Mobility and Gender in a Segmented Labor Market: A Closer Look iJy JEFFREY WADDOUPS aw/iDjETo ASSANE*

Male and female workers' labor segment location and intersegment mobility processes are compared to assess the existence and nature of inequalities in the structure of labor market opportunity. Findings indicate significant differences in segment location, upward occupational mobility and opportunity structures across gender groups.

ABSTRACT.

I Introduction A NUMBER OF STUDIES have examined differential labor market outcomes across gender groups focusing specifically on wage differentials (Polachek, 1979; England, 1982; Bergmann, 1989). An important and related issue in the labor market literature suggests that discriminatory barriers tend to prevent women from obtaining access to high status jobs to the same extent as do their male counterparts. The opportunity structure confronting female workers is, thus, postulated to be different than that facing men. This study extends labor market mobility research by analyzing differences in the nature of the opportunity structure facing male and female workers in the context of a segmented labor market. The labor market segmentation approach provides a useful framework for analyzing the issue of gender differences in occupational mobility.' Pomer (1984), for example, in a study influenced by labor market segmentation ideas, constructs low- paid and mainstream occupational strata. These strata somewhat correspond to secondary and primary labor markets. Using 1970 Census data, he Ends that female workers experience a substantially lower probability of upward mobility across strata than do their male counterparts. Similarly, Howell and Reese (1986) define core and peripheral industrial sectors and demonstrate * [Jeffrey Waddoups, PhD, is assistant professor of economics, and Djeto Assane, PhD, is assistant professor of economics, at the University of Nevada, Las Vegas, Las Vegas, NV 89154.] We gratefuily acknowledge the helpful comments of Nasser Daneshvary, Randall Krieg, Susan Averett, members of the Economics Department Workshop Series at University of Nevada, Las Vegas and an anonymous referee. We also acknowledge financial support from the First Interstate Bank Institute for Business Leadership. American Journal'of Economics and Sociology, Vol. 52, No. 4 (October, 1993). © 1993 American Journal of Economics and Sociology, inc.

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that female workers are both more likely to begin their careers in the periphery and less likely to make a transition to the core sector of the economy. While these studies are both influenced by ideas originating from the labor market segmentation framework, in critical respects they do not closely adhere to labor segment definitions as outlined in the literature (Piore, 1975; Gordon, Edwards and Reich, 1982). Pomer's work, for example, does not differentiate between upper and lower tiers of the primary segment, which tends to mask important differences in the occupational opportunity structure faced by female workers as compared to males. Howell and Reese (1986) analyze mobility through industrial rather than occupational segments. Though it is likely that secondary jobs are more concentrated in a peripheral sector, there is, nevertheless, not a one-to-one correspondence between industrial sectors and labor segments. The present study makes a unique contribution to the literature on occupational mobility in that it analyzes gender differences in the context of a tri-partite segment structure as proposed by Piore (1975) and Gordon, Edwards and Reich (1982),^ One of the important aspects of the tri-partite structure is that it allows for a partitioning of the primary segment into an upper and lower tier (independent and subordinate primary segments, respectively). This procedure has the potential to offer additional insights into gender differences in mobility, which are lacking in previous work. Three dimensions of gender differences in opportunity structure will be examined. Eirst, a segmentation view of the labor market is presented. Second, gender differences in segment location and upward intersegment mobility patterns across gender groups are examined; and third, an econometric model describing labor market opportunity is proposed and estimated. This model offers further insights into differences in male and female labor market mobility experience. II Labor Market Structure and Gender LABOR MARKET SEGMENTATION THEORY suggests that the U.S. labor market is composed of three qualitatively distinct segments, which may be defined as bounded submarkets and identified by both industrial and occupational characteristics. Mobility between these submarkets are hypothesized to be somewhat, though not totally, restricted. Levels and determinants of mobility between labor segments represent important aspects of the structure of labor market opportunity faced by workers. An upward move through the segment structure often represents a significant increase in earnings, prestige and stability.^

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The three labor segments proposed in the labor market segmentation literature are the independent primary, the subordinate primary and the secondary (Piore, 1975 and Gordon, Edwards and Reich, 1982). The independent primary segment is composed of professional, managerial and craft positions. Besides offering relatively good pay, favorable working conditions, benefits and substantial amounts of job security, administrative rules in these occupations typically are not codified and rigid; rather, they are embedded in an internalized code of conduct. In addition, a substantial amount of autonomy in work performance is allowed in these occupations. The subordinate primary segment is also comprised of jobs with desirable characteristics, but typically these occupations are characterized by specific tasks linked to relatively narrowly defined job classifications. Opportunities for job advancement characteristically depend on tenure in a particular organization. Administrative rules in this segment are usually institutionalized and impersonal, and are often accompanied by more or less well-defined grievance procedures. The secondary segment is composed of unstable jobs accompanied by poor pay and working conditions. Full-time secondary segment employment often generates insufficient income to lift a worker out of poverty. In addition, occupations in this segment typically are not thought to be part of a mobility chain through which a worker has access to promotion opportunities. Work rules in this segment are often unwritten and arbitrary with little or no institutionalized recourse for employee grievances. Previous work in the labor market segmentation framework indicates that female workers tend to be concentrated in secondary and subordinate primary employment, while white male workers are overrepresented in the independent primary segment (Ammot and Matthaei, 1991 and Gordon, Edwards and Reich, 1982). The fact that women tend to be concentrated in lower level segments suggests a lack of mobility, but there has been little research comparing the magnitude and nature of mobility differences across gender groups directly in a labor market segmentation ixamework. These issues are addressed in, the empirical section below. Ill Data and Sample Characteristics

containing labor market and other relevant characteristics of a group of male and female workers is required for the empirical portion of this study. The Panel Study of Income Dynamics (PSID), which includes yearly information of this sort, is a good data source for this study. PSID data from the years 1983-1985 is used to generate the saniple in the following way: for each LONGITUDINAL DATA

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individual, observations of labor segment location are observed in each of the three years. These observations are then used to determine if an upward transition has occurred during each of the two one-year intervals. An upward transition is defined as a move from the secondary to either of the two primary segments or a transition from the subordinate to the independent primary segment. Next, personal, household and market characteristics for each individual are observed for the years 1983 and 1984. In addition, only prime-aged (ages 25-54) workers are included in the sample. Truncating the sample in this manner avoids capturing the job-market instability of young workers and the movements out of the labor force cif older workers. Information on transitions and other individual characteristics for the 1983-84 and 1984-85 intervals are then stacked, thus creating a data set with a total of 5,155 individual-year observations. The sample of females is composed of household heads (generally single women with or without children), which is pooled with data on "wives" of male household heads. To arrive at our sarnple of males, male household heads are selected. Since upward intersegment mobility differences in prime-age male and female workers is the focus of attention, only those individuals engaging in wage labor are included in the sample. The sanlple is not entirely representative of the population of male and female workers in general. Segment Location and Intersegment Mobility

Research in the context of segmented labor markets requires occupations to be allocated into segments. Gordon (1986) provides a procedure corresponding closely to the requirements of labor market segmentation theory'' (see Appendix for examples of segment location for selected 3-digit occupations). Table 1 contains estimates of proportions of respondents in the various labor segments. Table 1 PERCENTAGES OF RESPONDENTS BY GENDER IN THE VARIOUS LABOR SEGMENTS Independent Primary Subord i nate Primary Secondary

Male 48.8 26.6 24.6

Female 26.4 46.2 27.4

Totals

3315

2351

Source: Calculated from Panel Study of Income Dynamics (PSID) data. Proportions are based on averages over the years 1983-84 for prime-age (25-54) household heads and wives. See Appendix for specific examples of three-digit occupations contained in each labor segment.

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Males are found to be more heavily represented in occupations of the independent primary segment than females, while female respondents are more likely to be located in the subordinate primary. Similar percentages of male and female labor force participants are situated in the secondary segment. Table 2 contains proportions of male and female workers who make the various transitions. These proportions may be thought of as unadjusted conditional transition probabilities. For example, given location in the secondary segment during one year the probability that a male worker will have made a transition to the subordinate primary segment by the next year is .128. The same probability for female workers is somewhat higher at .154. Females are thus found to be 20 percent more likely to move from the secondary to the subordinate primary segment than their male counterparts, but were 25 percent and 7 percent less likely to move to the independent primary from the secondary and subordinate primary, respectively. Though these results point to gender differentials in the intersegment mobility process, further analysis is required to determine if these differences persist after controlling for other variables that may affect intersegment mobility. This analysis is contained in the following section in the form of an econometric model relating a vector of explanatory variables to the transition probabilities. IV

Econometric Specification UPWARD INTERSEGMENT MOBILITY may be described as a stochastic process in which workers located in the secondary or subordinate primary segments in

Table 2 YEARLY PROPORTIONS OF MALE AND FEMALE WORKERS' UPWARD INTERSEGMENT TRANSITIONS Subordinate Primary Secondary to Male Female Subordinate Pri. to Male Female

.128 .154

Independent Primary .130 .104 .174 .163

Source: Proportions calculated from Panel Study of Income Dynamics (PSID) data. Estimates are based on averages of yearly transition rates for the years 1983/84 and 1984/85 for prime-age (25-54) household heads and wives.

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time t have a probability of transition to a higher level segment by time t + 1.' For ease of presentation, independent primary segment employment is defined as "state 1," subordinate primary employment is defined as "state 2" and secondary employment is defined as "state 3." The transition probability, Py, is therefore the conditional probability of making a transition to a higher level segment j (j = 1, 2) at time t 4- 1, given location in a lower level segment i (i = 2, 3) at time t. Labor market mobility for workers in employment state 3 may be to either state 2 or state 1, while for state 2 workers upward mobility is comprised of a move to state 1. Transition probabilities can be described by a logistic functional form written as Pi,(s = 1) = 1/1 + exp(-x;||3)(i = 2, 3), (j = 1, 2), (i ¥= j),

[1]

where s is a variable taking on the value 1 if the relevant transition occurs and the value of 0 otherwise, x is a vector of explanatory variables and ;S is a vector of unknown parameters. The log-odds of the transition probabilities are, therefore, expressed as a linear function of the explanatory variables by In [Pij(s = 1)/1 - Pij(s = D] = 4^(1 = 2, 3), (j = 1, 2), (i # j).

[2]

A description of these independent variables along with summary statistical results are found in Table 3. V

Estimation Results THE EMPIRICAL FINDINGS of the logit models are presented in Tables 4 and 5a-

c. The (3 estimates capture the marginal effect of independent variables on the log-odds of the transition probabilities. Table 3 DEFINITIONS AND DESCRIPTIVE STATISTICS OF INDEPENDENT VARIABLES Independent Mean & (Std. Dev.) Variables Female Male .15 (.36) MARSTAT 1 = single .28 (.45) REGION 1 = south .50 (.50) .47 (.50) CHILD 1 = children in household .50 (.50) d .52 (.48) EDUCl 1 = high school .27 (-44) .32 (.47) EDUC2 1 = some college or .38 (.48) .39 (.49) additional training EDUC3 1 = college degree .09 (.29) .08 (.27) AGE Age in years 36.34 (8.60) 35.11 (8.07) AGE2 Age squared 1394.42 (669.99) 1297.84 (620.23) JOBTEN Months with employer 55.76 (63.23) 57.53 (73.57) J0BTEN2 Months squared/100 71.06 (143.91) 87.20 (195.91) UNION 1 = union member .18 (.38) .11 (.32) UNEMP County unemployment rate 7.74 (2.99) 7.79 (3.07) RACE 1 = nonwhite .38 (.49) .41 (.49) Source: 1983-84 PSID data.

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Table 4 MAXIKUM LIKELIHOOD ESTIMATES OF LOGIT COEFFICIENTS FOR POOLED DATA Independent Pji ^31 Variables SEX MARSTAT REGION CHILD AGE AGB2 JOBTEN J0BTEN2 UNION UNEMP RACE EDUCl EDDC2 EDUC3 Constant Numtier -2LogLikelihood

-.7389 (6.3964)*** .1388 (.9462) -.1292 (1.0817) .0983 (.6971) -.1030 (1.5597)* .0013 (1.5550)* -.0002 (.1049) .0002 (.2220) -.5742 (2.8315)*** -.0010 (.0510) -.5122 (3.5018)*** .1837 (.8975) .1099 (.5787) .6235 (2.8360)*** .574 9 ( .4(516) 2590.0 2143.593

-.5332 (3.1973)*** .2151 (1.1497) -.0504 (.2978) -.1133 (.6320) -.0134 (.1439) .0001 (.0100) .0013 (.4207) -.0005 (.4346) -.5519 (2.1769)*** -.0577 (2.1721)*** -.2563 (1.4847)* .1558 (.7901) .5676 (2.8669)*** 1.3189 (3.4375)*** -.8755 (.5080) 1695.0 1208.589

.1617 (1.0650) -.0071 (-.0387) .1872 (1.1405') -.0153 (.0911) .0030 (.0332) -.0040 (.3452) -.0017 (.5798) .00002 (.0173) .4035 (1.9479)** -.0887 (3.4001)*** -.4798 (2.8938)*** .0122 (.0671) .4137 (2.2180)*** -.2191 (.4258) -.11^1 (.4705) 1718.0 1308.555

Source: 1983-85 PSID data on household heads and wives. Note: The absolute value of asymptotic t-ratios are in parentheses. The asterisks *, **, and *** represent one-tail significance test-S at the .1, .05 and .01 level, respectively. The effect of changes of the independent.variables on the probability of transitions may be obtained by calculating, 3p/3x = ^p(l-p). Where p is the transition probability evaluated at mean values of the independent variables. The estimates of partial derivatives are available upon request from the authors.

The empiricalfindingslocated in Table 4 are obtained using pooled data on both male and female workers controlling for gender. These results offer a measure of the direct impact of gender on upward intersegment mobility. The assumption underlying this formulation is that the structure of upward mobility for the two groups is identical. This somewhat restrictive assumption will be relaxed below. Table 4 results indicate that female workers are substantially less likely than their male counterparts to experience upward mobility from either the secondary

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American Journal of Economics and Sociology Table 5a

MAXIMUM LIKELIHOOD ESTIMATES OF LOGIT COEFFICIENTS (SUBORDINATE TO INDEPENDENT PRIMARY SEGMENT) Independent 1female Variables MARSTAT 1903 (.9180) REGION 0335 (.1900) CHILD 0780 (.3710) AGE 0298 (.3050) AGE 2.0005 (.3791) JOBTEN 0028 (.8761) J0BTEN2 0011 (.8475) UNION - 1 .1513 (2.6590)*** UNEMP 0017 (.0600) RACE 6600 (2.8995)*** EDUCl 5363 (1.4588)* EDUC2 3871 (1.0786) EDUC3 1.1021 (2.7412)*** CONSTANT - 1 . 8114 (.9647) 1497.0 n -2 Log 1079.947 Likelihood

Male .1005 (.4648) -.2752 (1.6794)** .2857 (1.4833)* -.1628 (1.7664)** .0020 (1.6670)** .0017 (.6274) -.0005 (.4850) -.3965 (1.6480)** -.0031 (.1212) -.4064 (2.0941)** .0312 (.1183) .0004 (.0009) .4239 (1.5525)* 1.8140 (1.1287) 1093 . 0 1093 .067

Source: See Table 4.

or subordinate primary segments to the independent primary (P31 and P21, respectively). Results of these estimations support the contention that access to the managerial, craft and professional occupations of the independent primarysegment remains relatively restricted for female workers. For mobility from the secondary to the subordinate primary segment (P32), gender does not play a statistically significant role. Female secondary segment workers do notfindtheir gender to be a significant barrier to subordinate primary segment employment. This result is not surprising in light of other studies documenting that females experience less desirable labor market outcomes than males, since subordinate primary jobs tend 10 be characterized by relatively lower levels of earnings and status than independent primary jobs (i.e. England, 1982; Bergmann, 1989). To examine the notion that male and female workers face a different mobility structure, equation 2 was estimated separately by gender. Equations located in Tables 5a-c indicate that in a number of important respects male and female workers face a different structure of upward intersegment mobility. Overall, educational attainment as a representation of human capital acquisition yields the most significant results. Formal education is presented as a series of three dummy variables corresponding to various levels of educational attainment.*" Higher levels of attainment such as a college degree or additional

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Table 5b MAXIMUM LIKELIHOOD ESTIMATES OF LOGIT COEFFICIENTS (SECONDARY TO INDEPENDENT PRIMARY SEGMENT) •31

Independent Female Male Variables .2480 (.9773) MARSTAT .2311 ( ,7675) .1132 (.5466) REGION .0681 ( ,2260) CHILD -.6275 (2 ,0386)** .1934 (.8689) .0033 (.0283) AGE -.0552 ( .3541) .0003 (.2135) AGE2 .0007 ( ,3466) .0035 (.2135) JOBTEN -.0023 ( ,4362) .0017 (.9689) JOBTEN2 -.0014 ( .6564) .6529 (2.1364)** UNION .3808 ( .8353) UNEMP -.0718 (1 .4914)* - .0510 (1.6008)* .4402 (2.2892)** RACE -.0320 ( .1341) .0336 (.1345) EDUCl .3852 (1 .1637) .6959 (1 .9402)** .4158 (1.7228)** 1.6177 (2 .9017)*** .9577 (1.7599)** ED0C3 1.0157 (.4683) -.6916 ( .2352) CONSTANT 758.0 937. 0 n -2 Log 758. 189 440.492 Likelihood Source:

See Table 4.

post high school training are positively correlated with upward mobility. Consistent with predictions found in labor market segmentation literature (Piore, 1975), schooling appears to be of little assistance to male secondary segment workers in their transitions to the subordinate primary (P32). For females, however, results indicate that additional training is positively correlated with the probability of making this transition. The other human capital variables, AGE and JOBTEN, are proxies for general labor market experience andfirmspecific human capital, respectively. The predicted impact of general labor market experience represented by AGE is somewhat ambiguous. On the one hand, increased time in the labor market may increase the likelihood of obtaining the information necessary to experience upward labor market transitions. Vietorisz and Harrison (1973), on the other hand, suggest that a feedback mechanism may exist, which generates behavior patterns appropriate for a worker's particular segment location. For example, the longer individuals are located in the secondary segment the more likely it is that they will develop traits incompatible with primary segment employment. This type of state dependence suggests a negative correlation between upward mobility and AGE.^

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American Journal of Economics and Sociology Table 5c

MAXIMUM LIKELIHOOD ESTIMATES OP LOGIT COEFFICIENTS (SECONDARY TO SUBORDINATE PRIMARY SEGMENT) ~32 Independent Female Male Variables MARSTAT 0255 (.1010) -.0034 (.0100) REGION 0699 (.2879) .4473 (1.9614)** CHILD 1323 (.5199) .1179 (.5135) AGE 1667 (1.2427) -.1427 (1.1466) AGE2 0024 (1.3923)* .0014 (.8401) JOBTEN 0056 (1.1927) .0015 (.3655) J0BTEN2 0021 (1.1640) -.0016 (.8477) UNION 2370 (.6963) .4917 (1.8609)** UNEMP 0959 (2.4350)*** -.0806 (2.2904)** RACE 5189 (-2.0574)** -.4652 (2.0651)** EDUCl 0918 (.3450) -.0560 (.2191) EDUC2 6534 (2.3672)*** .2041 (.7920) EDUC3 1638 (.2379) -.4462 (.5577) CONSTANT -3. 4973 (1.4006)* 1.7293 (.7618) n 798.0 920 .0 -2Log 603.961 692.834 Likelihood Source:

See Table 4.

Actual parameter estimates on the AGE variable are generally negative and not statistically significant. One exception is found in the P21 equation where male workers are more likely to make this transition with an increase in experience. No such effect is evident among similarly situated females. Acquisition of firm specific human capital is represented by the JOBTEN variable, where again the hypothesized effect on transition probabilities is somewhat ambiguous. Tenure with a firm is expected to increase the development of firm specific human capital such that the cost of changing jobs increases, which should lead to a negative correlation between upward mobility and tenure. On the other hand, individuals may obtain skills in one job, which qualify them for a higher-level occupation, suggesting a positive correlation (Sicherman and Galor, 1990). An interesting pattern of signs is observed on the JOBTEN parameter estimates. Signs are uniformly positive for male workers and negative for females, which suggests that the theory of career mobility as put forward by Sicherman and Galor (1990) is a better description of male than female labor market experience. It must be pointed out that this inference is weakened by the lack statistical significance on the estimates.

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Traditional division of labor in the household will likely cause variation in male and female workers' intersegment transition probabilities. Our results indicate that a child in the household exerts a negative impact on secondary segment females' probability of upward mobility, with no similar effect for males. The parameter estimates on the RACE variable suggest that being nonwhite is correlated negatively with probabilities of upward transitions. This pattern is observed in a majority of the equations estimated for both male and female workers. One exception is found in the female P31 equation, where being nonwhite appears not to be a significant deterrent to upward mobility. Finally, the level of economic activity is assumed to be positively related to upward intersegment mobility. Economic activity is measured by the unemployment rate in the respondents' county (UNEMP). Signs on the parameter estimates are largely consistent with expectations. Except for P21 equations, the level of economic activity appears to be an important predictor of transition probabilities for both tnale and female workers. VI Conclusion MALE AND FEMALE WORKERS

are not equally represented across the labor segment structure, and also tend to exhibit differences in intersegment mobility patterns. In addition, empiricalfindingsof the logit models suggest that, even after controlling for a number of variables, being female remains a significant barrier to entry into high status independent primary segment occupations. Mobility from the secondary to the subordinate primary, on the other hand, is found to be statistically unrelated to gender. Differences by gender in the structure of upward intersegment mobility are also evident. For example, relative to males, educational attainment is a more effective factor contributing to female mobility from secondary to primary segment occupations. Household related variables tend to affect women more than men, while race is a less important barrier to upward mobility for females than males. Finally, slack labor markets tend to act as a barrier to upward mobility for both men and women. Though previous work has compared labor market mobility experience by gender, this study is unique in the sense that it analyzes upward intersegment mobility in a tri-partite segment structure. A m^jor insight resulting from the use of this methodology confirms findings in the occupational segregation literature, which indicates that the most significant barriers in the segment structure facing female workers are located around the high status professional, managerial and craft employment of the independent primary segment.

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American Journal of Economics and Sociology Notes

1. Although there has been some question concerning the compatibility of the labor market segmentation approach with analysis of women's labor market outcomes (Albelda, 1986), others believe it has the potential to offer important insights into women's experience (Blau and Jusinius, 1976; Boston, 1990; Amott and Matthaei, 1991). 2. Similar research analyzing male mobility in a tri-partite segment structure has been conducted (see Rumberger and Carnoy, 1980; Rosenberg, 1980; Waddoups, 1991). In addition, Boston (1990) examines and compares male and female mobility using a labor market segmentation framework. His segment definitions, however, render it substantially different than either this study or other work in the area. 3. Waddoups, Daneshvary and Assane (1993) find that male secondary segment workers earn approximately 65 percent of their primary segment counterparts, and that a significantly lower level of occupational prestige is accompanied by location in the secondary rather than the two primary segments. 4. Gordon (1986) proposes that occupations be divided into three segments based on three conditions: first, segment categories reflect characteristics of jobs not of workers in the jobs; second, segments are not defined using information concerning outcomes such as wages and turnover rates; and third, both industrial and occupational characteristics are taken into account to capture segmentation occurring withinfirms.Occupations in which workers control a relatively high level of their work activity, which are not characterized by detailed and repetitive instructions, and which require relatively high levels of training are allocated to the independent primary segment. Semi-skilled and unskilled jobs in the ' 'core'' industries are allocated to the subordinate primary segment. Core industries are characterized by the following-, high levels of concentration, technologically progressive and capital intensive production processes and relatively high rates of unionization (Oster, 1979). All other occupations are placed in the secondary segment. 5. Upward transitions for secondary segment workers consist of movements to either the subordinate or indepetident primary segments, while upward mobility for subordinate primary workers is confined only to movements to the independent primary. 6. A series of education dummy variables is used than a single continuous variable because of the inherent discontinuity in the returns to an additional year of education. For example, it is reasonable to assume that the return (in terms of mobilicy) to the eleventh year of education is relatively smaller than the return to the twelfth year, which implies receipt of a high school diploma. A contintiOtis education Variable assumes that returns to an additional year of education are constant. 7. It is recognized that there is not a one to one correspondence between AGE and time spent in a particular segment; however, it is reasonable to assume that a correlation between the two exists.

References Albelda, Randy, 1986. "Occupational Segregation by Race and Gender, 1958-1981." Industrial and Labor Relations Review 39: 404-11. Amott, Teresa L. and Julie A. Matthaei, 1991. Race, Gender and Work: A Multicultural Economic History of the United States. Boston MA: South End Press. Bergman, Barbara, 1989. "Does the Market for 'Women's Labor Need Fixing?" The Journal oJ Economic Perspectives A: 43-60.

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Blau, Francine and Carol Jusinius, 1976. "Economists' Approach to Sex Segregation in the Labor Market," In Women and the Workplace, eds. Martha Blaxall and Barbara Reagan. Chicago, IL: U of Chicago P. Boston, Thomas D., 1990. "Segmented Labor Markets: New Evidence from a Study of Four RaceGender Groups." Industrial and Labor Relations Review 44: 99-115. Fngland, Paula, 1982. "The Failure of Human Capital Theory to Explain Occupational Sex Segregation." The Journal of Human Resources 18: 358-70. Gordon, David M., Richard Edwards and Michael Reich, 1982. Segmented Work, Divided Workers: The Historical Transformation of Work in the United States, Cambridge, MA: Cambridge UP. Howell, Frank M. and William A. Reese, 1986. "Sex and Mobility in the Dual Economy: From Entry to Midcareer." Work and Occupations 13: ll-9(>. Gordon, David M., 1986. "Procedure for Allocating Jobs into Labor Segments." working paper. The New School for Social Research. Oster, Gerry, 1979. "A factor Analytic Test of the Theory of the Dual Economy." Review of Economics and Statistics 62: 33-9. Polachek, Solomon, 1979. "Occupational Segregation Among "Women: Theory, Evidence, and a Prognosis." In Women in the Labor Market eds. Cynthia Lloyd, Emily Andrews and Curtis Gilroy. New York: Columbia UP. Piore, Michael, 1975. "Notes fot a Theory of Labor Market Stratification." In Labor Market Segmentation, eds. Richard C. Edwards, Michael Reich and David M. Edwards. Lexington, MA: D.C. Heath, 1975,125-50. Pomer, Marshall, 1984. "Upward Mobility of Low-Paid Workers: A Multivariate Model of Occupational Changers." Sociological Per^ectives 27: 427-42. Rosenberg, Sam, 1980. "Male Occupational Standing and the Dual Labor Market." Industrial Relations 19: 35-49. Rumberger, Russell W. and Martin Carnoy, 1980. "Segmentation in the U.S. Labor Market: Its Effects on the Mobility and Earnings of Whites and Blacks." Cambridge Journal ofEconomics 4: 117-32. Sicherman, Nachum and Oded Galor, 1990. "A Theory of Career Mobility." Journal of Political Economy 96:169-92. Vietorisz, Thomas and Bennett Harrison, 1973. "Labor Market Segmentation: Positive Feedback and Divergent Development." American Economic Review 43: 366-76. Waddoups, Jeffrey, 1991. "Racial Differences in Intersegment Mobility." Revietu ofBlack Political Economy 20: 23-43. Waddoups, Jeffrey, Nasser Daneshvary and Djeto Assane, 1993. "An Analysis of Occupational Upgrading Differentials Between Black and White Males" unpublished working paper. University of Nevada, Las Vegas.

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