Author: Andrew Berger-Gross
State and local unemployment rates are estimated and published on a monthly basis by LEAD and our partners at the U.S. Bureau of Labor Statistics (BLS). Each year around this time, as sure as the changing of the seasons, we use standard BLS methodologies to implement data revisions that affect unemployment rates published in prior years.
The story we initially tell about the economy during a given month is eventually modified as preliminary estimates are revised in the following month and in later years to reflect new information that comes in over time. While the monthly revisions to the unemployment rate are very small, the annual revisions can paint a substantially different picture of our economic health than the initially-published data that are covered extensively in the news media. (See for example our coverage of data revisions in 2016, 2015, and 2014.)
This year’s data revisions do not fundamentally alter North Carolina’s economic narrative. However, they do offer clarity regarding some recently-emerging labor market trends. Paramount among these trends are a short-term increase in the unemployment rate and a longer-term increase in the labor force participation rate.
The recent upturn in North Carolina’s unemployment rate is, on its face, somewhat concerning. We initially estimated the upward trend to have started in August; this year’s revisions depict the trend as having started in June. The unemployment rate increased 0.3 points between June and December, the largest six-month increase we’ve seen since March 2010, during the aftermath of the Great Recession.
However, we do not have enough evidence at this time to suggest that we are approaching an economic downturn. The number of initial claims for unemployment insurance—which typically spikes near the onset of economic slumps—has remained near historic lows despite a slight increase over the past year. Other timely indicators of North Carolina’s economic condition, such as job growth, remain robust. Although there is currently no cause for alarm, we will continue to monitor this trend over the course of the next several months.
The other noteworthy development revealed by this year’s data revisions is a clear upward trend in labor force participation, which had previously been obscured by the volatility of the initial estimates.
LEAD has reported extensively on North Carolina’s participation rate, which has trended downward since 2001 due primarily to demographic-related factors such as the aging of the Baby Boomer generation and the increasing prevalence of school enrollment among young adults.
This year’s data revisions show a surprising—and welcome—increase in the participation rate of 1.1 percentage points between December 2014 and December 2016. This is the largest 2-year increase that North Carolina has seen since March 2001, before the participation rate began its 13-year decline.
This recent increase is surprising because forecasters have predicted that participation rates will continue to decline over this decade, driven by retiring Baby Boomers. According to the Current Population Survey, North Carolina saw increasing participation rates among all age groups over the past two years, along with relative growth in the number of prime working age (25-to-54) residents, who tend to participate in the labor force at high rates. The increase is a welcome surprise because labor force participation is an important factor for economic growth. But it is difficult to predict whether this trend will persist in the years to come.
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As we do every year, we leave you some parting words regarding the impact of annual data revisions:
Why do economic trends sometimes change – and even reverse direction – upon later revision? This occurs because our knowledge about the labor market 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 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 the Local Area Unemployment Statistics (LAUS) program. The LAUS annual revision process involves updating data inputs that feed into the LAUS estimation model and running a more powerful version of the model to incorporate a more complete set of input data. As in previous years, the process of running the estimation model on a larger 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 it is possible 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.
- 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.