Author: Andrew Berger-Gross
The unemployment rate is one of the most closely-watched indicators of current labor market conditions. As North Carolina has continued to march down the path toward economic recovery we have seen the unemployment rate plunge from a high of 11.3% in March 2010 down to 5.3% in February 2015. However, recent data releases have shown a noticeable uptick in unemployment since February, appearing to reverse much of the progress we saw in the previous year.
Should you be concerned about this apparent turning point in the long-term trend? In this article we evaluate several potential explanations for the recent increase in North Carolina’s unemployment rate and what it may tell us about the state of our labor market. To sum up our findings:
- The recent increase in North Carolina’s unemployment rate is based on preliminary data that are subject to substantial revision. We should have a better sense of whether this increase truly reflects facts on the ground after the data are revised in 2016.
- New claims for unemployment insurance have not spiked, indicating that we are not entering recession territory.
- Oft-repeated claims that unemployed entrants to the labor force are inflating the unemployment rate are not supported by existing evidence.
- If we are truly seeing an increase in North Carolina’s unemployment rate, current evidence suggests that it may be driven by the labor market’s inability to match unemployed workers to the ample amount of jobs available in our state.
The unemployment rate is defined as the total number of unemployed persons divided by the number of labor force participants. The increase in North Carolina’s unemployment rate since February 2015 has been driven by an increasing number of unemployed.
Let’s address several potential explanations for this unusual unemployment uptick one-by-one. First, it is reasonable to question whether these data are accurately depicting the present state of our economy. LEAD has written extensively in the past about sources of uncertainty (or “error”) in economic statistics such as the unemployment rate. Federal and state statistical agencies are provided limited resources with which to track the health of our economy. In order to gauge current labor market conditions, these agencies are required to develop estimation methods that provide us with a “snapshot” based on the limited information we currently have available. Although these methods are rigorously tested and statistically sound, it is not possible for them to reflect conditions on the ground with 100% accuracy.
Very often, data revisions are required to ensure that previous snapshots are updated to reflect new information that is collected gradually over time. State unemployment rates are estimated using a statistical model; like most models, this model performs better when it is able to accommodate more information. State unemployment rates are often significantly revised after the end of each year, when this additional information is incorporated into the model. For example, recent data revisions canceled out a similar uptick in North Carolina’s unemployment rate in 2014, revealing a continuously tightening labor market throughout the period.
However, just for the sake of argument, let’s assume that we really are experiencing a slackening labor market. The last time we saw a multi-month increase in the unemployment rate was during the run-up to the Great Recession. Does this mean that another recession is right around the corner?
Probably not. Labor market watchers often track movements in the number of new unemployment insurance claims as an indicator of impending recession. These initial claims are typically interpreted as a proxy for layoffs, and historically we have seen spikes in initial claims before or during recessionary periods. Thankfully, such a spike does not appear to be evident in North Carolina, suggesting that we can rule out any talk of recession for the time being.
In the absence of mass layoffs, what else could be causing an increase in the unemployment rate? Media accounts have asserted that entrants to the labor force (including “formerly-discouraged workers”) are causing unemployment to increase around the country, including in North Carolina. We can test whether this assertion is true using publicly-available survey data.
The Current Population Survey provides information about the composition of the unemployed population, including whether they are already in the labor force (“experienced” unemployed) or whether they are entering the labor force for the first time or re-entering after a period of non-participation (“entrant” unemployed.) The most recent data currently available, covering the 12-month period from July 2014 to June 2015, shows that North Carolina is not seeing any increase in unemployed labor force entrants. Rather, the data depict a leveling off in the number of unemployed workers who are already in the labor force.
While it is possible that we’ll see a shift in this trend as the year progresses, for now there is no evidence to substantiate the assertion that the unemployment rate is being inflated by unemployed labor force entrants.
So if layoffs and the number of unemployed labor force entrants are both holding steady, what else could be causing an increase in unemployment? It might be that, for some reason, there is a mismatch between our unemployed population and the jobs currently available in the state. This is another issue that LEAD has covered in previous articles—although job vacancy rates are at historically high levels, the unemployment rate remains elevated. In other words: employers seemingly want to hire, but (to some extent) our unemployed population is not benefiting from this strong labor demand.
Matching problems in the labor market can be caused by any number of factors, including the hiring behavior of employers and the job search behavior of unemployed workers. Earlier research by LEAD found that North Carolina’s labor market mismatch was being driven by problems related to the long-term unemployed; however, more research is needed to determine whether these factors are still playing a role.
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.