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Youth Joblessness and Race: Evidence from the 1980 Census
Pages 367-409

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From page 367...
... 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.
From page 368...
... 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.
From page 369...
... 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.
From page 370...
... 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.
From page 371...
... 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.
From page 373...
... 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.
From page 374...
... 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)
From page 375...
... 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.
From page 376...
... 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 participation and the chance of unemployment-if participation is chosen.
From page 377...
... 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)
From page 378...
... If a separate conditional unemployment probability equation was estimated for each labor market, using only the young labor force participants in that labor market, the ecological correlation problem might be reduced. A Structural Model for the Youth Labor Market An alternative and even more direct approach to these problems of modeling the youth labor market is available.
From page 379...
... Employmentpopulation ratios are 68.6 percent for white males and 40.7 percent for black males. Among labor force participants, the white male unemployment rate is 18.5 percent, but the black male unemployment rate
From page 384...
... 384 Z o ~ o o o o ~ oo ~ ~ ~ -O ~ 1 J oO ~0 ~ O (U ~ 0~0~ 6 0 C:)
From page 385...
... 385 ZO - ~O= o om~ ooo~ m~ -OOCOJ - CC)
From page 386...
... Since young people generally make gradual transitions from school to work, years of education completed, especially those in excess of 12, ought to increase labor force participation and employment. The greater 2 3When these labor force and unemployment statistics for nonstudents are added to those reported in Table 8 for students, national unemployment rates comparable to those reported by the BLS emerge.
From page 387...
... -5td) , linear probability models of multiple choice are used to show quite clearly the gross racial differentials in the labor force behavior of young people.
From page 388...
... TABLE 5Ib) Linear Probability l\/odels: Civilian, N.onstudent Labor Force Pa rticipa,nts, Male Teenagers MODEL: blODEL21 SSE275.212078 F RATIO16.14 DFE1752 PROB>F0.0~)
From page 389...
... TABLE 5Id) Linear Probability Models: Civilian, Nonstudent Labor Force Participants, Female Teenagers FlODEL: MODEL21 SSE181.706169 F RATIO27.04 DFE1443 PROB>F0.0001 DFP VAR: U MSE0.125923 R-SQUARE0.0184 PARAMETERSTANDARD VARIABLE DFESTIMATEERROR T RATIOPROB>ITI I'4TERCEPT 10.1352180.009808031 13.78640.0001 COLOR 10.1662530.031970 5.20020.0001 tdODEL: MODEL22 SSE181.706169 F RATIO27.04 DFE1443 PROB>F0.0001 DEP VAR: M MSE0.125923 R-SQUARE0.0184 PARAMETERSTANDARD VARIABLE DFESTIMATEERROR T RATIOPROB>ITI INTERCEPT 10.8647820.009808031 88.17080.0001 COLOR 1-0.1662530.031970 -5.20020.0001 MODEL: MODEL31 SSE181.559482 F RATIO9.40 DFE1441 PROB>F0.0001 DEP VAR: U MSE0.125995 ~ R-SQUARE0.0192 PARAMETERSTANDARD VARIABLE DFESTIMATEERROR T RATIOPROB>ITI INTERCEPT 10.1353470.011872 11.40090.0001 SO 1 -0.000406997 0.021084 -0.0193 0.9846 COLOR 1 0.203636 0.047712 4.2680 0.0001 1NTRACT 1 -0.065849 0.064934 -1.0141 0.3107 [VlODEL: MODEL32 SSE 181.559482 F RATIO 9.40 DFE 1441 PROB>F 0.0001 DEP VAR: M MSE 0.125995 R-SQUARE 0.0192 PARAMETER STANDARD VARIABLE DF ESTIMATE ERROR T RATIO PROB>iTI INTERCEPT 1 0.864653 0.011872 72.8338 0.0001 SO 1 0.0004069971 0.021084 0.0193 0.9846 COLOR 1 -0.203636 0.047712 -4.2680 0.0001 1NTRACT 1 0.065849 0.064934 1.0141 0.3107 SOURCE: Bureau of the Census, "Public-Use Microdata Sample C" (Washington, D.C.: U.S.
From page 390...
... 2 6 Clearly, we must focus attention on conditional measures of unemployment if we are to say anything sensible about racial differences in youth unemployment. The much lower labor force participation of blacks and their higher unemployment rates tend to cancel out each other completely in unconditional measures of unemployment.
From page 391...
... , we know that an astounding 56.8 percent of the black male, nonstudent labor force participants live in the South. Were this not so, the national ratio of male unemployment rates by race would be much higher than 1.68.
From page 392...
... The Effect of Additional Explanatory Variables For the reasons stated above, it might be unwise to place much faith in structural estimates of the effect of race on unemployment based on single-equation techniques, or even on system techniques, ignoring the complex sample selection used to generate data on youth unemployment, wages, and school enrollment. Thus far, the focus has been on merely measuring gross effects of race on unemployment.
From page 393...
... TABLE 6tb} Conditional Unemployment Probabilities, Civilian, Nonstudent Labor Force Participants, Male Teenagers LOGISTIC REGRESSION PROCEDURE DEPENDENT VARIABLE: U 1754 OBSERVATIONS 346 POSITIVES 1408 NEGATIVES O OBSERVATIONS DELETED DUE TO MISSING VALUES LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT CONVERGENCE OBTAINED IN 5 ITERATIONS.
From page 394...
... TABLE 6(d) Conditional Unemployment Probabilities, Civilian, Nonstuclent Labor Force Participants, Female Teenagers LOGISTIC REGRESSION PROCEDURE DEPENDENT VARIABLE: U 1445 OBSERVATIONS 1227 U = 0 218 U = 1 O OBSERVATIONS DELETED DUE TO MISSING VALUES -2 LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY= 1225.96 MODEL CHI-SQUARE= 70.27 WITH 9 D.F.(SCORE STAT.)
From page 395...
... TABLE 7tb) Labor Force Participation Probabilities, Civilian, Nonstudent Male Teenagers LOGISTIC REGRESSION PROCEDURE DEPENDENT VARIABLE: N 2178 OBSERVATIONS 424 POSITIVES 1754 NEGATIVES O OBSERVATIONS DELETED DUE TO MISSING VALUES -2 LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY= 2147.20 CONVERGENCE OBTAINED lN 6 ITERATIONS.
From page 396...
... TABLE 7(d) Labor Force Participation Probabilities, Civilian, Nonstudent Female Teenagers LOGISTIC REGRESSION PROCEDURE DEPENDENT VARIABLE: N 2280 OBSERVATIONS 1445 N = 0 835 N = 1 O OBSERVATIONS DELETED DUE TO MISSING VALUES -2 LOG LIKELIHOOD FOR MODEL CONTAINING INTERCEPT ONLY= 2995.54 MODEL CHI-SQUARE= 461.55 WITH 10 D.F.
From page 397...
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From page 398...
... 398 Z O N C C~ (>J ~ N ~ (>J ~0 O OC< 0 ~ ~ ON~ONL~C>~ ·n LJ C ~ ~ J °O O r~ ~ J O ..........
From page 399...
... 399 z 0 ~ as o~ ~ ~ ~ ~ ~ ~ ~ oooO~)
From page 401...
... 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 participants; 2,123 of the 8,239 black students were participants.
From page 402...
... TABLE 9(b) Linear Probability Models: Civilian, Student Labor Force Participants, Male Teenagers MODEL: MODEL2 1 SSE 260.
From page 403...
... 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 [)
From page 404...
... 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.
From page 405...
... 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.
From page 406...
... 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.
From page 407...
... In Richard Freeman and David Wise, eds., The Youth Labor Market Problem: Its Nature, Causes, and Consequences. Chicago, Ill.: University of Chicago Press.
From page 408...
... Wise 1982 The youth labor market problem: its nature, causes, and consequences.
From page 409...
... Bureau of Labor Statistics, Special Labor Force Report No. Review 100~11~:39-43.


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