Current Employment Statistics Survey

Concurrent Seasonal Adjustment for Industry

Employment Statistics

 Bureau of Labor Statistics

 October 21, 2002
Concurrent Seasonal Adjustment for Industry Employment Statistics

The Current Employment Statistics (CES) Survey, conducted monthly by the Bureau of Labor

Statistics, obtains payroll employment, hours, and earnings from business establishments and

produces industry-based estimates. Accurate seasonal adjustment is an important component in the

usefulness of these monthly data. The CES program will convert to the North American Industry

Coding System (NAICS) with the publication of May 2003 first preliminary estimates, published in

June 2003. Simultaneous with this conversion CES will replace the current projected-factor

seasonal adjustment methodology with concurrent seasonal adjustment. This paper compares the

two seasonal adjustment methodologies, examines results from recent research evaluating the two

methods, and discusses the implications of a conversion to concurrent seasonal adjustment.

Introduction: The CES is a monthly survey of over 300,000 business establishments. The national

CES estimates of employment, hours, and earnings are some of the most timely and sensitive

economic indicators published by the federal government. They are widely viewed as a key

measure of the health of the economy and are closely tracked by both public and private policy

makers alike. 

Most CES data users are interested in the seasonally adjusted over-the-month employment changes

as a primary measure of overall national economic trends. While seasonally adjusted series go

through several monthly revisions and an annual benchmark revision before they are finalized, the

first published estimates are the most widely anticipated and analyzed. Thus it is important to use

the most efficient and reliable methods for seasonal adjustment of current months’ data. Currently,

CES uses seasonal adjustment methodology that applies forecasted seasonal factors to the

employment estimate. Twice a year seasonal factors are forecasted for six months into the future

and applied to the not seasonally adjusted estimates during the subsequent six months. Beginning

in 2003, CES will convert to concurrent seasonal adjustment in which new seasonal factors are

calculated each month using all relevant data, up to and including the current month. 

Background on CES Estimates: One of the benefits of CES is the timeliness of the estimates.

CES estimates are published each month after only 2 1⁄2 weeks of data collection. The primary

deadline for data receipts, referred to as “first closing”, is the last Friday of the reference month, and

preliminary estimates are published on the first Friday following the reference month. In order to

incorporate additional sample received after the primary deadline, each estimate undergoes two

monthly revisions before being finalized. The secondary cut-off, or “second closing”, is usually

three weeks after the primary deadline, and the third deadline, or “third closing” is three weeks after

the second. Therefore, for any given reference month, second closing estimates are published the

following month, and third closing estimates are published two months subsequent. 

CES estimates also undergo annual revisions called “benchmarks”. Each year, the sample-based

estimates for the previous year are adjusted to universe employment counts derived from State

unemployment insurance tax records. This constitutes the final estimate for all reference months in

the benchmark period. 
To seasonally adjust the estimates, CES uses X-12 ARIMA software developed by the US Census

Bureau. Seasonal adjustment factors are recalculated semi-annually, in April and November, and

projected factors are published in advance for the next 6 months. Currently, new seasonal factors

are published in June and December of each year. The June CES publication incorporates annual

benchmark revisions that include recalculation of seasonally adjusted data for the most recent 5

years. After 5 years of seasonal adjustment revisions, figures are frozen. For example, the March

2001 benchmark revision, published in June 2002, provided revised seasonally adjusted data for

1997 through the first quarter of 2002. 

Research approach: During the past several years, BLS has been researching the impact that a

change in seasonal adjustment methodology would have on both the CES data itself and on users.

Each month, parallel to the monthly production of CES seasonally adjusted data using projectedfactor methodology, CES runs concurrent seasonal adjustment for research purposes. The parallel

tests are structured in a way to measure only the effect of incorporating additional months of data

into the seasonal adjustment process. 

Current CES standard practice requires ten years of historical data to be used as input to the X-12

ARIMA model. The same historical data set was used for the experimental concurrent run.

Therefore, any prior adjustments originally made to the data during production, such as to account

for strikes or editing and screening, are included in the concurrent simulations as well. The only

difference in inputs between the two runs is that concurrent adjustment also incorporates up to 5

months of additional estimates when calculating the seasonally adjusted data.

In the parallel series, incorporation of revised seasonal factors is handled within the normal CES

monthly revisions procedures. With the calculation of first closing estimates for a current month,

the second closing and third closing estimates for the prior two months are revised on an unadjusted

basis to incorporate further sample receipts. Likewise, the concurrent seasonally adjusted data are

recalculated using revised second closing and third closing estimates, mirroring the current

production process. Finally, all published data types are seasonally adjusted under both methods;

however, because the all employee series is the most closely watched series published by CES, it is

the focus of this paper. 

Results: The two methods are compared in terms of (1) mean absolute revisions to the over-themonth changes evident from first preliminary estimate to the benchmarked series, (2) the variation

between monthly revisions, and (3) the smoothness of the seasonally adjusted series. Looking first

at the smoothness of the series, Figure 1 compares the third closing over-the-month changes of the

seasonally adjusted employment figures for Total Nonfarm from January 2001 to June 2002 for the

two methodologies. The dashed line shows the published over-the-month changes for third closing,

while the solid line shows the third closing over-the-month change for the experimental series (i.e.,

what the over-the-month change would have been if CES had been using concurrent seasonal

adjustment). As the graph illustrates, concurrent adjustment produces a slightly smoother

seasonally adjusted series with less variability in the over-the-month changes. 

Table 1 underscores the smoothness of the concurrent seasonally adjusted series for Total Nonfarm

plus all nine industry divisions. The smoothness ratio shown in column B of Table 1 is a comparison 
 Figure 1. Third Closing Over-the-Month Change, Total Nonfarm, January 2001 through June 2002

-500,000

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-300,000

-200,000

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0

100,000

200,000

300,000

400,000

Jan-01

Feb-01Mar-01Apr-01

May-01

Jun-01

Jul-01

Aug-01

Sep-01

Oct-01

Nov-01Dec-01

Jan-02

Feb-02Mar-02Apr-02

May-02

Jun-02

Month

Over-the-Month Change in Employment

Published

Concurrent

measure of variability in the third closing over-the-month change of the seasonally adjusted

estimate. The calculation compares the sum of the squared over-the month changes in the

concurrently adjusted series to the sum of the squared over-the-month changes in the projectedfactor adjusted series. A smoothness ratio below 1 indicates that concurrent seasonal adjustment

has less variability in the over-the-month changes than does a series adjusted using projected

seasonal factors. As Table 1 illustrates, concurrent adjustment produces a smoother seasonally

adjusted series for Total Nonfarm plus all nine industry divisions. Taken with the results from

Figure 1, this indicates that CES will benefit from a switch to concurrent seasonal adjustment by

producing employment series with less variability in the over-the-month changes.

 Table 1. Smoothness Ratio, January 2001 through June 2002

(A)

Group

(B)

Smoothness Ratio

(Third Closing)

Total Nonfarm .67

Mining .77

Construction .47

Manufacturing .87

TPU .78

Wholesale Trade .88

Retail Trade .56

FIRE .68

Services .58

Government .67
Results to this point focused solely on estimates of seasonally adjusted over-the-month changes in

employment. Also of interest is the revision to the estimate of the seasonally adjusted over-themonth change, both from first closing to the final benchmarked series, and between monthly

closings. Table 2 illustrates the size of the mean absolute revision to the over-the-month change

from first preliminary to the final benchmarked series for all nine major industry divisions and their

topside aggregate, total nonfarm. Column B shows the mean absolute revision in the over-themonth change for the 6-month projected method for March 1998 through March 2001, while

column C shows the same for the concurrent adjustment method. Column D shows the difference

between the two methodologies (concurrent minus 6-month projected). As the table illustrates, CES

employment estimates seasonally adjusted under the concurrent method have a smaller revision

from first closing estimates to final benchmarked series in eight of nine industry divisions plus total

nonfarm. In Wholesale Trade, the revision statistic was larger for concurrent adjustment, but only

by 0.2%. 

Table 2. Mean Absolute Revision in Over-the-Month Changes, March 1998 through March 2001

(A)

Group

(B)

CES Published Series

(6-month projected)

(C)

 Experimental Series

(Concurrent)

(D)

Difference

Total Nonfarm 77,973 64,973 -13,000

Mining 1,892 1,865 -27

Construction 22,892 17,838 -5,054

Manufacturing 13,757 12,487 -1,270

TPU 7,892 6,568 -1324

Wholesale Trade 11,135 11,162 27

Retail Trade 32,162 21,946 -10,216

FIRE 6,919 5,703 -1216

Services 38,784 29,703 -9,081

Government 23,135 17,432 -5,703

In addition to a smaller revision between first closing and the final benchmarked series, revisions in

the over-the-month changes between closings are of concern as well. In particular, there is the

potential for these monthly revisions between closings to increase under concurrent adjustment

because the seasonal factors can change with each iteration of the monthly adjustment process.

However, results indicate that, in addition to a smaller revision between first closing and the final

benchmarked series, concurrent seasonal adjustment leads to equal or even less variability in the

over-the-month changes between closings. 

Figure 2 shows the revision to the over-the-month change between seasonally adjusted first closing

and second closing total nonfarm estimates under both methods. The graph illustrates that, in

general, the concurrently adjusted series shows slightly less variability in the seasonally adjusted

over-the-month changes between revisions. Results were very similar for revisions between first

closing and third closing. 
Figure 2. Over-the-month changes between Revisions, 1st Closing to 2nd Closing Seasonally Adjusted 

Total Nonfarm All Employees Series, March 1998 – March 2002

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-100

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100

150

Mar-98

May-98

Jul-98

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Mar-99

May-99

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Jan-01

Mar-01

May-01

Jul-01

Sep-01

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Mar-02

Month

Employment (in thousands)

Published

Experimental

Likewise, Table 3 illustrates a comparison of revisions between closings for the currently published

CES series and the same series adjusted concurrently. As the table shows, the mean revision and

mean absolute revision in the over-the-month change does not differ between first closing and

second closing across the two methods. However, from first closing to third closing, both the mean

revision and mean absolute revision are lower in the concurrently adjusted series. These results,

when combined with results shown in Figure 2, suggest that concurrent seasonal adjustment will not

increase revisions between closings.

Table 3. Mean and Mean Absolute Revisions in Over-the-Month Changes, 

Total Nonfarm All Employees Series, March 1998 through March 2002

Type

CES Published Series

(6-month projected)

Experimental Series

(Concurrent) Difference

First closing to second closing 

Mean

Revision -4 -7 3

Mean

Absolute

Revision

37 34 -3

First closing to third closing 

Mean

Revision 19 4 -15

Mean

Absolute

Revision

48 36 -12
Summary of Advantages and Disadvantages of Concurrent Seasonal Adjustment

Advantages

More accurate seasonal factors – Concurrent seasonal adjustment is technically superior to the 6-

month forecasted factors because it takes into account the timeliest information available. Empirical

results from this analysis illustrate that seasonally adjusted CES data are closer to the final

benchmarked series under concurrent adjustment, leading to smaller revisions between first primary

estimates and the final benchmark series. Furthermore, monthly revisions between first closing and

third closing are slightly lower for concurrent adjustment.

Conversion to NAICS – Using concurrent seasonal adjustment will be especially advantageous

during the first few years following the CES conversion to NAICS because most of the NAICS

historical data will be reconstructed from the SIC-based sample. Only two years of NAICS history

from a NAICS-based sample will be available. Therefore, under the projected-factor method, in the

first year of the NAICS conversion, there would be only two historical NAICS-based estimates per

month used to calculate projected seasonal factors, while with concurrent adjustment three actual

NAICS-based estimates would be used (the previous two years of NAICS-based estimates plus the

current one). The additional observations will be valuable because X-12 weights the most recent

years more heavily than the past when calculating seasonal factors.

Familiarity with revisions – As discussed earlier, CES already revises two prior months of estimates

with each month’s release. As part of the current monthly production process, not-seasonally

adjusted estimates are revised for the previous two months, and projected seasonal factors are

applied to the revised estimate to calculate the new seasonally adjusted figures. No additional

revisions would occur under concurrent seasonal adjustment. Under concurrent adjustment, the

non-seasonally adjusted estimate for the previous two months would still be revised as before, and

the seasonally adjusted data for these months will be based on these revisions. 

Potential Disadvantage

Factors will not be available ahead of time – As discussed earlier, CES traditionally calculates

seasonal factors twice a year and projected factors are published in advance for the next six months.

Under concurrent seasonal adjustment, CES will not publish factors in advance because the new

seasonal factors are calculated each month. However, it is possible to make available beforehand

the ARIMA model specifications used by BLS so that the seasonal adjustment run can be replicated

if desired. 

Summary and Implementation Plans: The research done with the National CES employment

series indicates that the CES will benefit from conversion to concurrent adjustment through smaller

revisions to the over-the-month changes from the first closing estimates to the final benchmarked

estimate. Furthermore, it shows that concurrent adjustment would not increase revisions between

closings, and would actually reduce revisions from first closing to third closing. Based on these

results, simultaneous with the program’s conversion to NAICS industry coding in June, 2003, CES

will switch to concurrent seasonal adjustment methodology. At that time, the practice of publishing

forecasted seasonal factors will be discontinued.