This past week, the two key labor statistical reports — from ADP and the Bureau of Labor Statistics — showed impressive improvement in the labor market. These reports drove stocks to strong gains for the week, with the NASDAQ setting an 11-year high. But if we dig a little deeper into the data, we find that all is not what it seems.
On Feb. 1, ADP (Automatic Data Processing) released its monthly jobs report, which showed 176,000 jobs added for the month of January. Stocks rallied — with the Standard & Poor’s 500 adding 12 points that day— as the positive reaction to the data helped overcome some other not so bullish economic data. Two days later, the BLS report showed 234,000 jobs added in January. That announcement spurred an even greater jump for stocks, with the Dow gaining 156 points, the S&P 500 up 19, and the NASDAQ surpassing the 2900 mark and closing at 2,905, an 11-year high. The NASDAQ has not been at this level since before the tech bubble burst in early 2000.
Each and every week we receive multiple reports on the economy — from labor statistics, to Gross Domestic Product growth rates, to housing-related data, and scores of other reports on every facet of our economy. Some hold more weight than others, but all are watched closely by market participants to try to understand the future direction of the economy, and the impact that direction will have on asset values. The problem faced by professionals like myself and individual investors is trying to understand how these statistics are calculated, and then what all of this information really means in terms of its impact on the financial markets over the short-, intermediate- and long-terms.
Last week’s labor reports are no exception. And, like all of the statistics we receive on a weekly basis, the process of calculating these data points can be involved and challenging. If we burrow into the latest BLS labor report, we find that although there was an increase of 234,000 jobs in January, and job growth was widespread in the private sector with large employment gains in professional and business services, leisure and hospitality, and manufacturing, there were some concerning adjustments that were made to arrive at that figure.
This adjustment resulted in an increase in the estimated size of the civilian noninstitutional population in December by 1,510,000, the civilian labor force by 258,000, employment by 216,000, unemployment by 42,000, and persons not in the labor force by 1,252,000. Although the total unemployment rate was unaffected, (now at 8.3 percent, down from 8.5 percent in December and down 0.8 percent since August), the labor force participation rate and the employment-population ratio were each reduced by 0.3 percentage points. This was because the population increase was primarily among persons 55 and older and, to a lesser degree, persons 16 to 24 years of age. Both these age groups have lower levels of labor force participation than the general population.
This decrease in the participation rate, in plain English, means that they basically removed 1.5 million people from the calculation of the jobs data because a large percentage of the increase in population between 2000 and 2010 comes from people over 55 or younger than 24. I would submit to you that there are a very high number of people aged 16 to 24 and 55 and older who need to work to afford to live. By removing 1.5 million people from the calculations, the government has basically padded its numbers to make them look stronger than they actually are. Basically, the BLS is saying that there were 1.5 million fewer people out of work and looking for a job in January than December, based on the change in population data from the 2010 census. For any of those 1.5 million people who didn’t find a job in January, they are just as unemployed, and just as in need of a job today as they were in December, but they are not being counted anymore.
Most professionals and many individual investors understand that all data must be calculated with imperfect inputs and that many assumptions must be included in the formulation of these data. We need statistics to track progress over time. No matter how unreliable they may be, it is still better to have an imperfect data set than none at all. Investors should be careful not to place too much weight on any of these statistics, however, since they are often calculated with questionable methods, unreliable inputs and changing formulas making comparisons to historical data spotty at best.