(passmore et al,1983) health and youth employment

8/14/2019 (Passmore Et Al,1983) Health and Youth Employment

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Applied Economics, 1983, 15, 715-729

Health and youth employmentDA VID LYNN PASSMORE, UNAL A Y, SHERYL ROCKEL,BARBARA WADE and JAMES WISEThe Pennsylvania State University, University Park, Pennsylvania, USA

The employment of6.4%ofUnited States teenagers and young adults is limited by their health.These young people are less likely to have jobs than youths without health problems. Also, theywork fewer hours per week than the youth average, although they earn as much per hour asyouths without health limitations. The differences in satisfactionand prestige that youths enjoyfrom their jobs are not related to the presence of health conditions. Youths who reported healthconditions lasting their entire lives are more likely to have jobs than young people recentlyacquiring their conditions. These relationships are derived from analysesof responses of 11 412civilian noninstitutionalized youths to the 1979 Youth Survey of the National LongitudinalSurveys of Labor Force Behavior.

1. INTRODUCTIONThe education and employment needs of handicapped and disabled people received con-siderable attention from legislators and policy-makers in the United States during the 1960sand1970s. For instance, the Education Amendments of 1976 (Public Law 94-482) earmark 10 %ofthe allotment of federal money for vocational education to pay for 50 %of the costs of traininghandicapped students. Affirmative action in employment ofhandicapped people is emphasizedin the Rehabilitation Act of 1973 (Public Law 93-112, Section 503). Amendments to the 1973Act provide incentives for employers to train and to hire disabled people. President Kennedyestablished the President's Committee on Employment of the Handicapped in 1962, extendingpresidential efforts since the mid-1940s to highlight employment needs of handicappedAmericans.Although much of this attention centres on education and employment of handicapped anddisabled young people, systematic and comprehensive estimates of the influence of healthconditions on youth employment are not available to guide policy-making and legislation.Previous studies focused on adults (e.g., Levitan and Taggart, 1977; Wolfe, 1980) and ondisabilities that are defined strictly for determining who receives Social Security DisabilityInsurance benefits in the United States (e.g., Berkowitz et aI., 1976). Moreover, data that addresshealth and employment simultaneously are scarce (Rones, 1981, footnote 2).In this paper we report a study of the relationships between employment and the incidence,type and duration of health conditions that limit work of young people. Our data are from a0003-6846j83 $03.00+ .12 1983 Chapman and Hall Ltd. 715


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716 David Lynn Passmore, Unal Ay, Sheryl Rockel, Barbara Wade and James Wise1979 survey of a sample of teenagers and young adults in the United States. Labour force data,demographic characteristics and health information are merged in this survey. In addition, thedefinition of health applied in this survey is broader than the Social Security definition ofdisability. Acute and chronic health conditions are included as well as a variety of conditionsthat affect employment, such as pregnancy.In brief, we find that 6.4 %of youths in the United States believed during 1979 that theirhealth limited the amount or kind of work they could do. These youths are less likely toparticipate in the labour force and to be employed than youths without health problems.However, perceived health status is not related to job satisfaction, occupational prestige orhourly rate ofpay among employed youths. Employed youths with health problems work fewerhours each week than the working youth average.In the next section of this paper we describe the target population, sample, variables andanalyses used in this study. Then, results ofour analyses are tabulated and discussed. Also, thereis a brief statistical appendix that follows the summary and reference citations.

II. METHODTarget population and sampleWe analyse data collected by the 1979 Youth Survey of the National Longitudinal Surveys ofLabor Force Behavior that was administered by the National Opinion Research Center at theUniversity of Chicago and the Center for Human Resource Research at The Ohio StateUniversity (Borus et al., 1980, 'Questionnaire', p. 395 if). The data represent civiliannon institutionalized youths in the continental United States. These young people were 16 to 21years old in January 1979.Two multi-stage probability samples cover the youth population through a cross-sectionalsample and a supplemental sample (Borus et ai., 1980, Appendix A). The cross-sectional sampleyields an equal number of males and females who represent various racial, ethnic and incomegroups in their proper population proportions. Proportional samples often contain inadequatenumbers of minority group members for analysis. The supplemental sample producesadditional Hispanics, Blacks and poor non-Hispanics and non-Blacks.The full sample contains 1269314- to 21-year-olds. However, we analyse data from 1141216-to 21-year-olds who are not enrolled in the active armed forces. We estimate population valuesby multiplying each survey response by a unique number called a sampling weight. This weightreflects the probability of being included in the sample. In addition, it corrects oversampling ofminorities, biases in sampling and inconsistencies with independent census population counts(Borus et al., 1980, pp. 387-92). As a result, sample members' data are adjusted to representmembers of the youth population.Estimates of error and bias created by this sampling scheme are unavailable currentlyHowever, for at least two reasons employment estimates from the 1979 Youth Survey may differfrom estimates from the Current Population Survey (CPS) (US Department of Commerce,1978), the source of the United States government's monthly labour force data. First, the CPSuses, when necesSary, proxy respondents (sister . . . granny . . . a responsible adult) even though


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717ealth and youth employmentthey might be unable to recall or report accurately a youth's employment activity (see Borus etal., 1978 for evidence of the effects of different respondents on labour force data). Second, the1979 Youth Survey was administered from January to August 1979, a period of varyingemployment conditions. The CPS refers to employment activity during the wee..k containing the19th day ofany month. In addition, the 1979 Youth Survey, by omitting institutionalized youths(as does the CPS), probably underestimates the number of youths with health problems whoneither work nor look for work.Variables

Criterion variables. We examine six aspects of youth employment: labour force status;weekly hours worked; incidence of employment; job satisfaction; occupational prestige; andhourly rate of pay.The definition of labour force status we apply is also used in the CPS, the United StatesCensus of the Population and Housing, and many American government and private statisticaloperations. Youths are classified as employed, unemployed, or out of the labour force.Employed youths worked for pay at least one hour, or worked unpaid for at least 15 hours in afamily business, during the week prior to the administration of the 1979 Youth Survey.Unemployed youths did not hold jobs, but they made specific attempts to find work during fourweeks before the survey. Youths out of the labour force were neither employed nor unemployed.The labour force participation rate equals the number of 16- to 21-year-olds employed andunemployed (called the youth labour force) divided by the number of civilian non-institutionalized youths. The employment-population ratio equals the number employeddivided by the number in the population. The unemployment rate represents the percentage ofunemployed youths in the youth labour force.Weekly hours worked represents the number of hours youths work at all jobs during theweek. Values for this variable include zero hours for youths who are not working. The incidenceof employment indicates whether youths are employed during the week. These variables, andthose remaining to be described, are analysed only for youths not enrolled in school. Thisrestriction on the sample eliminates youths who are unavailable for serious commitment towork.

Job satisfaction is defined as a simple sum of responses to ten 1979 Youth Survey items. Theseitems contain statements about job advancement opportunities, safety, environment, pay,security, co-workers and supervision. Youths rate these statements using a four-point scale(4 = statement very true; 3 = somewhat true; 2 = not too true; 1 = not true at all).Occupational prestige is determined by assigning values of the Duncan Occupational StatusIndex (Duncan, 1961) to occupations in which youths are employed. This index is a weightedcombination of median income, median educational attainment, and the percentage of femalesin each of the occupations listed in the United States Census occupational classification system.The Duncan Index is stable over time (Hodge et al., 1966) and settings (Siegel, 1970).Youths' before-tax pay (by day, week, month, year) is converted to cents received per hour.Youths paid less than 50 cents or more than $15 per hour are excluded from our analyses.Predictor variables. We study relationships between aspects of youth employment and theincidence, type and duration of health limitations to work. Many variables are related to youth


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718 David Lynn Passmore, Unal Ay, Sheryl Rockel, Barbara Wade and James Wiseemployability in addition to health. We apply some ofthese variables as controls to obtain betterestimates of the net relationship between youth employment and reported health. Thesecontrols include gender, ethnic origin, age, marital status, high school completion, responsibilityfor the support of dependents, location of current residence and the local unemployment rate.Definitions of control variables appear in tables that contain the results of our study.The incidence of health limitations to work is measured by youths' reports of whether theirhealth limits the amount or kind of their employment. Alternate measurement methods couldlead to different assessments of these limitations. For instance, medical examinations are notused to corroborate these self-reports, as done in the 1971-74 Health and Nutrition Surveysconducted by the United States Center for Health Statistics (see Rones, 1981, footnote 2). Wolfe(1981) observed, though, that medical evaluations do not necessarily reduce uncertainty aboutwhether functional work limitations are caused by health (see Haber (1967) for a review of thesemeasurement problems). At best, the measure of health obtained for our study represents beliefsabout the effects of conditions on employment; at worst, it may represent a lame excuse for apoor work history.The availability of data from only a small number of youths in the sample with healthconditions that affect work prevents employment comparisons among detailed categories ofhealth conditions. Conditions cited include, among others, acne, asthma, fractured bones,circulatory problems, cancer and pregnancy. These types of health conditions are classifiedunder three variables. One variable represents whether the health limitation is due to apregnancy or a normal delivery. Another variable denotes that an accident or injury caused thehealth limitation. The third variable shows that the health limitation is a developmentaldisability or disease.The duration of the health condition in months measures the persistence of the healthcondition. On the one hand, long durations may indicate reduced opportunities for creating,maintaining or improving job knowledge, skills and attitudes. On the other hand, young peoplewith long durations may have opportunities to adjust psychologically, medically andoccupationally to their conditions. Previous research is unavailable which might enable us todetermine the expected direction of the relationship between the duration measure and youthemployment.AnalysesWe apply in our analyses simple tabulation and varieties of multiple regression. Not only doregression equations include health limitation variables, but also control variables are entered inequations to allow estimation of net relationships between criterion variables and healthlimitation variables.Labour force status is cross-tabulated by the incidence of health limitations to work. Jobsatisfaction, occupational prestige and hourly rate of pay are related to incidence of healthlimitations and control variables by using ordinary least squares regression methods. Actually,the natural logarithm of hourly rate of pay is the criterion variable specified, but results areexpressed iIi cents per hour.Zero weekly hours are recorded for 31 %of the youths in the sample because they are notworking. Tobit regression methods (Tobin, 1958) are applied to examine the relationships


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719ealth and youth employmentbetween the weekly hours worked, the incidence of health limitations and the control variables.Tobit methods are appropriate when cases are clustered at some limiting value on the criterionvariable. The Tobit regression coefficient is parsed in two ways: first, if health limitations arereported, the probability of working is computed; second, for working youths, the change inweekly hours worked associated with; the incidence of health limitations is calculated. Astatistical appendix to this paper displays the mathematics of this decomposition of the Tobitregression coefficient.The probability ofworking is decomposed further by relating the incidence of employment totypes ofhealth limitations and control variables by using logistic regression methods (Amemiya,1981, p. 1486). These methods are appropriate when a categorical criterion variable is analysed.Predicted values from the logistic equation are restricted within the range zero to + 1, which is aconvenient property for expressing the probability of working as a function of types of healthlimitations to work.Using logistic regression methods, relationships are examined among the incidence ofemployment, control variables and the duration of the health limitation in months. Youthsexperiencing their health condition their entire lives are assigned the highest value for theduration variable.

III. RESULTS

Labour force statusAs shown in Table 1, 6.4%of youths in the United States believed during 1979 that their healthlimited the amount or kind of work they could do. Youths with health limitations to workparticipate in the labour force about 16 % less frequently than other young people. Theiremployment-population ratio is about 17%lower, and their unemployment rate is 7.5%higher.Weekly hours workedTable 2 contains regression coefficient estimates from a T.obit analysis of weekly hours worked,incidence of health limitations and control variables. The directions of the signs of the controlvariables' coefficients are consistent with previous research on youth labour markets. Thecoefficient for the health limitation variable differs statistically from zero and is negative.Using mathematics developed in the statistical appendix to this paper, the health limitationvariable is divided into two pieces of information. First, the probability ofbeing employed (thatis, of having worked any hours) for youths reporting limitations is 20% lower than for thosewithout health limitations to work. Among youths employed and not in school, healthlimitations are associated with 14.27 fewer work hours during the week pt:eceding theadministration of the 1979 Youth Survey.Incidence of employment

By type of health limitation. The Tobit analysis of hours worked reveals that young peoplewith health limitations have a lower probability of being employed. Also shown in Table 2 are


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720 David Lynn Passmore, Unal Ay, Sheryl Rockel, Barbara Wade and James WiseTable 1. Labour force status by incidence of health limitations to work, reported by civiliannoninstitutionalized 16-21year-olds during 1979 (numbers in thousands)

Reported health limits to workStatus Total Yes NoPopulation 25550 1634 23916In the labour force 18234 920 17314Laboqr force participation rate [(Inthe labour force/population) x 100JEmployedEmployment-population ratio(employed/population)UnemployedUnemployment rate[(Unemployed/in the labour force)x l00J

71.4%148540.5813380

18.5%

56.3%6820.417238

25.9%

72.4%141720.5923142

18.1 %Out of the labour force 7316 714 6602Source: Estimated from responses to the 1979 Youth Survey of the National LongitudinalSurveys of Labor Force Behavior by a multi-stage probability sample of 11 412 youths from thecivilian noninstitutional populat ion of the continental United States between 16 and 21 yearsold in January 1979.

Table 2. Relationships ofweekly hours worked and incidence ofemployment to incidence and type ofhealthlimitations to work and characteristicsofcivilian noninstitutionalized 16-21-year-olds not enroned in schoolduring 1979 (N =3619)Incidence of employment

TobitregressionEstimate of coefficient Logisticpopulation for weekly regression Chances of employmentmean hours worked coefficient associated with(standard (standard (standard characteristic, holdingCharacteristic deviation) . error) error) others constant at mean aHealth statusHealth limit due to 0.03 -1.87pregnancy or delivery (0.17) (0.28) Than f9 of 100 lessHealth limit due to 0.02 -0.36 those

accident or injury (0.14) (0.27) without 8 of 100 lessHealth limit due to 0.04 -0.60 healthdisease or develop- (0.20) (0.19) limits 13 of 100 lessmental disabilityTotal 0.09(0.29)Any health limit 0.09 -18.06to work . (0.29) (0.07)o health limit 0.91 rc rc rc

to work (0.29)
http:///reader/full/develop-(0.20http:///reader/full/develop-(0.20http:///reader/full/develop-(0.20


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721ealth and youth employmentTable 2. (Cont.)

Estimate ofpopulationmean(standardCharacteristic deviation)

GenderMaleFemale

Ethnic originBlackHispanicWhite

AgeIn integer yearsas of January 1979Between 16-17Between 18-19Between 20-21

Marital statusEver marriedNever married

High school completionReceived diplomaor equivalentNot received

DependentsAt least oneNone

0.47(0.50)0.53(0.50)0.14(0.35)0.07(0.26)0.79(0.41)

19.60(1.43)0.08(0.27)0.37(0.48)0.55(0.50)0.29(0.45)0.71(0.45)

0.69(0.46)0.31(0.46)0.10(OJO)0.90(OJO)

Tobitregressioncoefficientfor weeklyhours worked(standarderror)

16.44(0.04)rc

-14.49(0.04)-2.74(0.05)rc

2.20(0.01)

-4.46(0.04)rc

17.12(0.04)rc

4.29(0.53)rc

Incidence of employment

Logisticregression Chances of employmentcoefficient associated with(standard characteristic. holdingerror) others constant at meana

1.05 22 of 100 greater(0.08) than femalesrc rc

-1.01(0.10) Than { 22 of 100 lesswhites 6 of 100 less-0.27(0.11)rc rc

-0.65(0.23) Than20-21 { 14 of 100 less-0.14 year(0.08) olds 3 of 100 lessrc rc

-0.24 5 of 100 less than(0.09) those nevermarriedrc rc

1.27 27 of 100 greater(0.08) than those withoutdiplOmarc rc

0.21 4 of 100 greater(0.11) than those withoutdependentsrc rc


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722 David Lynn Passmore, Unal Ay, Sheryl Rockel, Barbara Wade and James WiseTable 2. (Cont.)

Incidents of employmentTobitregressionEstimate of coefficient Logisticpopulation for weekly regression Chances of employmentmean hours worked coefficient associated with(standard (standard (standard characteristic, holdingCharacteristic deviation) error) error) others constant at mean 8'It:

Current residenceUrban 0.77(0.42) 1.00(0.04) 0.054(0.09) Logistic coefficientnot statisticallyRural 0.23 rc rc significantrc(0.42)

Local unemployment 2.58 -0.23 5 of 100 lessrate category (0.72) (0.05) for each higher(1 = < 3%; unemployed2 3.0-5.9%; category3 = 6.0-8.9 %;4 = 9.0-11.9 %;5 = 12.0-14.9%;6=15+%)Criterion 26.3 0.69variable mean (18.4) (0.46)(standard (includesdeviation) zeros)Intercept -40.40 0.42Goodness-of-fit test ModelX2= 874 ModelX2 = 712(see Tobin, (see Harrell,1958, 1980, p. 83)Section 4) with 13 dJ.,with 9 dJ., P < 0.001

P < 0.001

Source: See Table 1; sample members with missing data deleted from analyses.Note: Approximately 11.5 million youths were not enrolled in school during 1979; rc = reference category;- means variable not used in equation.8First partial derivative, evaluated at the mean, of the likelihood function maximized to estimate logisticregression coefficients. Equal to[exp L XJj)/(1 +exp L

j j where ais the estima,te of intercept; the estimate of logistic coefficient for variable j; the estimate oflogistic coefficient for characteristic of interest, and Xj the estimate of population mean for variable j.


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723ealth and youth employmentthe chances of being employed for three types of health limitations to work and a number ofcontrol variables. These results are derived from a logistic regression analysis of the incidence ofemployment, types of health limitations and control variables. Differences in chances of beingemployed for values of each control variable are in expected directions. Chances of beingemployed for youths with each type of health limitation are based on logistic regressioncoefficients that differ statistically from zero.One-third of youths who are not enrolled in school and who report health limitations arepregnant or post partum females. These young women are 39 %less likely to be employed thanyouths not reporting any health limitations to work. Developmental disabilities or diseasesafflict 45%of youths who report health limitations to work. These young people experience13 %less chance ofbeing employed than youths not reporting health problems. Youths who arelimited because of accidents or injuries are 8%less likely to be employed than youths withoutany limitations. They comprise 22 %of youths who are not in school and who report healthlimitations to work.By duration of health condition. The longer out-of-school youths have their healthconditions, the more likely they are to be employed. Based on predicted values from theequation shown in Table 3, youths experiencing their health conqitions for seven months (theshortest duration observed) have a 0.35 probability of being employed. For youths with healthconditions for 63 months (the arithmetic mean), the probability of being employed is 0.40. The.probability of being employed for youths who report health conditions lasting their entire livesis 0.54.

Table 3. Relationship of incidence of employment to duration of health conditions and charac-teristics of civilian noninstitutionalized 16-21-year-olds not enrolled in school and reporting healthlimitations to work during 1979 (N = 303)

Characteris icDuration ofhealth limitin monthsGenderMale

Female

Ethnic originBlackHispanicWhite

Estimate ofpopulation mean(standarddeviation)

63.37(71.89)0.21(0.44)(0.79)(0.44)0.13(0.34)0.05(0.21)0.82(0.30)

Logisticregressioncoefficient(standard error)

0.004(0.002)1.60(0.36)rc

-1.23(0.37)-0.43(0.47)rc

Chances ofemployment associatedwith characteristic,holding othersconstant at meana1.15 of 100 greaterfor each 12 monthslonger duration38 of 100 greater thanfemalesrc

30 of 100 less thanwhitesnsrc


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Health and youth employment 725Among youths who are not enrolled in school and who report health limitations to work,males, youths not responsible for supporting dependents and high school completers are morelikely to be employed than females, youths with dependents, and those who did not completehigh school (see Table 3). Blacks and youths who are married, divorced, separated or widowedare less likely to be employed than whites and those never married. These' relationships areuniformly larger in magnitude than those observed from the analysis of employment youthswith and without limitations (cf. entries for each characteristic in Tables 2 and 3).

Job satisfaction, prestige and paySummarized in Table 4 are ordinary least squares regressions ofjob satisfaction, occupationalprestige and hourly rate of pay with the incidence of health limitations and control variables.Small mean differences in satisfaction, prestige and pay are observed by incidence of healthlimitations. These results remain unchanged when the differences are adjusted by including

Table 4. Distribution ofjob satisfaction, occupational prestige andpay by incidence ofhealth limitations towork reported by civilian non institutionalized 16-21-year-olds not enrolled in school during 1979(N = 2524)

Estimate of popUlation mean(standard deviation)Reported health limits towork Difference Regression adjustedCriterion between (1) difference betweenvariable Total Yes (1) No (2) and (2) means '(1) and (2) meansa

Job satisfactionscore 21.2 20.9 21.2 -0.3 -0.4(0-40 range) (4.7) (5.1) (4.7)Duncan Occupat-ional Status Index 28.4 30.6 28.3 2.3 -1.0(0-100 range) (18.3) (17.8) (18.3)Hourly rateof pay (cents/hr) 368c 358c 369c llc lc(>$0.50,


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726 David Lynn Passmore, Unal Ay, Sheryl Rockel, Barbara Wade and James Wisecontrol variables in regression equations. Because the average youth who reports healthlimitations works about 14 fewer hours per week, a youth with a helath limitation could receiveless gross annual personal income from wages and salaries than a youth not reporting a healthlimitation.

IV. DISCUSSIONThe results


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727ealth and youth employmentworking, however, these youths are as satisfied with aspects of their jobs as other employedyouths. On the average, youths hold jobs that occupy the lower quarter of the occupationalstatus hierarchy in the United States economy. Employed youths who report health limitationsto work are no exception.

Youths who report health limitations to work earn about as much per hour as other youths.Similarity in pay might reflect rigidities (union agreements, perceptions of fairness, minimumwage laws, wage standards) that reduce employers' flexibility in making wage offers. Or perhapsthe comparable productivity ofyouths who report health problems is shown through their equalpay. More detailed studies ofwage determinants for youths with health limitations can includeinformation about type and quality of schooling, family conditions, spouses' employment andemployer characteristics. The reduction in weekly hours worked can also be studied with thesevariables. A major policy question is whether income supplements from government transferpayments reduce the Willingness of youths with health problems to work additional hours, eventhough they may be as productive as other youths. Or are reductions in work hours and incomethe result of inability of these young people to complete a full work week?

V. SUMMARYDuring 1979, about 6.4%of teenagers and young adults in the United States reported that theirhealth limited their employment. These youths participate in the labour force 16 % lessfrequently and suffer a 7.5 %higher unemployment rate than youths without health limitations.Youths not enrolled in school have 20 %fewer chances of being employed if they believe thattheir health limits their employment. Pregnant or post partum females are 39%less likely to havejobs than youths without health limitations. Youths afflicted by diseases and accidents are 13and 8% less likely, respectively, to be employed than those not reporting health limitations.Youths having health problems their entire lives are about 19 %more likely to be employed thanyouths who recently acquired their conditions. Also, young people who report healthlimitations, who are employed and who are not enrolled in school, work about 14 fewer hoursper week than the working youth average. Perceived health status is not related to jobsatisfaction, occupational prestige or hourly rate of pay. These relationships between youthemployment and health limitations to work are derived from analyses of responses of 11412civilian noninstitutionalized 16- to 21-year-olds in the United States to the 1979 Youth Surveyof the National Longitudinal Surveys of Labor Force Behavior.

ACKNOWLEDGEMENTSThe advice and assistance of our following Pennsylvania State University colleagues areappreciated: D. Anderson, H. Flexner, M. Hallberg, E. Herr, T. Long, 1. Rodgers, M. Scofield, 1.Selzer, D. Shapiro, S. Stephenson and F. Welch. The Penn State College ofEducation providedgenerous computing assistance for the completion of research reported in this paper.


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728 David Lynn Passmore, Unal Ay, Sheryl Rockel, Barbara Wade and James WiseSTATISTICAL APPENDIX

The decomposition Qf the regression coefficient for the incidence of health limitations in theTo bit analysis of weekly hours worked is a straightforward application of the decomposition ofeffects of participating in an income maintenance experiment on family earnings shown byStephenson and McDonald (1979). Their approach uses methods provided by Tobin (1958) andextended by Amemiya (1973) for handling criterion variables that are clustered at some limitingvalue. In our study, 31 %of the sample members worked zero hours.In Table 2, the Tobit regression coefficient for the incidence of health limitation variable, H L,estimates the'thange in the expected value of weekly hours worked, E[lt1:la for person i for aunit change in H L. A unit change in H L is the difference between youths with and without healthlimitations to work. Expressed in another way, the coefficient is the first derivative of the Tobitequation with respect to HL. Dropping subscripts, the derivative is decomposed as follows:

oE[lt1:lJ = E[lt1:l*J of(z) F() oE[lt1:l*JoHL oHL + z oHLwhere z = lt1:l/SE(lt1:l), the number of standard deviations a youth with the average hoursworked is above zero lt1:l [SE(lt1:l) is the standard error of the Tobit regression equationJ; F(z)is the probability that person with WH is above zero WH; E[WH*J the expected value of WH,given that person i is working; of(z)/oHL the change in probability of working, given thatperson i reports a health limitation to work; and oE[lt1:l*J/oHL the change in lt1:l, given thatperson i has a health limitation and is working. of(z)/oHL and oE[lt1:l*J/oHL of thedecomposition are evaluated at lt1:l in our study.Using data from the Tobit equation reported in Table 2, z = 26.3/28.8 = 0.91; F (z) = 0.81from normal probability table; and, because E[lt1:lJ = E[lt1:l* J F(z), E[lt1:l*J = 26.3/0.81= 32.5. Therefore, of(z)/oHL = F( (lt1:l - oE[lt1:lJ/oHL)/SE(lt1:l F(z) = F[(26.3

oE[lt1:lJ of(z)18.06)/28.8J-0.81 = -0.20. Because F(z) oE[lt1:l*J/oHL = oHL -E[lt1:l*J oHL =-18.06-[( -0.20)(32.51)] = -11.56; therefore, oE[WH*J/oHL = -11.56/(0.81) = -14.27.The probability of having no hours worked (not havirtg a job) is 0.20 greater if a youthreported a health limitation to work. Among employed youths, the report ofa health limitationis associated with 14.27 fewer hours worked per week.

REFERENCESAmemiya, T. (1973) Regression analysis when the dependent variable is truncated normal, Econometrica,

41, 997-1016.Amemiya, T. (1981) Qualitative response models: a survey, Journal ofEconomic Literature, 19, 1483-536.Berkowitz, M., Johnson, W. G., and Murphy, E. H. (1976) Public Policy Toward Disability, PraegerPublishers, New York.Borus, M. F., Mott, F. I., and Nestel, G. (1978) Counting Youth: A Comparison of Youth Labor ForceStatistics in the Current Population Survey and the National Longitudinal Surveys, The Ohio StateUniversity, Center for Human Resource Research, Columbus, Ohio (ERIC Document ReproductionService No. ED'155 491).
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729ealth and youth employmentorus, M. F., Crowley, 1. E., Rumberger, R. W., Santos, R.,and Shapiro, D. (1980) Pathways to thefuture: ALongitudinal Study of Young Americans (Preliminary report: youth and the labor market 1979), TheOhio State University Center for Human Resource Research, Columbus, Ohio (ERIC DocumentReproduction Service No. ED 195 796).n, O. D. (1961) A socioeconomic index for all occupations, in Occupations and Social Status, editedby A. J. Reiss, Free Press, New York.

L. D. (1967) Identifying the disabled: concepts and methods in the measurement ofdisability, SocialSecurity Bulletin, 30, 17-34.The LOGIST procedure, in SAS Supplemental Library User's Guide edited by P. S.Reinhart, SAS Institute, Cary, North Carolina.R. W., Siegel, P. M., and Rossi, P. H. (1966) Occupat ional prestige in the United States: 1925-1963,in Class, Status, and Power, 2nd edn, edited by R. Bendix and S. M. Lipset, Free Press, New York.S. A., and Taggart, R. (1977)Jobsfor the Disabled (Policy Studies in Employment and Welfare No.28), The Johns Hopkiris University Press, Baltimore, Maryland.

Can the Current Population Survey be used to identify the disabled? Monthly LaborReview, 104, 37-8. Social Stratification: Research andTheory for the 1970s, edited by E. O. Laumann, Bobbs-Merrill, New York.S. P. (1979) The short-run consequences of work experience while in school, in ExpandingEmployment Opportunities for Disadvantaged Youth (Special Report No. 37), National Commission onEmployment Policy, Washington, DC.S. P., and McDonald, J. F. (1979) Disaggregation of income maintenance impacts on familyearnings, Review of Economics and Statistics, 61, 354-60.

and future employability, in TheLingering Crisis of Youth Unemployment, edited by A. Adams and G. L. Mangum, W. E. UpjohnInstitute for Employment Research, Kalamazoo, Michigan.1. (1958) Estimation of relationships for limited dependent variables, Econometrica, 26, 24-36.

of Commerce, Bureau of the Census (1978) The Current Population Survey:Design and Methodology (Technical Paper No. 40), United States Government Printing Office,Washington, DC.B. L. (1980) How the disabled fare in the labor market, Monthly Labor Review, 103,48-52.B. L. (1981) The CPS, work, and disability: A reply, Monthly Labor Review, 104, 38-9.

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(passmore et al,1983) health and youth employment

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