Author: Andrew Berger-Gross
Individuals who watch North Carolina’s economic trends closely may have noticed a recent change in the labor force data available on the LEAD website, including the unemployment rate. Each year around this time, as sure as the changing of the seasons, LEAD and our partners at the BLS use standard methodologies to implement data revisions that affect labor force data published in prior years.
Revisions are necessary to ensure that these data accurately reflect the state of the economy, including information not available at the time of initial publication. These annual revisions can paint a different picture of our economic health than the preliminary data that are covered extensively in the news media. (See for example our review of data revisions in 2017, 2016, 2015, and 2014.)
The biggest story to emerge from this year’s data revisions is a flattening of North Carolina’s labor force trend.
Labor force participation had been trending downward since around 2000, driven by an aging population and increasing school enrollment. Last year’s revisions appeared to reveal a reversal in this trend, with a sharp increase in North Carolina’s participation rate starting in 2014 and reaching 62.0% at the end of 2016.
This year’s revisions temper that earlier revelation, showing instead a more gradual increase over time in our participation rate, which is now recorded at 61.4% as of the last month in 2017.
Labor force participation is an important factor for economic growth. The recent upturn in labor force participation, although more gradual than initially thought, remains a welcome development. However, before we get too excited, we should note that forecasters continue to predict declining participation rates in years ahead due to the aging of the population and the retirement of the Baby Boomer cohort.
Recent data revisions also throw cold water on statements previously made about the state’s unemployment rate. News accounts have interpreted unemployment rate increases at the end of 2017 as evidence of inadequate job growth and the worst conditions in seven months. The revised data demonstrate that these conclusions were premature—North Carolina’s unemployment rate did not spike upward at the end of the year, but in fact remained nearly unchanged for much of 2017 after declining earlier in the year.
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As we do every year, we leave you with some parting words regarding data revisions:
Why do economic trends sometimes shift – and even reverse direction – upon later revision? This occurs because our knowledge about the economy at any particular point in time is incomplete, and any attempt to estimate current conditions in the economy is bound to be clouded by uncertainty. LEAD has written previously about the numerous sources of error in the unemployment rate, all of which create some degree of uncertainty about economic conditions. Some of these sources of error are temporary and are resolved over time through a process of revisions.
The Local Area Unemployment Statistics (LAUS) program produces state and local unemployment rates. The LAUS annual revision process updates data inputs that feed into the LAUS estimation model and runs a more powerful version of the model to incorporate a more complete set of input data. As in previous years, the bulk of revisions to LAUS unemployment rates likely resulted from running the estimation model on a larger set of data.
So how should a labor market watcher such as yourself interpret the economic data that the news media report 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 it is possible that the unemployment rate data are misestimated and will be revised at a later time.
· Pay careful attention to published measures of uncertainty (such as the margin of error) as they can give you a general sense of how confident we are in the accuracy of the data. However, while these measures depict one particular source of error (sampling error), they do not account for every conceivable problem that might occur in the process of data estimation.
· Be judicious when interpreting the month-to-month movements in economic data. We recommend focusing instead on long-term trends. Monthly economic data are often noisy, subject to revision, and (most importantly) provide 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.
General disclaimers:
Data sources cited in this article are derived from surveys and administrative records and are subject to sampling and non-sampling error. Any mistakes in data management, analysis, or presentation are the author’s.