...the government faces a payment shock similar to those that sent legions of overstretched homeowners into default on their mortgages.

Do we really need to worry so much in the short term?

Six years ago, Jeff Frankel and I examined the implications of the borrow-and-spend policies of the Bush Administration [PDF]. We estimated the following relationship:

(1) i_t ^long = 0.001 + 1 × π_t + 0.077 E(d_t+2) + 0.280 (y_t-y_t^FE) + 0.005 i_t^* - 0.574 int_t

Adj.-R² = 0.51, N=17, Smpl 1988-2004. i is the long term interest rate on ten year bonds, π is the y/y inflation rate, E(d_t+2) is the two-year ahead expected debt-to-GDP ratio and (y_t-y_t^FE) is the output gap (both according to OECD), and i^* is the foreign interest rate, and int is foreign purchases of US Treasury debt. (This specification was also discussed in this March 2007 post.)

We can use these estimates to do a back of the envelope calculation of what happens in a year, going from end FY2009 to end FY2010, by taking the total differential of equation (1). The change will be given by:

(2) Δ i = Δ π + 0.077 Δ E(d_t+2) + 0.280 Δ (y_t-y_t^FE) - 0.574 Δ int

According to the August CBO Economic and Budget Outlook, public debt held by the public two years ahead will rise from 65.2 to 65.9 ppts of GDP (that is, end FY 2011 to end FY2012, in Summary Table 1). The OECD November Economic Outlook reports that the output gap in 2009 was -4.92 ppts of GDP, and is projected to be -5.36 in 2010. If East Asian bank purchases of Treasuries remain constant, then one finds that the inflation adjusted interest rate will rise by:

(0.659-0.652)×0.077 + (-0.0536+0.0492)×0.280 = 0.0005-0.0012 = -0.0007

That is, the interest rate would fall by 7 bps (holding expected inflation constant). In other words, real interest rates would stay roughly constant, ceteris paribus.

Now, what about foreign purchases of US treasury bills and notes? These ran about $333 billion through September [1], or about 2.3% of nominal GDP. Suppose this went to zero (!). Then real rates would rise 1.3%. Of course, one needs net foreign purchases to drop to zero, which seems to me unlikely.

Some caveats: These estimates were obtained using data that spanned a period without extraordinary Federal Reserve credit easing, and in the face of an unprecedented financial collapse. And, the relationship is not precisely estimated. But they're the estimates we -- or at least I -- have.

By the way, CBO predicts only a 0.8 ppts increase in the ten year rate going from 2009 to 2010, and an additional 0.3 ppts by 2011 (Summary Table 2).

I'll further observe that the article gives the impression that the jump in Federal debt was due to the stimulus bill. But in fact, as shown in this post, most of the debt accumulation has been accounted for by the decline in tax revenues associated with the recession.

No doubt, trouble on the fiscal front is real, and has long been brewing -- from the tax cuts of 2001 (if extended) to Medicare Part D. And, indeed, I argued for a lot more fiscal restraint when we were near full employment. [2] But, in my view, apocalypse not quite yet.

Although many commentators write as if there were a national housing market, there have been huge differences in the experience across communities. Dastrup and Carson examine the OFHEO matched-sale data for house prices as calculated separately for 358 U.S. standard metropolitan statistical areas. As seen in the map below, the magnitude of the price decline has differed greatly across U.S. communities, with the biggest drops in the southwest, Florida, and Michigan.

Magnitude of house price declines for 358 SMSAs. Source: Carson and Dastrup (2009).

Dastrup and Carson look at how the magnitudes of the price declines correlate with a number of other community characteristics such as overbuilding (as measured by growth in building permits relative to the local labor force), extent of subprime lending, owner-occupied units, and fundamentals such as median income. Dastrup and Carson find that all of these measures were statistically significantly related to the magnitude of the housing price decline. But by far the most important variable was the magnitude of the previous price run-up, which all by itself can account for more than half of the observed variance in the size of the price decline across different communities.

Magnitude of house price increase prior to peak (horizontal axis) versus magnitude of house price decline (vertical axis). Source: Carson and Dastrup (2009).

I see this as consistent with earlier research by Marco Del Negro and Christopher Otrok which documented a common national factor driving much of the U.S. housing price boom, which in the Del Negro-Ostrok specification was allowed to affect different communities with different coefficients. My understanding of what happened is that low interest rates and in particular a deterioration of underwriting standards fueled an increase in housing demand everywhere in the earlier part of this decade. The magnitude of the price increase that this produced differed across communities as a function of local conditions. When these aggregate factors reversed, so did the prices.

The more prices were artificially bid up, the more spectacularly they declined.

Postscript to potential employers: Sam's a great teacher, and looking for a job.

Various observers have continued to ascribe a central role to real RMB appreciation to effect global rebalancing. I think it's useful to remember that, given a Chinese trade balance in excess of 260 billion USD, appreciation can only have a certain impact. From Cheung, Chinn and Fujii (forthcoming):

...using a single equation error correction model, allowing for coefficient shifts with Chinese accession to WTO, leads to a statistically insignificant estimate of the price elasticity. In the 2000-06 period, the implied price elasticity is zero. Using this point estimate, then a 10% appreciation would actually lead to a shrinkage of the trade balance from 400.9 billion to 355.2 billion. This estimate of 45.7 billion (2000$) is somewhat less than the $88.6 billion current dollars reported in Marquez and Schindler (forthcoming) [working paper version].

The export equations take the form:

Δ exp _t = θ ₀ + ρ exp _t-1 + θ ₁ y^* _t-1 + θ ₂ r _t-1 + θ ₃ k _t-1 + σ ₁ Δ exp _t-1 + 2 lags of first differenced right hand side variables + quarterly dummies + WTO dummy + v _t

Where exp is log exports, y^* is log RoW GDP, r is the real exchange rate, and k is the Chinese capital stock. The import equations take the form:

Δ imp _t = β ₀ + φ imp _t-1 + β ₁ y _t-1 + β ₂ r _t-1 + γ₁ Δ imp _t-1 + 1 lag of first differenced right hand side variables + quarterly dummies + WTO dummy + u _t

Where imp is log imports, y is log GDP.

Separate regressions are run for ordinary and processing trade, over the 1993Q4-07Q1 period. The adjusted R²'s range from 0.77 to 0.92.

If we take the Goldstein-Lardy misalignment estimates of 20% to heart, then a 20% appreciation would lead to an approximately 91.4 billion (2000$) reduction in the Chinese trade balance. As best as I can tell, the export prices from China, and to China (as proxied by unit value indices for Hong Kong) have probably stayed constant relative to 2000; hence the nominal impact is around 90 billion USD (with considerable uncertainty surrounding this point estimate).

This leaves a large Chinese trade surplus in place, around 170 billion even before the rebound in the Chinese surplus anticipated as the world aggregate demand recovers. Now, to the extent that Chinese reserve accumulation is due to a current account surplus and capital inflows, one could hope that a revaluation would have an additional knock-on effect by deterring capital inflows (the argument would be revaluation would eliminate expectations of appreciation that would provide capital gains on holding RMB). That is, recall the balance of payments identity:

CA + KA + ORT ≡ 0

Where CA is the current account, KA is private capital account, and ORT is official reserves transactions.

Figure 2, drawn from Prasad and Sorkin (2009) shows that net inflows were not a major factor, at least in 2008.

This suggests to me that rebalancing requires as much or more Chinese fiscal stimulus and a concerted effort to encourage private consumption via enhancing the social safety net, in addition to RMB revaluation. (This can be seen in a Mundell Fleming framework, as applied to China [3]). And it also requires determined action from the US side as well (see here). If only we'd conducted a sane fiscal policy in 2001-08 [4], our range of action would now be wider in this respect.

...The increase in September was more than accounted for by a large positive contribution from nonfarm inventories (slower inventory paring in September than August). The level of monthly GDP in September was 0.9% above the third-quarter average at an annual rate. Average monthly increases of 0.3% per month during the fourth quarter support our latest tracking forecast of 3.3% growth in the fourth quarter.

The series are plotted below.

With regard to 2009Q3, the Macroeconomic Advisers estimate suggests the 2009Q3 second release will contain a downward revision from 13014.2 billion Ch.2005$ (SAAR) to 12984.6 billion. The e-forecasting estimate suggests roughly no change, at 13014 billion.

Berge and Jorda evaluate rules of the form that would declare the economy to be in a recession when some indicator Y_t falls below a specified threshold c, for example, saying that the economy is in a recession whenever GDP growth comes in below -0.6%. For any choice of the threshold c, there is some observed fraction of observations for which the economy wasn't in a recession and yet Y_t was less than c (the false positive rate), and a fraction of the time when the economy was in a recession and Y_t was less than c (the true positive rate). By choosing a lower value for c, there will be fewer false positives and fewer true positives.

The receiver operating characteristics curve plots the false positive rate on the horizontal axis and the true positive rate on the vertical axis, moving along the curve by specifying alternative possible values for c. For example, here's Berge and Jorda's estimate of the ROC for Y_t corresponding to the Chicago Fed National Activity Index. The greater the area under the ROC, the more useful that indicator Y_t would be for identifying recessions.

ROC curve for Chicago Fed National Activity Index. Source: Berge and Jorda (2009).

Berge and Jorda evaluate a number of possible indicator series Y_t that one might use for this purpose, and find that the Chicago Fed index is one of the best. If you put equal weight on the two kinds of errors you can make with this measure (declaring a false positive versus missing a true positive), Berge and Jorda calculate you'd use an optimal threshold of c = -0.82, that is, declare the economy to be in a recession whenever the Chicago Fed index falls below -0.82. The figure below plots the values for the Chicago Fed index, with shaded regions corresponding to recessions as dated by the NBER. On the basis of this indicator, Berge and Jorda would say that the U.S. recovery began in September, for which the index came in at -0.69, its first reading above -0.82.

Three-month average value for Chicago Fed National Activity Index with -0.82 threshold. Shaded regions correspond to NBER recession dates.

Another indicator that comes out well on the basis of the area under the ROC is the ISM Manufacturing PMI Composite Index, for which Berge and Jorda propose a threshold of c = 44.7. Note that this is below the Y_t = 50 reading at which as many managers are reporting improvement as report deterioration-- things need to be getting significantly worse before it would be characterized as a recession. By this indicator, the recovery began in July.

ISM manufacturing PMI with 44.7 threshold. Shaded regions correspond to NBER recession dates.

(1) y_t = α_t + β x _t + ε _t

(2) y_t = γ + δ x _t + Γ z _t + u _t

Where x is the relative price of widgets, z is a government procurement policy for widgets, and ε and u are serially uncorrelated mean zero errors, E(ε u) = 0. Note that there is a time varying constant in the supply equation, α _t.

How would one analyze the impact of a public policy, such as an increase in government procurement of widgets to place in public places, on the total number of widgets consumed?

First, solve the system for the endogenous variables. Suppose I want to know the reduced form expression for the quantity of widgets purchased. The invert the second equation, solving for x, and substituting into the first (supply) equation. This leads, after solving:

(3) y_t = [α _t(δ-γ)/(δ-β)] + [(βΓ)/(δ-β)] z_t + (δ/(δ-β))(ε_t - (β/δ) u _t)

Suppose I wanted to forecast widget consumption next year, taking into a government policy involving z. How would one best undertake this exercise? For myself, I would take the time differential of the above expression (3). Let the Δ(.) operator indicate the time difference, hence Δ y ≡ y_t+1-y_t.

(4) Δ y = [Δ α (δ-γ)/(δ-β)] + [(βΓ)/(δ-β)] Δ z + (δ/(δ-β))(Δ ε - (β/δ) Δ u )

Suppose additionally capital services demand (i.e., the derived factor demand) is given by:

(5) k_t = Θ y _t + e_t

Where e is another random error term. Since the error terms are random, and serially uncorrelated, then my best guess of the change in widget consumption (and widget industry capital usage) in the absence of a change in government procurement is:

(6) Δ y = Δ α [(δ-γ)/(δ-β)]

(6a) Δ k = Θ × Δ α [(δ-γ)/(δ-β)]

And my best guess of widget consumption with the government policy change is:

(7) Δ y = Δ α[ (δ-γ)/(δ-β) ] + (βΓ)/(δ-β) Δ z

(7a) Δ k = Θ × {Δ α[ (δ-γ)/(δ-β) ] + (βΓ)/(δ-β) Δ z}

Where I would use estimates of δ , β , γ , Γ , and Θ from the literature on widget supply and demand, presumably obtained by way of econometric studies. Δ α would be a variable based upon forecasts, presumably based upon observations on real time data.

Of course, what we observe in reality is:

(8) Δ y = Δ α [(δ-γ)/(δ-β)] + [(βΓ)/(δ-β)] Δ z + (δ/(δ-β))(Δ ε - (β/δ) Δ u)

(8a) Δ k = Θ × {Δ α [(δ-γ)/(δ-β)] + (βΓ)/(δ-β) Δ z + (δ/(δ-β))(Δ ε - (β/δ) Δ u )} + Δ e

Wherein (8) differs from (7) by virtue of the unpredictable errors, (δ/(δ-β)(Δ ε - (β/δ) Δ u )). Employment differs by Θ [(δ/(δ-β)(Δ ε - (β/δ) Δ u)] + Δ e

Thus far, I don't think many economists would have trouble with this methodological approach, although they could clearly argue against this particular set of exogenous variables in, say, the demand equation; or they could reasonably argue that expectations of future widget demand (as well as future government procurement policies with respect to widgets) should matter. But the key is thinking structurally, and about shocks to the three structural equations. In this analysis, I don't think one would want to compare (8) against (6), nor (8a) against (6a), since one is conflating shocks with policy effects.

Now, inexplicably, in current discourse, the troubles begin...

Now, let's think about what happens if (1) is short run aggregate supply of real GDP, and (2) is aggregate demand, where x is the price level, and z is government spending. Equation (3) now defines the output-labor relationship, of the nature examined in this post. The critics of the Administration's approach to estimating the number of jobs created are doing one of two things: (i) they are comparing (8a) against (6a) when the errors have been large, so the composite error (δ/(δ-β)) × (Δ ε - (β/δ) Δ u) is large and negative; or (ii) they are arguing for different estimates of the relevant coefficients. By far, (i) is more popular discourse (such as discussed in this post). Regarding (i), I'll note that there was a substantial deterioration in everybody's expectations regarding the course of the economy in January and February [1]. The more relevant comparison would be (8) against (6) (and (8a) against (6a)) where the shocks (that have now been realized) are added in. That is, compare (8) against (6'), and (8a) against (6a'):

(6') Δ y = Δ α [(δ-γ)/(δ-β)] + (δ/(δ-β))(Δ ε - (β/δ) Δ u)

(6a') Δ k = Θ × {Δ α [(δ-γ)/(δ-β)] + (δ/(δ-β))(Δ ε - (β/δ) Δ u )} + Δ e

More reasonable critiques rely upon (ii), although I have not seen detailed quantitative analyses which break down the sources of mis-prediction. (There is a third route, which involves arguing as an article of faith that there is going to be no, or negative, effect of the government widget procurement policy on widget consumption).

larger graph

From Temperature Anomaly FAQs:

The term "temperature anomaly" means a departure from a reference value or long-term average. A positive anomaly indicates that the observed temperature was warmer than the reference value, while a negative anomaly indicates that the observed temperature was cooler than the reference value.

The reference value used to create this graph was the average over the 1901-2000 period.

The table at the right summarizes the percent change between January 6 and November 11 in the cash prices of 19 commodities reported in the Wall Street Journal (downloaded via Webstract). The average commodity in this list has appreciated 37% since the start of the year.

A recent paper by Ke Tang and Wei Xiong documents an increasing tendency for commodity prices to move together over the last few years. A decade ago, what happened to oil prices was largely unrelated to movements in most other commodity prices. The graphs below show how the correlations between oil prices and the prices of four representative commodities have increased significantly over time.

Correlation (using a rolling sample beginning one year before indicated date) between returns on oil and specified commodity. Source: Tang and Xiong (2009).

One explanation I often see in the popular press is that movements in commodity prices are driven by changes in the value of the dollar relative to other currencies. However, the magnitude of movements in commodity prices greatly exceeds the size of changes in the exchange rate. For example, the table above shows that since the start of this year oil prices have increased five times as much as the dollar price of a euro; see also Steve Gordon's graphs. While the depreciation of the dollar is part of the story, most of the explanation must be found elsewhere.

Another important factor is resurging real economic growth outside the United States, which produces pressures for both the dollar to depreciate and the real price of commodities to appreciate. According to this theory, the increasing correlations between commodity prices results from the fact that countries like China are so much more important for the world economy today than they were a decade ago.

A third explanation is that investors are making increasing use of commodities as an investment class. Although Treasury Inflation Protected Securities offer a hedge against an increase in the U.S. consumer price index, they don't offer protection for foreign investors against depreciation of the dollar. Insofar as increases in the prices of commodities like oil may depress real economic activity, holding commodities as an investment also offers useful diversification against risks to equities. Particularly when interest rates are low, there is an incentive to hoard physical commodities as an investment vehicle.

The paper by Tang and Xiong proposes that the increased use of commodities as a financial investment accounts for the increasing correlation among commodity price changes over time. In support of that claim, they note the growing popularity of investment strategies based on the Goldman Sachs Commodity Index or the Dow Jones Commodity Index. Tang and Xiong document that correlations among commodities included in the indexes have increased faster than those not included. For example, one of the regressions they estimate relates the return on commodity i to equity returns, bond yields, the value of the dollar, and oil prices, where the coefficients are allowed to grow with time at different rates before and after 2004, and with different trends on these coefficients estimated for commodities included in indexes as for those excluded. The figure below shows their estimated time path for the coefficient on oil prices comparing the indexed and non-indexed groups.

Coefficient relating return on average commodity to return on oil as a function of time for commodities included in the GS or DJ indexes (top curve) and those excluded (bottom curve). Source: Tang and Xiong (2009).

For any of the explanations in this third class, one of the important challenges is to reconcile the story of commodity speculation with supply and demand for the underlying physical commodity. If we propose that speculators have driven the price of the commodity up, the physical quantity demanded should decline as a result. In order to be sustained, a coherent speculation-based theory of commodity price appreciation requires increased physical storage of the commodity.

The solid black curve in the figure below plots the typical U.S. crude oil stocks (excluding those held in the Strategic Petroleum Reserve) for each week of the year, based on the average over 1990-2007. The red line gives the actual values for 2008, which were significantly below the historical average, particularly in the spring of 2008 when oil prices were rising so dramatically. Those below-normal inventories were one reason I focused on what was going on to the fundamentals of supply and demand in trying to understand the behavior of oil markets in the first half of 2008.

Weekly U.S. crude oil ending stocks, excluding SPR, in thousands of barrels, from EIA. Black line: average over 1990-2007. Red: 2008. Green: 2009.

On the other hand, inventories of crude oil this year, shown in green above, have been substantially above normal, meaning that in the absence of that oil going into storage, we would have expected to see lower oil prices than we currently have.

Moreover, much of the current stockpiling may be taking place outside the United States. For example, Yves Smith noted this story from Bloomberg last August:

Copper, nickel and other base metals stockpiled by speculative Chinese investors including pig farmers may be sold when "market sentiment turns," said Scotia Capital Inc.

A price surge and easy bank credit this year encouraged pig farmers, stock brokers and businessmen to buy copper and nickel for speculation, Liu Na, an analyst with Scotia Capital, wrote in a note dated Aug. 17, citing reports from the state-owned China Central Television....

"These stockpiles are in 'weak hands' as speculators have no real use for base metals," Liu wrote. "When the market sentiment turns, they are very likely to turn into quick sellers, especially when the bank's money is involved."

I also found this November 3 story from the Financial Times of interest:

Gold prices continued to rise on Wednesday extending the all-time highs which followed India's central bank bought 200 tonnes of the precious metal, swapping dollars for bullion as the country's finance minister warned the economies of the US and Europe had "collapsed".

India's decision to exchange $6.7bn for gold equivalent to 8 per cent of world annual mine production sent the strongest signal yet that Asian countries were moving away from the US currency.

Policy-makers in the Federal Reserve have traditionally thought of inflation as a broad movement in all wages and prices, which to some extent is under their control, and viewed changes in relative commodity prices as outside their control. I believe that this is not the correct understanding of the current situation. Concerns about inflation, particularly on the part of foreign dollar-holders, are likely to show up first in the relative prices of internationally traded commodities. Insofar as these relative price changes can be destabilizing in themselves, it cannot be wise for U.S. policy-makers to ignore them.

From "'Created or saved' doesn't add up", by Joseph Lawler:

...[t]he "created or saved" numbers are meaningless. The administration purposefully devised the metric to be nebulous. Without a counterfactual, showing the trend of unemployment in the absence of the stimulus, it is impossible to know how many jobs the stimulus saved.

But this is completely counter to what I learned in economics, and how, for instance, the CBO conducts analysis. I assume Mr. Lawler doesn't dispute the impartiality of the CBO (but who knows?). Here's the way real macroeconomists conduct analysis:

As the President has discussed, analysis done within the Administration has shown how his tax cuts have substantially offset the series of adverse shocks that have been buffeting the economy. Simulations of a conventional macroeconomic model show that, without the tax cuts, the level of real GDP would have been about 2 percent lower in the middle of 2003. About 1.5 million fewer people would have jobs today. The job market is not what we would like it to be right now, but it would have been worse without the Administration's actions.

One can view the short-run effects of these tax cuts from a classic Keynesian perspective. The tax cuts let people keep more of the money they earned. This supported consumption and thus helped maintain the aggregate demand for goods and services. There is nothing novel about this. It is very conventional short-run stabilization policy: You can find it in all of the leading textbooks.

The writer is Greg Mankiw, discussing in 2007 a particular fiscal measure, namely the 2003 tax cuts (h/t, Brad Delong).

So let us return to how the Congressional Budget Office (CBO) conducted analysis. In their February analysis, they presented this set of results, based on a range of multipliers in the literature.

So GDP is estimated to be between 1.4 to 3.8 percentage points (ppts) higher than baseline in 2009Q4, due to the stimulus bill. The midpoint of this range is 2.6 ppts. Relatedly, the range of employment gain relative to baseline is 0.8 to 2.3 million; the midpoint of this range is 1.55 million.

Interestingly, taking the CEA's model based approach (Table 2, Joint Economic Committee testimony of 22 October), and assuming the same incremental growth rate in 09Q4 as in 09Q3, the implied deviation from baseline is 2.56 ppts, or right in the midpoint of the CBO's range.

Now using the error correction model that I estimated in last Tuesday's post (where the cointegrating relationship between log GDP and log nonfarm payroll employment is 0.37), I find the range of increased employment relative to baseline is between 0.68 and 1.84 million, slightly lower than the CBO range of 0.8 and 2.3 million. The estimated employment impact is 1.26 million, using the midpoint of the CBO range for impact on GDP.

I know counterfactuals and math are hard to fit on a bumper sticker. But one would hope that in an 800-plus word essay on economics (even if in Politico), some economic content could be included.

By the way, Jeff Frankel debunks a similar misapprehension in National Journal.

In a paper presented at the Brookings Institution last spring, I examined the post-sample forecasting performance of an equation originally published in 2003, which relates real GDP to past values of GDP and oil prices. I noted in April that if you had known in October 2007 the values of GDP through 2007:Q3 and what was about to happen to oil prices through 2008:Q2, you could have used that historical relation to predict the value of U.S. real GDP for 2008:Q3 with an accuracy better than 99.5%.

Solid line: 100 times the natural log of real GDP. Dotted line: dynamic forecast (1- to 9-quarters ahead) based on coefficients of univariate AR(4) estimated 1949:Q2 to 2001:Q3 and applied to GDP data through 2007:Q3. Dashed line: dynamic conditional forecast (1- to 9-quarters ahead) based on coefficients reported in equation (3.8) in Hamilton (2003) (which was estimated over 1949:Q2 to 2001:Q3) applied to GDP data through 2007:Q3 and conditioning on the ex-post realizations of the net oil price increase measure.

In the figure above I extend the earlier-reported forecast an additional four quarters and compare the projection with what actually happened to GDP through 2009:Q3. The dotted green line is a forecast formed in October 2007 of what would happen to U.S. GDP if you used nothing more than the values of GDP observed through 2007:Q3. Basically that forecast simply extrapolates the recent prior trend. The dashed red line is the forecast that uses GDP values only through 2007:Q3 but also uses knowledge of what was going to happen to oil prices between 2007:Q4 and 2009:Q3. If you treated oil prices as the only thing that matters for the economy, you would have predicted the bottom would be reached in 2009:Q1, flat growth between 2009:Q1 and 2009:Q2, and normal growth resuming in 2009:Q3. That's exactly the trajectory that GDP has taken so far, although the bottom in 2009:Q2 was 2-1/2 percent lower than would be predicted on the basis of oil prices alone.

I have no doubt that the problems with financial markets were a bigger factor than oil prices in the striking collapse in output in 2008:Q4 and 2009:Q1. The other approaches to measuring the contribution of oil to the downturn surveyed in my Brookings paper would estimate a smaller contribution of oil to the downturn than suggested by the figure above. On the other hand, all of the approaches surveyed in that paper suggest that oil made a material contribution to the initial downturn, and it seems hard to deny that that the severity of the financial crisis was exacerbated by the fact that the U.S. had spent three quarters in recession prior to the failure of Lehman in September 2008.

What do these estimates imply looking forward, with oil prices now back up to $80 a barrel? The relation used to produce the figure above assumes that there is a threshold effect before the next oil price shock would begin to do its damage. According to that relation, oil has to get back above $130 before it would matter again for GDP growth. On the other hand, the original research on which that relation is based acknowledged that there's really not a very compelling basis in the data for choosing among various plausible nonlinear possibilities. The other approaches surveyed in my Brookings study assume a simple linear relation, according to which the recent resurgence in oil prices would already begin to exert a drag on spending.

Another magnitude that I think is important to watch is the share of the budget of an average U.S. consumer that is devoted to energy purchases. This had fallen considerably in the 1990s, making it easier for many consumers to largely ignore modest energy price fluctuations. When this share rises above 6%, it seems to become a more significant factor. The consumer energy expenditure share peaked last summer at 6.8%, but collapsing energy prices subsequently brought it back down to 4.7%. The resurgence in oil prices this summer had pushed that share back up to 5.4% in September.

Energy expenditures as a fraction of consumer spending. Calculated as 100 times nominal monthly consumption expenditures on energy goods and services divided by total personal consumption expenditures. Data source: BEA Table 2.3.5U, "Personal Consumption Expenditures by Major Type of Product and Expenditure," obtained from Econstats. Dashed line is drawn at 6.0%.

And the price of oil is up another 15% since September.

So a year later, in September 2009, after living through a year of "disaster," how did real consumption expenditure (one economists' favorite measures of living standards) compare to what it was in September 2008?

He observes that consumption (as well as disposable income) were higher than they were a year ago.

Since we're concerned with living standards, as opposed to economic activity, I thought it of interest to look at per capita consumption. Since population is available only on quarterly basis, I compare consumption per capita in 2009Q3 to that in 2008Q3.

Per capita consumption is 0.3 0.9 percent (in log terms) below the level in 2008Q3. Moreover, per capita consumption is 6.5 6.1 percent below the 1967Q1-09Q3 trend (which grows at 2.9 2.2 percent per annum). [Corrections made 1pm Pacific]

Is that a disaster? Maybe not. But one wonders how much lower per capita consumption would have been in the absence of the actions undertaken by fiscal and monetary authorities around the world.

Update, 1pm Pacific

Thanks to confused for pointing out the monthly population series. Here is a plot of log montly personal consumption expenditures, and analogous trend:

Note that relative to September 2008, consumption is 0.65 percent lower (in log terms), and is 6.4 percent lower than the time trend.

Today, we're fortunate to have Joe Gagnon, senior fellow at the Peterson Institute for International Economics, as a guest contributor.

With short-term risk-free interest rates essentially at zero in the major developed economies, conventional monetary policy is in a liquidity trap. As a number of commentators have observed, printing zero-interest-rate money to buy zero-interest-rate assets has no real economic effect because the assets are near-perfect substitutes for money. But does that mean that central banks have lost their power? Jim Hamilton asserts that central bank purchases of other assets, with positive yields, can always create inflation, though he is silent as to whether they can affect output. Building on Gauti Eggertsson and Michael Woodford, Scott Sumner argues that central banks can boost output and inflation despite zero interest rates by raising the public's expectations of future inflation and thus lowering the real rate of interest. According to Sumner, purchases of a variety of assets are one way central banks can bolster these expectations.

Is there any evidence on the effect of central bank purchases of longer-term or riskier assets?

In recent months, central banks have purchased large quantities of longer-term assets. These purchases appear to have been effective at pushing down longer-term interest rates, which should stimulate economic activity. For example, the Federal Reserve (Fed) has purchased large quantities of longer-term agency-backed securities and Treasury bonds. The following table shows that Fed communications about such purchases had substantial effects on a range of long-term interest rates, including on assets that were not included in the purchase program, such as interest rate swaps and corporate bonds.

Since March 19, the Fed has not made any substantive changes to its planned purchases of longer-term assets. Over this period, the 10-year Treasury yield has risen about 75 basis points and the corporate yield has fallen about 200 basis points, reflecting a relaxation of the extreme financial strains and flight-to-quality that characterized the first few months of this year. Conventional fixed mortgage rates, a key target of the Fed's policy easing, have changed little on balance since late March.

Paul Krugman has argued that potential gains and losses when long-term interest rates move make a policy of purchasing such bonds especially risky, and that fiscal stimulus is a safer bet. However, central banks, including the Fed, have always held risky assets, including long-term bonds, foreign exchange reserves, loans to private banks, and even equities. In many cases, such assets comprise the bulk of the central bank's portfolio.

A good definition of expansionary monetary policy is the printing of money to purchase financial assets. Expansionary fiscal policy is the selling of financial assets to purchase goods and services, to cut taxes, or to increase transfers. On these definitions, both monetary and fiscal policy can be effective when short-term interest rates are zero.

This post written by Joe Gagnon

Sterling's paper is in part a response to earlier analyses by John Taylor (2008, 2009) and John Cochrane and Luigi Zingales who noted that the spread between the LIBOR interest rate (London Interbank Offered Rate) and the OIS (Overnight Index Swap) rose only gradually following the Lehman bankruptcy, leading these scholars to see Lehman as just one of many relevant developments at the time. But Sterling questions the meaningfulness of the LIBOR or OIS indicators during these weeks given that markets seized up and little trading activity was occurring in these instruments. Sterling instead proposes to take a look at Bloomberg Financial Conditions Index, which Bloomberg launched in August 2008. The index is based in part on the observations by Rick Mishkin on some of the regularities observed in earlier historical financial crises. The components of the Bloomberg index are as follows:

Source: Sterling (2009)

Here's Sterling's graph of the behavior of the Bloomberg index, in which the remarkable character of events following September 12 is pretty striking.

Source: Sterling (2009)

Even if the Lehman failure is agreed to as a definitive event, it is not clear to me that this establishes that all would have been fine if the Fed had only bailed out Lehman as they had Bear Stearns and AIG before. That question is inherently and unavoidably counterfactual. We can't know-- and decision-makers at the time couldn't know-- which domino might have been next to fall had this one been propped up.

But I think it is fair to conclude that the middle of September of 2008 marked a clear turning point in the unfortunate sequence of events through which we have recently come.

The above series is the Hodrick-Prescott filtered net exports to GDP series for the United States. (I've discussed the HP filter in the context of output gaps before [1].) If one believes that the HP filter properly identifies the cyclical versus trend components, then the plot shows the cyclical component of net exports; hence in this context cyclical net exports were in rough balance in 2007.

Of course, this series is much different than the one we are accustomed to. I plot the filtered and actual series in Figure 2.

While it would appear that the HP filtered series does capture essential features of business cycle fluctuations in net exports (consider how the cycles correlate with NBER-defined recessions), filtering does impart some substantively different properties to the series. For instance, the unfiltered series is highly persistent, with the autoregressive coefficient in an AR(1) specification equal to 0.98, standard error 0.013. On the other hand, the filtered series exhibits much lower persistence, with an AR coefficient of 0.79.

In many studies, the variable of interest is the ratio of real exports to real imports. Here are the unflitered and filtered series:

The degree of persistence is once again lower with the HP-filtered data, as is the variability, especially during the latter portion of the sample. The HP-filtered series also indicates almost no business cycle related fluctuation in the exports/imports ratio over the entire 1997-2007 period. Perhaps that's the right interpretation. It certainly does give one pause for thought.

What's the bottom line? For me, it's not that HP-filtering is necessarily a bad idea. It's just that one has to be real careful, and think about what is being extracted, and what remains, when applying any detrending technique.

For more formal discussion of the use of various filters, see Cogley and Nason [link updated 4pm Pacific].

Data source: Wardsauto.com

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U.S. light vehicle sales last month were up slightly from September and about the same as October 2008. Given how dismal those comparison months were, that's not saying much. Last month's sales were 3.5% below the average level of April through June, which, because sales usually decline a bit more than that in the fall, counts as a modest seasonally-adjusted improvement. We seem to be past the bottom for autos, but climbing back painfully slowly at this point.

Source: Calculated Risk

The same might be said of new home sales, which despite a slight setback in the most recently reported month (September), have definitely been gaining from the lows reached in March. But there's still a long way to go before new home sales would reach the average levels seen in the 1980s.

Source: Calculated Risk

Existing home sales, which don't contribute directly to GDP but which do help absorb some of the overhang of distressed properties for sale, have been growing more solidly, and NAR's pending home sales index is up 12.5% over the last two months.

Source: Calculated Risk

Other new indicators have also been mixed. The Manufacturing ISM PMI, an index summarizing the responses of managers answering their survey, registered its third consecutive month above 50, indicating more respondents said that conditions were improving than said things were getting worse.

Source: FRED

On the other hand, real personal consumption expenditures and real disposable personal income both dipped back down in September.

Source: FRED

Source: FRED

I remain convinced that the key indicator for a normal recovery will be a resumption of growth in U.S. employment. Unfortunately, ADP is estimating that the U.S. lost 203,000 private-sector jobs in October on a seasonally adjusted basis.

Bill McBride says we are a long way from normal. And I say, don't bet against Bill McBride.

In order to investigate this question, I examine the relationship between GDP and nonfarm payroll employment over the 1986-2009 period, which encompasses the "Great Moderation". Figure 1 illustrates the log GDP and log nonfarm payroll employment series.

I estimate the following error correction specification, which includes 4 lags of first differences, using OLS:

Δ nfp_t = 0.54 - 0.069 nfp_t-1 + 0.026 y _t-1 + 0.60 Δ nfp_t-1 + 0.173 Δ y _t-1 + ... + 0.00046 time - 0.0000017 time ²

Adj. R² = 0.85 SER = 0.0018, n = 95, DW = 2.02. Breusch-Godfrey LM test, 2 lags, F = 1.84 (p-val = 0.16). HAC robust standard errors. (Nonsignificant coefficients suppressed.)

The long run elasticity of employment with respect to GDP is 0.37, while the short run elasticity is 0.17.

I conduct dynamic simulations (using the regression estimated over the entire sample) for the five years after each recession: 1991Q2-1996Q1, 2002Q1-2006Q4. I also conduct a dynamic forecast for 2009Q3-2010Q4, using the WSJ October mean forecast for GDP growth over that period (discussed in this post) as the right hand side variable.

The results are shown in Figure 2:

The dynamic simulations initially underpredict nonfarm payroll employment, before overshooting (in the 1990's) and essentially being on target (in the 2000's). What does the model imply for the trajectory of employment going forward? The dynamic simulation for the 2009Q3 through 2010Q4 period is shown in Figure 3 (with employment expressed in levels, instead of logs).

(Note: for sticklers out there -- e.g., juan in comments to this post -- what I am conducting here for the 2009Q3-10Q4 period is a conditional forecast, since I am taking the GDP path forecasted by the WSJ survey as given).

I calculate the WSJ forecast for employment by adding the October mean prediction of seventeen thousand per month net job creation to the 2009Q3 figure (literally, this forecast is for October 2010, and should be 17,000 × 12 added to the October employment figure).

Hence, if historical correlations persist, then nonfarm payroll employment will continue to decline through 2010Q2. However, given the imprecision of the estimates, nonfarm payroll employment could begin rising as early as 2010Q2 (the upper gray line).

Of course, not only is there sampling uncertainty; there's also uncertainty regarding the true model. I've imposed cointegration in the estimation procedure (and according to the Johansen maximum likelihood procedure, one can reject the null hypothesis of no cointegration at the 20% level, allowing for deterministic trends in the data). But one could drop that assumption, and assume a relationship in first differences. I estimate:

Δ nfp _t = -0.001 + + 0.687 Δ nfp _t-1 + 0.210 Δ y _t + 0.113 Δ y _t-1

Adj. R² = 0.89 SER = 0.0015, n = 95, DW = 2.05.

The adjusted R² statistic is slightly higher in this ARMAX specification, but of course R² shouldn't be the key determinant of whether one specification is to be preferred over another. In fact, one might wish to impose the long run cointegrating relationship especially if longer horizon prediction is of central import. Hence, I compare the two (conditional) forecasts in Figure 4.

In this case, job losses taper off, and net job creation occurs in 2009Q3. Or, it could be that the error correction model is correct (cointegration between GDP and employment holds), but the recovery will be more akin to that of the 1970's and early 1980's, because of the depth of the downturn. That specification (which would not fit well for the past two recoveries) yields the salmon colored line in Figure 4, and predicts strong job creation in 2009Q2.

James Hamilton says recent output indicators (as of 10/18) are not consistent with a jobless recovery. Paul Ashworth says manufacturing employment has may have [correction added 11/4, 8:45am] already "stabilized", while Robert Gordon predicts a resumption of employment growth in 2010Q1. David Altig at Macroblog and Mary Daly, Bart Hobijn, Joyce Kwok at SF Fed enumerate the reasons for a slow start in employment growth.

Revisions

We're all tempted to make predictions on the basis of the last data point. And even more difficult to resist is the temptation to make definitive statements on the basis of data that are sure to be revised. For instance, we see this question from Casey Mulligan, "Where's the GDP Disaster?".

Last October, when we were told that spending and incomes were about to collapse, I predicted that "real GDP will not drop below $11 trillion (chained 2000 $)."

Professor Mulligan provides this graph.

I think this is an excellent time to recapitulate the hazards of making definitive assessments on the basis of data that are sure to be revised [0] [1]. To illustrate this point, I go back to the last recession, which according to the NBER extended from 2001Q1-01Q4.

I plot the vintages of GDP in Ch.1996$ available as of April 2002 (the advance release for the first quarter after the recession ended), and October 2003 (advance release for 2003Q3).

Note that GDP in the latter vintage was 1.6% lower (in log terms) in 2001Q2 than it was in the corresponding period according to the earlier vintage. This amounted to a 5 148.6 [corrected 11/1, 10:35am] billion Ch.1996$ difference.

Now, I replicate Professor Mulligan's graph. I draw Professor Mulligan's floor, along with real GDP, and an alternate for 09Q1-09Q2 that would obtain if GDP turned out to be 1.6% lower in a later vintage.

I calculate GDP in Ch.2000$ by dividing the 2005-base chained price index by the average value of the index in 2000, which is 88.648, and then dividing nominal GDP by this base-year-2000 index.

The graph indicates that in 09Q2, GDP was only 2.3% above Mulligan's floor.

And Conditional Forecasts

In some sense, the critical aspect of Professor Mulligan's argument that the events of 2008-09 were never going to be disasterous is that he made his projection conditional on none of the extraordinary measures undertaken by the Fed, nor on the the fiscal stimulus by the Federal government being implemented. It's useful to recap his statement from October:

NO DEPRESSION; NO SEVERE RECESSION

The medium term fundamentals point toward more real GDP, more employment, and (to a lesser degree) more consumption. Some employment and real GDP declines may occur in the short run, but they will be small by historical standards. Professor Cooley recently explained "The losses to date represent less than .5% of the work force. In the relatively mild recession of 2001 to 2002, job losses equaled about 1% of the work force. In the much more severe recession of 1981 to 1982, job losses totaled nearly 3% of the labor force--six times today's figure. And in the (truly) Great Depression--invoked, now, with an alarmist frequency--job losses between 1929 and the trough in 1933 were 21% of the labor force." Note that 21% over 3 1/2 years is an average decline of 2% every quarter for 14 consecutive quarters! If employment declines 2% in even one quarter, or 5% over a full year, I will admit well before 2010 that a severe recession is happening and that my 2010 forecasts are unlikely to be attained.

According to the BLS, national nonfarm employment was 136,783,000 (SA) at the end of 2006, as the housing price crash was getting underway. Real GDP was $11.4 trillion (chained 2000 $). Barring a nuclear war or other violent national disaster, employment will not drop below 134,000,000 and real GDP will not drop below $11 trillion. The many economists who predict a severe recession clearly disagree with me, because 134 million is only 2.4% below September's employment and only 2.0% below employment during the housing crash. Time will tell.

Now, I assume that Mulligan feels free to compare a forecast conditioned on no fiscal policy against one with fiscal policy to the extent he believes multipliers are near zero or even negative. And perhaps he believes money is neutral in the short run. If so, then of course it's fine to make the comparisons he does. But for those of us who believe that monetary and fiscal policy have textbook effects, then making that comparison is problematic. In the absence of these stimulus measures, I believe we may very well have breached that 11 trillion floor.

To see this, consider CBO's assessment that by 2009Q4, the stimulus package would have an impact of between 1.4 to 3.8 ppts of baseline GDP. The midpoint is 2.6 ppts. That's well within the range of the Mulligan floor.

So, to conclude, in my view, a "GDP disaster" would have occurred in the absence of aggressive actions by the Federal Reserve, the US Government, as well as fiscal and monetary authorities abroad.

Rate of growth of real GDP (annual rates), 1947:Q2 to 2009:Q3. Shaded regions represent dates of recessions as declared by NBER.

Consumption spending is the biggest component of GDP and the main contributor to third quarter growth, accounting by itself for 2.4 percentage points out of the 3.5% total, and with consumer purchases of motor vehicles and parts alone 3/5 of the contribution of consumption. Next in importance was inventory rebuilding, which added 0.9 percentage points to the total and could make a significant further contribution in the quarters ahead. Housing is finally making a positive rather than a negative contribution, and nonresidential fixed investment was a smaller drag than I had been expecting. Imports grew faster than exports, though I'm relieved that trade overall is coming back. The government sector made a smaller contribution than one might have thought given the fiscal stimulus, in part because lower state and local spending offset some of the increased federal spending. For a healthier long-run growth path I'd prefer to see business fixed investment and net exports adding rather than subtracting. But, compared with what we've been seeing recently, this overall is a quite welcome report.

With the new third quarter numbers, we are ready to calculate our Econbrowser Recession Indicator Index for the preceding quarter (2009:Q2). This is a pattern recognition algorithm for identifying recessions that waits one quarter for data revisions and clear trend identification before making an assessment. Based on the 2009:Q3 GDP numbers just released, the value that the algorithm assigns to the second quarter of 2009 is 84.6-- based on currently available data, it looks like the economy was still in recession as of the second quarter of this year. We'll declare the recession to be over when the index falls below 33. At that time, we'll use the full set of revised data available as of that date to assign a most probable date for the end of the recession.

The plotted value for each date is based solely on information as it would have been publicly available and reported as of one quarter after the indicated date, with 2009:Q2 the last date shown on the graph. Shaded regions represent dates of NBER recessions, which were not used in any way in constructing the index, and which were sometimes not reported until two years after the date.

Other takes on today's numbers were provided by WSJ Real Time, Calculated Risk, Jon Hilsenrath, Economix, and Justin Fox, whose general theme seems to be concerns about whether this growth will be sustained into 2010.

Figure 1 breaks down the contributions of overall growth into the broad national income accounting components (overall investment decomposed into fixed and inventory investment). Interestingly, the contribution of government is fairly modest in Q3 (Note change of vertical axis scale).

Figure 2 breaks down the government consumption growth contribution into that from Federal nondefense, defense, and state and local government.

Government spending on goods and services (not overall government expenditures) accounted for 0.48 percentage points (ppts). Federal nondefense expenditures accounted for 0.17 ppts, while defense accounted for 0.45 ppts. State and local spending accounted for negative 0.14 ppts. At this juncture, one could leap to the conclusion that the stimulus package, and other measures, had no effect on output. And I'm sure many will. But I think it pays to be a bit circumspect in this regard.

First, it's always helpful to recall that the advance estimate incorporates lots of estimates, and is subject to revisions (see this post).

Second, some individuals have argued that since a portion of the government component comes in the defense category, that should not be construed as being attributable to the stimulus package. But in point of fact, according to CRS ARRA does have some defense expenditures (mostly energy efficiency upgrading). One can see what contracts have been let by going to the http://www.Recovery.gov website (noncompetitive contracts here). As an open question, I'm not sure where Army Corps of Engineers expenditures fall in the categories (I think it's under defense as well, in which case the defense category would incorporate even more of the stimulus spending).

Third, the decline in state and local government spending's contribution is notable. Given the big budget shortfalls in state budgets [1], what this outcome tells me is in the absence of the transfers from the Federal government, the negative contribution would have been even larger.

The Joint Economic Committee held hearings today; J Steven Landefeld, the head of the BEA, stated in his testimony:

...let me conclude by describing how it is reflected in GDP and the national accounts. BEA's national accounts include the effects of the federal outlays and tax cuts included in the ARRA. Because most of the outlays and tax reductions from ARRA during the last three quarters were in the form of grants to state and local governments, tax reductions for individuals and businesses, and one-time payments to retirees, their effects on GDP show up indirectly through the effects on GDP components such as consumer spending, residential investment, and state and local government spending. Thus, BEA’s accounts do not directly identify the portion of GDP expenditures that is funded by ARRA. During each of the second and third quarters, the Making Work Pay Credit lowered personal taxes and raised disposable personal income about $50 billion (annual rate). During the second quarter, ARRA provided payments of $250 to beneficiaries of social security and other programs that raised disposable personal income about $55 billion. ARRA also provided special government benefits for unemployment assistance, for student aid, and for nutritional assistance; these special benefits raised disposable income about $49 billion in the third quarter and about $35 billion in the second quarter. ARRA also funded grants (such as Medicaid) and capital grants (such as highway construction) to state and local governments of about $75 billion in the third quarter and $85 billion in the second quarter.

Mark Zandi also testified. His testimony included this interesting table, reporting estimates of expenditures.

Zandi writes:

Criticism that only $175 billion of the $787 billion stimulus plan has been distributed through tax cuts and increased government spending is misplaced (see Table 2). What matters for economic growth is the pace of stimulus spending, which surged from nothing at the beginning of the year to about $80 billion in the third quarter. That is a big change in a short period and is why the economy is growing again after more than a year.

A competing view is presented by Kevin Hassett (of Dow 36,000) in his testimony.

In a previous set of papers, Oli Coibion, Michael LeBlanc and I examined the predictive power of energy futures post and paper.

In a new paper, Oli Coibion and I update our results regarding energy futures, and metal and agricultural commodities as well, through the end of August 2008, just before the financial crisis broke out in full force. From the paper:

This paper examines the relationship between spot and futures prices for commodities, including those for energy (crude oil, gasoline, heating oil markets and natural gas), precious and base metals (gold, silver, aluminum, copper, lead, nickel and tin), and agricultural commodities (corn, soybean and wheat). In particular, we examine whether futures prices are (1) an unbiased and/or (2) accurate predictor of subsequent spot prices. We find that while energy futures prices are generally unbiased predictors of future spot prices, there are certain notable exceptions. For both base and precious metals, the results are much less favorable to unbiasedness hypothesis. For precious metals and copper and lead, we strongly reject the null that β=1 at all three horizons. For the these other base metals, while we cannot reject that β=1, due to large standard errors. Finally, both corn and soybean futures have β close to 1, while wheat has β<1. Excepting oil and base metals, futures tend to outperform a random walk specification in out of sample forecasts.

The regression we run is:

s_t - s_t-k = β ₀ + β ₁ (f _t|t-k - s_t-k) + ε _t

Where s_t is the log spot price at time t, f_t|t-k is the log futures price at time t-k that matures at time t. The resulting β coefficients at the three month horizons are displayed in Figure 2.

Despite the bias in futures, along a RMSE dimension, futures outperform a random walk for most commodities, except for base metals (the out of sample period is 03M01 to 08M07). That being said, the outperformance relative to a random walk is seldom statistically significant.

(1) Capital adequacy. The key principle for preventing the "bank run" dynamics of the recent financial turmoil is to make sure that financial institutions have a sufficient cushion of equity capital to be able to absorb liquidation and delinquency losses on assets without sacrificing the institution's ability to repay short-term creditors. Equity capital is also a critical tool for addressing the core incentive problems arising from gambling with other people's money. As Chair Bernanke observed:

Through the course of the crisis, it became increasingly clear that many firms lacked adequate capital and liquidity to protect themselves as well as the financial system as a whole.

Professor Blinder elaborated:

the real leverage problems arose with (a) investment banks that operated (under a different regulatory regime [from commercial banks]) with 30 times leverage and more, and (b) gimmicks such as thinly-capitalized SIVs and conduits that (legally) avoided capital requirements...

Both Bernanke and Blinder further called attention to the problems with procyclical capital requirements. Standard capital requirements become looser when times are good, but that is exactly when it's most feasible and desirable for them to strengthen the equity cushion. Blinder advocated reverse convertible debentures proposed by Mark Flannery and the Squam Lake Working Group on Financial Regulation as a way to implement countercyclical capital requirements.

Though a conceptually different issue from equity capital, Blinder also favored requiring both mortgage originators and mortgage securitizers to retain 5% of any assets they create.

(2) Compensation. Blinder observed: "Pay plans that are structured in such a 'heads I win, tails I don't lose' way create powerful incentives for traders to go for broke gambling with OPM ('other people's money')." In his spoken remarks he added, "They did go for broke, and a lot of them achieved that objective."

Here were Bernanke's observations on the subject:

flawed compensation practices at financial institutions also contributed to the crisis. Compensation, not only at the top but throughout a banking organization, should appropriately link pay to performance and provide sound incentives. In particular, compensation plans that encourage, even inadvertently, excessive risk-taking can pose a threat to safety and soundness. The Federal Reserve has just issued proposed guidance that would require banking organizations to review their compensation practices to ensure they do not encourage excessive risk-taking, are subject to effective controls and risk management, and are supported by strong corporate governance including board-level oversight.

(3) Derivatives. Though Bernanke did not say much about the explosion of financial instruments such as credit default swaps and their role in propagating the crisis, Blinder highlighted the desirability of changes:

While the regulation of derivatives is fraught with peril, it is not hard to improve upon what we have now-- which is practically nothing. I have argued for years that the most important step the government could take would be to push as much derivatives trading as possible into organized exchanges....

The Treasury White Paper (p. 48) proposes to subject OTC derivatives to a "robust regime" of regulation that includes "conservative capital requirements," margins, reporting requirements, and "business conduct standards."

(4) Resolution mechanism. Finally, both Bernanke and Blinder stressed the need for a mechanism to supervise the liquidation of failing systemically important financial institutions. Blinder advocated:

we could develop a new resolution mechanism, perhaps patterned on what the FDIC now does with small banks (often before the bank's net worth goes negative), that would enable the authorities to wind down a systemically-important financial institution (including a non-bank) in an orderly fashion-- rather than just throwing it to the Chapter 11 wolves. This last idea is among the key ingredients of the Treasury's reform plan, has substantial support in Congress, and may well become law. If so, it would have several desirable effects.

• The TBTF doctrine would morph into “too big to be put into Chapter 11," but not "too big to be seized and its management thrown out." That change alone would go a long way toward reducing moral hazard.
• Taxpayers would (mostly) be relieved of the burdens of costly bailouts....
• Regulators would no longer have to keep large "zombie banks" (and non-banks) on life support for fear of the systemic consequences of shutting them down.

Bernanke endorsed this reform as well:

the Congress should create a new set of authorities to facilitate the orderly resolution of failing, systemically important financial firms.... In light of the experience of the past year, it is clear that we need an option other than bankruptcy or bailout for such firms.

A new resolution regime for nonbanks, analogous to the regime currently used by the Federal Deposit Insurance Corporation for banks, would permit the government to wind down a failing systemically important firm in a way that reduces the risks to financial stability and the economy. Importantly, to restore a meaningful degree of market discipline and to address the too-big-to-fail problem, it is essential that there be a credible process for imposing losses on the shareholders and creditors of the firm. Any resolution costs incurred by the government should be paid through an assessment on the financial industry and not borne by the taxpayers.

One detail I'd stress is the need for integration of the approaches to items (3) and (4) above. One of the problems that makes bankruptcy messy for these institutions is that outstanding derivatives contracts can assume a life of their own, sucking assets out of the firm as the market moves against the firm's bets and in practice giving these contracts seniority over conventional debt. From the perspective of society's best interests I don't think such seniority can be justified. I agree with the assertion in Blinder's spoken remarks that the economic costs of the latest recession exceed the cumulative potential efficiency benefits of what he referred to as "fancy finance."

I would propose that instruments such as the credit default swaps entered into by any systemically important financial institution should be subject to a regulatory stop-loss provision. In a standard clearinghouse mechanism, each party delivers collateral against the possibility of the market moving against their original bet. If the market moves too much, the loser either must add collateral or their position is wiped out. If the institution continues to deliver new margin capital, it can become like the compulsive gambler doubling down as the firm's equity cushion essential for financial stability bleeds away. Like the referee protecting a staggering boxer, the regulator needs the authority to declare "no mas" on an institution's commitment of new capital to such positions.

Bernanke concluded with the following:

we cannot lose sight of the need to reorient our supervisory approach and to strengthen our regulatory and legal framework to help prevent a recurrence of the events of the past two years.

To which I would only add, Amen!

CA ≡ (T-G) + (S-I)

Where CA is the current account, (T-G) is the consolidated government budget balance, and (S-I) is the private sector saving-investment balance. Figure 1 depicts the profound shifts that have occurred in these components (normalized by nominal GDP).

Note that I've omitted the statistical discrepancy which makes these items add up exactly.

How much of the recent shift in the net private saving is due to changes in personal saving (as opposed to corporate behavior)? Actually quite a bit. Of the 2.6 ppts shift in net private saving since 08Q1, 2.9 ppts is accounted for by the shift in personal saving.

How persistent will this shift in the personal saving rate be? This is the big question, in terms of the rebalancing issue (keeping in mind that the national saving identity is a tautology). Deutsche Bank provides an interesting set of calculations, which indicates how long it will take to hit the 20 year average net wealth/disposal personal income ratio.

Peter Hooper, Torsten Slok and Christine Dobridge write:

To try to gauge historical norms that households may aim for we appeal once again to average values that have prevailed over time. The 20-year average of household net worth is 533% of income. On this basis, net worth has returned about half way to its historical norm from the low reached in Q1. Chart 6 shows two prospective paths of adjustment back to the 20-year average, a 3-year path and a 5-year path. To follow these paths, we assume that households use half of their saving to pay down debt, and the other half to purchase assets. We also assume that income grows at 1% a year and asset values grow at the same rate. In order to rebuild wealth in three years then, households would need to raise their saving rate to 7% immediately and to 8% by 2012. In order to rebuild wealth in 5 years, however, households would need only a 2% to 3% saving rate. The saving rates implied by this wealth calculation are lower than the rates implied by the debt calculation. This is because net worth has risen since Q1 because of the rebound in the stock market. Net worth-toincome looks to have been about 500% in Q3; households have already made good progress towards their wealth target.

This set of calculations suggests at least a few years of relatively muted consumer behavior. The key factor is the rate at which households seek to reestablish their target net worth/income ratios.

It's interesting to contrast this perspective with that the "Blame it on Beijing" view, which holds Rest-of-World excess saving as the driver. I believe that when considering the US economy -- which is about three times as large as that of China (according to IMF WEO data) -- one can reasonably argue that what happens here is at least as important as what happens in East Asia (in contrast to some observers, I take Chairman Bernanke's recent speech, focusing on raising US national saving, as a welcome return to thinking about the primacy of US factors [speech text]).

Perspective 1: Everybody just panicked

The first interpretation of what went wrong is that financial markets were pricing risk correctly in 2006 but began to overprice risk in 2007. Keister and McAndrews analyzed a situation in which banks out-of-the-blue stop lending to each other, while Gorton interpreted events in terms of a classic bank run, in which the liquidation value of entities is feared to have fallen below their short-run liabilities, creating an incentive for lenders to refuse to renew short-term credit. In the benign version of this theory, the troubled entities would in fact be solvent if it were not for the "fire-sale" prices at which distressed assets must be sold in such an environment. If allowed to proceed unchecked, these fears could prove self-fulfilling and result in a rapid collapse of credit.

In terms of appropriate policy responses to this problem, I would distinguish between actions that might have helped if implemented earlier in the decade and options that were available if we begin the analysis in the fall of 2007. If we are looking at what might have been done years earlier that could have helped, the obvious answer is to consider regulatory reforms that might have prevented financial markets from reaching a point at which the liquidation spiral could be set off in the first place. Bank panics are not an inevitable result of private financial intermediation. The key principle for avoiding them is to ensure that the liabilities of financial institutions consist not just of short-term borrowing, but also of equity contributed by the owners. As long as this equity cushion exceeds potential liquidation losses, there is no incentive for short-run creditors to rush to get their cash back, and no insolvency for the bank in the event that the bank does experience a run. It was a regulatory failure to allow an explosion of off-balance sheet entities that borrowed short and lent long but were immune from bank capital requirements.

On the other hand, if we ask what policy options were available after we had entered the fall of 2007, this particular policy prescription is of no help, as the horses were already out and the barn had no capital. Since there are profound negative externalities from simply watching asset prices and lending collapse, there would seem to be a clear case for the Fed to fulfill the function of lender of last resort, lending and buying assets where others won't until the panic subsides and rational valuations return, and trying to do so in such a way that otherwise solvent enterprises were shielded from a panic bankruptcy.

Perspective 2: The core problem in credit markets preceded the crisis

An alternative perspective is that risk was incorrectly priced in the years leading up to the crisis with rationality only returning in 2007-2008. During 2004-2006 there was $2.7 trillion in new subprime and alt-A mortgage debt generated; (Ashcraft and Schuermann). Much of this was extended without documentation of the borrowers' income, little or no money down, negative amortization, and called for huge increases in the borrowers' monthly payments a few years into the loan. Yet somehow through the magic of securitization, this debt was repackaged into tranches that overwhelmingly received AAA credit ratings.

Such massive capital flows only made sense if one believed that house prices would continue to expand rapidly. Because this process was funneling such huge sums into the U.S. housing market, for a while house prices did just that, more than doubling between 2000 and 2005 according to the Case-Shiller 20-city house price index. U.S. household mortgage debt tripled in a little over a decade. According to this second interpretation, when house prices inevitably came crashing down, they brought with them defaults not just on the hybrid subprime and alt-A mortgages, but also put many otherwise sound borrowers underwater.

If it is claimed that the run-up in house prices and mortgage debt were a horrible miscalculation, what were the market failures that produced it? There is a long list of contributing factors. The originate-to-distribute model left the loan originators and securitizers with profits and lesser-informed buyers with the losses, creating agency problems; (Ashcraft and Schuermann). Intra-firm compensation schemes left decision-makers personally with the upside and stockholders with the downside, inducing excessive risk-taking; (Diamond and Rajan; Bebchuk and Spamann). The public-private GSEs Fannie Mae and Freddie Mac were woefully undercapitalized, giving private players the upside and the taxpayers the downside, and perhaps emboldening private securitizers to take even bigger risks (Hamilton). Both the compensation and procedures of the ratings agencies may have contributed to inaccurate perception of the safety of MBS (Ashcraft and Schermann), as did the mistaken perception that entities like AIG had the ability to insure against aggregate default risk. Moral hazard problems induced from the (ex post correct) belief that the U.S. government would absorb the downside on such gambles may have been another factor inducing excessive risk-taking.

If this perspective is the correct one, we can again distinguish between policies that would have made sense earlier in the decade and policies that were realistic options once we entered the crisis phase in 2008. If the above list of contributing market failures is correct, obviously addressing these with regulatory reforms before we reached the crisis point would have been the first-best option. On the other hand, if we condition on previous policy mistakes and ask what could have been done with options available in the fall of 2008, I disagree with those who reason that the way to correct the moral hazard problem is to hang tough in this situation and simply watch the losers go down. There are huge macroeconomic externalities from the resulting collapse of credit, which is why the government claiming it will not bail out the gamblers is not a credible strategy. Instead, this perspective suggests that the key policy question once we find ourselves in the fall of 2008 is how to allocate the necessary capital losses among lenders, stockholders, and the taxpayers in a way that minimizes the disruptive externalities of a credit collapse. If this is the correct perspective, the primary effect of targeted liquidity measures is simply to allocate these potential losses to the Federal Reserve. It is far from clear that this is the appropriate way for a democratic society to answer the question of who should bear the losses.

Finding the middle ground

I laid out the two perspectives above as diametrically opposed views. I nevertheless believe that the correct interpretation of events would acknowledge that each account contains some truth. It is hard to deny that there was some degree of misallocation of capital in the explosion of house prices and mortgage debt or that the resulting real estate price collapse was a key cause of the devaluation of securities and loss of bank equity that precipitated the banking panic phase. The remarks I presented at the Jackson Hole conference in August 2007 laid out precisely this scenario. We might disagree on how much of that $2.7 trillion in new subprime and alt-A debt represented a malfunctioning capital market, and characterize the middle ground between the two views in terms of choice of a number between 0 and 2.7. If that number is big enough, it may be that no realistically feasible level of bank equity would have been sufficient to assure solvency in the face of a deterioration of confidence, and there is certainly the potential for fire-sale asset price deterioration and a necessary role for the Federal Reserve to fulfill its role of lender of last resort. But obviously from this hybrid perspective, the Fed is performing a combination of liquidity provision and residual loss absorption through these operations, and would want to undertake the latter only with extreme care and thoughtfulness.

Conclusions

Participants in this session were asked to address two basic questions. The first is whether the Fed's targeted liquidity operations were necessary and effective. My answer is probably yes, though I would have a hard time persuading someone if they were not already convinced of that. The second question is whether such operations should be considered an important part of central banks' arsenal of tools in the future. To that my answer is categorically no. From virtually any perspective of our current problems, it would have made far more sense to address these problems with proper regulatory supervision prior to the crisis instead of targeted liquidity operations after the crisis unfolds.

You can view my complete set of comments prepared for the conference here.

Global macroeconomic imbalances -- massive borrowing by some countries and massive lending by others -- drove the financial boom and bubble that eventually burst into the current crisis. There is now nearly universal agreement that such imbalances cannot be sustained, and that the former deficit and surplus nations need to move toward macroeconomic balance.

However, rebalancing requires a fundamental reorientation of some of the world's major economies, a reorientation that will lead to major economic, social, and political tensions. Foreign borrowing by the deficit countries fed an orgy of consumption that was wildly popular so long as it continued; but the accumulated foreign debt that has resulted will now begin to impose sacrifices that will be just as wildly unpopular. Nations that have relied on foreign borrowing to fuel government and household spending will have to cut back drastically. They face a reduction in real wages, in consumption, in the standard of living. At the same time, nations that have relied on exports as the engine of economic growth will have to figure out how to power their economies without relying on foreign markets. These political economies dominated by powerful export interests face fundamental challenges to those interests, as their export orientation may no longer be sustainable.

In deficit and surplus nations alike, the attempt to adjust to a new international economic reality will almost certainly lead to major conflicts within nations and among nations. What are these conflicts likely to be, and what do they say about prospects for the future? For guidance, one turns to history and theory. First, this paper reviews some of the extensive historical record on how the world economy, and the countries within it, has attempted to redress macroeconomic imbalances. Then it explores what theory says about the adjustments necessary to rebalance, and how this is likely to play out in national and international political economies.

For some related commentary, see our joint work in this examination of the causes of the ongoing financial and economic crisis in this post (and this article).

...recent research questions the claim that the financial panics themselves contributed to their contemporaneous and severe employment downturns.

The post continues:

The timing was different in this recession -- the largest employment drops seemed to come immediately after the financial panic -- but a recent paper by Ravi Jagannathan, Mudit Kapoor and Ernst Schaumburg of Northwestern argues that the coincidence is just as misleading. They argue that the changing global economy -- with more employment of residents in developing countries like China -- created a glut of savings in those countries, and was destined to reduce employment in developed countries regardless of whether there had been a financial panic.

This paper was discussed earlier on this weblog, and Professor Jagannathan provided some clearly exposited counterarguments to my criticisms in his comments. Indeed, I think there's a lot more subtlety to that paper, and the posited causal chain, than is related above, even if I disagree with the main thesis (see [1]).

But even if you believe the Jagannathan, Kapoor, Schaumberg thesis in full, this does not invalidate the idea that banks provide useful intermediation services, and that failing banks, insolvent banks, or banks that are retrenching in terms of their lending will exert a contractionary influence on the economy. (After all, if one reads Bernanke's 1983 paper (AER 73(3)), you'll see him focus on the declining ratio of loans to the sum of checking and savings deposits, shown in his Table 1) It's just that you think that what caused the banks to overleverage is the "saving glut". (Another way to put it is you think the financial system is the proximate cause, and Beijing the ultimate cause, a la "Blame it on Beijing"). This interpretation is suggested to me by this passage in the conclusion to Jagannathan et al.:

...History might have taken an entirely different path with better risk management controls in place in the US but then again, financial innovation might just have found a different way of getting highly leveraged deals done off-shore or through creative accounting. The root cause of the excess liquidity in the global financial system must be addressed, otherwise we are just squeezing the proverbial balloon only to see it bulge out somewhere else. However, this does not negate the need for the development of improved risk management in the broadest sense in order to ensure financial stability and prosperity going forward.

One truly odd aspect of Professor Mulligan's discussion is the treatment of the banking system as the financial system. I've been hearing a lot about the "shadow financial system" for quite a while -- and I have some inkling there was trouble brewing there somewhat before the plunge in employment and output in September 2008. Below, I graph one aspect of the broader financial system.

So in my view, the financial system problems preceded the initial decline in employment and output. That doesn't preclude the possibility of the subsequent declines in employment and output causing further financial system problems. That I believe is what is called an adverse feedback loop.

More graphs from the real world in this post and the IMF's GFSR.

Postscript: Brad Delong also appears to find this post befuddling.

Since I'll be trying to answer this question quantitatively using some equations, I'll begin with some notation. Let u_t denote the unemployment rate as of the end of a particular quarter t; currently u_t = 9.8 for t corresponding to 2009:Q3. I'll presume that the question we're interested in is what sort of inflation rate we should expect over the next two years, and so I'll let π_t+8 denote the average inflation rate (quoted at an annual rate) over the next 8 quarters as measured by the price index for personal consumption expenditures (data from FRED). Of course at the current time (t = 2009:Q3) we don't yet know what the value of π_t+8 is going to be-- that's what we're trying to predict.

One way to come up with a prediction is to look at a regression of the historical values of π_t+8 that we currently know (that is, for t = 1948:Q1 through 2007:Q2) on the historical values of u_t. The results from this regression (with Newey-West standard errors in parentheses) and scatterplot of the raw data are given below.

Each circle corresponds to a particular quarter t between 1948:Q1 and 2007:Q2. Horizontal axis: value of unemployment rate in last month of quarter t. Vertical axis: average PCE inflation rate for two years that came subsequent to quarter t. Upward sloping line is the estimated regression relation.

This is of course a version of the famous Phillips Curve, according to which higher rates of unemployment are supposed to be associated with lower rates of inflation. But there's just one problem-- whereas the Phillips Curve is supposed to slope down, the relation we just estimated slopes up. If a given quarter's unemployment rate was above average, it's likely that the subsequent inflation rate was above average as well.

The traditional interpretation of this seemingly anomalous result is that there's another important component of the Phillips relation that was left out of the above regression, which is expectations of inflation. Particularly for the observations from the 1970s and 1980s, people at the time were expecting inflation to remain high. The traditional argument is that if we could add inflationary expectations as a shift variable to the regression, we would see the anticipated negative relation between unemployment and inflation.

One simple way to try to do this is to add lagged values of inflation to the above relation, that is, include π_t, π_t-1, and earlier values that would have been known as of quarter t, and see what the contribution of unemployment is to that modified regression. Results of that regression when we add the previous 3 years of inflation observations are given below. Note I haven't reported the individual estimated coefficients on π_t, π_t-1, and π_t-11 because that exceeds our quota for how many numbers can be reported in an Econbrowser entry.

If you allow for the possibility of changing inflation expectations in this way, the result is that the coefficient on unemployment switches from positive to negative, and the negative coefficient is quite statistically significant. In other words, if we're going to forecast inflation over the next two years on the basis of what inflation has been over the last three years along with the current unemployment rate, the unemployment rate would enter that forecast with a negative sign-- higher unemployment causes us to predict lower inflation.

The forecasts of the above regression (in blue) are compared with the actual values (in black) in the top panel below. We don't know what the actual values after 2007:Q2 are going to turn out to be yet. But the forecast of the model for the average inflation rate between 2009:Q4 and 2011:Q4 is -0.5%.

Top panel: Black line is the value of subsequent average 2-year inflation rate (π_t+8) corresponding to each indicated date t Blue line is the predicted value from the dynamic regression for each indicated date t. Bottom panel: unemployment rate as of last month of indicated quarter t.

Does that mean that deflation is the best forecast given the current high level of unemployment and the recent moderate behavior of inflation? It's certainly not a crazy forecast, given the historical correlations. But the critical question would seem to be whether the contribution of inflationary expectations in the current situation is adequately captured by the recent observed behavior of π_t.

If for further evidence on inflationary expectations you looked at the gap in yields between nominal Treasuries and TIPS, you'd say inflation expectations remain quite low-- currently the 10-year spread corresponds to anu average annual inflation rate under 2% for the next decade.

On the other hand, if your preferred indicator is dollar commodity prices and the sinking exchange rate, the claim that inflationary expectations will remain low is less compelling.

But regardless of where you stand on that question, I believe the Federal Reserve is correct in thinking that high levels of unemployment are a factor that will put downward pressure on inflation over the next two years. The question is how big a pull the dollar and commodities could prove to be in the other direction.

Today, we're fortunate to have Willem Thorbecke, Senior Research Fellow at Asian Development Bank Institute and a Consulting Fellow at Japan's Research Institute of Economy, Trade and Industry, as a guest contributor.

Asia's role in the propagation of the global recession has been a subject of study, but relatively little attention has been devoted to the interaction of exchange rates and production chains. The structure of East Asian production networks and the severity of the recession places a premium on policy coordination in the region.

Multinational corporations in East Asia have established value chains by slicing up production processes and allocating the production blocks across countries in the region based on relative endowments of capital, skill, labor, and infrastructure. As MNCs increase their tenure in developing Asia, they procure more from local firms. This leads to the formation of industrial clusters, and local engineers and skilled workers begin migrating among firms and sectors. They bring their accumulated human capital with them and disperse it across the economy, promoting technological assimilation and productivity growth.

For instance, Kraemer and Dedrick document that the lion's share of the international production of notebook PCs is produced in the Yangtze River Delta by Taiwanese Original Design Manufacturers (ODMs). These manufacturers form part of a network that includes branded firms such as HP, Apple, and Toshiba, suppliers of key parts and components, producers of basic industrial materials, and makers of operating systems and CPU. Local Chinese firms supply connectors, batteries, switches, and displays and are also active in molding, casting, forging, plating, and module-assembling. Both digital and human networks enable PC producers to react efficiently in real time to changes in consumer preferences and technology. Firms assembling the notebook PCs have also kept inventories lean by processing 98 percent of the orders within three days. Productivity growth within this value chain has been amazing.

The Achilles heel of Asian production networks, though, is its dependence on developed economies for final demand. As private demand in the U.S., Europe, and Japan fell during the crisis, exports produced within regional production networks collapsed. This in turn caused output and employment throughout Asia to plummet.

Signs are emerging that East Asian production networks are reviving. Figure 1 shows imports for processing into China and processed exports from China to the rest of the world. Imports for processing are goods that are brought into China for processing and re-export. Processed exports are final goods that are produced using imports for processing. The figure shows that imports for processing and processed exports both collapsed earlier this year. Since then, however, imports for processing have recovered 85 percent of their losses and processed exports 75 percent. Thus trade within East Asian production networks is recovering. This is the good news coming out of Asia.

The not so good news is that exchange rate arrangements within the region may not allow this revival to be sustained. While many Asian countries have adopted greater exchange rate flexibility, China has returned to a de facto dollar peg. This implies that exchange rates between Asian countries have become very volatile (see, e.g., Figure 2). In general the effect of exchange rate volatility on trade is ambiguous. Within East Asian production networks, however, both theoretical and empirical evidence indicates that exchange rate volatility deters trade (see Thorbecke (2008) and Hiyakawa and Kimura (2009)). This effect arises because the service link cost for production blocks separated by national borders is an increasing function of risk and uncertainty, and exchange rate volatility increases risk and uncertainty. In a recent survey of Japanese MNCs, Professor Takatoshi Ito and his co-authors found that exchange rate stability between Asian currencies is essential for the uninterrupted flow of parts and components within regional production networks.

In addition, since Asian economies do not only cooperate within production networks but also compete in third markets, China's exchange rate peg puts pressure on other countries in the region to prevent their exchange rates from appreciating. Thorbecke and Smith (2009) reported that exchange rate appreciations across Asian supply chain countries are necessary to reduce global imbalances. If the renminbi is kept fixed, it becomes much harder for the huge surpluses generated within East Asian production networks to lead to a generalized appreciation of Asian currencies.

A solution to this impasse would be for China to abandon its de facto dollar peg and adopt a regime characterized by a multiple-currency, basket-based reference rate with a reasonably wide band. In this case, there would be more stability between the renminbi and other Asian currencies. In addition, exchange rates in the region would be able to appreciate together in response to regional trade surpluses.

This move would be difficult for China. Labor-intensive exports and thus employment in these industries are sensitive to exchange rate appreciations. On the other hand, exchange rate appreciations would reduce the need for Chinese and other Asian central banks to continue accumulating U.S. Treasury securities. Private and social rates of return are much higher for investments in education, healthcare, and clean water than for investments in U.S. government securities. An appreciation of the RMB would also allow Chinese consumers to purchase more of the final manufactured goods that were previously exported to developed markets (Thorbecke, 2009). A stronger renminbi would thus allow Chinese workers to enjoy more of the fruits of their labor while reducing their dependence on final demand in the West.

A good policy mix for Asia would thus involve relatively stable intra-regional exchange rates that could appreciate together in response to regional trade surpluses combined with more spending on human capital. Stable exchange rates would help to strengthen regional production networks. Joint appreciations would prevent unpleasant outcomes such as beggar-thy-neighbor policies and excessive reserve accumulation while also reducing global imbalances and encouraging production for domestic markets. Spending on human capital would facilitate technology transfer by allowing firms in developing Asia to become more involved in the engineering and design aspects of production. If Asian countries could climb the value chain in this way and focus on knowledge-intensive activities rather than assembly operations, not only would living standards in developing Asia rise but the region could become an engine of growth for the rest of the world.

This post written by Willem Thorbecke

4-week average of seasonally adjusted weekly initial claims for unemployment insurance, from Department of Labor via Webstract. Vertical lines drawn at dates when the economic recovery began as judged by the National Bureau of Economic Research.

I first called attention on April 9 to the regularity that a peak in new claims for unemployment insurance usually means that a recovery from the recession will begin within two months. On May 7, I calculated that there was an 85% probability that the peak in new claims was behind us. Although August brought some setbacks to new claims, the last few weeks have confirmed the pattern of a significant drop in unemployment claims typical of economic recovery. Unfortunately, the level remains high enough that net job growth is likely still quite negative.

Black line: seasonally adjusted weekly new claims for unemployment insurance from January 1 through October 15, 2009. Blue line: 4-week average.

On Friday we received two quite favorable indicators from the Federal Reserve. The first is that the nation's capacity utilization rate has been steadily climbing since June. Calculated Risk regards that as another reliable indicator that the recession is over.

Source: FRED

Even more encouraging was the Fed's report that its index of industrial production rose by 0.7% in the month of September and at a 5.2% annual rate during the third quarter. Paul Krugman thinks that could mean a third-quarter GDP annual growth rate above 4%.

Source: FRED

That kind of growth is inconsistent with a jobless recovery. After the 2001 recession, we didn't see a GDP growth rate of that size until 2003:Q3. There are two separate feedback mechanisms operating. The first is that rapidly rising output will eventually bring hiring up with it. The second is that the high unemployment rates will bring more foreclosures and cause spending and output to sputter.

Which is it going to be? Nonfarm payroll employment is the key indicator to watch from here.

The Dollar

First, let's take a look at what a survey of approximately 50 banks and financial firms indicates, for the value of the dollar (Fed broad index) and the euro/dollar exchange rate.

The data are from FX4casts, which is the successor to Currency Forecasters Digest; Jeff Frankel and I used these survey data in our studies of expectations in foreign exchange markets [1] [2] [3]. Some additional results are reported in [4] and [5].

Both series are plotted in logs, and exchange rates defined such that up implies a stronger dollar. In both cases the 95 percent interval is shown. What is clear is the (geometric) mean forecast for the dollar implies relative stability. It's true that the lower bound implies some depreciation over the next year -- but no more than 5.6 percent decline (in log terms). That's hardly calamitous. But it would be helpful in terms of facilitating rebalancing (as I pointed out a few weeks ago [6] [7], dollar decline makes a lot of sense, perhaps even more than the 5.6 percent implied by the lower bound).

If one is more concerned about the dollar's value against major currencies (perhaps because of an interest in cross-border valuation effects on financial assets), one would want to see how the dollar evolves against the euro. Here, it appears the mean forecst implies strength over the medium term. Even the scenario for the weakest dollar implies no more than a 7.5 percent decline, at the 3 month horizon.

More from US Treasury's semi annual report and from Fred Bergsten in Foreign Affairs. Appendix I to the Treasury report discusses the dollar's reserve currency status, a topic discussed in a historical perspective by myself and Jeff Frankel here and here.

Double-dip Recession?

Anxiety remains (rightly so) that the economy will suffer a relapse, with the date perhaps in 2010 or 2011. The latest WSJ survey indicates little evidence of such fears.

The mean forecast suggests a story largely consistent with last month's forecasts. The biggest revisions (upward) came between the August and September surveys. Even then, it won't be until close to end-2010 that GDP reattains its previous peak. Forecast dispersion remains large, though, and the trimmed high (Dean Maki, Barclays) indicates reattainment around mid-2010.

The trimmed low (David Malpass) is, interestingly, kind of a "W", although not the typical discussed one; Malpass forecasts 0% SAAR growth in 2009Q4. Bart van Ark (Conference Board) has a drop in growth to 0.5% SAAR in 2010Q1.

Of course, these are conditional forecasts -- that is they incorporate assumptions regarding a certain combination of fiscal and monetary policies. I would guess most of them are assuming "reasonable" policy mixes. But some of the recent discussions of "W" relate to mistakes in policy making, such as a too-early monetary tightening (Roubini, Krugman) or inadequate stimulus (Krugman) or the time profile of the stimulus (Feldstein).

Federal Reserve assets, in billions of dollars, seasonally unadjusted, from Jan 1, 2007 to September 23, 2009. Wednesday values, from Federal Reserve H41 release. Agency: federal agency debt securities held outright; swaps: central bank liquidity swaps; Maiden 1: net portfolio holdings of Maiden Lane LLC; MMIFL: net portfolio holdings of LLCs funded through the Money Market Investor Funding Facility; MBS: mortgage-backed securities held outright; CPLF: net portfolio holdings of LLCs funded through the Commercial Paper Funding Facility; TALF: loans extended through Term Asset-Backed Securities Loan Facility; AIG: sum of credit extended to American International Group, Inc. plus net portfolio holdings of Maiden Lane II and III; ABCP: loans extended to Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility; PDCF: loans extended to primary dealer and other broker-dealer credit; discount: sum of primary credit, secondary credit, and seasonal credit; TAC: term auction credit; RP: repurchase agreements; misc: sum of float, gold stock, special drawing rights certificate account, and Treasury currency outstanding; other FR: Other Federal Reserve assets; treasuries: U.S. Treasury securities held outright.

Rather than trying to sort out which of the asset-side actions by the Fed were most effective, I was curious to see what conclusions emerge if you just lump all the Fed's targeted liquidity measures together, as proxied by the difference between the Fed's total assets and its holding of conventional Treasury securities. Thus for purposes of the discussion below I propose to measure the overall magnitude of targeted liquidity actions by the sum at any given date of everything other than the light blue area in the graph above.

One indicator of the financial stress to which the Fed was responding is the gap between the 3-month LIBOR rate and the 3-month T-bill rate, often described as the TED spread. This spread went through a series of four dramatic waves over the last two years. I looked at each of these four surges in the TED spread as potential case studies for the effects that targeted liquidity actions might have.

3-month LIBOR rate minus 3-month T-bill rate, in basis points, January 2007 to September 2009. Data from Webstract.

The first episode began with the freezing of BNP Paribus assets on August 9, 2007, subsequent to which the TED spread reached a peak of 242 basis points on August 20. Targeted liquidity operations consisted only of a quite minor and temporary expansion of repos and discount borrowing. The sole special action by the Fed that made it onto the New York Fed's Financial Turmoil Timeline is an announcement by the Fed on August 10 that it would provide liquidity as needed. It seems safe to conclude that in this case, interest rate spreads rose in spite of the Fed announcement, and eased for reasons other than an increase in the volume of targeted liquidity operations by the Fed.

Brick line: cumulative targeted Fed liquidity actions subsequent to August 8, 2007, as measured by (a) total Fed assets net of Fed Treasury holdings on Wednesday of indicated week minus (b) total Fed assets net of Fed Treasury holdings as of August 8 in billions of dollars. Green line: TED spread at indicated date, in basis points. Black line: August 10, 2007.

The second episode occurred in the fall of 2007 as problem bank assets became acknowledged. Taylor and Williams pointed to the fact that the TED spread turned down well before targeted liquidity operations were cranked up as evidence that the latter were not the cause of the former. However, Christensen, Lopez and Rudebusch noted that the Fed announced its intention to make aggressive use of the Term Auction Facility and currency swaps on December 12. The TED spread peaked at 225 basis points on December 12, and fell steadily after the Fed's announcement. This episode could thus be viewed as consistent with the claim that targeted Fed liquidity measures are a potentially powerful tool for changing this interest rate spread, so much so that simply announcing their intended implementation can have dramatic effects.

Brick line: cumulative targeted Fed liquidity actions subsequent to October 31, 2007, as measured by (a) total Fed assets net of Fed Treasury holdings on Wednesday of indicated week minus (b) total Fed assets net of Fed Treasury holdings as of October 31 in billions of dollars. Green line: TED spread at indicated date, in basis points. Black line: December 12, 2007.

In the spring of 2008, the TED spread exhibited a similar spike. In this case the magnitude of the Fed's targeted liquidity operations eventually grew to three times the size of what it had implemented in December. The Fed announced its new Primary Dealer Credit Facility on March 16 and also significantly expanded its repo positions and Term Auction Credit over the next several weeks. The TED spread peaked at 200 basis points on March 19. Christensen, Lopez and Rudebusch chose March 24, the date of Bear Stearns' rescue, as the key turning point, after which risk spreads became significantly lower than their model predicts they otherwise would have been.

Brick line: cumulative targeted Fed liquidity actions subsequent to February 20, 2008, as measured by (a) total Fed assets net of Fed Treasury holdings on Wednesday of indicated week minus (b) total Fed assets net of Fed Treasury holdings as of February 20 in billions of dollars. Green line: TED spread at indicated date, in basis points. Black line: March 24, 2008.

These actions were in turn dwarfed by steps adopted in the fall of 2008; note the scale on the vertical axis in the diagram below is five times as big as that in the previous three graphs. There were so many new Fed measures adopted at this time that it would be hopeless to single out any one. I've indicated Lehman's September 15 filing for bankruptcy as one important reference date on which we could all agree in the diagram below. Targeted Fed liquidity operations increased by $691 billion between September 3 and October 8, despite which the TED spread rose from 114 to 385 basis points and would continue to rise until peaking at 458 basis points on October 10. By November 12 nonstandard Fed assets had expanded by $1312 billion.

Brick line: cumulative targeted Fed liquidity actions subsequent to September 3, 2008, as measured by (a) total Fed assets net of Fed Treasury holdings on Wednesday of indicated week minus (b) total Fed assets net of Fed Treasury holdings as of September 3 in billions of dollars. Green line: TED spread at indicated date, in basis points. Black line: September 15, 2008.

To get a sense of the overall connection between targeted liquidity actions and the TED spread over this period, we can look at a scatter plot such as that shown below. Each square in this plot corresponds to a particular week over the period January 10, 2007 through September 16, 2009. The horizontal axis represents the change in targeted liquidity operations over that week, while the vertical axis records the change in the TED spread over that week. If we thought that the correlation between these two variables resulted from the effects of liquidity operations on the spread, we would have expected a negative correlation-- when the Fed expands its balance sheet, the intention is to bring the spread down rather than up. In fact the regression line relating the two variables has a positive slope-- if the Fed expanded its balance sheet in a given week, the TED spread was more likely to go up rather than down that week, though the slope is statistically indistinguishable from zero.

Horizontal axis: (a) total Fed assets net of Fed Treasury holdings on Wednesday of a given week minus (b) total Fed assets net of Fed Treasury holdings as of the previous Wednesday. Vertical axis: Wednesday-to-Wednesday change in TED spread for the same week. Straight line: regression relation. Sample period: January 10, 2007 to September 16, 2009.

Of course, the reason that the line slopes up rather than down is that the correlation does not simply reflect the response of the economy to the Fed's actions. It also results from the response of the Fed to the economy. Specifically, when the TED spread increased, the Fed responded by increasing its targeted liquidity operations. Presumably it is this endogenous response by the Fed that produces the overall positive correlation in the data. This is a familiar problem in interpreting statistical correlations, and unfortunately it is difficult for any method to resolve. All we can say is that, presumably because of this endogeneity of the Fed's response, a beneficial effect of targeted liquidity operations on credit spreads is not the dominant feature one sees in the data.

Or to put it another way, it's not that we saw that things got better whenever the Fed expanded its targeted liquidity operations. Instead the most we could claim that if the Fed had not implemented its actions, things would have been much worse.

...We argue that the large increase in the developed world's labor supply, triggered by geo-political events and technological innovations, is the major underlying cause of the global macro economic imbalances that led to the great recession. ...

... The inability of existing institutions in the US and the rest of the world to cope with this shock set the stage for the great recession: The inability of emerging economies to absorb savings through domestic investment and consumption due to inadequate national financial markets and difficulties in enforcing financial contracts; the currency controls motivated by immediate national objectives; and the inability of the US economy to adjust to the perverse incentives caused by huge money inflows leading to a breakdown of checks and balances at various financial institutions. ...

The paper concludes:

While there is plenty of blame to go around for mistakes, the macro forces triggered by the labor shock is like a tidal wave that needed to wash ashore no matter what. History might have taken an entirely different path with better risk management controls in place in the US but then again, financial innovation might just have found a different way of getting highly leveraged deals done off-shore or through creative accounting. The root cause of the excess liquidity in the global financial system must be addressed, otherwise we are just squeezing the proverbial balloon only to see it bulge out somewhere else. However, this does not negate the need for the development of improved risk management in the broadest sense in order to ensure financial stability and prosperity going forward.

China and India will continue to need to bring tens of millions of rural laborers into the productive workforce in the coming decades and the world economy must find a sustainable way of dealing with this influx. Clearly China's export led growth strategy of the past cannot continue indefinitely and domestic consumption will have to grow as a share of GDP. At the same time, Western economies will necessarily have to adjust to a new equilibrium in which commodities are scarcer and households face stiffer competition for jobs.

This is a long paper (38 pages of text and graphs), and there's lots to digest. It's interesting that US budget deficits and tax policies make not a single appearance in the text (well, not quite -- "low taxes" are made possible by the saving glut, on page 6; and the budget balance shows up as a figure on page 16). Pretty remarkable; below is the latest version of a picture I've presented many times on this blog.

In any case, my reasons for looking askance at the the "Blame It on Beijing" view are laid out here:

...while I won't say that the idea of saving flows coming from East Asia had some role in the financial crisis we're now undergoing is completely without content, I'd say one has to think about how those flows came about, as much as how big they are. We don't usually think of the rest-of-the-world driving macroeconomic events in the US (here's my take: [10]), and I still don't think it's time to start.

For my money, I think a more plausible worldview is provided by Maurice Obsteld and Ken Rogoff (h/t Mark Thoma). In the intro to "Global Imbalances and the Financial Crisis: Products of Common Causes," they write:

We too believe that the global imbalances and the financial crisis are intimately connected, but we take a more nuanced stance on the nature of the connections. In our view, both of these phenomena have their origins primarily in economic policies followed in a number of countries in the 2000s (including the United States) and in distortions that influenced the transmission of these policies through financial markets. The United States' ability to finance macroeconomic imbalances through easy foreign borrowing allowed it to postpone tough policy choices (something that was of course true in many other deficit countries as well). Not only was the U.S. able to borrow in dollars at nominal interest rates kept low by a loose monetary policy. Also, until around the autumn of 2008, exchange-rate and other asset-price movements kept U.S. net foreign liabilities growing at a rate far below the cumulative U.S. current account deficit. On the lending side, China's ability to sterilize the immense reserve purchases it placed in U.S. markets allowed it to maintain an undervalued currency and postpone rebalancing its own economy. Had seemingly easy postponement options not been available, the subsequent crisis might well have been mitigated, if not contained.

We certainly do not agree with the many commentators and scholars who argued that the global imbalances were an essentially benign phenomenon, a natural and inevitable corollary of backward financial development in emerging markets. These commentators, including Cooper (2007) and Dooley, Folkerts-Landau, and Garber (2005), as well as Caballero, Farhi, and Gourinchas (2008) and Mendoza, Quadrini, and Rios-Rull (2007), advanced frameworks in which the global imbalances were essentially a "win-win" phenomenon, with developing countries' residents (including governments) enjoying safety and liquidity for their savings, while rich countries (especially the dollarissuing United States) benefited from easier borrowing terms. The fundamental flaw in these analyses, of course, was the assumption that advanced-country capital markets, especially those of the United States, were fundamentally perfect, and so able to take on ever-increasing leverage risklessly. In our 2001 paper we ourselves underscored this point, identifying the rapid evolution of financial markets as posing new, untested hazards that might be triggered by a rapid change in the underlying equilibrium.

Obstfeld and Rogoff are pre-eminent economists working in the international finance field (as opposed to domestic macro and monetary economics). As Paul Krugman has pointed out, international finance types have typically been less enthralled about untramelled financial deregulation, and the self-regulating abilities of financial markets (currency and financial crises being a topic we discuss). So far, such skepticism seems to have been validated by recent events.

See also this discussion of an earlier presentation by Obstfeld and the Chinn and Frieden view.

Update 10/15/09, 10:40am:

ExxonMobil (XOM) has been producing a little over 2.4 million barrels of oil a day for the last year and a half, its lowest rate of production over the last decade. The dark blue line in the figure below shows the company's production each year since 1999. Four years ago, Stuart Staniford noted that ExxonMobil's 2001 annual report predicted 3% annual growth in production between 2001 and 2007. That projection appears as the red line in the graph below; didn't quite come out as planned. Stuart's theory was that the company correctly predicted the contribution of its new discoveries, but underestimated the declining production rates from mature fields.

ExxonMobil again predicted in 2006 that it could achieve 3% annual growth over 2006-2011. I've shown that forecast as the lighter blue line in the figure. We still have two more years to make that one right, I suppose.

Dark blue: ExxonMobil's annual net production of crude oil and natural gas liquids in millions of barrels per day. 1999-2008 from company's annual reports. 2009 based on average of 2009:Q1 and 2009:Q2. Red: forecast from the company's 2001 annual report. Light blue: forecast from the company's statements in 2006.

The Wall Street Journal reported on Wednesday that ExxonMobil is prepared to spend $4 billion to buy 1/4 interest in the Jubilee oil field off the coast of Ghana, which would represent 15% of the oil giant's 2008 capital and exploration budget. Alan von Altendorf thinks they can't make a good return unless they sell the oil for $100/barrel. Presumably the company is reckoning on more oil in the field than current estimates suggest. But even if von Altendorf's calculations are off by a factor of two, it still seems to signal a change in philosophy for a company that has historically been extremely careful with its investments in order to maintain its position as a very low-cost producer.

But what else is the company to do? It's not like they haven't tried to take advantage of Russia's or Venezuela's strong commitment to protect foreign investors or the peaceful aspirations of Nigerian rebels.

Chevron (CVX) and many other companies are finding clever new ways to get more oil out of mature U.S. fields. That may well succeed in slowing the rate at which production from those fields declines over time. But to get the plot in the graph above to slope up you really need to develop new fields.

The New York Times is encouraged by the "brisk pace of new discoveries" which the paper reports "have totaled about 10 billion barrels in the first half of the year".

The Oil Drum, always a party pooper, notes that the world likely consumed that much in the first four months of the year.

First, let's take a look at the exports-to-GDP and imports-to-GDP ratios.

What is clear is that in nominal terms, there has been a trend increase in trade openness, with a big (although not completely unprecedented) break at end-2008. However, nominal ratios incorporate terms of trade changes (think oil price increases in 2008); using log real ratios (I take logs because ratios of chain weighted indices don't have a ready interpretation), one sees the jagged movements smoothed out, but trends intact.

What these graphs hint at is a break in the pace of integration. To investigate this issue more formally, I estimate error correction models for both non-agricultural goods exports and non-oil goods imports.

For exports, the specification is:

Δ exp _t = θ ₀ + ρ exp _t-1 + θ ₁ y^* _t-1 + θ ₂ r _t-1 + σ ₁ Δ exp _t-1 + σ ₂ Δ y^* _t-1 + σ ₃ Δy ^*_t-2 + σ ₄ Δ r _t-1 + v _t

Note that the rest-of-world GDP variable (y^*) is the export weighted real GDP calculated by the Federal Reserve Board, for 1970q2-07q4; the 2008q1-09q2 data I estimated using a regression of first differenced GDP on a current and four lags of first differenced industrial country industrial production.

(Data sources: BEA 2009q2 3rd release for imports, exports, GDP; Federal Reserve Board for broad index of real dollar value; personal communication/Fed for rest-of-world export weighted GDP; and IMF International Financial Statistics for industrial country industrial production (nsa).)

I estimate an error correction version of this model over the 1973q3-2009q2 period, wherein there is a long run relation between the levels of imports, income and the real dollar.

In this specification, the long run elasticities are given by the ratio θ _i/ ρ . When this model is estimated over the 1973q3-2008q4 period, one obtains sensible estimates (in that higher foreign income or a weaker dollar induces greater exports in the long run).

This specification fits fairly well, with an adjusted R-squared of 0.34, and the serial correlation test again failing to reject. The long run coefficients are significant, as is the reversion coefficient (ρ).

One way of evaluating whether the 2008q4-09q1 observations are anomalous, in a statistically significant sense, is to examine the recursive residuals. A recursive residual is the time t prediction error based upon the regression estimated up to time period t-1, but using time t values of the X variables. Figure 3 depicts the 95% standard error band; an observation outside that band suggestive structural instability in the regression equation.

For imports, an analogous specification is estimated:

Where Imp is real imports, y is real income, and r is the real value of the dollar.

Δ imp _t = β ₀ + φ imp _t-1 + β ₁ y _t-1 + β ₂ r _t-1 + γ ₁ Δ imp _t-1 + γ ₂ Δ y _t-1 + γ ₃ Δ r _t-1 + u _t

The specification fits fairly well, with an adjusted R-squared of 0.35. The long term coefficients are statistically significant, while the reversion coefficient (φ) is also significant and negative.

The recursive residuals are shown in Figure 4:

One observation is that using a fairly flexible econometric form (the ECM) with pretty high estimated income elasticities, I cannot track the downturn in exports and imports. (The estimated short run export income elasticity is 2.8, while the import income elasticity is 1.8.)

Now, what is true is that a constant parameter specification such as these cannot accomodate the time variation that Freund stresses -- namely that income elasticities in the 2000's have been substantially higher than in the previous decades.

If one restricts the sample to 2000q1-09q2, the short run export income elasticity falls to 2.5, but rises to 2.5 for imports. Nonetheless, the one-step ahead recursive residuals test still rejects stability for 08q4 and 09q1.

It's possible that the addition of other regressors would, in conjunction with a shorter sample, eliminate this instability in the equations. Pursuing that avenue remains for future research.

A couple of other interesting results arise from the full sample estimation. The long run price elasticity of exports is 1.4, while that for imports is 0.6. These are substantially higher than those I've reported in the past [5]; however, these estimates are subject to considerable uncertainty. The 90% confidence interval for the long run exports price elasticity is [0.56,2.22] (recalling the long run elasticity is the ratio of two coefficients).

Arnold Kling has proposed a "recalculation" theory of macroeconomics:

My claim (which is not original with me-- it is recognizably Austrian) is that a recession can be thought of as a recalculation. Imagine a central planner who decides to radically change plans. He has a huge recalculation to make in order to figure out where to allocate labor and capital. He says to some people, "Wait a minute. I am thinking. Some of you just have to stand idle while I figure this out."

The market economy is like that central planner. We are undergoing a Great Recalculation....

In conventional, hydraulic macro, we think in terms of this one good called GDP, and the output gap is the difference between how much of this GDP stuff we could produce if everybody were working and how much we are actually producing with all the unemployment over and above "normal." We assume that "normal" unemployment, which is structural and frictional, is some roughly constant fraction of the labor force.

The way I look at things, we have a huge amount of structural and frictional unemployment these days. There is very little cyclical unemployment--limited to autos and household durable goods. If you measure the output gap using my definition of cyclical unemployment, then the output gap is tiny.

Paul Krugman finds this unconvincing:

the whole notion falls apart when you ask why, say, a housing boom-- which requires shifting resources into housing-- doesn't produce the same kind of unemployment as a housing bust that shifts resources out of housing.

It is not clear whether Paul is intending his observation as a refutation of all models that attribute a fall in potential output to sectoral imbalances, though Tyler Cowen seems to read his statement that way. To their discussion I'd like to add a paper I published in 1988. There I presented a model in which unemployment arises from a drop in the demand for the output of a particular sector. The unemployed workers could consider trying to retrain or relocate, or might instead decide to wait it out in hopes that the demand for their specialized skills will come back.

If instead of a drop in the demand for sector A there was a boom in the demand for sector B, it is true that some workers in sector A might choose to retrain or relocate, and be temporarily unemployed as a result. But the key kind of unemployment that I think this sort of model describes-- waiting for an opening in the particular area in which you've specialized-- is caused by drops in demand, not increases.

Insofar as the frictions in that model are of a physical, technological nature, increasing the money supply would simply cause inflation and not do anything to get people back to work. I should emphasize that I built that monetary neutrality into the model not because I think it is the best description of reality, but in order to illustrate more clearly that there is a type of cyclical unemployment that stimulating nominal aggregate nominal demand is useless for preventing.

My personal view is that real-world unemployment arises from the interaction of sectoral imbalances with frictions in the wage and price structure of the sort documented by Truman Bewley and Alan Blinder. The key empirical test, in my opinion, is at what point inflationary pressures begin to pick up. If Krugman is correct, we could have much bigger monetary and fiscal stimulus without seeing any increase in inflation. If the sectoral imbalances story is correct, it would be possible for inflation to accelerate even while unemployment remains quite high.

I would also emphasize that even with a sectoral-imbalance interpretation, there is a clear potential for fiscal policy to have made a positive contribution by preventing job losses caused by state and local government spending cuts. Under the "hydraulic macro" view, as long as the federal government increases spending by the same amount that state and local governments decrease spending, we'd be OK. Under the "sectoral imbalances" view, a $1 increase in federal spending combined with a $1 decrease in state spending is on balance contractionary, and that downturn would have been preventable by a simple fiscal transfer from the federal to the state level. I note that one-fifth of the seasonally adjusted job losses in September came from a decrease of 47,000 in the number of state and local government employees. I am still persuaded that the nation would have been better served with the stimulus bill I proposed in January in place of the American Recovery and Reinvestment Act. Whatever your position on that, I'm presuming we could all agree that things have not developed so far in the way that we would have hoped.

Source: Greg Mankiw

You can find more discussion from Arnold Kling ([1], [2]), Ryan Avent, Robert Waldmann, and Matthew Yglesias.

Today, we're fortunate to have Morris A. Davis, Assistant Professor of Real Estate and Urban Land Economics at University of Wisconsin School of Business, as a guest contributor.

Research by economists inside the Federal Reserve system have shown that two events typically lead homeowners to default on their mortgage (see here). First, the value of the house must be less than the value of the mortgage ("under water"). This is necessary but not sufficient (see here). Second, homeowners must experience a significant disruption and loss of income. The available data suggest there might be a big increase in foreclosures in the immediate future. Zillow estimates that 22 percent of the 50 million homeowners with mortgages are currently under water; unemployment rates are high and are expected to remain high for the next two years.

Why does unemployment lead to a foreclosure when a house is under water? Consider the case of Wisconsin. In Wisconsin, UI benefits are capped at $1,452 per month. According to the 2007 American Community Survey, the average mortgage payment including all mortgages and taxes in Wisconsin is approximately $1,200. If the unemployed make their mortgage payment, they have roughly $63 per week available for food, transportation, and other necessities. After spending down their assets, the unemployed have no choice but default. They cannot sell their house, because they would have to write the bank a check at closing. They cannot make their mortgage payment, because they would have no money left for food.

The Obama Administration's "HAMP" proposal to reduce foreclosures calls for modifying mortgage payments. Mortgage modifications are designed to help borrowers refinance from a "bad" subprime mortgage to a more conventional mortgage. The reason that many legislators and journalists think mortgage modifications are the solution to the foreclosure problem is that the delinquency rate on subprime mortgages is much higher than the delinquency rate on prime mortgages, 12 percent compared to under 3 percent in 2009:Q2.

Although the HAMP plan will help some borrowers, it likely will not help avoid the majority of people facing foreclosure. There are two related reasons for this. First, shown in the table below, in 2009:Q2, 52% of seriously delinquent homeowners have prime mortgages -- so a "bad" mortgage is not the underlying problem. Second, because of the way the HAMP rules are written, the unemployed are essentially ineligible to have their mortgage refinanced. And, the research is quite clear that unemployment is a trigger to foreclosure.

There are two proposals out there that will prevent foreclosures for unemployed homeowners. One plan is from a team of researchers at the Boston Fed (Chris Foote, Jeff Fuhrer, Paul Willen) and Federal Reserve Board (Eileen Mauskopf), the so-called "Boston Fed" plan. The second is from my colleagues Stephen Malpezzi, François Ortalo-Magn&eacute and I here at the Department of Real Estate at the Wisconsin School of Business. We call this the WI-FUR plan. Both plans address the foreclosure problem by providing the temporarily unemployed with temporary assistance. Both plans attempt to mitigate the "moral hazard" problem by specifying that the amount of temporary assistance is independent of the original mortgage amount.

For each unemployed person, the Boston Fed plan (here for details) computes the percentage disruption in income due to unemployment (i.e. the gap between the wage while last employed and the UI benefit), and then pays that percentage of the mortgage directly to the mortgage servicer. This payment could be either a loan or a grant. The loss in income must be demonstrable and significant, at least 25 percent.

The WI-FUR plan (here for details) specifies that all unemployed receiving UI benefits also receive a housing voucher that can be used to pay the mortgage. The housing voucher would be computed such that, on average in each state, homeowners pay 30% of their UI benefits on their mortgage -- the voucher would cover the balance. In Wisconsin, for example, we advocate for an average voucher of about $764. This would make up for the shortfall in a $1,200 mortgage payment if households pay 30% of their UI benefit ($436 = 0.30 × $1,452) towards their mortgage.

You could ask: Why prevent foreclosures? We've known or suspected for some time that foreclosures are costly in terms of time and process (see here); that foreclosures may have "spillover" effects on values of neighboring property (see here); and that foreclosures may affect the well-being of children (see here). But why prevent them now?

People that know me know that I was completely opposed to foreclosure relief in 2007 and 2008. At that time, my view was that homeowners that were being foreclosed on put little down on their property, so they had option value from homeownership with no risk. Further, during the foreclosure process, they lived in the house for free -- no mortgage payment and no taxes. The economic and political environment has changed quite a bit since 2008, and my opinion has changed. Now, I can give two reasons for why, in late 2009, we should implement either the Boston Fed or WI-FUR plan.

First, my opinion is that we experienced two very large (and likely connected) aggregate shocks. I mention this because I want to emphasize that it is possible the foreclosures we’re observing now are not necessarily the result of ex-ante bad or risky decisions by lenders or borrowers, but of decisions that look bad ex-post due to bad luck. The first shock is that house prices have fallen by 30 percent nationwide since the peak in mid-2006. I describe this as a shock because traders appeared to think a big decline in house prices was a possible but low probability event. For example, the "Meltdown" scenario in a Lehman Brothers outlook as of August 2005, assumed to occur with only a 5 percent probability, called for a cumulative decline in house prices of about 15 percent over 3 years: See here. The second shock was the massive recession we just experienced. I describe this as a shock because many economists had become convinced the volatility of major macro aggregates had been cut in half due to the "Great Moderation" -- see here for a recent paper. In a world with a great moderation, the severe recession we just experienced is a very unlikely event. When put together, the large decline in house prices (shock 1) and large increase in unemployment (shock 2) have resulted in an unprecedented surge in foreclosures.

Second, as a society we've committed to spend money to reduce foreclosures. So let’s spend it effectively. The Obama Administration has been authorized to spend $75 billion dollars to try to prevent foreclosures. The Treasury now is spending the money by paying servicers to modify mortgages. This plan might help some people, but it will not address the majority of future foreclosures from unemployed people with prime-rate mortgages. If we want to prevent foreclosures of this group of people, either the Boston Fed plan or the WI-FUR plan will get the job done.

This post written by Morris Davis.

First, to the current situation and outlook:

Note that the newly released IMF WEO forecasts project continued growth into 2010. Of course, that's just a forecast. In fact, preliminary real time estimates of 09Q3 GDP suggest a somewhat faster rebound in activity than the IMF anticipates.

But if indeed there is underlying weakness in 2010 or 2011, then it's a good thing the stimulus package incorporated substantial spending for long-gestating infrastructure projects. Indeed, I made this exact point (on repeated occasions [3] [4] [5]) -- that with the recession likely being of a prolonged nature, and the recovery relatively slow, the concerns about timing were misplaced. Nonethelss, I suspect that those many critics of the stimulus package will not be retracting their criticisms in 2010.

Just to remind readers, depicted below is the projected spend-out time profile as estimated by CBO back in February, and graphed in this February 16th post.

Mark Zandi's June 2009 quarter-by-quarter spend-out estimates are here:

As it turns out, Zandi's estimates for 2009Q2 were a bit low, but not too far off of the $99.8 billion cited by the CEA [6].

As the stimulus spending diminishes, the impact on 2010Q4 growth becomes negative, according to Zandi's estimates. However, it's important to recall that this is an impact on the first derivative. The level of output will still be above what it would be in the no-stimulus counterfactual -- in other words the output gap would be smaller than otherwise.

September was the worst month for U.S. auto sales since February, down 23% from September 2008 and down 41% from the August 2009 outlier.

Data source: Wardsauto.com

Many of us had wondered whether the cash-for-clunkers program would simply cause people who would have bought cars in September or October to buy instead in July and August. Now we seem to have an answer, though General Motors Sales Chief Mark LaNeve believes that low inventories also lost the industry 300,000 potential sales for September. If you average the three months of July, August, and September together, the impression is one of improvement since the terrible first quarter that's still left us below 2008:Q3. Inventory rebuilding should give a cyclical boost at some point, but at the moment this is not looking at all like the sharp recovery some had been hoping for.

Data source: Wardsauto.com

But the biggest worry remains employment. Initial claims for unemployment insurance and number of hours worked are often viewed as leading economic indicators. Initial claims peaked in March, but have improved little since August.

Seasonally adjusted new claims for unemployment insurance (red) and 4-week average (blue), in thousands.

Average hours per week in manufacturing fell back a bit last month, undoing some of the earlier rebound.

Source: FRED

Hours worked for the broader economy remain at the low point for this cycle.

Source: FRED

And total employment, generally regarded as a coincident economic indicator, continues to plummet, with a quarter million fewer Americans on payrolls in September compared with August (seasonally adjusted). That this is not as rapid a decline as we saw at the start of the year can no longer provide much comfort to anyone.

Source: Calculated Risk

The Aruoba-Diebold-Scotti Business Conditions Index also doesn't care much for the latest numbers, having moved back into significant negative readings.

Aruoba-Diebold-Scotti Business Conditions Index.

Although I expect the GDP numbers later this month to show positive growth for the quarter, further deterioration on jobs is bad news for critical factors like loan defaults and total spending.

Carpe Diem has his usual optimistic take on this. Wish I felt the same way.

The Dollar as the Key Reserve Currency

First, to Zoellick's comments. From Bloomberg:

"There is every reason to believe that the euro's acceptability could grow," Zoellick said in a speech today in Washington. "Of course, the U.S. dollar is and will remain a major currency. But the greenback's fortunes will depend heavily on U.S. choices" on inflation, the budget deficit and financial oversight, he said.

The World Bank chief outlined ways he sees the balance of power in the global economy shifting after the financial crisis. China and probably India will rise in influence and the U.S.'s economic might may be diminished as the international system is "overhauled before our eyes," he said.

In excerpts of his speech released yesterday, Zoellick said, "there will increasingly be other options to the dollar," and the U.S. shouldn't "take for granted" its standing. While didn't specifically mention the yen in the full text of the speech, he said Japan's "old export model" of growth may not be sustainable in a world that's less reliant on U.S. consumers.

As I've noted on previous occasions [0] [1], Jeff Frankel and I [pdf] have outlined the conditions under which the dollar could lose primary reserve currency status to the Euro. In short, calamitously bad policies that induce rapid currency depreciation, or high inflation, would do the trick. Our results, last updated in early 2008 [pdf], might seem somewhat out of date given all the turmoil that has occurred in the meantime. But it's important to realize that it's the relative performance (US versus euro area) that matters, and I see no greater reason to believe that the conditions are in place for a drastic "reversal of fortune" than before. (A slightly different perspective by Jeff Frankel here and here.) I'd note that part of our results for the euro displacing the dollar depended on a London greatly overtaking New York as a financial center; well, that remains to be seen.)

So, if one looks at the breathless commentary about the euro's share rising to new highs, well, that's true, insofar as one looks at known euro reserves and known allocations.

The picture looks a lot less clear when one tries to think about total reserves, a large share of which is of unknown (well, unreported) composition. As always, a little perspective is helpful. Figure 2 shows a longer sample (using different data), and the US dollar share normalized by total reserves, rather than allocated reserves.

Notice that the estimated dollar share was relatively low, during the mid-1980s, despite the extremely high trade weighted value of the dollar. Further observe that while the reported US dollar share is now at a record low level (a few percentage points below what it was at the end of 1995), this is the known share. The unallocated share is quite large, and making an educated guess that 60% of unallocated reserves are in dollars yields a somewhat different perspective (the 60% figure makes the end-2003 old series match the guesstimated series). Then the dollar share has been lower, particularly in the early 1990's.

In other words, in terms of analysis, the speech was much ado about nothing, although I may be missing some sort of political subtext (i.e., [2]).

The Dollar's Value, Post-Pittsburgh

What about all the talk about the dollar decline and rebalancing? [3] It was clear before the G-20 meetings that, regardless of what was said and agreed to, dollar adjustment would occur. The extent to which adjustment would occur was, admittedly, obscure before. But it still remains so, exactly because the trajectories of consumption and potential GDP in the US, Europe, and East Asia remain uncertain.

In thinking about the prospects for the dollar, I think it's useful to break the forces affecting it into separate pieces. In particular, reserve currency status is not directly linked to the dollar's value, although they are of course related. The dollar could lose value without losing primary reserve currency status, and could gain reserve share without gaining value. In addition, in thinking about the other forces that can affect the dollar in the absence of tectonic shifts in the dollars international currency role, one can identify a number of factors:

• Portfolio balance effects (dollar asset supply versus demand).
• Cyclical factors (demand for credit, relative price movements, interest rates).
• Safe haven factors
• Structural factors (composition of demand, trend output).

This is not an exhaustive list [4], but it highlights the variety of the relevant effects that have different influences at different horizons (I've skipped purchasing power parity, pure monetary models, etc.). Let me talk about each of these factors in turn.

Portfolio Balance. In this approach, one focuses on stocks of assets denominated in different currencies. In the standard (mean-variance/CAPM) approach, demand for assets depends upon the covariance of relative returns with dollar returns, and the coefficient of relative risk aversion. See this post for the technical exposition.

Increasing amounts of US government debt should, holding all else constant, result in a higher risk premium on dollar assets, or a weaker dollar, or both [5]. Of course, during the next few years, governments around the world will be issuing a lot of debt as well.

Rising default risk relative to other countries could also be put into the model; if the perceived default risk were to rise (once again relative to other countries' government debt), then the risk premium would again rise. But there was nothing at the G-20 meetings to infer a greater incipient increase in US debt than was previously forecast.

Cyclical Factors. Where the economy is in terms of the business cycle has an also impact on the exchange rate. For instance, the US was entering into recession before the euro area; and as shown in Figure 3 the dollar was declining before collapse of Lehman spurred the flight to safety in September 2008. In part, this correlation is due to the empirical strength of Taylor rule fundamentals (see [6], [7]). Now that we're at the zero interest bound in the US, that effect may be in abeyance. Nonetheless, if the US recovers before Europe (and Japan), then the relative price of US goods will rise, pushing up the dollar. So, I'm a bit mystified by the short term pessimism about the dollar's value (over the longer term, other concerns will dominate).

Safe Haven Effects Inspection of Figure 3 reinforces the notion that dollar weakness, for now, is a function of the return of risk appetite. The spike in the dollar from September 2008 through the beginning of 2009 was flight to the safety of Treasurys. In this sense, the dollar decline is a good thing as it highlights the success of policymaker measures to normalize the financial markets. (See for instance the 9/11 Bloomberg Financial Conditions Index, shown below.)

Structural Factors. One of the main points emanating from the G-20 meetings (communiqué) was the need for rebalancing [8]. In my previous post I observed that given the considerable evidence in favor of US consumer retrenchment, rebalancing would be more likely than in the past. However, one of the big questions involve what happens in China and East Asia. I am somewhat skeptical about China experiencing a consumption boom, and sustainable growth in government spending and investment; but Nick Lardy has a more optimistic outlook on these counts [9]. To the extent his more optimistic view turns out to be more accurate, then the adjustment that has to be effected by exchange rate changes will be smaller.

That being said, lower trend consumption growth is also consistent with a weaker dollar, holding all else constant. Once again, I don't see how the G-20 meetings would have induced a revision in views regarding the paths of consumption in the US and China.

On the other hand, the decline in existing home sales for August, future rise in foreclosures already baked in the cake, inventory of unsold homes, and expected continuing increases in unemployment all raise the possibility that house prices could resume their descent.

In other words, I don't see the fat lady singing just yet.

Nominal seasonally adjusted Case-Shiller 10- and 20-city indices. Source: Calculated Risk

Today, we're fortunate to have David Papell, Professor of Economics at University of Houston, as a guest contributor.

The Federal Open Market Committee voted last Wednesday to keep the federal funds target rate at a record low of between zero and 0.25 percent. If it was not constrained by the zero lower bound, should the federal funds rate be negative? If the answer is yes, this suggests that the rate should remain at its record low for a considerable period and provides a justification for continued increases in the Fed's balance sheet. If the answer is no, then the Fed may need to raise its interest rate target sooner rather than later.

There has been a lively debate on this topic in the context of the Taylor rule for monetary policy. The debate started with an article in the Financial Times, which cites a confidential Fed staff study that placed the implied Taylor rule rate at negative 5 percent. John Taylor, speaking at the Atlanta Fed, counters that the Fed got both the sign and the decimal point wrong and calculates the rate at 0.5 percent. Glenn Rudebusch, writing in the San Francisco Fed's Economic Letter, argues for negative 5 percent. Most recently, Taylor writes in a Bloomberg.com commentary that the rate should be zero.

In its original form, the Taylor rule states that the Fed's interest rate target should be one plus 1.5 times the inflation rate plus 0.5 times the output gap. According to the most recent Congressional Budget Office (CBO) projections, "core" CPI inflation, excluding food and energy, was 2.0 percent in 2008 but is expected to fall to 1.6 percent in 2009. The output gap is expected to be negative 7 percent in the second half of 2009 and the first half of 2010. Using 2.0 percent for inflation, the implied rate is 0.5 percent while, with 1.6 percent inflation, the rate is -0.1 percent, both around the Fed's current target.

The implied interest rate target can change if the Taylor rule is modified. It is often argued that, because monetary policy is forward-looking, policy evaluation with Taylor rules should be conducted using forecasts rather than actual data. The CBO forecasts that core inflation will fall further to 1.1 percent in 2010. Using this forecast, the implied rate falls to -0.85 percent. More dramatically, Rudebusch and others argue that the coefficient on the output gap in the Taylor rule should be 1.0 instead of 0.5. With inflation forecasts and the larger output gap coefficient, the implied Taylor rule interest rate falls to -4.35 percent.

In "Taylor Rules and the Great Inflation: Lessons from the 1970s for the Road Ahead for the Fed," written with Alex Nikolsko-Rzhevskyy of the University of Memphis, we use research on the natural rate of unemployment published in the 1970s to construct real-time output gap measures for the periods of peak unemployment in 1971 and 1975, and argue that real-time linear and quadratic detrended output gaps provide the best measure of what policymakers perceived the output gap to be at the time. Using these gaps to estimate Taylor rules, we conclude that:

• The Fed did not follow a stabilizing Taylor rule in the 1970s. In order for policy to be stabilizing, the federal funds rate needs to be raised more than point-for-point when inflation increases, so that the real interest rate rises. Our estimates never produce a coefficient on inflation that is significantly greater than one.
• The Fed did follow a stabilizing Taylor rule if inflation forecasts, rather than inflation rates, are used. These forecasts, however, systematically under-predicted inflation following the 1970s recessions and this does not constitute evidence of stabilizing policy.
• The Fed responded too strongly to negative output gaps, with estimated coefficients between 0.7 and 1.0.

In the 1970s, the Fed "stabilized" overly optimistic inflation forecasts and responded too strongly to output gaps, lowering interest rates too much -- especially during and following the 1970-1971 and 1974-1975 recessions, resulting in frequent recessions and the Great Inflation. What are the lessons from the 1970s for Fed policy today?

• The Fed should respond to inflation, not inflation forecasts, especially in an environment where large negative output gaps are causing forecasted inflation to fall.
• The Fed should not tinker with Taylor's output gap coefficient of 0.5.

Using the rule with Taylor's original coefficients, the experience of the 1970s suggests that, even if it could, the Fed should not lower its interest rate target below zero. If the incipient recovery takes hold and inflation stays the same or rises, it may need to raise rates sooner than many people think.

This post written by David Papell.

First, a little background. The traditional tool of monetary policy is an open market purchase, in which the Fed purchased U.S. Treasury securities that had previously been held by someone in the private sector. The Fed would pay for those securities by crediting deposits in an account that the selling bank had with the Federal Reserve. These reserve deposits of banks represent claims that the bank could use, if it wished, to withdraw green currency from the Federal Reserve. The volume of reserve deposits historically was extremely important in determining the interest rate at which banks would lend the deposits to one another overnight. The traditional understanding of monetary policy was that the Fed would use open market purchases to achieve its desired objectives for the overnight interest rate and the money supply.

If the Fed wished to implement a purely temporary increase in the volume of reserve deposits, historically the tool of choice was a repurchase agreement, in which the Fed would buy a particular security with a promise to return it at a specified future date. The purchase was again implemented by creation of reserve deposits, and when the security was returned, those deposits came back into the Fed.

The Fed began to see a new potential use for these repos after the initial banking difficulties in August 2007. Although repos were traditionally used as a device for temporarily injecting reserves, their structure amounts to a collateralized loan from the Fed to the counterparty. The Fed's objective subsequent to August 2007 was to increase the volume of its lending and support the market for certain securities that it could accept as collateral for repos. Thus the Fed utilized an expansion of repurchase agreements as one of the initial tools for responding to the crisis, simply rolling them over to create a de facto expanded lending facility.

The graph below tracks the various assets held by the Federal Reserve since the beginning of 2007. The height of the graph represents the total asset holdings at the end of each week, with the colors indicating the contribution of each category. Repos are represented by the turquoise band. This traditionally had been small and highly variable, but grew significantly in the early phases of the financial crisis. Later the Fed came to use direct loans through its Term Auction Facility in preference to repos. Since January, the Fed has been directly buying up mortgage backed securities and agency debt in the way it used to purchase Treasury securities.

Figure 1. Factors supplying reserve funds, in billions of dollars, seasonally unadjusted, from Jan 1, 2007 to September 23, 2009. Wednesday values, from Federal Reserve H41 release. Agency: federal agency debt securities held outright; swaps: central bank liquidity swaps; Maiden 1: net portfolio holdings of Maiden Lane LLC; MMIFL: net portfolio holdings of LLCs funded through the Money Market Investor Funding Facility; MBS: mortgage-backed securities held outright; CPLF: net portfolio holdings of LLCs funded through the Commercial Paper Funding Facility; TALF: loans extended through Term Asset-Backed Securities Loan Facility; AIG: sum of credit extended to American International Group, Inc. plus net portfolio holdings of Maiden Lane II and III; ABCP: loans extended to Asset-Backed Commercial Paper Money Market Mutual Fund Liquidity Facility; PDCF: loans extended to primary dealer and other broker-dealer credit; discount: sum of primary credit, secondary credit, and seasonal credit; TAC: term auction credit; RP: repurchase agreements; misc: sum of float, gold stock, special drawing rights certificate account, and Treasury currency outstanding; other FR: Other Federal Reserve assets; treasuries: U.S. Treasury securities held outright.

A separate question is what happens to all the reserve deposits created through this process. The Fed has never wanted to see the huge volume of reserves it created end up as currency held by the public, for fear this would be inflationary. It has relied on several devices to keep that from happening. One was use of the Treasury's account with the Fed, another traditional feature of Fed operations that ballooned as it became adapted to new purposes. The Fed asked the Treasury to borrow funds that it simply left in deposit in its account with the Fed. These idle reserves held by the Treasury absorbed some of the vast increase in new reserves created by the Fed.

A more important tool was that the Fed started paying interest on reserves in October 2008, and by November had increased that rate to the target fed funds rate itself. This created a very strong incentive for banks to simply hold reserves idle at the end of each day rather than lend them out on the overnight fed funds market. In effect, by paying interest on reserves, the Fed is borrowing directly from banks and using the proceeds for the various asset expansions detailed above.

The graph below shows the Fed's liabilities at the end of each week. The height of the graph is, by definition, exactly equal to the height of the previous graph at every date. The first graph tracks what assets the Fed acquired with its operations, while the second graph shows where the funds it created ended up. The surge in the Treasury account (in yellow) and excess reserves of member banks (green) explain why the huge expansion in the Fed's balance sheet has not translated so far into a massive increase in the quantity of currency held by the public (blue).