Click for next page ( 92


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 91
~ Occupational Sex Segregation: U Prospects for the 1980s ANDREA H. BELLER and KEE,OK KIM HAN Occupational segregation declined 2 to 3 times as rapidly during the 1970s as during the 1960s (Belier, in this volume). While this is encouraging, segregation continues at a high level overall; as of 1981, 61.7 per- cent of women (or men) would stir] have to change occupations for the occupational dis- tribution to become completely integrated by sex (Belier, in this volume). What pros- pects lie ahead for the remainder of this decade? To attempt to answer this question, we shall make several projections of occu- pational segregation to 1990. These projec- tions are based on the trends in segregation analyzed and reported in Beller (in this vol- ume). The best measure of occupational segre- gation, the segregation index, is computed as follows: St = i/2~ | m`'fit | ~ where m`' is the percentage of the male labor force employed in occupation i in year t, end fi' is the percentage of the female labor force employed in occupation i in year t. The index may take on a value between 0 and 100, where 0 represents perfect in- tegration and 100, complete segregation. Ike number tells the proportion of women (or of men) that would have to be redistributed among occupations for the occupational dis- tribution to reach complete equality be- tween the sexes. ASSUMPTIONS, DATA, AND METHODOLOGY In order to project the index of segrega- tion, we need to know the projected distri- bution of employment across occupations and the projected sex composition within each occupation. Occupational employment pro- jections for 1990, constructed by the Bureau of Labor Statistics (BLS), are published in Volume 2 of The National Industry-Occu- pation Employment Matrix (Department of Labor, BLS, 1981~. These projections are (1) based upon the bureau's intermediate labor force projections and assume a 4.5 percent unemployment rate in 1990. These projec- tions are discussed further in Appendix A. In constructing projections on the sex composition within occupations, we employ a number of techniques and entertain a va- riety of alternative assumptions. The basic 91

OCR for page 91
92 ANDREA H. BELLER AND KEE-OK KIM HAN . assumption underlying most of these pro- jections is that the sex composition of all occupations or of each occupation individ- ually continues to change during the 1980s along the path established during the 1970s. The data on which we base our projections come from the Current Population Survey (CPS), collected monthly by the Bureau of the Census either from the March Annual Demographic File (ADF) or from the un- published annual averages (AA) computed by BLS from the monthly data. First, we make three projections based upon infor- mation about the labor force as a whole, employing linear and logistic models. Then we make four projections based upon infor- mation for age cohorts; the equations are specified according to the linear spline model. All of these projections are based upon 255 3-digit census occupations. Linear Group Labor Force Projection (P1) First, as in Blau and Hendricks (1979), we assume that the sex composition in all occupations changes over time according to the same linear function. We estimate an equation in which the proportion of males in each occupation in 1981 is a linear func- tion of the proportion of males in that oc- cupation in 1972, of the percentage change in employment in that occupation over the period, and of the interaction between the proportion of males and the percentage change in employment. Using the AA data, the following equation is estimated: Pi,s~ = a + ~P`,72 + ~Y6Ei,s~-72 + 8(P`,72 * ~Ei~s~-72) + e`, (2) where Pi,' is the percentage of males in oc- cupation i in year t = 1972 or 1981, and AEi,~_72 is the percentage change in em- ployment in occupation i between 1972 and 1981. This mode! has a logical rationale. The proportion of males in an occupation de- pends on the initial proportion of males as well as on the growth in the occupation over the period. It is easier for women to enter growing occupations than to enter stable or declining ones. Moreover, the effect of the initial proportion on the present proportion might depend on the growth rate of the oc- cupation. Ibis model also has some draw- backs. Because it averages over all occu- pations, it will overestimate change in some occupations and underestimate change in others. Because it is linear rather than lo- gistic, the projected values for the propor- tion of males could exceed 1.0 or be nega- tive. To eliminate the effects of averaging, in our two other labor force projections we as- sume that each occupation's sex composition is a function of time. Linear Individual Labor Force Projection (P2) First, we specify the percentage of males in each occupation as a linear function of time: p' = a + bt + e`, (3) where t is 1,2, . . .11 for years = 1971 to 1974, 1977, and 1981.i But, since pi is a fraction between O and 1, the linear function might not be a good model, particularly near the extremes. ~ Eqs. (3) and (5) require a time series data set. Data for 1971 to 1974 and 1977 are from the Annual De- mographic Files (ADF) of the monthly CPS, while 1981 data are from the unpublished BES annual averages of the monthly CPS. The former are the only years for which we have the ADF, while the latter is the most recent year of data available. The AA data are somewhat more reliable statistically than are the monthly data. Eq. (2), which requires data only for the end points, is estimated with AA data alone, since we can remain within a single, statistically more reliable data set.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s . 93 Logistic Individual Labor Force Projection (P3) The logistic equation constrains the value of pi to lie between 0 and 1: eta where ,B is a vector of parameters to be es- timated, and t is the year as above. Eq. (4) can be rewritten in the "log odds ratio" form: (lPt) This equation, which can be estimated by ordinary least squares (OLS), will have het- eroscecIastic residuals, increasing the stand- ard errors; however, this is not important for purposes of prediction.2 A different approach to projecting the segregation of the work force in 1990 is to piece together information about population subgroups. Beller (in this volume) examined occupational segregation by work experi- ence cohort and found that (1) new entrants into the labor market are less segregated than is the rest of the labor force, and (2) between 1971 and 1977, occupational seg- regation declined for the entering cohort as it aged and between entering cohorts. Changes in occupational segregation can be projected by cohort and aggregated to the labor force. In order to accomplish this, we need to know the age-sex specific compo- sition of the civilian labor force in 1990; for- tunately it has been projected by the BLS (Department of Labor, BLS, 19791. Since we do not have the additional data for 1990 2 This equation should be modified to include an equation error, interpreted as a surrogate for omitted variables, in addition to the usual error term. We will assume that the variance of the equation error equals 0, as discussed in Medoff~1979~. In his empirical results the estimates of the equation modified to include equa- tion error were not much different from those that were not. needed to identify work experience cohorts, we make our projections based upon age cohorts. We project to 1990 the sex com- position of individual occupations for each age group. Then we use the BLS projections on cohort size to aggregate over groups and (4) to obtain the sex composition of each oc- cupation for the labor force as a whole. Com- bining these with the BLS occupational em- ployment projections, we compute the projected segregation index. The details of this approach are described in Appendix A. The advantage of using these projection methods is that we can incorporate specu- lations about what might happen under al- ternative scenarios for such factors as federal efforts on affi~-~ative action. The disadvan- tage is that projections for specified small subgroups of a population are likely to be less reliable than are projections for the whole population. We make four projections based upon age cohorts: conservative, moderate, moderately optimistic, and optimistic.3 All cohort projections are based upon trends between 1971 and 1977 only; the latter is the most recent year for which we have the ADF data containing the demographic de- tai} to identify age cohorts. Conservative Age Cohort Projection (P5) The conservative projection assumes that no further change occurs for a given cohort after 1977, partly because equal employ- ment opportunity (EEO) efforts have slowed down. Each cohort maintains the same sex composition within occupations as it ages, and the youngest cohort (16 to 24 years of 3 The assumptions about behavior and policy under- lying each of these projections do not generate the particular mathematical models we use. Rather the models postulate change as a linear function of time, since affirmative action policies and related behavior have changed over time. More complex mathematical models might also be consistent with the underlying assumptions about change.

OCR for page 91
94 ANDREA H. BELLER ~ KEE-OK KIM age) has the same occupational sex compo- sition in 1990 as in 1977: P`,j+ ~ (1990) = P`,j (1977), (6) and P$,1 (1990) = Pi,1 (1977), (7) where j is age cohorts 1 to 5, defined by the age groups in the 1990 BLS projections, shown in Appendix A. The assumption of no further change for an individual cohort need not imply no change for the labor force as a whole as long as younger cohorts are less segregated than older and ones are. As the labor force ages, younger, less segregated cohorts replace older, more segregated ones. Nonetheless, the assump- tion of no change for all cohorts after 1977 is quite conservative in light of the decline in segregation through 1981 shown by the aggregate labor force data (Belier, in this volume, Table 2-11. Thus, these conserva- tive assumptions may be viewed as yielding a lower-bound estimate on the projected de- cline in the index of segregation during the 1980s. Moderate Age Cohort Projection (P6) The moderate projection is constructed under the assumption that the rate of change in the sex composition of occupations for the youngest (entering) cohort will be the same between 1977 and 1990 as it was between 1971 and 1977 a period of considerable change. We might expect this if youthful attitudes and aspirations have changed, but equal opportunity efforts subside so that, as it becomes older, the rest of the labor force remains as segregated as it was in 1977. This projection applies Eq. (6) to older cohorts and projects change for the youngest cohort according to a linear spline function esti- mated on 1971 and 1977 data. The linear spline allows different segments of a contin- uous linear function to have different slopes (Poirier, 19761. It is likely that the sex com- position of highly male (more than 85 per- cent male) and highly female (less than 15 percent male) occupations will change at dif- ferent rates than will occupations with sex compositions in between. We estimate the following equation: Pi,~41977) = al + ,3~pi,~41971) + ~y~Fi,~1971) + 8,.M`,~1971) + al, (8) where Fin = 0.00ifpi,~ = 0.15tol.00, Fi,~ = 0.15pa, if pa < 0.15, MA = 0.00 if Pi,, = 0.00 to 0.85, Mi,~ = pa,0.85 if pa > 0.85. This equation is estimated for a six-year period, 1971 to 1977, and thus can be used to predict the value of pi,lkl983) using 1977 data for the independent variables. The 1977- 1983 growth rate in the proportion of males in occupation i for this cohort can then be used to predict pi,,fl9901. Optimistic and Moderately Optimistic Age Cohort Projections (P8 and P7) The optimistic projection (P8) is con- structed under the assumption that affirm- ative action, attitudes, and other factors con- tinue to change during the 1980s at the same rate as during the 1970s. Actually, this is quite optimistic given what we already know about the Reagan administration's proposed cutbacks in affirmative action and de-em- phasis on enforcement. Thus, we consider this to be an upper-bound estimate on how much change could occur under the best of circumstances. For P8, we assume that as each 1977 cohort ages to 1990 its rate of change in percentage of males in each oc- cupation is the same as for the similar cohort as it aged between 1971 and 1977, and that the rate of change between entering cohorts in 1977 and 1990 is the same as between entering cohorts in 1971 and 1977. For these

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s . . . 95 projections, we apply Eq. (8) to the young- est cohort and estimate the following equa- tion for ogler cohorts: P`,j+ ~1977) = of + ,Bjp`,j(1971) + ~yjF`,j(1971) + ~yMi,j(1971) + A. (9) If the mechanisms have been put into place, then Reagan's policies may succeed only in reducing but not in eliminating change. We consider it moderately optimistic (P7) to as- sume that the rate of change for each cohort during the 1980s is one-half the rate during the 1970s. The value of P`,j+1~1983) provides We moderately optimist/c projection, P7, and the value of Pi,j+141990), the optimistic pro- jection, P8. PROjECTIONS OF OCCUPATIONAL SEGREGATION, 1981 TO 1990 Segregation indexes computed using BLS occupation projections and our linear pro- jections on the sex composition of occupa- tions for the labor force yield only a modest decline in occupational segregation between 1981 and 1990. As shown in Table 6-1, col- umn 1, the segregation index is projected to decline by 1.69 percentage points to 59.97 TABLE 6-1 Actual and Projected Segregation Indexes, 1972, 1977, 1981, and 1990 Employment Year Unstandardized Standardized . . .. 1972 68.32 67.23 1977 64.15 64.02 1981 61.66 61.66 1990 Labor force Linear-group (P1) 59.97 59.35 Linear-individual (P2) 60.37 59.51 Logistic-individual (P3) 56.06 55.20 Combined-individual (P4) 59.91 59.03 Age cohort Conservative (P5) 62.11 60.89 Moderate (P6) 57.29 56.02 Moderately optimistic (P7) 50.02 49.09 Optimistic (P8) 42.20 41.33 NOTES: PI was computed based on the following equation for projecting percentage of males in occupation i: Pi,81 = - 016 + 970 P`72 - 036^ E`.8l_72 - 042 (Pi72 *^E`8~_72), (1.65) (75.61) (1.64) (1.36) R2 = .966, N = 262, t-values in parentheses. P2 and P3 were computed based upon separate linear or logistic equations for each occupation, respectively. P4 was computed by selecting the equation for each occupation with the highest R2 if either functional form was significant; if neither was significant assuming the 1981 value. P5 assumes no further change after 1977 in the percentage of males in each occupation for each age cohort; P6 assumes no change for all but the youngest cohort, which experiences a linear change for all but the youngest cohort, which experiences a linear rate of change; P7 assumes a linear rate of change of percentage of males in each occuaption for each age cohort at one-half the rate during the 1970s; P8 assumes a linear rate of change at the same rate as during the 1970s. Employment standardized indexes are standardized to 1981 employment totals. Projected segregation indexes include only 255 occupations because the BLS employment projections were unavailable for 7 of the occupations included in the analysis of trends in Beller (in this volume). SOURCES: Annual Demographic Files of Current Population Survey, 1972 to 1975 and 1978, computer tapes; and Bureau of Labor Statistics, annual averages of monthly Current Population Surveys, 1972, 1977, and 1981, unpublished tabulations.

OCR for page 91
96 ANDREA H. BELLER AND KEE-OK KIM HAN or by 1.29 percentage points to 60.37 ac- cording to the linear-group (P1) and linear- individual (P2) projections, respectively. These projected rates of decline in the over- all index are much slower than during the past decacle. A logistic rate of growth (P3) in the sex composition of individual occu- pations combines with the BLS occupation projections to predict a decline in the seg- regation index from 61.66 in 1981 to 56.06 in 1990. This projected 5.6 percentage point decline is around 84 percent of the actual 6.66 percentage point decline between 1972 and 1981. To decompose these changes, we stand- arclize the projected segregation indexes to the 1981 occupational distribution.4 Shown in column 2, the employment standardized segregation indexes lie slightly below the unstandardized indexes indicating that the occupational `distribution is projected (by the BLS) to change slightly toward more seg- regated occupations between 1981 and 1990. This contrasts with the slight change in the occupational distribution toward less seg- regated occupations during the 1970s. The projected employment standardized segre- gation index based upon logistic trends (P3) declines by 6.46 percentage points cluring the 1980s to 55.20, or by more than the 5.57 percentage point decline in the standard- or by 1.29 percentage points to 60.37 ac- 4 1he employment standardized index of segregation is defined as follows: where S`* = 1/22 ~ m* - ~ I, (MtiTtt) (Ttt-l) (1~) 2(Mt/Ttt) (T,'_1) (FATE`) (Ttt-l) (1~) (FtiTtt) (Tit-l) F`t is the number of females in occupation i in year t, M`' is the number of males in occupation i in year t, and T`' equals F.`' + M`' equals total employment in occupation i in year t. cording to the linear-group (P1) and linear- izec! index during the 1970s. Thus, accord- ing to logistic trends, changes in the sex composition of occupations toward a less segregated work force will be larger during the 1980s than during the 1970s; however, projected adverse changes in the occupa- tional distribution will more than offset these more favorable changes in sex composition. From these data we conclude that projected changes in the sex composition of occupa- lions during the 1980s based upon changes between 1971 and 1981 will decrease oc- cupational segregation less than in the past in part because of opposing changes in the occupational distribution toward increased segregation. However, these projections of occupa- tional sex composition may be somewhat off, because they project the female share of the labor force in 1990 as higher than the BLS projects them (Department of Labor, BLS, 1979, Table 5, p. 71. The BLS projects the female share of the labor force to grow from 41.0 in 1977 to 45.5 in 1990, whereas PI projects the female share (aggregated from occupational shares) to be 49.3 in 1990, P2 projects it to be 48.2, and P3 projects it to be 50.3. Since projections for a population (the labor force) tend to be more accurate than projections for a specified subset of that population (occupations), the BLS projec- tions suggest that our own projections some- what overestimate the female share of the labor force in 1ggo.s What, then, do these projections tell us? The sex composition of some occupations changed so rapidly during the 1970s that the rate cannot be sustained during the 1980s on the basis of projected growth in the fe- male labor force. For which subset of oc- cupations will the female rate of entry de- cline during the 1980s? Will it decline further 5 This difference may be somewhat mitigated by the fact that the BLS has consistently underestimated the growth rate of the female labor force (Lloyd and Niemi, 1979, p. 311, n. 19; Smith, 1977, p. 23~.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s 97 in the large, traditionally female occupations so that not only will the proportion of the female labor force that is in them decline, but also the female proportion of these oc- cupations will decline? If so, occupational segregation will decline more than is pro- jected. Or will it decline in the traditionally male occupations to which access has been increased so recently? If so, occupational segregation will decline less than is pro- jected. Obviously incentives can be created by public policy for movement in one di- rection or the other. If affirmative action continues to promote equal opportunity for women in nontraditional occupations, then traditionally female occupations will be- come relatively less attractive. If opportu- nities decline in nontraditional jobs, then female occupations will look relatively more attractive. As our experience during the 1970s suggests, federal policy can significantly af- fect occupational segregation (Belier, 1982a,b). Each of these projection methods has ad- vantages and disadvantages, and each makes projections that are quite reasonable for many individual occupations. The projected fe- male percentages of PI to P3, as well as the estimated annual linear trend of P2, are pre- sented in Appendix B. Table B-1. As these data show, the logistic growth model, P3, does very well in capturing the acceleration or deceleration in the rate of change in per- centage of females near the tails of the dis- tribution; however, it overprotects the fe- male share of the labor force the most. Ike linear-individual projection, P2, overpro- jects the female share the least. Since the truth probably lies somewhere in between, we construct a combined estimate. For each occupation we choose the individual equa- tion with the highest R2, where at least one is significant at the 10 percent level accord- ing to the F-statistic. Where neither the lin- ear nor the logistic equation is significant, we assume that no change occurs in percent female between 1981 and 1990. As shown in Table B-1, while the majority of occu- pations shows no significant trend in the percent female, more than one-fourth, 27.5 percent, shows a significant increase. These assumptions yield the combined-individual projection (P4) of the segregation index of 59.91, only slightly below P2. MAjOR COMPONENTS OF PROJECTED CHANGE IN THE INDEX Many of the trends toward decreasing segregation begun in the 1970s (Belier, in this volume) are projected to continue into the 1980s. However, counterbalancing these will be a major new source of increasing segregation. According to the P2 linear pro- jections, a number of predominantly male crafts and operative and laborer occupations are projected to grow (some rapidly) and to account for increasing proportions of the male labor force during the 1980s. According to the PI linear projections, if women take a larger share of the growth in these jobs as they did in the male occupations that grew rapidly during the 1970s, then these occu- pations may not increase overall segrega- tion. Several traditionally female occupa- tions are projected to continue to account for decreasing proportions of the female la- bor force, while some increase in segrega- tion will result from the expansion of the predominantly female health services oc- cupations. According to P2, occupations projected to continue contributing to declines in the seg- regation index during the 1980s are ele- mentary school teachers; telephone opera- tors; cooks, except private household; child care workers, private household; and maids and servants, private household. Declines in the segregation index during the 1980s are projected from some new sources as well: secondary school teachers; managers and administrators, not elsewhere classified; bookkeepers; waiters; and hairdressers and cosmotologists. Also projected to decrease the segregation index during the 1980s are two female occupations registered nurses

OCR for page 91
98 ANDREA H. BELLER AND KEE-OK KIM HAN and bank tellers that increased it during the 1970s. The occupation of miscellaneous clerical workers is projected to continue increasing the segregation index by a large amount. Also projected to increase the index during the 1980s is the rapidly growing secretarial occupation, which decreased it during the 1970s. Several female health services oc- cupations are projected to grow and to be- come more segregated: health aides, nurs- ing aides, and practical nurses.6 As mentioned before, the biggest change projected for the 1980s that will hinder fur- ther declines in occupational segregation is substantial growth in a number of highly male crafts and operative and laborer oc- cupations. The following occupations, with the projected percentage of females in pa- rentheses, will add a large amount to the 1990 segregation index if their female per- centage grows during the 1980s at the same linear rate as during the 1970s: carpenters (3.0 percent); auto mechanics (0.7 percent); heavy-equipment mechanics (2.8 percent); welders and flame-cutters (5.3 percent); ma- chine operatives, miscellaneous (27.7 per- cent); truck drivers (4.0 percent); and con- struction laborers (4.5 percent). Of these, women made significant inroads during the 1970s only into the occupations of carpen- ter, heavy-equipment mechanic, and truck driver. In all except machine operatives, the percentage of females is projected to grow between 1981 and 1990, but by nearly im- perceptible amounts. However, growth in these occupations need not hinder declines in occupational segregation; if these male blue-collar occupations respond to growth in the same way as the male white-colIar occupations did during the 1970s, allocating a higher share of new than of existing jobs 6 These occupations were identified on the basis of the linear trends for each individual occupation; they carry the assumption that the percentage of females during the 1980s follows the same trend as during the 1970s regardless of any changes in total occupational employment that might occur. to women, the PI projections show that the percentages of females could increase among carpenters (to 8.3 percent); auto mechanics (to 6.8 percent); heavy-equipment mechan- ics (to 9.4 percent); welclers and flame-cut- ters (to 11.2 percent); machine operatives, miscellaneous (to 34.1 percent); truck driv- ers (to 9.2 percent); and construction labor- ers (to 9.1 percent). Increasing the rate at which women enter those blue-collar oc- cupations that are projected to grow during the coming decade should be a major focus of any public policy designed to reduce oc- cupational segregation.7 PROJECTIONS BASED ON AGE COHORTS The four segregation indexes based upon projections of occupational sex composition within age cohorts vary considerably de- pending upon the assumptions. Based upon conservative assumptions, P5 projects only a slight decline in the index of segregation, from 64.15 in 1977 to 62.11 in 1990, slightly above the actual 1981 index. (The projected indexes for each age cohort, as well as the indexes for 1971 and 1977, appear in Ap- pendix B. Table B-2.) Note that these con- servative assumptions yield a projection only slightly above P1, P2, and P4. The con- servative projection appears especially so in light of the actual 1981 segregation index. Based upon moderate assumptions that further declines occur only for the youngest cohort after 1977, P6 projects a decline in the index of segregation to 57.29 in 1990, closest to the logistic projection, P3, of 56.06. (P6 projects a female share of the labor force of 48.3 percent in 1990.) While the linear spline equation predicts a substantial drop 7 Whether this projected growth in blue-collar jobs will be realized is questionable because of the present high unemployment rate contrasted with the 4.5 per- centage rate embedded in the BES occupation projec- tions for 1990. Moreover, as Carey (1981, p. 42) points out, job growth in blue-collar occupations is more sen- sitive to the underlying assumptions of the projections than in other major occupational categories.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s in segregation for the youngest cohort (see Table B-2), even changes of substantial mag- nitude restricted to a single cohort have a limited impact on the overall index. It would take many years of a continued influx of less segregated cohorts for the overall occupa- tional distribution to show a major decline in segregation. The moderately optimistic and the opti- mistic age cohort projections, P7 and P8, depart from the others in projecting a sig- nificant decline in the index of segregation during the 1980s. Based upon the assump- tion that the total change in percentage of males for each occupation as a cohort ages between 1977 and 1990 is the same as for the similar cohort between 1971 and 1977, i.e., one-half the rate of change, P7 projects the index of segregation overall to decline by 11.68 percentage points to 50.02 in 1990. P7 projects the female share of the labor force at 47 percent, which is close to the BLS projection of 45.5. Projection P8 is very optimistic and assumes that the rate of change in percentage of males by occupation for each cohort between 1977 and 1990 is the same as between 1971 and 1977 for the com- parable cohort, i.e., double the total change. P8 predicts a rather substantial drop in the segregation index of nearly 20 points to 42.20 and projects a female share of the labor force just over 50 percent. The age cohort projections differ from the labor force projections in their emphasis upon source of change. They assume that segre- gation is a characteristic of cohorts of indi- viduals rather than of occupations or of the labor force. Thus, change is based more upon the characteristics of the supply side of the labor market than, as in the earlier projec- tions, upon the demand side. If, on the one hand, segregation were primarily the result of the choices of each sex, then the opti- mistic projections suggest that occupational segregation could decline significantly dur- ing the 1980s. If, on the other hand, seg- regation were primarily the result of em- ployer practices and other demand-side factors, modest Farther declines are pre- 99 dieted for the 1980s. The truth may well lie in between. In the next section, we focus on changes in the characteristics of the sup- ply side of the professional labor market. PROJECTIONS TO 1990 OF SEGREGATION AMONG PROFESSIONAL OCCUPATIONS BASED ON COLLEGE MAjORS In our final set of projections on segre- gation among professional occupations we break with previous methodological pat- terns. So far we have projected trends in the sex composition of occupations either for the whole labor force or for a subgroup of that population. Here we use an aggregate time series regression framework in which the segregation indexes are variables. Since professional occupations generally require a college degree, segregation among profes- sional occupations is hypothesized to be a function of segregation by college field of study among recent bachelor's degree re- cinients. As long as the sexes face equal op- portunity in the job market, to the extent that a greater similarity arises in the edu- cational preparation of men and women, segregation in professional occupations should decline. We have chosen to specify a rela- tionship where segregation in professional occupations in year t is a function of seg- regation in college majors in year t3, a lag of three years. While many of last year's college graduates fill this year's job vacan- cies, several previous years' graduates may also, especially where postgraduate educa- tion or training is involved.8 We thus specify the following equation: S` = a + ~ St_3 + e`, (10) where S is the index of segregation among 8 We postulate a relationship for a single year, be- cause the number of years for which we have compa- rable time series data is limited. If more years of data were available, a more complex distributed-lag model in which occupational segregation in year t was a func- tion of several previous years of segregation among college graduates might be desirable.

OCR for page 91
100 professional occupations in year t, St_3 is the index of segregation among bachelor's degree recipients by major field of study in year t3, and t is 1974 to 1981. Ike data on the size and sex composition of college majors are taken from the National Center for Education Statistics' (NCES, 1980) Projections of Eclucation Statistics to 1988- 89, and the data for professional occupations are from the published AA data (Employ- ment and Earnings, various issues). The model and data are discussed in more detail in Appendix C. First, we computed a time series of seg- regation indexes for college major among bachelor's degree recipients and for profes- sional occupations, which appear in Table 6-2, columns (1) and (2), respectively. With these data we estimate Eq. (101; the esti- mated equation with t-values in parentheses 9 S: SO _ t 23.88 + 0.727 St3 (23.50) (29.20) (11) R2 = 0.99, N = 8. Initially we used the NCES projections of the number of female and male college graduates by fielc! of study through 1989 (NCES, 1980, pp. 66-71) to compute pro- jected segregation indexes for college ma- jors, as shown in Table 6-2, column (3). In contrast to the increasingly rapid decline in segregation among college majors during the 1970s (column [14), the NCES projections show a slight increase in segregation in 1979 and only a modest decline thereafter (col- umn f31). Then, using Eq. (11), we com- puted the projected segregation indexes for professional occupations, which appear in column (4). Likewise, the projected segre- 9 We also estimated this equation for 1-, 2-, 4-, and 5-year lags, and found the explanatory power greatest for the 3-year lag. Given the linear trend in the data, a 3-year lag is approximately equivalent to a S-year moving average. ANDREA H. BELLER AND KEE-OK KIM HAN cation indexes for professional occupations increase (in 1982) and then decline very slowly thereafter. In fact, the projected seg- regation index for professional occupations of 48.36 in 1990 based on the NCES pro- jections lies only slightly below the actual 1981 index 49.61. This value is slightly above one for professional occupations com- puted with the earlier combined-individual projection (P4), 47.05. We suspect from the distinct break in the series between 1978 and 1979 that the NCES projections under- estimate the extent of change in the sex com- position of college majors. is Assuming that the number of majors by field has been correctly projected by NCES but that the sex composition has not, we estimate the trend in the sex composition for each college major as a linear function of time and then project it into the future. We estimate equations of the following form for each major field: PF' = a + fit + e`, (12) where PF' is the percentage of females among bachelor's degree recipients in a major field in year t, t = 1969 to 1978, and ,B is the estimated trend. The estimated trends in percentage of fe- males by major field of study are presented in Appendix B. Table B-3, along with the actual 1969 and 1978 values and the pro- jected 1989 values. These estimates reveal two major trends among college students: a To check this we computed the actual segregation index among college fields of study of bachelor's degree recipients for 1979-1980 directly from data from Earned Degrees Conferred 1979-80 (NCES, 1981~. (The cate- gories used for 1978-1979 data were not comparable with those published in the NCES projections volume.) The actual index for 1980 was 34.53, below that based upon NCES projections. While these data are not strictly comparable with earlier data, because the NCES sup- plemented the data from Earned Degrees Conferred with information from additional sources (NCES, 1980, p. 49), they are nevertheless suggestive.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s Z01 TABLE 6-2 Actual and Projected Segregation Indexes for College Majors and Professional Occupations Actual Projection I Projection II Projection III College Majors Year (1) 1969 46.08 45.45 44.56 44.17 43.32 41.99 40.42 38.77 36.92 35.62 Professional Occupations (2) College Majors (3) Professional Occupations (4) College Majors (5) Professional Occupations (6) College Majors (7) Professional Occupations (8) 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 58.94 56.13 56.11 55.58 54.07 53.32 51.98 34.04 50.98 49.61 35.98 35.66 35.54 35.35 50.03 35.33 49.79 34.89 49.71 34.46 49.57 34.18 49.56 33.69 49.23 33.36 48.92 32.94 48.72 48.36 36.91 36.30 35.67 33.16 50.70 34. 18 50.26 33.32 49.80 32.47 49.43 31.70 48.72 30.97 48.09 30.55 47.48 30.43 46.92 46.39 35.22 34.04 51.19 32.85 50.33 31.67 49.47 30.48 48.61 29.30 47.75 28.12 46.89 26.93 46.03 25.75 45.17 24.56 44.31 23.38 43.45 42.59 NOTES: Projection I is computed based on NCES projections of degree recipients by major field of study. Projection II is computed based on projecting the previous linear trend (1969 to 1978) in the female proportion of degree recipients in each major field. Projection III is computed based on projecting the previous linear trend (1969 to 1978) in the segregation index for college majors. SOURCES: Cols. (1) (except 1980), (3), (5), and (7) are computed from NCES (1980, pp. 66-71). Col. (1), 1980; NCES (1981, pp. 19-24). Col. (2), 1974 to 1981 is computed from Department of Labor, BLS, Employment and Earnings, 1974 (March), 1975 June), and 1976 through 1981 January). Col. (2), 1972 is computed from BLS unpublished annual averages. (1) a decline in the percentage of females in nearly all traditionally female fields of study (public affairs and services, library sciences, letters, and education); and (2) an increase in the representation of women in all other fields. The largest upward trends are in the traditionally male disciplines of architecture (2.1 percent per year); agriculture and nat- ural resources (2.5 percent per year); ac- counting (2.4 percent per year); business and management (2.0 percent per year); and computer and information sciences (1.5 per- cent per year). In each of these fields, be- tween 1969 and 1978, the number of women grew to around 25 percent of all majors; according to our projections, they will be- come 40 to 50 percent of these majors by 1989. The female proportion of psychology majors also grew by 1.9 percent per year during the 1970s. For such trends to con- tinue the same conditions must prevail in the future as in the recent past, even at the higher number of female majors. That is, women must not encounter substantial re-

OCR for page 91
104 ANDREA H. BELLER AND KEE-OK KID] HAN Unlike the Census, the new Occupational Employment Statistics (OES) survey con- ducted by the BLS is a survey of jobs, not of people. Projections based on such a ma- trix avoid the problem of having to specify an unemployment rate, but the primary dis- advantage is the lack of consistency with CPS data, which are based on a count of persons rather than of jobs. The first set of these new projections appears in Carey (19811. As stated in the text of this paper, the BLS occupational employment projections r used assume that a stable Tong-run unem- ployment rate close to 4.5 percent will be achieved by the mid-1980s. This seems ex- ceptionally optimistic from the vantage point of the end of 1982 when, in September, the economy hit a high unemployment rate of 10.1 percent. How would an unemployment rate higher than 4.5 percent affect occupa- tional employment projections for 1990? Ac- cording to Carey (1981, p. 42), job growth in blue-collar occupations is more sensitive to the underlying assumptions than is job growth in other major occupational cate- gories. The need for additional blue-collar workers is very much affected by the de- mand for manufactured goods as well as by changes in productivity. Job growth in the white-collar and service categories generally is less sensitive to the underlying assump- tions than is blue-collar job growth. Most likely the employment estimates would ov- erestimate the size of blue-collar occupa- tional employment. This consideration should be kept in mind in examining the projec- tions of occupational segregation, because many blue-collar occupations are highly male, and the larger they are the more they add to the index of segregation. The BLS 1990 occupational employment projections "assume that the size, sex and age composition of the labor force wiD change as indicates] by the intermediate labor force projections published by the BLS in Em- ployment Projections of the 1980's." We use those projections for our projections by age cohort. Since the occupation projections are based upon these labor force projections, they should be consistent with them. Methodology of Projections Based on Age Cohorts In order to make projections by age co- hort, we need to know the sizes of age-sex cohorts for the projected year to supplement information on the projected occupational distribution and the projected sex compo- sition of each occupation. Age-sex-specific projections on the size of the civilian labor force in 1990, created by the BLS, are pre- sented in Employment Projections for the 1980's. They project the size of age cohorts, which we grouped into four categories: 16 to 24, 25 to 34, 35 to 44, and 45 + . Unfor- tunately these categories cannot be trans- formed into cohorts based on potential labor market experience, which creates more meaningful and presumably more homo- geneous groupings for labor force analyses. For example, the age group 16 to 24 contains a mixture of high school graduates already in their chosen field for several years and individuals who are still students and who work part time at jobs unrelated to their future occupations. In the analyses by ex- perience cohorts presented in another pa- per, we excluded individuals with 0 years of potential work experience (defined as AgeEducation6), hoping to eliminate students. Since we do not know the sizes of age-sex experience cohorts for the projected year, we must base our cohort projections on age rather than on work experience co- horts. Thus, we grouped our Annual De- mographic File data for 1971 and 1977 into age cohorts. Segregation indexes by age co- hort for 1971 and 1977 shown in Table B-2 differ from those by experience cohort (Belier, in this volume, Table 2-21. The youngest age cohort is more segregated than is the young- est experience cohort after exclusion of in-

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s 105 dividuals with 0 years of potential experi- ence. To project what happened to a specific cohort as it aged over time, we aged each group backward to 1977 by subtracting 13 years. This yielded age groups in 1977 of 16 to 21, 22 to 31, and 32 to 41. We added the age group 42 + to complete the labor force. We also added the youngest complete co- hort, 16 to 24 years of age, so that we could use it for the projections as well. Aging the groups backward! by 6 years to 1971 yielded age groups 16 to 25, 26 to 35, and 36 to 45. We added the group 46 + and the youngest age cohort, 16 to 24. This construction can be diagrammed as follows: Group 1971 1977 1990 +6 +13 2 3 4 5 16-24 ~ 16-24 ~ 16-24 16-25 16-21 I 26-35 22-31 25-34 36~5 `32-41 `35-44 46+42+ ~~45+ First, we must project the percentage of males in each occupation for each age group in 1990. To do this we choose three alter- native assumptions, which allow for (1) no change between 1977 and 1990; (2) changes in the youngest cohort, group 1, only; and (3) changes in all cohorts. Changes are pro- jected to occur at the same rate between 1977 and 1990 as a given group ages as the rate for the similar age group as it aged be- tween 1971 and 1977. The methodology of these three projections (labeled conserva- tive, moderate, and optimistic) is described in the text of this paper. This method of projecting assumes that the sex composition of occupations is char- acteristic of the particular age cohort in question rather than of the labor force or the occupation. That is, it directs our atten- tion to the characteristics of individuals, or the supply side of the labor market, more than do the projections based upon the whole labor force, which direct our attention more to the characteristics of employers, or the demand side. Of course, supply changes in response to perceived changes in demand. Women's aspirations change as they per- ceive reductions in the barriers to their en- try into traditionally male occupations (Res- kin and Hartmann, 1984~. Once we create projections of the per- centage of males in each occupation by age group, we must combine them to create pro- jections of the percentage of males in each occupation for the labor force as a whole. To obtain a labor force estimate from the cohort estimates, we use the BLS projec- tions on the age-sex distribution of the labor force in 1990. We know the projected num- ber of males, females, and total labor force for each age group. We also know from BLS projections the projected distribution of em- ployment across occupations for the civilian labor force as a whole. We need to combine these two pieces of information to deter- mine the occupational distribution of em- ployment for each age group, and we must make assumptions to do this. The simplest assumption is that the occupational distri- bution is the same for each age group as it is for the labor force as a whole; this is equiv- alent to standardizing the employment dis- tribution of each subgroup to the employ- ment distribution of the whole. (Thus, looking ahead, when we use the projected sex com- position within each occupation of each age group to compute segregation indexes, we will see differences in the indexes that are bases] upon differences in their occupational sex composition alone.) Another possible as- sumption is that each age group has the same occupational distribution of employment in 1990 as it did in 1977. This, too, is imper- fect, because the occupational distribution is projected to change. Consequently we opted for the first approach. We computed total employment, number of males, and number of females in occu-

OCR for page 91
106 ANDREA H. BELLER AND KEE-OK KIM HAN pation i in age group j in 1990 according to the following: Tij= zE x Tj, (A1) My = Tij X pa, ~9 = Tij My, (A2) (A3) where Tij is total employment in occupation i for age group j, where j = 1, . . . ,5 as in the diagram above; Ei is total employment in occupation i, given by BLS projections; T . . . r IS ClV1 1an a for force in age group 1, given by BLS projections; pa is proportion male in occupation i for age group j according to cohort projection n, where n = 1,2,3; Mn' is the number of males in occupation i for age group j according to cohort projection n; and Fill is the number of females in oc- cupation i for age groupj according to cohort projection n. These numbers are then aggregated over age groups to obtain the values for the ci- vilian labor force in 1990: Mn = ~ My, i Fn _ ~ Fn i ~ id, pn = Mn + Fn (A4) (A5) (A6) Using these projections of proportion of males by occupation and the BLS occupational employment projections for 1990, Ei, we compute four projected segregation indexes for 1990, P5 to P8. TABLE B-1 Actual and Projected Proportion Female for Detailed Occupations Actual Researchers and analysts, operas. and sys. Personnel and labor relations Pharmacists Physicians, medics, and osteopaths NEC, health technicians Technicians, chemical Draftsmen Technicians, elect. engineers Operators, radio Public relations Officers and manag., bank NEC, managers, office NEC, pub. admin., office and admin. .2171 .1989 .0000 .0105 .0235 .0417 .0396 .0991 .3097 .1349 .1006 .5821 .1169 .0629 .0549 .3514 .2989 .1874 .4190 .2006 .3850 .2941 .0615 .0378 .1126 .1053 .1423 .2551 .4977 .2517 .1376 .5787 .2574 .1929 .1124 .5738 .4545 .3735 .7024 .2900 .0152 .4291 .0093 .3247 .0056 .1043 .0028 .0817 .0124 .1560 .0090 .1650 .0100 .1910 .0188 .2741 .0133 .5342 .0088 .3076 .0030 .1909 .0100 .6052 .0155 .3022 .0120 .2459 .0079 .1515 .0380 .5988 .0173 .5073 .0165 .4222 .0233 .7300 .0057 .3100 Occ. Code Occupation Name I. Proportion Female Increasing 1 Accountants 3 Programmers, computer 10 Engineers, chemical 12 Engineers, electrical and electronic 13 Engineers, industrial 25 Foresters and conservationists 31 Lawyers 55 56 64 65 85 151 152 153 171 192 202 220 222 1972 1981 Annual Projected Rate 1.990 of Change* (P1) (P2) (p3) .5239 .5476 .3822 .4113 .0964 .4129 .0594 .2666 .2442 .6959 .1879 .7742 .2284 .3644 .4188 .5883 .6028 .6063 .3240 .3563 .1620 .1681 .6721 .6666 .3965 .4874 .2884 .3763 .1882 .3061 .9790 .8860 .5721 .5847 .5204 .5530 .9356 .8628 .3414 .3496 NOTES: Column (P1) contains the linear-group projection, column (P2) contains the linear-individual projection, and column (P3) contains the logistic-individual projection. NEC = not elsewhere classified. *Coefficient estimate on linear time trend in individual occupation projection, where estimate is significant.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s _ 107 TABLE B-1 (Continued) occ. Code Occupation Name 225 231 233 245 260 265 266 270 281 282 301 Bank tellers 305 Bookkeepers 315 Dispatchers and starters, vehicle 323 Expediters and production controllers 331 Mail carriers, post office 333 Messengers and office helpers 343 Computer and peripheral equip. operators 360 Clerks, payroll, and timekeep. 361 Postal clerks 375 Clerks, statistical 381 Storekeepers and stock clerks 390 Agents, ticket station and express 412 Bulldozer operators 415 Carpenters 422 Compositors and typesetters 430 Electricians 441 NEC, blue-collar supervisors 455 Locomotive engineers 470 Air coed., heating, refrigeration 481 Mechanics, heavy equipment 482 Mechanics and install. appliance home 550 Structural metal workers 552 Teleph. install and repairers 554 Teleph. linemen and splicers 601 Asbestos and insular. workers 631 Meat cutters, butchers, exe. manuf. 664 Shoemaking, machine operatives 666 Furnace tenders and stokers 703 Bus drivers 705 Delivery and route workers 715 Truck drivers 740 Animal caretakers, exe. farm 750 Carpenters' helpers 754 Garbage collectors 762 764 785 903 910 Actual 1972 1981 Annual Projected Rate 1990 of Change* (P1) (P2) .3038 .0135 .4036 .0119 .1362 .0105 .1966 .0080 .4683 .0256 .2363 .0128 .2973 .0211 .5000 .0114 .2000 .0114 .1199 .0071 .9355 .0043 .9116 .0039 .3805 .0249 .4096 .0113 .1548 .0090 .2766 .0202 .6407 .0290 .8097 .0135 .3802 .0101 .8006 .0090 .3501 .0099 .4722 .0188 .0099 .0013 .0180 .0013 .3506 .0210 .0168 .0012 .1134 .0033 .0213 .0019 .0047 .0007 .0179 .0012 .0455 .0033 .0122 .0011 .0982 .0076 .0506 .0060 .0377 .0056 .0809 .0038 .7260 .0122 .0128 .0012 .4732 .0122 .0856 .0057 .0266 .0018 .5699 .0251 .0167 .0020 .0278 .0011 .2479 .0093 .1576 .0042 .1122 .0073 .1899 .0066 .4725 .0192 (P3) .4648 .5422 .4391 .3049 .7241 .4173 .6537 .5934 .3553 .2506 .9586 .9399 .6883 .5159 .3091 .7250 .8555 .8965 .4919 .8667 .4689 .6732 .1623 .0610 .6181 .0496 .1562 .1108 .0434 .0472 .2108 .0488 .3495 .8681 .8507 .1402 .8248 .0527 .6071 .2172 .0686 .7822 .3307 .0385 .3530 .2017 .2881 .2759 .6688 NEC, purchasing agents and buyers Retail trade sales manag. and dept. heads Sales manag., exe. ret. trade NEC, manag. and admin. Adv. agents and sales work Ins. agents, brokers, and underwriters Vendors and carriers, news Agents and brokers, real est. Sales rep., manuf. indust. Sales rep., wholesale trade Stock handlers Vehicle washers and equip. cleaners Not specified laborers Janitors and sextons Bartenders .1326 .2736 .0292 .1214 .2273 .1179 2444 . .3668 .0700 .0460 .8715 .8794 .1628 .2308 .0667 .1410 .3776 .7174 .2669 .7090 .2290 .3178 .0000 .0049 .1647 .0043 .0701 .0000 .0000 .0070 .0152 .0000 .0194 .0000 .0000 .0348 .6184 .0123 .3414 .0247 .0042 .4125 .0160 .0118 .1687 .0909 .0396 .1051 .2786 .3426 .4148 .4732 .5299 .1944 .2242 .2424 .2724 .5063 .7100 .2867 .3508 .3193 .4734 .5440 .5916 .2596 .2870 .1681 .1837 .9501 .9749 .9327 .9517 .4065 .6186 .4561 .5056 .2026 .2444 .2934 .5129 .6596 .9335 .8367 .9446 .4025 .4824 .8364 .8913 .3958 .4550 .4941 .6625 .1239 .0232 .0831 .0303 .3803 .5491 .0720 .0255 .1606 .1434 .0784 .0334 .0628 .0125 .0936 .0285 .1178 .0673 .0787 .0191 .1378 .1684 .0771 .1072 .1000 .1037 .1307 .1135 .7223 .8539 .0507 .0201 .5038 .6043 .1420 .1393 .0923 .0401 .6387 .8100 .1150 .0351 .1731 .0347 .2911 .3267 .2149 .1920 .2181 .1778 .2407 .2503 .5156 .6604 (Continued)

OCR for page 91
108 ANDREA _ BELLER AND KEE-OK KIM HAN TABLE B-1 (Continued) Actual Annual Projected 1972 1981 Rate 1990 Occ. of Code Occupation Name Change* (P1) (P2) (P3) - 932 Attendants, recreation and amuse. .3120 .4477 .0155 .4814 .6146 .6230 962 Guards .0461 .1366 .0086 .2062 .2017 .2936 II. Proportion Female Decreasing 141 Teach., adult ed. 142 Teach., elem. ed. 385 Telephone operators 502 Millwrights 624 Graders and sorters, manuf. 822 Farm laborers, wage workers 912 Cooks, exe. private household 915 Waiters 933 NEC, attendants, personal serv. .3768 .4267 - .0219 .4759 .2106 .2364 .8505 .8359 - .0038 .8627 .8003 .7922 .9668 .9302 - .0040 .9469 .8984 .8480 .0000 .0000 - .0029 .0594 .0000 .0000 .7727 .6389 - .0162 .6869 .5119 .4772 .1535 .1594 - .0095 .1898 .0955 .1142 .6236 .5228 - .0109 .5616 .4344 .4318 .9173 .8967 - .0038 .9211 .8626 .8474 .6386 .5773 - .0117 .6077 .5135 .5096 III. Proportion Female No Significant Trend 2 Architects .0303 .0440 .0000 .1080 .0408 .0768 4 Syst. analysts, computer .1081 .2584 .0000 .2993 .3121 .3266 6 Engineers, aero- and astro-. .0000 .0122 .0000 .0467 .0124 .0245 11 Engineers, civil .0065 .0164 .0000 .0652 .0251 .0963 14 Engineers, mechanical .0000 .0243 .0000 .0652 .0396 .1713 23 N E C e n g i n e e r s .0000 .0290 .0000 .0544 .0335 .0524 32 Librarians .8278 .8580 .0000 .8745 .9020 .8914 44 S c i e n t i s t s , b i o 10 g i c a 1 .2778 .4035 .0000 .4609 .4525 .4563 45 Chemists .1008 .2090 .0000 .2577 .2866 .3452 75 Nurses, registered .9763 .9680 .0000 .9890 .9676 .9662 76 Therapists .5826 .7049 .0000 .7271 .7781 .7675 80 Clinical lab. techs. .7063 .7724 .0000 .7948 .7426 .7451 83 Radiologic techs. .6618 .6863 .0000 .7299 .5765 .5681 86 Clergy .0163 .0505 .0000 .0949 .0766 .0839 90 NEC, religious workers .5745 .4717 .0000 .5276 .4281 .4380 91 Economists .1176 .2484 .0000 .2927 .3620 .4014 93 Psychologists .3800 .4870 .0000 .5349 .4369 .4373 100 Social workers .5856 .6397 .0000 .6787 .5984 .5980 101 R e c r e a t i 0 n w 0 r k e r s .4457 .5798 .0000 .6288 .6511 .6487 140 Col. teach., not specified .2703 .3788 .0000 .8194 .4974 .4971 143 Teach., pre- and kindergart. .9681 .9833 .0000 1.0000 .9744 .9693 144 Teach., secondary sch. .4964 .5136 .0000 .5289 .4933 .4932 145 NEC, teach., exe. colt .7409 .7380 .0000 .7632 .7232 .7205 150 Technicians, ag. and biol., exe. health .2927 .4400 .0000 .4794 .5386 .5551 161 Surveyors .0141 .0114 .0000 .0563 .0188 .0743 162 NEC, techs., eng. and science .1437 .2469 .0000 .2970 .3083 .3218 163 Pilots, airplane .0000 .0125 .0000 .0769 .0152 .0278 174 Counselors, voca. and education .5000 .5323 .0000 .5648 .5559 .5558 180 Athletes and kindred .3077 .4351 .0000 .4610 .5434 .5495 183 Designers .1909 .2953 .0000 .3242 .4180 .4384 184 Editors and reporters .4110 .5050 .0000 .5465 .4908 .4901 185 Musicians and composers .3058 .2535 .0000 .3232 .1960 .2062 NOTES: Column (P1) contains the linear-group projection, column (P2) contains the linear-individual projection, and column (P3) contains the logistic-individual projection. NEC = not elsewhere classified. *Coefficient estimate on linear time trend in individual occupation projection, where estimate is significant.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s 109 TABLE B-1 (Continued) Actual Occ. Code Occupation Name 190 191 194 195 201 205 210 212 215 216 223 224 230 235 240 262 264 271 283 284 285 303 310 312 313 314 320 321 325 326 330 332 341 345 355 364 370 372 374 Clerks, shipping and receiving 376 Stenographers 382 Teacher aides 391 Typists 392 Weighers 394 Misc. clerical workers 402 Bakers 404 Boilermakers 405 Bookbinders 410 Brick- and stonemasons 413 Cabinetmakers 1972 1981 Annual Projected Rate 1990 of Change* (P1) .0000 .5424 .0000 .2868 .0000 .4272 .0000 .4107 .0000 .5311 .0000 .4735 .0000 .4042 .0000 .5442 .0000 .1545 .0000 .5572 .0000 .3034 .0000 .4121 .0000 .4356 .0000 .3506 .0000 .4010 .0000 .9663 .0000 .8295 .0000 .1915 .0000 .7504 .0000 .2502 .0000 .5146 .0000 .9286 .0000 .8960 .0000 .7469 .0000 .6740 .0000 .8051 .0000 .7842 .0000 .5754 .0000 .8619 .0000 .6189 .0000 .8600 .0000 .5136 .0000 .9329 .0000 .9494 .0000 .7156 .0000 .9961 .0000 1.0000 .0000 1.0000 .0000 .2734 .0000 .8790 .0000 .9634 .0000 .9891 .0000 .3996 .0000 .8716 .. .0000 .4427 .0000 .0840 .0000 .6064 .0000 .0842 .0000 .0904 (P2) (P3) .5559 .5558 .1829 .1776 .3115 .3171 .4774 .4896 .6337 .6356 .5175 .5198 .4803 .4986 .4941 .4937 .2487 .3676 .5134 .5134 .3114 .3140 .5071 .5347 .3893 .3893 .3923 .4001 .4255 .4313 .9726 .9720 .9441 .9106 .2186 .2461 .7298 .7302 .2032 .2040 .4648 .4654 .9654 .9362 .8715 .8715 .7731 .7631 .7942 .7702 .7804 .7831 .5988 .4914 .5879 .5875 .8068 .8027 .7291 .7181 .9261 .9012 .4977 .4978 .8085 .7311 .9866 .9701 .5438 .4820 .9792 .9770 .9851 .9733 .9921 .9922 .2667 .2769 .8164 .7413 .9680 .9536 .9745 .9728 .2969 .3035 .8911 .8778 4737 .4762 .0000 ~0010 .5671 .5783 .0000 .~Q2 .0279 .0258 Painters and sculptors Photographers NEC, writers, artists, entertainers Research, not specified Assessors, controllers, treasurers Buyer, whole. and retail Manager, credit and collect Administrators, health Inspectors, exe. construct., pub. admin. Manag. and superintendent, building admin. Officials, lodges, sac. and unions Mail super. and postmasters Manager, rest., bar, cafeteria Sch. admin., college Sch. admin., sec. and elem. Demonstrators Hucksters and peddlers Sales agent, stock and bond Sales clerks, retail trade Sales work, exe. clerks, retail Sales work, service and construe. Billing clerks Cashiers NEC, supervisors, clerical Collectors, bill and account Clerks, counter, exe. food Enumerators and interviewers NEC investigator and estimator File clerks Insur. adjusters, examiners, and investigators Library attend. and assist. Mail handlers, exe. post office Bookkeep. and billing operators Key punch operators NEC, office machine operators Receptionists Secretaries, legal NEC, secretaries in. . , .4264 .5121 .1558 .2424 .3333 .3853 .2791 .3936 .3448 .5000 .3292 .4346 .2535 .3788 .4746 .4954 .0619 .1019 .4265 .5063 .1875 .284s .3409 .3636 .3239 .404s .2530 .35O4 .26a4 .3668 .9375 .9619 .7304 .7964 .osso .1667 .6887 .7130 .1302 .1965 .2941 .4304 .8456 .8808 .8667 .8637 .s779 .7073 .4833 .6444 .7386 .7642 .820s .7544 .4339 .5444 .8493 .8371 .3519 .5753 .7518 .82sS .4375 .4767 .9130 .8936 .897s .9383 .6949 .6833 .9702 .9742 .9908 .9888 .9909 .9914 .1486 .2272 .9040 .8611 .8932 .9303 .9608 .9634 .2791 .3421 .7513 .8200 .289s .4148 .0000 .oooo .62s0 .5600 .ooss .oooo .oSoo .0267 (Continued)

OCR for page 91
110 ANDREA H. BELLER AND KEE-OK KIM HAN TABLE B-1 (Continued) Actual Occ. Code Occupation Name 652 Lathe and mill machine operatives .0488 653 NEC, precision machine operatives .1475 656 Punch and stamp press operatives .2739 662 Sawyers .0496 421 Cement and concrete finishers .0127 424 Crane, derrick, hoist operators .0467 425 Decorators and window dressers .5977 433 Install. and repair elect. power lines .0098 436 Excavat., grading, exe. bulldozer .0000 452 NEC, inspectors .0305 454 Job and die setters, metal .0106 461 Machinists .0054 471 Aircraft .0000 472 Auto body repairers .0000 473 Auto mechanics .0058 475 Repairers, data process. machine .0222 480 Farm implement .0000 484 Office machine .0000 485 Radio and television .0081 503 Molders, metal .0962 510 Painters, construct. and maintenance .0188 522 Plumbers and pipe fitters .0027 530 Printing press operator .0563 534 Roofers and slaters .0000 535 Sheetmetal workers and tinsmiths .0139 545 Stationary engineers .0105 551 Tailors .3226 561 Tool and die makers .0058 563 Upholsterers .1364 575 NEC, craft and kindred workers .0690 602 Assemblers .4671 604 Bottling and can operatives .3455 610 Checkers, examiners, inspect manuf. .4847 611 Clothing-ironers and pressers .7683 612 NEC, cutting operatives .2773 615 Drywall install. and lathers .0120 621 Filers, sanders, polishers, buffers .2213 622 Furnace tenders, smelters, pourers .0429 623 Garage work and gas stat attend. .0458 625 Produce graders and packers, exe. fact. and farm .7143 630 NEC, laundry and dry clean. opera. .6970 633 Meat cutters, butchers-manuf. .3258 634 Meat wrappers, retail trade .9000 640 NEC, mine operatives .0070 641 Mixing operatives .0202 642 Oilers and greasers, exe. auto .0435 643 Packers and wrappers, exe. meat and produce .6090 1972 1981 Annual Projected Rate 1990 of Change* (P1) (P2) (P3) .0594 .0000 .1754 .0000 .3208 .0000 .0909 .0000 .0000 .0000 .0070 .0000 .7222 .0000 .0086 .0000 .0062 .0000 .0839 .0000 .0532 .0000 .0388 .0000 .0325 .0000 .0098 .0000 .0058 .0000 .0700 .0000 .0000 .0000 .0400 .0000 .0367 .0000 .1731 .0000 .0575 .0000 .0044 .0000 .1091 .0000 .0000 .0000 .0397 .0000 .0165 .0000 .4091 .0000 .0237 .0000 .2222 .0000 .1746 .0000 .5231 .0000 .4231 .0000 .5356 .0000 .8067 .0000 .3137 .0000 .0127 .0000 .3186 .0000 .0323 .0000 .0559 .0000 .7333 .0000 .6614 .0000 .2917 .0000 .8980 .0000 .0192 .0000 .0390 .0000 .0526 .0000 .6339 .0000 .1331 .0568 .2521 .0835 .4124 3544 .1591 .1553 .1030 .0000 .0941 .0124 .7627 .8934 .0618 .0044 .0740 .0113 .1364 .1619 .1211 .0661 .0861 .0769 .0916 .0414 .0675 .0217 .0685 .0068 .1298 .0930 .0995 .0004 .1086 .0481 .1216 .0406 .2276 .1990 .1149 .1114 .0595 .0092 .1595 .1350 .0477 .0000 .1069 .0577 .0639 .0316 .5666 .5151 .0878 .0276 .2612 .1425 .2648 .4046 .5787 .5462 .4710 .4176 .5743 .5239 .8334 .7846 .3606 .3047 .1034 .0272 .3614 .2806 .1058 .0965 .1048 .0797 .7699 .8337 .7044 .6600 .3297 .2832 .9357 .8358 .0610 .0336 .0998 .0287 .1189 .0719 .6827 .6354 .0583 .0994 .3556 .6861 .0010 .0068 .8542 .0053 .0315 .2269 .0283 .2243 .0486 .1132 .0066 .2669 .0011 .0456 .0501 .2099 .2060 .0124 .1699 .0006 .0911 .1634 .5344 .0337 .1499 .6389 .5461 .4183 .5240 .7831 .3049 .0790 .2813 .7333 .1100 .8241 .6594 .2832 .7171 . 1333 .0330 .2183 .6352 NOTES: Column (P1) contains the linear-group projection, column (P2) contains the linear-individual projection, and column (P3) contains the logistic-individual projection. NEC = not elsewhere classified. *Coefficient estimate on linear time trend in individual occupation projection, where estimate is significant.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s 111 TABLE B-1 (Continued) ocC. Code Occupation Name l- 644 645 650 651 663 665 672 674 680 681 690 692 694 695 706 711 712 713 714 751 753 755 760 761 770 82 901 902 911 913 914 916 921 922 925 Nursing aides, orderlies, attend. 926 Practical nurses 942 Child care workers, exe. private 944 Hairdressers and cosmetologists 950 Housekeepers, exe. private house. 952 School monitors 954 Welfare service aides 960 Crossing guards and bridge attend. 961 Firemen, fire protection 964 Police and detectives 965 Sheriffs and bailiffs 980 Child care workers, priv. house. 984 Private house cleaners and servants Actual 1972 198 Annual Projected Rate 1990 of Change* (P1) .1667 .0000 .5177 .oooo .2500 .oooo .1119 .oooo .9603 .oooo .7292 .0000 .673s .oooo .5s20 .0000 .0465 .0000 .4546 .0000 .2913 .0000 .2781 .0000 .3526 .oooo .3452 .0000 .0579 .oooo .1000 .0000 .oooo .oooo .oooo .oooo .0932 .oooo .0215 .0000 .0966 .0000 .0463 .0000 .0227 .oooo .0101 .0000 .0595 .oooo .0645 .0000 .9657 .0000 .5534 .oooo .1982 .oooo .2892 .0000 .8370 .oooo .7342 .0000 .9784 .oooo .8454 .oooo .8665 .0000 .9772 .oooo .9544 .oooo .8936 .oooo .6992 .oooo .9722 .0000 .8837 .oooo .6222 .oooo .0095 .oooo .0557 .oooo .0725 .oooo .9774 .oooo .9519 .oooo (P2) (P3) .1469 .1458 .sso3 .5501 .2607 .2618 .2038 .2699 .9558 .9565 .6354 .6065 .6720 .6727 .5060 .5056 .0526 .0530 .5856 .5873 .2769 .2773 .3625 .3640 .3553 .3553 .4835 .4974 .0909 .2624 .1283 .5642 .0163 .0088 .0175 .0092 .0902 .0936 .0454 .0875 .1106 .1139 .0917 .1719 .0299 .0654 .0000 .0015 .0723 .0783 .1937 .2039 .9454 .9174 .5830 .5826 .2882 .3110 .3540 .3527 .8788 .8709 .7387 .7391 .9761 .9840 .9321 .9084 .8682 .8698 .9652 .9131 .9084 .8760 .8647 .8490 .6274 .6095 .0000 .9328 .9086 .9035 .5411 .5378 .0057 .0065 .0856 .0987 .1099 .3588 .9690 .9721 .9395 .9320 Painters, manufactured articles Photographic process workers Drill press operatives Grinding machine operatives Sewers and stitchers Solderers Spinners, twisters, and winders NEC, textile operatives Welders and flame-cutters NEC, winding operatives Machine operatives, misc. specified Machine operatives, not specified Misc. operatives Not specified operatives Fork lift and tow motor operatives Parking attendants RR brake operators and couplers RR switch operators ~ . . . . . , ~ .146 .4691 .2267 .0538 .9583 .7674 .6071 .5282 .0361 .4658 .2814 .2148 .3166 .3036 .ooss .0303 .oooo .oooo taxicab drivers and cnauneurs .0904 Construction laborers, exe. carpenters' helpers .0049 Freight and material handlers .0604 Gardeners and groundskeepers, exe. farm .0221 Longshore workers and stevedores .0000 Timber cutting and logging .0123 NEC, warehouse laborers .0267 Farm supervisors .0357 Cleaners, lodging quarters- exc. .9786 NEC, building interior cleaners .5509 Waiters' assistants .1367 Dishwashers .3578 Food counter and fountain workers .8208 NEC. food service workers, exe. private house. .7377 .9787 .7973 .8344 .9650 .9579 .9089 .7094 .8750 .8235 .6327 .0050 .0264 .0508 .9797 .9719 Dental assistants Health aides, exe. nursing .2314 .5679 .2936 .1499 .9908 .7299 .7174 .ss59 .1125 .5402 .3408 .3387 .3643 .4101 .1220 .1922 .0525 .0553 .1361 .0905 .1544 .1002 .0857 .0302 .0715 .1211 .0000 .5992 .2802 .3645 .8752 .7723 .0000 .8932 .9034 .0000 .9839 .9171 .7424 .9962 .9173 .6625 .0775 .1125 .1230 .9853 .9700

OCR for page 91
112 ANDREA H. BELLER AND KEE-OK KIM HAN TABLE B-2 Actual and Projected Segregation Indexes by Age Cohort, 1971 to 1990 1971 1977 Projected 1990 Group Age U ES Age U ES Age (P5) (P6) (P7) (P8) 1 16-24 68.64 67.14 16-24 64.68 61.86 16-24 61.96 37.08 46.60 37.08 2 16-25 68.31 67.33 16-21 65.49 66.04 - 3 26-35 68.94 68.90 22-31 62.67 61.81 25-34 66.18 66.18 47.82 39.61 4 36-45 70.85 70.77 32-41 66.30 67.48 35-44 61.91 61.91 55.02 49.63 5 46+ 68.62 69.16 42+ 66.86 67.61 45+ 68.19 68.19 58.17 48.90 Total 68.14 64.15 - 62.11 57.29 50.02 42.20 NOTES: The age groups are constructed so that each group can be followed as it ages over time. Thus, the difference between the 1971 and 1977 intervals is 6 years and between the 1977 and 1990 intervals, 13 years. U = unstandardized, and ES = standardized to the employment of the whole labor force in the given year. SOURCES: 1971 and 1977: Current Population Survey, Annual Demographic Files, 1972 and 1978, computer tapes; 1990: Department of Labor, BLS (1979, Table 4, p. 5~; Department of Labor, BLS (1981, Table 5, pp. 495- 502~. TABLE B-3 Actual and Projected Proportion Female for College Majors, 1969 to 1989 Actual Projected Percent Female Percent 1969 1978 Trenda Female, 1989 Field of Study (1) (2) (by (4) Social sciences 36.3 41.0 0.005 45.5 Psychology 42.9 58.8 0.019 79.4 Public affairs and services 71.6 49.4 -0.026 10.8 Library sciences 93.6 88.5 - 0.003 87.4 Architecture and environment design 4.3 23.7 0.021 47.0 Fine and applied arts 59.1 62.0 0.004 66.4 Foreign languages 73.1 75.9 0.003 79.7 Communications 42.2 46.9 0.007 51.0 Letters 63.4 57.1 - 0.008 46.9 Mathematics and statistics 37.4 41.1 0.005 47.5 Computer and information sciences 13.0 25.7 0.015 40.7 Engineering 0.7 7.4 0.006 11.5 Engineering technologies 0.5 2.8 0.003 6.4 Physical sciences 13.6 21.3 0.009 30.4 Biologic sciences 28.0 38.4 0.012 50.6 Agriculture and natural resources 3.8 24.6 0.025 50.1 Health care professions 76.9 80.5 0.004 84.0 Accounting 7.8 29.4 0.024 52.7 Business and management 9.1 26.4 0.020 45.4 Education 75.8 72.5 - 0.004 67.2 Other 53.1 58.4 0.007 66.1 All fields 43.7 47.1 52.3b a The trend value is the estimated coefficient on year in an OLS regression equation in which the percentage of females is regressed on time. All trend values are significant at the .05 level except communications, which is significant at .10 level. b Computed from the projected sex composition for each major field. SOURCE: NCES (1980, pp. 70-71~.

OCR for page 91
OCCUPATIONAL SEX SEGREGATION: PROSPECTS FOR THE 1980s 113 APPENDIX C PROJECTIONS FOR PROFESSIONAL OCCUPATIONS BASED UPON COLLEGE MAjORS The mode! used to project segregation among professional occupations based on segregation among college majors departs from the other models used in this paper. In this mode! the segregation index itself, rather than the sex composition of each oc- cupation, becomes the data point. We chose this model because attempts to relate the sex composition of a professional occupation to that of a specific college major proved unsuccessful. Thus, we hypothesized that the overall degree of segregation among col- lege graduates would affect the overall de- gree of segregation in professional occupa- tions. The data used for these projections for professional occupations are slightly di~er- ent from the data used earlier in this paper. To obtain a continuous time series, data were taken from the published BLS AA data (Em- ployment and Earnings, various issues). The female proportion of occupational employ- ment was first published in 1974. Initially each published category represented an in- dividual occupation or a combination of oc- cupations having a minimum employment estimate of 150,000; the sex distribution was included only where the basis of the esti- mate was at least 15,000. By this criterion the sex distribution was not published for many detailed categories. Consequently to obtain comparable data over time, more ag- gregate categories of professional occupa- tions were used in these analyses. Twenty- four separate categories were used for 1978 to 1981, 23 for 1975 to 1977, ant! 18 for 1974. We also used unpublished AA data for 1972 and used the same 24 categories as in 1978 to 1981. Thus, the segregation indexes used here differ slightly from the other ones re- ported in this paper, which include 59 dis- aggregated categories. For example, the segregation index for professional occupa- tions based on the published aggregated data is 49.61 in 1981, while that based on un- published detailed data is 50.55. As is com- mon, the aggregation tends to mask some segregation, but the effect here is small. It is even smaller in 1977, when the segre- gation index based on 23 aggregated cate- gories is 54.07 and based on 59 detailed oc- cupations is 54.35. The index baser! on unpublished aggregated data in 1972 is 58.94, while that based on detailed data is 59.44. Thus, the elect of this aggregation appears to be to somewhat overstate the decline in segregation in professional occupations dur- ing the 1970s. ACKNOWLEDGMENTS Discussions with Francine Blau, John Boyd, and Kenneth Stolarsky stimulated the development of ideas. Helpfi~l comments on an earlier draR were provided by Marianne Ferber, Barbara Reskin, and several re- viewers. Computational assistance by John Boyd and Alex Kwok is gratefully acknowI- edged. REFERENCES Becker, Gary S. 1971 The Economics of Discrimination. Chicago: University of Chicago Press. Beller, Andrea H. 1982a "The Impact of Equal Opportunity Policy on Sex Differentials in Earnings and Occupa- tions." American Economic Review, Papers and Proceedings (May):171-75. 1982b "Occupational Segregation by Sex: Determi- nants and Changes." Journal of Human Re- sources 17 (Summer):371-92. Blau, Francine D., and Wallace E. Hendricks 1979 "Occupational Segregation by Sex: Trends and Prospects." Journal of Human Resources 14 (Spring): 197-210. Carey, Max L. 1981 "Occupational Employment Growth Through 1990." Monthly Labor Review 104~8~:42-55. Department of Labor, Bureau of Labor Statistics 1979 Employment Projections for the 1980's. Bul- letin 2030. Washington, D.C.: U.S. Govern- ment Printing Office. June. 1981 The National Industry-Occupation Employ- ment Matrix, 1970, 1978 and Projected 1990. Bulletin 2086, Vol. 2. Washington, D.C.: U.S.

OCR for page 91
114 Government Printing Office. April. Employ- ment and Earnings. Various issues. Lloyd, Cynthia, and Beth Niemi 1979 The Economics of Sex Differentials. New York: Columbia University Press. Medoff, James L. 1979 "Layoffs and Alternatives Under Trade Unions in U. S. Manufacturing." American Economic Revieu; 69 June):380-95. National Center for Education Statistics 1980 Projections of Education Statistics to 1988-89. By Martin M. Frankel and Debra E. Gerald. April. 1981 Earned Degrees Conferred 1979-80. Septem- ber. Osterman, Paul 1982 "Affirmative Action and Opportunity: A Study ANDREA H. BELLER AND KEE-OK KIM HAN . . Of Female Quit Rates." Review of Economics and Statistics 64~4~:604-12. Poirier, Dale J. 1976 The Econometrics of Structural Change, With Special Emphasis on Spline Functions. Am- sterdam: North-Holland. Reskin, Barbara F., and Heidi I. Hartmann 1984 Women's Work, Men's Work: Sex Segregation on the Job. Washington, D.C.: National Acad- emy Press. Smith, Ralph E. 1977 "1he Impact of Macroeconomic Conditions on Employment Opportunities for Women." Pa- per No. 6, prepared for U.S. Congress, Joint Economic Committee, 94th Cong., 2d sess.