the context that flat taxes “are typically perceived as unfair.”)

Notably, the required course “An Insider View of Policymaking in the White House” was taught by conservative Cathy Taylor.

PWBM faculty director Kent Smetters led the final required course, “How Do Economists Predict the Economic Effects of Policies?” Smetters emphasized how the budget model team works closely with lawmakers. “Ninety percent of our time right now is spent on actually just doing private delivery for policymakers who come to us from both [parties],” Smetters said in the lecture. “They come to us typically before they’ve started writing legislation, before they’ve actually introduced something. Relative to the scoring agencies, we’re typically operating on the front end [of policymaking].”

Riffing toward the end of the lecture, Smet-

ters tried downplaying the model’s political influence, but still touted its analytical rigor. He said: “I like to say, [at] the Penn Wharton Budget Model, we’re terrible at politics, good at policy, and we don’t [do] advocacy.”

Other elective lectures included sessions on topics like cryptocurrency, environmental policy, Social Security, antitrust, fiscal imbalances (taught by Jagadeesh Gokhale), prescription drug policy, and others.

The concept of a private institution funded by corporate interests holding classes for policymakers that hew to a particular perspective has an analogue. From 1976 to 1999, the Law & Economics Center at George Mason University held a popular conference for judges, teaching conservative theories about economic efficiency and cost-benefit analysis. According to later research, it had a decided impact on the judges it trained, leading to more conservative rulings. You can see the same potential from Penn Wharton’s indoctrination sessions on economic policy.

The Penn Wharton Budget Model is not necessarily an extraordinary actor in Washington. Different groups have varying degrees of sway over certain lawmakers. Many of them are more narrowly ideologically focused, however; PWBM ’s power is derived from its claims to nonpartisanship and its ability to drive its message through the media. Yet it has an implicit motive: Dean Baker, an economist at the Center for Economic and Policy Research (CEPR), sees the budget model as one piece of a larger network in Washington pushing the view that budget

deficits are detrimental to the economy.

In the world of budget modeling, some lawmakers take the models too seriously, viewing success or failure through the lens of a budget score. One former staffer for Sen. Sanders, Lori Kearns, explained to the Prospect that ideally, models would be only one input a lawmaker considers when drafting policy.

Bringing economics down from the heavens is an almost impossible task. Unconventional perspectives are smeared. And anybody who questions orthodoxies is automatically cast as an ideological partisan. The entire field protects itself with what the South Korean economist Ha-Joon Chang calls an ecclesiastical “language of rulers”—whose entire purpose is to stifle debate. “Once you create this body of knowledge,” Chang said in a 2019 lecture, “you can basically bully other people into accepting your argument because other people cannot understand you.”

The Penn Wharton Budget Model has mastered the language of rulers at a quicker speed than most others. That’s why it has been so successful in its short life span. “The important thing to understand about the Penn Wharton model is that it’s not really supposed to be a model of the macroeconomy. It’s supposed to be a tool by which you could kill progressive policymaking,” Steinbaum said. “So the question [for PWBM] is, what assumptions do you make about how the macroeconomy works such that when you feed a progressive policy into it, it produces a prediction that says it will be bad for the economy?” n

How ModelDependent Polıcymaking Ignores Race

By Gabrielle Gurley

The Banker is the Hollywood version of the lives of two California African American real estate investors, Bernard Garrett (Anthony Mackie) and Joe Morris (Samuel L. Jackson). They embrace the idea that homeownership is a prime generator of wealth in America. But where to begin to confront racial discrimination? To snuff out housing segregation in their corner of 1950s Los Angeles, they land on whitewashing. Using white front men like Matt Steiner (Nicholas Hoult), they buy and renovate residential properties in white neighborhoods and move in Black people. The subterfuge works when they buy a downtown L.A. office building filled with bank offices, but Jim Crow has other ideas when they buy actual banks in Garrett’s native Texas. Bernard’s wife Eunice (Nia Long) sums up the inequities this way: “White people get to buy homes and businesses because banks will lend them money, Black people don’t.”

If this sanitized version of mid-century American racism stings, it’s even more disturbing, but not surprising, that African

Americans still resort to whitewashing today. Thanks to potent and persistent bias in the housing market, Black homeowners are often on the receiving end of low property appraisals, sometimes by hundreds of thousands of dollars. In 2020, a Marin County, California, couple received a home appraisal for far less than what they expected. To clap back at the first appraiser’s bigotry, they scheduled a second appraisal with a different person and removed family photos and artworks—anything that could point to their African American identity. A friend stood in as the owner. What a difference a white person makes: The value of the home jumped $500,000, from $995,000 to $1,482,500.

Black families can’t begin to benefit from the wealth that they’ve created by owning a home when white appraisers undervalue their homes. A 2018 Brookings study found that homes owned by African Americans in majority-Black areas are valued at 23 percent less than comparable homes in neighborhoods with fewer or no Black residents. Real estate agents use a related tactic, steering Black homebuyers toward lower-income neighborhoods of color and away from white

middle- and upper-middle-class neighborhoods with characteristics that drive up home values, like better schools, low crime, good services, transportation links, and cultural amenities.

The homebuyers who end up buying in less appealing, depressed, or dangerous communities see their home values stagnate or decline. There are also tax-related penalties that weigh down Black homeowners. Local property taxes are often higher for Black homeowners than white homeowners in the same neighborhoods. Most of the benefits of the mortgage interest deduction go to high-income white households who could afford a home without it. The overwhelming majority of Black households do not meet the parameters to qualify for that deduction, because even with their mortgage interest, they don’t have enough deductions to itemize.

The Fair Housing Act that Lyndon Johnson signed in 1968 sought to end the most egregious forms of systemic discrimination— redlining, restrictive covenants, and the like— and open up African Americans’ access to the housing market. Home loans and grants, spe-

Despite decades of policies aimed at creating new generations of homeowners, many African Americans grapple with a hostile housing sector. Where did the assumptions go wrong?

cial incentives for first-time homebuyers, and mortgage interest deductions were designed to chart new routes to wealth creation for people with more modest incomes.

But National Association of Realtors data makes plain that homeownership for Blacks has lagged. Even after the housing bubble collapse in 2008, the number of Americans who own their own homes has been steadily increasing, reaching 65.5 percent in 2021. Hispanic and Asian Americans made significant gains over the past decade compared to whites. But the Black-white homeownership gap has barely budged. The African American homeownership rate is 44 percent, nearly 30 percentage points behind the white rate of about 70 percent. After more than five decades and libraries of housing regulations, the gap is larger now than it was before the passage of the Fair Housing Act.

Why have housing policies designed to facilitate homeownership had such marginal effects on creating Black American homeowners? The problem starts with how white American economists, who are the majority of the field’s practitioners and the backbone of the federal policymaking sector, view the world. William Spriggs, the AFL- CIO’s chief

In his 2020 open letter to the Opportunity & Inclusive Growth Institute at the Federal Reserve Bank of Minneapolis shortly after George Floyd’s lynching, Spriggs explained how taking a different approach to race in public-policy modeling would shift that dynamic. “We will no longer look for marginal policies to create change, because we know that we will be skirting the real issues. We won’t be amazed that after too many years of approaching a systemic policy with marginal analysis and marginal policies, we are not going to get the change we need.” How do policy assumptions that fail to take race into account play out? As Nick Hanauer notes in this issue, budget models presume that worker wages perfectly reflect productivity. Racial discrimination isn’t accounted for, and because wages for Black workers are below average, it leads to the pernicious assumption that they are somehow less productive.

Here’s a good housing-related example. The Housing and Urban Development Act of 1968 spun off the Government National Mortgage Association (Ginnie Mae) from the Federal National Mortgage Association (Fannie Mae). Fannie Mae kept conventional mortgages in its portfolio. To open up

Housing and Urban Development, Agriculture, and Veterans Affairs.

The Ginnie Mae model, which rests on market mechanisms and private-sector implementation, assumed that the benefits of homeownership, such as asset appreciation and upward mobility, would accrue equally to all comers. Since private-sector lenders were subsidized and insured from losses by the federal government, they operated in a low-risk environment. These protections would allow the private sector to approve mortgages that would be specially tailored to unserved and underserved lowincome groups like African Americans.

The major assumption was that the policy would prod lenders to do the right thing without upsetting the systems that rested on segregation and racial bias. The economists’ model-centric way of looking at how the policy would play out, in other words, wrote racism out of the story.

Protected against loses by the full faith and credit of the federal government, white private-sector lenders instead turned to maximizing their profits, and embraced predatory lending practices. They steered many Black borrowers into subprime mortgages, to purchase fixer-uppers and other substandard housing in disadvantaged, segregated Black neighborhoods with limited curb appeal.

These homes were unlikely to appreciate in value, especially when a homeowner had limited access to credit to help budget for repairs or other improvements. Nevertheless, the assumption was that the American dream was available—as long as the homeowner kept up mortgage payments and there were no sudden socioeconomic shocks. When frictions in the system occur, like the subprime mortgage crisis, African American homeowners often suffer the first and greatest housing shocks. In fact, in the run-up of the bubble, mortgage lenders sought out African American families with home equity, selling them cash-out refinances and lines of credit that pushed them underwater and destroyed their wealth when everything collapsed.

economist and a member of the Prospect ’s board of directors, faults the field for reflexively setting aside race in policymaking, only to cast about for alternative interpretations when weak policies produce poor outcomes for African Americans.

homeownership to low-income people who could not qualify for conventional mortgage products, Ginnie Mae became the chief financing mechanism for home loan programs insured by the Federal Housing Administration and the Departments of

In short, Ginnie Mae assumed that lenders would adopt a color-blind approach to lending since they were insulated against losses. But their assumptions ran straight into the twin pillars of white supremacy, endemic housing segregation and racial bias. The United States pays a high price for stamping out African American wealth. A 2020 Citigroup report found that over

the past 20 years, the country’s failure to close gaps in housing, wages, education, and investment for African Americans cost the economy $16 trillion.

When economists study race, they acknowledge that there are different racial groups that have different economic outcomes—and many economists “are good at measuring that,” says Damon Jones, an associate professor at the University of Chicago Harris School of Public Policy. “But the step before that is to ask why there are different racial groups and what do those mean to you.” “There are a lot of scholars who think about race in other fields—how race is constructed and why,” he adds. “In economics, many economists don’t; they haven’t done that work. So that hinders what economists do next.”

Today, Black homebuyers who should qualify for conventional mortgages run into trouble when loan officers look at monthly expenses like car loans, student loans, and credit card debt. Those can produce poor credit scores and debt-to-income ratios (DTIs) that banks pick apart to deny mortgages. African Americans have higher rates of denials, since their DTIs tend to be worse. But lenders are also twice as likely to deny conventional mortgages to Blacks than to whites with the same DTIs; that is, the same earnings, and similar financial histories. Add FHA loans into the mix, and although applicants of color have a better chance of obtaining those loans, whites also have higher approval rates for those mortgages than African Americans.

Some mainstream economists, however, tend to evaluate policy remedies that place greater emphasis on the human capital an individual homebuyer brings to the table: their incomes, future labor prospects, education, and skills. Rather than analyze how systemic inequities affect African Americans and how those might be addressed in policy frameworks, they conclude that there are financial deficiencies that render them unqualified or otherwise inadequate, and those interpretations fuel their policy prescriptions.

Interpretations about individual inadequacies woven through the warp and woof of housing sector policymaking do not fully account for the failure of loan subsidies, first-time homebuying programs, and other measures designed to scale up wealth up for African Americans. These programs benefit a limited number of Black homebuyers

because many white people will reject the operating premise that the country needs to increase Black homeownership. Today, the federal government is fighting an uphill battle against deeply ingrained attitudes that show no sign of powering down. In white neighborhoods where some Blacks settle, whites often flee, convinced that more Black residents will move in and depress their property values.

“If you want to use the housing market as a way to give people more wealth, the housing market discriminates against property owned by Black people. The idea that this asset is a home and that it generates wealth works for white people,” says Jones. “The idea that it works for Black people is not going to work as much because of racism.”

Stratification economics considers the role of race in a society, the rationales behind the creation of racial hierarchies, who benefits from these frameworks, and how that dominant group or groups maintains and reinforces systems and institutions against other groups to maintain their own positions, preferences, and privileges. “The debate about what that residual difference is, is what leads to whether or not we address that racial disparity by trying to increase investments in education or something else, rather than directly thinking about how we can address the structural issues that generate that difference, or even how we better enforce antidiscrimination law to eliminate that difference,” says Valerie Wilson, director of the Economic Policy Institute’s Program on Race, Ethnicity, and the Economy.

Washington’s attention to combating housing discrimination is an on-again, off-again political exercise, with the most extreme swing in recent years coming

between the Trump administration’s blatant moves to erode fair-housing laws and the Biden administration’s flurry of responses, such as a Property Appraisal and Valuation Equity (PAVE) task force to investigate appraisal bias. In 2021, the Justice Department stepped up activity against housing discrimination, beginning with historic agreements against redlining, including securing the first-ever settlement against a mortgage company that avoided offering products in Black neighborhoods, and filing statements of interests in discrimination cases like the one presented by the Marin County homeowners.

Confronting racial discrimination has to be the starting point for policymakers, rather than creating programs tailored to “deficiencies” in individuals’ personal profiles. After nearly 60 years of fair-housing programs designed to improve wealth creation through homeownership, Black wealth is the same as it ever was, negligible compared to whites’. Few Black households benefit, while white homeowners get richer at dizzying rates. Nevertheless, many African Americans keep making the financial and psychic investments to own a home, even though more than enough white people are determined to devise new ways to close off these pathways to wealth creation every time a new set of antidiscrimination policies hits the books.

There are no laws preventing a Black person from buying a house today, and there are laws that prohibit discrimination based on race. But discrimination still flourishes—and trying to weave one’s way through the thicket of biases that housing actors use to bar entry to the so-called American dream is cold comfort to a Black homeowner faced with asking a white friend to stand in at an appraisal and pretend to own their home. n

Housing programs benefit a limited number of Black homebuyers because many white people will reject the operating premise that the country needs to increase Black homeownership.

Picking Over a Melting Planet

Government and the private sector rely increasingly on risk-modeling firms that claim they can zero in on exposure to climate change.

By Lee Harris

In the spring of 2011, heavy rainfall swelled the Mississippi River to record levels, flooding trailer parks and pushing up gas prices as refineries and fuel terminals along the waterway closed. Surveying the wreckage, Heather McTeer Toney, then mayor of Greenville, Mississippi, found a crucial partner in Mars, Inc., the international conglomerate that makes M&M’s and pet food.

Mars operates an 80-acre rice farm in Greenville—its largest factory in the world. The company sent senior officials and shared its in-house risk assessment with Toney, which helped her plan the city’s police and fire response, design street upgrades, identify points of weakness in the wastewater and levee systems, and work with the Army Corps of Engineers.

“Today, we would call that climate risk,” Toney told the Prospect, but at the time it was “just protecting infrastructure.”

Local officials, civil engineers, and homeowners describe a growing need for information on exposure to the risks of extreme weather. In the past five years, demand has exploded. But not all cities have an anchor business as willing to share as Mars, and many might prefer not to depend on private industry for public planning.

Financial markets and private com -

panies, meanwhile, are in an “arms race” for climate intelligence. Some firms have announced decarbonization plans, while others are pledging to double down on fossil fuels. Regulators, struggling to keep up, have asked for more disclosure.

Private climate risk modelers have been the beneficiaries of this gold rush. Their guidance falls into two buckets: physical risk, or material exposure of assets to hazards, or transition risk, which includes fallout from policy changes, impact on the financial system, and reputation.

Financial institutions have snapped up these modelers, driving rapid consolidation in the nascent industry. BlackRock, the world’s largest asset manager, runs a platform called Aladdin, which has been called the “central nervous system” of the investment industry. It recently acquired a climate modeler to launch Aladdin Climate, which tracks exposure to environmental risk. Credit rating agencies like Moody’s and S&P have joined in, acquiring their own climate rating firms.

The remaining independent modelers look, themselves, a lot like rating agencies. They are private entities vetting the soundness of everything from city infrastructure to financial portfolios. Their unregulated new products—vast troves of climate analytics, with little standardization—are already being used by federal agencies, incorporated

into municipal bond ratings, and influencing how investors spend money.

Observers widely agree that new modeling is an improvement on the backwardlooking and patchy status quo. But quality varies considerably. New consumer-facing products provide detailed assessments of exposure, but experts have warned that they could be overstating what current models are able to predict at specific properties, or over long time horizons.

“A lot of these bold, hyperlocal claims are greatly outpacing the science,” Daniel Swain, a climate scientist at UCLA , told the Prospect. Some industry leaders voiced frustration. “It’s a Wild West right now,” said Cal Inman, principal at the risk data firm ClimateCheck.

Physical scientists interviewed by the Prospect raised concerns about claims made by some of these firms, most of which are led and heavily staffed not by climate scientists but by lawyers, marketing specialists, public-policy experts, and economists.

Meanwhile, the federal government is seeking precise estimates of global warming’s economic impacts. But there too, the demand for certainty can leave regulators crafting exercises that create a false sense of security, relying on risk modelers who are selling precision in an inherently imprecise business, and formulating questions that

sound technical but convey little meaningful information.

Noah Kaufman, who until last year was the top climate economist at the White House working on the social cost of carbon, said he worries about the government’s reliance on risk models built around carbon pricing. “Everybody’s looking for the number—the damage value. And the truth is, it’s an impossible question,” Kaufman said. “We don’t know the dollar per ton. We just know it might be really big.”

When a prospective buyer browses homes on real estate sites Realtor.com and RedFin, she can now view scores ranking a property’s exposure to extreme weather, such as heat, flood, and fire. A RedFin study found that the tool is already having an impact on homebuying, making customers less likely to bid on homes deemed higher-risk.

Scores for both websites are provided by the First Street Foundation, a Brooklyn-based nonprofit. In fine print, RedFin advises customers that they should “independently investigate the property’s climate risks to their own personal satisfaction.”

But assessments of risk exposure are hardly a matter of personal taste. Some observers have argued that regulators should vet the information. Instead, the relationship runs in the other direction: Private climate intelligence firms are increasingly being tapped to supply the government with data and analysis.

The most prominent is First Street, whose dozens of government clients include the U.S. Treasury, the Federal Housing Finance Agency, the Consumer Financial Protection Bureau, and all 12 Federal Reserve Banks. First Street is cited in the president’s 2023 budget, and its data is used in a new White House “Climate and Economic Justice Screening Tool.”

First Street Founder and CEO Matthew Eby previously launched the digital market-

ing agency Anthro. Before that, he was vice president of consumer and brand marketing at The Weather Company, the world’s largest private weather firm.

Eby has argued that it is crucial to make climate risks feel immediate and specific. “If we can make it personal for them, if we make the impacts here and now, that’s what we’ve found as marketers is the best way to convey the impact of something as big as this,” he told a Virginia newspaper when he launched an earlier iteration of the flood tool.

While the heat index helps capture the human toll of extreme heat, conflating it with temperature is misleading.

Eby’s site bio is similarly eye-catching. “Under Matthew’s leadership,” it reads, “the Foundation created a first-of-its-kind, peerreviewed flood model, wildfire model, and extreme heat model to calculate the past, present, and future climate risk of every property in the United States. The Foundation has also calculated the associated economic damage for every property” (emphasis added). The foundation’s more modest Mission page claims only to provide “the most up to date science available.”

It is fiendishly difficult to predict the likelihood and severity of future natural catastrophes. The average, long-run trend of global heating has been well established for decades—as well as the fact that it intensifies extreme weather. To show those macro trends, scientists use general circulation models (GCM s), which simulate the atmosphere, oceans, land, and ice and compute their interactions using basic laws of physics.

First Street often attracts media attention when it floats a new product, as it did with a splashy press release last summer launching a new model on extreme heat.

“We need to be prepared for the inevitable, that a quarter of the country will soon fall inside the Extreme Heat Belt with temperatures exceeding 125°F and the results will be dire,” Eby is quoted saying.

Eby was actually referring to a spike in the heat index—a measure of temperature plus humidity—not to air temperature.

But GCMs suck up enormous computing power, making them costly to run. And while they are good at predicting averages over big areas, that makes them bad at forecasting local weather or catching anomalies. Scientists have tried creating higher-resolution physical models, but it has been slow going.

Not that relying on historical data is better. On the contrary: Extreme weather events are rare by definition, meaning there is a thin data set to draw on. There is also the issue of local climate variability, which, as climate risk expert Kate Mackenzie explains, makes it “harder to detect the ‘fingerprint’ of global warming” in any given event.

The catastrophe, or “cat risk,” modeling industry, which sells insurers and reinsurers

odds of losses on storms, has long relied on historical statistics, mixing in some proprietary assumptions and information on clients’ portfolios. For years, they openly disdained scientists who used physics equations in their models. One quipped that climate modelers were “doing brain surgery with a chain saw.”

Insurers could afford to use these backward-looking models as long as climate change was far off in the future, they reasoned, since they write short-term policies. But in the past five years, the industry has been pummeled by some of the highest losses on record, making insurers more interested in what climate modelers have to say.

Hot demand has driven rapid progress in physics-based modeling. But the best versions of these tools come with warning labels, including qualitative assessments based on local terrain data. Insurers and financial institutions are also more capable than ordinary consumers of absorbing uncertainties in modeling, since they apply models across a portfolio of locations.

First Street burst to national attention in 2020 with its launch of Flood Factor, which found that federal flood maps grossly underestimated risk. Almost twice as many properties were exposed than what FEMA claimed, First Street found. The New York Times published a glossy spread on the report.

First Street licensed its U.S. flood map data from Fathom, a U.K.-based research group. The firms have since split, and Fathom said it could not comment for this story. People familiar with the work told the Prospect that Fathom provided the backbone of the model before becoming concerned about First Street’s handling of it.

On its website, First Street says that Flood Factor’s methods “are going through blind reviews for traditional peer-reviewed scientific publication and have already been through an additional expert panel-review process.” That additional panel review, not the standard in academic science, involved First Street hand-picking three academics to give feedback.

“To me, that terminology of ‘peer review’ was a bit misleading and confusing,” Marco Tedesco, a climate scientist at Columbia who worked briefly with First Street, told the Prospect

Ed Kearns, First Street’s chief data officer, pointed out in an interview with the Prospect that the Fathom model underly-

ing their work is peer-reviewed, and even won an award. “Still today, we are using the Fathom U.S. model that Fathom created, but now we are making modifications to it and advances to it,” he said.

Asked how accurately First Street can approximate water level rise for any given property, Kearns said that “some of the work Fathom did shows that it’s around five centimeters or so.” (That’s before adding in other sources of error, he added.) But published work by Fathom suggests that their error range for flood depth is closer to one meter, which can be the difference between wet and dry, or between small and enormous losses.

In February, First Street launched a new product, Wind Factor, geared at predicting changes in storm- and hurricane-caused wind damage. Rather than using sparse historical data, it draws on a data set of some 50,000 “synthetic” hurricanes, examining their potential paths and damage.

At a launch event, Eby repeatedly emphasized the importance of trust, open science, and peer review. “Everything we do, we publish, we put through the peer review process,” he said.

But that is inaccurate. While First Street’s work has been published by journal publisher MDPI, including wildfire, extreme heat, and flooding models, other core products are not peer-reviewed. In fact, Wind Factor, which Eby was promoting at that event, has not been peer-reviewed. (Several critics pointed out to the Prospect that MDPI, the world’s largest publisher of open-access articles, is seen by many scientists as low-quality.)

Asked about peer review for Wind Factor, Kearns said that the model is based on peerreviewed work by Kerry Emanuel, a preeminent hurricane expert at MIT who sits on First Street’s advisory board. “Since the

method itself was published back in 2006, we thought, ‘We don’t need to get the technique peer-reviewed, but we can go after getting the results peer-reviewed,’” Kearns said. “It’s on our list of things to do.”

Reached by the Prospect, Emanuel enthusiastically endorsed First Street’s use of his work. “It’s just the way I hoped these tracks would be used someday,” he said. He emphasized that models currently in use are seriously outdated, and that it is particularly important to improve assessments of present-day risk.

Other experts expressed reluctance to criticize property-level predictions of modelers like First Street, saying that even where they may lack accuracy, they are a major improvement on existing tools. But studies have shown that overstating precision could encourage misallocation of capital. Rating agencies, for example, may downgrade debt based on inaccurate assessments of creditworthiness.

Tedesco wrote a paper with First Street’s then-head of data science, but before long grew uneasy with their methods and left to work with Cloud to Street, a flood insurance provider.

“I felt more comfortable because it was really more like a spin-off from academia, using a lot of academic tools to translate the research in a robust way to operational products. And this [First Street], to me, feels different. There’s no way to say, exactly, what is the role of academia,” Tedesco said.

First Street’s website lists a large bench of scientists and economists as part of its “full research lab team.” But several said they had little affiliation.

“I haven’t done any work for them,” said Brett Sanders, a professor of civil engineer-

Private climate risk modelers have been the beneficiaries of a growing need for information on exposure to the risks of extreme weather.

ing at UC Irvine. He believes that he is listed because he is working on a model of flood risk in Southern California, and he wanted to see how his model compared with First Street data. To access the information, he said, First Street asked him to agree to put his face and name on the website.

What work has University of Central Florida engineering professor Thomas Wahl done with First Street? “Not much, to be honest. My name is in there because we’re part of a big grant funded by the National Academies of Science,” he told the Prospect. Wahl and colleagues also reached out to access First Street data.

One academic listed on the site said that he had never even used the data he accessed through First Street.

The constant search for data is part of a bigger problem. Climate models are resource-intensive, partly due to computing power constraints. Big tech companies see an opportunity here. Supercomputing is increasingly performed in the cloud, hosted by Amazon, Microsoft, and Google. First Street is funded in part by Amazon Web Services, a cloud-computing subsidiary of Amazon. Microsoft and Google are both

sponsoring efforts to bypass physics-based modeling altogether.

Aditya Grover, a computer scientist at UCLA , is working on a machine learning model for weather and climate projections. Grover said his group teamed up with Microsoft in order to access their computer servers. He insisted that their tool remain open-source, he told the Prospect, but acknowledged that Microsoft may want to build out future uses and commercialize them, which he could not control.

These developments worry Swain, the climate scientist at UCLA , who also serves as an adviser to ClimateCheck, a competitor to First Street. “There’s an incentive to produce products that may or may not be correct, but to be the first,” he said. “That’s the tech industry, in a nutshell. Move fast and break things.”

Kearns said First Street has scaled these barriers to entry. “I don’t know any [competitor] that’s doing everything to as high degree of spatial resolution as we are. They’re usually driven off by the expense and the labor involved,” he said. It may not stay that way, he added. “As I’ve told other people, it’s like, Hey, if 30 well-determined and fairly smart

people in Brooklyn can create these products, you know, it’s not that hard.”

When the Federal Reserve released details in January about its first analysis of major banks’ exposure to climate change, climate scientists reacted with derision.

“Who’s up to take the measure of severe hurricanes under rapid warming 30 years from now?” one modeler wrote on Twitter. Another professor joked to his students that if they use the same techniques, “you will get a lot of points off.”

The Fed chose to focus its climate risk scenario on a future hurricane in the Northeast. Scientists wondered why it would pick one of the most complex and noisiest hazards for the exercise.

R. Saravanan, head of the Department of Atmospheric Sciences at Texas A&M University, suggested in a blog post that regulators were motivated by New York City’s recent experience with Hurricane Sandy. But while Sandy inflicted tens of billions of dollars in damage, it was only a Category 1 hurricane when it made landfall in the U.S. “Bankers may be surprised to learn that climate change might actually make weak

First Street’s models claim to be able “to calculate the past, present, and future climate risk of every property in the United States.”

storms like Sandy rarer in the future,” Saravanan wrote. Another danger of the model, he said, is that the Fed lets banks make a “smorgasbord” of modeling assumptions.

The Fed’s scenario analysis has no regulatory consequences—it does not trigger stricter capital requirements or supervision—so its main effect might be to create a false sense of security. But it points to a bigger problem: Where were the climate scientists when the stress test was designed?

“There’s clearly been a breakdown in interdisciplinary communication,” said Madison Condon, a law professor at Boston University who works on climate risk. “This is part of the bigger cultural trend of thinking that the economists are authoritative experts on all things—to think they could design a hurricane stress test, with no hurricane experts.”

A Fed spokesperson declined to say whether any climate scientists were involved with the design of the exercise. The Fed’s published work leading up to the scenario guidance was written by economists with no apparent background in climate science.

If climate experts doubt the Fed’s ability to vet models of physical climate risk, they are even more skeptical of its ability to interpret those effects on the economy.

Climate economists have long relied on integrated assessment models (IAM s), a technique pioneered by Bill Nordhaus. A neoclassical economist who won the Nobel Prize in 2018, Nordhaus is reviled by activists for drawing attention to the greenhouse gas effect in the 1990s—only to emphasize the high costs of taking action.

IAM s rely on damage functions, which model how climate change harms the economy. Most look at the historical relationship between temperature rise and GDP, and project that forward. The tool has been widely criticized by both progressive economists and scientists, and more recent damage functions have attempted to enumerate effects within specific sectors.

However, to model transition risk, the Fed is using tools developed by the Network for Greening the Financial System (NGFS), a coalition of central banks. The damage function used in NGFS’s latest scenarios examines how weather has historically impacted GDP, abstracting away from specific sectors or macroeconomic trends. It omits sea level rise, biodiversity and ecosystem damages, and ignores tipping points or sudden changes, like the collapse of a Florida-sized ice shelf, or a crisis in the troubled Florida housing market. It even

excludes climate change’s toll on human life, even though the authors acknowledge that years of life lost “constitute the major share of the costs of global warming in the United States.”

NGFS then describes climate impacts under three future scenarios. “They’re trying to come up with an approach simple enough that the banks would actually try to use it,” said Robert Brammer, an atmospheric and oceanic scientist at the University of Maryland.

But those three static scenarios mask huge amounts of uncertainty, Brammer said. “It’s like Goldilocks: cool, warm, hot.”

Policy models like those used by the Congressional Budget Office fail to capture the costs of a warming climate, or the benefits of the energy transition, outside the limited ten-year budget window, giving lawmakers little basis for action.

The Biden administration has made some headway with integrating climate risk into modeling. The White House’s long-term budget outlook estimated the economic impact of future greenhouse gas emissions, and says findings should be seen as a minimum estimate of likely costs, “in the context of substantial uncertainty.”

Yet the administration has also forged

The Federal Reserve’s climate stress test was developed by economists, not climate scientists.

ahead with carbon pricing initiatives, an issue on which climate hawks are split.

In January, the Department of Commerce released new guidance for environmental-economic decisions, in an attempt “to put nature on the national balance sheet.” The aim is to create “natural capital accounts,” to value critical natural resources like water and forests.

Basic economics introduces the concept of an “externality,” and pollution is the classic example: It’s costly, but you don’t have to pay for it. The solution is to “price it in.” Fifty years of environmental policy has proposed varying sorts of carbon pricing, a solution that is famously elegant and even more famously unworkable in the U.S. political system.

President Obama introduced the “social cost of carbon,” which he valued at $43 a ton. The Trump administration slashed that to $3–$5 a ton. Biden plans to raise the number to $190 a ton, a number that could inform everything from energy efficiency mandates to fuel efficiency rules and environmental reviews for major projects.

A higher cost of carbon has powerful supporters. It should be used “across government decision-making, not just in regulations,” Sen. Sheldon Whitehouse (D-RI), chairman of the Senate Budget Committee, told E&E News. “Think grants, permitting, purchasing, royalty rates, investment decisions, and trade agreements, just to name a few.”

Kaufman, who was until recently a senior climate economist in the White House, questioned the need for the government to set a single price. Regulators should stop “pretending we can quantify things that are not quantifiable,” he told the Prospect. “Do we really need a quantitative estimate of climate damages in 2100 under a certain scenario?”

The conundrum is that Congress runs on budgetary costs. Without showing how a policy is cost-effective, it won’t be passed into law.

But realist critics point out that the U.S. has repeatedly rejected carbon pricing. Climate policy experts Danny Cullenward and David Victor have shown that even where the U.S. superficially appears to have passed a carbon price—such as with cap-and-trade programs—direct regulation has done the heavy lifting.

The Inflation Reduction Act, the White House’s signature climate legislation, lavishly subsidizes green spending without penalizing fossil fuels. It is the opposite of carbon pricing, which is about making emissions more expensive. In that context, observers say, the administration’s carbon pricing efforts look vestigial.

During the 20th century, America’s worldclass weather forecasting provided data that drove improvements in crop yields, safe transportation, and warnings for freak storms. Scientific advancements in weather also informed strategy at the Department of Defense.

In recent years, the private sector has chipped away at this public good. Companies like AccuWeather have pushed Republicans to protect them from competition from the National Weather Service, which makes its predictions available for free online. These attempts got a boost in 2018, when President Trump nominated the CEO of AccuWeather to lead NOAA .

Arguing against creating a National Climate Service to accompany the National Weather Service, Rep. Frank Lucas (R-OK), chairman of the House Science Committee, has said that it would only “create more red tape and hurdles to our budding weather industry.”

Yet AccuWeather and its counterparts, which consult for the aviation and logistics sectors, rely on high-quality, freely available data provided by the U.S. government. In

medicine and drug development, the government funds basic research, but there is a large intermediate industry where pharmaceutical companies are expected to invest billions in direct research. There is no such intermediate industry in weather; the government produces most of the data, and then the private sector uses it directly.

The accuracy of weather prediction has improved dramatically over the past several decades. By contrast, while the federal government does produce climate-related economic models, its forecasting is orders of magnitude cruder.

In a forthcoming book, Doyne Farmer, a Houston-born physicist who now teaches at Oxford, emphasizes this divergence between weather and economic forecasting. Whereas weather is modeled from the bottom up, packed with local details, the economic models most in use are top-down aggregates, which typically rely on a single representative household rather than attempting to describe the behavior of real market participants.

To Farmer, who pioneered the field of complex systems science, this seems ludicrous. It is, he writes, as if meteorologists were to undertake the “utterly hopeless” exercise of “predicting the U.S. weather based solely on measurements of its average temperature, barometric pressure, and wind velocity.”

As the systemic risks of global warming come into view, many economists are becoming less sanguine about internalizing the costs of pollution. Michael Greenstone, the Milton Friedman Distinguished Service Professor in Economics at the University of Chicago, has called climate change “the ultimate negative externality.”

Perhaps climate change is just the mother of all externalities—an anomaly, given its scope and the way it links together disparate risks—in a world that is otherwise well described by neoclassical models. Seen this way, the climate emergency is just a bad fit for economics. Once its catastrophic harms are contained, we’ll be able to continue business as usual with smooth cost-benefit projections.

Or perhaps climate is just one risk-riddled complex process among many interactive and hazardous systems handed down by industrial modernity. On that view, climate is no aberration. Planetary catastrophe, long underestimated and still poorly described by neoclassical economists, exposes the flimsiness of their models. n

The Inflation Reduction Act, which lavishly subsidizes green spending without penalizing fossil fuels, is the opposite of carbon pricing.

The LeftForgottenEconomics Tradition

By Meg Jacobs

Joe Biden described his 2023 State of the Union address as a “blue-collar blueprint.” At a moment when inflation has been running above anything seen in the last four decades, the president championed greater investment in, price relief for, and empowerment to what he called forgotten places and people. “So many of you listening to me tonight, I know you feel it,” he said from the Capitol. “So many of you felt like you’ve just simply been forgotten. Amid the economic upheaval of the past four decades, too many people have been left behind and treated like they’re invisible.”

Amid this Rooseveltian, “forgotten man” rhetoric, there was one thing Biden did not mention: raising interest rates. The president ignored the main tool that macroeconomists have put forth as necessary to bringing down the high cost of living. Nor did he refer to the Federal Reserve or its chairman Jerome Powell.

Instead, he talked about hearing aids. Thanks to his administration, he said, “millions of Americans can now save thousands of dollars because they can finally get a hearing aid over the counter without a prescription.” The shift to an over-thecounter market aims to disempower a cartel of manufacturers that have kept prices artificially high, out of reach for those forgotten men and women.

Biden followed up by adding, “Look, capitalism without competition is not capitalism. It’s extortion. It’s exploitation.”

Inflation-fighting economists were firmly in the saddle four decades ago. Ronald Reagan pinned his hopes on “Morning in America” after a setback in the 1982 midterm elections. What he truly meant was that, after a rough couple of years, he had wrestled spiraling inflation to the ground by letting Paul Volcker, then the Fed chairman, ratchet up interest rates to unheard-of levels, nearly 20 percent in 1980 and 1981.

In a speech offered just before the 1980 election, the Hollywood actor had asked Americans to think about whether they were better off than they had been four years earlier. The answer was no. In this same speech, Reagan saluted another actor, John Wayne, an “American hero” who embodied all that was great about swashbuckling swift action. Volcker’s interest rate spike, driving the economy into a recession, epitomized these tough-guy tactics. John Wayne, Paul Volcker: same difference. One just had an advanced degree.

Biden’s post-midterm speech suggests a pivot away from this 40-year tyranny of monetarists, and their politics of ritual sacrifice for the middle class. The president emphasized taming capitalism over cooling the economy; limits on corporate power over limits on fiscal aid; people over bond markets.

Monetary economists’ rise was bolstered by the claim from Margaret Thatcher that

In the Progressive and New Deal eras, there was a markedly different response to rising prices, and a different usage of economic theory.

“there is no alternative” to their manner of thinking. But history provides evidence of a forgotten tradition, just like the forgotten people and places in Biden’s speech.

Instead of relying on interest rates to work their wonders, left-leaning economists have in the past seen questions of income distribution, industrial policy, and corporate concentration as key to resolving what historically and colloquially is called the high cost of living. They identified problems of power and worked to solve them, rather than myopically following the unyielding blueprints of the textbook. Whether the Biden administration will fully reconnect to this legacy will set the course of the nation.

The Institutionalists’ Rise

To capture that lineage, we have to go back to when economists began taking on fundamental social-policy questions. Before they were called economists, they called themselves political economists. One of the most iconic was Richard T. Ely, who founded the American Economic Association in 1885 and made Johns Hopkins and the University of Wisconsin the leading research institutes dedicated to the reform of American capitalism.

Universities ought not to be mere playgrounds for elite young men who socialized and networked before assuming their rightful place in government, business, and at the pulpit, Ely believed. Instead, experts would work with politicians to soften the edges of corporate capitalism. With likeminded advocates of the “Wisconsin Idea,” Ely favored a minimum wage, workers’ compensation, pension plans, and more. When he supported local unionization efforts for workers, his critics tried to run him off the Madison campus. In a signature moment that came to define the meaning of “academic freedom,” he kept his post when university trustees refrained from firing him.

Ely was no Marxist. He, too, believed in the market, as did the institutional economists for whom he laid the foundation. But instead of the utility-maximizing individuals of neoclassical economics, to him it was the presence of corporations that determined the distribution of a nation’s wealth. And it was the job of economists to push for exactly the kinds of public policies that would create the institutional basis for widely shared prosperity.

If the rise of corporations meant that workers could no longer set the terms of their own employment, the same was true

for the modern consumer. Instead of peering into the cracker barrel and bargaining or bartering with the local merchant, a buyer bought Nabiscos in a prepackaged box at the price that the National Biscuit Company dictated. In the first decade and a half of the new century, the cost of living went up more than 20 percent, before skyrocketing further during World War I. “The high cost of living,” said the Progressive Era president Woodrow Wilson, “is arranged by private understanding.”

In 1917, the urban reformer Frederic Howe published a book under that title, The High Cost of Living. Like Louis Brandeis, who before he became the first Jewish Supreme Court justice famously explained how corporations and financiers borrowed funds to earn enormous profits and exploit the working class in his book Other People’s Money, Howe exposed what he believed were nefarious forces at work: “Monopoly is responsible for the conditions which confront us.” Rising food prices, the focus of Howe’s work, arose because land speculators, meatpackers, cold storage operators, and railroad managers exerted undue influence, which it was the obligation of government to bring to light and counteract. President Wilson’s newly created Federal Trade Commission buttressed these exposés, revealing that the “Big Five” packers, responsible for distributing two-thirds of fresh meat, established pooling agreements to parcel out the market and set prices.

The modern consumer instinctively shared this view. As Howe was writing

about inflation, a group of women who recently moved to the city, and were now reliant on purchases in the marketplace rather than meat from their own farms, formed the Mothers’ Anti-High Price League. They wrote to President Wilson: “We, housewives of the City of New York, mothers and wives of workmen, desire to call your attention, Mr. President, to the fact that, in the midst of plenty, we and our families are facing starvation … The American standard of living cannot be maintained.” In cities across the country, consumers took to the streets in food riots, demanding meat at prices they could afford. Howe agreed.

That sage observer of American life, Walter Lippmann, remarked, “The real power emerging today in democratic politics is just the mass of people who are crying out against the ‘high cost of living’… To talk about ‘reasonable returns’ is to begin an attack on industrialism which will lead far beyond the present imaginations of the people who talk about it.”

From Morals to Economics

The attack on corporatism as a cause of the high cost of living led to a reaction from the industrial elite. Nothing, or no one, seemed to promise to revolutionize living standards as much as Henry Ford. Ford believed it was in the self-interest of the corporation to pay workers more money and to bring down the prices of the products they made, all in the service of creating a mass market. The purpose of the “five-dollar day,” introduced in 1914 at a time when job turnover was

Left-leaning economists have in the past identified problems of power and worked to solve them, rather than myopically following the unyielding blueprints of the textbook.

more than 300 percent, was to stabilize the workforce while mass production, made possible by the moving assembly line, would allow for drastically lower production costs. Well-paid workers would be able to own a house and buy a Model T.

Except if they couldn’t. William Green, president of the American Federation of Labor, said in the 1920s, “If America’s prosperity is to be maintained, it must be possible for the masses of workmen to buy and use the things they have produced.” Other labor leaders like Sidney Hillman and Rose Schneiderman, both of whom organized unskilled immigrants, including many teenage girls ignored by the AFL , echoed that view. Expressing a similar sentiment, the Wall Street economists Waddill Catchings and William T. Foster wrote in their 1927 book Business Without a Buyer, “The failure of consumer demand to keep pace with the output of consumers’ goods is the chief reason why prosperity ends in depression.”

These were not simply moral claims

about the dignity of work and the right to an American standard of living. They were economic claims. At precisely this moment in the Roaring Twenties, a young Herbert Hoover was successfully asserting in his presidential campaign that the country was “nearer to the final triumph over poverty than ever before in the history of any land.” Yet beneath the Fords and the flappers, there were signs of trouble. Between 1914 and 1926, Detroit doubled its auto production, but the population grew by only 15 percent. At the same time, firms were awarding more of the productivity dividend to profits rather than to wages. Farmers suffered from a massive deflation as commodity prices plunged after WWI. So who would buy all these products? How exactly would the modern marketplace sustain demand? Catchings and Foster called this “underconsumption.”

A new group of economists, largely of liberal sympathies and mostly tied to the labor movement, came to the fore. Let’s call them “purchasing-power progressives.” They

Henry Ford created the “five-dollar day” to stabilize the workforce, allowing well-paid workers to own a house and buy a Model T.

believed that even the most well-intentioned capitalists could not be trusted to make fundamental decisions about the allocation of the spoils of productivity among wages, prices, and profits. Instead, the state, organized labor, and a host of other organized groupings—like farmers or consumers—had to exercise what John Kenneth Galbraith would later call “countervailing powers.”

Two economists in particular serve to illustrate this new way of thinking. One was Paul Douglas, who published a seminal work in 1930 simply called Real Wages. Before he would become an illustrious liberal senator from Illinois, Douglas cut his teeth in the academy. In 1927, he introduced what became known as the Cobb-Douglas production function to measure labor and capital’s relative contributions to productivity, a formula that would become a staple of microeconomics.

However, Douglas’s abiding interest focused on what he called the “real wages” of workers. The inflation of the war years had been particularly corrosive to income

gains. As he concluded, “American labor as a whole, therefore, cannot legitimately be charged with having profiteered during the war. Rather, like Alice in Wonderland, it was compelled to run faster in order to stay in the same place.” If wages were to sustain demand, then workers needed a way to grab a greater share of the productivity dividend. Otherwise, they collected what department store magnate and liberal reformer Edward Filene called “counterfeit wages.”

The other innovator was the economist Gardiner Means, who in 1932 wrote The Modern Corporation and Private Property along with the legal scholar Adolf A. Berle Jr. Together, these two reformers—both were sons of Congregational ministers— believed they had diagnosed the problem at the heart of the modern economy. It was too great a concentration of corporate power, presided over by an unaccountable managerial elite.

Before the Great Depression, 200 corporations controlled more than half of all corporate wealth. That meant that rather than setting prices “by higgling and bargaining in the market place” (a reference to Adam Smith), the managers of these large firms, what Means called a “small body of officials,” instead resorted to “administered prices.” Where classical economic theory would have suggested that a downturn in the market would have led to lower prices, instead these corporate managers turned to scarcity, reducing wages, cutting production, and removing competition. “Modern industrial organization … destroyed the free market,” said Means.

The Great Depression confirmed the view of these purchasing-power progressives, both that corporations had too much power and that the state had to step in to ensure consumer demand in the form of higher wages and lower prices. The solution was not to break up corporations; these were not trust-busting anti-monopolists. Rather, they pursued an entirely new idea that would become the basis of the New Deal: to socialize the price- and wage-setting function of private enterprise.

Building From the Bottom Up

Franklin Roosevelt had never taken much interest in economics. But he cared deeply about restoring prosperity, and he instinctively believed that the problem was underconsumption. In a 1932 campaign speech delivered at the San Francisco Common-

wealth Club, written by Brains Truster Adolf Berle, Roosevelt said, “Our task now is not discovery or exploitation of natural resources, or necessarily producing more goods. It is the soberer, less dramatic business … of meeting the problem of underconsumption, of adjusting production to consumption, of distributing wealth and products more equitably, of adapting existing economic organizations to the service of the people.” Or as he put it in his first national radio address, “These unhappy times call for the building of plans that rest upon the forgotten, the unorganized but indispensable units of economic power … that build from the bottom up and not from the top down.”

In essence, Roosevelt’s New Deal enshrined a high-wage, low-price, fullemployment agenda into public policy— and that was before John Maynard Keynes published The General Theory of Employment, Interest, and Money in 1936, which would argue for much the same intention through countercyclical spending policy. The Agricultural Adjustment Act, the National Labor Relations Act, the Social Security Act—all these New Deal laws had the purchasing-power purpose in mind. As the new president put it, “The aim of this whole effort is to restore our rich domestic market by raising its vast consuming capacity.”

That was especially true in the case of labor’s newly institutionalized right to form unions and engage in collective bargaining. Known as the Wagner Act, the 1935 bill rested squarely on the purchasing-power idea. As Sen. Robert Wagner’s chief legislative aide, institutional economist Leon Keyserling, explained, “The failure of the total volume of wage payments to advance as fast as production and corporate surpluses has resulted in inadequate purchasing power, which has accentuated periodic depressions and disrupted the flow of interstate commerce.”

Wagner, the liberal senator from New York, explained upon introduction of his bill that labor rights were essential to recovery and were in the modern marketplace a legitimate prerogative of Congress. He talked about how denying workers their rights robbed them of participation “in our national endeavor to coordinate production and purchasing power.” There was, to date, no national endeavor. But if the country ever wanted to get out of the Depression and break the back of business cycles where profits increased and real wages fell, there would have to be.

Frances Perkins, Roosevelt’s labor secretary, captured what was at stake. What seemed like a matter-of-fact proto-Keynesian position belied an underlying New Deal radicalism. “If the wages of mill workers in the South should be raised to the point where workers could buy shoes, that would be a social revolution,” said Perkins. Indeed, these Southern female laborers were some of the most exploited workers, and, as Perkins understood, it would take the state stepping in to offer protections that not only improved working conditions and wages, but also bolstered working-class purchasing power. Perkins liked to say the New Deal was born the day she was among the throngs of outraged onlookers at the Triangle Shirtwaist Factory fire in 1911, when 146 teenage girls jumped to their death to escape the flames engulfing their sweatshop workplace. The Great Depression made it clear that empowering these workers was not only the right thing to do, but also the necessary thing, if the country did not want to suffer from the scourge of low wages and inhumane working conditions.

These reformers also fought to keep prices down, as high wages and low prices were two sides of the purchasing-power coin. In 1937, when the economy slid backwards, New Dealer Leon Henderson, described in a profile as “more like a truck driver than a cap-and-gown economist,” fully rejected

Roosevelt’s New Deal enshrined a high-wage, low-price, full-employment agenda into public policy.

the idea that government spending was the cause of inflation. No, he said, the problem was the sticky prices administered by quasimonopolistic corporations. This overran any gains made by collective bargaining and more robust wages, said this cigar-chomping “spittoon economist.”

Roosevelt did not disagree. In the spring of 1938, he adopted a Keynesian spending outlook as an antidote to what his enemies were calling the “Roosevelt Recession.” All along, he had supported large-scale public works as a way to restore the economy to

full, or fuller, employment. That was, after all, the goal of the Tennessee Valley Authority, which sought to build up an entire backward region of the country, and the same was true of the Public Works Administration and the Works Progress Administration, all of which created millions of jobs. When Roosevelt trimmed the budget in 1937 and recovery faltered, he turned to deficit spending as a deliberate strategy to pump up demand.

However, the structural issues of the corporation and its ability to distort the market

were never far from the fore of public policy. In 1938, Roosevelt appointed Henderson and Thurman Arnold to lead the Temporary National Economic Committee, a congressional panel dedicated to studying the concentration of economic power. While their tools were largely the new antitrust powers of the federal government, their target was what they saw as price rigidity. In practice, that meant that these economists would fight, in the words of Arnold, to lower the “price of pork chops, bread, spectacles, drugs, and plumbing,” using the power of the Antitrust Division to put pressure on corporations. At the same time, Roosevelt pushed hard for the passage of the Fair Labor Standards Act, which, as Henderson testified, would “help to prevent a mass reduction in wages and purchasing power.”

The effort to maintain mass purchasing power became a matter of national security in World War II. With the military placing orders for 250 million pairs of pants, 250 million pairs of underwear, half a billion socks, plus thousands of tanks and planes, hundreds of fighting ships, and billions of rounds of ammunition, inflation soon became the leading problem. Civilian goods disappeared just as wartime full employment drove up consumer demand. “The fight against inflation is not fought with bullets or with bombs,” said Roosevelt, “but it is equally as vital. It calls for unflagging vigilance and effective action … to prevent profiteering and unfair returns.”

Henderson, who as a young professor at Carnegie Tech gave class credit to students for attending a talk by Eugene Debs, was put in charge of the Office of Price Administration. From that perch, staffed with twice as many economists as in the Treasury Department, he imposed price controls and rationing across the entire economy. A young Richard Nixon, who worked in the tire-rationing department, found the whole experience repugnant. “We both believed in the capitalist system,” said the other Republican in the office. “But the other lawyers were using rationing and price control as a means of controlling profits.” Indeed.

Along with Henderson, no one did more to devise this system of controls than John Kenneth Galbraith. Two days after Pearl Harbor, the 6' 8" agricultural economist, now working for the OPA , went around Washington, without any real authority to do so, getting signatures on an order he drafted to freeze the sale of new tires. It

Frances Perkins, FDR’s labor secretary, saw the importance of empowering workers to increase purchasing power and grow the economy.

would take months and much congressional wrangling to institutionalize OPA’s power. But Galbraith believed the situation could not wait. He followed up the tire order with thousands of telegrams to the nation’s mayors, instructing them to enlist local police to enforce the ban. Soon, he would devise a system of rationing for gasoline and many other essential items. War required what Roosevelt called “equality of sacrifice.”

Armed with government-issued price lists printed in many different languages, housewives marched into local stores ready to do battle against profiteering. Was the butcher holding his thumb on the scale? Was there too much fat in a cut of meat? Was he letting his favorite customers buy on the black market? In response, some merchants called the OPA a “kitchen Gestapo,” and it was true; these shoppers now had the power of the state behind them. When shoppers spotted a violation, they could sue for overcharges. In any given week, OPA received more than 4.5 million

phone calls and 2.5 million letters. It was not surprising to Galbraith and others that the OPA was one of the most popular agencies, with more than three-quarters of the public supporting it, even—or especially— in the months after the war ended.

The Eclipse of the Institutionalists

This success bred contempt. Sen. Robert Taft, perhaps the leading Republican critic of the New Deal, told Chester Bowles, Henderson’s successor, “What you are doing is organizing consumers against business … It is absolutely un-American and contrary to law and contrary to the Constitution.” With that, Taft led anti–New Deal forces in a takedown of the purchasing-power agenda.

The first fight came soon after the war’s end. Organized labor, which had grown in strength from 10 million to 15 during the conflict, wanted to preserve its wage gains.

In November 1945, Walter Reuther led the United Automobile Workers on a strike against General Motors, demanding both

higher wages and lower prices. “Purchasing Power for Prosperity” was the slogan, devised by former Agricultural Adjustment Administration and SEC employee Donald Montgomery, a University of Wisconsin–trained labor economist.

Understanding what was at stake, GM refused. “The UAW- CIO is reaching for power,” GM said. “It leads surely to the day when union bosses … will seek to tell us what we can make, when we can make it, where we can make it, and how much we can charge.” George Romney of the Automobile Manufacturers Association, future governor of Michigan and father of Mitt, saw Reuther as “the most dangerous man in Detroit … No one is more skillful in bringing about the revolution without seeming to disturb the existing forms of society.”

New Deal opponents fought back hard elsewhere. Rather than submit to controls, meatpackers starved the public into submission by withholding cattle from market. At the critical moment in the fight over the

The shift from the Truman to the Eisenhower era saw the fade of New Deal economics.

peacetime extension of OPA in the fall of 1946, slaughtering was down 80 percent from a year earlier. “Had Enough?” the 1946 midterm GOP platform asked Americans. Richard Nixon, back from his stint at OPA and in the Navy, ran a successful congressional campaign in this so-called “beefsteak election,” which returned Republicans to power for the first time since 1930. Controls were now dead, wholesale meat prices soared 89 percent, and overall consumer prices shot up 16 percent. As Bernard Baruch, the longtime presidential adviser, noted, “Next to human slaughter, maiming, and destruction, [inflation] is the worst consequence of war. It creates lack of confidence of men in themselves and in their government.”

With OPA out of the way, the GOP next went after organized labor with the passage of the anti-union Taft-Hartley Act in 1947, demonizing labor as the source of inflation. Whereas New Dealers had once attacked monopoly prices, conservatives came after monopoly unionism. “The price of MONOPOLY comes out of your pocket,” explained one National Association of Manufacturers full-page ad.

In 1948, Harry Truman ran a successful campaign against the “Do-Nothing” GOP Congress, calling for a return of controls, repeal of Taft-Hartley, and a Fair Deal that included universal health care, public housing, and an expansion of education. This shift to the left did not come naturally to the former senator from Missouri. As a haberdasher, he had been more sympathetic to small business than to organized labor. But he was helped by Keyserling, the Wagner Act author, who was now a member of Truman’s Council of Economic Advisers. Keyserling believed his job was not “mere

forecasting” but rather to determine what the “components and composition of the GNP ought to be.”

But the more they succeeded, the more their opponents held their success against them. “Inflation has clearly become the breaking point of the Roosevelt coalition,” wrote Samuel Lubell in his classic work The Future of American Politics. Republicans built a coalition on the fear of inflation, bringing in white-collar workers on annual salaries, mortgage holders, and retirees on fixed incomes. In 1952, Dwight Eisenhower used the “high cost of living” to get elected as the first Republican president since the Great Depression, blaming “creeping inflation” largely on organized labor, which successfully negotiated cost-of-living adjustments into half of all union contracts.

By the time John F. Kennedy was in the White House, Paul Samuelson, author of the best-selling macroeconomics textbook, was a household name, and Keyserling was not. Paul Douglas, elected as a senator from Illinois in 1948, launched hearings into administered prices that tried to bring back that old New Deal magic, going after high-priced drugs and other consumer goods. But by and large, political attacks against the power of concentration proved more successful against labor—think of Robert Kennedy’s attack against labor’s mob bosses—than against corporations. As president, JFK famously took on U.S. Steel for raising prices, but mere jawboning was no substitute for the legal authority exercised by the OPA 20 years before.

The last gasp of price controls proved the point. In the 1970s, when inflation returned, onetime OPA critic Richard Nixon was in the White House. Understanding just how popular the wartime OPA had been, he imposed

wage and price controls in August 1971, much to the chagrin of his conservative advisers. But Nixon’s heart wasn’t in it; there were no legions of housewives, no dollars-and-cents shopping lists, no local investigation boards. Everyone understood that the intent was, as one Nixon insider put it, to “zap labor.” Instead of Ken Galbraith in charge of the Cost of Living Council, it was Donald Rumsfeld.

The failure of Nixon price controls once and for all solidified their death as a tool of economic management. Paul Volcker was not yet Fed chair. But there were those in the Ford administration, namely Alan Greenspan, who were already pushing for austerity. Reflecting the final eclipse of this purchasing-power agenda, Ronald Reagan famously fired the striking PATCO workers and threw his full force behind Volcker’s shock therapy. If anyone had to pay the price of inflation, it would be the American working class.

Build Back Better?

Biden is bringing back all the sound bites of the kind of labor liberalism once at the center of the economics profession—a “living wage,” the “forgotten man,” “profiteering,” “exploitation.” Sound bites are one thing, of course; policy is another. And here too we see signs of Biden channeling his inner Roosevelt. The Inflation Reduction Act, along with the CHIPS Act and the Infrastructure Investment and Jobs Act, reflects the kind of industrial policy and government spending not seen since the Interstate Highway Act.

Add to these Biden’s push against what he has described as “junk fees,” empowering Lina Khan, the chair of the Federal Trade Commission, to go after what Means long ago called administered prices. Biden, with the help of a younger generation of economists, is harking back to an earlier period. He has also lined up behind labor unions in a way that presidents haven’t done in a long time. “When we do all of these things,” Biden has said, “we increase productivity. We increase economic growth.”

Biden’s blue-collar blueprint is not only good politics, but good economics too. Only time will tell if we are seeing a real pivot back to the progressive agenda of time gone by. n

Meg Jacobs is a senior research scholar and and teaches history and public affairs at the Princeton School of Public and International Affairs. She is the author of  Pocketbook Politics: Economic Citizenship in Twentieth-Century America.

Biden is bringing back all of the sound bites of the kind of labor liberalism once at the center of the economics profession.

Ripping Off the Invısible Straitjacket

By Lindsay Owens

In the final year of my doctorate program, I was fortunate enough to win a fellowship that would bring me to Washington for a year to work as a legislative staffer in Congress. Fellowship in hand, I began interviewing with potential House and Senate offices. Several offices required me to complete a skills test as part of the interview process. One of those tests stood out.

The prompt was straightforward but rather challenging: Design a policy to increase net retirement savings for families in the United States without spending a single net federal government dollar. I got to work designing a proposal to allow individuals to divert their tax refunds into a tax-preferred retirement vehicle like an IRA (more or less coming up with what would soon become the Obama administration’s now-defunct “myRA” proposal). This was hardly a revolutionary or even ideal policy proposal—after all, the best way to increase retirement savings in the United States would be to increase incomes, so people actually have some money left over at the end of the month to save. But it fulfilled the parameters of the assignment and I got the job.

At the time, I thought the test was more like a wonky IQ test, a brain twister designed to help the congressional office determine whether I was smart enough to tackle the kinds of tough questions I would be expected to face when I arrived on the Hill. Now, after six years writing legislation as a senior Hill aide in the House and Senate, and a decade working in economic policy in Washington, I understand the assignment differently. The test was quite literally an assessment of whether I was capable of designing policy that could clear the primary gatekeepers in Congress: the scorekeepers at the Congressional Budget Office (CBO) and the Joint Committee on Taxation (JCT).

The exercise got at the fundamental element of legislative policymaking that is not well understood by those outside of Washington. The merits of a given policy are rarely evaluated based on whether the policy would help the most people, or even the people who need it most. Utilitarian or even normative questions about whether a policy is “right” or “good” are subjugated to narrow budgetary questions of how much a policy costs. This is doubly true when legislating via the arcane budget reconciliation process, on which Congress has relied almost exclusively to achieve major policy accom-

plishments since 2010, in the absence of a filibuster-proof majority for either party.

Legislative staffers must write policy with the scorekeepers in mind, at every step of the way. It’s convoluted, suboptimal, and frustrating—a bit like teaching kindergarten “to the test” instead of instilling a love of learning. Successful staffers study the scorekeepers and know how to game the rules, never letting the perfect be the enemy of the CBO -approved. This is how you end up with policies that sunset after only a short period of time, like the recent expiration of the one-year expansion of the Child Tax Credit and the impending 2025 expiration of the 2017 Trump Tax Cuts and Jobs Act. It’s how you get policies that don’t kick in until years after a bill is signed into law, like the 2021 proposal to offer Medicare dental that wouldn’t have gone into effect until 2028. And it’s how you wind up with perennial budget gimmicks, like shifting around the date for which Pension Benefit Guaranty Corporation premiums are due to move money from one budget year to another.

One of the most brilliant and effective legislative staffers I came across during my time on the Hill, a tax expert, took it a step further, making it a priority to establish a strong personal connection with the score -

We need better economic models, but we also need Congress to free itself from the self-imposed constraints of modeling on the policymaking process.

keepers at the Joint Committee on Taxation. By effectively bringing JCT along for every step of the policymaking process, they were able to eliminate any scoring surprises, and even influence scoring choices that would have a sizable impact along the way. This was legislation by charm offensive, not something the Constitution’s architects anticipated.

As is probably becoming clear, the problem is bigger than the scorekeepers,

whose biases and limitations have been discussed in this issue at length. It’s the way scorekeeping seeps into every nook and cranny of the policymaking process, shaping not just what makes it into law but also what gets dreamed up in the first place. It’s a bit like what the sociologist Donald MacKenzie described in his book An Engine, Not a Camera , when he explained that financial models that were meant to evaluate financial markets (a

camera) had instead started to shape the markets themselves (an engine).

Senators, congresspeople, and their staffs are supposed to be the engines of legislative policymaking, responding to and accountable to the preferences of their constituents. CBO is supposed to be the camera, taking a look at these policies and reflecting back their impacts on the federal budget and the broader economy. Over the last few decades, the roles have reversed, with

members of Congress simply writing policy that reflects back what modelers and scorekeepers dictate. It’s the CBO’s world, and Congress is just living in it.

The impacts of this reversal are pernicious. CBO is often weaponized during internal disagreements over policy. Committee chairs who want to close ranks before a markup will proclaim that they aren’t accepting amendments from their fellow committee members that “score” (i.e., cost money). Legislative staffers who don’t want to engage with a policy on the merits will tell their colleagues their boss would love to co-sponsor their legislation, but first wants to see the CBO score. This is a particularly annoying one since it’s very difficult to get the CBO to score legislative proposals from rank-andfile members unless they are poised to become law.

In these examples, the mere threat of a bad CBO score is shaping (or rather eliminating) policy that never even makes it to CBO’s desk. And there are outside modelers who bring their estimates to Washington, also with the power to strangle policy before it even gets off the ground. As this issue has indicated, those outside modelers often use the same assumptions that invariably frustrate the enactment of any policy that intervenes in markets or increases public investment.

One of the low points of my legislative career was being asked to nickel-and-dime a group of health care advocates over a universal long-term care provision (a policy I personally supported) that would cost hundreds of billions of dollars. The proposal was to be attached to Medicare for All legislation that already clocked in at tens of trillions of dollars, rendering the entire exercise a bit silly. It’s no wonder Congress hasn’t made any progress on the looming long-term care crisis, despite a rapidly aging population.

It’s not only what gets “scored” that

undermines effective policymaking, it’s also what doesn’t. As Nick Hanauer and others explain in this issue, congressional scorekeepers don’t assess impacts outside the ten-year budget window, like the economic benefits that accrue to society as the investments we make in early-childhood education mature when toddlers become workers.

Sometimes CBO doesn’t bother to estimate a set of relevant economic impacts at all. As Phil Rocco points out in his piece, CBO has basically taken a pass on estimating the economic impacts of climate change mitigation because “many of the linkages between climate change and the federal budget require further assessment.” In other words, Congress is kneecapped when trying to solve the climate crisis, because while the CBO will happily tally up the costs of reducing carbon emissions, they’ve determined that it’s sim-

ply too hard to estimate the economic benefits. As Lee Harris explains in this issue, this has led a group of private-sector climate risk modelers, armed with dodgy estimates that they allege can map the threat of extreme weather events with fine-grained precision, to enter the fray and sell the illusion of certitude to businesses, cities, and even parts of the federal government.

And it’s not just the budgetary impact of models that dictates the terms of policy debates in Washington. It’s also the way the scorekeepers model (or don’t model) the broader macroeconomic impacts of a proposal. Jobs numbers can sink a proposal, like when CBO determined that a $15 minimum wage would kill 1.4 million jobs, despite a plethora of economic evidence to the contrary. GDP estimates can as well. Good luck trying to move legislation after the CBO or

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the Penn Wharton Budget Model issues a dictum saying it will “shrink” the economy.

So if this issue hasn’t convinced you that our current system of legislative modeling and scorekeeping is broken, I’m not sure what will. But the really tough question we must address is how to reform this system, so Congress can do the important work their constituents sent them to Washington to do—solving the climate crisis; providing affordable child care, housing, higher education, and health care; and making the overdue investments in our families and communities that will help us to build a stable, healthy, and equitable economy in this country.

Fixing our modeling system will require two sets of reforms: developing better models, and rightsizing the role of models in our policymaking process. We can and should work on both along parallel tracks. The good news is that we are starting to see early signs of a modeling renaissance. The National Academy of Sciences has begun work to advance macroeconomic modeling of climate risk, and the White House Council of Economic Advisers has made this a priority as well. These could serve as a counterweight to the kind of private-sector climate models being offered by First Street and others.

Several think tanks are in the beginning stages of developing alternative budget and macroeconomic models. And American University just launched a new Institute for Macroeconomic and Policy Analysis (IMPA) to develop next-generation models that will better account for the modern features of markets, including financialization and market power. (Full disclosure: My Groundwork Collaborative colleague Rakeen Mabud is on the board of this effort.) Modelers in the United States can also look to their European colleagues, who have begun undertaking similar efforts.

We should welcome these efforts, and federal grantmaking institutions as well as philanthropic foundations should support them. CBO and those who share its assumptions can’t be the only game in town. New models can help CBO make needed adjustments to its model, so that it is more accurate, transparent, and better reflects the latest empirical economic research. CBO should also adopt a more transparent posture, opening up the guts

of its models and their underlying assumptions for other researchers to replicate, critique, and improve.

But better models won’t solve the underlying problem: that Congress has needlessly abdicated too much power to the scorekeepers. Voters elect representatives who they think will best represent their interests. They are effectively choosing a proxy, and they expect these individuals to use their judgment, not the CBO’s, when making decisions about which policies to advance. Elected leaders can and should exercise that judgment when faced with CBO estimates that are highly uncertain or don’t paint the full picture.

Sometimes the right thing to do (e.g., saving the planet) is obvious, and when this is the case our elected leaders can and should say so. And while economic estimates and budget scores may be helpful in comparing the relative benefits of multiple competing proposals for investing in our people, the fact that the United States should invest in our future should be self-evident. When common sense, or a gut check, or even a smell test will suffice, Congress should not overcomplicate it.

Models provide bits and pieces of useful information about the future impacts of a policy. Economic data is helpful, but it’s not the only form of data that matters. Most congressional offices tally constituent calls in favor of or against legislation. That’s a pretty useful piece of data too. Congress also receives reams of expert testimony in committee hearings. When ten out of ten early-childhood educators endorse a child care policy, Congress can listen to that and even favor that evidence over a boiled-down budget score

and some educated guesses at the longrun economic impact.

Rightsizing the role of scorekeepers doesn’t require dismissing them altogether. Nor does it have to reflect a deeper antiintellectual sentiment. In fact, placing economic estimates in the proper context, and using them judiciously and in conjunction with other relevant information, is precisely how modeling is intended to be used. It is the modern Congress that has bestowed upon the scorekeepers outsize power, not divine ordinance or even the Founding Fathers. Leaders in Congress have the unilateral ability to end their self-imposed tyranny from the scorekeepers. It’s past time they did so.

At the end of my time in Congress, I had authored a handful of bills and amendments that made it to the Oval Office for signature, spending a grand total of zero net federal dollars across them all. Although I think these proposals improved policy in a number of arenas—from labor policy to financial regulation to immigration—putting money behind them would have exponentially increased their impact. Until we adopt a better set of modeling tools, and rightsize the role of models and modelers as gatekeepers in our policymaking process, we’ll continue to under-deliver for Americans, achieving consistently suboptimal policy results. Or, as my late father used to say, we’ll continue to “get what we pay for.” n

Lindsay Owens is the executive director of the Groundwork Collaborative. She previously served as senior economic policy adviser to Sen. Elizabeth Warren, and deputy chief of staff and legislative director to Reps. Keith Ellison and Progressive Caucus Chair Pramila Jayapal.

Better models won’t solve the underlying problem: that Congress has needlessly abdicated too much power to the scorekeepers.

What unions do

By Randi Weingarten, President AMERICAN FEDERATION OF TEACHERS

Several years ago, The Atlantic ran a story whose headline made even me, a labor leader, scratch my head: “Union Membership: Very Sexy.” The gist was that higher wages, health benefits and job security—all associated with union membership—boost one’s chances of getting married. Belonging to a union doesn’t actually guarantee happily ever after, but it does help working people have a better life in the here and now.

Workers who join together in unions have higher pay and better healthcare and retirement benefits than nonunion workers, and the advantage is especially strong for Black, Hispanic and female workers and workers with disabilities. Those effects are life-changing.

So, too, is the work the American Federation of Teachers and other unions do. Most know us as an education union, but today the AFT is the nation’s fastest-growing healthcare union. And healthcare workers are under siege. That is why the AFT launched a $1 million campaign, “Code Red: Understaffing = Patient Care Crisis.” We are fighting to make sure that when someone in your family has a medical emergency, there are enough nurses on hand to provide quality care. Families are going through so much these days, and we see it in schools. So we’re pushing for 25,000 more community schools to meet kids’ academic, social, emotional and physical needs. We’re advocating for the pay, benefits and respect that educators deserve, and for the resources and learning conditions that students need—all of which will help solve the teacher shortage crisis and help kids recover and thrive. And we’re promoting experiential learning, including career and technical education, that engages students through critical thinking, teamwork and learning by doing. Unions’ “business” is making life better for people.

Americans’ support for labor unions is at the highest level in more than half a century. And a majority of nonunion workers (nearly 60 million American workers) say they would join a union if they could. Why, then, has union membership plummeted to the single digits?

One cause is five decades of efforts by corporations and their allies to decimate unions in the United States. Some of the most costly and concerted efforts to crush unionization drives are happening today—at Amazon, Starbucks and other hugely profitable corporations. There are few or no consequences for powerful employers when they

violate workers’ legal right to form a union. That’s why congressional Democrats have reintroduced the Protecting the Right to Organize (PRO) Act, with the support of President Joe Biden. Another reason for the decline in union membership is that, as journalist David Leonhardt has observed, many Republican government officials “treat organized labor as their political enemy.” When unions focus voters on economic issues, he wrote, “they become more likely to vote for a Democrat.” Rather than win voters by meeting their economic needs, these Republicans are trying to take voters’ economic freedom away, like former Wisconsin Gov. Scott Walker did and Florida Gov. Ron DeSantis is trying to do right now.

Columnist Jamelle Bouie described it this way: “Republicans and other conservatives … know that organized labor is a key obstacle to dismantling the social safety net.”

That’s true. Many conservative politicians see safety net programs as a piggy bank to pay for large tax cuts for the wealthy and for corporations, and they see unions as the last roadblock to eviscerating them. What are these programs? They protect us from the most devastating consequences of job losses, economic hardship and disability. They help shield children from the misery of extreme poverty. And they help elderly Americans afford the food, healthcare

and housing they need to survive. If protecting the most vulnerable Americans makes unions the enemy of politicians who would harm them, so be it.

Many right-wing politicians don’t just find us a nuisance to their agenda, they actively want to destroy unions, and work to pass “right-to-work” laws, which allow workers to take the benefits the union secures without paying for them. By using these laws to weaken unions, their end goal is to destroy their main political opponent and the last major check on their corporate cronies’ power.

Unions’ ‘business’ is making life better for people.

But workers are undaunted. In the last month alone, the AFT has welcomed into our union hospital staff in Vermont, nurses and other healthcare professionals in New Mexico, medical technicians in Michigan, and teachers and other school staff at the Maryland School for the Deaf. And Michigan is returning to its pro-worker roots by repealing the state’s 2012 “right-to-work” law.

Perhaps the strongest argument for unions and collective action is also the simplest: Individuals can be powerless to effect change, but together we can tilt the balance of power so working people and our families have better lives, our democracy works and our country can move forward. That is what a workers’ right to organize a union means. It’s a way to strive for something better for ourselves and for our country.

Weingarten, center, with healthcare workers at a safe-staffing briefing at the state Capitol in Hartford, Conn., on Jan. 23.

Photo: AFT