(Click figure to enlarge.)

Two Fridays ago, I blogged about some newly released Bureau of Labor Statistics (BLS) data from a monthly household survey. I was surprised later to see that Multiplier Effect was one of only a handful of websites to mention that non-seasonally adjusted data showed vastly different and perhaps more disturbing results than the widely reported deseasonalized numbers: a flat unemployment rate, a sharp fall in employment, and a rise in the number of people unemployed. All of the numbers I discussed were based on traditional concepts of unemployment, which have been familiar to newspaper readers for decades.

It is important to put such survey results in context, and I have now had time to finish putting together some further information on the effects of seasonal adjustment on the numbers released early this month. While the standard version of the unemployment rate is widely reported and debated, it does not include potential workers who are not considered to be in the labor force because they have not recently been looking for work. If the labor market were stronger, most of these individuals would almost certainly return to the workforce and find work. Hence, it is interesting to look at a broader measure of unemployment that includes at least some of those out of workforce who want to work, but have not recently been searching for a job.

One such statistic is the BLS’s own U-4 measure of “labor underutilization,” which includes those deemed to be “discouraged workers,” in addition to the unemployed. The seasonally adjusted version of U-4 dropped from 10.2 percent in December to 9.6 percent in January. In contrast, the non-seasonally adjusted version of this index rose from 9.9 percent to 10.4 percent, according to the BLS. Hence, the story we have told in blog entries over the last two weeks also seems to have some implications for a more comprehensive measure of the human cost of weakness in the labor market.

The simple methodology that I used in my most recent post on the BLS report to calculate the impact of seasonal adjustment on the month-to-month change in the unemployment rate can be extended to a broader but unofficial index. This time, my answer will be somewhat less exact, because we do not have complete information about the potential impact of the 2011 adjustments to BLS population estimates on my new calculations .

Using data from Table A-1 of the BLS news release, as well as partial information on the effects of population adjustments on the January data from Table C of the release summary, we can find the contribution of seasonal adjustment to the apparent change in the following seasonally adjusted makeshift unemployment index:

number unemployed + number out of the labor force who want to work

divided by

labor force + number out of the labor force who want to work

The BLS does not separately report this statistic to our knowledge, but it is similar in spirit to several other alternative gauges of labor underutilization reported in Table A-15 of the employment report. Hence, we report our findings with the caveat that the BLS certainly might not endorse the use of this improvised measure.

What we find is interesting: seasonally adjusted BLS numbers from table A-1 imply that our broad unemployment index dropped from 13.0 percent in December to 12.7 percent last month. These are large numbers indeed. However, removing the effects of seasonal adjustment on the underlying raw numbers, the broad index probably would have risen by at least .9 percent, from 13.0 percent in December to between 13.9 and 14.0 percent. Hence, using a similar methodology to last week’s post, one finds that the effect of seasonal adjustment on the change in the broad unemployment measure is even greater than the corresponding effect on the change in the usual measure.

By the way, other data in the recent government report suggest that this difference between seasonally adjusted and non-seasonally unadjusted figures can largely be accounted for by temporary layoffs whose impact on the data was removed by adjustment procedures.  In other words, many workers were laid off last month, but were not counted in the most widely reported January unemployment figures, because large numbers of layoffs are not unusual for that time of year.

In fact, the historical record shows that the effects of BLS seasonal adjustment procedures are often especially large for the January release, resulting in substantial downward statistical adjustments to the recorded change in the unemployment rate from the previous December. This effect is not often noted in the media, though non-seasonally adjusted BLS figures are made available in the same report as the headline unemployment rate. The blog seekingalpha wrote about this phenomenon last winter. The figure at the top of this post is similar to a graphic appearing in that blog entry.

Edited slightly for clarity and readability Feb. 16 at 12:11 pm.

The White House remedies for the mortgage meltdown have now been presented. Congress will debate the life extension, death, or rebirth of federal mortgage entities Fannie Mae and Freddie Mac during the coming weeks. When the noise has died down, don’t expect substantial change. But those who hope for genuine financial reform should, nonetheless, listen carefully not only to what Washington says, but to whom it says it. Will the new guidelines call on traditional home-loan bankers to make traditional loans? Or will we hear a shout-out to the investment bankers/mortgage traders who designed the mess? In any new financial structure for home loans, the single most important issue will be the ratio of debt to assets that the government will expect lenders to show. During the real estate boom, lenders were willing—and able—to provide mortgage brokers with financing for 100 percent or more of the value of a property with the expectation that real estate prices would rise. We witnessed the triumph of the trader over the banker: Profit relied on the sale or refinancing of the asset. For a mortgage originator or securitizer with no plans to hold on to the mortgage, what really matters has been the ability to place it, not the depth of the underwriting or the long-term financial prospects of the home resident. A traditional banker, on the other hand, might feel safe with a capital leverage ratio of twelve to one, with careful underwriting to ensure that the borrower would be able to make payments. With equity at risk, something close to that level of underwriting would be essential. The trader-think model virtually eliminated mortgage underwriting. What we saw instead has been succinctly described by L. Randall Wray in a Levy Institute Brief (http://www.levyinstitute.org/publications/?docid=1301): “Property valuation by assessors who were paid to overvalue real estate, credit ratings agencies who were paid to overrate securities, accountants who were paid to ignore problems, and monoline insurers whose promises were not backed by sufficient loss reserves…” Much of the activity didn’t even appear on the balance sheets. Mortgage brokers arranged for finance, investment banks packaged the securities, and the shadow banks — the managed money — held the securities. The debt to assets ratios for mortgages climbed. Investment bankers consolidated their liabilities into a single financial market that could have been called the Mortgages & More Shoppe. Mortgage-backed securities were included with commercial banking, and with other financial services where acceptable capital leverage ratios are much higher than for traditional home loans. (For money managers, capital leverage ratios can be 30 to 1 and up to several hundred, with even higher unknown and unquantifiable risk exposures.) Income flows took a backseat. Except for the home resident, that is. Because ultimately, all of these financial instruments came to rest on the shoulders of some homeowner trying to service her mortgage out of annual income flows which boiled down to, on average, five dollars worth of debt and only one dollar of income to service it. “In an ideal world,” Wray added, “A lot of the debts will cancel, the homeowner will not lose her job, and the FIRE (finance, insurance, and real estate) sector can continue to force 40 percent of… profits in its direction. But that is not the world in which we live. In our little slice of the blue planet, the homeowner missed some payments, the securities issued against her mortgage got downgraded, the monoline insurers went bust, the credit default swaps went bad when AIG failed, the economy slowed, the homeowner lost her job and then her house, real estate prices collapsed, and, in spite of its best efforts to save [the system], the federal government has not yet found a way out of the morass.” Whatever the fate of Fannie Mae and Freddie Mac, the coming federal recommendations need to lift underwriting standards up from that morass and back onto solid ground. According to January’s Financial Crisis Inquiry Commission report, about 13 million US homes have already or will soon face foreclosure. The investment bank traders who securitized those mortgages, with a few notable exceptions, have overwhelmingly escaped such suffering. Financial reform should change that equation by demanding a traditional, appropriate ratio of assets to debts in the real estate markets.

In Friday’s post, I pointed out that unemployment and employment numbers announced by the BLS had apparently been changed greatly by the process of adjusting for typical seasonal changes. These adjustments are meant to account, for example, for the fact that retail business is generally stronger than usual during the holiday season at the end of each year. Friday’s widely reported unemployment drop to 9.0 percent in January from 9.4 percent the previous month was a figure that had been seasonally adjusted by the BLS to remove such normal seasonal effects. Also reported by the BLS Friday in the same set of documents were non-seasonally adjusted numbers that showed an increase in unemployment from 9.1 percent in December 2010 to 9.8 percent in January 2011. Few internet news outlets seem to have reported these latter percentages or the underlying raw numbers used to calculate them. On the other hand, many blogs and other news sources mentioned that adjustments had been made to the official numbers to reflect improved estimates of population growth from recent surveys, resulting in a problem with comparing January’s numbers with December’s. Friday morning’s blog post contained a qualifying statement to the effect that these population-related statistical adjustments had probably affected the un-seasonally adjusted numbers that I reported in the same post. Here is what I have been able to figure out about the importance of these two factors in creating such a large difference between the seasonally adjusted and non-seasonally adjusted one-month changes in the unemployment rates reported by the BLS.

The seasonally adjusted drop in number of unemployed people was -622,000, according to the BLS figures reported Friday. Table C in the accompanying news release estimated that annual changes in population estimates made by the BLS each January had this year magnified the reduction in unemployment from December to January by +32,000 individuals, leaving a true drop of perhaps −590,000, once one removed the effect of the population adjustments. On the other hand, non-seasonally adjusted figures from the same economic news release (Table A-1) showed an increase in the number of unemployed people of +40,000. Hence, one can deduce that, at least to a rough approximation, seasonal adjustment resulted in a much larger swing than population-related adjustments in figures reported in the headlines yesterday. Namely, about 630,000 more people were unemployed last month, once one puts back in the effects of typical seasonal changes in unemployment, as estimated by the BLS, resulting in a swing in the estimated figures of approximately +.4 percent of the labor force. In other words, the reported reduction in the unemployment rate from 9.4 percent to 9.0 percent in January derived from household survey data can be accounted for almost entirely by seasonal adjustments applied by the BLS.

Minor corrections for readability made to the post above approximately 2:00 pm, February 6 by G. Hannsgen

A note on the unemployment figures released earlier this morning by the Bureau of Labor Statistics (BLS), reporting the results of a January survey of U.S. households: The seasonally adjusted unemployment rate fell from 9.4 percent in December to 9.0 percent last month, a healthy improvement. On the other hand, before seasonal adjustment, the unemployment rate rose from 9.1 percent in December to 9.8 percent in January. Raw data that are not seasonally adjusted show that the number of unemployed Americans rose by 940,000, while the number employed fell by 1,560,000. New adjustments for population changes, introduced by the BLS this month, affected these numbers by an amount that is possibly very large and that is not yet known to me. This latter problem probably affects raw numbers more than the overall unemployment rate. The seasonally unadjusted numbers used in this blog post can be found in table A-1 of the recent economic news release from the BLS.

In December, the Levy Institute issued a working paper that asked how the economy might be affected by the seemingly unusual fiscal and monetary policies implemented by the Fed and other central banks since 2008. The authors, Dimitri Papadimitriou and I, used a phrase that is not often spoken in this era by governments and central banks around the world: “monetizing the deficit.” This phrase traditionally describes the practice of financing a government deficit with money that is “printed” rather than borrowed or raised by taxation. We feel perhaps a little more comfortable with our use of these words in light of a recent blog entry on the Financial Times website Alphaville. The blog reports that the Fed has come close to running out of securities to buy in the markets for certain types of government bonds, having bought so many of them already. Hence, it is increasingly resorting to the purchase of recently issued bonds and notes, which it had apparently sought to avoid. This development makes the link between deficit spending and monetary policy initiatives such as the current round of “quantitative easing” in a monetary system like ours easier to grasp. If the Fed buys a Treasury security almost immediately after it is issued, there is less reason than ever to think of the financing process as anything other than the use of the Federal Reserve’s “printing press” to pay for government operations–an essential use of “monetization” to stimulate the economy and avoid drastic fiscal measures during a time of weak tax revenues. Some worry still, but this practice has been used many times by numerous governments around the world and seems unusual only in light of common but unrealistic beliefs about monetary systems and how they normally work. Hence, those in Congress should not give credence to arguments that it is necessary to eliminate entire government programs or freeze major parts of the federal budget in order to restore some fanciful state of budgetary normalcy.

February 10 addendum on recent news: A short and interesting article on the implementation of quantitative easing policies was posted very recently on the New York Fed’s website. The article mentions changes in the composition of the Fed’s asset purchases, including the recent increase in purchases of newer issues that was reported in the Alphaville blog entry linked to above.  On the other hand, the new piece, based on a speech by a Fed official, finds no evidence that the Fed’s purchases have caused “significant market strains.” The article covers some other important issues associated with the recent policy actions involving long-maturity securities and might be interesting to people wanting detailed information about these topics.