Continuation of NLS Discussion Paper 92-4
Part 3 of 3
This version of the paper was split for web delivery.
APPENDIX
A: Construction
Describing
In this appendix
described.
Earnings
the procedures
of a Data
Set from
afid Employment
and employment
These data We used both to describe the earnings and employment
The YNLS data are briefly discussed.
derivation.
year e~nings
There foUows a very dettiled
data ~e
data are often not avtilable for rdl jobs.
incomplete
md discussed.
usually atilable
It is very critical for the unemployment
discussion of data
It is established
for at least one job,
years being dropped
insur~ce
from the
analysis which requir~
earnings data be complete in W calendar years. Since e~nings
at the endof
experiences
This is not so critical for Section 3, where
earnings data may lead to only specific cdend~
on 1-s important
are
insurance,
Then the samples used in Section 3 are defied
that wtile in any cdendm
analysis.
Experierices””
used to construct data on e=nings
of youth, and in the subsequent mralysis of unemployment
eartings
the YNLS
that
data are most often missix~g
“intermittent p jobs, these missing data are imputed by methods described
this appendix.
The discussion is qtitk dettiled.
To mderstand
the body of this appendix,
it is sufficient
to read only Sections A. 1 and A.5, which are self-contained.
A. 1
General Considerations
The data used come from the first seven rounds of the National Longitudinal
Youth (Yh’LS ). This survey commenced
22 y~s
in 1979, when the respondents
old. Calendar year data can be constructed
Surve~ of
were between 14 and
for the seven years from 1978 to 1984.
In this study the first yem, in which there is relatively more missing data, is not amdyzed.
To avoid confusion about whether a quoted year is the cdendm
or the interview year from which data is obttined,
intemiews
year under investigation,
=e referred to by the survey
round number rather than the yem. Survey Round 1 w= conducted
.
in 1979, Survey Round
2 in 1980 mrd so on.
The prim~
data source is the YNLS Data Tape.
multi-file format tape for rowds
1 to 7 combined.
is used. This is rdso pubficly atilable,
from the public-rele~e
Work Histo~
The version used is the public-use
b addition, the YNLS Work History Tape
but this study uses a pre-release version whi& differs
in the fo~owing
76
tinor
respect,
To .consmve space the
public-release
version
omitted
data
for the sixth
the A and DLIALJOB
arrays still use information
6 through 10 will require use of the .4DDJOBS
held
since
the previous
(In the public-release
x~ersion.
on up to ten jobs, but to get data on jobs
files, w-hich are avtilable for most but not all
At each interview, between 25 and 47 of the 12,686 roses have more than five jobs. )
For geographic
information,
The complete
military
smple,
tenth job
The pre-release version has data on dl ten jobs.
ixlterx.iew.
years.
through
the Geocode
tapes are used.
sample of 12,686 people includes non-random
personnd.
However,
this study uses only the 6,111 people
designed to represent the noninstitutional
as of January
samples of the poor and
in the cross-section
civifian segment of youth aged 14 to 21
1, 1979. Otherwise there are few restrictions on the sample, md every effort
is made to keep sample sizes m large as possible.
made to clean or omit suspect data. One exception
In particular,
is that stat
only mizlimal attempts are
-d
stop dates for jobs and
gaps withixl jobs are checked for vflldi ty.
A..2
if’ork Htitoq
Data
The analysis is based on data on each job held since the last interview.
This is obttined
in the Employer Supplements and the “On Current Labor Force Status” section of the main
questionnaire.
This data is stored in a convenient
w,hich has weekl~ activity
respondent
form in the YNLS Work History Tape.
arrays that give codes for every employer in that week, or if the
is not employed
the major acti~.ity. WeeMy data on labor supply and earnings
can be obttined.
These data are aggregated
icdly
to form cdend~
year data. The calendar year data will typ
use data horn the two interviews that together spa
calendar year 1981 computed
earnings wi~ gen=dly
that calendar year. For exaple,
use data from both the 1980 ad
interviews (but will use other interviews if one or both of these inteniews
A major t=k is to determine which respondents
The term “missing”
data encompasses
(1) Data cannot be obttined
(2) Received
1981
are tissed),
should be omitted due to missing data,
the following situations:
due to noninterview.
income from mihtary semice.
(3) Data cannot be obtained
due to age 15 years or less (every job is obttined
?i
onIy for
respondents
16 y~rs
or older).
(4)
Job dates given are inconsistent;
(5)
Data cannot be obtained
quations;
e.g.
e.g. stop before start,
due to falling into > “class that is not asked the relevant
wage rates are not obttined
for d] jobs.
(These are coded as 4
i!).
the raw data. )
Missing in the sense used in the YNLS: the respondent
(6)
refused to answer (-1) or did not know (-2),
or shodd
w-as inked the question but
have been asked the question
but w-as not (-3).
Most data are “tissing”
A.2.1
Data
on Each
for the first five reasons.
Job in Each
For dl respondents
B:eek
16 yems and over, detailed information
is obttined
ci},ilian job held since the previous inter~-iew-. This information
started and stopped;
within this, dates for periods
work for the employer;
Unfortunately,
In many instances a respondent
A code tists
confusion
the following
data at eack interview.
employ~.is
during which the respondent
did Ilot
for every job,
will be working for the same employer
to fink jobs v,ith the same employer
terminology
Data on an employer is obttined
at different in-
across interviews.
is used, Data on a job is obttined
different interviews for the same employer.
job level, i.e.
includes the dates the job
hours per week usua~y w-orked at the job; and the usual wage rate.
the wage rate is not obttined
terviews.
for every “regular”
To avoid
directly from the job
by linking different job entries from
Most of the analysis in this study is done at tke
the fact that different jobs from different inter~,iews may be w,ith the same
ignored.
The key variables me based on the following
definitions,
drawn from the YNLS ques-
tiontire:
.
Job:
“Some jobs are odd jobs - that is, work done horn time to time, like occmiona[
or babysitting.
Others are m~lar
lawnmowi:lg
jobs - that is, jobs done on a more or less regdar
(Not counting the job you had Imt week), Since (DATE
OF LAST INTERVIEW’),
jobs you ‘ve had for pay been done on a more or 1=s regular b=is?
78
b~is.
have any
Please give me the names
of each of your
IN,TERVIEJV)
(not
Additiond
sponsored
borhood
employers
for all regular
counting
questions
the job
are asked
jobs
you ‘ve had for pay
(DATE
OF
LAST
you had last week),”
to ensue
recording
programs such as college work-study,
Youth Corps In-School,
since
of all jobs for pay with go~.ernment
high school cooperative
summer employment,
work-study,
Neigh-
and, employer t& credit,
The job last week is the job picked Up in the ‘~On Current Labor Force .Status” section of
the main questionnaire,
where questions virt”dly
the same w those in the mont~y
CPS are
asked, In particular, the respondent is wked to report any work at dl l~t week, not counting
work around the house, and to give details for the employer
worked the most hours last w~k,
tith
whom the respondent
This job may be either occasional
or regular.
(About
5
percent of the CPS jobs are occasional rather than regular in Surveys Round 3 to 6 for tke
full s~ple
of 12,686).
Note, for the items bdow,
we have about (EMPLOYER),
since (DATE
the respondent
ple~e
thifi
is ~ked
“For dl of the mst of the questions
only of the time you worked for (EMPLOYER)
OF LAST INTERVIEW’).
Gaps w-ithinJobs:
‘lFor one reason or another,
people often do not work for a week, a month, or even longrr.
For example, strikes, layoffs, and extended illnesses can cause people to miss work for a week
or longer. Between (DATE STARTED
JOB / NOW),
not counting paid vacations or paid sick leave?”
gaps are reported.
Hours per week:
“How, may
and (DATE
STOPPED
were there any periods of a full week or more during which you did not work
for this employer,
Up to 4 sud
JOB / LAST Ih’TERVIEW’)
hours per week (do/did)
you usua{ly work at this job?”
79
wage
“Altogether,
this job?
including
tips, overtime, ”and bonuses, ho~~.much (do/did)
Please ~ve me the amount you e=n(ed)
Security (are/were)
before
deductions
you usually earr] at
like trees and Social
taken out. Was that per hour, per day, per week, or. ~’hat7°
The wage
The wage is reported as hourly, dtily, weekly, bi -weeUy, ‘monthly and annual.
rate question is OAY =ked
if one or more of the following
job is the current job recorded
sponsored
program.
in the CPS section.
conditions” are satisfied:
(1) The
(2) The job is part of a government-
(3) .The job has been held for more than 9 weeks and is for 20 or more
hours per week md the respondent
is 16 years md older. Thus wages for intermittent
jobs
are missing.
The necessary jobs-related
data from the Employer Supplements
original rav, YNLS datfi set, and the YNLS Work History Tape.
as the data are stored as an euily
History tape h= useful constructed
accessed PL/I
v=iables
below,. The Work History tape is accompanied
description
of the PL/I
sud
are stored on both the
The latter is used here
data structure.
In addition,
as the A and DUALJOB
by documentation
the Work
mrays described
that includes a listing and
program that. created the v,ork history tape.
The work history program uses the raw. data on start and stop dates for jobs, and start
and stop dates for gaps within jobs, to construct
wee~y
activity
DUALJ OB arrays, which dettil every job that the respondent
who had no civilim
Working
job, additiond
or in the Mifitary”
data from the “Military”
had that week.
(For those
and “Gaps when R was not
or out of the labor force).
The dates are originally entmed to the day. Employment
employment
the A aIld
sections are used, and the arrays indicate whether the person
was in the active armed servicm, or unemployed
=
arrays, ded
for the whole week.
For example,
in any day of a week is treated
a job that begins on a Wednesday
treated as beginning on the previous Sunday, and a job that ends on a Wednesday
u ending on the foHowing Satwday.
.
If job start md stop dates me randody
across the week, tbe length of employment
a week.
Howeva,
disproportionate
is treated
distributed
at each job wiH on average be overstated
the bias is nowhere near as ~eat
number of jobs begin on a Monday
80
m this for the fo~owing
md end on a Friday.
is
reasons.
by
A
For jobs from
different survey rounds msociated
with the same employer,
the problem
start date of the job in the first survey that the employer is recorded
the job in the last survey that the employer is recorded.
within jobs are similuly
History Tape follows.
and the stop date of
The start and stop dates for gaps
treated, which imparts a potential
A detailed d~cription
arises only for the
bias in the opposite
directioJ1.
of the program that constructs jobs data from the YNLS Ivork
ACCUS to documentation
for this data set is assumed.
Hourly Wage and WWMy Wage for Each Job:
If the job is not for pay, the Work History program sets HOURLYWAGE
be recoded
to zero. H CLASSWORKER
equals -4 ) then HO URLIWAGE
equals 4 and (PAYRATE
equals O. For Rounds
to –4. This needs to
equals O or TIhIERATE
1 to 7 there are a total of 105 such
jobs.
The weekly wage is constructed
H OURSI$’EEK
This algorithm
in the ob~,ious fashion.
> 0 then WEEK LYJVAGE equals HO URLYW’AGE
can compute
This is the case when wages are reported
(then WEEKLY
WAGE
PAYRATE
= PAYRATE
/ 4.3) and yearly ( WEEKLYWAGE
done only in those cases where HO URSWEEK
of 105 such jobs.
WEEKLYWAGE
In the Work History progra,
males
is reported.
even if data on HOURSWEEK
as wee~y
(then WEEKLYW.4GE
/ 2), monthly
= PAYRATE
>=
O and
timm HO URSJVEEK.
WEEK LY\t’A GE ordy if HOURS WEEK
cases it is possible to construct WEEKLYWAGE
hi-weekly
H HOURLY~’AGE
In some
are tissing.
= PAYR.4TE),
(then WEEKLYWAGE
=
/ 52). These calculations
are
is missing. For rounds 1 to 7 there are a total
is truncated
to the nearest cat.
HOURLYWAGE
is truncated
to the ne~est
cent.
This
no difference if wages are reported at m hourly rate, the case for hdf the reported
wages. But for wages reported as daily, etc. the hourly wage and weekly wage will be shghtly
understated.
Missing Data Bemuse Not kterviewed:
If the respondent
tisses
one or more rounds of the survey, but is interviewed
round, data are not missing,
since dl the necessary questions are uked
81
at a later
for the period since
the luI
inter~iew,.
to missing
E the respondent
for weeks
subsequent
is not irrfervieu,ed
at a later
round,
then
data
are set
to the v,eek of the last inter~-iew.
Liissing Data because Active hlilitar~ SerTyice:
The WoIk History Tape includes start and stop dates for each period of active service in the
military.
Active service is service in “the branches coded 1 to 4 in the “Milituy”
the questionnaire;
viz. army, na~,y, sir force, marine corps.
Reserves or National Guards.
section of
It does not include any of the
The dates are used rather than a code of 7 in the A array, as
when a person in the mifitary rdso holds a civifian job the A array records the job rather
than military ser%.ice. For weeks in which the respondent
is in the active services, data are
set to missing.
Missing Data because Bad Dates:
The constructed
data are based on the A and DUALJOB
array s.’ These mdy include jobs for
which valid start and stop dates me a%-tilable. If the dates are invalid, there is no record of
the job in the A or DUALJOB
mray, and no indication
that the job is missin”g.””Similarly, if
the dates for gaps within jobs are invalid, there is no indication
that the gap is missing.
(Though
of the gap, =d
no indication
for the first gap there is a record, the A array being set
to 3). For weeks in which dates for jobs or gaps within jobs are invalid, annual computed
=rnings
are set to missing. Also, more stringel]t tests of date didity
A respondent
is treated as having a job if START
gap within jobs if WEEKSNOTWORKED
> -4
we used.
or STOP
> -4,
# O and WEEKSNOTWORKED
and having a
# -4.
Dates
for jobs are invalid for the following reasons:
(1)
START
(2) START
> STOP
+1 or START
< LASTINT
<0
–1 or STOP
(3) PEWODSTART
> PERIODSTOP
(4) PE~ODSTART
< START
or STOP
<0.
> INT +1.
or PERIODSTART
-1 or PERIODSTOP
e O or PE~ODSTOP
> STOP
<0.
+1.
.
In the first round, some of the dates for gaps within jobs are associated with the wrong job.
Ths
is an errQr in the YNLS Data Tape that will be detected by the above tats
cases), and the additiond
reasons for rejection:
82
(iIl mallY
(5) PERIODSTART
>=
O or PERIODSTOP
>=
O when WEEKSNOTWORKED
equals O or —4
(6) PERIODSTART
>=
O or PERIODSTOP
>= O when START=
The Work History program does only checks (1) and (3).
–4 or STOP = =4.
The presence of the “+1”
“- 1“ terms in the above twts may at first seem strange. It ‘is necessay
because of the w-a>
dates are treated for jobs held at the interview date. For example, if the respondent
job on 1/10/79
~d
= FLOOR(3ii/i)
tid.
was interviewed on 1/12/79,
= 53, in which case STOP
See ‘fDescIiption
then STOP,=
or
CEIL(3i5/i)
ended a
= 54 and INT
> INT even though the dates are obviouslf
of the NLSY 1979-1985
Work ~lstory
P“iogram” and the program
itself, for further det tils on its treatment of dates.
If the data fail the checks abo,,e, job dates zre treated as being in,did
to INT, or in the case of che&s (4) ad
(5) from START to STOP. Data are set to missing
for the weeks that these dat= lie in. Typically
Missing Data because of Mssing
jobs for which the wage rate W= deliberately
and for less thu
two calendar years w-ill be effected.
l~age:
As already noted, wage rates are not obtained
1 dow,Ii to 19 perceI1t in Wund
from LASTINT
for all intermittent jobs.
not requested r=ges
The percentage
of
from 29 percent in Round
i. In these cases the job is one held for less than 9 weeks
20 hours per week. For an additiond
2 percent of dl jobs, wage rates are
missing due to refusal, don ‘t know-, invalid skip, or code 7 for time unit rate of pay.
A.2.2
Weekly
Earnings
and ~’ork
Ezpetiences
Weekly hours ( WH) and weekly earnings (WE) from dl jobs this week are obtained
summing usual hours per w-k
A and DUALJOB
&tided
k
19i8
per week over ea~ job recorded in the weekly
Hourly eartings this wek
(WE/WH)
is simply computed
= WE
over weeks in the cdend~r
year is
by WH.
section
studied.
=ays.
and eatings
by
3 tiation
The cdenda
in WH, WE and WE/WH
year is standardized
through 1984 using the s~eme
at 52 weeks.
Weeks are docated
described bdow in Section A.2.3.
83
to the yeas
.
A.2.3
Annual
Computed
Earnings
and
To obtain annual work experience
Ii’ork
Experiences
or earnings data the baric approach
is tile follo~.illg,
From the A and DUA.LJOB urays” obtain the number of weeks in each calendar year at each
job.
Multiplying
by each job’s hours per week or eartings per week and summing across jobs
yields annual hours (AH) or ann.d
To make ACE comparable
*
computed
~nings
with annual reported
(ACE).
earnings
(ARE,
defined
below)
the
calendar year length for ACE and AH is determined by the number of work days.
When a w=k
year, according
spans two calendar years, jobs in that week are dlbcated
to the proportion
of the work week (Monday
Weeks begin on a Sunday, with week 1 commencing
on 1/1 /78.
1978:
Weeks 1 to 52.
1979:
53 to 104 and 0.2 x 105.
1980:
0.8 x 105 and 106 to 156 ~d
1981:
0.4 x 157 and 158 to 208 and 0.8 x 209.
1982:
0.2 X 209 md 210 to 261.
1983:
262 to 313.
1984:
314 to 365 and 0.2 x 366.
partly to each
to Friday) falling into each.
The calendar years are:
0.6 x 157.
Each calendar ye= is processed in turn, using the A and DUALJ OB entries” for the weeks
ill that partictiar
yea
to compute the number of weeks in each job held that cderrdar >ear.
Only job entries in the A and DUALJOB
arrays are processed.
In particular,
code 3 in the
A array is ignored here. It is picked up as missing data at a later stage.
For example,
considm the foUowing A and DUALJ OB entries:
Weeks
Then in cdendm
at job 301, ad
For eafi
150-170
171–190
191-210
A
201
301
302
DUALJOB
202
302
0
year 1981 there are 13,4 week
at job 201, 13.4 weeks at job 202, 20.0 weeks
38.8 weeks at job 302.
job held in the cdezldar year, mnud
as the product
earnings are computed
of the weeMy wage and the number of weks
84
in wkole do~ars
at the job, divided by 100 rmd
truncated
to an integer %.alue. Then sum over d] jobs..
Continuing
the earlier example suppose the weekly u-age for job 201 is 8000 cents: for
job 202 is 22490” cents, for job 301 is 11100 cents, for job 301 is 9500 cents, and for job 302
is 25704 cents. Then for calendar yem 1981:
Computed
earnings = FLOOR(800Q
.-.
x 13.4/100)+
FLOOR(22490
x 20.0/100)
+ FL00R(2j704
+ FLOOR(II1OO
x 13.4/100)
x 38.8/100)
.*
= 1072 + 3013 + 2220 + 9973
= $16,278
For computed
earnings to be comparable
with the reported
w-age for each job in each calendar year shodd
emnings data, the reported
be the a~.erage wage for the job that calendar
year.
Since the reported w,ageisthe usual wage receivedover the period worked sincethe last
inter\.iew,
thiswillbe the case on average for respondents interviewedon January 1 each
year, There will be no bias,
For respondents
interviewed
at other times, the reported
w-age for ea~
job w,ill not be
the average for the calendar year. But this v-ill not induce any biases. To see this, consider
the following simple example.
The respondent
works for only one employer, with the v.eekly
wage path:
Apr-Jun
Jan–hIar
Jul-Sept
Oit-Dec
1981
$200
$210
$220
$230
1982
$240
$250
$260
$270
1983
$280
$290
$300
$310
The average weekly wage for crdendar year 1982 is $255, md reported
times $2j5.
Suppose the respondent
4 survey on Mm&
is dw.ays intaviewed
emnings
will be 52
on Mar& 31. Then at the Round
31, 1982 he should report a usual wee~y
wage of $225 (the average of
.
$210, $220, S230, $240), and at the Round 5 survey on Mm&
of $265 shodd
be reported.
Computed
annual =nings
wage
are 13 times $225 plus 39 times $265,
which equals 52 times $255, as dmired. Agtin there is no bias.
85
31, 1983 a usual w~kly
There are clearlycaseswhere therewi~ be individud biases,due to jobs held only in the
first few weeks after or before an inter~,ieti, or intei}rieti.s not in the same month each year,
or interviews missed entirely, But there is no a priori reason to believe that these v-ill not
bdaI1ce out over au jobs ad
respondents,
Annual weeks worked (AWW)
is the nuber
job is recorded in the weeM.y A array. AEMPS
year, obttined
of weeks in the calendar year for which a
is the number of employers in the calendar
by summing over jobs hdd in the year but not double-counting
same employer.
(Reed
jobs with the
that work for an employer may appear as two jobs this year - one
horn the srrrvey this year and one from the smvey next year).
Annual weeks with a multiple job (AWMJ ) is the number of weeks in the crdendar year
for which a job is recorded in both the A =d
DUALJOB
arrays. The wo~
history data are
cleaned up to avoid spurious double counting in the survey week – in some cases the work
history data records both the entry from this survey and the entry from the next survey,
A.3
Annual
Repotied
Earnings
In the “On Assets and Income”
=ked
section of the questionnaire,
the amount rectived horn vaious
interview
date.
all respondents
me directly
income sources for the calendar year preceding
In this paper, annual reported
eartings
(ARE)
the
are the sum of wage and
salary emnings and own fmm or business earnings.
Respondents
interviews tde
are generally
interviewed
place ti January or Februa~,
by the end of April.
reported e=tings
in the emly part of the ye~.
About
hdf
the
and over 90 percent of inter!.iews are completed
The latest interview month is August.
So the recrdl period for the
questions is not too long.
The key vmiables me based on the fo~owing definitions:
Wage and Salary Eartings:
“During
.
19=,
how much did you receive horn wag=,
jobs, before deductions
for t=es
or anything dse?”.
from your military service).
Own Farm or Business Earnings:
86
srdary, commissions,
or tips horn dl
(Not counting arry money you received
“During
19=,
did you receive any money in income
ow-n rronfarm brrsin~s, partnership or professional
Construction
following:
lived
is for those respondents
under 18 years, never married,
at home.
These
respondents
from your
practi cc?”
of this mriable is straightforward,
The only complication
from your own farm?
It is the sum of the t.v,o components.
in Rounds
1 to 4 w,ho satisfied dl of the
never had a child, never enrolled in co~ege
were ~ked
a shorter set of income
questions.
and
Separate
questions were asked for whether or not the respondent received income from (A) working on
own busin~s
or farm and (B) interest on savings or my other income received periodically
or regulmly,
not counting
asked the mount
dlowmces
from parents.
received from A and B mmbined.
However, these respondents
were then
For these people data are treated as
missizlg if the answer is yes to both A and B. At most 30 respondents
in eack
of Rounds
2
to 4 are missing earnings data for this reason,
The mtin
interviewed,
reasons for missing data on reported
earnings are that the person was not
or that earnings from service in the military u,ere reported.
(There is a separate
question asked: “Did you receive any income from ser~,ice in the mifitary?”
is yes or missing, reported
emnings are treated as missing that ye=.
If the response
This separate question
is not asked in the shorter set of questions mentioned in the previous paragraph,
but respon-
dents asked the shorter set ae unlikely to be in the military, md if they are, they may still
be picked up as such in the computed
eartings
section).
The amount received horn each source of income is truncated at $75,001 for rounds 1 to 6,
and at $100,001 for Round 7. This occurred for 1 respondent in Round 4, and 4 r=porrdents
in each of Rounds 5, 6 and 7. In these instances, data are treated m missing. Note that it is
still possible for reported
truncated.
income to exceed $75,001 (or $100,001) if each component
Data are dso missing if the person was (erroneously)
to questions on either or both of the components
to the respondent
A.4
School
not asked or did not reply
of reported income.
Most of this w= due
not knowing the amount received from wages and salary.
Attendance
and Education
Data on shcool attendance
year,
is not
Level
and educational
level can be constructed
However, the school data are much more complete
87
for each calendar
from the Round 3 survey on, In
particular,
before 1980 it can Te determined
that a respondent
did not attend school at any
time in the year, but for those who did attend at some time the length of school attendance
cannot be determined
without further assumptions.
The school data come directly from qu~tions
attendance
Schooling”
section about
since the preceding interview at regular school: elementary school, middle school,
high school,
trtining
in the “Regdar
college or graduate
school.
Some questions about other types of sdools
and
programs are asked elsewhere but are not used here.
Monthly
school attendance
data are avtilable
a w~kly
month means attendance
data are often, but not always, a~,tilable.
attendance
mr8y is created by assuting
for every week (beginning
monthly data are unavailable,
in any
When
For weeks after this date
is not in SAOO1, while for weeks prior to
this date and after the preceding interview, school attendance
or uncertain attendance
that attenduce
Sunday) that falls in the month,
use the date last enrolled in school.
and before the current inter~.iew the respondent
When monthly
is uncertain.
during a calendar ye= leads to aclusion
Any attendance
of youth for that calendar
year horn the analysis in Section 3.
The level of education
regular school that you have completed
Round
1, and in subsequent
attended
or were emolled
of high school diploma
in regular school.
Vatiables
In addition,
16 years (ba&elors
degree) and 17 ye=s
A question on generalized equidency
and Samples
12 years of education,
Used in Section
degr=
(masters degree)
( GED) is “not used. Such
unless they obttin
further schooling.
in ea~
mlendar year or each week, me the
data for Section 3.
Annual Eartings
data
ACE_
Computed
= Annual
to
3
The following variables, for each individud
b~ic
separate questions on attainment
and atttirrment of college degree me used to increase education
youths will be assigned less thu
A.5
and gotten credit for? ”.. This is for dl youth in sur~-ey
rounds for dl youth that at my time since the last intervie~v
12 years (high school diploma),
where appropriate.
l’What is the highest grade or year of
is based on the question:
Earnings born dl jobs for which wage data is available
88
ACE
= Annual Computed
Earnings from all jobs.
Corrstru~t.ed ordy if wage data is
available for all jobs
ARE
= Annual Reported
Earnings (hfarch CPS type question)
ARE_
=. Annual Reported
Earnings constructed
Annual Work Experience
only if ACE >0.
data
DAEMP
= Dummy for whether employed
or not at any time during year
Aw~
= Annual Weeks Worked
AEMPS
= Number of EmpIoyers over the year
AH
=Annud
AH_
= Annual Hours at dl jobs for which wage data is not missing
ADMJ
= Dummy for w,hether or not simultaneously
Hours at dl jobs
held more than one job
in any week this year
AW’MJ
=. Annual weeks held mdtiple
job, given held a multiple job.
Weekly Earnings md WeeMy JVork Experience
data
WE = WeeMy earningsfrom d] jobs thisweek
IVH = Weekly Hours from dl jobs thisweek,
From these basic mriables,
tie additionally
tinstruct:
ARE/AWW
= Weekly Reported
Earnings
ACEJAWW
= WeeMy Computed
ARE/AH
= Hourly Reported
ACE_/AH_
= Hourly Computed
AWMJ/AWW
= Percentage
wE/wE
= Weekly Hourly Wage from dl jobs this w~k.
Emnings
E=fings
Earnings
of annual weeks with mdtiple
Finally for the wee~y data WE, WH md WE/WH
Ruge
(RR)
md Absolute
refer to Average,
Max,
Rmge
(AR)
Min, Relative
job, given hdd a multiple job
we construct Average, Mu,
Min, Relati~e
for the weekly data WE, WH and WE/WH.
Range
89
aid
Absolute
These
Range for a given individud
over the year across weeks with non-zero non-missing
data. As an example consider weekly
eartings:
A\’E (IVE) = (Sum of WE over tieeks with non-zero non-missing
M=
(WE)
= Mafimum
of WE over weeks with non-zero non-missing
M7E and W’H
Min (WE)
= Minimum of WE over weeks with non-zero non-missing
WE and W’H
RR (W’E)
= Ln (Mu
AR (W’E)
= MU
All emnings md
(WE)/Min
(WE)
(WE))
. Min (WE).
wage data are inflated to 1984 constant dollars by the Ml Items CPI for
urban consumers (Economic
Construction
Report
to President 1989 Table B–58 ).
of these vmiables is described in Sections A.2-A.4.
between ACE and ACE_.
ody
JVE and WH)/A\VITP
Annual computed
if emnings data are a~-tilable for d
earnings from all jobs for ~,hi~
data ~e
avtilable
A crutid
distinction is
eartirrgs from d] jobs, ACE, can be constructed
jobs held in the calex~dar year. Annual computed
u,age data is available, ACE_, can be constructed
for at least one job.
The smple
sizes for ACE_
if earnings
will be considerably
greater than those for ACE.
ACE is used for analysis of anmrd =nings.
earnings, is much l=ger,
can be constructed,
we additionally
Since the sample for ARE, annual reported
define ARE_,
to permit comparable
reported earnings given ACE
annual
samples for mdyzing
computed
and reported
earnings.
ACE.
is used for arrdysis of hourly earrrings to compute hourly computed earnings, given
earnings for at lmt
one job, we divide ACE-
by AH_,
anmrd
hours at dl jobs for Whick
wage data me atilable.
Regarding
sample compositions
used in the =dysis,
the apiricd
firnited to youth age 18 yems or more, not in the mfitmy
during the ye=,
.
and with education
and not in school at any time
of grade 8 or more. To ensure cell sizes of at least 30,
look at:
AGE 18-19
20-22
work in Section 3 is
Ye~s
ED 8-11, 12
8-11, 12, 13-15
79–84
79-84
90
23-24
8-11,
12, 13-15
25-27
8-11,
12, 13-15,
81-84
16+
83-S4
where
AGE = age in years at hiarch 12 of the calendar year.
ED
= highest grade of completed
schooling at the end of the calendar year.
Beyond that,whenever data is a~.ailable it is used. Since the number of missing observations
=ries
by data items, this leads to m~y
smples.
Sample sizes are given irr Tables A.1-M,
for men, and A.1–W,
for women.
For each cell,
the upper three entries are for samples A, B, and C; the second three entries ae for samples
D“, E and F; the third three entries are for samples G, H and 1; and the low,est three entries
are for samples J, K and L. Samples A to M are defined in Table A.2.
A listing of the
samples used for each of the variables in the tables in Section 3 is given in Table A.3.
The analysis of Section 3 is for d
youth not in the military or in school (sample A),
leading to a sample initially larger for women. This sample is used to compute
ment rate - DAEMP.
the employ-
Youth are dropped horn the analysis only to the extent that relevant
data are missing.
The primary data thoughout
this study are the work historydata on each job held
since,
the preceding interview.The analysisof thisdata in section3 is restrictedto tkose
v,ho worked at some stage during the year (sample B). This and subsequent samples are
smallerforwomen
than formen, except for youth with 12 years of eduation.
TO analyze weeks worked during the year and relatedvtiabl= - AWW,
ADMJ
- we need to exclude youth forwhom
AEhfPS
and
the datesofemployment forany job are missing
or intiid (sample c). About 2 percentof youth are excluded forthisreason,mostly in 1979
due to missing data in the first survey, and more often rode.
subsequent
samples but sample H.
91
Sample C is the b~is
for dl
25-27
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333 328 326
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92
91b
Table
Definitions
A: Persons not in military
A.2
of Samples
and not in school
this year AGE
18 and ED >=
>=
8.
B: Sample A less those w-ho did not work at any time in the year. It is assumed that those
who had jobs since the last survey, but for whom even the dates of employment
are bad
or missing, did work during the calendar year.
C: Sample B less those who did work but had missing or invalid dates for one or more
jobs.
This is the reference sample for fl
Earnings which do=
the ~dysis,
except that of Annual Reported
not require job dat~.
D-:- Sample C less those for whom
weekly hours (hours on all jobs held in each week) are nOt
avtilable for even one week of the year,
E: Sample C less those for v,hom hours are missing for one or more jobs held during tile
year,
F: Sample C l~s those for whom the hourly wage rate camot
be constructed
for even one
job held during the yem,
G: Sample
C less those for whom hourly
wage each week md
weekly emtings
are not
available for even one week of the yeu.
H: Sample A less those for whom annual reported earnings are zero or missiltg
I: Sample H less those for whom anual
weeks worked are missing or zero. (The intersectiml
of samples H and C).
J: Sample H less those for whom mnud
hours worked are missing or zero. (The intersection
of samples H and E).
K: Smple
.
C less those for whom the wage rate is missing on one or more jobs held during
the year.
L: Sample
K less those for whom
intersection
of s~ples
mnud
reported
H md K).
92
emtings
are zero or missing.
(The
Table
Samples
Table Variable
3.1
3.2
3.3
3.4
3.5
3.6
used
A.3
for Tables
3.1-3.6
Name
Sample
ARE
H
Annual Reported Earnings
ARE_
L
Anrmd
ACE
K
Annual Computed
Log(ARE_)
L
“Log Ann.d
Log(ACE)
L
Annual Computed
ARE/AWW
I
Weekly Reported
ACE/AWW
K
Weekly Computed
A\’E(WE)
G
Average Weekly Earnings
AR(WE)
G
Absolute
RR(WE)
G
Relative Range of Weekly Earnings
ARE/AH
J
Hourly Reported
ACE_/AH_
F
Hourly Computed
AVE( WE/WH)
G
Average Hourly Earnings per week
RR(WE/WH)
G
Relative Range of Hourly Earnings per week
AR(WE/WH)
G
Absolute
DAEMP
A
Employed
AWTW
c
Weeks worked in 52 week yem
AEMPS
c
Number of Employers over the year
ADMJ
c
Dummy for simdtmeous
AWMJ
M
Number of Weeks with mtitiple jobs
AWMJ / AWW
M
Fraction of weeks worked with multiple jobs
AH
E
Annual Hours
AH_
E
Annual Hours with non-missing
AVE(WH)
D
Average Weetiy
RR(WH)
D
Rdative
AR(WH)
D
Absolute
Reported Earnings constructedonly ifACE
Earnings
Reported
Earting:
if ACE >= O
Emnings
Eartings
Emings
Range of WeeMy Emnings
Earnings
Emnings
Range of Hourly Eartings
per week
during year
job holder
pay
Hours
Range of WeeUy Hours
Range of Weekly Hours
93
>=
O
To investigate
variation in w,eekly “hours within the year -–AVE(IVH),
AR(\~rH) - weeMy hours are required for at le~t
RR(IVH ) al,d
one week (and for W jobs in that v,eek) in
the year (sample D). Less than 0.25 percent of obser~-ations are lost for this reason.
For total hours worked over the year, - AH
and AH_–
w,e exclude from sample C youth
for w,hom hours worked at any job are missing (sample E). “About 1 percent of sample C is
lost due to missing hours data, primarily in 1979. AH_ .is the sum of hours at jobs for which
emnirrgs are known, while AH is the sum of hours at U jobs, ,regardess
of whether earnings
are known.
Hourly computed earnings - ACE_/AH_F). Thus eartiugs
me n=ded
the jobs used in constructing
To investigate
AR(WE)
requires data for both ACE-
for at least one job during tbe y=r
ACE. Ahost
md AH_ (sample
and hours are needed for
3 percent of sample C is lost.
variation in weekly eutings
within the yeu
- A\’E(W’E),
RR(t$’E)
- and hourly earnings across weeks v-ithin the year - AlrE(WE/WH),
and AR( WE/WH)
- we require both weekly earnings and hourly eartings
week in the year (smple
may be reported
and
RR(WE/\VH)
for at le=t
one
F). Almost 3 percent of sample C is lost. (Note that since earnings
w hourly, weeMy, monthly,
there are some cases when weeM~ earnings
are missing but hourly are not, and }.ice-versa, but for simplicity we have required that both
be known).
For youth who hold more than one job in any week of the yem, AVE(WE/IVH)
will differ from ACE_/AH_,
In addition
to the e=nings
data from the work history, data on calendar year reported
earnings - ARE – are separately avtilable (sample H). Reporting error mide, smple
be roughly smnple B less youth with missing reported eartings.
smder
than sample B, =d
is of size comparable
H should
Sample H is about 6 percent
to samples F md
G which essentially
require earnings and hours data for at least one job held during the year.
To compute
AWW
W*MY reported eunings
(sample I). This is the intersection
- ARE/AWW
of s=ples
- requires data on both ARE and
C md
H. The requirement
dates be known leads to a loss of 2 percent of sample H (sitil~ly
that job
about 2 percent of sample
B is lost for ttis reason).
TO compute
hourly
reported
e=tings
- ARE/AH
94
- requires
data on both
ARE
and AH
(sample J). This is the intersection
of samples H and E, The requirement that annual hours
be
know-n leads to a loss of about 1 percent of sample I.
To compute
,
annual computed
earnings at all jobs - ACE - requires earnings data for
dl jobs hdd in the year (saple
ACE_ which requires ~rtings
decrease in smple
Becaue.
K). This is much more stringent than data required for
for just one job in the year. Tables A,] indicate a significant
size.
so mmy
are not necessmily
youth have incomplete
comparable.
earnings data, the samples for ACE and ARE
A better compmison
is obttined
annual reported earnings given complete earnings data, ARE_
Finally, in investigating
multiple job holdings,
by restricting
(sample L).
AM’MJ and AW’MJ/AW\V,
restricted to multiple job holders (sample NI). Smple
analysis to
sizes are not reported
since most cells are very small. Sample sizes across dl years can be obtained
attention is
in Tables A. 1
by multiplying
sample sizes for sample C by ADhfJ reported ill Table 3.5.
The basic samples are sample A, the universe of respondents
respoxldents
v,ho were aployed
for Section 3; sample C,
during the year and for whom the dates of employment
are not missing or invalid (e.g.
st~t
date titer stop date); s-pie
H, those who reported
earnings during the year md for whom this data is not missing; and sample K, those for whom
wage data is a~,ailable on rdl jobs held during the yem.
Saple
K (and L) is considerably
smaller than the others, because the wage rate is not asked for jobs of less thari 20 hours
and/or
less than 9 weeks, udess
government-sponsored
A.6
Imputation
Sumfing
the job is the mtin job
job.
of Missing
Earnings
over dl yems ud
age-education
obser=tions
for men and 7,237 obsmvations
observations
for men and 5,298 observations
-tings
at the time of the survey or a
on some jobs are tissing
groups in Tables Al,
for women,
sample C has 7,285
while smple
K has only 5,261
for women. h any one cdendm
year, therefore,
for over a quarter of youth who work during the ymr. For
.
the mdysis
of unemployment
horn 1979 or the l=t
lost due to tissing
insurance,
date of s&ool
which requires a mmplete
attendance,
earnings,
e5
time series of e=nings
we~ over hdf the potential
sample will be
Since most of the jobs with missing v-ages are less important
than 9 weeks duration
and/or
impute some of these fisstig
less than 20 hours per u.=k,
intermittent jobs, of less
it seems reasonable
to try t@
wages.
—
Recallixlg that hours data are avtilable even if the wage is missing, all ob~,ious procedure
is to assign the difference betwmn annual reported earnings and annual computed
emings
for those jobs where wage data is avtilable to hours worked at jobs w~ithmissing w-ages, i.e.
the imputed
hourly wage is (A R& ACE_/ (AH-AH_),
measurement
errors in ARE and ACE_ are ~eatly
A we~kness of this approach
is that
magnified if wages =e missing for oxdy
a small fraction of hours worked, w,hich is often the cuse. For example,
measurement
error
leading to ACE_ greater than ARE leads to a negative imputed wage.
Other sources of information
calculated
(l)
are instead used. The imputed hourly wage is sequentially
w:
hourly wage for a job with the sme
interview
(appropriately
(2) average hourly earfings
vided these jobs ~count
employer reported in the preceding or .ubsequeI,t
deflated or inflated)
at other jobs with know-n earnings this .ur\,ey rou,ld, profor more than 5070 of total hours at jobs this intaview
(3) average hourly earnings at other jobs with known wages in the preceding
quent inter~.iew- (appropriately
deflated or inflated ), protided
or subse-
these jobs account for
more thm 80% of total hours at jobs that interview,.
Source
(2) is used only if (1) is unavailable,
unavailable.
The iflation
and (3) is used only if (1) and (2) are
factors for (1) and (3) are the aver~ge wage growth for the sample,
23.0%’ (horn surveys 1 to 2), 15.3% (2 to 3), 13.4% (3 to 4), 9.5% (4 to 5), 9.5%” (5”to 6)
(6 to 7). If data from both the preceding
md 11.6%
construct
md subsequent surveys are available to
(1) or (3), th&r average is used.
The imputed hourly wage is multiplied by actual hours per week to impute earnings per
week at the job.
Then weekly earfings
are constructed
.
by summing over actual or imputed
.
eartings
per w=k
at dl jobs held in the week.
This use of imputed
employment
insurmce.
eartingi
grmtly
Nonetheless,
increases the sample size for the analysis of un-
other critaia
96
such w interview
in dl yems lead to a
sample considerably
smaller than the potential 6,111. It should also be clear that the sample
for Section 4 onw-ards, which uses a work history over many yeas,
for Section 3, w.kich are for separate analyses of eack calendar year.
.
differs from tile samples
Appendix
This appendix
provides a description
B
of the procedures
used in the constructioxl
of the
data set analyzed in Sections 4 through 10. An assessment of the rehability and accuracy of
our imputed measures of UI entitlements is also presented here.
,
B-l
Sample
Selection
To obtain reasonably
procedure
reliable me=sures of weeMy earnings a stringent sample sdection
w= used to obttin a subsample of youths from the nationally-representative
ponent of the YNLS.
A youth had to satisfy 6 conditions
com-
to be included in the subsample.
First, to minimize the bimes that can mise from mistakes in recalling events furtker in the
pwt a youth must have been titer~-iewed in each of the first 7 years of the survey. Second, he
or she must have worked at least once after January 1979. Third, to assure reliable measures
of time employed
and nonemployed
an indi~-idual w-as required
to report ~.slid beginning
and ending dates for time periods spent working, between jobs and in the military. Fourth,
he or she must have left school and not returned prior to the Juuary
Fifth, a youth must have a reasonably
accurate and complete
1985 interview date.
time series of tither reported
or imputed weekly earnings beginning in January 1978 or the last date of schooI attendaIlce.
Finally, the respondent
must have started a nonemployment
spell after March 1979 or the
last date of schooI attendance.
Table B. 1 provides a summary of the number of youths affected by each of the successi~,e
screens. The resulting subsample of 3,028 individuals from the 6,111 youths in the nationallyrepresentative
through
B-2
component
10.
Imputation
The dettiled
constmct
of the YNLS is used for all of the empirical analyses in Sections 4
of VI Entitlements
work histories
avtilable
in the YNLS
provide
accurate measures of the amount of UI benefits atilable
a unique
opportunity
to nonemployed
to
youths,
.
Every State determines cm individud’s
Agibility
to receive UI and the amount of benefits
he or she is entitled to co~ect on the basis of the reason for leaving the latest employer and a
dettiled earnings history over a recent 52 week period, termed the “base period.”
98
While there
T~LE
Effect
Of
SaT,Dle
B .1
SelectiOn
Criteria
Screen
Nationally-Representative
Missed
Never
Of
Interview
Worked
Invalid
In
Sample
Dates
School
Incomplete
Alwavs
Weekly
Earnings
Iiistory
EmDloved
On
Samnle
..
size
Nufier
Ndfier
Eliminated
Remzininc
YNLS
6111
711
5400
185
5215
982
4233
745
3488
321
3167
139
302e
“
98a
is variation across States in the specificearnings history collectedand the definitionof the
base,period, the complete time seriesof weekly emnings
avtilablefrom the YNLS is suficient
to calculate dl of the earnings measures used by States to determine UI entitlements.
To establish whether a youth was disqualified
for separation
initiation
from hls or her last employer,
of a nonemployment
causes for st=ting
spell.
we utilized the self-reported
Respondents
a period of nonemployment.
into 8 reasons for the beginning
from receiving UT because
were provided
this range of responses
Briefly, these 8 causes are: (1) on
(3) quit for other than fafily or health reasons; (4) quit to join the
layoff; (2) discharged;
mmed forces; (5) quit for family or health re~ons;
(8) unknown or other reasons. Approximately
(6) quit to attend school;
(7) on strike;
25 percent of the nonemployme,lt
spells began
because of a layoff, another 10 percent resulted from dischmges ad
a quit for other th=
reason for the
a wide array of possible
We have condensed
of a period not working.
of the reason
family or health rewons.
20 percent started after
Qtits for family or health r-sons
and other
reasons account for almost dl of the remaining spdls
All States have disqualification
cause, discharge
for misconduct3B
provisions
for voluntarily
and direct involvement
leaving
in a labor
majority of State statutes do not directly specify what constitutes
operationally
disqutifies
broad concept
of the voluntary separation provision we have adopted
of digiblfity
separation
In addition
disqualifies
a youth if he or she reported
our
attachment”
level of earnings or a “mifimum
~figi~~ity
imputation
proc~ure
does
The
the nonemployment
This broad interpretation
of the
in Sections 5 through 10.
to satisfying the separation from work condition,
a ‘(permanent
The narrow defini tiorr
unless they were on layoff or were discharged.
provision is used in dl of the mdyses
must dso demonstrate
38
individuals
started because of causes 5, 6, 7 or 8 fisted above.
mitimum
the
“good cause,” most States
two methods to determine efigibihty based upon reason of separation.
voluntary
While
related reasons. As noted in Section 4, to determine the sensitivity of
our results to different interpretations
spd
dispute.
good
define this provision to include only causes involving the fault of the employer
or other employment
of eligibility
work without
an out-of-work
individual
to the labor force by atttining
either a
number of weeks of work in covered employment,
not account for the misconduct provisionbecause WC are
unable to distinguish between discharges for misconduct and other discharges
99
or possibly
both during the base period.
Act virtually
exclusions
dl employment
certtin
financed trtining
ofi”cers of primte
indi ~,iduals, agricultural
program,
corporations,
earnings in covered employment
employees
sponsored
jobs where a youth reported his or her occupation
spd.
and the constructed
trtiting
B.3
Constmction
and UI entitlements
of H.ork History
complex
determining
interactions
and 10wer thresholds
of weeks of eligibility
entitlements
a series of brackets
AS ~r=vjo”slY
noicd,
during the subsequent
contain
introduce
and entitlements
the b=e
(HQE)
period
and total
eligibility to collect U] as
benefit year.3g The specific
vary from State to State and
amount
further no&nearities
above.
( WBA)
k
addition,
and the number
into the relationship
betw~n
program rties ad
the three
work history.
md nonlin=rities
rdating
a set of dummy
the combination
of a nonemployment
progr_s
of this appendix.
throughout
the various earnings mewures
we have constructed
youth at the beginning
39
The accuracy of these imputed
of the last section
in both the weekly benefit
for the interactions
measures,
in covered
and the amount of benefits avtilable
is the subject
ehgibility
between
and an inditidud’s
To account
e=tings
(WE)
earnings from
on the rewon for the ititiatimr
to determine an indit.idual’s
well as the amount of benefits atilable
upper
programs ~d
of average u,eeMy earfings
earnings over the bwe period (BPE)
involve
the time series of weekly
highest earnings during any calendar qumter of the base period
rulm and regulations
and
Irariables
All States use some combination
(AWE),
members,
weekly time serim of earnings
enabled us to impute both UI digibility
of eligibility
fmily
as a fmmer or farm laborer.
to a youth at the beginning of each spell of nonemploymellt.
measures
The major
income, income from a family
with the laws of each State, the information
of a nonemployment
employment
of immediate
we excluded self-employment
Tax
workers, ptid parti cipaIlts
Thus, in constructing
farm or business, income from govmnment
In conjunction
Unemployment
has been covered by the UI system since .1977.
to coverage are self-employed
in a government
As a result of the Federd
of AWE,
vtiabl~
HQE
~d
that indicate
BPE
associated
which of
with a
spe~. As illustrated in Table B.2 each -rfings
thatntihzeinformation on weks worked (Wlr)
tion on AWE and BPE shce WW = BPE/AWE.
100
are combtning informa-
TABLE
Bracket
Definitions
B.2
for””AF-,
Zarn inas
Bracket
Ah=
RQE”” and
BPZ
Measure
B?~
HQE
1
$0.00-$99.99
2
$100.00-$149.99
$1000.00-$1999.99
$1500.00-$3999.99
3
$150.00-$199.99.
$2000.00-$3499.99
$4000.00-$7999.99
4
$200.00”-$299.99.
$3500.00-$5499.99
$8000.00-$14999.99
$5500.00
$15000.00
$0.00-$999.99
$0.00-$1499.99
>
5
$300.00
+
1
]
oOa
+
+
measure x,as divided into 5 brackets., The endpoints
axld upper
thresholds
me=ures.
determining
and
entitlements
for
the
Clearly, it w= not possible to account for all of the complexities
resulting in an unacceptably
bra&et.
UI eligibility
of each bracket correspond
For example,
sma~ number of spells associated
to
vaious
earl~ing
involved w-itbou t
with indjviduds
in any one
the BPE brackets were selected to capture the wriation
both across
States and over time in the tinimum
amount of total eanings
necessary to become eligible
for UI, as well as the minimum level of BPE needed to qualify for the maximum
of benefits avtilable.
The mitimum
level of BPE necessary
for this lower threshold,
U’BA
times
Similarly, the upper 3 brackets in BPE
introduced
the mtimum
by the mtimum
11’~)
under
vafious
to qrrdify for the mtimum
necessary
mount
State
potential
amount
to qurdify for UI benefits v=ied
from $150 in Hawaii in 1979 to more thm $3000 in 1985. The first 2 BPE
the nonlineariti~i
lower
the
were
of benefits
programs.
brackets ‘accouut
&osen
payable
to allow
(i.e.,
for
mtimum
The minimum level of BPE
benefits varied from about $4000 iIl Illinois
iu 1979 to $21,500 in Colorado in 1985,
and AU’E
Brackets in HQE
select the BPE
thresholds
brackets.
mtimum
additional
eligibility
The empirical
AU7E
lVBA
thresholds
requirements
influenced
specifications
describe a w~rk~
of type
Tables B.3-M
Accuracy
in Sections 7 through
type
define
and
on HQE
hax,e upper
used
ad
to
Iow,er
Variation in the miIlimum
of bra&ets
in AI1’~.
Finally,
the
or a weeks of work
ad
B.3-W
of Imputed
the accuracy
9 incorporate
a set of work history
Let a certain combination
a dummy
vtiable
WT; equal
of BPE,
HQE
and
to 1 if an individud
The empirical resdts in this report ~e
i or O otherwise.
The self-reported
to =sess
the cbice
to the procedure
greater than 1.5 times HQE
of BPE
definitionof 22 worker types for the men
B.4
similar
to the BPE hmits discussed above.
based on these bracket definitions.
is a worker
in a manner
dso effected the selection of the lower brackets in both HQE md Att’E.
requirement
controls
chosen
States w,hich base entitlements
in HQE eqni~.alent
M“B.4 ad
were
and 15 worker types for the women
b~ed
m
on the
defined in
respectivdy.
Memurw
m-ures
of UI Eligibility
and Entitlements
of UI receipt avsilable in the YNLS provide a
of our imputed
me~ures
101
of UI eligibility
opportunity
and entitlements.
M’bile
,
TABLE B.3–M
De fin”iz”L”on of
Earnings
Worker
Tvne
BPE
Work
%istory
Controls
Earnings
Brackets
HQE
for. Men
Worker
Tvpe
AW-
BPE
Bz.a.ckets
AWE
HQE
~
WTti
1
1-2
1
WT~ z
WT~
1
1-2
2
WTml 3
3
2
3
wTm3
1
1-2
WTfl ~
3
3
3
WTm~
1
1-2
4-5
wT~
~
‘3
2-3
4-5
WTm5
2
1
1-2
~~~
6
3
4-5
4-5
WTm6
2.
2
1
hlTmI 7
4
313
WTm7
2
2
2
WTml ~
4
3
WTm~
2
1-2
3-4
WTmI g
4
414
hTmg
2
3
3
WT& o
4
wTm~ ~
2
3-4
4-5
WT~ I
5
WT3 ::
3
WT=,Zz
5
,3
~
2;1
T=LE
Definition
Of
Za=nings
Woxk
BPE
I
II
WTWI
1
1-2
WTW~
1
1-2
WTW3
1.
WT. ~
Controls
Brackets.
HQE
2
f
4-5
4
5!5
for
4-5
I
5
I1
4-5
.Wo”rn<n
Earnings
AWT
2
B.3-W
History
Wozker
Tvne
‘3
Brackets
Worker
Tvve
BPE
HQE
AWE
WTwg
3
2
2-4
mw~ o
3
3
I
3
1
1-2
3-5
WTWII
3
3-5
2
1
1-2
WTW12
4
3
3
WTW5
2
2
1
WTwl 3
4
3
4-5
WTW6
2
2
2
WTwl 4
4
4-5
4-5
WTW7
2
1-4
3-5
WTW15
5
4-5
4-5
mw~
3
2
1
.
10la
4-5
the data avtilable
do not permit us to identify
indi~.idud
spells of UI receipt, the YNLS
does pro~-ide reliable calendar year measures of the total number of weeks of UI receipt,
tbe average 11’B.4 over the year and the months
follow.ing assessments, as weUw
of eligibility and entitlements
in which benefits w,ere received.
the analyses in Section4,
constructed
Thus, the
are based upon annudme=ures
from our measures impnted at the beginning
of a
.
period ofnonemployment.
Calculating
mnual
uds who experience
vduesfor,the
imputed
UI variables is strtight forward forindivid-
a single spe~ of nonemployment
that begins and ends w.ithirr a single
calendar year. This exercise is dso relatively simple for individuals who experience
nmremployment
spellsdl occurringwithin a calendaryear. Ambiguitiesmise w-hen a nonem-
ployment spell overlaps two calendar ye=rs, especially if a person experiences
period
of nonemployment
weeks of eligibility
example,
suppose
drrting a given year.
were allocated
When
to the beginning
this situation
more than one
arose the number
weeks of a nonemployment
of a 30
spell where 18 weeks occur in one calendar year and 12 weeks take
place in the second y-.
year and the remtining
In this case 18 weeks of eligibility would be assigned to the first
8 weds
would be allotted to the second dendm
Table B.4 presents a cross-tablulation
yem.
of our estimated eligibility to receive benefits wit]]
reported receipt of UI payments during a calendar year. The fist entry in ead
&gible
of
spell .40 For
a youth was efigible for 26 weeks of LII benefits at the beginning
week nonemployment
the frequency
multiple
cell represents
and the second entry denotes the percent of cues in each ce~. To be deemed
a youth must have b=n
retitled
to receive at least one week of UI benefits at. some
time during a calendar year. Two sets of resdts
are presented in the table corresponding
to
the two definitions of eligibility described above and in Section 4. The first set of 2 columns
presents the resdts
the second
for our broad interpretation
set refer to the n=row
definition
of the voluntary
of ehgitility,
sepmation
which ~sumes
provision
d
quitters
ad
are
ineligible.
~(>A simii=
problem tises
in the tiocation
of tbc number of weeks of unemployment
to ea~
dendar
two years. Again, the weeks Of unemployment were -snn]ed to occtIr at tile
beginning of the nonemployment spell. While this does not impact on the accuracy =aesmrent, it does effect
the cdmdar year rne~ures of efigibfity and utilization analyzed in Section 4.
year when a spell overlaps
102
T~LE
Freqaeficy Table””of Imputed
Eligibility
Definitions
(percentage
of cases
Broad
Ineligible
NOnrecipient
UI Recipient
I
I
4214
“B.4
and UI Receipt
for Both
of Eligibility
i.n each category
Definition
Narrow
ineligible
Eligible
1B69
(58.2)
(25.8)
260
892
(3,6)
(12.3)
102a
in parentheses)
I
I
Definition
Eligible
,5134
949
(71.0)
(13.1)
333
819
(4.6)
(11.3)
The results in Table 3.4 are very encouraging.
Using the broad definition of eligibility, an
obvious error w-as made in only 3.6 percent of the cases: i.e., cases where a person reported
receiving
U1 payments when we determined
they v,erc ineligible for benefits.
A furtker ex-
amination of these 260 c=es indicated that just 69 cases were judged to be indigible
of insufficient earnings in covered employment,
3
resulted from the self-reported
because
w,hile the other 191 incorrect” determinations
reason for beginning
a nonemployment”
spe~.
Surprisingly,
this type of error only occurs in 4.6 percent of the ewes un,der the narrow, interpretation
of the volnrrt ary sepmation
imputations
provision.
In addition,
it is possible that erroneous
were made for the cases where we determined
an individual
benefits but he or she did n,ot report receipt of any payments.
c==,
under the broad and nmrow definitions respectively,
eligibility
was efigible for UI
Alternativdy,
all 1869 or 949
could be the result of incomplete
take. up rates for benefits.
Table B.5 presents summary- statistim for the difference betwea
payments
and our imputed
W’BA for the years 1979 to 1984.
reported average benefit
Similar measures for the
difference betw,een weeks of rectipt ,and the imputed value for weeks of ehgibility are reported
in Table B.6. h
imputed
order to make the two measur=
measure for weeks of digibility
weeks of nonemployment.
used in this latter table comparable,
the
is set equal to the lesser of MrE or the number of
during the year. The resdts in Tables B.5 md B.6 provide further
evidence of the remarkable accuracy
of our imputed me~ures
103
of UI entitlements.
T~LE
Sumary
Statistics
~A
by
Year
for
Year
Lower
the
for
Difference
Broad
between
Definition
Quartile
of
Reported
and
Upper
Quartile
1979
-3
1
19
1980
-8
1
17
1981
-11
1
23
1982
-11
1.
21
1983
-19
0
21
1984
-11
2
29
Statistics
UI Receipt
bv Year
B.6
for the Difference
and Imputed
for
WE Adjusted
Broad
between
for Weeks
Definition
Reported
of
Weeks
Nonemployment
of Eliqibilitv
Year
Lower Quartile
Median
1979
-3
-1
1
1980
-3
-1
3
1981
-4
-1
1
1982
-1
-2
2
1983
-2
1
8
1984
-3
-1
3
i03a
Imputed
Eligibility
Median
TABLE
Su-ry
B .5
Upper
Quartile
of
References
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in A, J. Aurbach and M.
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Feldstein, eds., Handbook of Public Econornim,
B=rori,
J. M. and W. Mdow (1981), “Unemployment Insurance:
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There an Explanation?”
Paper #243.
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R. Ehrenberg,
Press.
Activit~,
No.
and Its
“Recent Trends in Insured and Uninsured Unemployment:
Is
Industrid Relations Section, Princeton University, Working
“The kcentive
cd., Ruearch
Burtless, G. 1983, ‘Why
The Recipients
Effects of the U.S. Unemployment Insurace
Tax”, in
Vol. 1, Greenwi~,
Corm.: JAI
in Labor Economics,
is Insured Unemployment
So Low?”
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Papers
on Economic
1, 225-249.
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