Just Released: Revised Data Reveal Continuous Labor Market Improvement in North Carolina

<p>Despite early indications of an economic slowdown, newly released data revisions show that North Carolina&rsquo;s unemployment rate improved continuously through 2013 and 2014. This article explains what labor market watchers can learn from these new data and provides some helpful tips for how to avoid getting caught off-guard by future data revisions.</p>

Author: Andrew Berger-Gross

Welcome, labor market watchers, to March 2015! The snow has melted, school is back in session, and you’ve finally dragged that Christmas tree out for curbside pickup. Now for the fun part: annual data revisions.

State and local unemployment rates are estimated and published every month by LEAD (and our partners in the federal-state statistical system), and these monthly estimates are then revised in the following month. Each year around this time, we conduct additional revisions that impact historical unemployment rates from previous years. While the monthly revisions to the unemployment rate are very small, the annual revisions have been quite large in recent years, often painting a substantially different picture of our economic health than the data initially published (and reported in the news media) on a monthly basis.

For example, according to the data published at the time, North Carolina and its neighboring states saw their unemployment rates surge upward during the middle part of 2014. The uptick in North Carolina’s rate was reported extensively in local news media, and some commentators went so far as to attribute this apparent worsening in labor market conditions to changes in public policy.

However, all of this hand-wringing was in vain. Newly released annual data revisions show that North Carolina did not experience any uptick in unemployment at all in 2014. Rather, the trend of a tightening labor market has proceeded unabated from 2013 through 2014.

So why do unemployment rates change, and in some cases, reverse direction upon later revision? The answer is that these numbers are estimates and our knowledge about the labor market at any particular point in time is incomplete. This fact is a persistent feature of any effort to measure the economy. LEAD has written previously about the numerous sources of error in the unemployment rate, all of which create some degree of uncertainty about actual conditions on the ground. Some of these sources of error are temporary and are resolved over time through a process of revisions.

State and local unemployment rates are produced by Local Area Unemployment Statistics (LAUS), a federal-state cooperative program. The LAUS annual revision process involves updating data inputs that feed into the LAUS estimation model and re-running the estimation model to incorporate a more complete set of input data. In addition, this year the Bureau of Labor Statistics implemented a new estimation model for LAUS; however, this new model is unlikely to result in major changes to the unemployment rate estimates. Rather, as in previous years, the process of re-running the estimation model on a more complete set of data was likely responsible for the bulk of revisions to the LAUS unemployment rates.

So how should a labor market watcher such as yourself interpret the economic data that are reported in the news media every month when these data are likely to be revised at a later date? Here are some guidelines that can prevent you from prematurely jumping to conclusions:

  • Consult data from different sources. For example, if the unemployment rate is increasing, are we also seeing declines in job creation or a slowdown in other economic indicators? If the answer is “no”, then there is a good chance that the unemployment rate data are erroneous and will be revised downward at a later time.
  • Pay careful attention to published measures of uncertainty (such as the margin of error). These measures usually depict one particular source of error — e.g., sampling error — and do not account for every conceivable problem that might occur in the process of data estimation. However, they can give you a general sense of how confident we are in the accuracy of the data.
  •  Ignore the month-to-month movements in economic data and focus instead on long-term trends. Monthly economic data are often noisy, subject to revision, and (most importantly) provide a little information about the overall direction of the economy. Long-term trends are much more stable, less affected by data revisions, and provide a wealth of information about what is happening in our economy and what we can expect in the future.

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