nitude of the redesign effects. Subsequent research has provided some refmed and alternative estimates of the redesign effects 4, but also has made it clear that there is enough

uncertainty about the best way to measure the redesign ef~

feets to make it inappropriate for BLS to officially revise

the historical series to adjust for them.

On the other hand, the magnitude of the population-control effects was already known for 1993 and could easily be

computed by the Bureau of the Census back to 1990, the

decennial census year. The main questions remaining about

revision for these effects were: (1) Was 1990 the limit, or

could defensible revisions be done further back in time; and

(2) was there sufficient justification for, in effect, creating

two breaks from one by separating the timing of the population-control and redesign effects. With respect to the first

question, 1990 was determined to be the limit, principally

because there is no adequate basis for computing estimates

of undercount for any time prior to 1990 that would be comparable to those in the new population controls. No previous census had a Post Enumeration Survey (PES) comparable to the 1990 PES that provided the basis for the

undercount estimates in the new controls. On the second

question, the main consideration was that the redesign effects were probably small, and in some cases negligible, relative to the population-control effects, for many important

labor force characteristics. The moving back in time of the

population-control effects would therefore provide a longer

current series of reasonably comparable data for those characteristics even though, technically, there might still be some

effect of the redesign from January 1994 forward. Of course,

for characteristics with significant defmitional changes in

1994, such as part time for economic reasons, the redesign

effects were not small relative to the population-control effects, and significant comparability breaks would still remain between 1993 and 1994 after population revisions.

The production of revised data for 1990-93 was based on

revised monthly CPS sample weights for each of the 48

months, computed using population controls consistent with

those used to weight the CPS samples during 1994. The

derivation of population controls and their use in computing sample weights was described in some detail in the February 1994 article. Public use microdata files with the reRevisions in Household Survey Data

Effective February 1996

Robert J. McIntire

It is an annual end-of-year practice for BLS to compute

and issue revised seasonally adjusted data for recent years

and projected seasonal factors for the first half of the new

year for the labor force time series derived from the household survey. 1 Revisions to the not seasonally adjusted data

for those series are rare 2, but such revisions have been made

this year to the data for 1990-93. This article discusses this

year's revisions to the not seasonally adjusted and seasonally adjusted data and describes the current seasonal adjustment methodology.

Revision of Not Seasonally Adjusted Data

All of the household survey data released since 1994

reflect changes introduced effective with the data for January 1994.3 For each series, the net effect of those changes

reflects a combination of two underlying effects. One is a

redesign effect resulting from the complete redesign of the

survey questionnaire and of the technology and systems used

to administer the questionnaire and process the data from it.

The other is a population-control effect, resulting from the

introduction of 1990 census-based population controls with

adjustment for the estimated net census undercount.

Tables and databases that include data from before and

after the 1994 changes contain notes or warnings that the

data beginning in 1994 are not directly comparable with data

for 1993 and earlier. Historical comparability is, however, a

desirable characteristic for time series. Accordingly, BLS

also has been concerned about trying to help users bridge

the comparability break to the extent possible, by providing

information about the redesign and population-control effects and by considering possible revisions to the historical

series. The February 1994 article indicated the size of the

population-control effects for various groups using 1993 annual data, and provided some estimates of the possible magRobert J. McIntire is an economist and Chief of the Division of Data

Development and Publications, Office of Employment and Unemployment

Statistics, Bureau of Labor Statistics.

IBudget-related shutdowns and the weather-related closing of many Federal agencies, including BLS and the Bureau of the Census, delayed the timing of revisions this year.

2 The last previous occurrence of such a revision was in early 1982. See

"Revisions in the Current Population Survey Beginning in January 1982" in

the February 1982 issue of this publication.

3 See "Revisions in the Current Population Survey Effective JanllllIY 1994"

in the February 1994 issue of this publication.

8

4 SeeAnne E. Polivka and Stephen M. Miller, ''The CPSAfter the Redesign:

Refocusing the Economic Lens," paper presented at NBERICRIW Labor

Statistics Measurement lssues Conference December 1994, BLS Working

Paper 269, March 1995.

, I

,

I

j

!

~
vised weights for 1990-93 are expected to be available later

in 1996. The aggregate data for all of the months were

retabulated using the revised sample weights, and

recomposited. (See the Explanatory Notes for the household

data at the back of this publication for information on

compositing. )

-

the major labor force series at least, the redesign effects are

small and need not be a major concern when making longer

term comparisons of data for these series across the 1993-94

period and back to 1990.

!

Revision of Seasonally Adjusted Data

:

,

Seasonal adjustment attempts to eliminate from economic

time series the influence of weather, holidays, the opening

and closing of schools, and other such seasonal events. Its

purpose is to make it easier to observe and analyze the cyclical and other nonseasonal movements in the series. The seasonality which the adjustment process endeavors to eliminate is represented by seasonal factors. The seasonal factors

used for current adjustment are estimates of how much the

original unadjusted values can be expected to deviate from

underlying trend-cycle levels due to annually recurring behavior as projected from average seasonal patterns in the

recent past. I

Even though seasonality involves regularly recurring patterns, it does tend to change over time, creating a need for

periodic reestimation of factors and revision of recently adjusted estimates. By including more recent data in the estimation process, the revision process can provide better estimates of how much the original, unadjusted estimates actually deviated from underlying trend-cycle levels during the

recent period, thereby improving the historical seasonally

adjusted data for that period. In addition, the new information is incorporated to produce the new projected factors to

be used for current seasonal adjustment.

Therefore, at the end of each calendar year, the Bureau of

Labor Statistics reestimates the seasonality of the unemployment, employment, and other labor force series derived from

the Current Population Survey (CPS) by including another

full year of data in the estimation. process. Based on this

annual reestimation, BLS issues the projected factors for the

first 6 months of the new year as well as revised estimates of

historical seasonally adjusted data.] Usually, the data for the

last 5 years are revised. This time, because of the revision of

the not seasonally adjusted data for 1990-93 discussed in

the first part of this article, the revisions extend back 6 years,

to January 1990. In addition, because of the processing delays caused by the recent shutdowns and the weather-related

closing of many Federal agencies; the seasonally adjusted

data for January 1996 also were subject to revision.

Table 2 contains the projected seasonal factors for the first

6 months of 1996 for the 12 component series used in the

computation of the seasonally adjusted civilian labor force

and unemployment rate. (See the section on aggregation

procedures later in the article.) Projected factors for the last .

6 months of 1996 will be published in the July issue of this

publication.

I

Effect of revised seasonal factors

One of the criteria used to evaluate alternative methods of

seasonal adjustment is how close initial estimates are to sub9

Effect. of new population controls

The effect of the new population controls on the national

estimates for 1990-93 were very consistent with the effects

described for 1993 in the February 1994 article cited previously. Table 1 displays the old and new data and the differences between them based on 1990 annual averages for several labor force status categories. Increases in levels were

concentrated in younger age groups, races other than white

or black, and persons of Hispanic origin. Data for older age

groups generally decreased as a result of these revisions.

Over the 4 years subject to revision, the annual average

estimates of the civilian noninstitutional population 16 years

and over increased at a gradually increasing rate, beginning

with an increase of about 1.1 million (0.6 percent) in 1990,

and ending with an increase of about 1.3 million (0.7 percent) in 1993. Because of the demographic distribution of

the population increase, particularly with respect to age, the

estimated levels of civilian labor force went up by almost as

much as the population, resulting in average increases of

0.1 in overall labor force participation rates and employment-population ratios. Within the civilian labor force, the

new controls caused average increases of 0.1 in the overall

unemployment rate because demographic groups with relatively high unemployment rates, such as youth and persons

of Hispanic origin, had relatively large upward population

adjustments.

As a result of the population-control effect having been

moved from January 1994 to January 1990, the over-theyear changes in the annual average levels of the major labor

force aggregates between 1993 and 1994 are now: An increase of 1.9 instead of 3.0 million for civilian labor force;

an increase of 2.8 instead of 3.8 million for employment;

and a decrease of 0.9 instead of 0.7 million in unemployment. Also, primarily because of the revisions to the not

seasonally adjusted data, the month-to-month changes from

December 1993 to January 1994 in the seasonally adjusted

levels of the major labor force aggregates are now much

closer to being within the bounds of what can be regarded as

"normal" month-to-month change for these series: An increase of 0.7 instead of 1.8 million for civilian labor force;

an increase of 0.5 instead of 1.3 million for employment;

and an increase of 0.3 instead of 0.5 million for unemployment. The direction of these month-to-month changes are

so consistent with our understanding of the redesign effects

that it is virtually certain that the December 1993 to January

1994 month-to-month changes remaining after revision do

reflect to some degree the effects of the redesign. On the

other hand, the fact that changes such as these occur occasionally under normal conditions supports the idea that, for
I

I

Ll

I

Table 1. Labor force status of the civilian noninstitutional population by sex, age, race, and Hispanic origin using 1980 and adjusted

1990 census population bases, 1990 annual averages '

(Numbers in thousands)

Both sexes Men ! Women

Labor force status, age, Adjusted Adjusted Adjusted

race, and Hispanic origin 1980 1990 Net 1980 1990 Net 1980 1990 Net

base base difference base base difference I base base difference

TOTAL

Civilian noninstitutional population •.••.•....•.••..•...... 188,049 189,164 1,115 89,850 90,377 727 98,399 98,787 388

Civilian labor force .•..•.....•....••.•...•••...••..•..•..•••••..• 124,787 125,840 1,053 68,234 69,011 777 56,554 56,829 275

Participation rate ..............•.••.•.••...••.•.•..•..•....... 68.4 66.5 .1 76.1 76.4 .3 57.5 57.5 .0

Employed ••••••..•...•.•••...•.••.......•..•••••.•..••..•.•••.••.•. 117,914 118,793 879 64,435 65,104 669 ) 53,479 53,689 210

Employment·population ratio .....•..•...•..•••.•....•. 62.7 62.8 .1 71.9 72.0 .1 54.3 54.3 .0

Unemployed .......•.....•...••..•.••............•••..•.••....... 6,874 7,047 173 3,799 3,906 107 3,075 3,140 65

Unemployment rate ........•..•..••..••.....••............. 5.5 5.6 .1 5.6 5.7 .1 5.4 5.5 .1

Not in labor force ...•.••..••.........................•.....••..•. 63,262 63,324 62 21,417 21,367 ·50 41,845 41,957 112

16 to 19 years

Civilian noninstitutional population .............•.•.•..•.. 13,794 14,520 726 6,947 7,347 400 6,847 7,173 326

Civilian labor force ...••.......•....•.••........•.............•.. 7,410 7,792 382 3,866 4,094 228 3,544 3,698 154

Participation rate ................•.•...........•....•.....••.. 53.7 53.7 .0 55.7 55.7 .0 51.8 51.6 ·.2

Employed .•..•.•.•••....••..•..•..........•...................•.... 6,261 6,581 320 3,237 3,427 190 ! 3,024 3,154 130

Employment·population ratio .......................... 45.4 45.3 '.1 46.6 46.6 .0 44.2 44.0 ·.2

Unemployed ..........................•.........................• 1,149 1,212 63 629 667 38 519 544 25

Unemployment rate ........................................ 15.5 15.5 .0 16.3 16.3 .0 14.7 14.7 .0

Not in labor force ..........................................••.... 6,385 6,727 342 3,081 3,253 172 3,304 3,474 170

20 to 24 year.

Civilian noninstitutional population ....................... 17,799 18,902 1,103 8,647 9,320 673 9,152 9,582 430

Civilian labor force .............................................. 13,843 14,700 857 7,291 7,866 575 6,552 6,834 282

Participation rate ............................................. 77.8 77.8 .0 84.3 84.4 .1 71.6 71.3 ·.3

Employed .......................................................... 12,622 13,401 779 6,625 7,151 526 5,997 6,250 253

Employment·population ratio .......................... 70.9 70.9 .0 76.6 76.7 .1 65.5 65.2 ·.3

Unemployed ..................................................... 1,221 1,299 78 666 715 49 555 584 29

Unemployment rate ........................................ 8.8 8.8 .0 9.1 9.1 .0 8.5 8.5 .0

Not in labor force ..........................................•.•.•. 3,956 4,202 246 1,356 1,454 98 2,600 2,748 148

25 to 54 year.

Civilian (loninstitutional population ..........•...........• 105,498 105,777 279 51,641 51,884 243 53,856 53,893 37

Civilian labor force ................•.•..•.•.•...•...•...•.•.....• 88,140 88,322 182 48,259 48,456 197 39,881 39,866 ·15

Participation rate ..•.......•...........•............•......... 83.5 83.5 .0 93.4 93.4 .0 I ,.:~ 74.0 '.1

Employed •...••.•.•.•.•...•.•.•...•.•....•....•...•.......•........ 84,139 84,277 138 46,071 46,240 169 38,037 ·31

Employment·population ratio •..•....•................• 79.8 79.7 '.1 89.2 89.1 -.1 70.7 70.6 -.1

Unemployed ..•...•.•...•.•...•...•.....•...•.....•.....•.•.•...• 4,001 4,045 44 2,188 2,216 28 1,813 1,829 16

Unemployment rate ..•...........•...•.....•.......•.•••... 4.5 4.6 .1 4.5 4.6 .1 4.5 4.6 .1

Not in labor force ......•.........•.........•.....•.........•..... 17,358 17,455 97 3,383 3,428 45 13,975 14,027 52

55 years and over

Civilian noninstitutional population ................•...... 50,959 49,966 -993 22,415 21,826 -589 28,544 28,139 -405

Civilian labor force •.•.......•.....•.•.•.•...•.•.•...•...•.•..... 15,395 15,026 -369 8,818 8,594 -224 6,577 6,431 -146

Participation rate ......•.•...•..............•...•.•.•......... 30.2 30.1 -.1 39.3 39.4 .1 23.0 22.9 -.1

Employed .....................•............•....................... 14,892 14,535 -357 8,502 8,286 -216 ,: 6,390 6,248 -142

Employment-population ratio ..•...•.•.•.•.•.•.•....... 29.2 29.1 -.1 37.9 38.0 .1 22.4 22.2 -.2

Unemployed ..•...•.•.•.......•...•. :•...•.•........•..•.•..•.•.• 503 491 -12 316 308 -8 i 187 183 -4

Unemployment rate ..............•.•.•..................... 3.3 3.3 .0 3.6 3.6 .0 2.8 2.9 .1

Not In labor force .....•...............•......................•... 35,563 • 34,940 -623 13,597 13,232 -365 I 21,966 21,708 -258

White

CiVilian nonlflstltutlonal population ...•.................•. 160,415 160,625 210 77,082 77,369 287 83,332 83,256 -76

Civilian labor force ............•.•...................•........... 107,177 107,447 270 59,298 59,638 340 47,879 47,809 -70

Participation rate ......•..•.....•..•...•...........•....••..•. 66.8 66.9 .1 76.9 77.1 .2

I

57.5 57.4 -.1

Employed ..•.•..•..•.•..•..........•.•.....•••.•......•.•........•. 102,087 102,261 174 56,432 56,703 271 45,654 45,558 -96

Employment-population ratio .....••..••.•.••.•.••••••. 63.6 63.7 .1 73.2 73.3 .1 54.8 54.7 -.1

Unemployed •.•.•..•..•..•.......•....•.....•.........•.......... 5,091 5,186 95 2,866 2,935 69 2,225 2,251 26

Unemployment rate •..••...•.•..•.........•.•..•....••..... 4.7 4.8 .1 4.8 4.9 .1

I,

4.6 4.7 .1

Not in labor force •••..•..•...•....•.....•..•......••.•.•.••••••.. 53,237 53,178 -59 17,785 17,731 -54 ': 35,453 35,447 -6

Black

Ii Civilian noninstitulional population .....•.......•••.•..•.• 21,300 21,477 177 9,567 9,573 8 11,733 11,904 171

Civilian labor force •.••.••.••••...••...•.•.•...••..............•. . 13,493 13,740 247 6,708 6,802 94

If

6,785 6,938 153

Participation rate ••..••...•••..•..••..•.••.•..••....•..•.•...• 63.3 64.0 .7 70.1 71.0 .9 57.8 58.3 .5

Employed •....•.•••.•••.•.••.....••..•..••.•.•..•.•..•..•.........• 11,966 12,175 209 5,915 5,995 80 6,051 6,180 129

Employment-population ratio ....•..•........•........• 56.2 56.7 .5 61.8 62.6 .8

i

51.6 51.9 .3

Unemployed •••••.•...•.•..•....•.•..•...•.••.•.•......•..•.•...• 1,527 1,565 38 793 806 13 734 758 24

Unemployment rate ......••.•.•.••.••..••.•...•.......••..• 11.3 11.4 .1 11.8 11.9 .1 10.8 10.9 .1

Not in labor force •.•.•.•..•.•...•.•.•.•.•...•......•.....•...•... 7,808 7,737 -71 2,859 2,772 -87

:

4,948 4,965 17

Hispanic origin

i

Civilian noninstitutional population ............••..•..•... 14,297 15,904 1,607 7,087 8,041 954 7,210 7,863 653

Civilian labor force •.......••..••.•....••.....•.................• 9,576 10,720 1,144 5,755 6,546 791 3,821 4,174 353

Participation rate .•..................•..•..•.•.•.•....•...•..• 67.0 67.4 .4 81.2 81.4 .2 53.0 53.1 .1

Employed .•••.•.••..•••...•.....•.•.........................•.•.... 8,808 9,845 1,037 5,304 6,021 717 , 3,504 3,823 319

Employment-population ratio ••...•..•.............•... 61.6 61.9 .3 74.8 74.9 .1 i 48.6 48.6 .0

Unemployed ..•.......••..••..•......•......................•.•... 769 876 107 451 524 73 317 351 34

Unemployment rate ..•....•.•..•.•.•.•...•.•............... B.O 8.2 .2 7.B $.0 .2 B.3 8.4 .1

Not in laborforce ................................................ 4,721 5,184 463 1,332 1,495 163 3,389 3,689 300

10
I

r.tJ\e 2. pre-1994 adjustment and Janua~une 1996 seasonal adjustment factors for the 12 major civilian labor fOrce components

Procedure and series

-47

(I)

Prior Season8Jadjustment factors

adjustment f----.----....-~--_._---.._---_,_---

factors January February March April

(1) .895 .907 .936 .996

.776 .873 .891 .956 1.026

.860 .616 .645 .802 .862

.853 .708 .622 .699 .858

(1) .986 .989 .993 .996

(I) .995 1.002 1.000 1.000

.938 1.164 1.164 1.107 1.019

.976 1.047 .998 .980 .945

Multiplicative adjustment

:.' (Divide factor into Original value)

'·ltural employment:

'20 years and over ..........••...... , .

n, 20 years and over .....•................

, 16to19years .

en, 16 to 19 years •........•..................

ricultural employment:

, 20 years and over .

iW~C!men,20 years and over .....................•

~ployment:

~j1Men,20 years and over .

,,,Women, 20 years and over .

Additive adjustment

(Subtract factor from original value)

Nonagriculturalemployment:

Men, 16 to 19 years .

Women, 16to 19 years ...........................•

-68

-96

Unemployment:

'Men, 16 to 19 years ................................•

Women, 16 to 19 years .

1 No prior adjustment was done.

sequentrevisions. Policymakers and analysts must make determinations based on current information, and so it is important that the initial estimates of current factors for the

s~onal adjustment of major economic series produce estimates of level and change that are as close as possible to the

improved estimates that will be made after more data become available. Even though the current revisions of the

seasonally adjusted data for January 1995 through January

1996 are not final, these are the first revisions for that period, and first revisions are usually more substantial than,

andoften indicate the direction of, any subsequent revisions.

Therefore, it is appropriate to compare these first revisions

with the initial estimates. Table 3 shows the civilian unemployment rate for January 1995 through January 1996 as

first computed and as revised, as well as the changes due to

revision. Rounded to one decimal place as published, the

rate changes in only 2 of the 13 months, and only by 0.1

percentage point. Clearly, the trend observed in the initial

estimates was sustained in the revisions.

Adjustment methods and procedures

The official seasonal adjustment procedure for the labor

force series is the X-II ARIMA program, which was developed at Statistics Canada during the 1970's as an extension

of and improvement to the widely used X-II method developed at the U.S. Bureau of the Census in the 196O's.5 The

X-U ARIMA method improves current estimates for most

-28

-27

17

38

-292

-213

-295

-239

-269

-210

-229

-204

May June

I

1.062

1.053

1.081

.956

i

1.000

1.000

1.087

1.087

1.563

1.537

-31

-59

10

-42

-53

-59

1.005

.993

i

i.965

..923

I

i

I

I

:-186

i -92

I

.939

1.022

382

370

223

228

I

Table 3. Seasonally adjuated unemployment rates and

change due to revision, January 199s-.January 1996

Month As first As Change computed revised

5.7 5.7 .0

5.4 5.4 .0

5.5 5.5 .0

5.4 5.7 -0.1

5.7 5.6 -.1

5.6 5.6 .0

5.7 5.7 .0

5.6 5.6 .0

5.6 5.6 .0

5.5 5.5 .0

5.6 5.6 .0

5.6 5.6 .0

5.8 5.8 .0

1995

January .

February :...............•..•................•.

March ...............••........•.•...................

April ................••................................

May ................•.........•.......................

June .•...............................................

July ...........•...................••.........••.......

August .

September .

October ..••..........•.........•........•..........

November .•.........•.........•...................

December .

1996

January ...••..........•.....•..•...................

series by allowing recent observations, especially those of

the last 6 months, to weigh more heavily in the estimates of

current and recent seasonal factors than did the X-ll alone.

, The primary documentation for the X-ll ARIMA procedure is The X-llARIMA Seasonal Adjustment Method, by Estela Bee Dagum (Statistics

Canada Catalogue No. 12-564B, January 1983). (ARIMA is an acronym for

AutoRegressive Integrated Moving Average.) The X-II method was originally described in The X-ll Variant o/the Census Method II Seasonal Adjustment Program, by Julius Shiskin,Alan Young, and John Musgrave (Technical Paper No. 15, Bureau of the Census, 1967).,

I

11
Table 4.ARIMA models used In end-of-1995 seasonal

adjustment for the 12 major clvman labor force components

Series Model Transfonnatlon

Agricultural employment:

Men, 20 years and over ••.••.•••••..• (1,0,0)(0,1,1) LOG

Women, 20 years and over ••...••.. (0,1,1)(0,1,1) LOG

Men, 16to 19 years ••••..•..........••• (0,1,2)(0,1,1) NONE

Women, 16 to 19 years .••••.....•..•• (2,1,2)(0,1,1) NONE

Nonagricultural employment:

Men, 20 years and over ••.••••••••..• (0,1,1)(0,1,1) LOG

Women, 20 years and over ••••••..• (0,1,4)(0,1,1) LOG

Men, 16to 19 years ..••..••.••••...•••• (2,1,0)(0,1,1) NONE

Women, 16 to 19 years •••.••••..•••••• (2,1,0)(0,1,1) NONE

Unemployment:

Men, 20 years and over ••.•••••••••.. (0,1,3)(0,1,1) LOG

Women, 20 years and over •••.••••. (0,1,1)(0,1,1) LOG

Men, 16to 19 years •••••....••••.•..••• (0,1,1)(0,1,1) NONE

Women, 16 to 19 years ••••••....•.••. (2,1,2)(0,1,1) NONE

The method provides this improvement through the use of

ARIMA models to extend the data series by 12 months. The

X-ll algorithm for seasonal adjustment is then applied to

the extended series.

ARlMA models. ARIMA projections are based only on the

past experience observed in a series itself. ARIMA models

have proved to have good properties for short-term projection or extrapolation of a large class of time series, especially in a seasonal adjustment context, since the extrapolations tend to track intra-year movements quite well. The

ARIMA models in the X-II ARIMA program used to seasonally adjust the labor force series are of the Box-Jenkins

type. 6 They can generally be described with the notation:

(p,d,q)(P.D,Q) TRANSFORMATION,

Where:

(1) p is the number of regular (nonseasonal)

autoregressive parameters

(2) d is the number of regular differences

(3) q is the number of regular moving average

parameters

(4) P is the number of seasonal autoregressive

parameters

(5) D is the number of seasonal differences

(6) Q is the number of seasonal moving average

parameters

(7) TRANSFORMATION may be NONE,

LOG, or POWER(n).

6 For a more detailed discussion of ARIMA models, refer to previously

cited Dagum (1983) and to G.E.P. Box and G.M. Jenkins, 7Ime SeriesAnalysis, FOTrlcasting, and Control (SanFranclsco, Holden Day, 1970); and C.WJ.

Granger and P. Newbold, FOTrlcasting Economic 7Jme Series (New York,

Academic Press, 1977).

12

I

While the lettered elements within the parentheses of the

model specifications can theoretically take on many values,

in practice, only small values are useful.

For each labor force series which has been extended based

on an ARIMA model, the model has been specifically chosen as well suited to the particular series, based on a set of

established criteria. The criteria essentially require a model

to: (1) Fit the series well, (2) have low average forecasting

errors in the last 3 years prior to the projected year, and (3)

produce residuals (the differences between the observed val- .

ues and the values forecast by the model for the observed

period) which follow a random pattern. Acceptable ARIMA

models have been identified and were used for 167 of the

173 labor force series which were directly adjusted at the

end of 1995, including all 12 major civilian labor force components, whose ARIMA models are shown in table 4. The

models for two of those major components-unemployment

for women 16-19 and women 20 years and over-are different from those used last year. The six remaining series (of

the 173) for which acceptable models were not identified

were simply run through the X-ll part of the programwithout any ARIMA extrapolations. .

X-ll procedures. The procedures used for this year's adjustment of the labor force series within the X-ll part of the

process were different from the standard procedures of most

previous years in three respects. All three were intended to

help the seasonal adjustment process deal more effectively

or more easily with the effects discussed earlier-the population-control effects that now begin in January 1990, and

the redesign effects that begin in January 1994.

First, like last year, prior adjustment factors were still used

in the X-II ARIMA runs for many of the series to link the

pre-I994 datil with the subsequent data for purposes of seasonal adjustment. Even after the revisions to the unadjusted

data for 1990-93, there were still unusual changes in many

of the series between December 1993 and January 1994 that

could have caused distortion in the seasonal decomposition

if no adjustment were done. The prior adjustment factors

used a year ago 7 were recomputed to adjust for the effects of

the revisions to the not seasonally adjusted data for 1990-

93. Smaller prior adjustments resulted for mariy series, because the population-control effects were usually in the same

direction as the redesign effects. For some series, including 4

of the 12 major components, prior adjustment was no longer

necessary at all. However, for series such as part time for economic reasons and full-time employed, the population-control

and redesign effects were in offsetting directions, and larger

prior adjustments were needed, For each series still warranting prior adjustment, the factors were applied to all pre-1994

observations in X.;l1 ARIMA. The prior adjustment factors

used for the 12 major components are shown in table 2 alongside the seasonal adjustment factors.

,

7 For a discussion of the original derivation of those factors, see "Revision

of Seasonally Adjusted Labor Force Series" in the January 1995 issue of

this publication.
, second, also like last year, the shorter "3x3" seasonal factor

moving averages were chosen in place of the normal "3x5"

default ones for all series. This allowed the 1994 and 1995

datato contribute more weight (and the pre-I994 "old CPS"

data less weight) to the revised seasonal factors for 1994

arid1995 and the projected seasonal factors for 1996 Computed in these runs. The temporary use of the shorter sea-

, seShal factor moving averages in these early years of the re-

,aes:ignedCPS should enable the seasonal adjustment results

"to,jiiore quickly adapt to any changes in seasonal patterns

tliit'might have occurred in the labor force series due to the

cbaDgesintroduced at the beginning of 1994.

'Tbird, a 6-year time span, including data from January

1990 through December 1995, was used for this year's adjustment of all the labor force series, instead of the usual 10-

yearspan. This was done primarily for the sake of keeping

the seasonal adjustment process no more complicated than

it hasto be to serve its principal objectives. Inclusion of pre1990 data in these runs would have required the computation and use of additional prior adjustment factors for many

seriesthat would have had little or no effect on the seasonal

adjustment of current data. To benefit the revisions of historical seasonally adjusted data back to 1990, the AR1MA

option that computes a year of backcasted data in addition

to the usual forecasted year was chosen for all of the directly

adjusted series with identified AR1MA models.

The X-II method of seasonal adiustment contained in the

X-ll AR1MA procedure assumes that the original series,

including the 12 extrapolated observations if an AR1MA

model has been applied, is either the product or the sum of 3

components-trend-cycle, seasonal, and irregular. The

method uses either a ratio-to- or difference-from-movingaverage approach to estimate the components, depending

on whether the multiplicative or additive model is used. The

seasonally adjusted series values are computed by dividing

each month's original value by the corresponding seasonal

factor if the multiplicative model is used, or by subtracting

the factor if the additive model is used. Of the 12 major

civilian labor force components, the 4 teenage unemployment and nonagricultural employment series were adjusted

using the additive model, and the other 8 series with the

multiplicative model. Of all the 173 directly adjusted series,

48 were adjusted with the additive model, including most

teenage employment and unemployment series, for which

the seasonal components were found to be fairly independent of the trend-cycle.

Moving-holiday adjustment. Two of the series directly adjusted with multiplicative models were seasonally adjusted

using the moving-holiday extension of X-ll AR1MA which

wasdeveloped at BLS. Both holiday-adjusted series-at work

on part-time schedules for noneconomic reasons, usually

work part time, all industries and nonagricultural industrieS-had tested as having significant and well-defined effects in their April data related to the timing of Easter. A

detailed discussion of the nature of the Easter effect in these

series and of the procedure used to control for it as part of

the seasonal' adjustment process was included in the January 1990 version of this article.

Six-month updates. The current official practice for the seasonal adjustment of the labor force series involves the running of all directly adjusted series through X-II ARIMA

twice each year, usually 8fter receipt of June and December

data, with 6 months of projected factors. drawn from each

run and historical revisions drawn from the end-of-year run.

This practice allows, among other things, the prior publication of seasonal factors, which historically bas been regarded

by BLS and other statistical agencies as an important way of

ensuring the openness of their seasonal adjustment procedures, especially where very sensitive indicators such as the

unemployment rate have been involved. A number of research studies, including a 1987 paper on the labor force

series," have indicated that the alternative practice of concurrent adjustment, where the seasonal adjustment procedure is run with all available data each month and factors

cannot be published ahead of time, generally produces initial seasonally adjusted estimates requiring smaller revisions

than those produced by adjusbnent using projected factors.

BLS is continuing to compute and evaluate concurrent adjustment for the labor force series.

i

I

Aggregation procedures I

BLS maintains and publishes several hundred seasonally

adjusted labor force series in addition to the 173 directly

adjusted series discussed above. These additional series are

produced by arithmetically combining or aggregating the

directly adjusted series with each other or, in some cases,

with series on population which are not seasonally adjusted

because they are not considered to have any significant seasonal variation. For example, the seasonally adjusted levels

of total unemployment, civilian employment, and civilian

labor force, and the seasonally adjusted unemployment rate

for all civilian workers, are all produced by aggregation of

some or all of the seasonally adjusted results for the 12 major civilian labor force components. The seasonally adjusted

level of total unemployment is the sum of the seasonally

adjusted levels of unemployment for the four age-sex

groups-men and women 16 to 19, and men and women 20

years and over. Seasonally' adjusted civilian employment is

the sum of the seasonally adjusted levels of employment for

the eight employment components-the same four age-sex

, groups as noted above employed in nonagricultural and agricultural industries. The seasonally adjusted civilian labor

force is the sum of all 12 components. The seasonally adjusted civilian unemployment rate is calculated by taking

the total seasonally adjusted unemployment level as a percent of the total seasonally adjusted civilian labor force.

,

a G.R. Methee and R.J. McIntire, "An Evaluation of Concurrent Seasonal

Adjustment for the Major Labor Force Series," in the 1987 Proceedings of

the Business and Economic Skllistics Section, American StatisticalAssociation.

13
The principal reason for producing many of the major

seasonally adjusted estimates for the labor force by aggregation rather than by direct adjustment is that this approach

ensures that the major seasonally adjusted totals will be arithmetically consistent with at least one major set of components. If the totals were directly adjusted along with the

components, such consistency would not, in all likelihood,

occur, since the X-ll is not a sum-preserving procedure;

that is, the sum of the result for two or more directly adjusted series will not generally be the same as the result of

directly adjusting the sum of the unadjusted versions of the

same series. Another factor is that it would generally be inappropriate to apply seasonal factors computed for an aggregate series to the components of the aggregate. The various labor force components tend to have significantly different patterns of seasonal variation; for example, teenage unemployment tends to peak: in June, while unemployment of

adult men tends to peak: in the winter months of January

and February. In order to estimate properly these varying

seasonal patterns, it is necessary to adjust the components

directly. Of course, one of the implications of producing seasonally adjusted estimates for many major series by aggregation is that exact factors cannot be projected for those se14

ries. However, implicit seasonal adjustment factors can be

calculated after the fact by taking the ratio of the unadjusted

aggregate to the seasonally adjusted aggregate, or, for additive implicit factors, the difference between those two aggregates.

Availability of revised series

This issue of Employment and Earnings contains revised

monthly data for 1990 through January 1996 for many seasonally adjusted labor force series. These revisions replace

the seasonally adjusted estimates published previously for

that period.

Additional data for any of the several hundred seasonally

adjusted labor force series, as well as the January-June 1996

factors for any of the directly adjusted series beyond the 12

major components, can be obtained from BLS upon request.

Requests for data or inquiries concerning seasonal adjustment methodology or the availability of machine-readable

files of labor force data should be addressed to the Division

of Data Development and Publications, Office of Employment and Unemployment Statistics, Bureau of Labor Statistics, Washington, DC 20212.