A warning from the unemployment line

The weekly initial claims for unemployment data was released today and it is getting close to being a concern of mine.

Year over year change, initial unemployed claims - Source Federal Reserve

As can be seen from this chart of *non seasonally adjusted and *4 week average of seasonally adjusted data, (the noisiest and smoothest interpretations of this data series) the rate of decline has effectively stopped. Yes there were some spikes upwards during the hurricane Sandy and blizzard Nemo, but the trend has started to move upwards after spending most of 2011 and 2012 fluctuating around an annual decline in jobs  unemployment claims of ~40,000.

We are now near the break-even mark and if it starts to consistently go positive, (more people laid off now as compared to last year) this would be a very strong warning flag to the US economy.

Considering the recent payroll tax hike and other tax increases recently imposed it is possible we may see this happen.

Employment, seasonality, and noise

The monthly employment report can be a high volatility day for the markets.  The ADP report (BusinessInsider.com) came in above expectations and this was most likely the reason for early weakness in bond prices. (before the Fed Minutes release.)  However how one looks at the data can change your perspective.

Monthly employment numbers, ADP and Federal
Source: Federal Reserve

ADP data is shown in red, with Federal data shown in blue. Looking at the data, it does look quite random, great recession notwithstanding. It jerks up and down with no apparent order and the ADP data does not appear to track the Federal data well.  No wonder the markets can be volatile on employment release days.

Let us look at the data a slightly different way ...

Year over year change in employment, ADP and Federal
Source: Federal Reserve

Same data, just looking at a year over year percentage change instead of an absolute monthly change.  Looks a little different doesn't it?  There are minor variations in the two curves but not much.  Looking at the data this way however, it appears we are on the downslope of employment growth and that great ADP number which was released today doesn't look so impressive does it?

I'm not implying what tomorrow's employment report will look like, just a hint that how you look at the data can make a big difference in what conclusions to draw.

Seasonality and initial unemployment claims

Weekly initial unemployment claims were released today and they showed an increase to 399,000, countering the recent downward trend.  Details can be found at the DOL

I would not make much of the current gyrations, up and down. As the graph below shows, right now we are at the highest seasonal peak of layoffs/firings.  Each year you can see a massive increase in initial claims just around the new year. This makes estimating the trend very difficult.  (Translation, put very large error bars on data around the new year)

I wonder how many financial models out there reduce the weight of this data (and other series) when they are less accurate?  

Here's the two data series on a year over year basis.  While initial claims are still declining the rate of decline appears to have leveled off. 

Source: Federal Reserve

Seasonality and the Baltic Dry Index

I'm doing some research on other economic indicators showing seasonality for a later post. An article on the BDI (Baltic Dry Index) and how shipping rates show a seasonal tendency caught my eye.

From the Financial Times:

It is with interest, therefore, that we note Icap’s latest monthly shipping report — which errs towards the notion that the move wasn’t necessarily so unusual.

According to the broker, for example, the BDI has fallen in June on 20 separate occasions since 1985 — making the recent declines relatively consistent.
As they noted:
..no other month can claim to have such a poor track record, although the month of July has shown a similar tendency towards weakness over the years.

As you can see from the graph from Stockcharts the BDI can be ... volatile.

It's important to know if a data series contains seasonal affects to it.  That's one reason I tend to show data on a year over year format which eliminates time of year influences.