A Dash of Insight

Intuition and Economics

(This is the second installment of our series describing how the BLS might respond to critics of the Birth/Death adjustment.  See Part One here.)

Everyone uses intuition, making decisions that seem instinctive rather than the result of a carefully reasoned process.  This type of decision making has actually been the subject of formal study.  For example, Yehezkel Dror's Public Policymaking Reexamined highlighted the use of "extra-rational" processes to make decisions.

The TV version of this is almost a cliche', but we can still learn from it.  The veteran cop and the rookie are on patrol.  The savvy vet senses something wrong.  A few questions make clear that there is a serious criminal behind someone's bland exterior.  There are even some studies supporting the cliche'.

It is noteworthy that those whose intuition is helpful are those most experienced and adept in their business.  A top poker player or bridge player has great "table feel."  Many famous traders are noted for their ability to read the tape.

So here is a question:  Why do people who are not expert in economics believe that they have such great economic intuition?  For most of us, it is better to rely upon data.

Intuition about Job Creation

A case in point is the vise-like grip of misinformation about measuring job creation.  Here is an example relating to the last employment report.  Regular readers of "A Dash" know that we respect the work and the significant influence of John Mauldin.  Many people rely upon his weekly letters.  We were disappointed to read the following:

...Last month saw the number of unemployed rise by 345,000. What was not in the headline data was that 217,000 of those jobs were estimated from the "birth-death" ratio. The US economy creates new businesses that do not get counted in the data, so the BLS estimates what that number is, using previous data patterns. When the economy turns, it overestimates new jobs in recessions and underestimates them in recoveries. No conspiracy, it is just the best methodology we currently have.

But does anyone really think 200,000 jobs were created last month? The real number of lost jobs is worse than the headline. And next month the birth-death number will likely be over 200,000 again.

Mr. Mauldin clearly believes that new jobs are not being created.  He is so confident that he poses it as a rhetorical question.  He shares this intuition with nearly everyone.  It seems obvious.  If labor conditions are tight, there are no new jobs.

Intuition about economics leads people to think in black and white terms, rather than seeing quantitative responses and changes at the margin.  Instead, let us consider actual data, the most recent available from the Business Dynamics series.  It uses the records from state unemployment offices -- no projections, and no one is paying insurance premiums on non-existent jobs.  Here is the summary table of seasonally adjusted data.  (The full year data illustrate the same point).

Table A. Three-month private sector gross job gains and losses,
seasonally adjusted

------------------------------------------------------------------------
     |  3 months ended  
     |-------------------------------------
     | Sept. | Dec. | Mar. | June | Sept. 
  Category  | 2007 | 2007 | 2008 | 2008 | 2008 
     |-------------------------------------
     |  Levels (in thousands) 
----------------------------------|-------------------------------------
     | | | | | 
Gross job gains...................| 7,323| 7,676| 7,130| 7,258| 6,822 
 At expanding establishments.....| 5,849| 6,220| 5,731| 5,858| 5,504 
 At opening establishments.......| 1,474| 1,456| 1,399| 1,400| 1,318 
     | | | | | 
Gross job losses..................| 7,564| 7,366| 7,400| 7,751| 7,754 
 At contracting establishments...| 6,209| 6,010| 6,047| 6,277| 6,383 
 At closing establishments.......| 1,355| 1,356| 1,353| 1,474| 1,371 
     | | | | | 
Net employment change (1).........| -241| 310| -270| -493| -932 
     |-------------------------------------
     |  Rates (percent)  
     |-------------------------------------
Gross job gains...................| 6.4| 6.8| 6.2| 6.4| 6.1 
 At expanding establishments.....| 5.1| 5.5| 5.0| 5.2| 4.9 
 At opening establishments.......| 1.3| 1.3| 1.2| 1.2| 1.2 
     | | | | | 
Gross job losses..................| 6.7| 6.5| 6.5| 6.8| 6.9 
 At contracting establishments...| 5.5| 5.3| 5.3| 5.5| 5.7 
 At closing establishments.......| 1.2| 1.2| 1.2| 1.3| 1.2 
     | | | | | 
Net employment change (1).........| -.3| .3| -.3| -.4| -.8 
------------------------------------------------------------------------
 1 The net employment change is the difference between total gross job
gains and total gross job losses. See the Technical Note for further
information.

{Emphasis added to the line showing job gains at opening establishments}

We can see that in the third quarter of 2008, there were over 1.3 million job gains at new establishments and 5.5 million new jobs at continuing businesses.  The entire table covers data during the current recession.  The results from the 2001 recession were similar.

Briefly put, there is a massive turnover of jobs lost and jobs created every quarter.  The net changes that we see in the news are much smaller by comparison.

Measuring New Jobs

How should the BLS account for the job gains at new establishments?  

Most readers will be surprised to learn that the Birth/Death adjustment is not the principal method.  More importantly, the BLS basically ignores non-responding firms in the business survey.  It knows that some of these are business deaths, but not which nor how many.

The BLS uses a two-step process, described as follows:

Step One - Employment losses from business deaths are excluded from the sample in order to offset the missing employment gains from new business births. Because employment increases from births nearly offset employment decreases from deaths in most months (as illustrated above by the BED data), this step accounts for most of the net of business birth and death employment.

Operationally this is accomplished in the following manner each month. Business deaths that are non-respondents to the survey are automatically excluded because they have no current month data. Death establishments that report zero employment to the survey for the current month are treated the same as non-respondents and also excluded. As a result, the over-the-month change calculation from the sample is based solely on continuing businesses.

For the months subsequent to a business death, the deaths are "kept alive" in the CES estimation process; the growth rate of the continuing units in the sample is applied to them each month. This estimates for the growth of the new business births in the months after their birth but before they can be brought into the sample.

This step accounts for most of the net birth/death employment but not all of it. The residual net employment that is not captured by this step is estimated through an econometric model, described below as Step 2.

Step Two - Modeling for the residual of net/birth death employment change. In this step, the CES adjusts its sample-based estimates for the residual net birth/death employment that step 1 misses. This adjustment is derived from an econometric technique known as Auto Regressive Integrated Moving Average (ARIMA) modeling. ARIMA is a standard econometric modeling technique that is often used to estimate relatively stable series. CES refits the ARIMA models each year, for each basic estimation cell, as part of its annual benchmarking process.

The inputs to the ARIMA model are historical observations of the residual net birth/death employment that is not captured by either the sample or the step 1 imputation described above. These historical observations are derived empirically, from the most recent five years of QCEW historical data.

The Birth/Death adjustment that gets so much attention is Step Two in the process.  The critics mistakenly ignore Step One, even though that is the more significant part of the process.  Step One is also sensitive to economic changes, as noted in this paper:

The imputation part of the procedure is directly related to the current sample and is
therefore sensitive to employment trend shifts and turning points.

Summary

In Part One of this series we showed data proving that the BLS Birth/Death adjustment has improved the monthly estimates of payroll employment changes for every quarter since it has been used.

This article, Part Two, shows the reason behind the result.

• There is massive new job creation at all times, even in recessions.  In bad economic times the job gains are offset by even larger losses.
• The BLS estimates most of the job creation by "keeping alive" some of the business deaths.  This process helps to reflect economic turning points since it follows the results from the rest of the sample.
• The Birth/Death adjustment deals only with the residual new jobs.  It has no separate economic meaning.

The BLS critics are mistaken in looking only at the Birth/Death adjustment, when that is only part of the two-step process --- and the less significant part at that.

In Part Three of this series we will look at a few of the specific claims by critics.

The BLS Responds to Birth/Death Adjustment Critics

OK, our title is false advertising.  The Bureau of Labor Statistics crew is not allowed to surf the Net making comments on blogs, nor do they have a blog of their own.  It might be a good idea, but it is an unlikely move given budget constraints.

This is too bad, since there is near-universal criticism of their methodology. Many go much further.  A Google search will reveal plenty of aggressive name-calling critics.  The criticism has been so loud and pervasive that hardly anyone in the blogosphere or trading worlds believes in the monthly non-farm payroll report.  Many sites routinely mention the birth/death adjustment so that the reader can mentally subtract these "phantom" or "magical" jobs.

This presents an interesting situation.  What if the BLS approach is correct and accurate?  Those understanding this would do better in gauging economic changes.

Our Mission

Since the BLS is not going to respond directly to critics, we propose to use their existing results and words to address some of the key points.  In this article, we will show the strength of the BLS methods with only indirect references to the many critics.  In future articles we will directly analyze and expose pervasive errors on this topic.  Reader questions are invited.

We have three steps:  Showing the accuracy of the birth/death adjustment, explaining the b/d role in job creation, and showing how the research design effectively captures economic changes.

This article takes up the first of these issues.

Accuracy

Estimating the number of jobs and the monthly change in jobs is a daunting challenge.  There is a way of keeping score.  As we wrote last October:

Each year the BLS makes a "benchmark revision" to the payroll employment series based on the establishment survey.  The purpose of this is to make sure the survey data are consistent with the actual count of jobs from state unemployment insurance tax records.

The state data is much better, of course, but it is not available in a timely fashion.  The benchmarking is a reality check.  It allows the BLS to see how well they did with the monthly estimates.  Each October, along with the report on September employment, the BLS releases the preliminary version of these benchmark revisions.

This is the report card for the BLS.

This should be a non-controversial test, since it relies upon actual state data, not projections.  No employer is going to pay extra taxes, so this count does not include any "phantom jobs."

The better the BLS methods, the smaller the benchmark revisions.  If the Birth/Death adjustment is effective, it makes the revisions smaller.

And it does!!

Here is a nice chart showing the effects.

The blue line is the actual count.  Just compare the red line to the green line.  The red line shows what the estimate would have reported without any birth/death adjustment.  The green line shows the effect of birth/death.

The birth/death adjustment improves the job change estimate in every quarter since it has been introduced.

Conclusion to Part One

Most of the BLS critics have been offering the same complaints for many years, but no one ever asks whether they were correct.  The closest the BLS team will come is the paper they published last October.

In this article we have emphasized that something about the birth/death adjustment is good, very good.  It improves the job change estimates in every quarter.

This seems counter-intuitive.  How can we have new job creation in such difficult economic times?  Most people believe their intuition rather than the data.

In the next article in this series we will explain this mystery.

Employment Situation Report Preview

Each month we ask the question, "What change in payroll employment would be consistent with other economic data from the same time period (the middle of the prior month)?

This is not a forecast, per se, since we do not posit any causal relationship among these variables.  They are all concomitant indicators of economic activity.  We use the four-week moving average of initial unemployment claims, the University of Michigan sentiment survey, and the ISM manufacturing report.  We carefully choose data from the correct time period.  Even though the ISM report was released today, the survey is obviously from earlier in the month.

None of these indicators have improved very much, so we continue our negative outlook on employment.  We were surprised last month when the job losses were less than we (and nearly everyone else) expected.  We are still looking for losses over 550K, much worse than the consensus loss of about 400 K.

Since our analysis is based upon the final data, after all revisions, the ultimate accuracy may not be known until next year!  That is when final benchmark revisions are done.  Also, the sampling error (90% confidence interval) alone on the payroll survey is more than +/-100K jobs.

Other Predictions

In addition to the consensus forecasts, there are various predictions using proprietary data.  These are all interesting.

TrimTabs uses data from income tax deposits of salaried employees.  They expect job losses of 472,000.

ADP uses data from their payroll administration business, information that no one else has.  They have attempted to gear their results to the "official" government report.  They forecast a loss of 473,000 jobs, amazingly close to TrimTabs.

New entrant Wanted Technologies uses an algorithm reflecting online job ads.  They have a startling forecast:  a loss of "only" 260,000 jobs.  Furthermore, they made their call on June 19th.  And why not?  That was the right time frame to match the payroll survey, and their online job data is more readily available in real time.

Conclusions

Our own prediction of the jobs report has no special inputs -- just the analysis of concurrent economic data.  We are surprised to be the most bearish of the group.  As noted, the error band is wide.  The market will react wildly without regard to the sampling error or other issues.

We do have a few predictions that we can make with more confidence:

• Whatever the job loss, the unemployment rate will move higher.  The demographic factors at work require job gains of at least 150,000 (and probably more) just to maintain current unemployment levels.  The unemployment rate is an important social and political indicator, but it will lag in reflecting an economic change.
• The assembled punditry will state, whatever the number, that it should have been worse because the government is incorrectly projecting job creation. 
• If the result is really good, the rumor mill will start, as it did last month.  When the market spiked on a better-than-expected report for May, the rumors quickly circulated that it was an error -- a government worker had a "fat finger."  Those circulating this rumor (and those believing it) have absolutely no concept about how government reports are assembled, how many people are involved, and how many check points there are.

It just shows that if you want to be short going into this report, you can have confidence that the Bearish Blogging Network (TM OldProf) will have your back.  They will take advantage of the blogosphere to spin at high speed.  The official sources have to wait for a news conference or an interview to reply.  This is plenty of time to cover your shorts.

It is an attractive trade for any hedge fund manager.  Take last month as an example.  You could come in short and be an instant winner on a bad number.  If the report was positive,  you sell more on the spike (averaging up in price).  You then cash in on the silly "fat finger" rumor and the expected monthly spin on the birth/death adjustment.

How Can this Work?

It is amazing.  Take a roomful of traders.  Ask them whether government or a trading desk is more efficient.  We know what they would say.  Trading desks can execute baskets with a keystroke.  There are "fat finger" examples and also stories about interns sitting on keyboards.

Does anyone really think that a very complicated government report is generated in the same way?  Well the silly story was good enough to move the market last month.

Interpreting Housing Indicators

Finding the right economic indicators is a challenge for investors.  Often the same data are presented in several different ways.  How does one make the right choice?

Today's data on home prices from S&P Case-Shiller provides a useful example.  As everyone knows, prices are down significantly from peak values and the annual data have a strong seasonal component.  There are three quite different approaches.

Month-over month changes.  The 20-city home price index for April fell by 0.6% from March.  This decline was reported by some media sources, but ignores the seasonality in the data.  When the seasonal effect is strong, it can be quite misleading.

Year ago changes.  Most solve the seasonality problem by comparing the prices in April, 2009, to those in April of 2008.  Sources using this approach cited a price decline of 18.1%.  This ran as a headline on some stories and as a subtitle on CNBC.

The problem with these year-over-year changes is that it is difficult to see improvement fast enough to be helpful for investment decisions.  Let us illustrate this with an unlikely and extreme example.  Suppose that the index went up 10% from April to May.  The year-over-year value would still be a decline of 9.2%.

To avoid this problem, those using the year-over-year method compare the annual change in one month to that of another.  The conclusion often reached is that prices are declining at a lower rate.  This is not correct.  In the example given, prices would be increasing, not declining at a lower rate.  It is not easy to get real insight from a string of year-over-year numbers.

Seasonally adjusted data.  S&P also puts out a seasonally adjusted version of the series.  This allows the user to focus on the month to month change, the real time movement of greatest interest, while removing the regular seasonal pattern.  Using this approach, prices declined by 0.9%, worse than suggested by the other two methods.

Conclusion

Using seasonally adjusted data is frequently the best solution for this sort of problem.  Many of our fellow data consumers are suspicious of any adjustments to raw data.  They are then forced to make their own seat of the pants guesstimates about how important the changes are.

Calculated Risk, a favorite and featured source, also focuses on the seasonally adjusted data.  You can check out the latest update to this series, comparing it to the bank stress test assumptions, in this article.

Can Investors Learn from the Lottery?

On a recent trip to Wisconsin we chanced upon a television commercial for the Wisconsin Lottery.  It was insidious, offensive, and probably effective.

The commercial was for a crossword scratch game, the kind where you can be an instant winner.  It showed a nerdy guy doing a regular crossword puzzle, and then turning his skill to the lottery version.  He was a winner.

Here is a look at the card:

If one looks at the back of the card, there is a different message.  The overall return on this "investment" is 3:7 to 1 against the player.

Overall Odds	1:3.7
Odds	$5 1:7 $10 1:16 $15 1:61 $20 1:51 $25 1:50 $50 1:90 $100 1:1,500 $500 1:44,445 $5,000 1:600,000 $50,000 1:600,000

We purchased a ticket and got an "oh, so close" experience.  When we inquired about the game, the vendor asked, "How many?"   He was surprised that we took only one.

There are several themes here:

1. The "investment" appeals to someone who needs/wants to hit it big;
2. There is an illusion of skill in the game;
3. There is a poor perception of the actual chances of victory.
4. The game itself is designed to nurture the worst instincts of the player.

Other Lottery Examples

There is an occasional case where a lottery has a positive expectancy because of a carried-over jackpot.  We remember when a group of bored CBOE traders sent a clerk to O'Hare with instructions to fly to Pittsburgh.  He went to a nearby lottery vendor and bought tickets for hours before flying back.  No luck, but at least the play had edge, even with the cost of the plane ticket.

Normally the lottery is a straightforward losing proposition.  Despite this, various promoters offer lottery strategies and analysis of the "tendencies" in various state lotteries.  The vendors of strategies use the terminology of successful players in other sports, e.g., they "wheel" a key number.

Over twenty years ago there was a publication called Gambling Times.  Most of the authors wrote about games where it was possible to achieve a positive expectancy  -- sports betting, blackjack, horse racing, and poker.  There was some treatment of craps, where you could get a lot of play while losing little edge by following "best" strategy.

And then there was the lottery "expert".  She was something of a joke with the other authors, since everyone knew that her methods had no edge.  The magazine ran her columns because they were popular, not because they provided any "investment edge."

There was one quotation, going something like this ----

"In the Pennsylvania Lottery, certain numbers like to come up with their near neighbors."

Wow!  What an incredibly dumb statement!  This was long before Fooled by Randomness,  a book we liked so much that we bought multiple copies as gifts for clients.  Anyone who understood odds knew that this claim was completely bogus.  It was an early example of the fact that people can see patterns in anything.

Whatever one thinks about the methodology, this was a successful business model.  She now has testimonials from many winning players, used as evidence for her newest work.  No one knows how much was invested to generate those winners since losers do not write letters.

Lessons?

It is our expectation to draw upon this article, showing the similarities with many other investment decisions and the sort of information provided in support.

While we have strong feelings about the public policy implications of states exploiting the poor for additional gambling revenues, that is not our focus.  We write about investments and about how people can get the information they need.

Meanwhile, we invite reader suggestions for their own similar experiences.

Forecasting the Jobs Report

MarketWatch tells us that today's selling was concern about the labor market and the resulting economic contribution of consumers.

We sympathize with journalists who need a daily lead to explain modest market moves.  We are amazed that market participants suddenly started to worry about Friday's jobless report.

"Forecasting" the Employment Report

Here at "A Dash"  we have a good model for payroll employment changes, but it is not really a forecast.  We look at concurrent economic data and ask what change in employment would be consistent with the other indicators.

Other people also make forecasts or predictions.  It is a strange game, because the "truth" does not matter.  Everyone is trying to forecast the BLS version of truth.  Here is how we put it in an explanation from two years ago:

• The Bureau of Labor Statistics (BLS) does not actually measure the change in jobs from month to month!  We know this may seem confusing.  The change in jobs is what everyone talks about, but it is not what the BLS measures.  They try to estimate the total number of jobs, using survey techniques.  They then compare the estimate from one month to the estimate from the next to calculate the change.

The result:  They can be great at estimating the total, and still have a huge error band for the change.  If you want more explanation on this point, we covered it here.

• The original report is revised for two reasons, but not because the government is cooking the books.  The first reason is that many of the businesses in the survey do not send their reports in on time.  What a surprise!  Some businesses NEVER respond.  The BLS does two revisions, based on more complete returns, and then declares the result to be final -- for a time.  The second reason for revision is that the BLS sample for the survey includes only businesses that existed at the start of the year.  The dynamic economy is gaining and losing businesses all of the time.  The BLS eventually takes actual data from state employment offices and compares it to their own count.  They adjust the methodology based upon the actual count, using something called a birth/death model.

We now believe that there is a recent negative pattern in revisions.  We think it is related to the seasonal adjustment methodology, and we invited other researchers to collaborate with us in investigating this premise.

Current "Predictions"

As we noted, our own approach looks at several other economic variables from the same time frame as the survey.  Our model is linked to the final data series.  There is no point in trying to model the first report, which is known to be either biased or less accurate.

Given the continuing weak picture in initial jobless claims, University of Michigan sentiment, and the ISM report, we expect May job losses of over 600K, greater than the consensus.

Most economists do not reveal the basis for their forecasts, but there are some exceptions.

Today's ADP data draw upon proprietary information about actual job changes, an excellent source.  It is completely possible that ADP could do better on job changes than the BLS, if only because that is exactly what they measure.  Since the initial BLS report is the "official" number, that has become the ADP target.  ADP sees job losses of 536,000 in the private sector.

Wanted Technologies forecasts a job loss of 565,000.  Their method is a proprietary regression model including online advertising for jobs and prior BLS data.  Their methodology is careful and accurate.  Like the rest of us, they are looking at the final revised data.  Interestingly, they show that their "forecasts" are more accurate in calling the final revision than the BLS does itself from initial data.

Good idea.  We should test that on our own results!

Our Take

It is interesting that our approach and those using different methods are so close in the predictions.  Our own estimates have been too bearish, but later revisions have shown us to be very accurate.  And remember, the sampling error alone is more than 100K jobs.

Whatever the exact number, we are a long way from significant improvement in employment.  We expect the old, big-firm employers to add workers only slowly.  While there is vibrant job creation, misunderstood by most, it is fighting a losing battle with job losses.

Problems with Housing Data

US equities responded favorably to morning news about pending home sales.  The data showed a third consecutive month of gains, actually up 6.7% over the prior month.  Some pundits favor the year-over-year comparison, which was up 3.2%.  It certainly seems like good news.

Commentators quickly pointed out some problems with the data ---- the sample size is small and sampling error is large.  Pending sales do not always translate into actual sales.  It is only one month.  Etc.

The Sad Truth about Housing Data

Housing is at the epicenter of the financial crisis.  Home values affect wealth, personal consumption, and the need for further write downs in "legacy" (formerly known as toxic) assets.  We would love to have good data about housing.

Forget it.  Nearly all of the housing series are flawed with significant discontinuities or conceptual problems.  No matter what the data report, there will be plenty of opportunity for pundits to dispute the results for the next year or so.

Here are some of the problems:

• Pent-up supply, and pent-up demand.  Most pundits claim that there are many homes ready to hit the market as soon as things improve a bit.  We believe that there are also many latent buyers, waiting for the right combination of loan availability and price.  Neither of these assertions has any hard data.
• Foreclosures.  The principal media and blog observations show the percentage increase in foreclosures.  This is an alarming increase from a small base.  Interpreting this series is guesswork.  There was a moratorium on foreclosures as the Obama proposals worked through the legislative process.  That gave a false sense that foreclosures were lower.  Since non-foreclosure sales are generally at higher prices, it made prices seem higher.  Now that the moratorium has ended, we are seeing the opposite -- more foreclosures and lower prices.  Those looking at the data series will be deceived by both effects.
• Tax credit effects.  New buyers have until the end of November to collect a tax credit of $8000.  It is reasonable to expect any first-time buyer considering a home purchase to act in the next few months.  This may draw forward demand, leading to a reduction in purchasers after the credit expires.
• Financing effects.  There is a sense that mortgage rates have bottomed, and moved higher.  This may stimulate some to act more quickly.  The increase in pending sales was quite dramatic in some regions -- over 30% in the Northeast.

Our Take

Like everyone, we watch housing data closely.  We also solicit anecdotal evidence from many sources.  We know of first-time buyers, using the credit, who put 5% down via FHA and got the seller to cover closing costs.  It is a great opportunity for qualified young buyers.  There is some Internet mythology that there are no 5% loans.  That is incorrect.

Our major conclusion?  Most of the pundits are too confident in their predictions.  We see so many who expect prices to move much lower, but there is little supporting data.

We continue to look for good indicators on housing, and welcome comments.  Our major observation relates to the calculation of "months of inventory."  This measure takes the known inventory and divides by the annualized rate of sales.

At this point, the rate of sales is so low that even modest increases will dramatically reduce the months of inventory.

ISM Signals Expanding Economy

Most economic observers have noted that any encouraging data has so far taken the form of a reduction in the pace of declines.  This is a necessary, but not a sufficient condition for improvement.

Anyone looking for leading indicators of economic growth wants to see more than a green shoot or two, but that is how beginnings occur.

Today we have the first real-time data indicator consistent with actual economic growth -- the ISM manufacturing report for May.  This came in at 42.8%, slightly better than expectations.

Interpreting the data requires some care.  A reading below 50 indicates contraction in manufacturing.  Since manufacturing has played a declining role in the US economy for many years, the question is what level of decline is still consistent with overall economic growth.

The ISM analyzes this question and reports as follows:

A PMI in excess of 41.2 percent, over a period of time, generally indicates an expansion of the overall economy. Therefore, the PMI indicates growth in the overall economy following seven months of decline, and continuing contraction in the manufacturing sector.

The rate of economic growth corresponding to the May reading is only 0.5%.  It is small.  It is only one month.  But it is, perhaps, a start.

It is also occurring before much of the stimulus package has had an effect.

The ISM also notes that the New Orders Index, considered to be a forward indicator, has bounced rapidly from the December '08 low of 23.1 to show actual (small) expansion with May's reading of 51.1.

People are Finding Jobs -- More are Losing Them

Every Thursday we see the same discussion of the weekly jobless claims reports.  It is not helpful, even though this is a good real-time indicator.

Some commentators note that initial claims, usually measured by the four-week moving average, have stabilized and even show some decline.  Economist Robert Gordon, a long-time member of the NBER dating committee, believes that this indicates an imminent end to the recession.  Look here for a nice summary by the Good News Economist, and here for the full Gordon article.  Check out the convincing charts and tables.  Gordon writes as follows:

If we refine the NBER weekly trough date to be the third week in the NBER trough month, then in four of the past five recessions the new claims peak leads the NBER weekly trough by a range of only four to six weeks, and in the fifth recession the new claims peak lags the NBER weekly trough by two weeks. Since new claims have recently reached a peak in the week ending 4 April 2009, it is tempting to conclude that the monthly trough of the US recession could come as early as the middle of May 2009 – a date earlier than most analysts appear to expect. 

Meanwhile, many observers highlight the scary chart showing continuing claims.  As we noted last week, this indicator is helpful in showing individual pain from the recession, but not for predictive purposes.  Continuing claims are not a consistent series because of changes in the duration of unemployment benefits.  Those presenting this chart have an obligation to address this issue.

Instead, most point to it as evidence that the recession will be prolonged.  It is not good evidence for this argument.

An Exercise

Continuing claims are nearly 6.8 million, up 110,000 from last week.  It is a deeply disturbing fact, representing plenty of distress.

Now try this.  Take the initial claims level of about 620,000.  Multiply this by the number of weeks of benefits.  In normal times this is 26 weeks, but it is now as long as 72 weeks in some states.  (It is still not long enough for some).  Let us take 52 weeks as a conservative number for many states.  Now multiply.

This shows that more than 30 million people lose jobs in a year.  It also shows that 80% find new jobs.

This conclusion is supported by various reports from the BLS.  The most authoritative sources use the business dynamics series, citing data from state unemployment records.  In data released last week, but ignored by everyone, the BLS reported on the most recent available data, from the third quarter of 2008.

The number of job gains was 6.8 million.  That is over 100,000 jobs for every business day.

The problem is that job losses were 7.8 million, a net loss of a million jobs in the quarter.  The fourth quarter data will be much worse.

Getting an Accurate Picture of Labor Dynamics

There are several important conclusions.

• Looking at changes of 100K or so in continuing claims is misleading.  This is statistical noise compared to the overall impacts.
• The critics of the BLS on job creation measurement (including the many birth/death model critics) are completely wrong.  There are many jobs being created.  The problem is that job creation is not keeping up with job losses.
• The extent of the impact is better represented by the 30 million or so people who have lost jobs in a year, and also their colleagues who feel threatened.  This is a big damper on consumption.
• The Obama Administration efforts to "count" job gains from stimulus do not capture the story.

Anyone who looks at the actual data gets a more accurate picture.  There is a sea change of job losses and gains.  It helps to understand why the initial claims series is so important.

To our continuing surprise, none of the mainstream media sources have reported this story.  It is almost as if they are following the bloggers rather than doing their own research.

Street Fighters: Good Information and Good Fun

Kate Kelly's book, Street Fighters:   The Last 72 Hours of Bear Stearns, the Toughest Firm on Wall Street,  now on our recommended reading list, is a great source of information and fun to read.  It is well-sourced, authoritative, and always interesting.

Does it provide, through a look at Bear, the answers to our financial crisis?  We think not, but that is part of the fun.  The reader can collect information -- raw data -- with real confidence.  There will be many accounts of the financial crisis.  Anyone seeking a complete understanding should consult many sources.

The Approach

Street Fighters tells an engaging tale focused upon how a mighty firm was reduced to rubble in three days.  You know the ending before you start reading, but it is no less engaging.  The author has a nice sense of the characters and has done extensive research into backgrounds.  We not only learn about the major players, we learn what everyone else thought about them.

Such an approach is open to challenge.  Kelly provides footnotes for sources, and acknowledges disagreement.  It is convincing support  for her narrative.

The Result

The reader is treated to a view from several perspectives.  It is an insider's take on the politics within an investment bank.  There is genuine conflict over risk and which products to feature. Even the most jaded reader may have some sympathy for a wealthy guy who spent a lifetime building up his company and his position, only to lose it all in a few days.  This is "inside baseball" at its best.

The story is dramatic and well-told.

Assorted Insights

The reader has raw data to draw conclusions on several interesting points.  Here are some that stood out for us.  Yours might be different.  Please consider the following:

• Significance of CNBC.  David Faber had a story about firms not trading with Bear.  It was big news, but it was later denied by those in question.  The damage was already done.   The issue is how much information one needs to go with a story like this, when the story itself can affect the outcome.  Should Faber have verified more completely before going with this story?  Would it have made a difference?
• Significance of Kelly and the WSJ.  Many readers will already be familiar with the three-part series in the Wall Street Journal.  In the book, Kelly asserts that the series itself -- criticizing Cayne's leadership -- had an impact within the firm.
• Hank Paulson's Role.  Paulson is portrayed as dictating a punishingly low stock price for Bear.  Historians will combine this information with additional information, includeing his reversal on the use of TARP funds, the decision to force TARP on all of the major banks, and other similar decisions.  From our perspective as public policy experts, this is an extraordinary and arbitrary use of powers.  It is on a scale that is without precedent for a Treasury Secretary.
• The Fed Role.  The decision of the Fed to expand lending to include investment banks, only two days after the Bear failure, was extremely arbitrary with respect to timing.  We should all be concerned when public officials make decisions about which firms (and which investors) live or die, and do so without clear rationale.  Bear was allowed to die while others were saved.

Conclusions

Kelly's conclusion is that Ace Greenberg built a firm on some principles and Jimmy Cayne violated those and lost it all.  We are not convinced.

We can now see what happened to many other firms.  It would not have mattered if Bear's leverage and risk had been a little less.  Kelly is probably right in suggesting that Bear was an unloved firm on the Street, and therefore first to be challenged.

It was beyond her scope to consider other causes, although there is a paragraph or two on the trading in Bear stock.  This was something we watched daily on our trading screen.  Those betting against the firm could trade in the thin Credit Default Swaps market (CDS), buy puts (where premiums exploded in issues that were far out of the money), short the stock, pull your hedge fund accounts, and spread rumors.

These events were all taking place.  The sequence of causation will never be determined.  What we do know is that any business depending upon confidence and credit can be destroyed in three days. Those aiding the destruction can make millions as it happens.   If that is a verdict on a business model, the entire banking industry is in question.

Final Take

The book is fun to read and has plenty of raw data with authoritative sources.  You should read it, and combine what you learn with other information.  The story of the 2008 crisis is complicated.  We look forward to reviewing other books on the subject.