Impact of coronavirus (COVID-19) pandemic on Job Openings and Labor Turnover data for March 2020
The Job Openings and Labor Turnover Survey (JOLTS) national estimates of job openings, hires, and separations have been affected by the coronavirus (COVID-19) pandemic. The questions and answers below address the impact. Although the impact cannot be precisely quantified for March, it is clear that decreasing job openings, hires, and quits and increasing layoffs and discharges can be attributed to the effect of the pandemic.
- How are people who were laid off counted by JOLTS? The reference periods of the Job Openings and Labor Turnover Survey are the pay period that includes the 12th of the month for employment, the last business day of the month for job openings, and the entire calendar month for hires and separations. The JOLTS program does not estimate employment, but does collect employment during the collection process to validate reported job openings, hires, and separations. Employees who are laid off from their job at any time during the month are reported in the layoffs and discharges measure, and in total separations. See the JOLTS frequently asked questions for detailed information on the data definitions.
- Was data collection (response) affected by the COVID-19 pandemic? Yes. Data collection for the survey was affected by the COVID-19 pandemic. While 42 percent of data are usually collected by phone onsite at the JOLTS data collection center, most phone respondents were asked to report electronically by our data collection website. However, data collection was adversely impacted by the inability to reach some respondents that normally respond by phone. The JOLTS response rate for March was 67 percent, while response rates prior to the pandemic averaged 77 percent. See Table 1.
Table 1. Response outcomes for the Job Openings and Labor Turnover Survey
|Outcome and Mode
|12-month average through February 2020
|Percent Responding by
|Percent Responding by
- Was there an impact on the JOLTS estimation process due to the COVID-19 pandemic? In anticipation of issues from the pandemic, the JOLTS program reviewed all estimation and methodological procedures. An adjustment has been made to the JOLTS estimation methodology in order to more accurately reflect the effects of the pandemic. The current estimation process includes an alignment of JOLTS hires minus separations to the over-the-month change in the Current Employment Statistics (CES) employment estimates. JOLTS and CES have different reference periods. The reference period for JOLTS hires and separations is for the entire month. In contrast, the reference period for CES employment is the pay period including the 12th of the month.
The CES employment change for March was the difference between the February and March employment values. Consequently, hires and separations during the latter half of March were not reflected in the CES reference period but are included in the JOLTS reference period.
Because of the pandemic and the ensuing economic disruption in the latter half of March, high levels of layoffs and discharges were reported to JOLTS. This led to an extremely large discrepancy between the JOLTS implied employment change (hires minus separations) and the CES employment change. The alignment portion of the JOLTS estimation process was designed to correct a long-term divergence between the JOLTS implied employment change and the CES employment change. JOLTS hires minus separations and CES employment change generally approximate one another. Alignment was developed to correct small monthly discrepancies between the two series that build up over time; it was not designed to address a substantial one-month divergence between the two series. The alignment process was suppressed in order to produce the highest quality JOLTS estimates possible in this unique situation.
- Were there modifications to the seasonal adjustment methodology for the JOLTS survey? Yes. Seasonal adjustment factors can be either multiplicative or additive. A multiplicative seasonal effect is assumed to be proportional to the level of the series. A sudden large increase in the level of the series will be accompanied by a proportionally large seasonal effect. In contrast, an additive seasonal effect is assumed to be unaffected by the level of the series. In times of relative economic stability, the multiplicative option is generally preferred over the additive option. However, in the presence of a large level shift in a time series, multiplicative seasonal adjustment factors can result in systematic over- or underadjustment of the series; in such cases, additive seasonal adjustment factors are preferred since they tend to track seasonal fluctuations in the series more accurately and have smaller revisions.
Most data series that had outliers in March used multiplicative seasonal adjustment factors. Therefore, BLS staff decided to specify all JOLTS series in March as additive. In accordance with the survey’s usual practice, the seasonal adjustment models and factors will be reviewed at the end of the calendar year, when 5 years of seasonally adjusted estimates will be subject to revision.
More information about seasonal adjustment is available in the JOLTS Handbook of Methods chapter.
Last Modified Date: May 15, 2020