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Youth Joblessness and Race:
Evidence from the 1980 Census
George Cave
In 1983 the Census Bureau released microdata based on the "long-
form" questionnaire completed by about one-fifth of the respondents in
the 1980 Census. "Public-Use ~ ~ ~ ~ ~ ~~~
Microdata Sample C" identities the retype
of area"--central city, urban fringe, rural, and so on--for a full 1
percent of the U.S. population (Bureau of the Census, 1983~. The large
size of this data set enables researchers to study the impact of area
type and many other factors on a multitude of individual variables
measured by the questionnaire. In addition to the 1 percent population
sample, a 0.1 percent subsample provides data on 226,947 individuals
surveyed in the 1980 Census.
This paper compares the data on unemployment and other labor force
behavior reported for black youths with that reported for white youths.
The key question addressed is, Do black youths face special problems in
the labor market due to their race? A related question is whether
correcting black and white youth labor force statistics for location,
education, family income, and other factors tends to eliminate the
racial differences. This paper, like most others in the empirical
literature on youth unemployment, uses simple single-equation methods
to correct for these factors. However, the results must be interpreted
very carefully for several reasons. First, most coefficients estimated
on data for individuals are subject to "ecological correlation bias" if
the labor market characteristics of the respondents' local areas are
missing from the data set. Second, the most common single-equation or
system methods may not estimate structural coefficients for individuals'
and employers' behavior. Third, single-equation methods introduce
simultaneity bias if, for example, the probability of unemployment
influences the probability of labor force participation. Finally, even
system methods may ignore some simultaneity and overcorrect for factors
other than race. To some extent, residential location within the local
labor market, quantity and quality of education, family income, and so
on are, like unemployment, partly the consequences of race in the labor
market. To ignore the effect of race on these determinants of labor
George Cave is on the staff of the Manpower Demonstration Research
Corporation.
367
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368
force status is to ignore the indirect labor market effects of race on
unemployment.
This paper is organized as follows. First, the data sets and the
statistical methods used most extensively in this study are described.
Next, a brief overview is given of the seasonal, cyclical, frictional,
and structural components that complicate empirical analysis of the
youth unemployment problem. This section also includes a survey of
several earlier empirical analyses. The empirical analysis of the
labor force status of out-of-school black and white teenagers included
in the Census microdata follows. Then the very different behavior of
those teenagers who were enrolled in school at any time during the two
months before Census day is explored. The paper ends with a summary of
the major findings.
THE DATA
The 0.1 percent subsample of "Public-Use Microdata Sample C" has
226,947 self-weighting observations on individuals. Of these, 8,653
are young men aged 16-19. Because only 1,190 of these young men are
black, stratifying the sample by region, education, and other factors
produces some data cells with no nonwhites. The problem becomes even
more severe when students are excluded from this group; there remain
2,061 white males, but only 372 black males. Fortunately, it is easy
to increase the number of nonwhites by a factor of 10 by using the full
1 percent sample of nonwhites. However, calculations based on
stratified samples containing nonwhites from the full C sample but only
those whites in the 0.1 percent subsample require special techniques.
Heteroscedasticity could arise from the 10-fold greater chance a
nonwhite respondent had to get into such stratified samples. Still,
the huge Census microdata samples enable appropriately cautious
researchers to home in on interesting subgroups in ways that smaller
samples do not permit.
The main dependent variables used here reflect labor force status
during the week of the Census survey.) Unfortunately, questions that
would have identified "discouraged" workers during the survey week were
not asked. However, analyzing nonstudents separately picks up some
part of the often-neglected behavior of those who are not in school yet
are neither employed nor unemployed.
. .
This measure of labor force behavior is the most common. Alter-
natives are available: the number of weeks spent in unemployment and in
employment in 1979 are recorded for everyone 16 years and older in
"Public Use Microdata Sample C." Survey-week labor force behavior is
related systematically to weeks and spells of unemployment over the
course of a year; see Betsey (1978) and Hanoch (1976~. Using survey-
week behavior does not distinguish between the short-term and long-term
unemployed.
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369
Other variables available for all respondents include census region,
type of area, householder status, age, race, marital status, disabil-
ities, years of school completed, whether the respondent has ever
worked, and income status. For those with at least some employment in
1979, earnings, usual weekly hours, and industry in which employed are
also available. Unfortunately, crucial variables that are not available
include actual hourly wages, the number of spells of unemployment in
1979, the number of job offers refused during job search, and eligibil-
ity for unemployment compensation.
METHODOLOGY
Two basic methods are used in this analysis to compare black and
white labor force behavior. Both attempt to explain dummy variables
for employment, unemployment, and nonparticipation. When black and
white samples are combined, and when race is one of the independent
variables, the coefficient of race shows the increase in the
probability of the behavior, conditional on the other independent
variables, that can be attributed to being black. As in Freeman
(1982), linear probability models (LPMs) are estimated because they
explain quite simply some important relationships among the three
dependent variables. However, because of well-known econometric
difficulties with linear probabilities, logistic methods are used as
well .2
Using both methods, linear and logistic, equations are estimated
for two types of dependent variables, unconditional and conditional.
Unemployment and employment equations are estimated both for the entire
population and for labor force participants only. In these models, the
coefficients for conditional employment and unemployment have the same
magnitude, but different signs. The next section points out structural
interpretations for the conditional equations; these reflect employer
behavior and make the unconditional equations reduced forms confounding
employer and individual structural coefficients.
OVERVIEW OF THE YOUTH UNEMPLOYMENT PROBLEM
The Many Faces of Unemployment
Even though economists have produced a large literature on
unemployment and take many separate approaches to the subject ,3 not
2See Nerlove and Press (1973:Ch. 2). The LPM predicts probabilities
outside the unit interval, is subject to heteroscedasticity, and in
general does not fit the statistical assumptions underlying least
squares regression.
3 Two important strands of this literature are largely theoretical:
macroeconomic general equilibrium and wage-search distributions. Hey
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370
much fundamental progress has been made in explaining unemployment. An
adequate economic explanation of unemployment would separate relevant
factors reflecting the preferences of individuals for consumption of
goods and uses of time from factors constraining individuals' choices
about consumption and work. Moreover, such an explanation would
systematize many of the stylized facts about unemployment. Further,
such an explanation would yield empirically testable hypotheses for
existing data about unemployment.
One problem is that the same word, unemployment, is used to denote
many very different phenomena. For a long time, empirical work on
unemployment among individuals has tried to classify such unemployment
as "seasonal," "cyclical," "frictional," or "structural," although it
has been recognized that a given spell of unemployment for a given
individual might be very difficult to categorize.
Seasonal and Cyclical Unemployment
One sort of seasonal unemployment is a characteristic of certain
occupations, such as construction work. Workers committed to such
occupations generally do not take other kinds of jobs during the
off-season, perhaps because their wages reflect compensating differ-
entials for the known risk of unemployment at certain times of the
year. This sort of demand-side unemployment is unlikely to affect
young people, who generally have not yet committed themselves to
occupations.
The failure of schools and colleges to stagger their vacation
periods produces another kind of seasonal unemployment, 4 which can be
attributed to the supply side of the youth labor market. A deluge of
young people compete for relatively few jobs each summer. If the kinds
of jobs young people take during their summer vacations paid lower
wages, some have argued, 5 the problem would be smaller.
Cyclical unemployment occurs less predictably and is tied to the
business cycle and to cycles of product demand within industries.
There has been a great deal of recent work on the nature of long-term
contracts between firms and workers who are periodically laid off
temporarily and then rehired. Feldstein {1976) estimated that 75
(1981) provides a survey. Some important articles in the empirical
literature are cited in the next section.
however, Clark and Summers (1982:209) cite gross flow evidence that
demand for young workers, on the whole, adjusted remarkably well to
increased supply during the summer over the years 1968-1976. They
surmise from preliminary statistical work that federal Neighborhood
Youth Corps and CETA programs may explain their surprising findings on
this point.
5See Brown (1981) for a survey of many of the issues surrounding
minimum wage differentials for youth.
OCR for page 371
371
percent of laid-off workers in manufacturing subsequently are rehired
by the same employer. He cited 1975 evidence that 41 percent of
unemployed men aged 25-64 who had been laid off had made no attempt to
find jobs during the previous month. However, since young people tend
not to have made solid commitments to particular firms or even to
particular industries, temporary-layoff theories are less-convincing
explanations for their unemployment than for the unemployment of older
workers.
The next subsection provides rough evidence that even cyclical
unemployment tied to the business cycle is less important for younger
people than for more-established workers. This lack of cyclical
sensitivity is reassuring, since elsewhere in the paper I focus on
cross-sectional data pertaining only to March 1980 and calendar 1979.
Cyclical Sensitivity of Youth Unemployment
Because I talk about a single cross-section of individuals in the
remainder of the paper, I do not have much to say about cyclical
influences on youth unemployment. How important are they?
Persistent unemployment of at least 3.5 percent (measured as annual
averages of monthly Current Population Survey estimates) has afflicted
the U.S. civilian labor force since the mid-1950s. The aggregate rate
fell from 5.5 percent in 1954 to 4.1 percent in 1956. Then it jumped
to 6.8 percent with the 1958 recession. It fell again to 5.5 percent
in 1959 and 1960 and then rose abruptly to 6.7 percent for 1961. With
the exception of a slight faltering in 1963, it fell steadily from its
1961 level until it reached the post-1953 trough of 3.5 percent in
1969, a war year e Since then, as Table 1 shows, it rose in 1970 and
1971, fell in 1972 and 1973, rose through 1975 to a three-decade peak,
and fell to the 1979 low preceding the most recent recession.
Although the aggregate time series is sensitive enough to reveal
broad trends, it masks a great deal of the labor market behavior that
disaggregation reveals. In addition to aggregate unemployment rates,
Table 1 shows unemployment rates for certain sex, age, and race groups,
including groups of teenagers 16-19 years old. With the exception that
teenage unemployment was below 12 percent for the three years preceding
1958, 1969 was the post-1953 trough for each of the disaggregated
series as well as for the aggregate unemployment rate. But many of the
disaggregated series have kept the same rank relative to each other
ever since 1950. Moreover, some of the series are much more stable
over time than others.
Table 1 shows coefficients of variation (ratios of standard
deviations to means) for the aggregate series and for 20 disaggregated
groups for the period between 1972 and 1982. It also shows the rank of
each coefficient of variation (c.v.) among the 21 coefficients
reported. The aggregate series ranks 14th at 21.09 percent. All 7
groups of teenagers rank above the aggregate series; in fact, the
teenage groups account for 4 of the top 4 groups and 7 of the top 11
groups. The coefficient for the top group, black women aged 16-19, is
barely a third of the aggregate coefficient. At the other end of the
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Representative terms from entire chapter:
labor market
372
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373
scale, groups containing older men account for all of the bottom six
ranks. Older women tend to rank just below the groups of teenagers.
Thus, from a disaggregation of time series using coefficients of
variation to index instability, it can be seen that older men's
unemployment is most susceptible to macroeconomic forces exerted on all
demographic groups over time, while teenage unemployment is influenced
least by such forces.
Frictional Wage-Search Unemployment
Frictional wage-search unemployment results from the dynamics of
labor markets. People move in and out of the labor market as they age,
as their skills change, as wage levels rise and fall, and as family
needs and financial fortunes vary. 6 Jobs are created as firms are
established, as plants are opened in new areas, and as older workers
are forced to retire.7 But it may take a few weeks for would-be
workers and firms with vacancies to search out and find each other. A
job applicant might not take the first offer of wages and working
conditions, and a firm might not be willing to meet the first appli-
cant's wage bid. Such "search" unemployment might affect young people
disproportionately more than adults, B because they are making gradual
transitions from full-time schooling to full-time labor force partici-
pation. Young people experiment with industries and occupations before
making lifetime commitments. Sometimes they have parents to support
extended periods away from both school and the labor force, and some-
times they might misreport such nonparticipation as unemployment.
According to "search theories" of frictional unemployment, 9
heterogeneity among individuals and among firms leads firms to search
for workers and individuals to search for vacancies. The latter type
of search has virtually been identified with unemployment by many labor
economists, usually under the restrictive assumption that the utility
function governing individual behavior is defined over discounted
future wages, net of search costs but ignoring foregone leisure.
6When movement into the labor force exceeds steady state levels,
structural unemployment may arise in addition to frictional
unemployment. It may take some time for employers to adjust their
hiring and wage policies to the increased supply of potential
employees.
7Destruction of jobs by the same kinds of processes may lead to
structural unemployment for established workers at the same time that
it creates frictional unemployment for new entrants into the same or
another job market.
but see note 15 below.
See Hey (1981:Ch. 5) for an accessible survey of this literature.
374
According to these theories, every worker is unemployed because he
turns down all proffered vacancies until he has been offered his
acceptance wage. The acceptance wage is the wage such that there is no
marginal gain in expected utility from continuing to search. Though
firms search for workers, if only in the sense that they do not
necessarily hire the first applicant for a given vacancy, most search
theories tend to ignore this phenomenon and attribute unemployment
solely to workers' searching for firms. The testable assertion here--
that the unemployed have refused actual job offers--has not been pitted
against empirical evidence very often. But when it has been tested,
the idea that most of the unemployed have refused wage offers has not
fared well.~°
Structural Unemployment
In contrast to voluntary, frictional unemployment is the notion of
involuntary, structural unemployment, which is defined by Killingsworth
(1978:22) as "joblessness--usually long-term--which results from basic
changes in the economic structure: new technology, the decline of some
industries and the growth of new ones, geographic relocation of
industries, permanent changes in consumer tastes, changes in labor
force characteristics, and so on."
For unskilled workers, among whom are most young people, legal
minimum wages or high union wage scales may be an important barrier to
Thor example, Rosenfeld (1977) has reported empirical work on the
3,238 out of 4,668 unemployed in the May 1976 CPS who answered
supplementary questions on their job-search behavior. The high
nonresponse rate and low potential for disaggregation indicate a need
for more special surveys of this kind. Yet the implications of the
respondents' answers for the validity of search unemployment theories
seem clear. Since only 32 percent were on layoff, and more than 81
percent of laid-off workers reported some effort to find an interim
job, only 6 percent of the unemployment could have been seasonal or
cyclical in the sense used in this paper. Search unemployment due to
high acceptance wages seemed less than universal: 22 percent stated
willingness to accept jobs paying less than the federal minimum wage,
then $2.30 per hour; another 33 percent were willing to take a wage
between the minimum and S2.99; and only 22 percent reported an
acceptance wage of $4.00 or more. Finally, only 10 percent of those
who were unemployed four weeks or more and who had contacted at least
one employer reported having refused any job offers.
i~Killingsworth (1978) recently retold the postwar history of this
old idea.
375
employment. 12 In an important theoretical paper, Weiss (1980) shows
that even in the absence of legal minimum wages or union pay scales,
firms may find it optimal to set wages fairly high and to refuse to
employ members of certain demographic groups, even though they are
willing to work for lower wages.
At the risk of excluding some dislocated workers from the
definition, 3 the structurally unemployed may be thought of as those
who have searched for jobs but found no employers willing to hire
them. This simple definition maximizes the contrast between frictional
unemployment and structural unemployment, while remaining consistent
with neoclassical labor economics. The frictionally unemployed will
join the ranks of the employed as soon as they lower their acceptance
wages. But lowering their acceptance wages will not help the struc-
turally unemployed find worker 4 they have not refused any wage offers.
There is some empirical evidence that the notion that the
unemployed are refusing wage offers is especially inappropriate for
young men.~5 If this is true, then empirical models of search
t2See Demsetz (1961~. But a legal minimum wage or union wage scale
need not be binding constraints on employer behavior if there are
other, higher, wage rigidities.
i3Lucas (1978) seems to. We might conceivably argue that the
laid-off skilled steelworker in Pittsburgh who won't sell his house and
take a minimum-wage job is voluntarily unemployed, but we cannot argue
as easily that an unskilled teenager who cannot get that same job is
unemployed voluntarily.
~ 4 Indeed, in Weiss's (1980) model it is precisely the positive
relationship between the acceptance wage and expected productivity that
causes the unemployment of workers with low acceptance wages. Compare
Lucas (1978:354~: "The unemployed worker at any time can always find
some job at once. . . e However miserable one's current work options,
one can always choose to accept them."
Stephenson (1976) analyzed 281 respondents of the 300 unemployed
males aged 18-21 with 8-12 years of education who sought full-time jobs
in November 1971 at the Indianapolis state employment service office.
He states (on p. 110~: "Nearly 90 percent of both white and black
youths, when describing the search before their last job, said they
took their first offer. In contrast to the search literature which
usually implies a choice among several offers, the central search
problem of young men may be to find a single offer." Cave (1983)
exploits this insight in modeling unemployment among unskilled
workers. Of course, because of its self-selection, Stephenson's sample
may not be representative of all unemployed youth in Indianapolis at
the time.
376
unemployment 6 may not be appropriate for individual data on youth
unemployment. Alternative models directly analogous to those used on
aggregate date t 7 are proposed at the end of the next subsection.
Implications for Empirical Work on Youth Unemployment
Young people's behavior in labor markets is even more complex than
the behavior of their elders. Unlike prime-age males, young people
have nonparticipation as a real option, and they exercise it often.
Whether they are in or out of the labor market, they must make another
constrained choice their elders rarely face--whether to stay enrolled
in school. Moreover, they work part-time rather than full-time more
often than older people do. Empirical studies of youth labor markets
must deal in some fashion with the joint determination of school
enrollment, military status, labor force participation, hours worked
per week, and wages. There is important simultaneity between par-
ticipation and the chance of unemployment-if participation is chosen.
There is also simultaneity between the number of years of education a
person has and the chance he or she will find a place in the labor
force.
In addition to the simultaneity problems, there are problems of
definition for the labor force variables. Several very different kinds
of behavior are reported as the same empirical phenomenon, "youth
unemployment." For someone who has quit school permanently and who
cannot rely on family financial support, reported unemployment may
reflect a chronic inability to find any hours of employment at any wage
level. This kind of involuntary, structural unemployment may
constitute what Conant (1961) called "social dynamite," and it has
grave implications for adult poverty and crime. At the other end
of the spectrum of interpretations of these statistics, reported
unemployment in a particular week may reflect brief job search or
normal experimentation with possible careers. For someone who has
i6Since the search literature is mainly theoretical, there are few
empirical search models to criticize. Kiefer and Neumann (1979, 1981)
are careful to use data on permanently laid-off men for whom their
sophisticated search model seems especially appropriate.
i7For example, Fleisher and Rhodes (1976~.
i8Recent empirical work, though not conclusive because of poor data,
tends to make Conant's fears seem ill-founded. Freeman and Wise (1982)
briefly survey work that, based on longitudinal data, finds no
significant effect of employment history per se on later labor force
behavior, once persistent individual skill and motivation differences
have been controlled for. But Cave (1981) and Levy (1982) criticize
these results as possibly reflecting unavoidable selection bias against
the relatively small demographic group Conant worried about.
377
completed his or her education fairly recently, reported unemployment
may reflect a single episode of leisurely job search or unrealistic
wage expectations. Summer unemployment by those who have never left
school ought to be treated as possibly the common experience of many
young people. Someone who has worked long enough in the past to be
eligible for state unemployment compensation may misreport actual
nonparticipation as unemployment.
A further problem of definition arises because many chronically
jobless youths may not show up in unemployment statistics at all.
Discouraged workers are counted among those who are not even part of
the labor force in a particular week. They may have been unemployed
for a long period in the recent past, they may be permanent school-
leavers, and they may have low reservation wages, but unless they
engage in specific search activities they are not counted as part of
the unemployed labor force. In addition to being too inclusive an
indicator of chronic joblessness, youth unemployment may be too
exclusive.
Another important statistical problem plaguing empirical work on
youth labor force behavior has sometimes been called "ecological
correlation bias" (see Freeman, 1982:115~.~9 Much of the empirical
work on unemployment has used the Standard Metropolitan Statistical
Area (SMSA), not the individual, as the unit of observation.20 The
proportion of variation in labor force behavior across areas that is
generally explained by SMSA regressions is much higher than the pro-
portion of variation across individuals that is explained by regressions
using individuals. Individual regressions may attribute to individual
characteristics (such as education, race, and family income) explanatory
power that really belongs to area variables (such as the density of
employment opportunities) that are correlated with the individual
characteristics. It is surprising that few studies of individual labor
force behavior have made use of area information that may be available
(albeit at great cost) even in microdata. 2 ~
i9Rosen (1984), reporting on data from a BLS cooperative
federal-state statistical program, indicates that there is a great deal
of variation in unemployment among local areas. In 1979, local
unemployment rates ranged from 40 percent in Menominee County,
Wisconsin, to less than 1 percent in Sioux County, Nebraska.
20Examples are Gilman (1965), Kalachek (1969), Fleisher and Rhodes
(1976), and part of Freeman (1982~. These SMSA regressions generally
explain a much higher proportion of variation in SMSA unemployment than
is explained (typically well under 10 percent) in individual
regressions.
2 ~Abowd and Killingsworth (1984) are an exception, although one might
quibble with their choice of geographic area variables to match with
individual data.
399
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401
out of the labor force, as one might expect for students at the middle
of the spring term in 1980--56.2 percent of all white male students and
74.2 percent of the black male students did not participate in the
labor force. The black-white ratio is only 1.32, just half the ratio
of participation rates for black and white nonstudents reported in the
last section. Employment-to-population ratios, however, are worse for
black students relative to white students than they were for black
nonstudents relative to white nonstudents. The fraction of all white
male students who had jobs was 38.6 percent; this was 1.97 times the
fraction of all black male students who had jobs. The ratio for male
nonstudents was only 1.67. The employment-population ratio for white
female students was 38.2 percent, 2.06 times the ratio for black female
students. However, 2.06 is almost as high as 1.94, the analogous ratio
for female nonstudents.
Labor Force Participants
Of the 4,856 white male students, 2,125 were labor force partici-
pants; 2,123 of the 8,239 black students were participants. Even though
the original sample contained 2.2 million people, only 505 unemployed
black male teen students made it into this final group. Of the 2,125
white student participants, 205 were unemployed. This means that the
black unemployment rate among teenage students was 23.8 percent, while
the white unemployment rate was 11.8 percent. Note that the ratio of
the black rate to the white rate is worse for male students than it was
for male nonstudents: 2.02, compared with 1.65 as computed from Table
4, or compared with 1.68, as computed from the LPM regressions on the
0.1 percent sample reported in Table 5(a).
Table 8(h) shows that 2,013 of the 4,811 white female students were
labor force participants; part (g) of the same table shows that 2,065
of the 8,505 black female students participated in the labor force.
Some 176 of the whites and 489 of the blacks were unemployed, which
yields female student rates of 8.7 percent and 23.7 percent, respec-
tively. The black-white ratio of female student unemployment rates
computed from this table is 2.71. Since parts (g) and (h) of Table 4
imply a nonstudent ratio of 2.38, the racial ratio of unemployment
rates is worse for female students as well as for male students.
Gross Effect of Race on Unemployment
In Tables 9(a)-9(d) are linear probability models exactly like
those in Table 5, but this time they are estimated for students.
"MODEL01" of Table eta) shows that the gross racial differential in
male unemployment-population ratios was 1.55 percent and of marginal
statistical significance. 3 ° Part (c) of the table shows a slightly
3 ° Like the LPM coefficient, the logistic coefficient is small and
barely significant at the 7 percent level. In the sample restricted to
labor force participants, the logistic coefficient on race is large and
has an asymptotic t-statistic of 5.8.
J
402
TABLE 9(a) Linear Probability Models: Civilian, Student Male Teenagers
-
MO[)Fl: MOF)~-l()1 SSF:,?87.511459 F ilATIO3.28
DFE5660 PROR>F0.(17()~4
DEP VAR: U MSE0.050797 R-SQUARE().()()()G
VARIAL31EDEF8TIMA1EERROR -r RAr loPRot~>l T I
1 N l ~ i\Ct- i' 11() . 051483(-).0()3234298 15.9177().()()()1
COLOR1().()15515(-).()()85723()1 1.8()'39().()-7()14
MODEI: MODEL() ~SSE1,?72.lt8 ~ RATIO124.'
[)FE5660 PROB>F().()()()1
DEP VAR: M MSF0.224820 R-SQUAREt-).()215
VARIABLEE)rESTIMATEERROR T RAI-IOPROB>ITI
INIE-~(,FPl1().38612()C).0(-)68()lt213 5t, . 74 72() . ()(:)() 1
COl()I\1-().2()1257().018(134 -11.1598().(:)(-)()1
MO[~LI: M()~tI()3 SSE1346.9G6 ~ RAl-Io1()().,21
DFE5G6() I'F?~.E().()()(11
DEP VAl<: N t4SE0.23798() R-SQUARE().(-)17lJ
VARIAl3IF[)tES1IMA1EE[lfIIl
lN[Ll\(~Ll,!1().562391().~(:)7()(-)()527 8(-).33Gl~(:).(~()~)1
COIOFl1().185-lit2(-).(-)1855Il 1().()1()6().()()()1
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate
0.1 percent subsample.
TABLE 9(b) Linear Probability Models: Civilian, Student Labor Force
Participants, Male Teenagers
MODEL: MODEL2 1 SSE 260. 2237()3 F RAT I O 36. 46
DF E 2326 PROB>F t).00()1
DEl' VAR: U MSE 0.111876 R-SQUARE 0. ()154
PAKAM E r E R S TAN DAR D
VAR I ABl ~DE EST I MATE ERROR T RAT I O PROB> I T I
I Nr~Rcr ~r 1 (1. 1176li7 0.007255864 16.2141 ().00()1
OOLOR 1 (:).148363 0.(:)24572 6.()38() (-).(:)0(-)1
__ __ __ _ ___ _____ ___ _ _____ _ __________________ _ __ _________ ________ _ ___ _ ___
h10[)E 1: MOr)t L,?? SSE 260.223703 F RAT IO 3G.4G
DFE 2326 PROB>F ().()(:)01
r)EP VAll: M MSE ().111876 R-SQUARE 0. ()154
PARAMETER STANDARD
VAR I AE3I ~[)F FST I MAIE ERROR T RAT I O PROB> I T I
INIERCEPI 1 ().882353 0.007255864 121.G055 0.00(-)1
COI ()~< 1 -().148363 ().()24572 -6.038() 0.0()()1
MO[)E l: MOL)[ L 3 1 SSE 259.708068 ~ RAT I 0 1 3. 7()
D r E 2324 P RO B> F () . C) 001
DEP VAR: U MSE 0.11175() R-SQUARE (). (:)174
PARAME T E ~STANDARD
VAfl I Al3LE- ~DE ~sr I MAI E ERROR T RAT I C PROB> I l- I
1 N r ~ R(,~ i' ~1 () .12713(1 O .00855751 14.8559 O . ()0() 1
SC) 1 -(). (!33641 0.016118 -2.0871 (). ()37()
C()l 0~< 1 0.153358 (~.()3789'' 4.()469 (-).()()()1
IN1-RACT 1 0.()()9351145 0.050459 (:).1853 ().853()
MOL)El: MO[)LI 32 SSE 259. 708()68 F RAT I O 1 3. -1()
DFE 2324 PROB>F 0.0001
DEP VAR: M MSE 0.111750 R-SQUARE 0.0174
PARAME r ER STANDARD
VAR IABl E DF ESt I MATE ERROR T RAT I O PROB> I T I
1 N r F KCE ~-r 1 0 . 872870 0.00855751 102.0005 O .00(-) 1
SO 1 0.033G41 0.016118 2.0871 0.0370
COLOR 1 -().153358 ().037895 -4. (:)469 0.00() 1
1 N T RAC T 1 - (-) .00935114 0.050459 - 0.1853 0.8530
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate
0.1 percent subsample.
403
TABLE 9(c) Linear Probability Models: Civilian, Student Female T.
t1ODEL: MODEL01 SSE211.346319 F RATIO3.77
DFE5683 PROB>F0.0524
F)EP VAR: U MSE0.037189 R-SQUARE0.0007
VARIABLEDFESTIMATEERROR T RATIOPROB>ITI
INTERCEPT10.0365830.002780295 13.15790.0001
COLOR10.0137600.007090884 1.94060.0524
tA()DEL: MODEL02 SSE1256.516 F RATIO155.97
DFE5683 PROB>F0.0001
()tP VAR: M MSE0.221101 R-SQUARE0.0267
VARIABLEDFESTIMATEERROR T RATIOPROB>ITI
INTERCEPT10.3818330.006779186 56.32440.0001
O()LOR1-0.2159290.017290 -12.48890.0001
MODEL: MODEL03 SSE1318.858 F RATIO130.26
DFE5683 PROB>F0.0001
DEP VAR: ~MSE0.232071 R-SQUARE0.0224
VARIABLEDFESTIMATEERROR T RATIOPROB>ITI
INTERCEPT10.5815840.006945323 83.73750.0001
(,OLOR10.2021690.017713 11.41330.0001
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate
0.1 percent subsample.
TABLE 9(d) Linear Probability Models: Civilian, Student Labor Force
Participants, Female Teenagers
FlODEL: MODEL21 SSE194.368636 F RAT1041.33
DFE2200 PROB>F0.0001
DfP VAR: U MSE0.088349 R-SQUARE0.0184
PARAMETERSTANDARD
VARIABLEDFESTIMATEERROR T RATIOPROB>ITI
INTERCEPT10.0874320.006624908 13.19740.0001
COLOR10.1453730.022613 6.42870.0001
MODEL: MODEL22 SSE194.368636 F RATIO41.33
DFE2200 PROB>F0.0001
[)~-P VAR: M MSE0.088349 R-SQUARE0.0184
PARAMETER
V,~RIABLEDFESTIMATE
T RATIO PROB>ITI
INTERCEPT 1 0.912568 0.006624908 137.7481 0.0001
C()LOR 1 -0.145373 0.022613 -6.4287 0.0001
_______________________________________________________________________
F1()DEL: MODEL31 SSE194.338377 F RATIO13.88
DFE2198 PROB>F0.0001
OFP VAR: U MSE0.088416 R-SQUARE0.0186
PARAMETERSTANDARD
VARIABLE DFESTIMATEERROR T RATIOPROB>ITI
INTERCEPT 10.0898330.007846721 11.44850.0001
SO 1-0.008377060.014656 -0.57160.5677
C()LOR 10.1400520.032831 4.26590.0001
1~TRACT 10.0137860.045803 0.30100.7635
MODEL: MODEL32 SSE 194.338377 F RAT10 13.88
DFE 2198 PROB>F 0.0001
DEP VAR: M MSE 0.088416 R-SQUARE 0.0186
PARAMETER STANDARD
VARIABLE DF ESTIMATE ERROR T RATIO PROB>ITI
INTERCEPT 1 0.910167 0.007846721 115.9933 0.0001
SO 1 0.008377063 0.014656 0.5716 0.5677
(-OLOR 1 -0.140052 0.032831 -4.2659 0.0001
1NTRACT 1 -0.013786 0.045803 -0.3010 0.7635
SOURCE: Bureau of the Census, ''Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate
0.1 percent subsample.
404
more significant racial differential of 1.38 percent for females.
However, as was the case for nonstudents, a regression for a sample
restricted to labor force participants tells a very different story.
Table 9(b) gives as the gross differential between black and white
male student unemployment rates a highly significant 14.8 percent.
This difference is greater than the white unemployment rate, 11.8
percent. When computed from this regression, the ratio of black to
white student unemployment rates is 2.26. When computed from Table 8
the ratio is 2.02. Once again, the difference between the two computa-
tions stems from the reduced sampling error in the tenfold larger
sample summarized in the earlier table. "MODEL31" shows that, once
again, the racial differential is larger outside the South than in the
South.
Part (d) of Table 9 shows a gross female student racial unemployment
differential that is very close to the male differential; it is 14.5
percent and highly significant. "MODEL31" shows something different
for women, however. The implied ratios of black to white student
unemployment rates are 2.89 in the South and 2.56 outside the South.
For men, the ratio was larger outside the South.
Effect of Additional Explanatory Variables
Tables 10(a)-10 (d) show the same equations for students that were
estimated for nonstudents in Table 6. There are two dramatic changes.
First, for male students but not for female students, race loses its
significant effect entirely, regardless of whether age is included.
Second, for male students but not for female students, household
income, net of the teenager's earnings, emerges from insignificance as
a strong explainer of variation in unemployment. When age is included
in the equation, household income is the greatest reducer of chi-square
for young men, stronger even than education.
SUMMARY
This paper presented a very brief review of the economic literature
on unemployment, in particular the implications for empirical work on
youth unemployment and labor force participation. New structural models
for use with microdata were developed. These models may reduce two
important sources of bias in estimates of the impact of race on
unemployment: simultaneity and ecological correlation.
Original empirical work based on 1980 Census microdata shows, using
simple, single-equation methods, that the labor force participation
decision cannot be ignored in estimating the impact of race on
unemployment. For students and nonstudents, male and female, there is
no gross racial differential to be explained if unemployment is measured
as the ratio of the number of unemployed to the size of the population.
However, when samples are restricted to labor force participants, large
and significant racial differentials emerge. These racial differentials
vary by sex, region, and school enrollment status. A racial difference
405
TABLE 10(a) Conditional Employment Probabilities Using Age as a Regressor,
Civilian, Student Labor Force Participants, Male Teenagers
LOGISTIC REGRESSION PROCEDURE
DEPENDENT VARIABLE: U
2328 OBSERVATIONS
304 POSITIVES
2024 NEGATIVES 7
O OBSERVATIONS DELETED DUE TO MISSING VALUES
-2 LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY= 1804.1$
CONVERGENCE OBTAINED IN 6 ITERATIONS.
MAX ABSOLUTE DERIVATIVE=0.4640D-05.
MODEL CHI-SQUARE= 73.14 WITH 10 D.F.
D=0.031.
-2 LOG L= 1731.04.
P=0.0
VARIABLEBETA STD. ERROR CHI-SQUARE P D
INTERCEPT 1.76G78657 1.173324442.27 0.1321
EDUCATN -0.12846921 0.062821744.18 0.0409 0.002
AGE -0.10924862 0.084958741.65 0.1985 0.001
HH1NCOME -0.01426909 0.0042830611.10 0.0009 0.005
FAM -0.11351487 0.777431070.02 0.8839 0.000
D1SAB1L 0.94116955 0.366565856.59 0.0102 0.003
SO -0.38920228 0.161541405.80 0.0160 0.002
CC -0.20908409 0.172255551.47 0.2248 0.001
SOXCOL 0.46637668 0.388383681.44 0.2298 0.001
CCXCOL 0.92305443 0.395068085.46 0.0195 0.002
COLOR 0.24863929 0.392565500.40 0.5265 0.000
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate 0.1
percent subsample.
TABLE 10(b) Conditional Employment Probabilities, Civilian, Student Labor Force
Participants, Male Teenagers
LOGISTIC REGRESSION PROCEDURE
DEPENDENT VARIABLE: U
2328 OBSERVATIONS
304 POSITIVES
2024 NEGATIVES ,
O OBSERVATIONS DELETED DUE TO MISSING VALUES
-2 LOG LIKEllHOOD FOR MODEL
CONVERGENCE OBTAINED IN 6 ITERATIONS.
MAX ABSOLUTE DERIVATIVE=0.2649D-05.
MODEL CHl-SQUARE= 71.46 ~lTH 9 D. F.
CONTAINING INTERCEPT ONLY= 1804.18
D=0.030.
-2 LOG L= 1-732.72.
P=0.0
VARIABLEBETA STD. ERROR CHI-SQUARE P D
INTERCEPT 0.40037328 0.50537597 0.63 0.4282
EDUCATE -0.1829C)315 O.C)45268()(3 16.33 O.C)()()1 0.()()7
HHINCOME -O.C)1358426 0.00422821 10.32 0.0013 0.004
FAM -0.19656901 0.77373926 0.()6 0.7995 ().()()()
D1SAB1L 0.92588989 0.36581134 6.41 0.0114 0.()()3
SO -0.388~)3809 ().16150578 5.77 ().0163 0.0(-)2
CC -0.21812()70 C).172C)960() 1.61 ().2(95~) 0.()()1
SOXCOL 0.44365482 0.38765566 1.31 ().252l' 0.()()1
CCXCOL 0.91135256 0.39467425 5.33 C).()2()9 ().C)~)2
COLOR 0.25223778 0.39166524 0.41 0.5196 O.O()C)
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate 0.1
percent subsample.
406
TABLE 10(c) Conditional Employment Probabilities Using Age as a Regressor,
Civilian, Student Labor Force Participants, Female Teenagers
LOCI s r IC REGRESSION PROCEDURE
DEPENDENT VARIABLE: U
2202 OBSERVATIONS
1982 U = 0
220 U = 1
O OBSERVATIONS DELETED DUE TO MISSING VALUES
-2 LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY= 1430.79
MODEL CHI-SQUARE= 61.55 WITH 10 D.F. (SCORE STAT.) P=0.0 .
CONVERGENCE OBlAINED IN 6 ITERATIONS.R= 0.147.
MAX ABSOLUTE DERIVATIVE=0.1323D-11.-2 LOG L= 1379.95.
MODEL CHI-SQUARE= 50.84 WITH 10 D.F.(-2 LOG L.R.) P=0.0000.
VARIABLEBETA STD. ERROR CHI-SQUARE P R
INTERCEPT 1.75277707 1.39271281 1.58 0.2082
EDUCATN -0.07694844 0.09504671 0.66 0.4182 0.000
AGE -0.18154598 0.11397084 2.54 0.1112 -0.019
HH1NCOME -0.00251025 0.00444995 0.32 0.5727 0.000
FAM 0.20474210 0.45725416 0.20 0.6543 0.000
D1SAB1L 1.04000882 0.47274892 4.84 0.0278 0.045
SO -0.10730316 0.17961398 0.36 0.5502 0.000
CC 0.05299589 0.18402893 0.08 0.7734 0.000
SOXCOL 0.15040473 0.40052623 0.14 0.7073 0.000
CCXCOL -0.44346755 0.40144515 1.22 0.2693 0.000
COLOR 1.40902455 0.36442253 14.95 0.0001 0.095
_
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate 0.1
percent subsample.
TABLE 10(d) Conditional Employment Probabilities, Civilian, Student Labor
Force Partici pa nts, Female Teenagers
LOGISTIC REGRESSION PROCEDURE
DEPENDENT VARIABLE: U
2202 OBSERVATIONS
1982 U = 0
220 U = 1
O OBSERVATIONS DELETED DUE TO MISSING VALUES
-2 LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY= 1430.79
MODEL CHI-SQUARE= 59.45 WITH 9 D.F.
CONVERGENCE OBTAINED lN 6 ITERATIONS.
MAX ABSOLUTE DERIVATIVE=0.0 .
MODEL CHI-SQUARE= 48.14 WITH 9 D.F.
(SCORE STAT.) P=0.0 .
R= 0.145.
-2 LOG L= 1382.65.
(-2 LOG L.R.) P=0.0000.
VARIABLEBETA STD. ERROR CHI-SQUARE P
INTERCEPT -0.25593762 0.641359740.16 0.6899
EDUCATN -0.18882782 0.0573203710.85 0.0010 -0.079
RH1NCOME -0.00182823 0.004408510.17 0.6784 0.000
FAM 0.17669396 0.455456500.15 0.6981 0.000
D1SAB1L 0.99716482 0.470698124.49 0.0341 0.042
SO -0.12178188 0.179837480.46 0.4983 0.000
CC 0.03780462 0.183842860.04 0.8371 0.000
SOXCOL 0.11184032 0.399101050.08 0.7793 0.000
CCXCOL -0.45539480 0.400617711.29 0.2557 0.000
COLOR 1.41227175 0.3629883715.14 0.0001 0.096
SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
Department of Commerce, 1983): blacks, noninmate 1 percent sample; whites, noninmate 0.1
percent subsample.
407
in labor force participation tends in each case to offset almost com-
pletely the racial difference in the unemployment rate, so that there
are no significant differences in unemployment-population ratios by
race. Two very different structural interpretations of these findings
are higher reservation wages for blacks and discouraged-worker effects.
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