Labor force behavior in the process of socioeconomic attainment: New scales added to classical models

SOCIAL SCIENCE RESEARCH u),

256-270 (1991)

Labor Force Behavior in the Process of Socioeconomic Attainment: New Scales Added to Classical Models* CLIFFORD C. CLOGG, NIMFA OGENA, Department

of Sociology

and Population Issues State University

AND HEE-CHOON Research

Center,

SHIN

Pennsylvania

We show that new scales summarizing complex types of labor force behavior are strongly related to socioeconomic attainments for males aged 25-64, net of standard predictors measuring background, schooling, and experience or first-job status. Baseline status-attainment models are reconsidered with data from the 1973 Occupational Changes in a Generation study (OCG-II), with both the models and the data viewed as an important test case. One goal is to examine whether the “effects” of labor force behavior remain when background status and other factors are statistically controlled. Results provide evidence for the importance of proximate labor market experiences (i.e., recent labor-force careers) and worker marginality (i.e., underemployment) in models predicting occupational status and especially in models predicting log-earnings. The perspective advocated here can be used in longitudinal analyses involving labor force careers as predictors of attainments over the life CyCk. 0 1991 Academic Press, IIIC.

The purpose of this note is to further investigate the explanatory value of new scales given by Clogg et al. (1990) in the context of classical sociological models of status attainment. Those four scales summarize complex forms of labor force behavior which can be added as covariates in most existing models that would be estimated with most existing data bases, The scales were derived from a basic 16 x 10 cross classification of labor-force measures designed to characterize the labor-market matching process. This cross classification was in turn derived by combining virtually all of the relevant labor-force items contained in standard surveys. Our purpose is not to give yet another refinement of an old model of social stratification, but rather to demonstrate how information on an individual’s labor force history might be added to any model explaining variability in socioeconomic outcomes such as occupational status, earn* This research was supported in part by a grant from the Russell Sage Foundation. The authors are indebted to Dennis P. Hogan, Kevin Leicht, Daniel T. Lichter, and two referees for helpful comments. Jeff Hayes helped us explain some discrepancies between our results and those of Featherman and Hauser. 2.56 0049-089x/91 $3.00 Copyright Q 1991 by Academic Press, Inc. All rights of reproduction in any form reserved.

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ings, or poverty status. Certain baseline models given by Featherman and Hauser (1978) and the OCG-II data (“Occupational Changes in a Generation-1973 Replicate”) are used as the point of reference. We view this exercise as an important test case in the effort to bring labor force concepts into models of social stratification. The new scales ought to have at least moderate explanatory power and substantively meaningful effects when added to the classical attainment models. If this is not the case, then the measurement amd modeling efforts reflected by Clogg et al. (1990) or Clogg and Eliason (1990) should not be seriously entertained in longitudinal research attempting to explain the attainment process over the life cycle (see Tienda et al., 1991). We next consider the images of attainment processes implicit in classical and modern models and suggest how labor force concepts might be added to those models. Classical attainment models (Blau and Duncan, 1967; Featherman and Hauser, 1978; Jencks et al., 1983; see also Kalleberg and Berg, 1987) incorporate background factors, ascribed characteristics such as race, and schooling levels to explain interindividual variability in occupational status or earnings. The image of the labor-market matching process in these models is one where employers reward workers based on their human capital (education) and possibly ascribed characteristics such as race or gender (see, e.g., Marini, 1989). Attainment models estimate the “returns” workers receive from characteristics like schooling or other factors assumed to be closely tied to productivity. More recent models bring in firm-level characteristics (Baron and Bielby, 1980), labor market segmentation or other structural heterogeneity (Hodson and Kaufman, 1982; Lincoln and Kalleberg, 1985), demand-side factors such as sex-based occupational segregation (Bridges, 1980), or the importance of career lines for those already placed into internal labor markets (see Althauser and Kalleberg, 1990). Most researchers recognize that such refinements are required in order to build realistic representations of the social processes involved in matching workers to socioeconomic outcomes. These new models are best suited for describing the matching process that takes place once workers are placed into internal labor markets. That is, they tell us what happens to workers after they are attached to the firm or to the full-time labor force, or they try to uncover structural or contextual influences defined in terms of variability within or across internal labor markets. They do not represent the processes associated with external labor markets, where business-cycle fluctuations shape the chances of obtaining secure employment in internal labor markets. Experiences such as unemployment, movement between part-year and fullyear work, shifts between part-time and full-time employment, and even movement to and from the labor force are some of the behaviors that ought to be used to define these processes. The prevalence of such experiences is much greater than has been assumed. For example, Clogg et

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al. (1990) estimate that nearly 40% of the labor force has had experiences of this general kind in an interval as short as one year. Such experiences accumulated over an interval of time can be called labor force careers, which we wish to distinguish sharply from occupational or job careers [see the review of the latter in Althauser and Kalleberg (1990)]. The image of the matching process invoked here is one where employers take account of labor force careers in addition to human capital and ascribed characteristics in allocating socioeconomic rewards. That is, the worker’s recent experiences in the external labor market are regarded as an additional factor used by employers to allocate jobs, earnings, and status or power, perhaps as a device to screen out workers thought to have low potential for productive employment. We operationalize labor force careers using indicators of labor force behavior that have a long history in labor force surveys collected in the United States. The major data sets used currently for empirical research on social stratification-Current Population Survey, Survey of Income and Program Participation, Panel Study of Income Dynamics, and the National Longitudinal Surveys-were actually designed with the goal of measuring dynamic aspects of labor force behavior, not cross-sectional snapshots of occupational status or earnings. These surveys can be used to construct a complex set of measures based on concepts such as part-time or partyear work, stretches and spells of unemployment, job search behavior for nonworkers or part-time workers, changes in income or earnings, unemployment or changes in employment status, and various types of labor force participation, including types that can be recognized as forms of underemployment (Clogg, 1979; Clogg et al., 1990). All these except change in earnings are considered in the definition of the new scales used here. (See Bureau of Labor Statistics (1987), Mellor and Parks (1988), and U.S. Bureau of the Census (1989) for discussion and many additional references.) THE NEW LABOR FORCE SCALES The new scales were developed in Clogg et al. (1990) using association models (Goodman, 1984; Becker and Clogg, 1989) applied to a 16 x 10 contingency table cross-classifying types of labor market experiences (in the preceding year) and underemployment status or labor force status held currently. The former were used to develop a scaling of the composite typology called E here; E has 16 categories representing special types of labor force behavior.’ The E categories summarize much more than labor 1 E stands for a set of categories that measure types of labor market experiences. This should not be confused with labor force experience as utilized in the literature. The latter stands for the length of time in the labor force or in employment and is usually measured by age - years of schooling - 6 (for males at least). (Better measures of actual time in

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force activity or spells of unemployment; they represent stability or instability in labor-market experiences that reflect the forces of demand for labor during the previous year. The other set of categories, based on a modification of the Labor Utilization Framework given by Clogg et al. (1986), were used to develop scales of labor force position, called P. This variable has 10 categories, so that the crosstable is 16 x 10 (160 cells or “types” are recognized). Descriptions of procedures, interpretations of the scales, and the logic of scale building with association models are provided by Clogg et al. (1990) and Clogg and Eliason (1990). In this section we summarize that analysis and provide enough information for interpreting results from the regression analyses using the scales. It is important to note that the universe on which the scales were originally calibrated is the civilian, noninstitutionalized population over age 16 as of March 1982, with no other exclusions. The categories used to consturct the P measures, which represent labor force positions currently held in the survey week in March, are as follows: 1. NILF = not in the labor force, 2. UR = unemployed-new entrants and reentrants, 3. UQ = unemployed-quits, job losses, 4. UL = unemployed-layoffs, 5. HI = part-time employed (less than 35 hours a week)-involuntary because no full-time work is available, 6. HE = part-time employed+conomic reasons (e.g., slack work),2 7. HV = part-time employed-voluntary, 8. I = underemployed by virtue of low earnings-see Clogg and Sullivan (1983), 9. M = occupationally mismatched (overeducated for current occupation)-see Clogg and Shockey (1984), 10. ADm = adequate full-time employed-full-time workers with adequate earnings. Note that P levels 2-4 disaggregate the usual unemployment category, and that P levels 5-7 disaggregate part-time work. The eamings-underemployment category (I) has the disadvantage that it refers to wages over the previous year, adjusted for weeks worked, which is compared to 1.25 times the poverty threshold, also adjusted to an average weekly wage. employment or the labor force are availablefrom longitudinal data.) The latter measure is included in earnings models (Tables 4 and 5) and stands for a supply-side factor (i.e., the years of experience that a worker can offer to acquire higher wages, increased job stability, or other rewards). * The HI and HE categories are similar but they are not the same. “Economic reasons” that define HE refer to downturns in work hours in the worker’s firm, for example. On the other hand, involuntary part-time work (HI) refers to holding a part-time job because the worker was unable to find a full-time job. These categories are also empirically different; see the scale values associated with them in Table 1.

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Unlike the other categories, this one refers to average experience over the previous year rather than to the survey week in March. The M (mismatch) category is unique to the Labor Utilization Framework, but in this analysis virtually the same conclusions would have been reached if this category had been combined with the ADFT category (see, e.g., Fig. 1 in the study by Clogg et al. (1990) and scale values for this category in Table 1).3 The 16 categories of past-year’s labor market experiences are based on combinations of five general items in the CPS files which also appear, in some fashion or another, in every major labor force survey conducted in the United States. These are weeks looking for work, reasons for working part time (in the past year), part-time and part-year worker status (combined from two items), weeks looking for work for nonworkers, and weeks looking for part-year workers. The reader is referred to Clogg et al. (1990), especially the Appendix and Table 1, for details. Essentially, the goal was to classify workers at the extremes (E category 1 = no work in previous year along with no search behavior, and E category 16 = fulltime, full-year worker), with categories in between representing various kinds of instability, search behavior, or marginal employment, where these are behavioral concepts based on self-reported labor market experiences of an entire calendar year. The scale values appear in Table 1. These were obtained from an association model applied to the 16 x 10 cross classification of E and P. This model estimates scores for row and column categories which are consistent with a simple interpretation of odds ratios. These scores were identified by forcing zero correlation between Pl and P2 and between El and E2. In the sample where these scores were estimated, the scores had mean zero and standard deviation one as identifying restrictions. An important aspect of the table and the scores obtained from it is that the economically inactive (E = 1 of P = 1) are included to anchor the scales and to avoid selection biases in the scale construction. The Pl scores range from a low of - 1.22 (not in the labor force) to a high of about 1.0 (Mismatch or Adequate Full-Time). The lowest scores for labor force participants occur for unemployed entrants (- .51) and voluntary parttime persons (- .21), indicating that these two groups are the most similar of all labor force participants to those outside the labor force. If we restrict attention to all those currently in the labor force (i.e., exclude category P = l), then the P2 scores essentially contrast full-time employment with part-time or marginal employment. Note that positive values are assigned ’ From Table 1 below we see that the scores same as for the adequate, full-time employed mismatch category could have been combined change in the inferences.

for the mismatch category (ADFT) category. This with the ADFT category

are virtually the implies that the with no major

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TABLE 1 Scale Values for Categories of Labor Market Experiences (El and E2) and Categories of Current Labor Force Position (Pl and P2) Category 1 2 3 4 5 6 7 8 9 10

6 7 8 9 10 11 12 13 14 1.5 16

First dimension

Description”

Current labor force position (Pl and P2) Not in labor force -1.22 Unemployed-entrants -.51 Unemployed+luits .27 .52 Unemployed-layoffs .36 Part-time-involuntary Part-time---economic .72 - .21 Part-time-voluntary Low income .31 Mismatch 1.08 Adequate full-time .99 Previous year’s labor market No work, no look No work, look 15+ weeks No work, look 1-14 weeks Part-time, part-year, no look Part-time, part-year, look 15+ stretch Part-time, part-year, look 15+ or more stretches Part-time, part-year, look 1-14 stretch Part-time, part-year, look 1-14 or more stretches Full-time, part-year, no look Full-time, part-year, look 15+ stretch Full-time, Part-year, look 15+ or more stretches Full-time, part-year, look 1-14 stretch Full-time, part-year, look 1-14 or more stretches Part-time, full-year, voluntary Part-time, full-year, involuntary Full-time, full-year

Second dimension - .45 .70 1.15 1.13 2.59 1.63 2.56 1.40 -.49 - .57

experiences (El and E2) -1.28 -.25 - .74 -.66 in one -.09

- .61 .94 .26 1.81 1.78

in two

- .Ol

1.52

in one

-.24

1.88

in two

-.12

1.75

in one

- .03 .35

- .33 .67

in two

.32

1.00

in one

.46

.40

in two

.51

.61

-.12 1.39 1.07

2.67 3.54 -.48

’ See text for descriptions and also Clogg, Eliason, and Wahl (1990). Scale values are drawn from Table 6 and definitions of experience types appear in Table 1 and the appendix of that source. Scale values obtained from the RC(2) association model applied to the crossclassification of the two variables in the March 1982 Current Population Survey, for all civilian persons not in institutions aged 16 and over.

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to the latter group and negative values to the former, so a negative correlation with outcomes is to be expected. The score values for E types represent what can be called a vertical dimension of labor force behavior (or stability/instability) for El and a more complicated set of contrasts for E2. Note that the lowest score is - 1.28 for the “no work, no look” category, which is the behavioral analogue to not-in-the-labor-force. The highest scores appear for the last two categories (over 1.0). The somewhat higher score for category 15 compared to category 16 (the latter referring to full-time, full-year workers) is obviously an anomaly. The relative frequency of this type is trivial (less than 1%) in the original sample as well as in the OCG-II sample, so the scale value requires no further comment. It is difficult to summarize the E2 scores, but meaningful ways of doing this appear in the study by Clogg et al. (1990). “E2” is essentially a contrast between stability (negative values) and instability (positive values). Note that the first category of E will be excluded if we select only those with earnings in the previous year. We do not comment further on the validity of the scores but note that this can be inferred to some extent from the analysis given next. Indeed, one objective of the analysis is to assess the validity of these scales from their predictive power in realistic models. For example, if the sign of the relationships involving these variables were different from what is expected, or if the scales do not contribute to prediction, then this would force us to cast the new scales aside. The E and P categories were duplicated in the OCG-II file, and the scores in Table 1 were assigned to the categories to create the four covariates that we shall now add to basic attainment models. Note that we have used the scale scores obtained from the entire civilian, noninstitutionalized sample in 1982, a period marking the worst recession (decline in demand) since the Great Depression, in our analysis of the OCG-II data (referring to 1973).4 Our scaling procedure was thus carried out with an independent sample, which avoids the potential circularity in logic had we used scale values constructed directly from the OCG-II data. ADDING THE NEW SCALES TO STANDARD All’AINMENT MODELS Our point of reference is the basic attainment model reported in Table 5.6 of Featherman and Hauser (1978, p. 235). As in that source, all 4 The 1982 CPS data were used partly because the marginal or unstable experience types (E categories) were at very high levels during the recession, so the scale values could be estimated with greater precision. As shown in Clogg, Eliason, and Wahl (1990), however, changes in marginal distributions, not scale values or correlations between them, change the most through nearly a decade of recent experience. The assumption is that these scale values apply also to the 1972/1973 period covered in OCG-II.

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analyses are restricted to men aged 25-64 in the experienced civilian labor force, which implies that occupation has been coded for all cases included. Also, the estimates reflect conventional adjustments for sample weights.5 The outcome variables are occupational status, as measured by the Duncan Socio-Economic Index (SEI), and the natural logarithm of workrelated earnings in the previous year (LOG-INCOME). The standard predictors used to form baseline models are drawn from Featherman and Hauser (1978), which essentially replicates the work of Blau and Duncan (1967). These are FOCC (father’s or other family head’s occupational status), FEDUC (father’s level of schooling), SIBLING (number of siblings), FORIG (farm origin, scored as a dummy variable), BROKENF (1 = broken family), BLACK (1 = black), REDUC (respondent’s level of schooling in years), X (standard proxy for years of labor force experience , Age - Years of Schooling - 6),6 X2 (the square of X), and DUNCF (occupational status of the first job). Occupational Status Table 2 gives results from five models that predict the Duncan SEI. The same cases were used to estimate all models, so standard assumptions about missing value exclusions are implicit (see Little and Rubin, 1987). It is important to emphasize the fact that our typologies E and P are completely exhaustive, so any potential bias created by deletion of cases cannot be attributed to our new scales. Hi is the “basic” model including background factors but excluding schooling and first-job status, while H5 is the basic model including schooling and first-job status in addition to background. Slight differences between these two models and those reported by Featherman and Hauser (1978, p. 235) arose, perhaps because of differences in the use of sample weights. H2 adds to Hi the four labor force scales given earlier. In this model, all four scales have large effects, but the proportion of variance explained increases only modestly (about 2%). H3 adds to H2 the yearsof-schooling variable, REDUC. Adding REDUC to the model increases explained variability by 19%. H4 adds status of first job; comparing H4 ’ Negative or zero earnings were treated as “missing” in regressions involving LOGINCOME. Conventional (listwise) missing-value exclusions were applied when estimating regression relationships, and the same cases were used for all models estimated for a given socioeconomic outcome. For occupational status, all persons with a reported occupation were included. Similarly, all persons with reported earnings were included for the earnings regressions. For log-earnings regressions, however, 255 cases with zero or negative earnings were deleted, which appears to be a slightly different method than was used in Featherman and Hauser and affects comparability slightly. ’ Note that X is the usual proxy for “experience”; but this measures the gross amount of potential experience in the labor force and is hence very different from the measures of labor market experiences captured in our El and E2 measures. See note 1 also.

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TABLE 2 Occupational Status (Duncan SEI) Predicted from Standard Models and Models Incorporating New Labor Force Scales (Males Age 25-64) Predictor Background:

Schooling: First job:

FOCC FEDUC SIBLING FORIG BROKENF BLACK REDUC DUNCF El E2 Pl P2

Constant R2

H,*

HZ

H3

,224 ,869 - ,968 - 3.935 -3.657 -8.511

,234 ,852 - .925 -4.089 - 3.355 -7.802

,133 -.oJ6 -.163 - ,861 - ,595 -5.465 4.016

Labor force behavior scales 4.855 4.385 - 4.268 - 2.482 2.494 1.382 .619 .223 33.214 25.341 - 19.922 ,197 ,218 ,411

H5 ,065 - L001 - ,083 -1.002

-* -5.024

,062 -.009 -3% - ,828 - I--575 -5.447

2.342 ,372

2.416 .374

3.894 -2.551 .881 -.066 - 7.627 ,488

-3.688 ,479

Note. See text for definition of variables. Insignificant coefficients (Ztailed test at .05 level) underlined. N of cases (unweighted number) is 19,012; there are no “missing values” on labor force scales. Standard (listwise) defaults on missing value exclusions were applied. The model predicting the Duncan SE1 with the labor force behavior scales alone (results not shown) gave RZ = ,028 (Pl not significant). Note also that age is not included in the models (Featherman and Hauser considered separate analyses by age groups using these basic models). * H, is the baseline model in Table 5.6 in Featherman and Hauser (1978, p. 235). Our coefficients differ slightly from theirs (.247, ,886, - 1.182, -4.825, -2.470, - 8.649, and [constant] 33.32, with R* = ,206). We have been unable to account for the slight discrepancies, although differences in the use of sample weights (SPSS” defaults used here) might explain them. Also, Featherman and Hauser used pairwise deletion of missing cases, not listwise deletion.

and H5 shows that the measures of current position (Pl and P2) do not have significant effects on current status once REDUC is controlled (compare also Hz and H3). The inference is that measures of current labor force (or underemployment) status need not be considered once the other predictors are included. There is thus no need to “adjust” occupational status scores for current labor force status (cf. Clogg and Eliason, 1990). On the other hand, the two experience measures, El and E2, continue to have strong effects in the most comprehensive models considered. These summary measures of labor force behavior appear to be strongly related to occupational status net of background, schooling level, and first job status, implying that recent labor market experiences exert independent effects on current occupational attainment. Whether the behaviors cap-

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tured in the E measures sort workers in a selective manner (reflecting a supply-side constraint) or whether employers allocate occupational rewards using worker characteristics captured in the E measures as screening devices (reflecting a demand-side constraint) is something that cannot be answered with these data. Comparisons with schooling effects are useful to acquire a sense of how important the effects of labor force behavior are. In the OCG-II sample, the standard deviation of El is 59.’ A change in El of this amount is almost equivalent (59 x 3.894 = 2.30) to the effect of 1 year of schooling (2.34). The maximum contrast possible for the El variable (see Table 1) is about 2.75 (compare the score for level 3 of E with the score for level 16), and such a contrast would lead to a “net” difference in occupational attainment of about 11 units on the Duncan SEI. This is about the same as a 4.5 year contrast in years of schooling. Although the increment to explanatory power (R*) is modest, the effects of both El and E2 are substantial as judged by comparisons with schooling effects.’ Log-Earnings A more exhaustive list of models is considered for log-earnings with results in Table 3. As previously, H1 is the baseline model including only background factors and ascribed characteristics. Adding the new scales to this model gives H2, and the consequence is dramatic: R* is increased from about 8% to over 26%. Models H3-H5 add REDUC, X, and X2 in succession, giving increments to explanatory power comparable to those reported by Featherman and Hauser (1978). But note that the coefficient values for El, E2, Pl, and P2 are essentially unchanged with the inclusion of schooling and experience. This adds credibility to our claim that the new scales add some new characteristic to the model, or that the influences are indeed independent or incremental effects. H6 is the standard Featherman-Hauser model including background, schooling, and experience, with an R* value of 18%. Note that the model with the labor force scales added to this set (model H,) leads to a substantial increment to explanatory power, with the majority of the effect obviously attributable to the measures of past-year’s labor force behavior. Note that with this model the signs are all in the expected direction: positive for vertical dimension of past behavior, negative for the special contrast represented as E2, positive for the vertical dimension of current ’ Note that El has about 60% of the variability in the OCG-II sample (measured in terms of standard deviations) compared to the original, 1982 CPS file where the scale was developed. The restriction here to males aged 25-64 accounts for the reduction in variability. ’ If we assess “strength of effects” or “relative importance” with t ratios or standardized regression coefficients, El and E2 appear to be much less important than REDUC and DUNCF but almost as important as any background variable except BLACK.

266 Log-Earnings Predictor FOCC FEDUC SIBLING FORIG BROKENF BLACK

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TABLE 3 Predicted from Standard Models and Models Incorporating Force Scales HI*

Hz

H,

.002 .012 -.016 -.126 -.085 -.318

.083 .Oll -.013 -.129 -.068 -.269

.OOl -.a01 -.003 -.084 -.030 -.241

REDUC

H,

Background .OOl .OOl .002 a04 -.004 -.004 -.103 -.091 -.027 - .032 -.222 -.220

2.198 .079

1.690 ,261

He

H,

H,

I-&

,001 ,006 - ,004 L050 - ,053 - .354

,001 ,006 -.002 .065 -.043 - ,337

,001 ,004 -.004 -.090 -.033 - ,221

Schooling and experience ,055 ,064 .061 .070

,070

,061

,061

- ,050 ,001

- ,041 ,001

- ,034 ,001 ,350 - ,331

-6.951 ,848 ,183

.369 - ,308 ,180 - ,057 -8.192 ,623 ,333

,006 ,390 -.343 ,191 -.044

K

New Labor

,001 ,004 -.005 -.082 -.043 -.255

$ El E2 Pl P2 A Constant RZ

OGENA,

-.ool .033

-.OOl .044

Labor force behavior scales ,398 ,382 ,368 -.314 -.315 -.308 ,201 ,202 ,182 -.067 -.073 -.058 1.074 ,308

,819 ,318

,598 ,332

,826 .183

,591 ,331

Note. Notes for Tables 1 and 2 apply here as well. Cases with negative or zero earnings treated as missing. N of (unweighted) cases is 20.741. * H, is the baseline from Featherman and Hauser (1978, p. 235). They report an R* of ,027 (we think ,072 should have been reported), and our results differ somewhat from those they reported for this model. The model predicting log-income from the labor force behavior scales alone (results not shown) gave R2 = ,243 (all coefficients significant).

position Pl, and negative for the special contrast represented as P2 (high positive values for underemployed workers, negative values for full-time employed persons, which should yield a negative relationship). The increment to explanatory power is impressive. For example, comparing H6 and H5 shows that the new scales almost double the R2 value (increase from 18 to over 33%). We now consider two other specifications in order to examine some objections that might be made about the models presented thus far. The possibility of sample selection bias is one obvious factor that ought to be examined. Heckman’s (1979) method was used to check for this. The experience types summarized in the E measures were designed, to some extent at least, to characterize types of workers that might be treated as missing in conventional earnings attainment models.9 As such, including 9 For example, some researchers would model earnings only for those who are continuously employed full-time, which is essentially our labor force “type” E = 16 in Table 1.

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the new scales for these types can be viewed as a partial adjustment for selection effects. We now estimate models that attempt to control for selection bias without using the information in our new scales. A probit selection equation was estimated from the existing set of independent variables except the labor force behavior scales, and the inverse of the Mills ratio estimated from this equation was added as a covariate in the model for log-earnings. The result is model H7, which produces almost no change in the inferences. Finally, H8 incorporates the same selectionadjustment factor in the model including the new labor force scales (compare H8 with H5 in this instance). Although the selection covariate is significant in both regressions, there is virtually no change in the magnitudes of the coefficients for any of the new scales. In this case, there appears to be little value in adding explicit adjustments for sample selection (i.e., selecting only those respondents with earnings), and we conclude that our new scales reflect something quite different from sampleselection adjustments that might have been defined without reference to the labor force measures given here. A logical difficulty with the models considered thus far is that they include effects of current fabor force position (Pl and P2), when in fact the estimand is (the logarithm of) earnings reported over the previous year. There is no simple way to sidestep the issue of incorrect time lags of this sort with the cross-sectional data used here. Current position in the labor force, as measured by Pl and P2, is highly associated with pastyear’s labor market experiences, so much so that it is natural to ask whether the P measures need to be included once the E measures have been included. In the 1982 CPS sample where these scales were derived, Pl and El were highly correlated (r = .856) and P2 and E2 were correlated moderately (r = .570).” We next consider deleting the P measureswhich is suggested by the high intercorrelation of these with the E measures-to avoid the logical difficulty of explaining earnings in the previous year with variables that summarize labor force position 3 months into the next calendar year. Deleting the P measures produces model Hg in Table 3, and we find that this model gives virtually the same conclusions as either one of the preferred models considered thus far (HS and H,) and has virtually the same explanatory power. In our preferred model predicting occupational status (H, in Table 2), neither P measure had a significant effect. Even though both effects are significant in a strict sense when log-earnings is considered, we would recommend deleting the P measures when modeling attainments from cross-sectional data of this type. Note that model Hg still lo See Clogg, Eliason, and Wahl (1990, Tables 6, 7, and 12) for further evidence on this point, including disaggregation by age and sex and results for nine CPS files covering the interval from 1976 to 1986.

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adds nearly a 15% absolute increment to the proportion of variance explained, nearly a doubling of the R2 value of the standard FeathermanHauser model (H6). In other words, it is not necessary to include the P measures in the occupation regressions (the effects were insignificant) or in the earnings regressions (deleting the P measures does not greatly affect either predictability or the magnitudes of the other effects). DISCUSSION Results in the present note suggest the importance of examining how labor force careers interact with and help to determine socioeconomic outcomes over the life course. This task has not been given high priority in contemporary research on the sociology of labor markets (cf. Kalleberg and Berg, 1987), and we hope that our results will encourage a reorientation of the research agenda. For regressions predicting occupational status, we found that the two measures of labor force experience (El and E2) increased explanatory power modestly and had strong effects in the direction that would have been predicted a priori. The measures of current labor force position were not important once the other effects were included, which implies, for example, that there is no need to consider covariate adjustments for the occupational status of, say, part-time workers compared to full-time workers with the same occupational level. The most compelling results, however, arose when considering log-earnings models adding the new scales. The increment in explanatory power is impressive indeed with earnings regressions (a 15% increment to R2). Almost all of the added effect could be attributed to the two measures of labor force experiences over the previous year, El and E2. On the log scale, for example, a unit increase in our El measure is associated with the same magnitude of change in earnings as a 5.7 year increase in years of schooling (model HS, in Table 3); the effects of both El and E2 were the “most important” effects in the regression as judged by any of the standard criteria for relative importance. We believe that the case has been made for seriously considering labor force experiences-or labor force histories of individual workers-as a major factor in the processes that determine earnings. Longitudinal analyses using measures of labor force experiences similar to those used here are the next step in this line of research. Other research teams besides ours are studying this topic at the present time (e.g., Tienda et al., 1991). Important questions that need to be answered include these: (1) Can the analogue to our E (or P) measures be defined for longitudinal analysis? We have implicitly assumed that the same “scale” values should apply regardless of the worker’s age, but perhaps some variability with age or time since completion of formal schooling ought to be allowed. If we attempt to use information from all or most items that capture the complexity of the labor force career, some type of scaling method will

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have to be used. (2) How might a model allowing for influences of the labor force career be specified? One strategy is simply to define E for each year in a cohort’s experience (E,, El, . . . E-i), and then create the scale summaries of each of those. The most straightforward model of attainment level at year t for a real cohort would then include the appropriate number of lagged E-type variables, however scaled. Results in this note amount to the case where E,-, only is used and where all effects are pooled across cohorts. But there are obviously other strategies as well, such as defining E for the entire period between some starting point and the current age of the worker. (3) How can we model effects of labor force shocks (recessions or upturns)? The relative frequency of the types recognized in both the E and P measures change with recession and growth, which is one of the main reasons why these measures can be called labor force measures. It is possible that business cycle influences could be taken into account by either appropriate lagging of effects or possibly by letting the scale summaries vary across periods of growth and recession. (In the study by Clogg et al. (1990), however, it was shown that marginal distributions rather than scale values produced from association models changed the most over time.) The importance of this question arises, for example, if it is believed that labor force “entry” during a recession is different from “entry” in periods of stability. (4) Finally, how should we specify the causal chain of influences that determines not only attainments at some point in time but also the labor force career lines (and attainments) throughout the life course? The dramatically high level of flow among labor force states over the short term (Clogg et al., 1990; Lichter et al., manuscript submitted for publication) must itself be “explained” in the kind of structural model that is called for by this agenda. These questions represent to us the main challenges that lie ahead in bringing the concept of the labor force career into the center of our modeling efforts. REFERENCES Althauser, R. P., and Kalleberg, A. L. (1990), “Identifying career lines and internal labor markets within firms: A study in the interrelationships of theory and methods,” in Social Mobility and Social Structure, (R. L. Breiger, Ed.), pp. 308-356, Cambridge Univ. Press, New York. Baron, J. N., and Bielby, W. T. (1980). “Bringing the firms back in: Stratification, segmentation, and the organization of work,” American Sociological Review 45, 737-765. Becker, M. P., and Clogg, C. C. (1989) “Analysis of sets of two-way contingency tables using association models,” Journal of the American Statistical Association 84, 142-1.51. Blau, P. M., and Duncan, 0. D. (1967). The American Occupational Structure, Wiley, New York. Bridges, W. P. (1980), “Industry marginality and female employment: A new appraisal,” American Sociological Review 45, 58-75. Bureau of Labor Statistics (1987), Linking Employment Problems to Economic Status, Bulletin 2282, Government Printing Office, Washington, DC.

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Clark, K. B., and Summers, L. H. (1979), “Labor market dynamics and unemployment: A reconsideration,” Brookings Papers on Economic Activity, No. 1, pp. 13-72. Clogg, C. C. (1979), Measuring Underemployment, Academic Press, New York. Clogg, C. C., and Eliason, S. R. (1990). “The relationship between labor force behavior and occupational attainment,” Research in Social Stratification and Mobility 9, 159180. Clogg, C. C., Eliason, S. R., and Wahl, R. J. (1990), “Labor-market experiences and laborforce outcomes,” American Journal of Sociology 95, 1536-1576. Clogg, C. C., and Shockey, J. W. (1984), “Mismatch between occupation and schooling: A prevalence measure, recent trends, and demographic analysis,” Demography 21,

233-257. Clogg, C. C., and Sullivan, T. A. (1983), “Labor force composition and underemployment trends, 1969-1980,” Social Indicators Research l2, 117-152. Clogg, C. C., Sullivan, T. A., and Mutchler, J. E. (1986), “On measuring underemployment and inequality in the labor force,” Social Indicators Research 13, 375-393. Featherman, D. L., and Hauser, R. M. (1978), Opportunity and Change, Academic Press, New York. Goodman, L. A. (1984), The Analysis of Cross-Classifications Having Ordered Categories, Harvard Univ. Press, Cambridge, MA. Heckman, J. J. (1979), “Sample selection bias as a specification error,” Econometrica 45, 153-161. Jencks, C., Crouse, J., and Mueser, P. (1983). “The Wisconsin Model of Status Attainment: A national replication with improved measures of ability and aspirations,” Sociology of Education 56, 3-19. Kalleberg, A. L., and Berg, I. (1987), Work and Industry: Structure, Markets, and Processes, Plenum, New York. Lichter, D. L., Landry, D. A., and Clogg, C. C. (1991), “Measuring short-term labor force mobility with the labor utilization framework,” Social Science Research, in press. Lincoln, J. R., and Kalleberg, A. L. (1985), “Work organization and workforce commitment: A study of plants and employees in the U.S. and Japan,” American Sociological Review

50, 738-760. Little, R. J. A., and Rubin, D. B. (1987) Statistical Analysis With Missing Data, Wiley, New York. Marini, M. M. (1989), “Sex differences in earnings in the United States,” Annual Review of Sociology 15, 343-380. Mellor, E. F., and Parks, W. (1988), “A year’s work: Labor force activity from a different perspective,” Monthly Labor Review 111 (September), 13-18. Tienda, M., Wilson, F. D., and Wu, L. L. (1991), “Calibrating underemployment with longitudinal data: Insights, problems, and prospects,” Paper presented at the 1991 Annual Meetings of the Population Association of America. U.S. Bureau of the Census (1989), Spells of Job Search and Layoff. . and Their Outcomes, Current Population Reports, Series P-70, No. 16-RD-2, Government Printing Office, Washington, DC.