Reliability of JOLTS Estimates
Confidence Intervals and Comparing JOLTS Estimates
All sample estimates have inherent variability. The true population value estimated from a sample
may be lower or higher than the sample point estimate. Comparing sample estimates to determine if
they are different in terms of statistical significance requires the use of a statistical measure called
the standard error of the estimate. Using standard errors allows the data user to probabilistically
determine if the difference between sample estimates is within the bound of natural variability or if
the difference exceeds the bound of natural variability. In other words, the data user can determine
if the difference between sample estimates is considered statistically significant.
Sampling and Nonsampling Error
All sample estimates, including the JOLTS estimates, are subject to both sampling and nonsampling
error. When a sample rather than the entire population is surveyed, there is a chance that the sample
estimates may differ from the "true" population values they represent. The exact difference, or
sampling error, varies depending on the particular sample selected. Sampling error is a generic term
for standard error.
Sample estimates also are affected by nonsampling error. Nonsampling error can occur for many
reasons, including the failure to include a segment of the population, the inability to obtain data
from all units in the sample, the inability or unwillingness of respondents to provide data on a
timely basis, mistakes made by respondents, errors made in the collection or processing of the data,
and sampling and nonsampling error in the independent population controls of employment (also
known as benchmark employment) data used in JOLTS estimation. This nonsampling error may be
partially reflected in the standard error of an estimate. For more information on nonsampling error
and benchmarking, see the JOLTS Handbook of Methods.
Significance testing is a technique used to compare estimates using the estimates’ standard errors.
The BLS significance testing is generally conducted at the 90% level of confidence. That means
that there is a 90% chance, or level of confidence, that an estimate based on the sample will differ
by no more than 1.645 standard errors from the population value. The 1.645 is the tdistribution
critical value used to construct a 90% confidence interval. For a 95% confidence interval, the value
is 1.96.
Confidence Intervals
A confidence interval is an interval around an estimate created by a lower bound and an upper
bound. The lower bound is calculated by subtracting from the estimate the product of the estimate’s
standard error and the appropriate tdistribution critical value. As mentioned above, the t
distribution critical value is 1.645 for a 90% confidence interval. That same product is added to the
estimate to calculate the upper bound of the confidence interval.
Data users can compare two or more estimates by creating confidence intervals around each
estimate and determining if the intervals overlap. If the intervals overlap, the two estimates are not
different from each other in a statistically significant way; if the intervals do not overlap, the two
estimates are different from each other in a statistically significant way. JOLTS publishes results of
significance testing for monthly changes (overthemonth change) and for annual changes (over
theyear change) since these are the most common comparisons likely to be made by JOLTS data
users. These results are accessible on the
JOLTS Supplemental Table of Contents page.
There are two ways to test for significance using JOLTS products:
 Use the significant change tables, and
 Use the median standard errors.
Using the Significant Change Tables
The JOLTS program publishes significant change tables
that evaluate overthemonth change and overtheyear change. The significant change tables provide the minimum change necessary in
order for a change to be statistically significant.
In the following example, the minimum significant change values from the significant change tables
are used to determine whether the monthly (overthemonth) change is statistically significant. Note
that the process described below is identical for rates and levels.
Table 1: Job openings estimated rate and level changes between May 2013 and June 2013, and test of significance, seasonally adjusted
Industry and Region 
Estimated overthemonth change, rates 
Minimum significant change 
Pass test of significance 
Estimated overthemonth change, levels 
Minimum significant change 
Pass test of significance 
Total 
0 
0.1 

29 
208 

 The total nonfarm Job Openings estimate increased by 29,000 from May 2013 to June 2013.
 The minimum significant change necessary to conclude that this increase in Job Openings is
statistically significant is 208,000. The minimum significant change in the table above is based on
the median standard error of the overthemonth changes of job openings. These median standard
errors are updated annually and the median is calculated over the life of the JOLTS survey.
 Since the absolute value of the increase (29,000= 29,000) does not exceed the minimum change
necessary to conclude that this increase in Job Openings is statistically significant (208,000), then
the comparison does not pass the test of significance. The overthemonth change in Job Openings
in this example is not statistically significant at the 90% confidence level. In such a case, the “Pass
test of significance” column in the table above remains blank.
In the following example, the minimum significant change values from the significant change tables
are used to determine whether the annual (overtheyear) change is statistically significant.
Table 11: Layoffs and discharges estimated rate and level changes between June 2012 and June 2013, and test of significance, not seasonally adjusted
Industry and Region 
Estimated overtheyear change, rates 
Minimum significant change 
Pass test of significance 
Estimated overtheyear change, levels 
Minimum significant change 
Pass test of significance 
Total 
0.2 
0.2 

254 
203 
YES 
 The total nonfarm Layoffs and Discharges estimate decreased by 254,000 from June 2012 to
June 2013.
 The minimum significant change necessary to conclude that this decrease in Layoffs and
Discharges is statistically significant is 203,000. The minimum significant change in the table above
is based on the median standard error of the overtheyear changes of Layoffs and Discharges.
 Since the absolute value of the decrease (254,000= 254,000) exceeds the minimum change
necessary to conclude that this change in Layoffs and Discharges is statistically significant
(203,000), then the comparison passes the test of significance. The overtheyear change in Layoffs
and Discharges in this example is statistically significant at the 90% confidence level. In such a
case, the “Pass test of significance” column in the table above has a “YES.” It is, therefore, possible
to identify all the significant changes at a glance at the significant change tables since the estimates
with a significant change have a “YES” in the “Pass test of significance” column.
Using the Median Standard Errors
The significant change tables can be used to evaluate overthemonth and overtheyear changes. If
the user wants to evaluate something other than overthemonth or overtheyear changes, then the
user will need the median standard error of the estimate rather than the median standard error of the
overthemonth change or the median standard error of the overtheyear change.
The median standard error tables are available on the
JOLTS median standard errors page.
These tables contain standard errors for the level and rate of each data element (job openings, hires, etc.)
for each published industry. To determine whether the change between estimates is statistically significant using
median standard errors it is necessary to construct a confidence interval around the estimates being
compared.
To construct the confidence interval for a given estimate, follow these steps:
 Choose your level of confidence. In this example, we use a 90% confidence level.
 Select the estimate for which you would like to develop the confidence interval.
 Using the median standard error tables, find the corresponding median standard error
for the data element (either level or rate) and industry that you selected in step 2.
 Multiply the standard error by the tdistribution critical value (1.645 at the 90%
confidence level).
 Create the confidence interval by adding and subtracting the product in step 4 to/from
the selected estimate to generate the upper and lower limits of the confidence
interval.
The process above must be repeated for each estimate that the data user is comparing.
In the following example, confidence intervals are calculated for the seasonally adjusted total
private job openings level for January 2013 and June 2013. From those two intervals, a
determination of whether the change between the two time periods is statistically significant may be
made.
 January 2013 total private job openings level, seasonally adjusted: 3,194,000
 Median Standard Error: 94,597
 94,597*1.645 = 155,612
 Add and subtract the value above from the January job openings estimate:
 Lower bound: 3,194,000 – 155,612 = 3,038,388
 Upper bound: 3,194,000 + 155,612 = 3,349,612
From the bounds calculated above, we can say with 90% confidence that the population value for
the January 2013 seasonally adjusted job openings level is between 3,038,388 and 3,349,612.
 June 2013 total private job openings level, seasonally adjusted: 3,534,000
 Median Standard Error: 94,597
 94,597*1.645 = 155,612
 Add and subtract the value above from the June job openings estimate:
 Lower bound: 3,534,000 – 155,612 = 3,378,388
 Upper bound: 3,534,000 + 155,612 = 3,689,612
We can say with 90% confidence that the population value for the June 2013 seasonally adjusted
job openings level is between 3,378,388 and 3,689,612.
Since the 90% confidence interval for the January 2013 seasonally adjusted total private job
openings (3,038,388  3,349,612) does not overlap the 90% confidence interval for the June 2013
seasonally adjusted total private job openings (3,378,388  3,689,612), it can be concluded that the
change in seasonally adjusted job openings between January 2013 and June 2013 is statistically
significant at the 90% confidence level.
Last Modified Date: December 20, 2013