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Should Governments Tax Unhealthy Foods and Drinks?

With obesity and diabetes at record levels, many public health experts believe governments should tax soda, sweets, junk food, and other unhealthy foods and drinks. Denmark, Finland, France, Hungary, and Mexico have such taxes. So do Berkeley, California and the Navajo Nation. Celebrity chef Jamie Oliver is waging a high-profile campaign to get Britain to tax sugar, and the Washington Post has endorsed the same for the United States.

Do such taxes make sense? My Urban Institute colleagues Maeve Gearing and John Iselin and I explore that question in a new report, Should We Tax Unhealthy Foods and Drinks?

Many nutrients and ingredients have been suggested as possible targets for taxes, including fat, saturated fat, salt, artificial sweeteners, and caffeine. Our sense, though, is that only sugar might be a plausible candidate.

Sugar in foods and drinks contributes to obesity, diabetes, and other conditions. By increasing the price of products that contain sugar, taxes can get people to consume less of them and thus improve nutrition and health. Health care costs would be lower, and people would live healthier, longer lives. Governments could put the resulting revenue to good use, perhaps by helping low-income families or cutting other taxes.

That’s the pro case for a sugar tax, and it’s a good one. But policymakers need to consider the downsides too. Taxes impose real costs on consumers who pay the tax or switch to other options that may be more expensive, less enjoyable, or less convenient.

That burden would be particularly large for lower-income families. We find that a US tax on sugar-sweetened beverages would be highly regressive, imposing more than four times as much burden, relative to income, on people in the bottom fifth of the income distribution as on those in the top fifth.

Another issue is how well sugar consumption tracks potential health costs and risks. If you are trying to discourage something harmful, taxes work best when there is a tight relationship between the “dose” that gets taxed and the “response” of concern. Taxes on cigarettes and carbon are well-targeted given tight links to lung cancer and climate change, respectively. The dose-response relationship for sugar, however, varies across individuals depending on their metabolisms, lifestyle, and health. Taxes cannot capture that variation; someone facing grave risks pays the same sugar tax rate as someone facing minute ones. That limits what taxes alone can accomplish.

In addition, people may switch to foods and drinks that are also unhealthy. If governments tax only sugary soda, for example, some people will switch to juice, which sounds healthier but packs a lot of sugar. It’s vital to understand how potential taxes affect entire diets, not just consumption of targeted products.

A final concern, beyond the scope of our report, is whether taxing sugar is an appropriate role for government. Some people strongly object to an expanding “nanny state” using taxes to influence personal choices. Others view taxes as acceptable only if individual choices impose costs on others. Eating and drinking sugar causes such “externalities” when insurance spreads resulting health care costs across other people. Others go further and view taxes as an acceptable way to reduce “internalities” as well, the overlooked harms consumers impose on themselves.

Policymakers must weigh all those concerns when considering whether to tax sugar. If they decide to do so, they should focus on content, not proxies like drink volume or sales value. Mexico, for example, taxes sweetened drinks based on their volume, a peso per liter. That encourages consumers to reduce how much they drink but does nothing to encourage less sugary alternatives. That’s a big deal because sugar content ranges enormously. Some drinks have less than 10 grams of sugar (2 ½ teaspoons) per serving, while others have 30 grams (7 ½ teaspoons) or more. Far better would be a content-based tax that encourages switching from the 30-gram drinks to the 10-gram ones.

Focusing on sugar content would bring another benefit. Most sugar tax discussions focus on changing consumer choices. But consumers aren’t in this alone. Food and beverage companies and retailers determine what products they make, market, and sell. Taxing drink volumes or the sales value of sugary food gives these companies no incentive to develop and market lower-sugar alternatives. Taxing sugar content, however, would encourage them to explore all avenues for reducing the sugar in what we eat and drink.

Note: I updated this post on December 15.

Should Governments Tax Products That Are Fun But Harmful?

Should you face an extra tax if you drink soda? Eat potato chips? Uncork some wine? Light up a cigarette or joint? Toast yourself in a tanning booth? Many governments think so. Mexico taxes junk food. Berkeley taxes sugary soft drinks. Countless governments tax alcohol and tobacco. Several states tax marijuana. And thanks to health reform, the U.S. government taxes indoor tanning.

One rationale for these taxes is that some personal choices impose costs on other people, what economists call externalities. Your drinking threatens bystanders if you get behind the wheel. Tanning-induced skin cancer drives up health insurance costs.

Another rationale is that people sometimes overlook costs they themselves face, known as internalities. Limited self-control, inattention, or poor information can cause people to eat too many sweets, drink too much alcohol, or take up smoking only to later regret the harm.

In a new paper, Should We Tax Internalities Like Externalities?, I examine whether the internality rationale is as strong as the externality one. Economists have long argued that taxes can be a good way to put a price on externalities like the pollutants causing climate change, but does the same logic apply to internalities?

People who look down on certain activities sometimes think so, with taxes being a way to discourage “sinful” conduct. People who prioritize public health often favor such taxes as a way to encourage healthier behavior. Those who emphasize personal responsibility, by contrast, often oppose such taxes as infringing on individual autonomy—the overreaching “nanny state.”

Economists don’t have much to say about sin. But we do have ideas about balancing health and consumer autonomy. One approach is to focus on efficiency: How do the benefits of a tax compare to its costs? Internalities and externalities both involve people consuming too much because they overlook some costs. Taxes can serve as a proxy for those overlooked costs and reduce consumption to a more beneficial level. In that way, the logic of taxing internalities is identical to that for taxing externalities.

But that equivalence comes with a caveat. Internality taxes should be targeted only at harms we overlook. If people recognize the health risks of eating bacon but still choose to do so, there is no efficiency rationale for a tax. Informed consumers have decided the pleasure is worth the risk. Efficiency thus differs sharply from sin and public health views that would tax harmful products regardless of whether consumers appreciate the risks.

Economists often temper cost-benefit comparisons with concerns about the distribution of gains and losses. At first glance, taxes on internalities and externalities generate similar equity concerns. Both target consumption, so both may fall more heavily on poor families, which tend to spend larger shares of their incomes. But there’s another caveat. Internality taxes do not target consumption in general. Instead, they target products whose future costs consumers often overlook. Nearly everyone does that, but it may be more of a problem for people with low incomes. The stress of poverty, for example, can make it more difficult to evaluate the long-term costs of decisions today. As a result, taxes aimed at internalities are more likely to hit low-income families than are those aimed at externalities.

A third, paternalistic perspective focuses on people who overlook harms. Do internality taxes help them? To meet that standard, the benefit consumers get from reducing purchases must exceed their new tax burden. That can be a high hurdle. If consumers only buy a little less, they may end up with small health gains but a large tax bill. That might be a success from an efficiency perspective since the tax revenue ultimately helps someone. But it’s a loss from the perspective of affected consumers.

The economic case for taxing internalities is thus weaker than for taxing externalities. Internality taxes raise greater distributional concerns, and they place a new burden on the people they are intended to help. Internality taxes can still make sense if consumers find it easy to cut back on taxed products (so health gains are large relative to the new tax burden), if overlooked health risks are very large (as with smoking), or if governments rebate revenues to affected consumers. But when those conditions do not hold, we should be skeptical.

Can Nudges Improve Government?

Behavioral “nudges” can increase college enrollment by low-income students, boost health insurance take up, encourage federal workers to save for retirement, cut delinquencies on student loans, reduce vendor fraud, and save paper, according to the first annual report of the White House’s “nudge” unit.

President Obama established the unit—officially known as the Social and Behavioral Sciences Team (SBST)—to use insights from psychology, behavioral economics, and other decision sciences to improve federal programs and operations. Those social sciences increasingly appreciate what regular folks have long known: people are imperfect. We procrastinate. We avoid making choices. We get confused and discouraged by complex forms. We forget to do things. We sometimes lack the energy to weigh decisions thoroughly, so we act based on what we think our peers do or how choices are framed. And we sometimes cut corners when we think no one is looking.

Changing how people engage with the choices they face—“nudging” them—can reduce those imperfections and substantially affect their decisions. The SBST is exploring how that insight can improve government activities. To date, it’s completed more than 15 pilots exploring such questions as:

Can prompted choice and sending reminders increase service-member participation in employee retirement plans? Yes.
Can personalized text messages reduce “summer melt,” the failure to enroll of low-income students accepted to colleges? Yes.
Can reminder emails reduce student loan delinquencies? Yes, modestly.
Can a simple change to a form reduce vendor low-balling of the fees they owe the government? Yes, a bit.
Can redesigning a collection letter increase debt recovery? No, at least not the letter that SBST tested.
Can notifying doctors that they are especially high prescribers of controlled substances reduce inappropriate prescriptions in Medicare? No, but SBST is trying new notifications.
Can a pop-up box get employees to print double-sided rather than single-sided? Yes.

These examples run the gamut from the life-changing to the almost trivial. But they illustrate a common theme: details matter. Policy debates usually focus on high-level issues. Should health insurance be offered on exchanges? Should student loan repayments be limited as a share of a borrower’s income? But after such issues are settled, their impact depends on how policies are implemented. The nitty-gritty of designing forms, deciding how and when to prompt people, and framing and communicating options really matter.

The SBST’s first year also demonstrates the importance of testing new approaches before rolling them out at large scale. It isn’t enough to recognize that particular nudges can influence people. Where possible, agencies should test different approaches to see how they work in specific circumstances. Letters comparing your behavior to your peers’ may encourage people to conserve electricity and pay their taxes, for example, but as one pilot found, that doesn’t mean that they will get doctors to prescribe fewer opioids.

On Tuesday, President Obama signed an executive order making the SBST a permanent part of the White House and directing government agencies to use behavioral sciences to improve their programs and operations. That move is consistent with a larger, bipartisan effort to bring more evidence to bear on the design and implementation of federal programs. The government shouldn’t operate in the dark when there’s an opportunity to use evidence to make programs more efficient and effective.

That potential comes with responsibility, however. One of the most important lessons from behavioral science is that framing matters. Government nudges are a perfect case in point. I’ve been characterizing the SBST’s efforts as “pilots” and “testing new approaches” to improve government activities. Those words are innocuous or positive. As a recent headline illustrates, however, this effort can also be characterized as “President Obama Orders Behavioral Experiments on American People.” That sounds much more ominous.

That characterization reflects concern about the goals of government nudging and the oversight of experiments. Are they really trying to improve our government and lives? Or are they manipulating us to do whatever Uncle Sam wants?

The most effective response is transparency. Tell the American people about the experiments, their goals, and their results. The SBST deserves good marks on that dimension. Its first report provides a good deal of information about each of the pilot studies, both the successes and the failures. As behavioral approaches spread, the government should build on that transparency to ensure that policymakers, media, and the public have the evidence they need to judge their merits.

Everything You Should Know about Taxing Carbon

Climate change is hot. From the pope’s encyclical to the upcoming United Nations conference in Paris, leaders are debating how to slow and eventually stop the warming of our planet.

We economists think we have an answer: put a price on carbon dioxide and the other gases driving climate change. When emissions are free, businesses, consumers, and governments pollute without thinking. But put a price on that pollution and watch how clean they become.

That’s the theory. And it’s a good one. But translating it from the economist’s whiteboard to reality is challenging. A carbon price that works well in principle may stumble in practice. A real carbon price will inevitably fall short of the theoretical ideal. Practical design challenges thus deserve close attention.

To help policymakers, analysts, and the public address those challenges, Eric Toder, Lydia Austin, and I have published a new report, “Taxing Carbon: What, Why, and How,” on putting a price on carbon.

Some highlights:

Lawmakers could put a price on carbon either by levying a tax or by setting a limit on emissions and allowing trading of emission rights. These approaches have much in common. Politically, however, a carbon tax is on the upswing. Cap and trade failed in 2010, while interest in taxing carbon is growing, including three bills in Congress and endorsements from analysts of diverse ideological stripes.

Carbon prices already exist. At least 15 governments tax carbon outright, and more than 25 have emissions trading systems. Those efforts have demonstrated that the economists’ logic holds. If you put a price on carbon, people emit less.

Figuring out the appropriate tax rate is hard. The Obama administration estimates that the “social cost of carbon” is currently about $42 per metric ton. But the right figure could easily be double that, or half. That uncertainty is not a reason to not tax carbon. But it does mean we should maintain flexibility to revisit the price as new evidence arrives.

Taxing carbon could reduce the need for regulations, tax breaks, and other subsidies that currently encourage cleaner energy. Rolling back those policies, in particular EPA regulations for existing power plants, may make policy sense and will likely be essential to the politics of enacting a carbon tax. But the details matter. Rolling back existing policies makes more sense with a carbon tax that’s high and broad, than with one that’s low and narrow.

By itself, a carbon tax would be regressive: low-income families would bear a greater burden, relative to their incomes, than would high-income families. We can reduce that burden, or even reverse it, by recycling some carbon revenue into refundable tax credits or other tax cuts focused on low-income families.

By itself, a carbon tax would weaken the overall economy, at least for several decades. That too can be reduced, and perhaps even reversed, by recycling some carbon revenue into offsetting tax cuts, such as to corporate income taxes.

Unfortunately, there’s a tradeoff. The most progressive recycling options do the least to help economic growth. And the recycling options that do the most for growth would leave the tax system less progressive.

A global agreement on carbon reductions would be preferable to the United States acting alone. Given the nation’s size and contribution to global emissions, a unilateral tax would make a difference, but would damage the competitiveness of some US industries. Special relief for these sectors could reduce the benefits of the tax, but may be necessary both practically and politically.

A carbon tax won’t be perfect. Done well, however, it could efficiently reduce the emissions that cause climate change and encourage innovation in cleaner technologies. The resulting revenue could finance tax reductions, spending priorities, or deficit reduction—policies that could offset the tax’s distributional and economic burdens, improve the environment, or otherwise lift Americans’ well-being.

The challenge is designing a carbon tax that delivers on that potential. We hope our new report helps elevate what will surely be a heated debate.

Return of the Cicadas

The world’s most fascinating insects live in the eastern United States. Periodical cicadas spend most of their lives underground, sucking up nutrients from tree and shrub roots. But every 13 or 17 years, they emerge en masse to reproduce. If you are in the right spot (this year, parts of Kansas and the Mississippi Valley), you can see thousands or tens of thousands all at once. (For information on Broods XXIII and IV, which are emerging this year, see magicicada.org).

Film-maker Samuel Orr is making a full-length documentary of their remarkable lives. His promotion video is an outstanding documentary in its own right:

Full disclosure: I provided some financial support for Sam’s documentary. And I posted a similar post two years ago.

Three Things You Should Know About Dynamic Scoring

The House recently changed the rules of budget scoring: The Congressional Budget Office and the Joint Committee on Taxation will now account for macroeconomic effects when estimating the budget impacts of major legislation. Here are three things you should know as we await the first official dynamic score.

1. Spending and regulations matter, not just taxes

You might think dynamic scoring is just about taxes. It’s not. Spending and regulatory policies can also move the economy. Take the Affordable Care Act. CBO estimates that the law’s insurance subsidies will reduce labor supply by 1.5 to 2.0 percent from 2017 to 2024, some 2 to 2.5 million full-time equivalent workers. If CBO and JCT do a dynamic score of the House’s latest ACA repeal, this effect will be front and center.

The same goes for immigration reform. In 2013 (and in 2006), CBO and JCT included some macroeconomic effects in their score of comprehensive immigration reform, though they did not do a fully dynamic score. Under today’s rules, reform would show an even bigger boost to the economy and more long-term deficit reduction than the agencies projected in the earlier bills.

2. Dynamic scoring isn’t new

For more than a decade, CBO and JCT have published dynamic analyses using multiple models and a range of assumptions. For example, JCT projected former House Ways & Means Committee chairman Dave Camp’s tax reform plan would boost the size of the economy (not its growth rate) by 0.1 to 1.6 percent over 2014 to 2023. The big step in dynamic scoring will be winnowing such multiple estimates into the single set of projections required for official scores.

Observers understandably worry about how the scorekeepers will do that. For example, what will JCT and CBO do with certain forward-looking models that require assumptions not just about the policy in question but also about policy decisions Congress will make in the future? If the agencies score a tax cut today, do they also have to include future tax increases or spending cuts to pay for it, even if Congress doesn’t specify them? If so, how should the agencies decide what those offsetting policies are? Does the existence of such models undermine dynamic scoring from the start?

Happily, we already have a good sense of what the agencies will do, and no, the existence of such models doesn’t hamstring them. At least twice a year, CBO and JCT construct baseline budget projections under existing law. That law often includes scheduled policy changes, most notably the (in)famous “fiscal cliff” at the end of 2012. CBO and JCT had to include the macro effects of the cliff in their budget baseline at that time, even though they had no idea whether and how Congress might offset those policies further in the future. That’s dynamic scoring in all its glory, just applied to the baseline rather than analyzing new legislation. CBO and JCT didn’t need to assume hypothetical future policies to score the fiscal cliff, and they won’t need to in scoring legislation either.

3. Dynamic scoring won’t live up to the hype, on either side

Some advocates hope that dynamic scoring will usher in a new era of tax cuts and entitlement reforms. Some opponents fear that they are right.

Reality will be more muted. Dynamic scores of tax cuts, for example, will include the pro-growth incentive effects that advocates emphasize, leading to more work and private investment. But they will also account for offsetting effects, such as higher deficits crowding out investment or people working less because their incomes rise. As previous CBO analyses have shown, the net of those effects often reveals less growth than advocates hope. Indeed, don’t be surprised if dynamic scoring sometimes shows tax cuts are more expensive than conventionally estimated; that can easily happen if pro-growth incentives aren’t large enough to offset anti-growth effects.

Detractors also worry that dynamic scoring is an invitation for JCT or CBO to cherry pick model assumptions to favor the majority’s policy goals. Doing so runs against the DNA of both organizations. Even if it didn’t, the discipline of twice-yearly budget baselines discourages cherry picking. Neither agency wants to publish rosy dynamic scenarios that are inconsistent with how they construct their budget baselines. You don’t want to forecast higher GDP when scoring a tax bill enacted in October, and have that GDP disappear in the January baseline.

I am cautiously optimistic about dynamic scoring. Done well, it can help Congress and the public better understand the fiscal effects of major policies. There are still some process issues to resolve, most notably how investments might be handled, but we should welcome the potential for better information.

For more views, see the dynamic scoring forum at TaxVox, the blog of the Tax Policy Center.

Tax Policy and Investment by Startups and Innovative Firms

Our tax system includes many provisions to boost business investment, particularly by startups and innovative firms. In a new Tax Policy Center study, Joe Rosenberg and I find that those incentives are often blunted by other features of the tax code:

We examine how tax policies alter investment incentives, with a particular focus on startup and innovative businesses. Consistent with prior work, we find that existing policies impose widely varying effective tax rates on investments in different industries and activities, favor debt over equity, and favor pass-through entities over corporations. Targeted tax incentives lower the cost of capital for small businesses, startups, and those that invest in intellectual property. Those advantages are weakened, and in some cases reversed, however, by two factors. First, businesses that invest heavily in new ideas rely more on higher-taxed equity than do firms that focus on tangible investment. Second, startups that initially make losses face limits on their ability to realize the full value of tax deductions and credits. These limits can more than offset the advantage provided by tax incentives. We also examine the effects of potential tax reforms that would reduce the corporate income tax rate and achieve more equal tax treatment across the various forms of business investment.

Spin Alert: DOE Loans Are Losing Money, Not Making Profits

The Department of Energy snookered the media last week with a report that seems to show that its clean energy lending programs are profitable. “Remember Solyndra? Those loans are making money,” went a typical headline.

Unfortunately, that’s not true. Taxpayers are losing money on DOE lending. Less than originally expected, and less than you would expect given media coverage of Solyndra, Fisker, and a few other failed loans. But smaller losses are still losses, not profits.

To understand DOE’s spin, consider a simple example. Suppose your spouse borrows $10,000 from a bank at 5 percent interest over 10 years so that you can lend it to your friend Bob on the same terms.

Everything goes well in the first year. Bob pays you, and your spouse pays the bank.

If your aunt asks how the deal is going, what would you say? A good answer would be, “We are breaking even; let’s hope Bob keeps paying us back.” You and your spouse are in this together, the loans from the bank and to Bob offset one another, and your best hope is for that to continue.

If DOE were asked, however, it apparently would say, “Things are great; Bob paid me $500 in interest, and I am on track to earn $5,000.” DOE takes credit for the interest that companies pay on their loans, but it doesn’t subtract—or even report—the interest costs that taxpayers pay to finance those loans. That’s like claiming profits on your loan to Bob, while ignoring the interest your spouse pays the bank.

DOE’s report does not address this issue, except in a footnote in a table (cut and pasted above) revealing that its $810 million of “interest earned” was “calculated without respect to Treasury’s borrowing cost.” In other words, DOE reports gross interest received, not the net interest taxpayers have earned after subtracting Treasury borrowing costs. The incomplete figures in the table seem to suggest that DOE has eked out a $30 million profit on its lending ($810 million in interest less $780 million in loan losses). But when we account for Treasury borrowing costs, taxpayers are actually well behind.

The report does not allow us to say just how far behind. We do know, however, that DOE loans are typically made at small, sometimes zero, spreads above Treasury rates. So a large portion of DOE’s “interest earned” must have been offset by borrowing costs. That puts taxpayer losses in the hundreds of millions of dollars.

The same concern applies to DOE’s statement that interest payments on these loans will eventually top $5 billion. Some media outlets are reporting that as $5 billion of profit. It’s not. That $5 billion does not include the cost of Treasury financing or of any defaults. DOE’s $5 billion figure is like claiming your loan to Bob is scheduled to bring in $5,000 in interest; it’s technically true, but tells you nothing about profits. Indeed, the Obama administration still predicts that DOE’s loans will lose money over their lifetimes.

DOE’s lending programs should not be evaluated solely or even primarily based on their profitability or lack thereof. What matters is their overall social impact. How much are they advancing new technologies? How much are they reducing future pollution? Have they created jobs and economic growth? And are any gains worth the taxpayer subsidies? Those are the questions we should be trying to answer.

If DOE wants to play the profitability card, however, it should do so in an accurate and transparent way. Last week’s report falls woefully short. DOE owes taxpayers an honest accounting of the financial performance of its lending programs.

P.S. In recent work, I raised concerns about how the government budgets for lending programs. One issue is whether we should measure profitability against Treasury borrowing rates (as currently done) or against market rates (which the government could earn by unsubsidized lending). I expected that issue to arise in DOE’s accounting. Instead, the agency ignored the cost of capital entirely. Budget policymakers eliminated that ploy more than two decades ago, so it is stunning DOE would resurrect it.

A “Normal” Budget Isn’t Really Normal

Treasury closed the financial books on fiscal 2014 last week. As my colleague Howard Gleckman noted, the top line figures all came in close to their 40-year averages. The $483 billion deficit was about 2.8 percent of gross domestic product, for example, slightly below the 3.2 percent average of the past four decades. Tax revenues clocked in at 17.5 percent of GDP, a smidgen above their 17.3 percent 40-year average. And spending was 20.3 percent, a bit below its 20.5 percent average.

Taxes, spending, and deficits thus appear to be back to “normal.” If anything, fiscal policy in 2014 was slightly tighter than the average of the past four decades.

That’s all true, as a matter of arithmetic. But should we use the past 40 years as a benchmark for normal budget policy?

It’s common to do so. The Congressional Budget Office often reports 40-year averages to help put budget figures in context. I’ve invoked 40-year averages as much as anyone.

But what has been the result of that “normal” policy? From 1975 to today, the federal debt swelled from less than 25 percent of GDP to more than 70 percent. I don’t think many people would view that as normal. Or maybe it is normal, but not in a good way.

Just before the Great Recession, the federal debt was only 35 percent of GDP. Over the previous four decades (1968 through 2007), the deficit had averaged 2.3 percent of GDP, almost a percentage point lower than today’s 40-year average.

That comparison illustrates the problem with mechanically using 40-year averages as a benchmark for normal. A few extreme years can skew the figures. In 2007, we would have said deficits around 2 percent of GDP were normal. Today, the post-Great Recession average tempts us to think of 3 percent as normal. The Great Recession has similarly skewed up average spending (from 19.9 percent to 20.5 percent) and skewed down average taxes (from 17.6 percent to 17.3 percent).

As recent years demonstrate, we don’t want a normal budget every year. When the economy is weak, it makes sense for taxes to fall and spending and deficits to rise. When the economy is strong, deficits should come down, perhaps even disappear, through a mix of higher revenues and lower spending.

Looking over the business cycle, however, it is useful to have some budget benchmarks. A mechanical calculation of 40-year averages won’t serve. Instead, we need more objective benchmarks. On Twitter, Brad Delong suggested one benchmark for deficits: the level that would keep the debt-to-GDP ratio constant. I welcome other suggestions.

The $300 Billion Question: How Should Congress Budget for Federal Lending Programs?

Lending programs create special challenges for federal budgeting. So special, in fact, that the Congressional Budget Office estimates their budget effects two different ways. According to official budget rules, taxpayers will earn more than $200 billion over the next decade from new student loans, mortgage guarantees, and the Export-Import Bank. According to an alternative that CBO favors, taxpayers will lose more than $100 billion.

Those competing estimates pose a $300 billion question: Which budgeting approach is best?

As I document in a new report and policy brief, the answer is neither one. Each approach tells only part of the story. Congress would be better served by a new approach that fairly reflects all the fiscal effects of lending.

Compared with what?

If lending programs perform as CBO expects, they will bring in new money that the government can use to reduce the deficit, increase spending, or cut taxes. In that sense, taxpayers may come out more than $200 billion ahead.

But these programs do not fully compensate taxpayers for their financial risk. If the government took the same risk by making loans and guarantees at fair market rates—perhaps by investing in publicly traded bonds—taxpayers would make much more. Taxpayers are subsidizing the students, homeowners, and companies that borrow through these programs. In that sense, taxpayers come out more than $100 billion behind.

The same issue can arise in personal life. Suppose your aunt asks for a $10,000 loan to start a business. You’ve got exactly that much in a government bond fund earning 2.5 percent, and she offers to pay 5 percent. She’s got a good head for business, so the risk of default is very low; realistically you expect a 4 percent annual return.

The loan sounds like a winner, right? Her 4 percent beats the bond fund’s 2.5 percent, if you can handle the risk. But there’s one other thing: your brother-in-law, equally good at business, would like a similar loan, and he’s willing to pay 6 percent, with an expected net of 5 percent.

Now the loan to your aunt sounds like a loser. Your brother-in-law’s 5 percent beats her 4 percent. You might still prefer to lend to her, but you would come out behind in financial terms.

The competing CBO estimates reflect this dichotomy. One approach compares the financial returns of lending with doing nothing (the $200 billion gain in CBO’s case, 4 percent versus 2.5 percent in yours). The other compares the returns with taking similar risks and being fully compensated (the $100 billion loss in CBO’s case, 4 percent versus 5 percent in yours).

Both comparisons provide useful information. If you want to predict the government’s future fiscal condition, you should compare the financial returns of lending with doing nothing. If you want to measure the subsidies given to borrowers, you should compare returns with the fair market alternative.

When you discuss your aunt’s proposal with your spouse, you would be wise to mention not only the potential financial gain (“4 percent is better than 2.5 percent”) but the subsidy to your aunt (“4 percent is less than the 5 percent your brother would pay”). Only then can you have an open discussion of your family’s financial priorities.

Today’s approaches

The same information is necessary for an open discussion of federal budgeting. But official budget rules, created by the Federal Credit Reform Act of 1990 (FCRA), require CBO to use just the first approach in its budget analyses. Official estimates thus measure the fiscal effects of lending, not the subsidies provided to borrowers. CBO rightly believes, however, that policy deliberations are incomplete without measuring the subsidies, which CBO calculates separately using an approach known as fair value.

Policy analysts have vigorously debated the pros and cons of FCRA and fair value for years. Neither side has scored a decisive win for a simple reason: both approaches are incomplete. Fair value measures subsidies well, but tells us nothing about fiscal effects; this is its missing-money problem. FCRA measures lifetime fiscal effects well, but tells us nothing about subsidies.

By recording expected fiscal gains the moment a loan is made, moreover, FCRA makes lending appear to be a magic money machine. Lending may pay off over time, but the gains do not happen the moment the loan’s ink is dry. Like any lender, the government must be patient to earn those returns. It must hold the loan, perhaps for many years, and bear the associated financial risk.

A better approach

For those reasons, I believe we should replace both approaches with a more accurate budgeting method, which I call expected returns. As the report and brief describe, the expected-returns approach forecasts the fiscal effects of a loan by projecting the government’s expected returns year by year, rather than collapsing them into a single value at the time the loan is made, as both FCRA and fair value do.

Expected returns accurately tracks the fiscal effects of lending over time, thus avoiding both fair value’s missing-money problem and FCRA’s magic-money-machine problem. It also provides a natural framework for reporting the fiscal effects of lending and the subsidies to borrowers. Expected returns would give policymakers and the public a more accurate assessment of federal lending than either of the approaches we use now.