The Relative Productivity of Private vs Public Sector Employees

"How does the compensation of federal civilian employees compare with that of employees in the private sector?" is a question that the Congressional Budget Office once asked and answered, finding that the combined cash income and benefits that Uncle Sam's employees are paid is a lot more generous than what their peers in the private sector earn, even after controlling for factors like education, years of experience and job descriptions.

But we wondered how does the productivity of federal civilian employees compare with that of employees in the private sector? After all, if a civilian employee of the U.S. federal government is more productive than their similarly educated and experienced peer doing the same or similar job in the private sector, that difference could well justify their more generous compensation. If everything else is equal, it makes sense that a person who is more economically productive in doing a job would be compensated more than a less productive person doing the same work.

Thanks to a natural experiment, we're about to find out just how productive federal bureaucrats really are with respect to their direct peers in the private sector!

This summer, as part of the cost-cutting measures related to the budget sequester required by the Budget Control Act of 2011, President Obama acted to discontinue the operations of the Department of Labor's International Labor Comparisons (ILC) program, which converts the economic statistics produced by other nations' governments to adhere to U.S. standards and definitions, which allows for direct apples-to-apples comparisons to be made between the nations' economic data. Here's the announcement of the program's elimination that appeared in the Federal Register on 25 June 2013:

The International Labor Comparisons (ILC) program adjusted foreign data to a common framework of concepts, definitions, and classifications to facilitate data comparisons between the United States and other countries. ILC data were used to assess United States economic performance relative to other countries, as well as to evaluate the competitive position of the United States in international markets.

On March 1, 2013, President Obama ordered into effect the across-the-board spending cuts (commonly referred to as sequestration) required by the Balanced Budget and Emergency Deficit Control Act, as amended. In order to achieve these budget cuts and protect core programs, The Bureau of Labor Statistics is eliminating the International Labor Comparisons program. Subject to BLS policies and procedures, the underlying data and the methodology used to produce the data will be available upon request.

Shutting down the ILC program had been one of the President's budget objectives for some time. The Washington Post described the ILC's operations and President Obama's desire to cut the program back on 3 March 2010:

President Obama's budget would eliminate the International Labor Comparisons office and transfer its 16 economists to expand the bureau's work tracking inflation and occupational trends. The White House says the cut, estimated to save $2 million, is one of many difficult decisions the president was forced to make to control spending.

On 27 June 2013, the non-profit Conference Board announced that it would take over reporting the international labor comparisons. (The Conference Board is the same outfit that conducts the Consumer Confidence Survey and that reports the Index of Leading Economic Indicators, both of which are frequently cited in the media.)

The Conference Board announced today that it will continue a statistical program on international labor statistics that is to be eliminated by the federal government due to across-the-board spending cuts. The Bureau of Labor Statistics, a unit of the United States Department of Labor, has announced that it will shut down the International Labor Comparison (ILC) program on July 1.

The program provides businesses, government agencies, academics, and the public with high-quality data on manufacturing productivity, unit labor costs, consumer price, wage rates, and employment and unemployment for up to 34 countries. It adjusts data to a common framework of concepts, definitions, and classifications to facilitate data comparisons across countries. ILC data are used to assess United States economic performance relative to other countries, as well as to evaluate the competitive position of the United States in international markets. The Conference Board will continue the program on its current basis and make the data available to the public at no cost. The Conference Board will implement the transition of the program over the summer.

"Every large company needs access to this data, and it can only be gathered effectively by leveraging non-commercial relationships between various government and statistical agencies around the world," said Jon Spector, President and CEO of The Conference Board. "If a government agency cannot continue to maintain this information, it requires an independent institution to take over the task."

Clearly, the Conference Board believes that there is value in sustaining the output of the International Labor Comparisons program. But the question we wanted to answer is "how many people will they seek to hire to do the work?"

Since the private sector Conference Board wasn't doing the work previously, to take on the new work, it would very likely have to both retask its current employees to add to their current job responsibilities while also creating new jobs specifically to do the additional work.

The number of dedicated new hires would be especially revealing because that would provide a direct indication of the relative productivity of people doing the exact same jobs in both the public and private sector. If the number of new hires in the private sector required to do the work turns out to be greater than the number of dedicated federal employees who were previously doing it, that would be a clear indication that the federal bureaucrats are more productive than their private sector peers, and thus are deserving of a higher level of compensation.

As best as we can tell from its job postings since its announcement, the private sector Conference Board will hire at least two and possibly three people to do the work that would appear to have required 16 dedicated bureaucrats when the same work was done by the U.S. federal government. Here are the job descriptions for the positions that the Conference Board is seeking to fill related to this work:

• Senior Economist International Labor Comparison


• Research Analyst, Economics International Labor Comparisons Program


• Research Assistant, Economics

The last Research Assistant position doesn't reference the International Labor Comparisons program, which means that it isn't the primary purpose of the position, but it's clear from the job description that the person hired to fill the opening could very well be tasked with work related to the ILC program. We should also recognize that it is possible that the job posting for the Research Analyst position that does specifically reference the ILC work may represent more than one opening with the same job description, but there is no indication that is the case in the description for the position, so we tend to think that is not the case.

Those things noted, these job postings suggest that the private sector Conference Board believes it will take the addition of no more than 3 people to do the same work that 16 bureaucrats were dedicated to doing as employees of the U.S. federal government. That would mean that the federal government employees who were previously doing the work would appear to be less than one-fifth as productive as their private sector peers in working to produce the same output.

We therefore find that the higher level of compensation for civilian federal government employees is not justifiable on the basis of their relative productivity with respect to similarly skilled and experienced workers in the private sector.

In fact, the lower apparent productivity of federal bureaucrats would also be a big reason why cuts to government spending reduces the nation's GDP by considerably less than the actual amount of the spending reduction.

Speaking of those 16 displaced federal government employees, since it appears that they have been reassigned to other areas within the Bureau of Labor Statistics, whose remaining workload is unchanged following the elimination of the International Labor Comparisons program, that means that the collective productivity of the federal government's employees at the BLS has decreased.

Of course, that is exactly what we should expect to happen when more people become involved in generating an unchanged level of output. But at least, in doing less work per person than before, but for the same pay, that means that their total compensation per person has become just ever so much greater than their private sector peers.

It's all just another perk of working for Uncle Sam!

References

Conference Board. The Conference Board Takes Over International Labor Statistics Program from the U.S. Government. [Online Article]. 27 June 2013.

Labor Statistics Bureau. International Labor Comparisons. [Online Article]. Federal Register. 25 June 2013.

MacGillis, Alec. Obama Administration Plans to Close International Labor Comparisons Office. [Online Article]. Washington Post. 3 March 2010.

Keeping the Big Dominoes from Falling

Can the failure of a single, small entity possibly cause the collapse of a much larger entity?

The answer, of course, is yes, provided that the failure of the small entity triggers the release of a greater amount of destructive energy than it took to make it fail. If that release of pent-up destructive energy then triggers the subsequent failure of a larger entity, then the potential for a sustained chain reaction that grows in scale exists. This is called a cascading failure.

It's kind of like dominoes, where a small domino can be set to knock down a larger one, which can then spark off a chain reaction where larger and larger dominoes can be made to fall (HT: PhysOrg).

Here, once the dominoes are lined up and have begun to fall, the only thing that can avoid the collapse of the entire system is an active intervention that creates a gap to interrupt the systemic failure.

That's kind of like how a crime wave can be suppressed in a community through a dramatic increase in police activity after it has started, provided the community wants the crime to stop. Note the parallels to a cascading failure in the basic description below for how a crime wave can take hold in the quoted passage below:

Crime can happen anywhere, but it usually doesn't.

Researchers have noticed that criminal activity seems to be concentrated in self-perpetuating hotspots. Crime leads to more crime. Then, from these epicenters, crime spreads outward through the community.

Mathematicians have a model they've used to study this kind of behavior. It's called a reaction-diffusion-advection system, and criminologists have found it a useful way to analyze issues like "near-repeat victimization" – the observation that single neighborhoods, and even single households, see a disproportionate share of crime.

What criminologists Nancy Rodríguez, Lenya Ryzhik and Henri Berestycki have recently found however is that whether a crime wave can propagate itself depends greatly upon the attitude that the people within the community have with respect to crime itself.

Here, if a community is "pro-crime" or is neutral toward crime, or rather, when a community is not positively supportive of law enforcement, a crime wave cannot be suppressed once it has begun. But if the community is opposed to crime, or is positively supportive of law enforcement, then the crime wave can be arrested, provided a large enough intervention can be deployed to create a gap in the criminal activity. It all comes down to the prevailing choice of the community.

This choice is significant, because a crime wave spreading through a pro-crime population can't be entirely stopped. You end up, Rodríguez said, "always having crime everywhere."

The news is nearly as bad when the population has a neutral attitude toward crime. Although crime primarily persists in hotspots, waves of crime are just as unstoppable.

But if the population has an overall anti-crime stance – meaning that the population is more reluctant to engage in criminal activity – two outcomes are possible. High crime rates can spread, but so can waves of zero criminal activity. And, unlike in the other scenarios, high crime rates can be stopped by adding in a "gap."

In the world of the model, the gap is a stretch of space where the incentive to commit a crime is zero. This corresponds to real-life disincentives to commit crimes, such as an increased police presence, with longer gaps representing more anti-crime efforts.

Rodríguez found that a long enough gap – a large enough police crackdown, for instance – will completely contain a crime outbreak.

The same logic applies toward financial systems, where the only thing that prevents an economic collapse from occurring after the failure of a small entity is the trust that people have in government and financial institutions for the interventions they might undertake to create sufficient gaps in the financial system to prevent the small failure from propagating into a wide-scale one.

If people increasingly distrust those institutions, then the risk of a cascading failure grows, because the interventions that might otherwise be effective in arresting a series of failures after they have begun would be rendered impotent.

That, more than anything else, is what is at stake in the abuse of power scandals and increase in crony capitalism in Washington D.C. Or for that matter, in the ongoing economic crises within the European Union.

In both cases, we'll only know if the balance has been tipped from stability toward instability as once-small failures can no longer be contained from spreading to cause larger and larger failures.

Elsewhere on the Web

The WSJ reports that the Bank of International Settlements may have finally discovered how to create the needed gap to keep the failure of a "too-big-too-fail" bank (ala Lehman Brothers) from collapsing the entire financial system.

Update 7 June 2013: A sharp-eyed reader points us directly to the BIS study.

Going Fractal with the S&P 500: An After Action Report

When should you sell your stocks?

We asked that specific question several weeks ago, before we exploited the fractal nature of stock prices in developing an objective statistics-based method for determining when to tell if an upward microtrend for stocks was about to break.

We then tracked stock prices closely using that method, as it didn't take long before the recent upward microtrend we were tracking would appear to have broken, as stock prices suddenly dropped below the statistically-expected, or "normal", target range:

But then, one day earlier, we said that maybe that isn't such a good signal to follow in deciding to pull the trigger for selling stocks, explaining the technical reasons why, and then suggesting a more "conservative approach" for deciding when to actually sell:

We should also recognize that since we're using both a power law-based regression analysis and a statistics-based approach to define these curves, there is a possibility that data points falling outside the red-dashed curves could be outliers for the established trend, rather than an indication that a state of order in the market has broken down. A more conservative approach for making a decision to sell in these circumstances would be to wait for the 20-day moving average of stock prices to fall outside the "normal" range for stock prices with respect to their trailing year dividends per share, which would be a confirmation that a previous state of relative order in the market has ended.

And then, four days after we said that, we explained why the rally for the S&P 500 wasn't really over.

Tired of all this jumping about back and forth in time? Good! Let's catch up to the present and update that earlier chart, keeping everything the same except for the daily stock prices and their 20-day moving average, which is now updated through 13 May 2013. Pay close attention especially to the moving average....

As you can see, the 20-day moving average never moved outside the statistically-expected "normal" target range. And thus, this more conservative approach for deciding when to sell stocks, or in this case, for deciding to not sell stocks, was the right approach to follow. At least through yesterday.

We'd be happy to tell you what's going to happen next, but we don't like to repeat ourselves and we're not sure if we haven't already. Sometimes, there's just no telling if we're even telling you these things in anything close to the right order!

Stranded in the Food Desert

Science is a wonderful tool for understanding how our world really works. But if the science supporting a given understanding is flawed, or worse, if it is slanted in favor of a politically-favored outcome, it can become the justification for excessively wasteful activities. When science crosses that line, it is transformed from something that is worthy of respect into junk science.

This is the story of Michelle Obama and her fight against the food deserts of America. The story begins on 24 February 2010, when the First Lady of the United States of America used her White House platform to introduce the little-understood concept of the newly-discovered "food deserts" of America to Americans as part of a media blitz:

As part of Lets Move!, the campaign to end childhood obesity, First Lady Michelle Obama is taking on food deserts. These are nutritional wastelands that exist across America in both urban and rural communities where parents and children simply do not have access to a supermarket. Some 23.5 million Americans – including 6.5 million children – currently live in food deserts. Watch the video below and learn what the First Lady is doing to help families in these areas across the country.

Food deserts sound horrible. Isn't it good that the First Lady is doing something about this awful problem that would appear to be plaguing America's most poor, yet obese citizens, who suffer because they are deprived from having large supermarkets stocked with nutritious foods within walking distance of where they live?

Or is the First Lady relying upon junk science to justify the wasteful expenditure of taxpayer money to benefit her and the President's political cronies? After all, there was already plenty of evidence back in 2010 that indicated that food deserts were more a junk science-fueled political talking point than a real factor that significantly contributed to making poor Americans obese, as the original 2006 study proclaiming the crisis in President Obama's home base of Chicago was funded by LaSalle Bank of Chicago, then the largest business lender in the city, who would directly profit from investments to "remedy" the situation.

Fortunately, respectable science can help provide the answers to these questions. The U.S. Centers for Disease Control very recently published a peer-reviewed scientific study of the impact that a lack of nearby access to nutritious foods, such as might be found in one of the First Lady's food deserts, actually has upon the Body Mass Index (BMI) of the Americans who live within such regions. Here are the results and conclusion for their study of 97,678 adults in the state of California (home to 1 out of every 8 Americans):

Results

Food outlets within walking distance (≤1.0 mile) were not strongly associated with dietary intake, BMI, or probabilities of a BMI of 25.0 or more or a BMI of 30.0 or more. We found significant associations between fast-food outlets and dietary intake and between supermarkets and BMI and probabilities of a BMI of 25.0 or more and a BMI of 30.0 or more for food environments beyond walking distance (>1.0 mile).

Conclusion

We found no strong evidence that food outlets near homes are associated with dietary intake or BMI. We replicated some associations reported previously but only for areas that are larger than what typically is considered a neighborhood. A likely reason for the null finding is that shopping patterns are weakly related, if at all, to neighborhoods in the United States because of access to motorized transportation.

Economist Jacob Geller reviewed the study's statistical results:

If you look at the statistical tables, they’re pretty striking. Even where there is statistical significance — which is the exception to the rule — the size of the effect is so tiny, it’s like practically nothing. For example, on the margin, adding one full-service supermarket within a one-mile radius of your house is associated with an average BMI decrease in your neighborhood of .115. That is a difference of just one pound. (see back-of-the-envelope calculations here)

So there is really no relationship, according to this one recent study of nearly 100,000 Californians, between the distance between your body and a full-service supermarket (or any other kind of food store), and whether or not you are obese. Distance, which is a proxy for access (the idea of a food desert is that the nearest supermarket, which has fresh produce, is distant), is for all practical purposes a non-factor.

We created the following tool so you can see what Michelle Obama's publicity campaign to direct large and/or politically well-connected retailers to spend millions of dollars to open or expand stores in the "disadvantaged" regions identified by the U.S. government as supposed food deserts would have in terms of your own weight. And the cool part is that the math is such that we can figure out just how much that would be for you from just your height!

Your Height
Input Data	Values
Your Height [inches]

Your "Food Desert" Weight
Calculated Results	Values
Amount in Weight [pounds]

If you're accessing this tool on a site that republishes our RSS news feed, please click here to access the original, functioning version of this tool!

Our tool indicates how much of your weight would be affected by whether you lived within a food desert. If you live in an area identified by the U.S. government as a food desert, it indicates how much more you would weigh, and if you were to move out of that area, it is how much you would lose. To put your result into proper context, the weight of an adult American normally fluctuates by up to 5 pounds during the course of a single day.

Did we mention large and/or politically well-connected retailers are involved? That's actually how we know that the whole food desert publicity campaign is really about crony capitalism more than it is about dealing with the health problems of obesity. Because in truth, if it were a real problem that could be fixed by opening new store locations, it would be a lot easier, cheaper and faster for small "Mom and Pop"-style grocery businesses to fit themselves into the already existing and available retail spaces within such deprived communities as the supposed food deserts of America.

But since the whole food desert concept would seem to be based on junk science rather than the more respectable kind, it is perhaps too much to ask for the solutions advanced by the politicians taking charge of the crisis to solve a legitimate problem.

References

Carras, Michelle Colder. Normal Body Weight Fluctuation. LiveStrong.com. http://www.livestrong.com/article/29567-normal-body-weight-fluctuation/. 7 May 2011.

Croft, Cammie. Food desert? What’s a food desert? White House Blog. http://www.whitehouse.gov/blog/2010/02/24/taking-food-deserts. 24 February 2010.

Gallagher, Mari. Examining the Impact of Food Deserts on Public Health in Chicago. http://www.marigallagher.com/site_media/dynamic/project_files/Chicago_Food_Desert_Report.pdf. Mari Gallagher Research & Consulting Group, sponsored by LaSalle Bank. 2006.

Geller, Jacob A. The Problem of "Food Deserts" Is Not All About Access. http://jacobageller.com/2013/04/the-problem-of-food-deserts-is-not-all-about-access/. 3 April 2013.

Hattori A, An R, Sturm R. Neighborhood Food Outlets, Diet, and Obesity Among California Adults, 2007 and 2009. Prev Chronic Dis 2013;10:120123. DOI: http://dx.doi.org/10.5888/pcd10.120123. 14 March 2013.

McWhorter, John. The Root: The Myth of the Food Desert. National Public Radio. http://www.npr.org/2010/12/15/132076786/the-root-the-myth-of-the-food-desert. 15 December 2010.

Mooney, Alexander. First Lady Takes on 'Food Deserts'. CNN: The 1600 Report. http://whitehouse.blogs.cnn.com/2011/07/20/first-lady-takes-on-%E2%80%98food-deserts%E2%80%99/. 20 July 2011.

Political Calculations. How To Detect Junk Science. http://politicalcalculations.blogspot.com/2009/08/how-to-detect-junk-science.html. 19 August 2009.

White House. Transcript of President Obama's Remarks at 2013 White House Science Fair. White House Photos and Video. http://www.whitehouse.gov/photos-and-video/video/2013/04/22/president-obama-tours-2013-white-house-science-fair#transcript. 22 April 2013.

Wright, Ann. Interactive Web Tool Maps Food Deserts, Provides Key Data. http://www.letsmove.gov/blog/2011/05/03/interactive-web-tool-maps-food-deserts-provides-key-data. LetsMove.gov. 3 May 2011.

New Jobless Claims: Outliers, Volatility, Incompetence and Incentives

The United States would appear to have entered a new trend for new jobless claims, one characterized by outliers and volatility. Our chart below showing the residual distribution of the number of new jobless claims filed each week shows just how far off from normal things have become since 22 September 2012, which we're marking as the end of the previous trend:

In fact, the data is so volatile right now that we're not able to accurately determine what the real trend in new jobless claims might be. For that, we'll need anywhere from six to ten non-outlier data points, where at present, we only have five (or four, if we discount the first data point marking the shift from the previous trend).

The newest and most extreme outlier is an outcome of the economic disruption caused by Hurricane Sandy. Here, we note that it may be some time before the data settles down enough to reveal its current trend. At present, we're showing a flat trend for the data points that aren't attributable to special causes, but this is a temporary choice on our part to provide a reference from which we can determine the magnitude of the outliers in the data.

What's particularly upsetting is that the two of the outlying points are man-made, the result of the state of California's Employment Development Department's failure to process tens of thousands of new claims filed in the first week of October 2012. That failure then produced an outlier in the opposite direction in the following week, as the state played catch up.

No disciplinary action related to the mishandling of these unemployment insurance claims in California's Employment Development Department has yet been taken.

Those problems are deep and ongoing. The California State Auditor, Elaine M. Howle, issued a report on 13 November 2012 finding that the California Employment Development Department's management of the state's unemployment program still does not meet acceptable performance levels related to core benefits measures, such as the timely processing of unemployment insurance claims.

In addition, California's state auditor found that the agency has failed to implement a number of reforms it specified following a 2010 audit of the agency, but interestingly, noted that the agency made a unique effort to achieve one of them - the one that allowed it to receive $839 million in economic stimulus funds in return for establishing an "alternate base period" for determining benefit eligibility for jobless claim filers, which it put on the fast track and received in July 2011.

So apparently, if you want to get California's Employment Development Department to achieve a goal, such as the timely processing and reporting of initial unemployment insurance claims, one needs to dangle a check with a lot of zeroes on it in front of the state agency's management to provide a sufficient incentive for them to do so.

The Real Trend for New Jobless Claims

What's wrong with using moving averages to assess trends in economic data? After all, it's a practice that is endorsed by the Federal Reserve.

That question arises today because of the WSJ's Josh Mitchell's analysis from last Friday, which suggests that initial jobless claims are trending down:

The number of Americans applying for unemployment insurance has fallen sharply since summer. The four-week moving average of first-time jobless claims inched up by 750 last week to 365,500. But the figure was below mid-June’s level of more than 387,000 claims. That is a sign that employers are laying off fewer workers and the job market could be healing.

And so it would seem, except there was a huge problem in the data for the week ending 6 October 2012, which might best be described as a California-sized hole in the data:

Last week, California reported a large drop in applications, pushing down the overall figure to the lowest since February 2008.

This week, it reported a significant increase as it processed applications delayed from the previous week. (Read More: Why Jobless Claims May Not Be as Good as Market Thinks.)

A department spokesman says the seasonally adjusted numbers "are being distorted ... by an issue of timing."

Since the Labor Department's four-week moving average for new jobless claims includes this data reporting anomaly, where one week's data was recorded more than 10% below where it might otherwise have been reported (if the California state government's Employment Development Department had properly processed and reported its claims from that week), this measure fails to provide an accurate depiction of the current trends for new jobless claims in the U.S.

In this case, the effect of the data reporting anomaly makes the situation for new jobless claims look better than it really is. Worse, at this writing, that data anomaly will be used to calculate the moving average in new jobless claims for another two weeks yet, which means that the distorted impression it creates will be with us through the Tuesday, 6 November 2012 national election, until it is no longer included in the Labor Department's calculation of the four-week moving average for the new jobless claims report it will issue on Thursday, 8 November 2012!

It is the moving average's inability to recognize data outliers that represents one of its greatest weaknesses. Based on only four data points, the four week moving average just isn't capable of accounting with outliers in a practical manner!

That's where the statistical control chart-style approach we've developed over the last several years can do a much better job. Here's what the recent data looks like from our perspective:

The major trends in the chart above, identified by the letters "I" through "K", are described in greater detail here (along with Trends "A" through "H"!)

The current trend, "K", began after the national average for gasoline prices fell below $3.50 per gallon in early June 2012. As gasoline prices have generally risen since that time, we've observed a generally rising trend in the number of new jobless claims filed each week.

Treating the 6 October 2012 data point as an outlier, we find that through the week ending 13 October 2012, the number of new jobless claims being filed each week, a proxy for the rate of job layoffs in the U.S., is currently rising at an average rate of over 1800 per week - a rate that is faster than at any time since the U.S. last fell into deep recession in mid-2008. This rate of increase also excludes another data point outlier for the week ending 14 July 2012, where the BLS' seasonal adjustments didn't account for a delay in the typical mid-year plant shutdowns in the automotive industry.

That's likely a bit inflated, as the state of California is playing catch up in reporting the large number of claims it failed to process in the week ending 6 October 2012. If we exclude the initially-reported data for last week, we find that the number of new jobless claims in the U.S. is rising at a rate of over 1500 per week. That would still be the fastest rate of increase recorded for new jobless claims in the U.S. since the nation fell into deep recession in mid-2008.

As for whether it ever makes sense for using a moving average to evaluate this kind of data, we'll say that it does, but only for certain unique occasions. Since our statistics based method needs at least 6 to 10 data points to establish whether a trend exists, a moving average might provide a useful picture in the limited period of time following when a break in an established trend occurs until our method can define the new trend.

But once that trend is defined, our method provides a more accurate picture of what's actually going on in the economy. Right now, the real trend for new jobless claims indicates a rising number of layoffs each week in the U.S., which is something the Labor Department's four-week moving average will confirm in the form of a sudden increase after the election.

The Answer to the Question

Last April, we were asked to contribute a question to Tim Kane's quarterly survey of economics bloggers for 2012-Q2. After reflecting on the potential causes for the very statistically unlikely number of upward revisions to the number of new jobless claims filed each week over the past year, we submitted the following question:

The Wall Street Journal has reported that the advance number of seasonally-adjusted initial unemployment insurance claims filed each week has been revised upward in 56 of the past 57 weeks. If this were a coin toss, the probability of that occurring would be 2,528,336,632,909,751 to 1, which makes the event extremely statistically unlikely. What is the primary reason behind why the BLS' collection and reporting of the initial data would appear to be so deeply flawed?

1. Seasonal adjustments that no longer reflect reality well enough to account for the difference between initial and later revised counts.


2. Inadequate counting practices by the 50+ state agencies that report this data weekly to the BLS.


3. Deliberate delays in reporting the full scale of the worst case increases in the number of claims recorded each week to create a rosier impression of the data.

Here are the survey results:

We selected the second explanation in the live voting for the survey, but we were surprised that it didn't win an outright majority. Today however, thanks to a large anomaly in reporting the number of new jobless claims last week, we have an answer to the question that would seem to confirm "Inadequate counting practices by the 50+ state agencies that report this data weekly to the BLS" as the correct answer to the question. James Pethokoukis reports on Strategas' Don Rissmiller's analysis of the reporting anomaly:

One of the driving reasons for the less-than-expected growth was one state, which was expected to report a large increase but instead reported a decline. Unfortunately we will have to wait until next week's release to find out which state it was; the state breakdown is always one week delayed.

And also JPMorgan's Daniel Silver's view, in which he identifies which state had the problem:

We won't know for sure which state caused the large drop in claims during the week ending October 6 until the state-level data become available next week. It was likely a state with a large population and we suspect that it was California based on the occasional massive swings that have occurred in its claims data in the past.

Fox Business' Dunstan Prial and Peter Barnes report on what the BLS has to say about the derelect data counting and reporting practices of the state in question:

Because one state left out a chunk of its weekly reports from last week, the jobless claims numbers will likely be revised upward in the coming week.

The spokesman said the lack of reporting affected the season adjustment for the week, likely by about 30,000 fewer claims.

In addition, the spokesman explained to FOX Business that this large state has a history of reporting "volatile" numbers at the beginning of quarters and that the Labor Department has complained and tried to work with the state to more accurately report its claims but with little success.

"There is no explanation" for the volatility. "We have tried and tried to work with them. It's like playing hardball with them," the spokesman said.

The spokesman said that the unprocessed claims are likely to show up in the numbers in the next week or two. "We should see some sort of catch up."

That apparently was too much truth for one day, as another BLS spokesman tried to walk back the observation, we suspect after getting some rather loud and angry calls from one particular state's employment security department:

Later Thursday, another Labor Department spokesman issued a statement in effort to clarify what the agency described as “confusion” over the data. The latter statement seemed to refute the department's earlier explanation.

"The decline in claims this week was driven by smaller than expected increases in most states and because of drops in claims in a number of states where we were expecting an increase," the statement said. "No single state was responsible for the majority of the decline in initial unemployment insurance claims."

We'll let Pierpont Securities' Stephen Stanley get the final word, as reported by Marketwatch:

Added Stephen Stanley of Pierpont Securities: "The formula for the size of a claimant's benefit check is derived based on an average of their last few quarters of pay (the more you were earning before being laid off, the bigger your unemployment check would be). Thus, in many cases, it pays for a laid-off worker to game the formula by waiting until the beginning of the next calendar quarter to file (if they can wait that long), as they may have been getting paid more in the quarter when they were laid off than in the quarter that rolls out of the equation if they wait."

"As a result, there is an accumulation of claims that are likely submitted over a period of several weeks but not processed until the turn of the quarter. Apparently, the state in question (and it pretty much has to be California to account for anything close to 30,000) forgot to include that stockpile of unprocessed claims in their tally for this week (which is the first week of a new calendar quarter). Since the seasonal factors expected an unadjusted surge of almost 20% in the period to account for the quarterly filing pattern, failure to adhere to that pattern in the raw data (unadjusted claims were only up 8.6%) creates a big drop seasonally adjusted."

So we see that inadequate counting practices by the 50+ state agencies that report this data weekly to the BLS, and for the week ending 6 October 2012, one large state in particular, is responsible for the ongoing issues with the continual undercounting of the initial number of seasonally adjusted initial unemployment insurance claims filed each week.

Which Obama Policy Killed the Recovery?

Not long ago, we used stock market-style technical analysis to reveal that the policies implemented during President George W. Bush's tenure in office were most likely responsible for launching the economic recovery that followed the December 2007 recession and that the policies implemented during President Barack H. Obama's tenure in office were most likely responsible for derailing that recovery.

Since technical analysis is such a weak analytical technique, we thought we'd revisit that topic today using a combination of regression analysis and statistical analysis, focusing in on the one data metric that really defines a recession for most people: job layoffs.

Our chart below uses the Bureau of Labor Statistics' data for the seasonally-adjusted number of initial unemployment insurance benefit claims filed each week for the period spanning 6 July 2008 through 26 September 2010, which tracks the number of job layoffs that occurred during this period. We've annotated the chart to show the major events that coincided with major shifts in the trend in job layoffs or that affected the trend in data for short periods of time.

In the chart above, we've also indicated Mark Thoma's "effectiveness lag", which represents the typical amount of time from when federal government policies are implemented to when they have a noticeable impact, which he indicates is 6 to 18 months long. We note that economists have long recognized this real world phenomenon and that its existence is widely accepted (except perhaps by philosophy professors who would seem to care more about credentials than thought.)

Here, the chart shows that the rate of layoffs in the U.S. was increasing at an average rate of 7.599 per week from August 2008 up until they peaked in March 2009. Along the way, we note an unusual outlier where the number of layoffs plunged in late December-early January, which corresponds to the emergency bridge loan that the Bush administration extended to failing companies in the auto industry to avoid massive layoffs during the Christmas and New Year's holidays in the U.S.

Those massive layoffs were only postponed however to the period from mid-January 2009 through February 2009, as the number of weekly layoffs in the U.S. stopped rising and topped out at this time. Without the Bush administration bridge loan to the auto industry, we note that the peak in layoffs during the recession would have occurred earlier.

After topping out, the pace of layoffs began to fall at a rate of 4,109 per week, which held up through mid-November 2009. There is one near-outlier recorded during this period, which coincides with the Obama administration's "Cash for Clunkers" program, which temporarily saw the number of weekly layoffs fall while it ran. And then, because the program could no longer be sustained, the rate of layoffs in the U.S. resumed the path they had previously been on.

All that changed after 29 November 2009 however, which President Obama's signature achievement of his administration, the bill that the Patient Protection and Affordable Care Act (aka "ObamaCare") was introduced in the House of Representatives as H.R. 3962. The introduction of this bill, the result of the Democratic Party's supermajority in the U.S. Congress which meant it was likely to pass, and the uncertainty it unleashed for employers combined with the higher costs it mandated for them, derailed the economic recovery. We observe that derailment in the sudden deceleration of the pace of recovery as the number of layoffs in the U.S. each week dropped from falling at an average rate of 4,109 per week to just 766 per week all the way through 19 September 2010.

And that, in a nutshell, is what President Obama did to kill off the strong economic recovery President Obama inherited and replaced it with the weak jobs recovery that has dominated ever since. The major trends in U.S. layoffs through all this period is described here, which explains the letters shown on the chart above!

The Quality of Government-Produced Economic Data

China's economy entered into recession in December 2011.

That's very old news to readers of Political Calculations, a little-read blog that somehow managed to scoop a number of financial institutions and even the New York Times in reporting on the poor health of China's economy back in February 2012.

At least now we know why so many of these organizations were so far behind the curve in understanding that a large-scale slowdown in China's economy has been underway for nearly seven months now - they appear to actually rely upon the Chinese government for their economic data. The New York Times might perhaps be finally recognizing that error in judgment:

HONG KONG — As the Chinese economy continues to sputter, prominent corporate executives in China and Western economists say there is evidence that local and provincial officials are falsifying economic statistics to disguise the true depth of the troubles.

The article goes on to detail evidence of the slowdown that has shown up in recent months, mostly from an accumulation of stocks of coal, copper and other commodities, including the nation's rates of electricity production and consumption, which had previously been taken as a good measure of China's overall economic health.

But the thing that stands out to us is that they've known for decades that China's economic statistics were less than trustworthy, although for completely predictable reasons, they have become more unreliable during the past year:

Questions about the quality and accuracy of Chinese economic data are longstanding, but the concerns now being raised are unusual. This year is the first time since 1989 that a sharp economic slowdown has coincided with the once-a-decade changeover in the country’s top leadership.

Officials at all levels of government are under pressure to report good economic results to Beijing as they wait for promotions, demotions and transfers to cascade down from Beijing. So narrower and seemingly more obscure measures of economic activity are being falsified, according to the executives and economists.

"The government officials don’t want to see the negative," so they tell power managers to report usage declines as zero change, said a chief executive in the power sector.

As a result, a number of global financial institutions, who rely on China's economic data in assessing the potential for their investments in the country, were effectively caught with their pants down earlier this month:

Many Chinese economic indicators already show a slowdown this spring, with fixed-asset investment growing at its weakest pace in May since 2001. The annual growth rate for industrial production has edged below 10 percent, while electricity generation was up only 3.2 percent in May from a year earlier and up only 1.5 percent in April.

The question is whether the actual slowdown is even worse. Skewed government data would help explain why prices for commodities like oil, coal and copper fell heavily this spring even though official Chinese statistics show a more modest deceleration in economic activity.

Manipulation of official statistics would also provide a clue why some wholesalers of consumer goods and construction materials say sales are now as dismal as in early 2009.

Keeping accurate statistics for internal use by policy makers while releasing less grim figures to the public and financial markets may also help explain why China’s central bank suddenly and unexpectedly cut interest rates earlier this month.

Whoops! When the economic tide shifts, the worst thing that anyone in the financial world can be is late. Millions, and perhaps billions, of dollars are lost whenever that happens.

So how did a little-read blog manage to report that China's economy had fallen into recession over five months ago, well ahead of all these other venerable institutions?

Easy. That little read blog didn't use China's statistics to assess that nation's economic health. Instead, we used data collected and reported by the U.S. Census on the monthly value of the international trade between the two nations, which we think would be pretty difficult for Chinese officials to fabricate. As it happens, we've found that the year-over-year growth rate of that trade makes it possible to accurately diagnose the relative economic health of each nation, making this kind of analysis an excellent alternative to China's official government statistics for assessing the actual state of that nation's economy.

Speaking of which, here is what it looks like today:

The U.S. Census will update its foreign trade data through May 2012 on 11 July 2012.

In this chart, assessing China's relative economic health may be done by examining the data series shown with the blue data points, which correspond to the year-over-year growth rate of U.S. exports to China. Here, a national economy will demand more goods and services from outside its borders when it is is experiencing strong economic growth, which shows up as a positive growth rate - the more strongly the economy grows, the higher the positive value.

But when that growth rate turns negative or is near-zero, which we'll define as growing at just single digit rates, that communicates that the national economy in question is experiencing at least a significant economic slowdown.

What we observe in our chart above is that China's economy is trudging along at a very sluggish pace. And for all practical purposes, has been since October 2011.

Meanwhile, the data series shown with the red points, which correspond to the year-over-year growth rates of China's exports to the U.S., indicate that as of April 2012, the U.S. economy has been growing more strongly than the Chinese economy.

With the U.S. economy now passing through the equivalent of a microrecession however, we anticipate the May 2012 trade data will fall back toward the zero growth line on the chart.

But then, we didn't rely on the U.S. government's official economic data to work out that the U.S. economy would be struggling at this point of time. We used an alternative data source to first make that call over a year ago....

It's just a good practice to not rely too much on "official" data reports, which can frequently be really off track. That's also a big reason why our readers our rarely surprised by sudden and unexpected economic news.

Really? Unexpected? Again?!

Back in August 2010, we became concerned that the professional U.S. news media really were being caught with their pants down far too often when reporting economic news, which they were perpetually describing as "unexpected".

So, we decided to help them out. We shared how we've become able to project the future for the number of seasonally-adjusted new jobless claims each week with such a high degree of accuracy. After all, using those methods, we've been able to transform this particular economic statistic into the most easy to forecast of all economic data.

So, imagine our surprise when we decided to pay attention to their reporting of the most recent new unemployment insurance claims from Thursday, 14 June 2012 when, well, let's let Bloomberg tell the story....

Claims for jobless benefits unexpectedly climbed by 6,000 to 386,000 in the week ended June 9 from a revised 380,000 the prior week that was more than first estimated, Labor Department figures showed today in Washington. Economists projected claims would fall to 375,000, according to the median estimate in a Bloomberg News survey.

Really? Unexpected? Again?! Who exactly are these economists that keep getting surveyed who it seems really couldn't forecast their way out of a wet paper bag?

Let's try something - let's take our statistics-based analytical method and find out just how far back in time we could have put ourselves in the right ballpark for predicting last week's numbers!

For our methods, we need at least six weeks worth of data to establish whether a trend exists, and ideally ten weeks to get a decent statistical picture of it (obviously, the more data the better - these values represent the minimum values we need to put the picture together). Since we confirmed in an update on 3 May 2012 that a new trend in the number of seasonally-adjusted new jobless claims had indeed taken effect, pegging its beginning to the week ending 18 February 2012.

Ten weeks later puts us at the data report for the week ending 5 May 2012. Here is the chart with the projections that we could have generated at that time, using the BLS' revised data through 28 April 2012:

Now, let's fill in all the data that has been recorded since....

Based upon just ten weeks worth of data, we could easily have projected the range where each subsequent data point would be some 95% of the time for each week up to at least a month and a half later.

Of course, since we have more data now, why not use it? Here's what the chart looks like today based on the data reported through the week ending 9 June 2012:

And now you know what "expected" really looks like. In fact, we'll give you 68.2% odds of the data for the week ending 16 June 2012 coming in between 375,378 and 393,652, and 95% odds that it will fall between 366,240 and 402,789. At least while the current trend holds (it may not for much longer, which we'll touch on tomorrow!)

Rebuilding Trust on Wall Street

Trust. How can Wall Street firms regain the trust they lost from the long list of scandals that have arisen in recent years without any effective response from President Obama's Justice Department? Better yet, how can the U.S. government regain the trust it has lost as a consequence of its unwillingness to pursue clear indications of fraud where high ranking politicos with close connections to the government that is supposed to be on the investors' side in such cases might be involved?

In the wake of scandals like MF Global, where literally millions of dollars in the company's investors' accounts were drained without the consent of the account holders at the discretion of prominent Democratic party fundraiser and former governor of New Jersey John Corzine, the lack of an effective response to date from President Obama's Securities and Exchange Commission, Federal Bureau of Investigation, or other federal law enforcement agency to the known facts of the scandal has done little to restore the trust of investors.

Worse, it seems like the wrongdoers are being given a free pass - a get out of jail free card - provided they have certain political connections. Under those kind of conditions, there's no way that anyone can reasonably trust the system in which Wall Street is trying to work today.

That has consequences, as the real needs of millions of people cannot be met efficiently if that system cannot be trusted, as potential investors either hold back or have to devote significant efforts to scrutinizing the firms with whom they do business to ensure the integrity of their investments are not at risk of being lost to fraud or other bad behavior. Markets, whether on Wall Street or Main Street, need trust to work, and a high level of trust among nearly all participants to really reach their full potential.

So what if, instead of relying upon the whims of a government that appears to take political affiliations into account in determining whether or not it might pursue criminal prosecutions, we used a market mechanism instead to protect investors from fraud? Chris Whalen recently discussed an old idea that we find especially intriguing:

Equity receivers have been around for a long time and have been very effective at combating fraud in their limited applications in recent history. We believe that taking steps now to make their role and visibility more prominent will go a very long way toward rebuilding the trust that Wall Street needs to reach its potential.

The alternative would be to continue weighting down the markets with more and more regulations that keep it far away from being able to achieve its potential, but somehow, never seem to affect the money-making schemes or personal pocketbooks of the crooked and politically-connected.

Enabling Disability Fraud

We were inspired by Climateer Investing's summary of the econoblogosphere's ongoing analysis of the increasing level of disability fraud in the U.S., where hundreds of thousands of people would appear to be ending up after their extended unemployment insurance benefits expire, to ask two new questions: which Americans are benefiting from the fraud and how are they getting away with it?

To answer the first question, we started with the annual age distribution data that the Social Security Administration publishes on the number and age of that agency's disability benefit recipients. Starting with the pre-recession years of 2006 and 2007, the recession years of 2008 and 2009, as well as the post-recession years of 2010 and 2011, we created the following chart showing the number of people for each age recorded by Social Security for each year:

We see that nearly 90% of the increase in the number of people claiming disability benefits from Social Security has taken place for people Age 46 or older, with that increase outnumbering the increase in younger individuals by a factor of nearly 10 to 1.

Next, we compared a given year's number of disability benefit recipients to the previous year's number of disability benefit recipients who were one year younger. Doing this allows us to see the net number of people added to Social Security's disability rolls in each year:

Here, we see once again that it is mainly older Americans who have cashed in on Social Security's disability benefits. But this time, we see something we didn't expect - there is a very pronounced spike in the number of disability claims being awarded in each year, regardless of the condition of the U.S. economy, coinciding with Age 50.

That didn't make much sense - why would 50 year old people have such a surge in enrollment for Social Security disability benefits? Do people just suddenly break down at Age 50?

We found the answer in a blog post for a law firm that specializes in disability claims from 31 October 2005 - it's because the federal government gives people Age 50 or older a free pass for being able to claim disability benefits:

Why is age 50 so important in a SSDI case?

It goes without saying, the older you are, the better chance you have of being awarded disability. Age 50 is the “cut off” point for claimants filing for social security disability. If you had two claimants with nearly identical disabilities and backgrounds and only one of them is older than 50, the older claimant is more likely to receive benefits than the younger claimant. Claimants younger than 50 simply have a harder burden to overcome, although it is not impossible.

Why is it harder for younger claimants to receive disability benefits? If you are disabled it does not matter how old you are, right? Well not exactly. The social security administration has stated that even if a claimant cannot perform substantially all sedentary work, it does not mean that they are entitled to receive benefits. The reason being your background may dictate you working in another field. The SSA will look at your age, education, work experience, etc and determine if you have any transferable work skills that enable you to work despite your disability. This becomes important when you have a disability that prohibits you from doing substantially all sedentary work and you are below age 50. The SSA believes that claimants under age 50 have not yet reached an age that is old enough to limit their ability to adjust to other work. Is it fair, probably not especially if you are 47 and have the same disability as a claimant who is 51. But in defense of the SSA policy, there has to be some point where advanced age significantly becomes a factor.

Claimants under age 50 are put up against the task of having to rebut the testimony of a vocational expert at their hearing. This is a difficult task for many claimants. Vocational experts have often times heard several cases and have years of experience. Social security disability attorneys deal with vocational experts on a daily basis. If you find yourself in this situation, you are better off having counsel on your side to handle the cross examination of a vocational expert.

Before Age 50, the federal government puts obstacles in the way of those who might falsely claim disability benefits by actively challenging their claims. But once an individual reaches Age 50, it removes that barrier to preventing fraud.

We therefore find that the government's bureaucratic policies are enabling large scale disability fraud by not challenging all claims made by those applying to receive disability benefits. What's more, because individuals on disability status are no longer counted as being part of the U.S. labor force, the federal government is also guilty of falsifying employment situation reports, which are providing a false picture of the health of the U.S. job market.

That in turn is keeping resources that might otherwise improve that situation from being used for doing so. Because why would a policy maker take action to change the current policies of the federal government if the numbers say no action is needed?

New Jobless Claims: Thunderdome Edition!

Previously, we advanced two possible hypotheses that might explain what is currently happening with the number of seasonally-adjusted initial unemployment insurance claim applications being filed in the U.S. each week:

1. The number of new jobless claims filed each week is in the process of leveling out somewhere between 370,000 and 380,000, which is about 60,000-70,000 higher than the typical levels that were recorded before the December 2007 recession began.


2. Rising oil and gasoline prices in the U.S. have derailed the most recent falling trend in new jobless claims, and a new, negative trend has begun where the number of claims filed each week is rising.

In that post, we indicated that we might not know which hypothesis was correct until sometime this summer. But that was before the U.S. Bureau of Labor Statistics released its initial estimate of the number of new jobless benefit claim filings on Thursday, 26 April 2012. Now, it is very possible we might know the answer as early as this upcoming Thursday, 3 May 2012.

We've updated both charts showing our two hypotheses to incorporate the data as it stands as of the BLS' 26 April 2012 report. The first chart illustrates our first hypothesis:

In this chart, we would seem to be realizing our first hypothesis, in that the indicated trend, which we've identified as Trend I, is in the process of flatlining.

Now take a closer look. Focusing in on the data from 4 February 2012 through 31 March 2012, we see that the mean trend line for all data reported since 3 December 2011 has shifted in the past week so that all but one of these data points are below the line.

Following the well-established rules developed by Western Electric over half a century ago to determine whether or not an existing trend has broken down after having been in statistical equilibrium, which are visually depicted in the bell-curve image (it's not there for decoration!), we find that all it would take for us to declare this hypothesis to be false is for the most recent data, for the week ending 21 April 2012, to be revised upward by more that 2,000 claims, as the resulting change in the mean trend line will place the data for these nine consecutive weeks below it.

If the BLS keeps to its recent track record, it will definitely be revising the number of new jobless claims recorded for the week ending 21 April 2012 upward when it revises its data for that week this Thursday, 3 May 2012 - the only question is by how much.

Our second chart shows what the new trend would look like at this point in time:

This Thursday, our two hypotheses regarding the current trend in new jobless benefit claim filings will enter the theoretical Thunderdome, and very possibly, only one will leave. Stay tuned!

Major Update (3 May 2012): Hypothesis Two Confirmed!

Trend I is dead. Trend J has begun. All hail J!

The official announcement is here (jobless claims for 21 April 2012 revised upward by 4,000....)