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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.

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.

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.

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.

ELO Report

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.

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.

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.

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