EconWeekly

Did the recession end in May?

2009-06-18T18:39:00.004-05:00

It’s looking more and more as if the recession in the U.S. ended in May. To support that claim I have looked at initial unemployment insurance (UI) weekly claims and the pattern that that time series has followed in previous recessions. The four-week moving average of not seasonally adjusted weekly claims has peaked less than eight weeks before the end* of the each recession since 1970 (see chart).

Click on the chart to enlarge.

The lead time was eight weeks in the 1973-75 recession, seven weeks in two recessions (1980 and 1981-82), six in 1970, five in 2001, and zero in 1991. The moving average declined essentially monotonically after each of those identified peaks.

During the current recession, the moving average saw a peak on the week that ended on April 4, 2009. That would put the end of the recession somewhere between April and May (zero to eight weeks later). And if the past is any indication, most likely in May.

The usual caveat applies: the pattern-recognition method is based on only six episodes of recession, and six is a very small number to make any claim on the significance of this finding. Moreover, each episode is spaced by several years from each other, so the hypothetical relationship between UI claims and the business cycle may have changed since last time.

I can’t help, nonetheless, to find some solace in that chart.

*Here I take the NBER dating of the business cycles. The NBER, however, provides the month in which a business cycle trough occurs, not the week. I take the liberty of dating the end of each recession on the last week of the month when the NBER identifies a business cycle trough.

Recession buzz: April update

2008-05-15T21:27:00.007-05:00

Recession talk decreased slightly again in April. According to an index which includes eight newspapers, each publication included an average of 1.96 articles per day using the word “recession,” down from 2.16 in March and significantly below the level of 3.70 in January (see chart 1). The decrease occurred in spite of the continued softening of the job market in March, which was known the first week of April. The release of the first-quarter growth of GDP and the meeting of the FOMC committee, which might have generated some R-talk, occurred on the last days of the month, so their impact on R-word statistics probably straddled April and May.

Chart 1 (click to enlarge)

In spite of the diminishing buzz, the current level of talk is still consistent with an economy in recession, judging from historical trends (see chart 2).

Chart 2 (click to enlarge)

Previous updates: January, February , March

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Ranking of WSJ forecasters: May update

2008-05-08T17:50:00.002-05:00

The GDP estimate for the first quarter is out, so it's time to update my ranking of the Wall Street Journal forecasters. (Remember: to pit forecasters against each other I use the Root Mean Squared Error (RMSE) and the most accurate forecaster is the one with the lowest RMSE, among all who have submitted at least ten forecasts since May of 2004. I only include the predictions submitted in the months of February, May, August and November for quarters Q1, Q2, Q3 and Q4, respectively. The Q1 forecast is the one submitted in February, the Q2 forecast is the one posted in May, and so on.)

According to the latest ranking, the most accurate forecaster is Gene Huang of FedEx, pushing Gary Thayer of A.G. Edwards to second place. Resler and Hoffman keep third and fourth place, respectively. The median forecast falls from 6th to 10th. The lanterne rouge continues to be James Smith of the University of North Carolina. At 2.6%, his forecast for 2008:Q1 was as inaccurate as usual.

The naive forecast, which is equal to the growth rate observed during the previous quarter, would have been spot-on this time, since GDP grew by 0.6% in both Q4 and Q1. Historically, however, it has performed worse than any forecaster but one.

Only Edward Leamer of UCLA Anderson Forecast was right on the mark. The predictions of 38 of the 52 forecasters were within one percentage point of the truth.

Top-20 WSJ forecasters, by Root Mean Squared Error (RMSE), as of 2008:Q1

Rank	Forecaster	Firm	RMSE	Absolute deviation for 2008:Q1
1	Gene Huang	FedEx Corp.	0.95	0.4
2	Gary Thayer*	A.G. Edwards	0.95	--
3	David Resler	Nomura Securities International	1.01	0.8
4	Stuart Hoffman	PNC Financial Services Group	1.01	0.6
5	Allen Sinai	Decision Economics Inc.	1.01	0.1
6	Dana Johnson	Comerica Bank	1.02	0.1
7	Nicholas S. Perna	Perna Associates	1.02	0.3
8	Mike Cosgrove	Econoclast	1.02	0.4
9	J. Prakken and C. Varvares	Macroeconomic Advisers	1.03	0.1
--	Median	--	1.05	0.6
10	Scott Anderson	Wells Fargo & Co.	1.07	0.4
11	Nairmen Behravesh	Global Insight	1.08	0.9
12	John Lonski	Moody's Investors Service	1.08	0.8
13	Edward Leamer	UCLA Anderson Forecast	1.08	0
14	R. Berner and D. Greenlaw	Morgan Stanley	1.08	1.3
15	Douglas Duncan	Mortgage Bankers Association	1.09	0.2
16	Robert DiClemente*	Citibank SSB	1.09	--
17	Diane Swonk	Mesirow Financial	1.09	0.1
18	Neal Soss	Credit Suisse	1.11	0.1
19	Dean Maki	Barclays Capital	1.12	0.4
20	David Rosenberg	Merrill Lynch	1.12	1

Source: WSJ's survey of forecasters and author's calculations.
*Not in WSJ group of forecasters, as of February 2008.

The May forecast for Q2 is also available now. On average, the top five forecasters according to my ranking predict growth of -0.1%. The median of all forecasts is 0.3%.

Previous ranking

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A ranking of WSJ forecasters

2008-04-23T16:54:00.018-05:00

For a number of years, The Wall Street Journal has been conducting a survey of economic forecasts. The newspaper is kind enough to publish the prediction of each forecaster, so we can entertain ourselves observing how they fare.

I restrict my attention to quarterly forecasts of GDP growth. Between 2004 and 2007, 27% of the predictions were within 0.5 percentage points of the actual outcome (see Table 1), whereas 56% (27% + 29%) were within one percentage point. Or, if you’re a member of the empty-glass club, 44% missed the target by more than one point.

Table 1. Distribution of accuracy of forecasts (2004-2007)

Forecasts within...	Percentage
0.5 percentage points (p.p.) of actual value	27.2
0.5 - 1 p.p.	28.9
1 - 1.5 p.p.	22.6
1.5 - 2 p.p.	13.7
2 - 2.5 p.p.	5.2
over 2.5 p.p.	2.4
All	100

Source: WSJ's survey of forecasters and author's calculations

To pit forecasters against each other I use the Root Mean Squared Error (RMSE), a one-number summary of the deviations of several forecasts. The RMSE punishes both positive and negative deviations equally, but penalizes big errors proportionally more than small ones*. I also can use it to form confidence intervals.

According to the RMSE measure, the most accurate forecaster is Gary Thayer, of the firm A.G. Edwards, although he no longer participates in the survey. The second most-accurate forecaster, and still in the panel, is Gene Huang of FedEx. (See Table 2.) The best forecaster is able to predict GDP growth within 1.67 percentage points, at a 90% level of confidence. (That means that if he posted 100 forecasts, 90 of them would deviate from the actual GDP growth rate by plus or minus 1.67 percentage points.)

Table 2. Top-20 WSJ forecasters, by Root Mean Squared Error (RMSE)

Rank	Forecaster	Firm	RMSE	Forecasts' 90% confidence margin (p.p.)
1	Gary Thayer*	A.G. Edwards	0.95	1.67
2	Gene Huang	FedEx Corp.	0.98	1.72
3	David Resler	Nomura Securities International	1.02	1.79
4	Stuart Hoffman*	PNC Financial Services Group	1.03	1.82
5	Allen Sinai	Decision Economics Inc.	1.05	1.83
--	Median forecast	--	1.05	1.83
6	Mike Cosgrove	Econoclast	1.05	1.84
7	Nicholas S. Perna	Perna Associates	1.05	1.85
8	Dana Johnson	Comerica Bank	1.06	1.88
9	J. Prakken and C. Varvares	Macroeconomic Advisers	1.06	1.86
10	R. Berner and D. Greenlaw*	Morgan Stanley	1.07	1.87
11	Nairmen Behravesh	Global Insight	1.09	1.90
12	Robert DiClemente*	Citibank SSB	1.09	1.92
13	John Lonski	Moody's Investors Service	1.09	1.91
14	Scott Anderson	Wells Fargo & Co.	1.11	1.97
15	Douglas Duncan	Mortgage Bankers Association	1.12	1.97
16	David Rosenberg	Merrill Lynch	1.13	1.97
17	Diane Swonk	Mesirow Financial	1.13	1.99
18	David Lereah*	National Association of Realtors	1.13	1.99
19	Neal Soss	CSFB	1.15	2.01
20	Paul Kasriel	The Northern Trust	1.15	2.02

Source: WSJ's survey of forecasters and author's calculations.
*Not in WSJ group of forecasts anymore, as of November 2007.

It is well known that, over time, a group’s forecast is closer to the mark than almost any particular individual’s. Among the WSJ panel it’s no different: the median forecast is sixth in the ranking, out of 47. The same conclusion applies to the average forecast (average and median are very close to the each other in every release of the WSJ survey).

The top participants in the group hold but a tiny advantage over the rest. Even the 20th most accurate person has a margin of error of just over 2 percentage points, versus 1.67 points for the top forecaster. It’s not surprising then that rankings tend to change frequently. For example, at the end of 2006 the top five forecasters were (latest ranking in parentheses): Thayer (1), Rosenberg (17), Perna (8), Sinai (5) and Lonski (14).

Catching a “hot streak” seems to be exceedingly difficult too. Suppose that we define “winning” as being among the 50% most-accurate accurate forecasts for a given quarter. (A rather modest victory, may I say.) By that measure, only 37% of successes were followed by a second win, 31% of two-in-a-row’s were followed by a third success, and just 17% of those were followed by a fourth one.

Can a simple predictor outperform the pros? Michael Bryan of the Federal Reserve of Cleveland, whose commentary I follow in this post, asks that question. He compares the predictions in the Survey of Professional Forecasters (SPF) with the naïve forecast that next period’s outcome will be the same as the latest observed outcome. In terms of my data, that is the prediction that GDP growth in, say, 2008:Q1 will be the same as in 2007:Q4.

Bryan finds that 53% of economists made worse predictions than the naïve forecast. The WSJ panel shows much better marksmanship. All of them performed better than the naïve forecast, except one. (The exception is James F. Smith of Western Carolina University, and by a long shot. His RMSE is 2.83, whereas that of the naïve forecast is 1.89. Compare with the values in Table 2.)

By Clay Bennett
In five days we will have an advance estimate of how much the economy grew during the first quarter. The naïve forecast indicates 0.6 percentage points. The median WSJ forecast in April is exactly zero —neither cold nor hot, as a friend of mine likes to say. Gene Huang, the top forecaster of the hour, says it will be 0.8%. Which one will be closer to the mark?

* For the ranking of forecasters by RMSE, I include all the participants in the survey who submitted at least ten GDP forecasts between May of 2004 and December of 2007. I only include the predictions submitted at the beginning of the months of February, May, August and November for quarters Q1, Q2, Q3 and Q4, respectively. “Actual” GDP growth is taken to be the advance estimate, released one month after the end of the corresponding quarter. Given the timing of the forecasts and of the advance releases of GDP growth, each forecast appeared about three months before the actual outcome was known.

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The Fed's new tools (II)

2008-04-18T16:35:00.011-05:00

Last week I described the traditional tools of the Fed (open market operations and the discount window) and an old, but less well-known one (repurchase agreements). Then I described the first innovation, the Term Auction Facility, inaugurated in December.

This week I’ll go over the forms of lending introduced in 2008, and then I'll discuss the options that the Fed is rumored to be considering next.

* * *

In 2007 the Federal Reserve made an effort to provide liquidity through channels other than open market operations and repos. To that effect, it created the Term Discount Window Program (TDWP) and the Term Auction Facility (TAF), as I explained last week. Both of those facilities, however, are available only to depository institutions.

So far I’ve been using the ambiguous term “banks” to refer to institutions that borrow funds or buy Treasurys from the Fed. There are however two broad classes of “banks”: depository institutions and primary dealers. Depository institutions are allowed to accept deposits. Primary dealers, on the other hand, are investment banks and brokers that trade in Treasurys with the Federal Reserve. Bear Stearns and Lehman Brothers are two examples in the latter group. As of today, there are 20 of them.

One defining characteristic of depository institutions is that they can use a broad range of assets to secure their loans from the Fed. The discount window, the TDWP and the TAF all accept a set of assets known as “discount collateral.” That includes pretty much all paper of investment quality, including performing sub-prime mortgages. Primary dealers, on the other hand, only have access to open-market operations (OMOs) and repos. The latter only can be obtained after posting General Collateral —that is paper issued by the Treasury or US agencies only.

Following problems in the mortgage and real estate markets last summer, primary dealers found it increasingly hard to obtain short-term financing because nobody would take their suspicious assets as collateral —or would do so only at very high prices. The Fed stepped up to the plate by opening the Term Securities Lending Facility (TSLF) to primary dealers, on March 27. Roughly speaking, a TSLF loan is an exchange of risky securities for Treasuries for 28 days between Federal Reserve and primary dealers. The range of acceptable collateral, although not as wide as at the discount window, includes some types of paper issued by non-agency institutions (AAA/Aaa-rated private label RMBS and CMBS).

To be sure, the Fed has had a securities lending program for a number of years. The novelty of the TSLF is that it extends the range of acceptable collateral beyond Treasuries. A second novelty is that the term of the loans increases from overnight to 28 days.

Unlike the other tools I have discussed, the TSLF does not have an effect on reserve balances by design. This allows the Fed to pursue its recent strategy of providing liquidity to the banking system without increasing the monetary base.

This is what these loans would look like on the balance sheet of the Fed:

Changes in the Fed's balance sheet after a $1,000M TSLF loan
Assets	US government securities	-1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	TSLF loan	+1,000
	Other assets	0
Liabilities	Currency in circulation	0
Liabilities	Reserve balances	0

In March the Fed inaugurated a second form of lending: the Primary Dealer Credit Facility (PDCF). This venue provides overnight cash loans to all primary dealers, at the same interest rate as the discount window does, and by pledging the same type of collateral. With the PDCF the Federal Reserve has de facto opened the discount window to primary dealers.

PDCF loans increase the monetary base (read the FAQ). Because this facility is meant to oil the credit market, not to provide a monetary stimulus, the Fed will continue to offset the increase in reserves using "a number of tools, including, but not necessarily limited to, outright sales of Treasury securities, reverse repurchase agreements, redemptions of Treasury securities, and changes in the sizes of conventional RP transactions." Here's what a PDCF loan looks like, after it has been offset:

Changes in the Fed's balance sheet after a $1,000M PDCF loan, offset by an open market operation
Assets	US government securities	-1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	PDCF loan	+1,000
	TSLF loan	0
	Other assets	0
Liabilities	Currency in circulation	-1,000 + 1,000
Liabilities	Reserve balances	0

The composition of the Fed’s assets has changed substantially over the last nine months. Here’s the balance sheet of the Fed again, in December and March:

Federal Reserve's balance sheet, $ millions
Assets		Aug. 15, 2007	Mar. 19, 2008
	US government securities	789,601	660,484
	Repurchase agreements	24,000	62,000
	Reverse repurchase agreements	-31,941	-46,143
	Term Auction Facility loans	0	80,000
	Primary Dealers Credit Facility	0	28,800
	Direct loans	264	125
	Other assets	37,058	36,603
Liabilities	Currency in circulation	813,085	818,362
Liabilities	Reserve balances	5,897	3,507

Source: Federal Reserve, H.4.1 release.

With its new tools, the Fed has provided liquidity without printing much money. In a way, the Fed has become a pawnbroker.

The future?

Loans to commercial banks and primary dealers, from one facility or another, represent now a much larger fraction of assets (see chart, from the Wall Street Journal). The fraction of Treasurys has declined to 53% from 87%.

The concern now is that the Fed may run out of Treasurys. In theory, the Fed could continue extending loans indefinitely. The problem is that, with no Treasurys left over, the Fed would not be able to offset expansions of the monetary base, as it’s been doing for months. Reserve balances would balloon, pushing down the federal funds interest rate to zero. So the Fed is now pondering the following alternatives:

1) Purchase mortgage-backed securities directly —as opposed to taking them as collateral, as it does through the discount window programs and the PDCF. The Fed could finance such purchases by selling Treasurys, and in that case reserve balances would not be affected. But the amount of Treasurys in the Fed’s balance sheet is, as I said, limited and shrinking rapidly.

2) Have the Treasury issue more debt than it needs and deposit the cash at the Fed. The extra cash would be separate from reserve balances, and thus a priori wouldn’t have any impact on the fed funds rate. The Fed would use that cash to purchase Treasurys. This is what this maneuver would look like:

Changes in the Fed's balance sheet after taking $1,000M worth of Treasury deposits, after an issue of "unnecessary" Treasurys
Assets	US government securities	+1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	PDCF loan	0
	TSLF loan	0
	Other assets	0
Liabilities	Currency in circulation	0
	Reserve balances	0
	Treasury deposits	+1,000

While lending conditions don't improve, the new funds would soon turn into loans to banks, so the actual effect on the balance sheet would be (assuming the funds are loaned through the discount window; other forms of lending would affect different lines in the asset side of the balance sheet):

Changes in the Fed's balance sheet after taking $1,000M worth of Treasury deposits, after an issue of "unnecessary" Treasurys

Assets

US government securities

Repurchase agreements

Reverse repurchase agreements

Direct loans

+1,000

PDCF loan

TSLF loan

Other assets

Liabilities

Currency in circulation

Reserve balances

Treasury deposits

+1,000

This would change the way we view sovereign debt. Traditionally, the government’s power to raise taxes and set public expenditures have determined the creditworthiness of sovereign debt. With this plan, the value of the government’s debt obligations would become contingent on the portfolio of dodgy securities that the Fed accepts as collateral.

3) Let the Fed issue its own debt. The Fed would use the funds to purchase securities or make loans. A new entry would appear in the list of Federal Reserve’s liabilities:

Changes in the Fed's balance sheet after issuing $1,000M worth of its own debt

Assets

US government securities

Repurchase agreements

Reverse repurchase agreements

Direct loans

+1,000

PDCF loan

TSLF loan

Other assets

Liabilities

Currency in circulation

Reserve balances

Federal Reserve bonds

+1,000

4) Remunerate reserves. Reserve balances are like checking accounts: they don’t earn interest. For that reason banks have little incentive to hold more reserves than they need to meet the Fed’s requirements and clear transactions. Any excess reserves are loaned to other banks. As Greg Ip explains, “if the Fed paid, say, 2% interest on reserves, banks would have no incentive to lend out excess reserves once the federal funds rate fell to that level.”

This measure would lead to a higher equilibrium level of reserve balances, for a given value of the federal funds interest rate. It would also reduce the amount of inter-bank lending, as banks would keep more of their cash in their safe-deposit box at the Fed. That lending would be replaced by loans from the Federal Reserve.

This reviews my review of the Fed's new monetary policy. Will these new tools make it to the textbooks? It’s hard to tell whether the particular facilities (TAF, TSLF, etc.) will survive. But I think that some standardized form of loans to non-depository institutions will stay, and that the Fed will become willing to accept dodgier collateral than it traditionally has.

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The odds of getting an H-1B visa

2008-04-16T17:16:00.010-05:00

I just read about this. US Citizenship and Immigration Services (USCIS) received 163,000 applications for H1-B visas by April 7. Since that number exceeds the visa cap, the government will not consider any more applications this year (Source: USCIS, via the H1B data blog). 31,200 of the applications were from individuals with an advanced degree. The government quotas are: 20,000 for applicants with an advanced degree and 65,000 for the rest. (I wrote about the H1-B visa system a couple of weeks ago.)

The USCIS will hold two lotteries this week. The first one is for applicants with an advanced degree from a US institution (MA or higher). Applicants who are not selected in that first lottery will be pooled with the rest of the applications in the second lottery.

Unless my math is failing me, the probability of getting a work visa is then 80.4% for advanced-degree holders, and 45.5% for the rest of the applicants.

UPDATE: I'm really behind on this. USCIS already conducted the lottery (they did it on April 14, two days ago). Lucky applicants should get a notification by early June. I really recommend reading H1B data if you want timely information.

Recession buzz: March update

2008-04-14T16:35:00.013-05:00

Recession buzz remained stable from February at a moderate to elevated level. The Washington Post and the New York Times printed an average of 4.8 stories per day containing the word “recession,” 0.2 more than in February. And among the set of eight major US newspapers that I track every month, each one published 2.2 stories per day, about the same as in February (see Chart 1).

Chart 1 (click to enlarge)

Note to Chart 1: since the unemployment rate in any given month is released by the BLS at the beginning of the next calendar month, I plot the lagged unemployment rate alongside the current-month R-word count.

Now that the first quarter is over, I can also report The Economist’s R-word index, which is the number of stories per quarter containing the gloomy term in the New York Times and the Washington Post. That index reached 855, a value consistent with what we've seen at the beginning of recent recessions (see Chart 2).

Chart 2 (click to enlarge)

In March bloggers and googlers expressed about the same interest in writing and searching about recessions, respectively, as in February (see below).

I don't think that buzz will escalate in April. Near the end of the month, however, we'll know the preliminary figure of GDP growth during the first quarter. That will generate lots of R-talk, especially if the number is negative or zero.

Previous updates: January, February

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The Fed's new tools (I)

2008-04-11T01:48:00.014-05:00

By popular demand, I improved and expanded the notes I published a couple of weeks ago about the new tools of the Federal Reserve. I have added instruments that are not in place yet but the Fed considers using. I have ended up with a very long post, so I have broken it down into two parts. The second part will come out next week.

(I thought somebody would like to use these posts as a refresher, a summary, or even class notes. Jim Hamilton has a few great posts on the subject: September 23, December 14, December 16, March 15. The New York Federal Reserve made its own pocket version. And Greg Ip wrote a rather educational piece. Enjoy.)

UPDATE (4/13/2008): The link to the New York Fed's pocket version does not work any more. But you can find that document here now. Sorry about that.

* * *

The central bank has a balance sheet, as any other bank. As assets, it holds primarily securities issued by the government and loans to banks. As liabilities, it has currency (the cash in your pockets) and reserve balances. Reserves are deposits that regular banks keep at the central bank. When a bank needs currency it withdraws from its deposit, effectively turning it into notes and coins that you and I can use. As you will see in a minute, reserves are a key element in monetary policy.

This was the balance sheet of the Federal Reserve on August 15, 2007:

Federal Reserve's balance sheet, $ millions (Aug. 15, 2007)
Assets	US government securities	789,601
	Repurchase agreements	24,000
	Reverse repurchase agreements	-31,941
	Direct loans	264
	Other assets	37,058
Liabilities	Currency in circulation	813,085
Liabilities	Reserve balances	5,897

Source: Federal Reserve, H.4.1 release.

(For the moment, regard “repurchase agreements” as loans to banks.)

The sum of currency in circulation and reserve balances is the monetary base (M0). The Federal Reserve’s target, however, is not M0 but the federal funds rate.

Banks keep deposits at the Fed to meet reserve minimums required by the Fed and to clear financial transactions. Institutions with balances in excess of reserve requirements lend reserves to institutions that don't have enough. The interest rate on those loans, typically overnight, is called the federal funds rate. That’s a market rate, determined by the supply and demand of such funds. The more reserves, the lower the fed funds rate, and vice versa.

Source: Federal Reserve Bank of New York
The Fed does not set the federal funds rate. When you read in the newspapers that “the Fed cut interest rates by 25 basis points,” what they mean is that the Fed reduced its target for the federal funds interest rate by 0.25%, not the actual rate. But the Federal Reserve can push the actual rate close to its desired target by affecting the amount of reserves available.

Until now, macroeconomics textbooks have been telling us that central banks use two tools to affect the federal funds rate. Looking at them through the balance sheet will help us understand what the U.S. central bank has been up to recently:

• Open market operations (OMO). This is an outright purchase of Treasurys (government securities): the Fed takes securities from banks and credits the banks’ reserve balances. A higher level of loanable reserve balances mean lower interest rates. Voilà: the Fed just "cut" interest rates. Eventually, banks withdraw from their reserves at the central bank and turn them into cash. So an open market operation amounts to withdrawing Treasurys from the hands of banks and replacing them with cash. That would be an expansionary move of monetary policy. The central bank can also reduce the amount of cash in circulation, by doing just the opposite: selling government securities and absorbing cash.

Suppose that the Fed pumps $1,000 million in the banking system through an open market operation. The Fed’s balance sheet would experience the following changes, once banks have withdrawn the new funds from their reserve accounts:

Changes in the Fed's balance sheet after a $1,000M open market operation
Assets	US government securities	+1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	+1,000
Liabilities	Reserve balances	0

• Direct loan. We usually refer to this tool as the discount window. The central bank simply lends money to a bank, at a set interest rate. The central bank increases its balance of loans, and simultaneously credits the reserves that the borrowing bank holds at the Fed.

The loan is secured by collateral, i.e. the Fed would seize assets in the event of default. But there is no flow of securities from the bank to the Fed or vice versa —just cash from the Fed to the bank. There is a long list of assets that banks can use as collateral. The term of the loan is generally one day, but sometimes it’s longer for small banks.

This is what happens to the Fed’s balance sheet when it extends a loan through the discount window (and once the borrower has withdrawn its new reserves):

Changes in the Fed's balance sheet after a $1,000M discount window loan
Assets	US government securities	0
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	+1,000
	Other assets	0
Liabilities	Currency in circulation	+1,000
Liabilities	Reserve balances	0

Even though the price of a such loan is set by the Fed, not the market, a discount-window loan can affect the federal funds interest rate by increasing the amount of available reserves.

Asking for a direct loan usually means that the bank was not able to obtain liquidity any other way. For that reason banks that request discount window loans are subject to scrutiny by the central bank, and watched closely by other banks. And the interest rate charged for direct loans is higher than the federal funds rate. For those reasons, the discount window is used rarely and in small amounts.

A tool that is more frequently used than OMOs but that textbooks often don't mention is:

• Repurchase agreements (or “repos”). A repurchase agreement is a loan from the Fed to a bank. The Fed credits the bank’s reserves and increases its entry of repo claims on banks.

This is what happens to the Fed’s balance sheet:

Changes in the Fed's balance sheet after a $1,000M repurchase agreement
Assets	US government securities	0
	Repurchase agreements	+1,000
	Reverse repurchase agreements	0
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	+1,000
Liabilities	Reserve balances	0

From a financial point of view a repo is not that different from a discount window loan.

The effect of a repo loan on reserves is “self-reversing.” Unlike open market operations, repos automatically restore reserve balances to their original level: at the maturity of the loan, the Fed debits reserve balances and removes the repo loan from its assets.

The loan is guaranteed by assets pledged by the borrower. The set of acceptable collateral is called General Collateral. It includes things other than US government securities (agency obligations and mortgage-backed securities) but not as many as the list of discount window collateral .

Many textbooks don’t mention repos because they are considered a type of open market operation (OMO). The New York Federal Reserve itself calls them “temporary OMOs” sometimes. By nature, however, a repo is a collateralized loan, not a purchase or sale of Treasurys.

Sometimes the Fed does not want to increase the amount of reserves in the banking system, because it estimates that the amount available is appropriate. Still, for some reason, banks can’t get enough liquidity from their peers in the federal funds market and keep coming to the Fed for loans.

As an example, last summer some U.S. banks started experiencing losses from their portfolios of mortgage-related securities. Nobody knew who those banks were, or how large those losses could be. So banks started hoarding reserve balances rather than lending them out. Some institutions were unable to find as much liquidity as they wanted because nobody would lend it to them (at a reasonable price).

In those situations the Fed conducts moneyless monetary policy, acting as counterparty without actually changing the amount of cash in the economy. How? It enters repurchase agreements, which create new reserves. Then it offsets those new reserves by selling some of its own government securities (or letting them mature without purchasing more), and thus withdraws cash from the banking system.

Here’s how a repurchase agreement would change the Fed’s balance sheet, after offsetting it with an open market operation:

Changes in the Fed's balance sheet after a $1,000M repurchase agreement, offset by an open market operation
Assets	US government securities	-1,000
	Repurchase agreements	+1,000
	Reverse repurchase agreements	0
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	0 (-1,000 + 1,000)
Liabilities	Reserve balances	0

In that case the repos don’t have any bottom-line effect on liquidity: they merely change the composition of the Fed’s assets and provide temporary cash to the borrowing banks.

In the second half of 2007 these offsetting operations became more frequent, and his is why Jim Hamilton writes that the Fed has been doing monetary policy using the asset side of the balance sheet. Another way to see it is that the Fed took the money out of monetary policy, because its actions barely affected the monetary base (reserves plus currency in circulation).

Also in the summer the Fed introduced the first of its new tools:

• Term Discount Window Program (TDWP). The first innovation introduced by the Fed doesn’t really deserve a name of its own. Under the TDWP, announced on August 17, the Fed makes discount-window loans for as long as 30 days. On March 16 it prolonged the maximum maturity to 90 days. The range of collateral at this facility is exactly the same as at the discount window, and so are the changes in the Fed's balance sheet. These loans are restricted to institutions eligible for primary discount-window credit —basically, banks with a strong balance sheet.

Many institutions didn’t want to use the discount window or its sister the TDWP because of the stigma that it carries. But liquidity was still dear, so on December 12 the Fed stepped up to the plate, with the

• Term Auction Facility (TAF). The TAF represents an improvement with respect to repos in their capacity to provide liquidity. First, it widens the range of collateral it accepts, from General Collateral to discount window collateral. Second, it provides funds for a longer term, eliminating the need to roll over the loans every day or every week. And third, unlike discount window loans, the interest rate is determined in the marketplace, so the money goes to the institutions that value it most.

By themselves, TAF loans would increase both assets and liabilities of the Fed, just like open market operations and repos. But once again the Fed offset those loans by selling securities and withdrawing cash from the system. Example:

Changes in the Fed's balance sheet after a $1,000M TAF loan, offset by an open market operation
Assets	US government securities	-1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	TAF loans	+1,000
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	0 (-1,000 + 1,000)
Liabilities	Reserve balances	0

The Fed extends the loan, which is an asset for the lender, and credits the bank's reserve account. (In the table I assume that the borrower withdraws the funds from the reserve account, so they're turned into currency in circulation.) Just like a repo, loans through the new facility require borrowers to use assets as collateral for the duration of the loan. But the collateral doesn't show up in the balance sheet, because the Fed does not take ownership of it. At the same time, the Fed sells $1,000M worth of government securities, absorbing that same amount of cash from the banking system.

And here’s the simplified balance sheet on December 26 and August 15:

Federal Reserve's balance sheet, $ millions
Assets		Aug. 15, 2007	Dec. 26, 2007
	US government securities	789,601	754,612
	Repurchase agreements	24,000	42,500
	Reverse repurchase agreements	-31,941	-40,542
	Term Auction Facility loans	0	20,000
	Direct loans	264	4,535
	Other assets	37,058	52,869
Liabilities	Currency in circulation	813,085	829,193
Liabilities	Reserve balances	5,897	4,781

Source: Federal Reserve, H.4.1 release.

The balance of TAF loans grew from $20bn to $100bn between December 26 and April 9.

To be continued...

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That pesky LFP

2008-04-04T06:30:00.003-05:00

“Americans delay retirement as housing, stocks swoon” was the headline of a recent story in the Wall Street Journal (WSJ). As a description of what's going on, it should be taken with a rock of salt.

According to the front-page article, “millions of retirement-age Americans, stung by the recent economic pall, suddenly are having to reassess their plans —with many forced to quickly change course,” (emphasis mine). The Journal itself provides evidence to the contrary. They print a chart (left) that shows that the proportion of people ages 55 to 64 in the work force has been increasing since at least 1990. Seniors have been postponing retirement for 17 years now! And recent numbers do not indicate a switch of gears, but a continuation of a long-run tendency.

It is still possible that the thick-lined graph can’t capture recent changes adequately. Or maybe what the writer meant is that the time series is above its trend. After all, the labor force participation (LFP) of people in that age group did increase steadily between August 2007 and February 2008. To examine this possibility, I calculate the trend (for the nerds: I use the Hodrick-Prescott filter). Chart 1 below shows the trend along with the raw time series. If my calculations are correct, observed LFP in February was indeed about 0.5 percentage points too high.

Chart 1 (click to enlarge)

The writer tells us that the LFP in February was up 1.5 percentage points from last April. But that month the participation rate was 0.6 percentage points below trend. In other words, she is comparing a point that is well below expected values with one that is well above, leading to an overstatement of the facts.

She goes on to say that the surge in participation “translates to more than an additional million people in the job pool” since April 2007. After a few simple calculations I find that an accurate statement would be: “In April 2007 there were 197,000 fewer seniors than expected in the labor force, whereas in February 2008 there were 172,000 more than expected. Population aging plus the growth of trend LFP mean that between April and February we should have expected an increase of 659,000. But because of deviations from the trend on both ends, the actual increase was 829,000.” But that won’t draw many eyeballs.

Another reason to downplay the recent numbers is that blips like this are frequent and short-lived (see Chart 1).

The central point of the WSJ article, however, is that some seniors have been putting off retirement because “falling real-estate and stock markets are erode their savings.” Historically, falling asset prices are not strongly correlated to the LFP of people on the verge of retirement. In 1990-92 home prices fell and stocks barely grew, but participation did not go up. Falling asset prices is thus not a sufficient condition. Or perhaps, as the WSJ insinuates, it takes a double whammy to throw older workers off the retirement track.

Falling markets is not a necessary condition either. Since 1990, the LFP of people ages 55 to 64 has risen in three bursts: 1995-98, 2002-2003, and 2005-2007 (see charts above). Now, 1995-98 and 2000-2003 were periods of rising home prices. And a bull stock market dominated 1995-98. I guess that back then journalists would have written that folks were waiting for the markets to peak before retiring. You gotta explain things somehow.

Of course, all the historical data in the world cannot refute that this time falling asset prices may be postponing retirement. As I showed above, though, the change in LFP so far is quite small.

And now that I’m here: the business cycle cannot explain the LFP of senior workers either. For most demographic groups participation in the workforce is pro-cyclical. It should fall —at least below trend— in periods of high unemployment and low wages. Retiree wannabes do not conform to the pattern. Their LFP rose above trend during the 1990-91 recession, as well as during the weak labor market of 2002-2003 (see Chart 1). That LFP has a mind of its own, I tell you.

What I’ve learned from all this is that the fraction of working seniors has been rising for many years. I’m not sure whether the main driving force is better health or the increase in longevity risk —that is, people work longer because they need to fund longer periods of retirement. Another possibility is that the LFP figures are inflated because they include workers in semi-retirement, i.e. people who have a paid job but do not do it for a living. For example, part-timers or people doing partially pro-bono work. Those pesky senior workers…

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Alien arguments

2008-03-29T13:59:00.009-05:00

It’s again that time of the year when some people fill out a lot of forms and everyone gets mad at the government. No, it’s not taxes; it’s the visa program for skilled workers.

April 1st marks the beginning of the annual application period. The government sets a general quota of 65,000 H-1B visas, plus 20,000 for people with a graduate degree from a U.S. institution. Last year over 100,000 applications swamped the immigration service on the very first day. This year people are expecting an even bigger excess demand. A lottery will decide who gets to live and work in the U.S.

Over the next two weeks we shall witness a repetition of last year’s debate. On one side, businesses and pro-immigration groups advocate lifting the cap. Skilled labor, they say, gives the U.S. an edge in high human-capital sectors, particularly science and technology, and contributes to faster growth. The country should, therefore, welcome as much skilled labor as employers will bear. On the other corner, some professionals in the IT industry and protectionists would like to restrict the hiring of foreigners, if not kill the program altogether. Their main complaint is that employers just want to hire foreign computer programmers and engineers on the cheap.

I have been indoctrinated to believe in the virtues of the free movement of goods, capital and labor. But if we’re going to make any progress in this quarrel, both sides should come clean. Free-traders must acknowledge that immigrants will lower wages in some sectors; some Americans will lose their jobs to foreign nationals. And any system is susceptible to abuse from greedy employers. Failing to mention the negatives does little service to the cause. And simply stating that “the country is better off on the whole” won’t cut it.

Protectionists should accept that the job market is not always a zero-sum game. In many instances, a job “lost” to a foreigner generates several other complementary positions, whether horizontally or vertically. Immigration detractors must also admit that, if skilled people don’t move in, capital and entire companies will move out. Don’t forget that Canada and the UK, just to mention two close rivals, have much friendlier immigration policies than the U.S.

Evidence of the effects of immigration is tenuous, which keeps skepticism alive. For example, a 2007 study by Ottaviano and Peri reported that immigrant and native workers are not perfect substitutes within skill groups —their study included all education levels. Their conclusion, another paper discovered, hinges on a disputable assumption, and has been promptly proven wrong. (George Borjas briefs us in his blog. The paper also provides a short review of the literature on the substitutability of immigrants and natives.)

About.com: political humor
Even more recently, a study by the National Foundation for American Policy, featured by the Wall Street Journal and the Washington Post, found that “there is a positive and statistically significant association between the number of positions requested in H-1B labor condition applications and the percentage change in total employment. The data show that for every H-1B position requested, U.S. technology companies increase their employment by 5 workers.” As the authors admit, it is possible that the hiring of H-1B’s and natives are both driven by business conditions. The study does not prove causation, just correlation —of course this “detail” didn’t make it to the newspapers.

Lotteries, which the government started using last year to allocate visas, provide economists with a natural experiment. If we could get company-level data on number of visas obtained, salaries, and payrolls, we could take advantage of the randomization scheme to get clean estimates of the effects of foreign hires.

Sometimes the evidence is just manipulated. For example, immigration skeptics like to point out that in many industries aliens’ salaries are below market rates. But according to a report by the U.S. Citizenship and Immigration Services (USCIS), 82 percent of first-time visa applicants are below 35 years old, so their work experience is also significantly shorter than average. Even among applicants for renewals, 65 percent are below 35. Comparing their wage with the overall industry average is misleading, if not malicious.

Closer monitoring would warm up protectionists to the H-1B program too. Employers are supposed to pay aliens no less than the prevailing salary. But USCIS examines the employers’ stated offers, and doesn’t have the resources to properly monitor actual salaries. Access to Social Security records and government payroll surveys would go some way towards preventing fraud.

A closer correspondence between supply and demand numbers should please everyone too. The visa limit of 65,000 —effectively a cap on the supply of skilled foreign labor—bears little relationship to demand. It was already 65,000 back in 1990, and it didn’t change through 1998. In 1999 the cap was finally raised to 115,000, perhaps because of the rapid growth of the IT sector in the late 1990s. Then, just as the dot-com bubble was imploding, it was raised again to 195,000 in 2002 and 2003. Employers filed fewer than 80,000 applications each of those years. In 2004, as the job market recovered, the limit was cut to 65,000, plus 20,000 for aliens with U.S.-earned graduate degrees. USCIS could coordinate with the Department of Labor and issue a number of visas that is related to payroll forecasts.

And reform of the green card program should appeal to immigration opponents and enthusiasts alike. Nowadays, a “temporary” work visa lasts three years and is renewable for three more. During that time, many H-1B aliens in their 20s and 30s grow roots in the country, whether they are supposed to or not. It’s only natural that they eventually seek to stay permanently. That process sometimes lasts longer than the work visa, especially for Chinese and Indian citizens. While the green card application is pending, most workers don’t switch jobs because that would push them to the end of the waiting line. As a result, they’re bound to one employer, reducing their bargaining power and putting downward pressure on industry wages.

Two things could, therefore, narrow the chasm between anti- and pro-immigration groups. First, a healthy dose of honest economics. We economists are in the best position to offer an accurate and dispassionate answer to the questions of which and how many Americans are displaced by educated foreign nationals, how much they affect salaries, and how a shortage of skilled labor fosters outsourcing and company relocation. Once we settle that, Congress can decide whether we need 20,000, 200,000, or two million visas. Second, give more resources to USCIS, to prevent fraud and reassure protectionists.

Toning down language would help too. The Economist qualified the system as an “idiocracy.” That is the kind of attitude that polarizes public opinion and stalls reform. And if nothing is done, next year we’ll be having this discussion again, while thousands of high-skill jobs trickle overseas —along with their employers.

UPDATE (4/3/2008): Felix Salmon, Dean Baker and Free Exchange take on the issue.

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Brief announcement

2008-03-27T07:00:00.006-05:00

This week's post will come out particularly late (some time late Saturday)--I have an important deadline this Friday. Sorry, folks.

I'll leave you with a couple of great comics from phdcomics.com:

Changes to the previous post

2008-03-25T10:01:00.002-05:00

After reading comments from readers and doing my homework I've made some changes to the previous post, where I describe recent innovations in the Federal Reserve's modus operandi. The text in bold face is either new or has been rewritten from the previous version. I've also added a couple of tables to illustrate what happens to the balance sheet in the case of TSLF and PDCF loans.

This is a summary of the changes:

1. The range of collateral for TSLF loans is the same as that for TAF loans, which is the same as for the discount window. This range is wider, however, than for repurchase agreements.

2. The TSLF is a bonds-for-bonds transaction, and therefore doesn't change the monetary base. The Federal Reserve takes possession of the borrower's securities for 28 days, and lends government securities. The PDCF is a cash-for-bonds transaction, which does change the monetary base. The Fed will offset PDCF loans by conducting direct purchases of government securities, reverse repos, or by letting its Treasurys expire.

Thanks to everyone who left a comment or sent me an e-mail.

How the Fed took the money out of monetary policy

2008-03-22T17:31:00.013-05:00

UPDATE: I wrote an expanded, better version of this post, in two parts: Part I and Part II.

The Federal Reserve used to have only a few tools to do its job —that is, until it got the genie out of the bottle. Sometimes quietly, other times conspicuously, the Fed is surely changing the way it creates liquidity.

(Jim Hamilton has been narrating these changes since the summer. Part of this post is my one-stop account. Jim’s posts, which are much better, are here: September 23, December 14, December 16, March 15.)

The central bank has a balance sheet, just like any other bank. As assets, it holds government securities, loans to depository institutions (banks), and other assets. As liabilities, it has currency (the cash in your pockets) and reserve balances. Reserves are deposits that banks keep at the central bank. When a bank needs currency it withdraws from its deposit, effectively turning it into bills and coins that you and I can use.

Until now, macroeconomics textbooks have been telling us that central banks use three tools to control the amount of currency in circulation. Looking at them from an accounting perspective will help us understand what the Federal Reserve has been up to recently:

1) Open market operation. This is an outright purchase of government securities from banks. When conducting this operation, the central bank increases its assets and credits banks’ reserve balances. Eventually, banks withdraw from their reserves at the central bank and turn them into cash. So an open market operation amounts to withdrawing government securities from the economy and replacing them with cash. The central bank can also reduce the amount of cash in circulation, by doing just the opposite: selling government securities and absorbing cash. By far, an open market operation is the best-know of the central bank’s tools.
This is a simplified version of the U.S. Federal Reserve’s balance sheet on August 15, 2007:

Federal Reserve's balance sheet, $ millions (Aug. 15, 2007)
Assets	US government securities	789,601
	Repurchase agreements	24,000
	Reverse repurchase agreements	-31,941
	Direct loans	264
	Other assets	37,058
Liabilities	Currency in circulation	813,085
Liabilities	Reserve balances	5,897

Source: Federal Reserve, H.4.1 release.

(For the moment, regard “repurchase agreements” as government securities.)
Suppose that on August 16, 2007, the Fed pumped $1,000 million in the system through an open market operation. The Fed’s balance sheet would experience the following changes, once banks have withdrawn the new funds from their reserve accounts:

Changes in the Fed's balance sheet after a $1,000M open market operation
Assets	US government securities	+1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	+1,000
Liabilities	Reserve balances	0

2) Direct loan. This tool is usually referred to as the “discount window.” The central bank simply lends money to a bank. The borrower must pledge collateral with a value that exceeds that of the direct loan. The central bank increases its balance of loans, and simultaneously credits the reserves of the borrowing bank. Then the bank withdraws from its reserves, effectively turning them into currency in circulation. Asking for a direct loan usually means that the bank was not able to obtain liquidity in the inter-bank market. Moreover, borrowers are also subject to scrutiny by the central bank, and watched by other banks. And the interest rate charged for direct loans is higher than the inter-bank rate. For those reasons, the discount window is used rarely and in small amounts.

3) Reserve requirements. Banks are required to keep a certain amount of reserves at the central bank. If the central bank increases that requirement, banks are forced to withdraw currency from the economy and put it in their reserve account. The central bank can also do the opposite, i.e. increase the amount of currency in circulation by lowering the reserve requirement. This tool is the least often used.

Normally banks obtain liquidity for their daily operations in the inter-bank market, where they borrow from and lend to each other at the going interest rate. Last summer some U.S. banks started experiencing losses from their portfolios of mortgages and securitized mortgages. Nobody knew which banks would suffer losses in the future, or how large they could be. So banks starting growing wary of lending to each other, and it became more expensive—or just plain impossible—to raise as much liquidity as needed.

The Fed stepped in to help. Instead of providing liquidity through outright open market operations, it increased the use of an operation that is more frequently used, yet less well known: repurchase agreements. These are short-term loans, usually overnight, extended by the Fed to banks. As collateral, banks transfer high-quality securities to the central bank for the duration of the loan. At expiration, the loan is repaid and the bank takes back its securities.

From an accounting perspective, the repo increases the central bank’s assets and potential currency in circulation, much like an open market operation does. This, for example, is what happened between August 8 and August 15.

Soon after, the Fed decided that it didn’t want to increase the potential amount of liquidity in the system, which affects short-term interest rates and inflation. So it offset the repurchase agreements by selling some of its own government securities (or letting them expire without purchasing more), and thus withdrawing cash from the system. So the repos didn’t have any bottom-line effect on liquidity: they merely changed the composition of the Fed’s assets and provided temporary cash to the borrowing banks. This is why Jim Hamilton writes that the Fed has been doing monetary policy using the asset side of the balance sheet. Another way to see it is that the Fed has been conducting money-less monetary policy, because its actions barely affect the monetary base (reserves plus currency in circulation).

Here’s how a repurchase agreement would change the Fed’s balance sheet, after offsetting it with an open market operation:

Changes in the Fed's balance sheet after a $1,000M repurchase agreement, offset by an open market operation
Assets	US government securities	-1,000
	Repurchase agreements	+1,000
	Reverse repurchase agreements	0
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	0 (-1,000 + 1,000)
Liabilities	Reserve balances	0

After doing this for months, and aware that banks were not getting as much liquidity as they wanted, in December the Fed unveiled the Term Auction Facility (TAF). As its name suggests, this is an auction for a limited amount of Fed’s loans. Just like a repo, loans through the new facility require borrowers to use assets as collateral to the Fed for the duration of the loan. But the TAF represents an improvement with respect to repos in their capacity to provide liquidity. First, it lowers the bar for the type of assets that the Fed accepts, which are the same as those for the discount window. Second, it is more targeted than repos: the bidding system ensures that the limited loans go to the banks that value them most.

By themselves, TAF loans would increase both assets and liabilities of the Fed, just like open market operations and repos. But, once again, the Fed partially offset those loans by selling securities and withdrawing cash from the system. Here’s the simplified balance sheet on December 26 and August 15:

Federal Reserve's balance sheet, $ millions
Assets		Aug. 15, 2007	Dec. 26, 2007
	US government securities	789,601	754,612
	Repurchase agreements	24,000	42,500
	Reverse repurchase agreements	-31,941	-40,542
	Term Auction Facility loans	0	20,000
	Direct loans	264	4,535
	Other assets	37,058	52,869
Liabilities	Currency in circulation	813,085	829,193
Liabilities	Reserve balances	5,897	4,781

Source: Federal Reserve, H.4.1 release.

The balance of TAF loans grew from $20bn to $60bn between December 26 and March 12.

Still, all these liquidity venues are available only to members of the Federal Reserve system, which I have been calling “banks” and whose proper name is “depository institutions.” There is another set of financial intermediaries and investors, such as Bear Stearns or Lehman Brothers. They have been as affected by the liquidity crisis as much as banks have, but don’t have direct access to neither the discount window nor TAF.

So the Fed has announced two new facilities for those institutions. The first one is the Term Securities Lending Facility (TSLF), to open on March 27. At this new window, all primary dealers -all banks and brokers that trade in government securities with the Fed- are allowed to borrow up to $200bn of government securities for 28 days. The minimum quality of the assets seems to be the same as those for than for the TAF (they include federal agency debt, federal agency residential-mortgage-backed securities (MBS), and non-agency AAA/Aaa-rated private-label residential MBS). But in contrast with TAF this new facility lends government securities, not cash. Through the TSLF the Federal Reserve will be temporarily swapping safe government securities for risky assets. This is how these loans would look like on the balance sheet:

Changes in the Fed's balance sheet after a $1,000M TSLF loan
Assets	US government securities	-1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	TSLF loan	+1,000
	Other assets	0
Liabilities	Currency in circulation	0
Liabilities	Reserve balances	0

The second institution is the Primary Dealer Credit Facility (PDCF), which started operating on March 17. This venue provides overnight cash loans to all primary dealers, at the discount window interest rate, and accepts ~~even riskier~~ the same type of collateral. ~~they accept all collateral eligible for repos, plus investment-grade corporate securities, municipal securities, MBS and asset-backed securities.~~ With the PDCF, all primary dealers have de facto access to the discount window, from which only depository institutions could borrow before. The loan will increase the monetary base (read the PDCF FAQ). To offset the increase, the Fed will utilize "a number of tools, including, but not necessarily limited to, outright sales of Treasury securities, reverse repurchase agreements, redemptions of Treasury securities, and changes in the sizes of conventional RP transactions." Here's what a PDCF loan looks like, after it has been offset:

Changes in the Fed's balance sheet after a $1,000M PDCF loan, offset by an open market operation
Assets	US government securities	-1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Direct loans	0
	PDCF loan	+1,000
	TSLF loan	0
	Other assets	0
Liabilities	Currency in circulation	-1,000 + 1,000
Liabilities	Reserve balances	0

In fact, the Federal Reserve has included PDCF as a sub-entry within "Other loans" in the balance sheet, next to the discount window loans, because PDCF and discount window are in fact one and the same facility.

Unlike the TAF, neither TSLF nor PDCF will increase the assets of the Fed. It will temporarily decrease balances of government securities, and increase those of sketchy securities. And because participant institutions don’t have Fed reserves, TSLF loans don’t affect the monetary base. These two venues circumvent the necessity to conduct open market operations so that the monetary base doesn’t change.

Here’s the balance Fed again, in December and after the PDCF opened:

Federal Reserve's balance sheet, $ millions
Assets		Dec. 26, 2007	Mar. 19, 2008
	US government securities	754,612	660,484
	Repurchase agreements	42,500	62,000
	Reverse repurchase agreements	-40,542	-46,143
	Term Auction Facility loans	20,000	80,000
	Primary Dealers Credit Facility	0	28,800
	Direct loans	4,535	125
	Other assets	52,869	36,603
Liabilities	Currency in circulation	829,193	818,362
Liabilities	Reserve balances	4,781	3,507

Source: Federal Reserve, H.4.1 release.

With its new tools, the Fed has provided liquidity without printing much money. It has temporarily absorbed risky and illiquid securities, and supplied government securities, which are risk-free. So instead of monetary policy, in the sense we traditionally have thought about it, the Fed has become a risk-absorber (temporarily, we hope). Or, to put it less kindly, a pawnbroker.

Will these new tools make it to the textbooks? It’s hard to tell whether the particular facilities (TAF, TSLF, etc.) will survive. I think that some unified, generalized form of credit to non-depository institutions will stay. But I’ll have to write about that another time.

Addendum:

Somebody asked me how the Fed conducts an "offsetting" open market operation when the Fed extends a TAF loan. This table summarizes it:

Changes in the Fed's balance sheet after a $1,000M TAF loan with an offsetting open market operation
Assets	US government securities	-1,000
	Repurchase agreements	0
	Reverse repurchase agreements	0
	Teerm Auction Facility loans	+1,000
	Direct loans	0
	Other assets	0
Liabilities	Currency in circulation	(-1,000 + 1,000)
Liabilities	Reserve balances	0

The Fed extends the loan, which is an asset for the lender, and credits the bank's reserve account. (In the table I assume that the borrower withdraws the funds from the reserve account, so they're turned into currency in circulation.) The collateral doesn't show up in the balance sheet, because the Fed does not take ownership of it. At the same time, the Fed sells $1,000M worth of government securities, absorbing that same amount of cash from the banking system.

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Recession buzz: February update

2008-03-14T01:41:00.014-05:00

(Click to enlarge.)

The Economist’s R-word index, which counts the stories in the New York Times (NYT) and the Washington Post (WP) that use the word recession, reached a seven-year high on the first month of 2008. On average, each newspaper published 7.1* stories per day containing the feared term. Since 1976, that level of coverage has only occurred when a recession is already under way —not that that means much, since we have seen only four such episodes in that period.

Both blog chatter, as measured by Technorati, and the number of Google searches for the R-word, were consistent with the buzz created by the press. (See charts above. Hat-tip to Aaron Schiff for the Technorati chart.)

In February, recession hum quieted down from 7.1 to 4.6 stories per day. A more comprehensive index** also fell sharply, from 3.7 to 2.2. Did that improve the public’s view of the economy?

Hardly. Most of the data released in February showed a slight worsening of the economic situation, and public opinion reacted accordingly. The average closing price of recession contracts at intrade, the prediction market, increased slightly from 63.3 to 64.8 between January and February. According to the Wall Street Journal’s forecasters, who are polled early in the month, the median estimate of the probability of a recession over the next 12 months went up from 50% to 65%. And consumer confidence declined from 87.9 to 75, also between January and February. (As a counterpoint, the preliminary figure of Michigan consumer sentiment inched up to 70.0 from 69.6.)

Why gloom is spreading but recession chatter has diminished is easy to explain. Extensive coverage of the stimulus package and of the Federal Reserve interest rate cuts inflated the R-word index in January, as I explained last month. In February, R-talk returned to levels more in line with actual economic conditions.

How much use of the R-word is justified? In February, about six stories per day and newspaper was reasonable (only for the NYT and the WP); at least that’s what my statistical model says.

The model, which I have changed since last month, makes predictions based on a set of economic variables and their lags. (Technical details below. Suggestions welcome.) The prediction tracked actual values quite closely from February through December (except in October). It missed the January buzz by a long shot, and predicted that we would see six stories per day in February, one and a half more than we actually did.

In March we will probably see an increase in recession chatter. My model forecasts 5.7 stories per day and newspaper, but that’s probably an underestimation. The Fed will make sure of that.

* Assuming that each newspaper publishes 26 issues per month.

**Counts the number of stories with the word “recession” in the following newspapers: Chicago Tribune, Daily News, LA Times, New York Times, Philadelphia Inquirer, Wall Street Journal, Washington Post and USA Today.

Statistical model:
VAR, with monthly data, from January of 1976 through the latest month available. Each equation includes six lags. The variables are: the R-word index, the unemployment rate, the change in nonfarm payrolls, the slope of the yield curve (10-year minus 1-year), the growth of personal consumption expenditures on durable goods accumulated over the current and previous two months, and the growth of the industrial production index, also accumulated over the same period. I also include a set of monthly dummies and a dummy variable that equals 1 if the NBER makes a recession announcement. The unemployment rate is the first release reported by the BLS. The change in payrolls mimics the one reported by the BLS, that is, it is equal to the first estimate of payrolls for month t, minus the revised (first update) figure for month t-1. Both unemployment and payroll figures come from ALFRED. The yields on the ten-year bond and the one-year Treasury bill are monthly averages, from FRED. Durable expenditures come from the NIPA accounts, via FRED, and the industrial production index is from the Federal Reserve, also via FRED.

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Technorati tags
economics, news, media bias, recession, media

Productivity trends

2008-03-08T14:25:00.012-06:00

Productivity is the main determinant of long-run growth. In the United States, between 1958 and 2007, the average growth of output per capita has been 2% per year, whereas the average growth of productivity —output produced in one hour of work— has been 2.1%. But productivity growth fluctuates a lot, and the latest changes are uncomfortable to look at.

At the end of 2007 trend productivity growth was 1.7%, 1.3 percentage points down from the fourth quarter of 2001. Now productivity increases at the same pace as it did before the information revolution of the 1990s (see Chart 1). Why?

My story is one of sectoral shifts. The second half of the 1990s was marked by technological improvements in telecommunications. Cell-phones, e-mail, software and, above all, the internet, did two things: they lowered the cost of gathering information, and facilitated interactions among workers, both within and between firms. Both boosted productivity in knowledge-based industries. Not only that: those technologies created new jobs and even entire sub-industries.

Over time those technologies became well established. Investment in equipment slowed down. New job openings were filled by people coming from other industries, who were less productive than incumbent workers. To put it in two sentences: 1995-2001 was a period of technological deepening and creation of new services and industries; 2001-2007 were years of re-allocation of workers towards services.

The data are suggestive: during the business cycle of July 1990 to March 2001, the decrease in payrolls in the manufacturing sector accounted for just 4% of the total change in jobs in the private economy (see Chart 2). Since March 2001, the bleed of manufacturing accounted for 76% of the change in employment.

But another change is in the works. During the current business cycle, most jobs have been created in industries where it is difficult to increase productivity. Health services are a notorious example: 62% of the increase in non-farm payrolls happened in the health care industry. (Hat tip to Michael Mandel, from BusinessWeek.) It is hard to increase the value of services per hour in that sector, because we still rely on a large number of doctors, nurses, orderlies and administrative personnel to deliver one unit of output. Without having done an in-depth analysis, I would say that we have made enormous technological progress in diagnosing and treating health conditions, but that those technologies don’t save any labor.

A similar argument applies to the leisure and hospitality industry (hotels, restaurants, etc.), which absorbed 39% of the change in employment. Until we teach a robot to fry your eggs and make your bed, productivity will increase slowly.

* * *

Productivity is the most important, but not the only driver of growth. An economy produces more output per capita by: employing a higher fraction of the population, working longer hours, or squeezing more output per hour. Chart 3 shows the trend growth rates of each of those components. (I call them “trend” because they are constructed using smoothed time series. Technical details below.) The sum of the three series is equal to the growth rate of output per capita.

Increased participation in the labor force (top panel in chart 3) has grown by less than 0.5% in most decades. Hours per worker (middle panel) have declined decade after decade, reducing the growth of output per capita. But output per hour (bottom panel), also known as productivity, has grown routinely at annual rates well over 1%, and in some decades 2%.

Going forward, there are reasons to believe that productivity gains will become even more important. In a nutshell: the fraction of people who work is going to decrease. The fall of the participation rate already subtracted 0.12 percentage points from the growth rate of output per capita between 1998 and 2007 (see chart 3, top panel). Historically, the employment-population ratio has increased thanks to women, whose participation has increased since WWII. That trend has probably played out. The female participation rate reached a historical maximum of 58% in 1999, and since then it has stayed roughly constant.

More importantly, the large generation of baby-boomers born between 1946 and 1964 will gradually retire from 2011 through 2030. That will push the participation rate down because their descendants, the X and Y generations, are not numerous enough to replace them. And immigrants don’t improve the employment-population ratio much because they add to both the numerator and the denominator.

Adding to those demographic trends, men in prime working age (25-54) have continued their slow, secular exit from the labor force. Their participation has declined from a maximum of 95% in 1969 down to 87% in 2007. Where did those men go? Some of them just replaced women as homemakers, but that cannot be the whole story. The New York Times published a story by David Leonhardt this week (hat tip: Vox Baby). He thinks that “these nonemployed workers tend to be those who have been left behind by the economic changes of the last generation. Their jobs have been replaced by technology or have gone overseas, and they can no longer find work that pays as well.”

Now let’s put together these pieces: the economy is re-allocating resources towards health and labor-intensive services; population aging will increase the demand for those services and reduce the number of people who provide them; and the information technologies of the “e-era” raise productivity at decreasing rates. Can the U.S. economy continue to deliver a growth rate of 2% in output per capita? And, if so, what will be the next driver of productivity?

Technical details: I use time series on hours, employment and non-farm business output (from BLS productivity data base), and on the employment-population ratio (from the CPS data base). First of all, I calculate quarter-over-quarter growth rates for each time series, by taking differences in logarithms, and then I annualize them by multiplying by four. Then I apply the Hodrick-Prescott filter to each growth rate series, with smoothing parameter equal to 5000. Finally, the growth rate of productivity is the smoothed growth rate of output minus the smoothed growth rate of hours; the growth rate of hours per worker is the smoothed growth rate of hours minus the smoothed growth rate of employment. The growth rate of output per capita (displayed on chart 2) is the sum of the growth rate of participation and the constructed growth rates of hours per worker and productivity.

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economics, productivity, growth, macroeconomics, participation rate

Woohoo! I made my first $0.10!

2008-03-06T15:51:00.017-06:00

...on EconWeekly: A generous reader gave me a tip today.

If you visit the website you'll see this at the end of each post:

starting with my post from last week. (For people reading this on an RSS reader: there's a link to the tipping button at the end of the post. The company that created the tipping system still needs to figure out how to "embed the button" in the RSS feed. There's also a permanent link near the top right corner of www.econweekly.com.)

This is a service provided by tipjoy and makes it significantly easier to tip the creators of online content. It is more convenient than the "tip jars" that blogs and websites have used so far.

Felix Salmon and Aaron Schiff explain how it works. Here is tipjoy's FAQ.

Basically, the first time you want to give a tip you enter your e-mail address. Tipjoy will later request that you confirm your account, and they'll add the tip to your tab. The amount of the tip depends on what the tipped person specified. It can be $0.05, $0.10, $0.25 or $0.50.

You can pay your tab at any time, in amounts no smaller than $5 (you can have a credit and deduct the tips you give from there). Tip owners can collect their money as soon as they have a $5 balance in paid tips. For now, tipjoy can only pay in the form of gift cards at Amazon or by giving the money to charities. In the future, it looks like they'll be able to pay cash. Oh, and they keep 3% of earned tips.

When will this take off? There need to be enough websites that receive tips to make it worthwhile for readers to sign up for the service. For authors, there need to be enough tippers around to make it worthwhile to insert the buttons. On both sides, however, transactions costs are small.

My main concern is that users will neglect to pay their accumulated tab. Because the payment of individual tips is deferred (the tipper's credit card is not charged immediately) it's easy to imagine that tippers can accumulate unpaid tips and postpone the actual payment indefinitely. If tipjoy has no way to enforce the payment, the tipping never actually happens.

We'll see. For the moment, I'm letting the (still virtual) dough roll in, one dime at a time.

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In Spain, mortgage funding is different

2008-03-01T20:05:00.025-06:00

Spain is different. The slogan, which the tourism industry used in the 1950s to celebrate the country’s identity and culture, is nowadays something of a joke. Among Spaniards, the old line is an expression of self-deprecation, of a sentiment of quirkiness and inferiority, which constitutes a fundamental part of the Spanish ethos. When it comes to funding mortgages, however, Spain is different in a good way.

Like so many other parts of the developed world, Spain has experienced a housing fever over the last ten years. Banks have thus been lending at a hectic pace. Because bank deposits do not provide reliable and sufficient liquidity to fund the mortgages, lenders everywhere have resorted to mortgage-backed securities (MBS) and mortgage bonds.

An investor in a “standard” MBS holds a claim on the issuer of the security, not on the mortgagee, even if he buys securities directly from the originator of the mortgages.

In Spain, however, at least one link in the chain of investors has a stake in the original loans. In order to securitize mortgages, Spanish banks first issue participations, which are shares in each of the mortgages included in a pool. A holder of participations receives a percentage of the interest and principal of the mortgages from the originator, who in turn receives the payments from the Janes and Joes (or Pacos and Dolores) who obtained the mortgage loans. The participation holder has thus a claim on both the originator and the mortgagee. More importantly, the originator retains a certain fraction —100 minus the percentage of the participation— of each and every mortgage created, turning originator and participation holder into co-creditors. In a second step, holders of the participations may form pools and then issue securities that represent stakes in those pools of participations.

This scheme provides a form of risk-sharing between originators and participation holders, which is conducive to higher lending standards. Originators are not off the hook when they sell the participations, because they keep a share of each mortgage. That makes them more careful when assessing the creditworthiness of mortgage applicants, and reduces the chance of overly loose credit standards. And participation holders have a stronger incentive to keep an eye on individual mortgagees than if they had a claim on the originating institution only. (Read this post at Calculated Risk about this subject.)

* * *

The second source of mortgage funding for originators is the so-called covered bond. This is a debt instrument issued by a credit institution and secured by a pool of mortgage loans or public debt or even MBS themselves. These bonds pay coupons and principal, just like any other bond. The investor has a claim on the issuer of the bond and, if the latter defaults, on the pool of loans (not on individual loans).

In most European countries, where covered bonds are popular, a set of cover assets is set aside for each bond issue. In Spain, however, all mortgages of the issuer constitute collateral for the bond. The risk of the bond depends thus on thousands of separate individual loans, with varying credit quality, not just sub-prime or fixed-rate 30-year loans.

The amount of outstanding bonds has experienced an astonishing growth in Spain, thanks in part to the participation of regional savings banks. That has some people fretting, because the business of each of those small institutions is too dependent on local economic conditions. But those banks, known as “cajas” o “caixes,” formed a coalition that has become the largest issuer of covered bonds in the country: AyT Cédulas Cajas. (The composition of the coalition varies by issue.) By pooling their mortgages into a single issue, the risk of the bond depends on dozens of local real estate markets, spread all over the country.

But perhaps the most important reason why the Spanish financial system is unlikely to suffer a meltdown is the virtual absence of Special Investment Vehicles (SIV) and conduits. These animals allow banks to move mortgage-backed securities off their balance sheets, thus obscuring the exposure of individual institutions and escaping capital requirements.

Not in Spain. The Bank of Spain, scarred by a financial crisis in the 1980s, demanded a long time ago that lenders post an 8% capital charge against SIV assets. The result: a relative absence of off-balance mortgage risk. Surely, Spanish banks have suffered losses, but much smaller than those of their European neighbors. That is remarkable considering the size of Spain’s mortgage-backed issues, only second to the UK’s. (Read this article in the Financial Times, Feb. 1) From here, then, my sincere bravo to the Bank of Spain.

* * *

Six months after the beginning of the credit crisis, back in August 2007, Spanish lenders show signs of liquidity stress. The market for covered bonds is practically inactive, and there is an obvious mismatch between the maturity of mortgages (20, 30 or 50 years) and the liquidity needs of originators.

But the policy of the European Central Bank (ECB) is proving very useful here: Europe’s banker has long allowed its members to use mortgage-backed bonds as collateral. So Spanish lenders are simply issuing covered bonds, keeping them instead of selling them to investors, and using them to borrow from the ECB at the weekly auctions: unprecedented, odd, but nothing to worry about.

The default rates of Spanish mortgagees are still near historical lows, even after months of rising mortgage payments. If the macroeconomic forecasts for the eurozone are roughly accurate, growth will slow down, inflation will subside, and the ECB will cut interest rates, providing relief to debtors. So when the liquidity crisis goes away, Spanish banks will be sitting on piles of solvent mortgage loans. There is no looming solvency crisis here.

UPDATE (3/6/08):
I found this graph of default rates on mortgage loans here.

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Technorati tags:
economics, mortgage funding, Spanish economy, mortgage backed securities, collateralization

EZer taxes

2008-02-22T19:56:00.017-06:00

Imagine if you didn’t have to file a tax return. Imagine if, come T-day, the only thing you needed to do to comply with your tax obligations was to sign a form and mail it. And imagine if this could be done without changing a comma of the tax code. This is not a pipe dream—millions of citizens in different parts of the world already do it.

Austan Goolsbee, professor at the University of Chicago Graduate School of Business and head economic adviser to Barack Obama, is proposing to let the Internal Revenue Service, America’s tax man, put together drafts of individual tax returns and mail them to taxpayers. Experts know the system as “Tax Agency Reconciliation” (TAR). Goolsbee has had the good sense to re-baptize as “Simple Return.”

Tax collection agencies receive all the information they need to fill out the returns of many taxpayers. By law, employers and financial institutions send the data to them. The time spent by filers collecting statements, putting the numbers in the right boxes of the tax form, figuring out the standard deductions and exemptions, and calculating the tax bill--not to mention the fees paid to tax prepapers--are thus a waste.

Sweden and Denmark use the system. In Spain, with seven years of TAR experience, some filers can even request and confirm their pre-filled tax returns by sending a text message. Some Spaniards don’t even have to sacrifice precious TV time: they can do their taxes through their interactive, digital TV sets. (I encourage readers who know of other countries in the EZer Club to let me know in the comments or by e-mail. I’d like to make a list. If you respond, please specify whether the country does TAR or exact withholding.)

The obvious benefit of pre-filled tax returns is the time savings for filers. In the U.S., the average compliance time for the 1040EZ form, the simplest there is, is three hours and 46 minutes. The other types of tax form take over ten hours. Goolsbee estimates that, if his Simple Return applied to 40% of taxpayers, it would save 225 million hours and more than $2 billion in fees.

The fiercest opposition to TAR would thus come from tax preparers. Thousands of jobs, they’ll clamor, will be lost. For an economist, this is the easiest criticism to counter. Those jobs are not providing any service other than helping to comply with a pointlessly dense tax code. Let tax shops fold, and their workers will find jobs producing goods and services that actually add to social welfare. Creating employment by keeping an unwieldy tax code makes as little sense as digging a hole in the desert and then employing jobless people to fill it. If only Congress were brave enough to hold this argument against lobbyists… (Regarding this topic, I believe there was a lively discussion in the comments following Steven Levitt’s post . Read my own rant about the broken windows fallacy.)

Mailing pre-filled tax returns is not an intrusion on private business either. As Goolsbee argues, governments allow online filing and provide printed tax tables, and nobody opposes to those services on the grounds that they undermine the tax preparation business.

Receiving a pre-filled return in the mail does feel a bit imposing though. Some people will see TAR as an intrusion on individual freedom. It doesn’t need to be. Individuals will be allowed to scrap the return prepared by the government and fill out a new one. And if the taxpayer ignores the pre-filled return, and doesn’t fill out her own, her taxes won’t be filed, so TAR doesn’t infringe on voluntary compliance. The key is to disclose, every year and to every taxpayer, that the return sent by the government is not a tax bill, but a draft that can be turned into a final return if the individual chooses to do so.

To be sure, TAR would not eliminate the need to file a tax return for everybody. People who itemize their deductions, or who don’t have all their earnings reported to the government by a third party, cannot use the pre-filled form. Goolsbee estimates that, at most, 40% of all taxpayers could benefit from a TAR system; and that’s only if the Alternative Minimum Tax is reformed. In Spain, 30 to 40 percent are eligible. Taxpayers who don’t qualify tend to file more complicated tax returns, and thus spend more time and money on filing, than those who are eligible. So the benefits of TAR go mostly to people with low-to-middle income or simple household finances, who spend most of their tax preparation time (or money) gathering and filling out documents, not mining the tax code for deductions.

The best way to reduce the cost of compliance for everyone is to simplify the tax code. This can be done by scrapping the income tax as we know it today, or by eliminating tax deductions, exemptions, and exceptions for special groups. But such changes face even taller political obstacles than TAR. So, since the tax-instructions booklet is not going to get thinner any time soon, let your tax man deliver a pre-filled return—and spend some more quality time with your TV.

Technorati tags:
economics, tax agency reconciliation, TAR, tax compliance, taxes

On inflation expectations

2008-02-15T22:12:00.012-06:00

With Federal Reserve and government doing their best to stimulate demand, people have started looking at inflation. The worry is that the economy is not as sick as our policymakers think, and so the fiscal and monetary medicines are excessive. Markets disagree.

Expected inflation is an important determinant of future inflation. If the public expects higher inflation, workers demand higher wages, prompting employers to raise the price of their goods, which results in higher actual inflation.

Markets in fixed-income securities provide timely information about inflation expectations. Treasury inflation-protected securities (TIPS) deliver interest and principal payments that are tied to inflation. Payments from regular Treasury notes, on the other hand, are not indexed to inflation. The difference between the yield rates of the two types of securities must be equal to the inflation rate expected by the markets—otherwise there would be an arbitrage opportunity. In practice, because of technical issues, the yield spread is only an approximation to expected inflation, and people call it the break-even inflation (BEI) instead. (More on this below.) From here on I use BEI and “expected inflation” interchangeably.

Because the Treasury has created notes with different maturities, we can use the spread between nominal and TIPS securities to gauge inflation expectations for different horizons. For example, today’s difference between the yield of five-year TIPS and that of five-year nominal notes is approximately equal to the inflation rate expected over the five years starting now (2008-2012).

The Fed is interested in long-term inflation expectations, because in the short term prices are affected by transitory or volatile factors, such as commodity prices. One measure of long-term expectations, which we can also derive from yields, is the five-year, five-year forward rate. That is an approximation to the rate of inflation expected for the five years starting five years from now. Today, that would be the period from 2013 through 2017.

* * *

Chart 1 (click to enlarge)
Earlier this month Greg Ip of the Wall Street Journal posted a graph showing the five-year, five-year forward BEI, which generated some discussion in the econ blogosphere. Felix Salmon and Greg Mankiw worried over signs of increasing inflation coming from that graph. Mankiw went as far as saying that the rise in expected inflation is “consistent with the hypothesis that policymakers are overreacting to some economic news with excessive monetary and fiscal stimulus.” Following up on knzn’s analysis (Feb. 3), I find that the worries about inflation in the far-future are overstated—and that inflation expectations over the near-future have been overlooked.

Using knzn’s back-of-the-envelope method, I have produced my own time series of forward BEI, which matches the one posted by Ip quite closely (see chart 1). The graph shows that starting on January 15, the rate of inflation expected for the far future (2013-2017) started increasing abruptly. By the time Ip’s graph was produced, January 30, the forward BEI had increased by 16 basis points.

That is not unusual. We have seen increases of similar or larger size in 2007: between March 9 and March 27 (15 b.p.), May 26 to June 13 (25 b.p.), and between September 11 and September 20 (16 b.p.). But each of those spikes partially reversed over time. In fact, after September 20, the time series began a protracted downward trend that left expectations at the end of 2007 below their level at the end of the summer.

Chart 2 (click to enlarge)

Let’s zoom in on the picture (chart 2). Expected inflation for the far future, the forward rate, did rise in the second half of January. Interestingly, most of the rise happened between January 16 and January 22, perhaps fueled by discussion of the fiscal stimulus package (the President made a call for tax relief on January 18). I guess markets don’t have much faith on the fiscal discipline of the government.
More relevant to the immediate future of the economy: over the second half of January the spot BEI—the rate of expected inflation for 2008-2012—went down. Inflation expectations briefly increased after the January 22 rate cut. But overall, between the 15th and the 30th, expected inflation for the near future fell slightly.

On January 30th and subsequent days the spot BEI fell, which is quite exceptional, because it tends to increase every time the Fed eases—just look at the record in chart 2. In February inflation expectations for the near-future have continued to abate.

Chart 3 (click to enlarge)
Just in case the leaves don't let me see the tree, let me now zoom out and smooth out the time series (see chart 3). The recent rise in inflation expectations for the far future (the forward rate) to which Mankiw and Salmon referred, barely registers. In fact, those expectations have remained quite stable throughout 2007. On the other hand, expected inflation for the near future (the spot rate) started a downward trend in mid-2006. And January certainly didn’t put an end to that trend.

What do we make of this? Worries about an economic slowdown have been simmering ever since house prices began falling, back in 2006. They have intensified as the credit crisis unfolds. Much like knzn, I think that markets expect a deceleration of demand, and hence of prices. Generally speaking, monetary policy has not convinced the public that the slowdown can be avoided, and neither has the fiscal stimulus package. Regarding the far future, inflation expectations are contained.

Addendum: why isn’t the break-even inflation (BEI) equal to expected inflation?

Earlier I wrote that the spread between TIPS and nominal notes is only an approximation to expected inflation. Here I include a list of reasons why the equality doesn’t hold exactly. Please let me know if I miss something.

1. Compound bias
From the Fisher identity

i – r = pi + pi*r

By taking the spread between nominal (i) and real (r) interest rates, we ignore the interaction term pi*r. The BEI rate therefore overestimates expected inflation. If we take the yield on TIPS as an estimate of r, it’s easy to correct for this (just divide the spread by (1+r)). This bias, however, is tiny in the US nowadays, since interest rates are in the one to five percent range most of the time.

2. Inflation lag
Every day, the principal of TIPS is adjusted using the change in the Consumer Price Index. In principle, since the CPI is published only once a month, and with some delay, the adjusted principal would be updated using a lagged measure of inflation. Investors would require compensation for the difference between current and lagged CPI, and the BEI would overestimate (underestimate) expected inflation if lagged inflation were higher (lower) than current inflation.

In practice, we need not worry about this bias in the US, since the Treasury seems to have come up with daily inflation adjustments—I suppose by extrapolation of past CPI figures. Also, the bias is tiny, since monthly CPI increases are small, and not systematic, since the rate of inflation is not consistently increasing or decreasing month-to-month over long periods of time.

3. Protection against deflation
The principal of a TIPS is protected from deflation. At maturity, the investor receives the greatest between the original principal or the inflation-adjusted principal. Because this protection is valuable, the yield on TIPS is lower than otherwise, and the BEI overestimates expected inflation. In practice this bias is negligible, because the probability of deflation is extremely low.

4. Inflation risk
TIPS offer protection against inflation volatility. If investors are risk averse and inflation changes over time, TIPS are more valuable than securities whose value suffers from inflation risk. The yield will be lower, and the BEI will overestimate inflation expectations.

5. Liquidity premium
TIPS are less liquid than nominal notes. Because liquidity is valuable, the price of TIPS is lower and their yield is higher than if these securities were as liquid as nominal notes. For this reason the BEI underestimates expected inflation.

At times of high market volatility, some investors “fly” to liquid securities, in this case nominal Treasury notes, driving yields on those securities down, and introducing a negative bias to BEI as an estimator of inflation expectations.

6. Differences in the duration of the securities
In real terms, the payments from TIPS are constant, whereas the payments from a nominal note decline. The inflation-protected security has therefore a longer duration—sensitivity to interest rate changes—than the nominal security, with respect to the real interest rate.

Technorati tags:
economics, inflation expectations, expected inflation, inflation, TIPS

The fiscal stimulus: ineffective or wrong?

2008-02-08T13:44:00.000-06:00

The latest economic data show that output growth has weakened and unemployment is creeping up. The government is worried, with good reason, that the economy is going through a pronounced slowdown, perhaps even a recession. To limit the damage, Congress yesterday approved a battery of fiscal measures. By my reckoning, however, the plan will at best provide a short-lived nudge to consumption, but not employment; at worst, it’ll do nothing.

Starting in May, the government will send $600 checks to individuals ($1,200 for couples and an extra $300 for each child). People who earn too little to pay income taxes, but make more than $3,000, will receive a $300 payment. Payments will total $106 billion and will add to the budget deficit.

Cash outlays are supposed to boost private consumption expenditures and accelerate overall growth. $106b may seem a small stimulus for a $14 trillion economy, but the payments are expected to have a “multiplier effect”: higher demand will prompt businesses to hire more workers, and increased employment will further stimulate private consumption, which in turn will induce more hiring. The process continues ad infinitum. The outlays, therefore, can have a final effect on aggregate demand that is many times bigger than the initial stimulus —hence the name “multiplier.”

The effectiveness of the measure hinges on two factors. First, the fraction of the government outlays that will be spent immediately. According to Bruce Bartlett, previous experiences with tax rebates in 1975 and 2001 indicate that it's small. The recent study by Elmendorf and Furman indicates that it's a 50 percent.

The second requirement, which has received less attention, is that businesses will respond to the initial surge in demand by hiring new workers. If they don’t, then the fiscal package will have no second-round impact on demand, and the stimulus to consumption will total just $50b.

Because the first two quarters of 2008 will be marked by considerable uncertainty about the course of the economy in the medium term, the announcement of the fiscal plan will not have an immediate effect on hiring. Manufacturers may ratchet up their inventories, in anticipation of the small jolt of demand in May, but they will do so by using overtime and temp workers, rather than hiring permanent employees. In the services sector, we won’t see any change in employment until the late spring, and even then employers will similarly meet spikes in demand with overtime hours and temp workers, at least initially. If, come June, forecasts have improved, we may see employment pick up over the fall. But by then the effect of the government checks will have played out. In conclusion, the fiscal package won’t provide any significant boost to employment.

A less obvious reason to reject the stimulus is that the slowdown in aggregate demand is necessary, even healthy. Most of the growth experienced between 2002 and 2006 was based on low interest rates, over-valued real estate, and loose lending standards.

Chart 1, from a story by Michael Mandel at BusinessWeek, tells it all. Mandel estimates that, “if consumer spending had tracked the overall economy over the past decade as it has in the past, Americans today would be spending about $600 billion less a year. The extra spending has amounted to a total of about $3 trillion since 2001.” That extra spending was financed with debt. Quite literally, Americans were borrowing their prosperity from the future —not exactly a sustainable growth path.

Chart 1 (left) and 2 (right). Click to enlarge.

The growth of productivity, the value of output per hour worked, confirms the hypothesis that consumer expenditures were out of line with real income gains, at least over the last five years. Robert Gordon of Northwestern University estimates that trend productivity growth peaked in 2002, and has slowed down ever since (see Chart 2, via Michael Mandel’s blog). The gap between long-term growth of GDP and consumption, on the other hand, has widened over the same period.

So, if the recent growth rate of expenditures was excessive, why is Congress rushing to prop it up? More importantly given that the stimulus will be financed with future tax increases: why are legislators borrowing even more from future prosperity? The answers to these questions have a lot to do with politics and very little with economics.

Notice the hodgepodge of enigmatic measures included in the fiscal package. Congress grants payments of $300 to low-income seniors and disabled veterans, but not to other disabled people. It allows federal housing agencies to insure jumbo mortgages, as if subsidies to the purchase of expensive homes was going to parachute the economy. And it includes specific provisions to prevent illegal immigrants from claiming payments, precluding illegals from contributing to the consumption surge, however small that may be. So, if you think about it for a minute, what Congress did is give itself a votes-buying package, which does stimulate something: re-election.

Technorati tags:
economics, macroeconomics, fiscal policy, fiscal stimulus, stabilization policy

Recession buzz

2008-02-02T23:42:00.002-06:00

Chart 1 (click to enlarge)

It’s been hard for news readers to avoid the word “recession” this January. The number of newspaper stories mentioning it has certainly been overwhelming (see Chart 1). Weak economic data might seem to justify the gloom. Growth has slowed down and the labor market has weakened. Still, we haven’t seen a single quarter of negative growth, and the employment figures have been equivocal, and certainly not recessionary. So, given what we know about the state of the economy, is all this recession chatter justified, or are journalists getting carried away?

To answer that question, I have put together data on the tone of economic reporting in the newspapers, as well as on indicators of the health of the real economy. Then I have estimated a statistical model and compared the level of pessimism of the newspapers with the actual mood that one would expect based on the known state of the economy. The results are pretty exciting. So exciting, in fact, that I plan on updating and reporting my calculations every month, here on EconWeekly.

My measure of usage of the word “recession” is The Economist’s R-word index: the number of stories containing that word in the New York Times and the Washington Post. The index is a surprisingly good indicator of economic slowdowns. It never fails to rise sharply at the beginning of recessions. (See Chart 2.) And in spite of its simplicity, it captures the sentiment of the newspapers pretty well. Mark Doms and Norman Morin, of the Federal Reserve Board, constructed a much fancier recession index for a research project on the subject, containing dozens of media sources and carefully filtering the search terms. And yet, the difference between their measure and The Economist’s R-word index is almost always small. (See Figure 4.1 in Doms and Morin’s paper.)

Chart 2 (click to enlarge)

To gauge the present and immediate future of the economy, I include the following variables in my statistical model: the unemployment rate, the growth of the S&P500 index, the growth of the price of oil, the growth of personal consumption expenditures, and the spread between the ten-year bond and the one-year Treasury bill. (Econometrics jocks can find the details of the statistical model below.)

My model shows that newspapers have indeed been too gloomy this past month. In January, known economic conditions would have justified about 200 stories mentioning the word “recession”; the actual count was around 300. Up until December, however, newspaper mood was approximately in line with the actual state of the economy. (See Chart 3.) Why did newspaper sentiment diverge from economic fundamentals last month?

Chart 3 (click to enlarge)

In January we witnessed a sequence of unusual events. There was ongoing talk about the fiscal stimulus package, which is being introduced precisely to avoid an economic slowdown. The President sketched a plan on January 18, then the House of Representatives announced theirs a week later, and then the Senate considered changing it. Then there was a mini crash in the stock market, followed by the surprise cut of the Federal Reserve’s target interest rate on January 22, and then another cut at the Fed’s scheduled meeting on the 30th. Every newspaper story that reported any of these events most likely included the word “recession.”

But, at least in part, I believe that the buzz has to do with incentives in the news industry. Even when reporting facts, every media outlet strives to agree with the views of its audience. Fox News would lose its parish if it started “showing” that the Iraq surge was wrong and ineffective, and the Wall Street Journal would clash against the opinions of its readers if it started “proving” that the Bush tax cuts were a bad idea. Maintaining an audience depends vitally on conforming to their prior expectations. (Note to self: what do EconWeekly readers expect?)

Economics reporting is a bit different because the state of the economy can be measured and verified more objectively. As a result, views are more homogeneous across audiences. Still, media outlets need to take into account three factors which determine the views news consumers, and therefore the choice of tone and volume of economic reports: intrinsic pessimism, past reports on the state of the economy, and reports from other media outlets.

Bryan Caplan of George Mason University has identified pessimism as one of the four capital biases of the average Joe. (Read this summary.) People routinely see negative trends in long-term living standards, wages, inequality, etc. The gloom extends to the state of the economy at any given moment. About half of Americans have been thinking that we are in a recession, or on the brink of one, since October! Where that pessimism comes from, I have no idea. David Hume, Caplan says, thought that “the humour of blaming the present, and admiring the past, is strongly rooted in human nature.” It sounds appealing. But whichever the reason, the media recognize the appeal of worrying reports about the economy —and deliver.

Inherent pessimism influences the interpretation that the media put on any given piece of hard data. But once the newspapers set clouds in the horizon, their incentives to deliver negative news become stronger, because they need to conform to the readers’ expectations. A newspaper that changed its view on the state of the economy would go against the prior views —plus, it would be accused of the horrible crime of flip-flopping. A newspaper has therefore an incentive to keep a certain mood even on something as relatively objective as the state of the economy. Past negative reports will lead to more negative reports in the future, feeding a cycle of pessimism, unless new hard data against such views are so strong that the paper is forced to tone it down over time.

Finally, people are exposed to reports from more than one source of information, even if it’s secondhand. Any newspaper that strayed from the average mood of all other newspapers would conflict with the established view, alienating itself. Any given outlet has thus an incentive to stay in line with the tone of all the major media, resulting in “herd behavior”: the tendency to base decisions (in this case the tone of the news) on the behavior of the rest of the community (other media outlets).

The combination of natural pessimism and the need to conform to the public’s views, therefore, explains why sometimes reporting on the economy is not consistent with actual events, as is the case now. Only policymakers, animal spirits and time can determine whether we’ll see a recession in 2008. For now, skip the editorials on economics.

Statistical model:
VAR, with monthly data, from January of 1976 through the latest month available. Each equation includes six lags. The variables are: the R-word index, the unemployment rate, the change in nonfarm payrolls, the slope of the yield curve (10-year minus 1-year), the growth of personal consumption expenditures on durable goods accumulated over the current and previous two months, and the growth of the industrial production index, also accumulated over the same period. I also include a set of monthly dummies and a dummy variable that equals 1 if the NBER announced a decline in real GDP. The unemployment rate is the first release reported by the BLS. The change in payrolls mimics the one reported by the BLS, that is, it is equal to the first estimate of payrolls for month t, minus the revised (first update) figure for month t-1. Both unemployment and payroll figures come from ALFRED. The yields on the ten-year bond and the one-year Treasury bill are monthly averages, from FRED. Durable expenditures come from the NIPA accounts, via FRED, and the industrial production index is from the Federal Reserve, also via FRED.

Technorati tags:
economics, news, media bias, recession, media

On college endowments

2008-01-25T13:23:00.000-06:00

According to a study released yesterday by the National Association of College and University Business Officers (NACUBO), the endowment fund of Harvard University is worth $34.6 billion, a 19.8% percent higher than a year ago. 76 colleges and universities sit on endowments over $1b. Even more impressively, almost every one of the 733 institutions analyzed reports a double-digit increase in the value of its fund. (Look up the endowment of your alma mater here.)

Chart 1 (click to enlarge)

The increase in the value of the endowments has been the result of at least two factors: risk taking and stock market bonanza. First, higher-education institutions invest large portions of their wealth on high-risk, high-return securities. On a dollar-weighted average, in 2007 they held a 47.4% of their funds in equities, an 18.2% in hedge funds, a 5.4% in private equity, and a 3.6% on venture capital investments. Wealthier universities hold riskier portfolios than the average. (See Chart 1.)

Second, average stock prices have increased almost every single year for over 25 years. In spite of the burst of the dot-com bubble in 2000, the inflation-adjusted Dow Jones Industrial Average Index ended 2007 at a level five times higher than in 1982. Even the most passive portfolio manager would have achieved high returns in this stock market.

Chart 2 (click to enlarge)

No surprise then that most universities have performed so well. Over the last ten years, the return on most endowments beats the S&P 500 index, which grew at a healthy 7.1% annual rate itself. (See Chart 2.) In the case of the largest portfolios, universities beat the market by a long shot.

News of these fabulous riches has prompted some sectors to demand that universities share more of their wealth. Lawmakers remind them that, as tax-exempt institutions, “they’re supposed to offer public benefit in return for (that) exemption.” Private foundations, which are also tax-exempt, are required by law to spend 5% each year; the average for colleges is 4.6%, with little variation across levels of wealth (see data). Parents, on the other hand, don’t understand why tuition keeps going up while universities continue to amass wealth. Little do they suspect that the cost of college is stoked by the self-interest of parents and students themselves, not that of universities.

The classic explanation for the rise of tuition is that the college premium —the positive gap between the earnings of college graduates and high school graduates— has increased the demand for college education, thereby raising its equilibrium price.

More interestingly, the stock market has also made tuition rates go up, according to a paper* by my former colleague at the University of Chicago Pablo Peña (pdf). Rises in asset prices increase the amount of resources available to universities. Part of that wealth is spent on inputs that improve the quality of education: more and better qualified professors, and newer and more sophisticated facilities, such as labs, computers and libraries, for instance. Higher quality, in turn, increases the amount of human capital accumulated in college, and ultimately affects life-time earnings, i.e. the returns to education. Prestige considerations may be at work too: celebrity professors and state-of-the-art facilities increase the reputation of the institution, adding to the value of the diploma. Therefore, larger endowments spur the demand for college education, and drive up tuition rates.

Differences in the value of endowments across universities are vast: the combined value of the top ten colleges represents 35% of total endowment assets. In light of Pablo’s theory, the implications of this inequality depend on what universities and colleges spend their money on.

If they continue to use their wealth to improve the quality of the service they provide, demand for college education and tuition levels will continue to rise. Differences in tuition rates and education quality between top-notch and second-tier institutions will continue to widen too, since endowments and asset returns are highly concentrated. Also, because the ablest students —those with highest SAT scores or best records of achievement in high school— benefit the most from the quality of college education, the matching of the best students with the best institutions will intensify. Differences in the quality of students across colleges will increase.

On the other hand, universities could start using their endowments to increase capacity or subsidize the cost of college. In this unlikely scenario, the equilibrium price of higher education will probably decline, the quality of college education will drop, and the college premium —the earnings of college graduates vis-à-vis high-school graduates— will drop.

Selected institutions have recently been announcing that they will increase financial aid. Recent announcements might suggest that this could actually happen among selected institutions. Harvard and Dartmouth have eliminated loans from their aid packages and will be giving grants instead; and Yale has followed in their footsteps. These de facto cuts in average tuition rates are not going to change the system. First, they won’t change the quality of education at top universities, for which the foregone tuition revenue is peanuts. Second, they won’t reduce the cost of attendance of the average college student, because the number of institutions that can afford foregoing tuition revenues is small.

But improved aid packages at top schools will make their programs affordable to the brightest students, regardless of their financial situation. If the newfound altruism of the Harvards and Yales has any effect, that will be an even more pronounced assortative matching of colleges and applicants by quality. The scope of these developments is very limited, but it’s good news —at least for believers, like myself, in a free, merit-based education system.

More data:
Tuition rates: table of nominal rates (html), graph of real rates (pdf, Figure 1)

Technorati tags: economics, education, college, endowments, college tuition

Income mobility and education

2008-01-19T18:23:00.000-06:00

As goats on a tree, reaching for the best leaves, we all strive to be the ones at the top. But even in America, the land of opportunity, only good climbers make it. And lately even the fittest seem to be having a hard time.

The table below shows the percentage of people who moved from a given group in the income distribution to any other one, between 1994 and 2004. (The data come from the Panel Study of Income Dynamics, and the income measure is household taxable earnings.) The lowest degree of income mobility occurs among the poorest and the wealthiest: 58% of households in the bottom 20% of the distribution stay there, and 60% of those in the wealthiest quintile don’t move either.

Pooling people from all ages together, however, can be misleading. The typical earnings profile over a lifetime is hump-shaped: earnings start low, rise up until the individual is in her 50s, then begin a slow decline, and fall sharply with retirement. Because of this non-monotonicity, movements up and down the earnings distribution may have little to do with climbing the social ladder.

As an example: suppose the economy is populated by two individuals, one of whom is 35 and earns $45,000, and the other one is 55 and makes $75,000. So the older person is at the top of the distribution. Ten years later, the young individual has accumulated experience and earns $65,000, whereas the older person, now 65, has retired and doesn’t earn any labor income. The younger individual is at the top of the earnings distribution now. If we were oblivious to the age of these individuals, this two-person society would look remarkably mobile: the poorer person moved to the top and vice versa. In reality, the observed mobility is the product of the normal course of earnings over peoples’ lives.

The fortunes of a person are more likely to change early in life. Twentysomethings are less likely to be attached to a house, a family, or a job. They job-hop, experiment, go back to school. Over time, some people land a dream job —or a “comfort job”— and stay there. And some others simply grow roots: they have mortgages to pay, and spouses and kids to drag along. We also become more risk averse with age.

The data bear these intuitions: 67% of households whose head was between 22 and 29 in 1994 had switched quintiles ten years later; 54% of those between 30 and 39 did so, about the same as among the 40-49 age group.

Things get much more interesting when I look at mobility within education groups. Schooling is probably the single most important factor determining a person’s chance to “make it.” People with less education are less employable. They also experience smaller changes in productivity, so their earnings curve is less steep. And they have fewer opportunities to fill high-powered positions —the sort that provide a pay boost if one is successful. In this, however, the evidence doesn't support my expectations.

Between 1975 and 1985, and within the group of college graduates, 61% of households moved to a different economic class, whereas 59% of high school graduates were mobile -barely a difference. And twenty years later, 54% of college grads and 60% of people with a high school degree were mobile. (See chart.)

Click to enlarge

What made the economic ladder more slippery for college grads? Following the reasoning above, maybe people have less appetite for risk, and are taking jobs that are safer but also offer fewer opportunities to leap-frog over income classes. Starting up a business, for instance, is one of the riskiest endeavors one could pursue. But statistics show that animal spirits have not subdued —the fraction of entrepreneurs and self-employed has risen over the last 30 years.

A second explanation is that unobserved ability, not education, is behind opportunity. A couple of decades ago earning a college degree was a major feat. Only the well-off, highly-motivated and bright ever put their feet in a University. Nowadays going to college is almost a given. As a result a college degree has become a weaker signal of one’s competence. Highly capable individuals still get ahead, but the vast majority of college graduates do not belong to that breed.

Finally, but not less importantly, it might be a problem of too many grads chasing too few jobs with incentive-based pay. In spite of all the talk about stock options, the number of positions with (significant) variable compensation has grown more slowly than the body of individuals with a University diploma. More well-educated people land jobs without the power or the incentives to rise fast on the pay scale.

This calcification of the white-collar society is worrying. More and more individuals go to graduate school in order to earn that M.B.A., M.A., or even Ph.D., that will give them an edge over their peers. That behavior is perfectly rational, and yet self-defeating. The latest batches of college grads remind me of hamsters on a wheel rather than goats.

Technorati tags: economics, income mobility, inequality, income inequality, upward mobility

Personal bankruptcy and consumption smoothing

2008-01-12T10:59:00.000-06:00

The welfare effects of bankruptcy legislation are not correctly understood. Policymakers and the general public think, for the most part, that laws that protect borrowers in the event of default are beneficial to consumers. In practice, however, those laws have negative effects on the households that need credit most — and, ironically, those whom the legislation was intended to protect.

Traditionally, Chapter 7 has been the most popular type of bankruptcy filing. Under that section of the Bankruptcy Code, a filer relinquishes her assets, minus a certain exempted amount, and in return is discharged from her unsecured debt (credit card debt, personal loans, student loans, etc.).

State law sets those exempted amounts. In Illinois, for instance, exemptions are: $7,500 for home equity, $1,200 for motor vehicles, $750 for tools of the trade, and $2,000 for any other generic property. So suppose that you file for bankruptcy in the “Land of Lincoln,” and that you have $20,000 worth of home equity, and a car with a market value of $600. Then you can sell the house and keep $7,500 of the proceeds, and sell your car and keep the $600 (since that’s below the $1,200 limit).

Since 1978, with the passage of the Bankruptcy Reform Act (BRA), there’s also a federal exemption. Some states allow filers to choose between the state and the federal amounts. Obviously, if given the opportunity, filers use whichever is highest.

There is an enormous disparity of bankruptcy exemptions across states, even after accounting for the existence of the federal limits. For example, in 2006 the states of Texas, Florida, Oklahoma, Iowa, Kansas, South Dakota, and the District of Columbia, all allowed for an unlimited homestead exemption. In the states of Ohio and Virginia, at the other extreme, the limit is set at $5,000 (and those states don’t allow for the application of the federal exemption). The map below shows the maximum exemption that a married homeowner could claim in 2003, after combining homestead and non-homestead amounts, and taking the highest of the state and federal limit (where the federal limit is available). The limits also vary over time, although high-exemption states tend to remain the same over the years.

Bankruptcy exemptions under Chapter 7 of the Bankruptcy Code
(in 2003, for a home owner)
Click to enlarge

The amount of the exemption provides insurance for the debtor’s consumption. Suppose that a debtor suffers a setback, such as illness or unemployment, and that she is forced to default on her credit card debt and student loans. In the absence of any exemption, creditors would take a blanket security interest in all of the debtor’s possessions. The existence of an exemption means that she is left with at least a small amount of assets after filing for bankruptcy. Legislators see it as a way to provide a “fresh start.” An alternative view is that a certain amount of assets, and hence consumption, are insured against negative events.

On the other side of the coin, lenders are hurt by this form of consumer protection. Higher exemptions reduce the payments received by the lender in the event of default, and increase the probability of bankruptcy, since the borrower’s punishment for doing so becomes smaller. Creditors rationally respond to higher exemptions by raising interest rates and rationing credit. This rationing may take the form of fewer households with access to debt, smaller loans, or both. Fewer and smaller loans reduce the amount of consumption that households can finance with debt in times of low income.

In theory, then, bankruptcy exemptions have an ambiguous effect on consumption smoothing. Higher exemptions allow bankrupt households to keep more assets; but those same higher exemptions reduce the supply of credit. It is, therefore, an empirical matter whether higher limits enhance or detract from the role of debt as a consumption insurance mechanism.

To answer that question, I put together data on consumption and lay-offs of American households (from the Panel Study of Income Dynamics), as well as bankruptcy exemptions, for as many years as I could get consistent data for. (In practice, that is 1976 through 2003, with the exception of 1994-1997.) The idea is to estimate by how much a family’s consumption is reduced when its main income earner gets laid off, and see how much the hit to consumption changes with the bankruptcy exemption.

As a warm-up and point of reference, I estimate that, without taking into account the exemptions, a household whose breadwinner gets laid off reduces its consumption by five to six percent. Once I include bankruptcy laws in the econometric analysis, I find that households that live in states with unlimited exemptions reduce their consumption by 16 to 18 percent. Households in the top third of the distribution of (limited) exemptions reduce their consumption by nine to ten percent. For households with lower exemptions the effect of unemployment on consumption is low and statistically insignificant. (See chart.)

Click to enlarge

My interpretation of the results is that consumer debt is an important mechanism of consumption insurance. People use loans and credit card debt not only to finance big-ticket items, but also to make ends meet when disaster strikes. Legislation that makes it harder to obtain debt, such as bankruptcy exemptions or interest rate caps, ends up punishing the weakest: people with low wealth, who could make the most use of credit as an insurance device.

Don’t get me wrong: this is not a call to eliminate bankruptcy exemptions. There is a place for them as a means to provide safety to people who have been struck by unexpected events. A zero-exemption policy would probably expand credit supply — at the cost of leaving thousands of families destitute and without a chance to recover. But exorbitant homestead exemptions go way beyond providing a chance for a “fresh start.” Likewise, there’s no reason why people should be allowed to keep $60,000 worth of personal property, as they can do in Texas.

Surely, medical expenses can easily run into the hundreds of thousands of dollars. But that’s a reason to reform health insurance. Limiting the enforceability of credit contracts is a bad way to lay out safety nets.

This post was based on my own research. The write-up of the paper is still in the making. It will be available on my website by January 28. In the meantime, you can have a look at the slides I prepared for a presentation this Friday.

Technorati tags: economics, personal bankruptcy, consumption, bankruptcy exemption, consumption smoothing

A bash for confidence indexes

2008-01-04T00:01:00.000-06:00

Every month the University of Michigan and the Conference Board conduct a survey of households’ confidence on the state of the economy. Each pollster asks several questions and summarizes the results with an index, which is closely watched for signs of consumer distress. Last November, the Michigan index fell by 4.8 points from October; the Conference Board Index dipped by 7.9 points. Supposedly this is bad news because worried consumers are thrifty consumers. Don’t let the surveys fool you: they are almost complete rubbish — unless you know how to use them.

At first glance, both the Michigan index (MI) and the Conference Board index (CI) are correlated with the business cycle: they sink around the beginning of a recession and rebound near the end (see chart nearby, originally published by the Wall Street Journal). They even seem to track the quarter-to-quarter growth of consumption expenditures. Look a bit closer, however, and you’ll see that confidence and reality get out of synch sometimes. For instance, both the MI and the CI were abnormally low relative to consumption growth in 1992-1993, and again during 2002 and 2003. The indices dipped during the Asian crisis of 1998, but consumption growth didn’t budge; conversely, expenditure growth fell dramatically in early 1995 even though sentiment didn’t change.

Formal statistical analyses have found that consumer sentiment says very little that forecasters don’t know already. That is, once this quarter’s spending, interest rates, etc. are known, it does not help much to predict future spending growth. Confidence and expectations matter. The issue, I reckon, is that these particular indices fail to capture them.

A cursory look at the guts of the MI and the CI will convince you that they are literally meaningless. Each of them is a mishmash of five opinions — which, by the way, are not the same for both surveys (see table below). The questionnaires represent but the pollster’s guess of what determines spending. There’s no guarantee that the questions are the ones that actually matter.

Click to enlarge

For instance, the MI doesn’t include questions on job security, whereas the CI doesn’t ask about present personal finances. The potential irrelevance of the surveys becomes painfully clear when one examines the first question of the MI: “Do you think now is a good or bad time for people to buy major household items?” With such a specific wording, that question should predict expenditures on cars, appliances, furniture and such, i.e. durable goods. But once past purchases are included into the forecasting model, confidence and expenditures are barely correlated. [1]

Even if one of the indexes had the right composition, there’s no reason why all the questions should be given equal weights. Personal finances and availability of jobs, for example, may influence a consumer’s expenditures more than overall business conditions; short-term prospects should matter more than distant ones. In both the MI and the CI, however, every question counts the same.

Despite my bashing of the indexes, the surveys are worth keeping. Each of them contains some question that can help predict one or other component of expenditures. More specifically, the Conference Board’s questions about job prospects help forecast expenditures on durable goods: sentiment about the current job situation (question number two in the table) significantly predicts purchases of vehicles and other durables; expectations about future jobs (question four) predicts expenditures on vehicles only. [1] The Michigan survey, on the other hand, contains questions which are not used in the indexes. It would be worth exploring whether they are useful for forecasters.

Unfortunately, the component questions are not accessible to most people. If they are, it’s only with significant delay. And even if they were published timely, most people wouldn’t be able to use them because they can’t handle the number crunching. So here’s my advice for the everyday news consumer. First, don’t draw any conclusions from month-to-month changes of the indexes, no matter how large they are. Start believing them only after several months of consecutive rises or declines. Second, the Conference Board index is a better predictor than the Michigan index, because the latter doesn’t include any question about jobs. Third, rather than sentiment indicators, pay attention to data on the labor market: the unemployment rate and the payroll numbers, for example, averaged over at least three months. Not only do they gauge consumers’ confidence more accurately than the confidence indexes themselves: they influence spending decisions directly (the more unemployment, the less disposable income).

In all fairness, the intention of the MI and the CI was never to forecast any specific variable. They were designed over 40 years ago as a rough measure of the households’ view of the state of the economy. Even if the surveys captured expectations correctly, it should be up to economists, not statisticians, pollsters or newspapers, to figure out how those expectations translate into realized outcomes. Some day we’ll know how to do it. I’m pretty confident.

References and further reading:

[1] Bram and Ludvigson (1998) Does consumer confidence forecast household expenditure? A sentiment index horserace (pdf)

[2] Carroll, Fuhrer and Wilcox (1994) Does consumer sentiment forecast household spending? If so, why? (pdf)

[3] Croushore (2006) Consumer confidence surveys: can they help us forecast consumer spending in real time? (pdf)

Technorati tags: economics, consumer confidence, consumer sentiment, forecasting, expectations