Commodities After The Crash, No Way But Up

By: Andrew_McKillop

After a suspiciously short and likely programmed commodities crash we can only have the right hand leg of a "V" profile for commodity prices - until and unless big things happen with the major currencies and national debt crises of most OECD countries, or we have a global economic crash. To be sure, this is the context for highly classic speculative frenzies, where fundamentals are put on the back burner, and the front burners are turned to full on.

By May 9, after a week of slaughter on the precious metals, energy, food and metals exchanges there was no place else to go but up. The commodities rout of April 29-May 6 knocked at least US$ 90 billion of nominal or paper value off the estimated value of all 24 commodities included in the S&P GSCI index. The value of market tradable paper in this group of leading commodities fell to about US$ 805 billion on May 6, from around US$ 891 billion on April 29, according to Bloomberg.

This was a classic - and short - bear squeeze needing very large falls in the nominal value of "underlying assets", commodities themselves, to temporarily drive out the speculators who had been bidding up prices. Signalling the probable degree of advanced preparation and warning for informed players - some call them insiders - Goldman Sachs Group Inc. added its own weight to the rebound. After forecasting the plunge and adding its weight to it in the days preceding April 29, it was predicting price recovery for commodities (announced as "a possible recovery" by GS Group Inc.) exactly one week later, on May 6.

WHAT HAD CHANGED ?

Through March and April in a process dating from the start of 2011, investment funds made near-record bets on commodity price gains, pushing indexes like the Rogers RICI, the CRB, CMCI and the S&P GSCI to their highest levels since late summer 2008. Commodities beat stocks, bonds and the dollar to the end of April, the longest winning streak in at least 14 years. Behind the euphoria was the spectre of penury, with relentless industrial growth in China and India, and other Emerging economies pressuring natural resource production capacities and stockpiles.

More realistically and carefully excluded from market-correct explanations, and even closer behind the euphoria, the continuing fall of the US dollar's world value - the dollar being used to price and transact more than 72 percent of the world's total commodity trades - can only intensify any fundamental factor levering up commodity prices. Retreat of the Eurozone-16's money, the euro, from its current unsustainable highs (measured against the structurally weak dollar) will also tend to bolster commodity price gains against stocks, bonds and currencies, due to the euro now being used to transact - if not price - increasing volumes of physical commodity trades.

THE DOLLAR REMAINS WEAK

To be sure, the Obama team's hunting-and-shooting triumph in Abbotabad followed by a fishing trip to the Oman sea could only drive up the world value of the dollar, but long-term trends and economic reality show the true trend. The US dollar index, measuring the dollar's performance against 6 other currencies, but weighted to give the dollar's performance against the euro some 58% of index weight shows long-term decline as the only main trend - until and unless the euro falls.

Taking performance of the US dollar index July 2010 we have this read out:

The Abottabad adventure is signalled by the 1.2% upward blip on the chart's right hand edge, above, but rather little in this chart allows us to believe there can or might be a longer-term upward recovery in the dollar - although the index will improve considerably when or if European Union monetary disorder goes into higher gear, and the overvalued euro moves backward.

OIL FRENZY

Oil prices best reflect the weakening US dollar and the onrush of central bank "injections" - rather than hits and misses on presidential palaces in Tripoli, and hits on demonstrators in a range of countries from Bahrain and Yemen to Syria and Tunisia, with the largest focus always on possible civil unrest in Saudi Arabia, potentially affecting oil production and exports. Oil prices also reflect claims by the OECD's International Energy Agency and large oil companies like Total, claiming Asian oil demand is very strong, while the US Energy Department's TWIP shows relatively high US oil inventories, and European oil consumption is in many countries still 5% - 10% below 2008 demand level.

The one-liner marketspeak for this context of fundamentals which can be read any way, and unknown geopolitical trends is that the balance of risks and fundamentals still points to a supply-constrained world, not only for oil but almost all other commodities, except US-only shale gas. The financial and monetary risk of a sudden plunge into steep recession, as in 2008, driven by national debt funding crisis, also generates so much new liquidity on financial markets, including commodity markets, that until the global economy crashes commodity price must rise.

Taking estimates for Quantitative Easing by the US Fed, Europe's ECB, Japan's BOJ and other central banks as 20 trillion dollars since end-2008 we can relate this to the world value of the physical oil trade at a year average barrel price of US$ 100. The issued money and near-money covers more than 12 years of world total traded oil supplies. Oil is by far the world's biggest traded commodity, priced and transacted to a dominant extent in dollars - so the real question, here, is how can its price not rise ?

The unnatural fall in oil prices during the Apr 29 - May 6 crash is shown by the US benchmark West Texas Intermediate falling nearly US$19, and the rest-of-world benchmark Brent falling US$ 21 in the 5 days of last week. From the bottom point of the "V", rebound could only be as much as 3.5%-per-day in a technical rebound offering zero risk gains for the best-informed market makers and players.

THE REAL GATEKEEPERS

To be sure there is no watchdog in a mass speculative bidding spree that favours commodities more than equities, but the very small size of most commodity markets makes for self-limiting feedback. This size limitation applies firstly to value and turnover, relative to vastly bigger equities markets, and to the special characteristic of commodity markets: physical deliveries and transactions.

Silver is the best recent example - despite the fantastic drubbing taken by silver prices during the Apr 29 - May 6 crash, the largest global silver market, the Comex, is in permanent physical shortage of the metal. Several of the food commodity markets are highly vulnerable to cornering and to physical under-supply. With rising prices, soothsaying of the Goldman Sachs type will give way to physical rigging, producing what many analysts already claim is happening: market movements with no relation at all to underlying fundamentals.

The twist is these analysts usually judge commodity prices as overpriced and likely to fall back 10%, or 20% or even 30% from current levels, without warning. The opposite is also possible, and more likely under current conditions: that is massive unexplained price rises in a few days of frenzied trading.

The gatekeepers are the precious metals. When or if gold prices rise above US$ 2000 per ounce, and silver prices attain some level above US$ 65 per ounce, this is a deadly challenge to the US dollar, and its fiat friends and also-rans, starting with the euro, which is already seriously overvalued. From high and sustained gold and silver price levels, the gates of inflation will very surely open wide - making panic rises of interest rates, and slump into global economic recession almost inevitable.

See the original article >>

Commodity Bounce Likely In The Short Term

by Dr. Joe Duarte

Markets Are Headed For Crucial Week 

 

Traders are clearly bent on testing the resolve of those who sold commodities off last week, setting this week up to become pivotal for all markets.

Last week’s nearly 9% pullback in the CRB Index was impressive, especially when key components, like silver and oil are considered individually. Yet, if you step away from the drama, the pullback falls within the realm of an intermediate term correction in what may still be a long running bull market.

Chart Courtesy of StockCharts.com

The key to the selloff was the presence of computer trading. So, in fact, what we saw last week was a repeat of the “Flash Crash” in the stock market, which happened just twelve months ago. As sell stops got hit, the computer programs began to sell, then the margin calls came in, and more sell stops got hit, setting up a vicious cycle. Silver and oil clearly took the brunt of it. Oil may have been moved by the death of Osama bin Laden. Selling in silver accelerated once the news that the Soros funds were sellers hit the mainstream press.

According to The Wall Street Journal, the cascading selling in crude oil on Thursday came as a result of “sell orders” which had been set in place to protect profits by traders (sell stops). And “once the liquidation gained momentum, each successive price drop triggered a wave of these automatic sell orders, sending crude sliding further—and starting another round of selling.” The selling was described as a “panic.” One trader told the Journal that at one point “the orders (to sell) were just pouring in.” The Journal added: “Much of the selling, however, wasn’t carried out by traders, but rather their computers, which were programmed to sell once the price of oil hit certain levels—$105.50 and $102.70, for example. Investors, not companies that produce and consume oil such as refiners, are the primary users of such sell orders.” So, the selling was speculator or trader driven, which should come as no surprise to anyone as “Those speculative investors were present in the oil market in near record numbers on Thursday. Open interest, or the total number of open contracts, of the Nymex’s main oil-futures contract surged to a record 1.65 million.”

Chart Courtesy of StockCharts.com

So the key to success in this market now is to remain patient. Crude oil and silver will have their bounce. West Texas Intermediate has support as far down as the $92 area, its 200 day moving average, while resistance is in the $100-$105 area. It will take a few days for this situation to sort itself out, but at least we have some parameters. And yes, what happened on the way down, could also happen on the way up, as computer related buy stops could get hit and the market could melt up.

Chart Courtesy of StockCharts.com

Silver, on its own right, also has support near the $30 area, with resistance in the $35-$40 price range. Silver bulls are more like bulldogs, with plenty of them shrugging off the selling last week and reaffirming their support for the metal. One set of bulls in the silver market that won’t be coming back soon are likely to be small investors who got in within the last two weeks as prices were blowing off.

Conclusion 

The markets are in the midst of a very risky period which is being exacerbated by the presence of mechanical, computer driven trading, in stocks, and commodities. There has been little evidence of the bond or currency markets being as vulnerable to this kind of activity, as of now. But it makes sense to consider that as another possible event in the future.

The key difference between bonds and currencies and stocks and commodities is that the two former markets are much deeper in their liquidity and their size. That means that smaller amounts of money can get lost in the bid and ask process of the bonds and currencies while stocks and especially the often relatively small commodity markets are much easier to move.

What’s the best recipe for success right now? Patience. Stick with stocks that are working and let the commodities do their thing for the next day or two. As those key resistance and support areas, listed above, are approached start considering your trades. We’ll be monitoring the markets and updating as needed. We suggest monitoring our Twitter feed regularly as we’ll be updating changes there as soon as they are updated on the web site.

See the original article >>

Learning about Long Term Unemployment (II)

by Menzie Chinn

Last Monday, I discussed some of the findings from the conference on causes and consequences of, and policy responses to, long term unemployment, which brought to UW Madison Prakash Loungani, an Advisor in IMF’s Research Department, Kenneth Scheve, Professor of Political Science at Yale, Phillip Swagel, Professor of Public Policy at the University of Maryland, and a former Assistant Secretary of Treasury for Economic Policy, Rob Valletta, Research Advisor at the Federal Reserve Bank of San Francisco, and Kenneth Troske, Professor of Economics from the University of Kentucky. In today’s post, I will discuss the presentations and papers by Ken Scheve and Phillip Swagel and Ken Troske.

Figure 1: Median duration of unemployment, official BLS series (blue line, left scale), overall unemployment rate (decimal form, right scale), unemployed over 27 weeks as a ratio of civilian labor force (red, right scale). NBER defined recession dates shaded gray. Source: BLS via FREDII, NBER, and author’s calculations.

On Policy

From Swagel and Troske’s paper, “Training: A Targeted Policy Proposal”:

In an era of limited government resources, we look to focus training dollars in a way that will have a meaningful and cost-effective impact in improving the lifetime incomes of those receiving training. The targeted groups are people with low skills and incomes but substantial motivation and evidence that they will benefit from training such as consistent labor force attachment. Training support is meant to provide an opportunity for people to garner the basic skills needed to move up the occupational ladder.

People with low skills already have substantial incentives to get training, since acquiring new skills is a pathway to higher incomes. Many low-skilled workers, however, face substantial obstacles to utilizing training, including the potential cost of training. For people of modest financial means and limited access to credit, even the cost of community college could be an insurmountable obstacle, as could the cost of paying for others to care for dependents while taking training. Moreover, many workers in low-skill occupations are not likely to receive on-the-job training—a garage attendant or janitor, for example, might have the motivation to consistently show up to work and the non-cognitive and social skills to perform at a high level but still never have the opportunity to advance. A lump sum of training funds could allow such a worker to take remedial courses and learn the skills needed to move up the occupational ladder. Training could help allow a garage attendant or janitor to become an MRI technician or home health care aide.

The empirical training literature suggests as well that motivation is a key determinant of the success of training programs. The second component of our proposal thus targets groups such as single mothers who have considerable motivation to move up.

Our proposal would extend training support to low-skilled people who are currently employed. Some $18 billion of federal money is now spent on a welter of programs related to training and job search, but workers are generally not eligible for federal training assistance while they remain employed. We turn this on its head and make evidence of reliable employment—of strong labor force attachment, in the jargon—a qualifier for assistance. This thus serves to increase skills for those taking it up and as an incentive for employment for those not currently eligible.

The paper takes as given that there is going to be an extended period of elevated unemployment, and that some of this unemployment is structural in nature. The specific proposal is described thusly:

We would provide an annual training benefit of $2,000 for two years delivered as an individual training account as in the current WIA model for a total of $4,000. This would be a one-time benefit for an individual (once per lifetime). The funds in the account could be used for approved local training providers (often community colleges). This amount compares to the $2,713 average annual tuition and fees at a community college in 2010-2011 (from the American Association of Community Colleges 2011 Fact Sheet); participants in this program would typically continue to work at least part-time while taking up training, meaning that $2,000 would likely cover the cost of an appropriate program of part-time courses. The focus would be on basic skills; this is not meant to replace Pell Grants in funding post-secondary education and the menu of approved providers and training opportunities would be explicitly linked to development of basic skills.
The training account would allow for part-time and intermittent enrollment for up to five years, in recognition of the reality that many workers will find their training interrupted for life events.

For me, perhaps the most interesting aspect of the paper was the point that we know remarkably little about what works and doesn’t work in terms of retraining. (Some literaure here and here.) While the authors conclude we should focus on the individuals the empirical literature says benefit from retraining, they also argue forcefully for the need for additional research — and this is not merely a throwaway line. In other words, those truly interested in helping the unemployed believe more research is necessary to see what constitutes a cost-effective use of resources in retraining (i.e., better to light a candle than curse the darkness … and cut funding).

I had two observations regarding the proposal. The first was that the focus on the trainable represented essentially an abdication of responsibility for those who evidenced low payoffs to retraining. While this decision might make sense from a benefit-cost perspective, the weakness of the empirical evidence suggested caution. Moreover, even if new empirical evidence were to buttress the earlier findings, trade adjustment assistance might still be worthwhile because it is part of an implicit bargain that a free trade regime that induces costs on some workers is associated with compensation for those bearing those costs.

The Political Implications

Ken Scheve’s paper, “Envy and Altruism in Hard Times”, did not directly address unemployment, but was very relevant to how the public views policy interventions that help various groups. From the conclusion:

Mass political behavior in the midst of an economic crisis provides a unique lens for studying distributive political conflict and the determinants of political opinion and behavior. This paper points to any one of the millions of citizens who have voted, marched, or rioted to
advocate or protest one policy position or another in their national political debate on how
best to respond to the crisis and asks why did those citizens take the positions that they
did and why did they often seem so invested in the debate. It seems likely that self-interest
plays an important role in answering these questions. Having often already lost much in
the crisis itself, individual citizens are acutely aware of the consequences of policy change
on their individual welfare. Moreover, economic crises are often periods of significant policy
change with long-lasting distributional consequences. In short, with so much at stake,
it would be surprising if self-interest did not inform policy opinions and behavior in the
national debate. However, the theatre of these political debates suggests the possibility that
other considerations may also be central to determining the positions that citizens take and
their behavior in the political process. The German or American taxpayer or Greek or
Spanish civil servant is not outraged simply because they will lose from some new policy
under consideration though that may be part of the story. Rather, their policy position and
outrage is in part because the policy alternative under consideration either resonates or is
in conflict with their sense of fairness.

In this paper, I investigate how one specific understanding of fairness- inequality aversion
influences individual policy opinions about economic policymaking in the context of an economic crisis. I argue that attitudes about inequality — both envy and altruism — lead to
systematic differences in support for trade protection across different sectors of the economy,
in support for taxing banking incomes, and in support for higher income taxes. In each pol-
icy domain, individuals not only consider how policy alternatives affect their own interests
but also how they affect the incomes of others relative to their own.

The paper provides empirical evidence from a set of original survey experiments on a
national sample of respondents in the United States (and I will shortly complement this
with analogous experiments in France). First, I show experimentally how variation in the in-
comes of the beneficiaries of various policies influence support for those policies. I show that
opinions about trade, financial market regulation, and tax policy vary systematically with
information provided about the incomes of those affected by policy alternatives. Respondents are generally more supportive of policies that benefit lower income recipients or create
costs for higher income recipients. Second, I adopt a specific formalization of inequality
aversion, incorporate this utility function into standard models of policymaking, and esti-
mate structurally an equation of policy preferences. I find that individuals have the social
preferences of altruism and envy assumed in these models though the relative importance
of these motivations vary across issue areas. Econometrically identifying these preferences
lends considerable support to the main claim of this paper that envy and altruism play a
central role in distributive political conflict over economic policies during times of economic
crises. That said, the evidence presented here should be viewed as pointing in the direction
of an important role for inequality aversion but it must be recognized that it is possible for
alternative mechanisms to generate the pattern of preferences observed across the experiments. It must also be said that many such alternatives seem more plausible for one policy area than another and so fail to simply explain the pattern across all experiments in the
way that inequality aversion does. Nonetheless, exploring new experiments and analyses to
evaluate alternative mechanisms seems a productive task for future research.

The way in which envy and altruism are operationalized is as follows:

As the author notes, perhaps a better way of characterizing the concept is inequality aversion. The extent to which this inequality aversion shows up, in the case of trade policy, is highlighted in this excerpt:

Table 1 reports the mean estimates for each treatment category and difference-in-means estimates for each combination of treatments. These results provide substantial evidence that support for sector-specific trade barriers are influenced by the average wage of workers in the industry.

Support for new trade barriers is 11 percentage points higher (a 33% increase) for respondents who considered protection for an industry with a low wage versus respondents who considered protection for an industry with an average wage. This difference was 20 percentage points (an over 80% increase) for respondents who considered protection for an industry with a low wage versus respondents who considered protection for an industry with a high wage. The differences between the middle and high wage treatments are also substantively and statistically significant.

Both Phill Swagel and Ken Scheve had remarks in the morning panel. Professor Scheve’s remarks were quite relevant to the issue of what to expect in terms of policy changes. He observes that while the Great Depression induced a big change in views about intervention in the economy, it might be the case that that experience is the exception, rather than the rule. Even before the end of the Great Depression, views toward helping the unemployed had hardened considerably, despite high unemployment. Using more recent data, he observed that there is little correlation between unemployment and the view that “Government should see to it that people have jobs and a good income and unemployment.” (from the National Election Studies).

Figure from Scheve presentation.

Statistical analysis does confirm that the unemployed do have different views of policies aimed at helping the unemployed. However, even if the differential is statistically and numerically significant, even now when the unemployed represent a large share of the labor force, the overall impact on preferences is not sufficiently large to have a large impact on the policy process. This is in addition to the following two points:

• Bartels (2008) and others have argued that political representatives are more responsive to high-income constituent opinion than low-income constituent opinion.
  
• Dominant role played by interest groups in policy process.

Some commentary. As I have thought about this presentation over the past week and a half, it seems to me that is where economic analysts have an important role in the policy process. If the unemployed and otherwise disenfranchised cannot speak up (or act) for themselves, then it is incumbent upon economists to ensure that critically important resources not be wasted (that is the clinical perspective; there is of course the moral imperative, but that differs from person to person, so I will not presume), as in Christina Romer’s recent commentary.

And More on Current Politics

Professor Phill Swagel’s morning presentation [not available online] made several points. The first was that the fault for the Great Recession does not lie entirely with the Bush-Cheney Administration. The depth and extent of the recession is attributable to the collapse in confidence in policymakers, which was exacerbated by the failures of the Obama Administration to forge a bipartisan stimulus package. This collapse in confidence in turn induced a process of massive deleveraging.

Agreeing with Drs. Valletta and Loungani, Professor Swagel stated that the bulk of unemployment is primarily cyclical in nature; however, the longer the unemployment persists, the more of it will be structural in nature.

More on Professor Swagel’s views on policy here.

Not an Ivory Tower Conference

There are some Econbrowser readers who love to take me to task for my devotion to models and analytical frameworks, allegedly without reference to the real world. The conference’s morning panel brought in an audience of policymakers and others — including those who had first-hand experience with the phenomenon of long term unemployment. From the Capital Times:

Nobody needs to remind Jeanne Hime what hard times look like.

After 30 years as a union electrician, Hime watched her hometown manufacturing plant close down, disrupting the lives of hundreds of working families in Darke County, Ohio.

“These weren’t people who could just pick up and find a job somewhere else,” says Hime. “They had lifetime roots in the community and didn’t want to leave.”

Now retired and living in Mount Horeb, Hime isn’t confident the good factory jobs will ever return. And she takes exception to those who dismiss the current unemployment situation as simply a cyclical turn of the economy.

“People like me have been burned too many times,” she says. “Why should they believe anything is going to change?”

While some would say the models and the real-world experiences have little in common, the responses of the panelists demonstrated that the development and interpretation of the models cannot exist in a vacuum.

La Follette School of Public Affairs Professor Tim Smeeding, who heads the Institute for Poverty on the UW-Madison, expressed more concerns about the long-term disenfranchised. He says no one has an answer for the 30-year olds with no job skills or the 12 million Americans on probation or parole.


“These are the folks at very the bottom of the hiring pool,” he says.

Dealing with those issues will require analytical thinking, and empirical work, to determine what works — and does not work — in helping people — just as the paper of Swagel and Troske highlighted. The faster we dispense with such false dichotomies, the faster we can get to work.

See the original article >>

Learning about Long Term Unemployment (I)

by Menzie Chinn

On Thursday, we brought together an impressive array of scholars to discuss the causes and consequences of, and policy responses to, long term unemployment, including Prakash Loungani, Advisor in the IMF’s Research Department, Kenneth Scheve, Professor of Political Science at Yale, Phillip Swagel, Professor of Public Policy at the University of Maryland, and a former Assistant Secretary of Treasury for Economic Policy, Rob Valletta, Research Advisor at the Federal Reserve Bank of San Francisco, Dan Aaronson, Director of Microeconomic Research at the Chicago Fed, and Kenneth Troske, Professor of Economics from the University of Kentucky. And that was in addition to the researchers from the University of Wisconsin-Madison (more on them below). For me, this was a tremendous learning experience. But like all good conferences, by the end I understood that I knew less than I thought I knew about long term unemployment. In today’s post, I will discuss the presentations and papers by Prakash Loungani and Rob Valletta; in the next post, I’ll cover the findings of Ken Scheve and Phillip Swagel and Ken Troske.

Prakash Loungani’s paper (with Jinzhu Chen, Prakash Kannan, and Bharat Trehan) provided one approach to trying to determine the sources of long term unemployment. They proxy shifts in the structure of the economy with a measure of the dispersion of stock returns (Figure 4 from the paper). They then use a vector autoregression to identify impulse response functions for unemployment at various horizons. Long term unemployment responds positively to this index, as shown in Figure 6.

Figure 4 from Chen, Kannan, Loungani, Trehan (2011).

Figure 6 from Chen, Kannan, Loungani, Trehan (2011).

I’m always fascinated by empirical relationships that appear to be robust, and this one, at least so far, does not seem particularly fragile. Loungani observes that the results are robust to the inclusion of an alternative measure of dispersion (Bloom’s measure (Econometrica, 2009)). The results imply the following for the cyclical/structural components at various durations of unemployment (a slide from the morning presentation).

Figure from Loungani presentation.

I have two observations here: The first is that the cyclical structural component is larger for those with longer unemployment duration, which is consistent with intuition. The second is that even at the longest duration category, no more than 40 percent is structural.

Dan Aaronson, Director of Microeconomic Research at the Chicago Fed discussed the paper. He noted that it was remarkable that nearly half the rise in long term unemployment was explained by one variable. One of his key concerns was that the outliers in both series associated with the Great Recession naturally made the dispersion variable successful. I also wondered whether the strength of the identified relationship would persist in a sample truncated before the Great Recession. Dr. Loungani observed that the relationship still prevailed in a short subsample, although he had not conducted a formal out-of-sample forecasting exercise.

Dr. Aaronson also observed that in a Mortensen-Pissarides matching framework [lecture notes on MP model], with reasonable calibration, no more than two percentage points of the increase of the unemployment can be structural. He illustrates this point in this figure:

Slide 6, Aaronson discussion

He stressed that something closer to one percentage point was more likely an estimate.

Rob Valletta addressed the question of whether rising unemployment duration in the United States was due to a composition effect, or a change in behavior, over the long term. Carefully addressing the changes in the construction of the survey, he concluded

“…After accounting for changes in the [Current Population Survey] survey and using a more complete and appropriate set of conditioning variables than has been used in past work, the results suggest limited changes in unemployment duration over the past three decades. However, conditional durations have been longer during the recent severe recession and its aftermath than in the similar episode during the early 1980s, primarily due to higher labor force attachment for women and lower unemployment exit rates among the very long-term unemployed.”

This finding is illustrated in the paper’s Figure 1, which plots the unemployment rate against the unadjusted and adjusted duration. The key point is there is no pronounced trend in the adjusted series, until the Great Recession. This suggests the compositional effect dominates.

Figure 1 from Valletta (2011).

Dr. Valletta concludes:

The higher durations in the recent recession appear largely due to: (i) increased labor force attachment among women, as reflected in the patterns for female labor force entrants
(consistent with the arguments of Abraham and Shimer 2002); and (ii) lower unemployment exit rates among the very long-term unemployed (2 years or more). Both of these groups are generally ineligible for extended UI benefits. These findings suggest that there has been little or no change in the behavior of unemployed individuals over the past three decades, including a limited impact of the historically unprecedented extensions of unemployment insurance benefits over the past 3 years.

The discussant, UW Professor Rasmus Lentz, observed that typically, one would want to estimate the hazard rate of exiting unemployment, which is straightforward with a sample of the flow, whereas the CPS is a sample of the stock; the hazard rate can still be estimated if the proper adjustments are undertaken. The analysis of generated of synthetic cohorts that Valletta implements is another approach to estimating the hazard rate.

He also observed that one could obtain a more structural interpretation of the results by including the vacancy rate as a regressor — that is the structure of the market implies a covariance between job finding and tightness. However, if all one wants to do is to is to estimate how job finding has changed over time, it’s not clear that one needs to include a measure of unemployment or market tightness. [minor edits -- mdc 11:40am]

In the discussion, I noted the fact that the mean duration figure did not completely convey the full story. In terms of the topic of the conference, I asked him to interpret further the lower exit rate out of the group unemployed over 99 weeks. Dr. Valletta agreed that this result was consistent with rising structural unemployment for those who have been unemployed for an extended period, but that this was quantitatively a small figure.

Summing Up

How to interpret these findings? I think it’s useful to recall the points that Dr. Valletta made in the morning session. In particular, there are at least three definitions of structural unemployment:

• often equated with persistent (long-term) unemployment; but this can be cyclical (disappears as economy recovers)
  
• mismatch between skills/location of workers and jobs (common
  definition)
  
• sources of unemployment (other than “frictional”) that contribute to a higher equilibrium “natural rate of unemployment” or NAIRU (“nonaccelerating inflation rate of unemployment”).

So, my reading of the views at the meeting were that there was a consensus that most of the unemployment increase since the onset of the Great Recession was cyclical in nature. Even when one takes the higher estimates of structural unemployment, structural unemployment still does not account for more than two percentage points of the increased amount of unemployment. Citing joint research with Mary Daly and Bart Hobijn, Dr. Valletta puts the estimate at 1.25 ppts, while Dr. Aaronson thought 1 ppts was reasonable.

The entire agenda for the conference, organized by myself and Mark Copelovitch, and sponsored by the La Follette School and the UW Center for World Affairs and the Global Economy, is here, while a recent Institute for Research on Poverty conference agenda is here. Recent posts on the subject are here, here, and here. The Economist has a long article on male unemployment in the latest issue. And for a survey of the costs of high unemployment, see this November ILO-IMF report.

See the original article >>

USING THE ISM CYCLE TO INVEST

by Cullen Roche

We’re at an interesting point in the business cycle. There are signs that growth is waning, however, it doesn’t yet appear like a time to panic. The ISM index has been particularly notable. Last week’s ISM Services index showed a steep decline despite continued growth. As a diffusion index the ISM indices undergo cyclical patterns. The recent high readings are reassuring, but unlikely to sustain themselves.

As growth peaks the investment cycle changes dramatically. Goldman Sachs has a very good note on this change. They see the ISM index as forecasting a slowing business cycle, but not a collapse in growth. This warrants a more defensive investment posture:

“A high level of growth favors cyclical parts of the equity market. A slowing rate of change signals a shift to become more defensive at the sector level. A transition points to significant turnover in 8 of 10 S&P sectors and 17 of 24 S&P industry groups.

ISM has remained above 50 for 20 straight months and above 60 for the past four months. However, the index may have peaked at 61.4 in February and is essentially unchanged over the past year so the business cycle signals are mixed. S&P 500 and most sectors pause around the ISM peak allowing time for investors to confirm a transition is underway and re-allocate portfolios into later cycle parts of the market. Around previous peaks S&P 500 has consolidated gains for about three months before resuming median positive returns of 13% as ISM falls from peak to 50. Every cycle is unique and return distributions are wide.”

See the original article >>

THE FINANCING PYRAMID

by Cullen Roche

Last week’s volatile action in the markets began to show some of the confusion that market participants are experiencing. Much of this confusion has been due to QE2 and the uncertainty regarding the end of the program. As we near the end of the program it’s difficult to ascertain exactly what QE2 has done. One thing is clear, however – QE2 has contributed to significant speculation in markets. This has been most apparent in the continuing surge in margin debt. I discussed this a few weeks:

As we noted earlier this year, margin debt has tended to correlate fairly closely with the direction of the equity market. And according to the latest data from the NYSE, margin debt continues to move higher. In an effort to ride the coattails of the Fed and QE2′s “can’t lose” environment, investors have dipped into their borrowings to buy equities. David Rosenberg highlights the speculative fervor that this now represents. Current levels of margin debt are now consistent with the Nasdaq bubble and just shy of the levels seen before the credit crisis (via Gluskin Sheff):

“If there is one sure way to tell that the Fed has managed to create and nurture a speculative-led rally in the equity market, look no further than what is happening to investor-based leverage growth – it’s exploding off the page. Yes, that’s right. Debit balances at margin accounts skyrocketed $20.7 billion in February. Only two other times historically have we seen leverage rise so much so fast and both times it was during a manic phase – during the tech bubble of the late 1990s and the credit bubble just a short four years ago. 

To put that $20.7 billion incremental leverage in on month into proper perspective, it represents a 7.2% jump, or an increase of no less than 129% at an annual rate. And, it’s not just February – the rising use of credit to buy stocks has zoomed ahead at a 64% annual rate in the past three months. If and when the markets breaks, the problem in trying to contain the downside momentum is that there are no short left to cover, which actually helps as a shock absorber. The Fed has successfully cleaned out the short community, and the extent to which we see margins being called away may very well accentuate and downside pressure…if it should come.”

Late last week the CIO of a boutique investment firm emailed me with some superb thoughts on the increasing debt in financial markets. He referred to the current environment as the “financial pyramid”:

“If this financing pyramid is near correct, then prices are simply a reflection of leverage rather than an inflation of money. If there is only speculative non-bank horizontal money, then whatever commodity (non-monetary) inflation exists must be transitory, because it relies on a permanent Ponzi condition (leveraged commodities holdings that depend on prices being higher to satisfy liabilities). So it seems that the central banks distortions of the last several years are creating some imbalances that are unintended and unwanted, which is to increase speculative volatility in things like oil, which goes from $40 to $150 to $50 to $130 over and over. Paper profits change accounts but the real economy is not theoretically affected, except that it is held hostage to this casino game of rapidly changing prices for basic materials and necessities that businesses and consumers use to make decisions. So the economy is in actuality disrupted by the casino, the casino creates no net wealth, and everyone is worse off as this charade continues.”

That’s one of the best summaries of the effects of QE2 I have yet seen. Most interestingly, Ben Bernanke is likely to be right about one thing – the increases in commodity prices are likely to be transitory and the continual fear mongering over high inflation is likely to be wrong. On the other hand, with growth having peaked when QE2 began, I think it’s important that policy makers begin to ask whether QE2 isn’t having a negative impact on the economy. If the air comes out of the financing pyramid I think that question will likely answer itself. Hopefully, the Fed won’t respond to any market or economic downside with more of the same misguided medicine.

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