As soon as the US government released the unemployment results for September showing that nonfarm employment increased by 114,000 (anemic) but yet the rate of unemployment dropped substantially from 8.1 to 7.8 many of the conservative politicians and econimic analysts cried foul. Jack Welsh, the former GE CEO tweeted"If you can't debate then you fix the numbers" and a CNBC personality said: "I told you that they would get the number below 8% just before the elections". All of the above comes under the category of sour grapes. No one who knows anything about the BLS would ever make such an accusation because it is baseless and is something that will be next to impossible to achieve . Over 50 different individuals work on these figures and not a single one has the power to manipulate them. The following is a great explanation of what the unemployment figures mean, as presented by Greg Manikw one of "star" economists in the US.
If you go to the recent release from the BLS, you can find these two sentences a few paragraphs apart:
Total employment rose by 873,000 in September.
Total nonfarm payroll employment increased by 114,000 in September.
To a layman, this may seem confusing. The first statement suggests a robust labor market, the second a more lackluster one. What is going on?
The issue is that there are two surveys. The first estimate of employment comes from the survey of households; the second is from the survey of establishments. I thought readers might like to hear what my favorite intermediate macro textbook says about this issue. Here is an excerpt:
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Because the BLS conducts two surveys of labor-market
conditions, it produces two measures of total employment. From the household
survey, it obtains an estimate of the number of people who say they are
working. From the establishment survey, it obtains an estimate of the number of
workers firms have on their payrolls.
One might expect these two measures of employment to be identical,
but that is not the case. Although they are positively correlated, the two
measures can diverge, especially over short periods of time. A particularly
large divergence occurred in the early 2000s, as the economy recovered from the
recession of 2001. From November 2001 to August 2003, the establishment survey
showed a decline in employment of 1.0 million, while the household survey
showed an increase of 1.4 million. Some commentators said the economy was
experiencing a “jobless recovery,” but this description applied only to the
establishment data, not to the household data.
Why might these two measures of employment diverge? Part of the
explanation is that the surveys measure different things. For example, a person
who runs his or her own business is self-employed. The household survey counts
that person as working, whereas the establishment survey does not because that
person does not show up on any firm’s payroll. As another example, a person who
holds two jobs is counted as one employed person in the household survey but is
counted twice in the establishment survey because that person would show up on
the payroll of two firms.
Another part of the explanation for the divergence is that surveys
are imperfect. For example, when new firms start up, it may take some time
before those firms are included in the establishment survey. The BLS tries to
estimate employment at start-ups, but the model it uses to produce these
estimates is one possible source of error. A different problem arises from how
the household survey extrapolates employment among the surveyed households to
the entire population. If the BLS uses incorrect estimates of the size of the
population, these errors will be reflected in its estimates of household
employment. One possible source of incorrect population estimates is changes in
the rate of immigration, both legal and illegal.
In the end, the divergence between the household and establishment
surveys from 2001 to 2003 remains a mystery. Some economists believe that the
establishment survey is the more accurate one because it has a larger sample.
Yet one recent study suggests that the best measure of employment is an average
of the two surveys.
[George Perry, “Gauging Employment: Is the Professional Wisdom
Wrong?,” Brookings Papers on Economic
Activity (2005): 2.]
More important than the specifics of these surveys or this particular episode when they diverged is the broader lesson: all economic statistics are imperfect. Although they contain valuable information about what is happening in the economy, each one should be interpreted with a healthy dose of caution and a bit of skepticism.
