The Southern Economic Association will be holding its meeting in San Antonio beginning on Saturday. I bring this up because I have put together a session entitled, “Nominal Income Targeting, the Productivity Norm, and Monetary Stability.” The panel consists of George Selgin, Scott Sumner, David Beckworth, and myself. We will be presenting on Monday morning at 10 a.m. Here is a list of the presentations:

— George Selgin, “Greenspan’s Bubble: The Productivity Connection”

— Scott Sumner, “Could Nominal Income Targeting Have Prevented the Current Recession?”

— David Beckworth, “Is the Federal Reserve a Monetary Superpower?”

— Josh Hendrickson, “Nominal Spending and the Great Moderation: Theory and Evidence”

I have been rather busy as of late and haven’t had time to post. While you are waiting, you might enjoy a few podcasts that I have been listening to while driving and working out:

• Charles Plosser on Bloomberg on the Economy with Tom Keene (the link is to an .mp3 file)
• Scott Sumner on EconTalk discussing monetary policy.
• Charles Calomiris on EconTalk discussing the financial crisis.

Allan Meltzer is one of the greatest monetary economists to have ever lived (although credit is also due to his long-time collaborator Karl Brunner, who Meltzer would be the first to acknowledge). This is not hyperbole. Meltzer’s work emphasizes the role of imperfect information, uncertainty, and transactions costs in developing an understanding of the role of money in exchange (AER, 1971 with Karl Brunner), the business cycle (with Alex Cukierman and Karl Brunner, JME 1983), evaluating alternate monetary regimes (1986 JME), and the role of monetary and fiscal policy (to be concise, on monetary policy, with Cukierman, Econometrica 1986; Economic Inquiry, 1987; with Mascaro, JME, 1983; with Brunner Carnegie-Rochester Series, 1983; on fiscal policy, with Scott Richard, JPE, 1981; with Brunner, 1993). This is not to mention Meltzer’s work on the history of the Federal Reserve and the monetary transmission mechanism.

For all the talk about economics needing to find a new vision, the work of Allan Meltzer (and his co-authors) represents a strong foundation on which to build.

What’s more, Meltzer has long embraced Keynes’s vision of uncertainty. More importantly, and unlike most others who have done so, he has recognized that uncertainty plagues not only individuals and firms, but also government policy makers.

With that being said, it was particularly disheartening to see Brad DeLong address Meltzer as follows:

Exercise some moral responsibility, Allan.

Shameless partisan hack.

Why would DeLong write such a thing? It seems that he doesn’t much like Meltzer’s critique of quantifying jobs “saved or created” by the stimulus package. Specifically, in a memo to John Boehner, Meltzer writes:

There is no greater recognition of the failure of the stimulus program to create jobs than the efforts to mislead the public into believing the program had saved thousands, or millions, of jobs.

One can search economic textbooks forever without finding a concept called “jobs saved.” It doesn’t exist for good reason: how can anyone know that his or her job has been saved?

The Administration can make up any number it pleases. The number has no meaning.

DeLong argues that this is ridiculous and appeals to authority by citing Milton Friedman arguing that the Depression was worsened by the Fed’s failure to prevent the money stock from declining:

If the concept of “jobs saved” does not exist, how come Milton Friedman says that an extra $1 billion of open market operations in late 1931 would have stopped the Great Depression in its tracks.

He continues:

You can critique models. You can critique parameters. You can critique parameters. You can critique how the calculations are done, but you cannot deny their existence, for the kind of counterfactualcalculations that Milton Friedman does are, of course, the steady diet of what economists and other policy analysts do every day.

Of course, if we expand the earlier quote of Allan Meltzer, we would find that this is precisely what Meltzer is doing in his memo. Here is the full quote:

There is no greater recognition of the failure of the stimulus program to create jobs than the efforts to mislead the public into believing the program had saved thousands, or millions, of jobs.

One can search economic textbooks forever without finding a concept called “jobs saved.” It doesn’t exist for good reason: how can anyone know that his or her job has been saved?

The Administration can make up any number it pleases. The number has no meaning. The Council of Economic Advisers gets a number for jobs saved using the same model that Dr. Christina Romer and Jared Bernstein used when they forecast that the $787 stimulus program would keep the worst unemployment rate in this recession at about eight percent. But as we all know, since that bill became law, our economy has shed some three million jobs and the unemployment rate is nearing double digits.

In other words, Meltzer believes that the model that was used to assess the benefits of the stimulus package is significantly flawed and an impractical guide to assess job creation. In addition, his earlier point is that there is no meaningful basis on which to calculate jobs “saved or created”.

Greg Mankiw has made much the same point:

That is, I do not object to claims such as,

A: “Based on our models of the economy, we believe there would be X million fewer jobs today without the stimulus.”

But it is absurd to suggest that you can say,

B: “We have measured how many jobs the stimulus has saved or created, and the number is X.”

Economists are capable of making statements such as A, but it is beyond our ken to make statements such as B. Statement B is,of course, much stronger than statement A, as it purports to be based on data rather than on models. Unfortunately, we are hearing statements like B much too often from administration officials.

Going beyond Mankiw’s description above, statements exemplified by A are dependent on the model that is used. Several questions need to be addressed. How useful is the model? What are the assumptions? How does it compare with other models in the literature?

These questions have been addressed in the paper by John Cogan, Tobias Cwik, John Taylor, and Volker Wieland entitled, “New Keynesian Versus Old Keynesian Government Spending Multipliers”. The model used by Bernstein and Romer is an Old Keynesian model. These authors compare and contrast the Bernstein-Romer model with the Smets-Wouters model that exemplifies the current consensus in macroeconomic thought. Here is their conclusion:

We find that the government spending multipliers from permanent increases in federal government purchases are much less in new Keynesian models than in old Keynesian models. The differences are even larger when one estimates the impacts of the actual path of government purchases in fiscal packages, such as the one enacted in February 2009 in the United States or similar ones discussed in other countries. The multipliers are less than one as consumption and investment are crowded out. The impact in the first year is very small. And as the government purchases decline in the later years of the simulation, the multipliers turn negative.

The estimates reported here of the impact of such packages are in stark contrast to those reported in the paper by Christina Romer and Jared Bernstein. They report impacts on GDP for a broad fiscal package that are six times larger than those implied by government spending multipliers in a typical new Keynesian model and our calculations based on generous assumptions of the impacts of tax rebates and transfers on GDP. They also report job estimates that are six times larger than these alternative models, and the impacts on private sector jobs are likely to be at variance with the alternative models by an even larger amount. At the least, our findings raise serious doubts about the robustness of the models and the approach currently used for practical fiscal policy evaluation.

Allan Meltzer clearly thinks that the model used by Bernstein and Romer is flawed. The work of Cogan, Cwik, Taylor, and Wieland suggests that there is reason to believe that Meltzer is correct to doubt the model.

In addition, Meltzer’s critique of the success of the stimulus package also raises important questions about attributing the recovery to the stimulus package. Specifically, he cites the role of “Cash for Clunkers” and new homebuyer tax credits in contributing to third quarter growth, neither of which was in the stimulus package.

Allan Meltzer is one of the greatest monetary economists to have ever lived. He deserves much better treatment than to be called a “shameless partisan hack”.

UPDATE: Mario Rizzo writes:

As we have been saying here, the claims that the fiscal stimulus has saved or created X number of jobs is not a simple empirical question. It must be an inference from a model that tells us what would have happened in the absence of that stimulus. Collecting reports from various firms or local governments about their job situations will not do. At best these individual reports are based on pop-theories on the part of the reporters about what would have happened.

The current administration has unveiled an entirely new metric for measuring the success of stimulus spending. Rather than claim credit for “creating” jobs, they have focused on jobs that were “created or saved” by the stimulus. This, of course, is a preposterous notion. How do we know that a job was saved? The idea of transparent reporting from the government is welcome, but transparent reporting is only part of the problem. What precisely is the definition of a “saved” job? This might seem a bit facetious, but bear with me.

Suppose that a municipality receives money to pave a road. They hire a private firm to do the job. The firm was planning on laying off (we’ll say) 10 workers. However, given the new job, the firm keeps those 10 men on payroll. This seems pretty straightforward. It’s not. These 10 workers might be kept on the payroll until the completion of this job and let go thereafter. Does this still count as a job saved? How long does the person have to remain employed for it to be considered a job “saved”? Near as I can tell, this doesn’t factor in to the decision-making.

Consider another example. Suppose that a state or municipality announces that they are going to lay off teachers or police officers. If stimulus funds keep these individuals employed, this is considered a job that was saved. However, how do we know that state and local governments weren’t, at the very least, exaggerating the number of individuals that were going to lose their jobs in a ploy for more stimulus money?

Of course, all of this ignores the financing. The government doesn’t have money, it must borrow and tax in order to spend money. Thus, any metric of job creation measures gross job creation, but what we are really concerned with is net job creation.

With that being said, the number that has been released regarding the “saved or created” jobs was estimated to be between 640,329 and 1 million jobs. That means that the stimulus has cost between $160,000 and $250,000 per job. (Jared Bernstein calls that “calculator abuse.”)

White House officials have been quick to mention that these numbers do not include jobs that were saved or created through the temporary tax cuts. As I have mentioned numerous times on the blog, temporary tax cuts don’t work. John Taylor has documented this fact for the last two rebate checks. Thus, it would seem that including the cost of these tax cuts would actually inflate the cost per job.

Ultimately, I am not entirely sure what we are to get from the “jobs saved or created” metric. There doesn’t seem to be any true objective way to quantify such a thing. Regardless, based on the data on jobs and growth up to this point, one can hardly conclude that the stimulus has been successful.

UPDATE: John Taylor breaks down the GDP numbers and concludes that the “stimulus did not fuel GDP growth.”

Casey Mulligan writes that he is “still waiting for mistakes that underestimate the potentcy of the stimulus.”

As we emerge from the financial crisis, it is important to develop a framework for dealing with failing institutions. In particular, the nature of the doctrine of “too big to fail” must be addressed and re-examined. Recently, John Taylor and Larry White have spoken out about the need for a rule of law rather than a discretionary authority. Their comments and my thoughts are below the fold.

John Taylor’s recent testimony to Congress outlines his view on the path forward. Here is a brief excerpt:

Two main alternative proposals are currently under consideration. One has been put forth as part of the Administration’s financial reform proposals. It would establish a special resolution regime under which the Secretary of the Treasury, with the approval of the President and agreement of the regulatory authorities, could apply an expanded FDIC-like resolution process to any financial firm if its failure would have “serious adverse effects on the financial system or the economy.” The firm would be placed into conservatorship or receivership and t he government could provide the firm with loans, purchase its assets, or guarantee its liabilities.

The other proposal would have the failing financial firm go through a bankruptcy process designed specifically to deal with some of the financial firm’s assets and liabilities, which are an integral part of the financial system. The bankruptcy proposal in H.R 3310 is an example of such an approach. The conceptual idea is that the bankruptcy would permit important financial transactions to continue without significant disruption during bankruptcy.

In my view the expanded resolution regime has significant disadvantages in comparison with a bankruptcy process designed specifically for financial firms. First, the new resolution regime would essentially institutionalize the kinds of bailouts that have occurred in the recent crisis. Hence, rather than providing an alternative to policy of bailouts, it would permanently establish such a policy. Second, the expanded resolution authority would be operated with a considerable degree of discretion about when to start the intervention and about the priority to give different creditors. In contrast a bankruptcy process relies on an established rule of law rather than the discretion, and treats creditors in a known way that is understood by lenders and investors in advance. Compared to the resolution authority, bankruptcy is a more predictable process.

Larry White echoes much of this sentiment in a recent speech:

If everyone knows that the rule of law will be followed, such that nobody gets bailed out, the incentive for imprudence disappears along with the hook into taxpayers. I don’t mean that no financial firm will ever act imprudently, but that there won’t be a system-wide malincentive producing an epidemic of imprudence. If it is known that nobody is “too big to fail”, or too well connected to fail, then lenders will not let financial firms leverage up cheaply in the belief that they will be protected.

Each of these documents is worth your perusal.

Ultimately, the debate about the limits of the Fed’s abilities as lender of last resort and the doctrine of “too big to fail” boil down to the same principles that arose in the rules versus discretion debate for monetary policy. Discretion generates uncertainty in that the behavior of the actor cannot be predicted. As John Taylor has documented so well in his recent research, the erratic and inconsistent behavior of the Federal Reserve and the Treasury during the financial crisis can explain why the crisis (and corresponding economic performance) got so much worse in the months of September and October of last year.

Moving forward, policy needs to be guided not by the discretion of the central bank or the Treasury secretary, but rather by the rule of law. Orderly and predictable bankruptcy procedures for all firms and the elimination of the doctrine of “too big to fail” would go a long way toward making potential future financial crises less severe (and might prevent others altogether).

The prisoner’s dilemma illustrated via YouTube:

HT: John Taylor

UPDATE: Scott Sumner writes, “I’ve never been more proud to be human.”

Why didn’t anyone tell me that John Taylor is blogging?

In any event, Taylor does some of the best work in the profession — thoughtful, careful, and persuasive. Definitely check out the blog.

Those who know me personally can attest to the fact that I am a big sports fan. More importantly, I am a sports fan who pays close attention to statistics and what those statistics mean — especially in baseball. One of my biggest pet peeves as a sports fan is when an announcer refers to a player as “clutch”. This bothers me because it is usually after pointing out that a particular batter is 6 for 7 with the bases loaded (or runners in scoring position or . . . ) thereby ignoring the relevance of sample size. It is with great pleasure that I discovered J.C. Bradbury’s recent post on clutch hitting. Here is an overview:

I used probit models to estimate the likelihood that a player would get a hit (1 = hit; 0 = otherwise), or get on base (1= hit, walk, or hbp; 0 = otherwise) controlling for the player’s seasonal performance in that area (AVG or OBP), RISP 1989–91 performance in that area, whether the the platoon advantage was in effect (1 = platton; 0 = otherwise), and the pitcher’s ability in that area. To test hitting power, I used the count regression negative binomial method to estimate the expected number of total bases during the plate appearance and used his RSIP SLG 1989–1991 as a proxy for clutch skill in this area.

[...]

In samples of this size, statistical significance isn’t difficult to achieve; therefore, it isn’t surprising that in all but two instances the variables are significant. The two that are insignificant are the past RISP performance in batting average and slugging average. Thus, clutch ability doesn’t appear to be strong here.

However, the estimate of a clutch effect is statistically significant for getting on base. Is this evidence for clutch ability? Well, let’s interpret the coefficient. Every one-unit increase in RISP OBP is associated with a 0.00018 increase in the likelihood of getting on base; thus, a player increasing his RISP OBP by 0.010 (10 OBP points) increases his on-base probability by 0.0000018. For practical purposes, there is no effect.

There is no such thing as “clutch”.

Again.

Arnold Kling writes:

What happened one year ago that caused the economy to tank over the winter?

(a) a credit crunch. Banks would not lend to one another, and they cut back on credit to businesses, which in turn caused the contraction in economic activity.

(b) a recalculation. People found out that their housing wealth was lower, so they spent less. The home construction, real estate brokerage, mortgage lending, and securitization industries found out that their services were in much less demand than they had been, and they cut back. Finally, Ben Bernanke and Henry Paulson shouted “The Great Depression might come back!” in this crowded theater, and everybody ran for the exits. For example, law firms started telling new hires to go do something else for a while.

(c) people woke up to find that the elves and helicopters had left less money lying around.

(d) people woke up to find that the Fed had lowered its de facto inflation target.

The economists I consider to be most sensible are pushing some combination of (a) and (b). I differ from the consensus in that I push (b) exclusively and minimize (a). Lots of folks–defenders of Bernanke in particular–push (a) more than (b). What Scott Sumner and David Beckworth wish to defend is (c) and/or (d).

If given the choice between these four descriptions, I would also choose (a) or (b), but mostly because (c) and (d) are misguided caricatures of what David Beckworth, Scott Sumner, and myself have been discussing. Those who believe that monetary policy was tight do not believe that there was “less money lying around.” While David and Scott might disagree on method, I think that this phenomenon can best be understood by an understanding of the quantity theory of money.

First, it is important to understand what the quantity theory is not. For example, Arnold Kling has admitted that he might be taking his “anti-monetarism to extremes” and that his motivation is to free many of us from old habits. For example, he writes:

Another habit I want to try to break is the habit of thinking that nominal income is proportional to money. At any given moment, one can take the ratio of PY/M and say “there’s your proportion for you,” but you can do that if you define M as mackerel as easily as if you define M as the monetary base.

Indeed, I am in agreement that it is meaningless to discuss variables based on their proportion to nominal income unless there is good reason. In fact, Friedman and Scwartz raised this very point in “Money and Business Cycles” (p. 213 in the reprinted version in Friedman, 1969):

The stock of money displays a consistent cyclical behavior which is closely related to the cyclical behavior of the economy at large. This much the factual evidence summarized above puts beyond reasonable doubt.

That evidence alone is much less decisive about the direction of influence . . . It might be, so far as we know, that one could marshal a similar body of evidence demonstrating that the production of dressmakers’ pins has displayed over the past nine decades a regular cyclical pattern; that the pin pattern reaches a peak well before the reference peak and a trough well before the reference trough; that it amplitude is highly correlated with the amplitude of the movements in general business.

[...]

Most economists would be willing to dismiss out of hand the pin theory even on such evidence; most economists would take seriously the monetary theory even on much less evidence, which is not by any means the same as saying that they would be persuaded by the evidence. Whence the difference? Primarily, the difference is that we have other kinds of evidence.

What Kling really seems to be suggesting is that not all changes in nominal income (and perhaps prices) can be explained by changes in the money supply. Again, this is not something that a quantity theorist would argue with. In his New Palgrave article on the quantity theory, Milton Friedman writes:

Changes in prices and nominal income can be produced either by changes in the real balances that people wish to hold or by changes in the nominal balances available for them to hold. Indeed, it is a tautology, summarized in the famous quantity equations, that all changes in nominal income can be attributed to one or the other . . . The quantity theory is not that tautology.

This provides the perfect segue into describing what the quantity theory is and why it is important to understanding the recession. (It is important to note that this is not how the quantity theory has been traditionally described. The quantity theory has come in a variety of forms and what follows is broadly consistent the QT.)

Recall the equation of exchange:

MV = Py

where M is money, V is velocity, P is the price level, and y is real output. The money supply itself, however, is a multiple of the monetary base (the currency in circulation plus bank reserves). Thus, we can rewrite the equation of exchange as:

mBV = Py

where m is the money multiplier, B is the monetary base, and V is now the velocity of the monetary base and V remains velocity as described above. This distinction is important because it stresses the interaction of the money multiplier and the monetary base. The money multiplier is a function of the reserve-to-deposit ratio, r, and the currency-to-deposit, c, ratio:

m = m(r, c)

where m_r (.), m_c (.) < 0 (m_i denotes the derivative w.r.t. i). Further, it is important to note that given M = mB, a decline in the money multiplier would reduce broader money aggregates through multiple deposit destruction.

Given this information, it would now be prudent to discuss the recession in light of this framework. The recession can largely be viewed in two stages. The first stage ran from Dec. 2007 to around the end of August 2008. This first stage was somewhat mild, or at least on par with a typical recession. The second stage, however, began in late August and early September 2008. There are two major events that correspond with this change: the collapse of Lehman Brothers and the Bernanke-Paulson testimony and TARP debacle. (John Taylor's research suggests the latter was more important to understanding the crisis.)

There are two effects that followed. First, the currency component began to rise considerably (note that this is in percentage change from the previous year):

Second, reserves increased substantially:

The sharp increase in excess reserves shown above can be attributed to increased uncertainty and to the Fed’s decision to pay excess reserves beginning in October.

These increases in currency and reserves relative to deposits all serve to reduce the money multiplier, m, as well as lead to reductions in velocity as spending falls. David Beckworth has depicted this phenomenon quite well graphically.

This decline in m and V should lead to a sharp decline in nominal spending. Thus, when we are talking about tight money we are not referencing a sudden decline in the amount of money that is lying around, but rather the failure of policy to answer the decline in m with a corresponding increase in B (I have noted that the Fed has performed relatively admirably in this case — at least in comparison to history). In fact, this is an insight that can be gathered from Friedman and Schwart’z Monetary History of the United States in their discussion of the Depression as the Fed allowed the money supply to fall because it did not increase the monetary base to offset changes in c (and in some cases, r).

What’s more, it is not necessarily that the Fed lowered their de facto inflation target, but rather that tight monetary policy created expectations of lower inflation (and lower nominal spending). Given that there is some endogeneity with respect to inflation expectations, there are other ways to measure the stance of monetary policy. David Beckworth has broken out the VARs again to analyze whether monetary policy can explain the decline in nominal spending. He shows that monetary policy explains a quite sizable portion of the decline in nominal spending (the precise size depends on the model specification and the stance of monetary policy).

Taken together, I would think that the evidence and theory presented here are at least somewhat compelling. At least certainly more so than (c) and (d) as described above.