Something interesting to keep in mind about the 409,000 new first time jobless claims, is that that figure is the seasonally adjusted figure. The actual, unadjusted figure for first time jobless claims was 577,279 for the same period, up 52,038 from the week ending Christmas Day. Quite a substantial difference from 409,000 (up 21,000), hey?
So what is this "seasonal adjustment" anyway? The US Census Bureau has a pretty good FAQ on Seasonal Adjustment, and here's the explanation they give: "Seasonal adjustment is the process of estimating and removing seasonal effects from a time series in order to better reveal certain non-seasonal features. Examples of seasonal effects include a July drop in automobile production as factories retool for new models and increases in heating oil production during September in anticipation of the winter heating season."
As to why seasonal adjustment is used, the explanation is that "seasonal movements are often large enough that they mask other characteristics of the data that are of interest to analysts of current economic trends. For example, if a month has a different seasonal tendency towards high or low values it can be difficult to detect the general direction of a time series' recent monthly movement (increase, decrease, turning point, no change, consistency with another economic indicator, etc). Seasonal adjustment produces data in which the values of neighboring months are usually easier to compare. Many data users prefer seasonally adjusted data because they want to see those characteristics that seasonal movements tend to mask, especially changes in the direction of the series."
And finally, here's what they have to say about what what sort of seasonal effects are removed: "Seasonal adjustment procedures for monthly time series estimate effects that occur in the same calendar month with similar magnitude and direction from year to year. In series whose seasonal effects come primarily from weather (rather than from, say Christmas sales or economic activity tied to the school year of the travel season), the seasonal factors are estimates of average weather effects for each month, for example the average January decrease in new home construction n the Northeastern region of the US due to cold and storms. Seasonal adjustment does not account for abnormal weather conditions or for year-to-year changes in weather. It is important to note that seasonal factors are estimates based on present and past experience and that future data may show a different pattern of seasonal factors."
In a nutshell, this seems to say that the actual numbers do not fit nicely into mathematical models. They are unwieldy, and they do not take into account various factors that could make the results look worse (or better) than they really are. As a result, in order to make the actual numbers fit the model and to try to account for these various factors, a "fudge factor" is applied.
It's tempting to point at this and scream "ZOMG THEY"RE FAKING THE DATA!!!!! The government is lying to us!!!!! Blood of Liberty! BLOOD OF LIBERTY!!! Information MUST BE FREE!!!! Free Assange! ASSANGE!!!!!!!!"
But, before you work yourself into a "9/11 Truth" kind of conspiratorial hysteria, have a look at Report r539cy from the US Department of Labor. This report, run using the Labor Department's own data, is Unemployment Insurance Weekly Claims Data from 1/2/2010 through 12/11/2010. Fifty weeks of data.
|Initial Claims||Continued Claims||I.U.R||Covered Employment|
What's interesting in that 50 weeks of data, the Seasonally Adjusted figures are only better than the Non-Seasonally Adjusted figures 16 times. The other 34 weeks, the SA figures were worse than the NSA figures.
So, before you whip yourself into a hysterical anti-authoritarian frenzy, ask yourself why - if there really is a conspiracy to "cook the numbers" - the numbers would be cooked so that almost 70% of the time the situation looks even worse than it actually is? I mean, really. That's almost the worst conspiracy ever.
Feel free to look at the NSA figures to see how many people are actually applying for UI in any given week. Then look at the SA figures to see how, assuming the correct seasonal factor was chosen, the given week compares to other weeks.
 I can't find the official list of these factors for first time jobless claims. I would assume that it includes things like the potential impact of seasonal positions ending in December (for the Christmas shopping season), massive one-time layoffs from a single large employer, companies that lay employees off for part of the year and then rehire them (**cough**Faygo**cough**cough**), and so forth.
 It's all right to admit that you had this response. I had it, mostly because I tend to assume that the government lies. All the time.
 I tried to run it through 12/31/2010, but apparently everything after 12/11 isn't in the database yet.
 1/2, 1/9, 1/16, 1/23, 1/30, 2/6, 2/27, 3/6, 4/10, 7/3, 7/10, 7/17, 11/6, 11/20, 12/4, and 12/11.
 No guarantee, there. Economics isn't physics, after all.