Do you remember that iconic banner? Yes, we had 3.5% real GDP growth last quarter. However, it is premature to declare “Mission Accomplished”. We are facing the specter of double digit unemployment lingering throughout 2010.

Short-term versus Long-term

The growth that we have seen is largely a result of government moving economic activity from the future to the present. The most visible example of this was the cash for clunkers program. Consumers could get up to $4,500 for trading their car in before the deadline. This attracted a lot of people that probably would have bought new cars anyways in the future (and now they won’t).

Indeed, 1.7% of the 3.5% GDP growth was vehicle related. Would we see the headlines, “Economic Growth in Third Quarter Heralds End of Recession“, if the GDP print was 1.8% [actual 3.5% minus auto contribution 1.7%=1.8%]?

This is but one example of the short-term stimulus spending. Another example which has been in the press recently is the phase out of the homebuyer tax credit which has likely accounted for some of the action in the housing market. “Existing home sales have largest percentage jump since 1983“. Again, we are just shifting consumption from the future to the present. Oh, by the way, even though was saw one piece of good news, 8.3% increase in existing sales, let me remind you that new and existing sales are still way below their peak. More seriously if you track housing starts, permits and mortgage applications, they all spell even worse news.

I have mentioned this before. There appears to be a bias in the news to report good news and sweep the less favorable news under the carpet. Let’s look at “New Privately Owned Housing Units Authorized by Building Permits” seasonally adjusted. In September 2009, the number was 575,000. The peak was September 2005 with 2,263,000 units. So we have dropped a staggering 75%!

Some more perspective. The last time we were below one million units was the 1991 recession where we hit a low of 786,000 units in January 1991. The low in the double dip recession of the early 1980s was 731,000 units. In the oil recession of 1975, we dropped to 726,000 in January 1975. Remember, the population has grown. Note that the data begins in 1960. The numbers we are experiencing are historic lows.

If we population adjust these numbers, today’s permits look even worse. The graph below shows “population-scaled” building permits. This is an apples to apples comparison. Essentially, it allows us to look at the permit activity in 1960 and ask, what would permits be in January 1960 if we had the population of September 2009?

There are three points here.

1. The government programs may be able to shift some activity from the longer term to the shorter term. However, this will not necessary “jump start” the housing market. This market has a long way to go before recovering.

2. The housing market will provide a continued drag on economic growth both directly (less construction and associated activity) and indirectly (negative wealth effect, i.e. people will not spend as much if their wealth decreases).

3. The depth of the housing crisis will likely cause a second wave of financial crisis as more an more people default on their “prime” mortgages. I have mentioned this before. If there is a significant housing recovery, this could mitigate the second wave. However, I just don’t see the data to make the case for a strong recovery in the housing market.

Read the rest of this entry »

The Washington Times recently featured  Aaron Chatterji and Christopher Gergen’s views on ways in which successful social enterprises can scale up to serve larger populations.  Read their column here.

The Raleigh News & Observer recently featured Professor (and physician) Marco Huesch’s thoughts on health care reform, highlighting ways in which physicians can lead innovation.  Read his thoughts here.

By Paul Zipkin

Here we are in early fall, 2009. It was a fairly calm summer. There were no major financial disasters. The stock market revived somewhat. The economy is still troubled, but compared to the unremittingly awful news during the previous year, it’s been a good time. I believe some credit for this happy turn of events is due to the bank stress tests conducted by the government during the spring.

The tests calculated what would happen over the next couple of years under two scenarios, a base-case scenario and a bad scenario reflecting worse economic conditions. They considered each bank’s entire position, not just standard deposits and loans but also fancy mortgage-backed securities and swaps. The question was, how much capital would each bank need to continue operating normally under the bad scenario?

The method itself is not at all novel. Many businesses, military forces and other organizations do this sort of scenario planning all the time. No one knows exactly what will happen in the future, so it makes sense to simulate several possible futures, to estimate whether the organization’s plans are robust.

But, amazingly, this was the first time the government conducted such a test of our financial system. We do have elaborate financial regulations, but those specify procedures and check whether they have been followed. In other words, they look backwards, not forwards. To look ahead at several plausible scenarios, the exact same ones for all banks, and to include all the banks’ exposures – that was unprecedented. Of course, the inspiration for the stress tests was the sequence of ugly surprises in 2008. Nobody knew how dire the situation was at Lehman and the others until it was too late.

Banks are supposed to do this sort of thing for themselves, of course, but it’s not clear how seriously they take these “risk management” exercises. Anyway, they are not required to use the same methods and certainly not to make the results public.

Before the results were revealed, many observers criticized the tests. The tests were not rigorous enough, the bad scenario was too tame, they said. (The Treasury invited some of this carping by calling the bad scenario the “worst-case” scenario. It was not that.) But the criticism largely fell silent once the tests were complete and the results announced on May 7.
Read the rest of this entry »

Is anybody listening out there? Those ‘in the know’ expected 180,000 job losses. Some thought 150,000. Optimists thought job gains. In the end, we bled 283,000 jobs.

It was no surprise to me. The message has been clear in the past two Duke-CFO quarterly surveys. Companies are still in cutting mode. Employment is not going to significantly improve when we have 551,000 new claims for unemployment insurance. So what if the four-week moving average of claims has decreased by 6,250 to 548,000. We need some three handles to stabilize employment and we are way far from that territory.

What to Watch For

The following are very important for those of us that carefully track employment:

• Forget continuing claims. They are misleading. Any headline like “Good News, Continuing Claims for Unemployment are Down” is likely written by someone who doesn’t really understand the data and the Federal programs. Continuing claims might be lower because people roll off the standard unemployment program and hit extended benefits and the emergency program (EUC). You need to count correctly. Notice that the extended benefits and EUC are reported with a two week lag. The most recent data showed that the number of people on emergency programs increased by 99,832 to 3,275,213.
• Extended benefits and EUC are important to watch because we have a problem with the duration of unemployment. It is taking longer to find jobs and this is another sign that the economy is not coming around.
• Watch the average number of hours worked in a week. The number of hours worked has been decreasing. People are sometimes voluntarily working fewer hours to save their jobs (or fellow workers’ jobs). Working fewer hours is a “shadow” unemployment that is not counted in the official numbers.
• I like the Bureau of Labor Statistics measure “U6″. This measure looks at total unemployment by considering unemployed, “marginally attached” workers as well as people working part-time that really want to work full time. This rate is a staggering 17%. It has never been this high.
• Initial claims as mentioned above need to be in the 300,000 range to stabilize employment. We are nowhere near that.
• The scale of the unemployment problem is important because we are approaching no-man’s land. What I mean is all the data to analyze the impact of unemployment comes after 1947. We are already in a recession situation worse than any other since WWII. Yes, this is not the Great Depression – but we don’t really have good data on the Great Depression. It is a challenge to quantitatively analyze some problems (I have an example below). To be clear here, we have lost 7.2 million jobs during this recession. We will lose more. How about this comparison? If we lose an additional 1.7 million jobs, i.e. go to 8.9 million losses from 7.2 million, the loss of jobs on a population adjusted basis will equal the losses in the past four recessions — and that includes the nasty double dip of the early 1980s.

What Worries Me the Most

Suppose you statistically examine the relationship between unemployment and mortgage defaults. There is a moderate positive association as you might expect (higher unemployment leads to higher defaults). Indeed, this moderate positive association forms the basis for the stress tests that banks have (finally) done.

What if this model is flawed? What if the financial institutions have underestimated the number of defaults? Here are the reasons why the models could be wrong:

• The estimates are largely based on mild recessions. This is not a mild recession. We have not seen anything like it in all of the available data. Essentially, we are extrapolating outside of historical experience. Usually when this occurs, we explicitly account for the extra uncertainty by being more conservative. I have my doubts that the financial institutions have factored in this extra uncertainty.
• While first point is important, the second one is more important. There is a critical omitted variable in the analysis. People statistically analyze default versus unemployment — but you must take housing values into account. If your house is worth less than what is owed on the mortgage, you are far more likely to default than if your house is worth more than the mortgage. In most of the data used in the standard analysis, houses are worth more than the loans. In other words, the fact that potentially 50% of houses with mortgages are under water, greatly increases the sensitivity of default to unemployment. This suggests that there is much more downside than financial institutions have prepared for. It suggests a second wave of credit crisis.
• Even the financial institutions know that defaults have not peaked yet. People are still holding on. Expect a surge in defaults, foreclosures and bankruptcies to happen next year.
• The social stigma attached to defaulting on your mortgage is gone. Keeping up the Jones’ means you get to default too. Anyways, those banks took all that government money making it easier for them to be the bad guys.

Other Tidbits

• As predicted, the Cash for Clunkers simply shifted car purchases. GM sales down 42%. In the end, the problems in the auto industry remain. Any bets on Chapter 22?
• There is a lot of push back on basic financial regulatory initiatives – like the requirement to offer vanilla mortgages. A lot of the push back is from small financial institutions who cannot compete in such a market against the likes of BAC and Citi. The real issue here is why do we need 7,000 banks in the U.S.? It would be far better for both customers and the economy to have a massive shakeout. In addition, it is completely dysfunctional to have different banking regulations depending upon the state that the bank resides in.
• The FDIC is bankrupt. For the last year, I have said that there are 1,000 banks that need to go down. Now we are in the odd situation where the FDIC cannot close down so many banks because they simply don’t have the funds.

Follow me on Twitter.

See below my monthly employment graph that standardizes the job losses (based on the size of the labor force) across different recessions.