Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs

Transcription

1 Australian Perry & Wilson: Journal of The Labour Accord Economics, and Strikes Vol. 7, No. 3, September 2004, pp Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs Yew Liang Lee Business School, The University of Western Australia Paul W. Miller * Business School, The University of Western Australia Abstract There has not been any major change in gender occupational segregation in recent years in Australia. The analyses presented in this paper, using data from the 1996 Census of Population and Housing, suggest that this occupational segregation stems more from gender differences in entry-level occupations than from gender differences in occupational mobility. Whether this is good or bad is unclear, as imposing the male occupational distribution on females will disadvantage them in terms of earnings. 1. Introduction One of the striking features of the Australian labour market is the high degree of occupational segregation on the basis of gender. Female workers are over-represented in the Intermediate and Elementary Clerical and Service Workers occupations. They are under-represented in the Managers and Administrators, Tradespersons and Related Workers and Intermediate Production and Transport Workers occupational categories. As many as one-half of women would have to change their occupation in order for their occupational distribution to be congruent to that of males. This is often argued to reflect barriers faced by women in the labour market. There were various legislative reforms during the 1980s that might have impacted on the extent of occupational segregation on the basis of gender. For example, the Sex Discrimination Act of 1984 prohibited discrimination based on sex, marital status, pregnancy and family responsibilities. The Affirmative Action Act of 1986 established proactive strategies aimed at compensating for the effects of past discrimination. According to Kidd and Meng (1997), however, these reforms had only a limited impact on the occupational distributions of males and females over the course of the 1980s. This study uses Census data to examine the nature of gender occupational segregation in the Australian labour market. It complements past research through a focus on the related issues of the variation in occupational Address for correspondence: Paul W. Miller, Economics M251, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. paul.miller@uwa.edu.au * Professor Miller acknowledges financial assistance from the Australian Research Council. We wish to thank Alison Preston and an anonymous referee for comments. The Centre for Labour Market Research, 2004

2 356 Australian Journal of Labour Economics, September 2004 segregation on the basis of gender across age groups, and on differences in occupational distributions across age groups within each gender. A conundrum arises: while the degree of occupational segregation on the basis of gender does not appear to have diminished over the decade reviewed, the extent of occupational segregation on the basis of gender drops as teenagers become young adults. In other words, occupational mobility among young females can lead to their distribution across occupations becoming more like that of males, and this seems to be an equilibrium feature of the labour market that coexists with the persistent overall level of occupational segregation. A key to understanding how the degree of gender-based occupational segregation can be reduced may be to understand the patterns of occupational mobility between younger and older females, and between younger and older males. The structure of this paper is as follows. Section 2 examines the aggregate occupational distributions of males and females between 1986 and Section 3 provides an analysis of the occupational distributions of males and females that has a focus on age. There appears to be considerable occupational mobility as individuals age, and this leads to a greater similarity in the occupational distributions of men and women. This issue is pursued in section 4, using multinomial logit models of occupational choice. Concluding comments are offered in section Occupational Distributions: Examination of occupational distributions across years requires consistency in the occupational classification adopted. Consequently, when recent Census data are used, valid comparisons can be made only from 1986 onwards, where occupation has been coded according to the Australian Standard Classification of Occupations (ASCO). 1 The classification of occupations for the 1986 and 1991 Censuses was in terms of the first edition of ASCO. This is a hierarchically structured classification, based on the kind of work defined in terms of skill level and skill specialisation. A second version of ASCO was developed and applied to the classification of occupations in the 1996 Census. Whilst the concept of skill level remained unchanged in the second edition of ASCO, the operational criteria used to measure skill level were refined to reflect competency-based initiatives in employment and training and to increase the emphasis on entry requirements to an occupation. The distributions of workers across the eight ASCO (first edition) major groups for the 1986 Census are presented in the left-hand panel of table 1. These data relate to all workers. That is, they include those employed fulltime and those employed on a part-time basis. They also include both employees and the self-employed/employers. As discussed below, previous studies that have used alternative sample selection rules suggest that the main findings from this type of analysis are not likely to be sensitive to these aspects of the definition of the sample. 1 In the 1981 Census, occupation was coded according to the Classification and Classified List of Occupations (CCLO), which is quite different from the ASCO: see Appendix A.

3 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 357 The two largest proportions of employed persons are clerks (18 per cent) and tradespersons (16 per cent). The largest proportion of female workers are employed in clerical jobs (33 per cent), and the largest proportion of male workers as tradespersons (23 per cent). There are similar proportions of male and female workers in major groups such as Professionals (12 per cent), Para-professionals (6-7 per cent), and Labourers and Related Workers (13-16 per cent). The proportion of male managers and administrators is, however, almost twice that of their female counterparts (15 per cent versus 8 per cent). Most of the results reported for the 1986 Census carry over to the 1991 Census (presented in the right-hand panel of table 1). In the 1991 Census, Sales and Personal Service Workers makes up the second largest group of employed persons, which is marginally more than Tradespersons. Table 1 Percentage Distribution of Workers Across Occupations By Gender, 1986 and 1991 Censuses of Population and Housing Occupation Males Females Total Males Females Total Managers and Administrators Professionals Para-Professionals Tradespersons Clerks Sales and Personal Services Plant and Machine Operators Labourers and Related Workers Total (a) (a) Due to rounding, total may not add to 100. Source: 1986 and 1991 Censuses of Population and Housing. The occupational distribution of employed persons in the 1996 Census is reported in table 2. The categorisation of occupations into nine major groups using the second edition of ASCO shows that 18 per cent of workers are Professionals, followed closely by Intermediate Clerical and Service Workers (16 per cent). These two groups form the largest proportions of employed persons. The separation of workers into males and females shows that the largest proportions of male and female workers are Tradespersons and Related Workers (21 per cent) and Intermediate Clerical and Service Workers (26 per cent), respectively. Clearly, the results from the three Censuses show a pattern of male- and female-dominated occupations. An assessment of the extent of this segregation can be formed by computing the index of dissimilarity (Duncan and Duncan (1955)). This index is defined as, where p im is the percentage representation of male workers in the ith occupational group and p if is the percentage representation of female workers in the same occupational group. 2 The index can be interpreted as the percentage of male 2 This index has a number of weaknesses (see, Karmel and MacLachlan (1988)). As it is used here as a simple summary measure, these weaknesses should not have any major impact on the interpretation of the findings.

4 358 Australian Journal of Labour Economics, September 2004 (female) workers that would have to move across occupations to be distributed the same way as female (male) workers. In other words, the index of dissimilarity, which varies between 0 and 100, is a comparison between male and female workers that measures their relative separation (high dissimilarity) or integration (low dissimilarity) across all occupations. Table 2 Percentage Distribution of Workers Across Occupations By Gender, 1996 Census of Population and Housing Occupation Males Female Total Managers and Administrators Professionals Associate Professionals Tradespersons and Related Workers Advanced Clerical and Service Workers Intermediate Clerical and Service Workers Intermediate Production and Transport Workers Elementary Clerical and Service Workers Labourers and Related Workers Total (a) (a) Due to rounding, total may not add to 100. Source: 1996 Census of Population and Housing. Table 3 presents indices of dissimilarity between male and female workers across occupations for all Censuses. Each of the indices is around 38. The fact that there is little variation in the value of the index of dissimilarity computed for 1986, 1991 and 1996 suggests there has been little change in the degree of occupational segregation on the basis of gender in the Australian labour market during the late 1980s and early 1990s. Do the differences between male and female occupational distributions matter? One way of assessing this is through computation of inter- and intra-occupational components of the overall gender wage differential using the method proposed by Treiman and Hartmann (1981). They distinguish these components using the following equation: (1) where and are the mean aggregate earnings of males and females respectively, and are the mean earnings of males and females respectively in the i th occupation and P im and P if are as defined above. In the above decomposition, is the difference in mean earnings of males and females. The term is a measure of the extent to which the different occupational distributions of males and females contribute to the gender difference in mean earnings (i.e., the interoccupational component). The term is a measure of the extent to which differences in the mean earnings of males and females within

5 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 359 each occupation contribute to the gender difference in mean earnings (i.e., the intra-occupational component). This methodology is in the spirit of the Blinder (1973) decomposition, but it does not require models of either occupational choice or earnings determination to be estimated. The method is subject to the index number problem that pervades the Blinder decomposition, but remains an informative device for assessing the origins of the gender difference in mean earnings. This methodology was applied to the 1996 Census data. Two sets of calculations were performed. The first was based on the nine major occupational groups. It showed that the inter-occupational component was negative, though small in value. In other words, all the gender difference in earnings derives from women receiving a lower rate of pay within each occupation. The quite different occupational distributions of men and women actually lead to women having slightly higher earnings than would otherwise have been the case. In other words, the different occupational distributions of men and women do not matter if equality of earnings is the objective. As Jones (1983) has reported, female workers tend to be underrepresented in both the high status and low status occupations such that the net effect of the occupational segregation is minimal. A similar finding is derived when the exercise is repeated using the 44 minor occupational groups for which information is available in the 1996 Census unit record files. In this case, the inter-occupational component is positive, but again it is economically unimportant (see also, Kidd and Shannon (1996) and Miller (1987), where it is also noted that the unimportance of the inter-occupational component to the overall gender wage differential is not sensitive to the degree of occupational aggregation used). The research that has been previously reported in the Australian literature suggests that this result is not associated with differences between males and females in the part-time/ full-time mix: Kidd and Meng (1997), for example, report that occupational segregation increases the mean female wage among full-time workers. Moreover, study of the Canadian and British labour markets has generated similar findings. For example, Kidd and Shannon (1994) conclude that intraoccupational wage effects account for most of the gender wage differential in Canada, and Miller (1987) shows that almost 90 per cent of the gender wage differential in Britain could be attributed to wage differences within occupations. Thus, the conclusion that is to be drawn from this section is that occupational segregation on the basis of gender has persisted in the Australian labour market in the face of legislative reform aimed, in part, at addressing this phenomenon. This occupational segregation has little, if any, impact on the gender earnings differential. These facts are reasonably well known in the literature that has addressed gender issues in the Australian labour market (see, for example, Jones (1983), Kidd and Meng (1997)). But do these patterns vary across age groups? For example, younger females might have been able to make greater inroads into male-dominated occupations than older females, particularly as they will have entered the labour market after the Sex Discrimination Act and Affirmative Action Act.

6 360 Australian Journal of Labour Economics, September 2004 Table 3 Indices of Dissimilarity on the Basis of Gender, 1981, 1986, 1991, 1996 Censuses of Population and Housing Occupational Number of Occupational Census Classification Categories Dissimilarity Index 1986 ASCO ASCO ASCO Sources: 1986, 1991, 1996 Censuses of Population and Housing. 3. Occupational Distributions and Age The index of dissimilarity can be computed for various sub-groups of the population (see, Lewis (1985)). One disaggregation that is particularly useful for this analysis is by age. This set of analyses was computed using 1996 Census data. Five different age groups are used, namely years ; years ; years ; years ; and 55 years and above. 3 Indices of dissimilarity between male and female workers across major groups are computed for each of these age groups. The numbers employed in the various age groups are presented in Appendix B. This Appendix also contains a breakdown of employment in the various age groups into fulltime and part-time components. The results of this index of dissimilarity analysis, reported in table 4, show that the value of the index decreases with age. For example, for the year-old group, almost 52 per cent of male (female) workers would need to move to a different major group to be distributed the same way as female (male) workers. On the other hand, for workers aged 55 years and above, the proportion of male (female) workers who would need to move to another major group to have congruent occupational distributions of male and female workers is only around 32 per cent, or 20 percentage points lower than for the year-old group. Thus, the table 4 results indicate high dissimilarity between male and female workers across major groups for younger workers compared to older workers. Lewis (1985) also reports that the value of the index of occupational segregation decreases with age. Thus the fact that younger females would have entered the labour market under arguably more gender-neutral circumstances does not appear to be a dominant factor in determining the extent of occupational segregation. The table 4 results suggest that as female workers age they move into sections of the labour market which are characterised by increasingly lower degrees of occupational segregation. However, while this is happening the overall degree of occupational segregation on the basis of gender remains the same (see section 2). As argued in the Introduction, a key to understanding how the degree of gender-based occupational segregation can be reduced may be to understand the patterns of occupational mobility between younger and older females, and between younger and older males. Table 5 presents indices of dissimilarity on the basis of age computed using major groups for both males and females. Each cell in this table records the percentage of the age group in the top row (or left-hand column) that would 3 The choice in this regard is largely dictated by the categorizations available in the published Census data. Lewis (1985) uses the same age brackets, except for the last age group 55 years and above where he further categorises it into 55-59, 60-64, and 70+.

7 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 361 have to move across major group occupations to have the same occupational distribution as the age group listed in the left-hand column (or top row). Thus, the figure shows that per cent of year old males (or of year old males) would need to shift across occupations to have an occupational distribution congruent to that of year olds (15-19 year olds). These results show marked differences between and year olds and the older age groups, in that the indices for all comparisons for age groups other than year olds and year olds are 14 or below, whereas those involving year olds and year olds are between 14 and 43. This pattern may emerge simply as a result of there being well defined age-related career paths characterised by frequent occupational changes in the early years, and greater stability of employment in later years. Initial occupational advancement followed by little occupational change seems to be the stereotype career path in the Australian labour market. The table 5 results also suggest that the differences between young and old females do not appear to match the differences in the index of dissimilarity in table 4. Thus, the changes in occupational mixes within the male and female workforces that generate the pattern across age groups in the indices of dissimilarity in table 4 are seemingly quite complex. These provide the focus for the multinomial logit models presented in the next section. Table 4 Index of Dissimilarity on the Basis of Gender by Age Group, 1996 Census of Population and Housing Age Group Dissimilarity Index years years years years years and over Source: 1996 Census of Population and Housing. Table 5 Index of Dissimilarity on the Basis of Age, 1996 Census of Population and Housing (A) Males Age Group (B) Females Age Group Source: 1996 Census of Population and Housing Household Sample File.

8 362 Australian Journal of Labour Economics, September Multinomial Logit Model A multinomial logit model is an appropriate method 4 for the study of the distribution of workers across occupations as it allows an assessment of whether workers attributes affect membership of the various occupational categories and also facilitates decompositions analogous to those made popular by Blinder (1973). Consider the model where u i ~ N(0,1) and Y * is an unobservable variable which indexes the perceived goodness of an occupational group. Y can be interpreted as the underlying tendency to be in an occupational group, which is not observed. Instead, discrete data on actual occupational membership are observed. These are in the form of the variable Y i = 1, 2,, k, where k is the number of occupational groups. X is a n x 1 vector of explanatory (or exogenous) variables. With the multinomial logit specification, the conditional probability (P ih X i ) that individual i with observed characteristics (X i ) is in occupational group h may be expressed as (2) (3) Since there are k occupational groups and each individual must select one occupation, only k-1 sets of coefficients are uniquely defined. Normalising by setting the first set of coefficients to zero (i.e., β 1 = 0), the log odds ratio can be written as (4) where β h provides a measure of the impact of the explanatory variables X on the log odds of being in occupational group h as opposed to occupational group 1. From (3), k predicted probabilities for any individual with characteristics X i can be derived. These probabilities may then be used to obtain a predicted occupational distribution for the sample. This is done by taking the predicted probabilities of belonging to a specific occupational group and summing them over all observations, thus generating the expected number of sample members who would belong to the specified occupation if the model were an accurate description of reality. Hence, predictions of the proportion of sample individuals who would be in each occupation can be obtained. 4 Miller and Volker (1985) show that while it is possible to use an ordered probability model, it is difficult to establish a preference for either model. The multinomial logit model involves fewer parameter restrictions (Miller and Volker (1985)) and will be used in this analysis.

9 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 363 These analyses are conducted using unit record data from the 1996 Census of Population and Housing (Australian Bureau of Statistics (1998)). The estimations are based on all employed persons in the particular age and gender groups that are targeted. This broad sample is used owing to the apparent insensitivity of the results to choice of sample, as demonstrated by comparisons across past studies where the choice of sample has varied, and the sample selection problems that arise when partitioning the sample on the basis of endogenous variables, such as employment type (full-time versus part-time; wage and salary earner versus self-employed). The estimations are, however, limited to those that report valid data on all variables used in the analysis. Of note here is that only individuals who had left school are included in this section of the study. There are several aspects of this modelling strategy that require consideration. First, the elements of the vector X need to be specified. Previous studies have differed in this regard, depending on their focus. For example, Miller and Volker (1985) relate occupation to initial occupation, initial education level, subsequent education, labour market experience, and birthplace. They reason that initial occupation should be included in the model specification as it enables the determination of occupational mobility over time. Education obtained subsequent to the first job was also included in the model for this reason. Brown, Moon and Zoloth (1980) specify the occupational attainment of an individual as a function of employers willingness to hire that person and of the individual s desire to work in specific occupations. Willingness of employers to hire an individual is dependent on personal qualifications such as education, training, and experience. They suggest that the individual s desire for membership of a particular occupation can be captured by three arguments in a utility function, namely income, the pure consumption aspects of working (i.e., tastes for the work involved), and family size which may induce an individual to seek a career providing stable employment. Kidd and Meng (1997) specify their model of occupational attainment as a function of a set of variables (primarily education and labour market experience) capturing the interaction between market demand for an occupation and an individual s occupational supply decisions. While the specification of the estimating equation adopted in each case appears to be determined by the nature of the data set, the most important variables are education and labour market experience. These, along with marital status, presence of children, home ownership and birthplace form the basis of the set of explanatory variables used in the current study. As the models are estimated on data partitioned by age, however, explicit controls for age or experience are not included in the estimating equations. Second, the level of aggregation for the occupation variable to be used in the multinomial logit model needs to be determined. Most studies use a relatively small number of occupational categories. For example, both Miller and Volker (1985) and Brown et al. (1980) use eight categories. Kidd and Shannon (1996), in examining the effect of changing the number of occupational categories, conclude that it is expedient to use a limited number of occupational categories.

10 364 Australian Journal of Labour Economics, September 2004 Multinomial logit models of occupational outcomes were estimated for all male workers and for all female workers, and separately for 10-year age categories within each gender group. 5 The latter is the distinguishing feature of this study, builds on the analyses presented in section 2, and offers the point of departure for the discussion that follows. The estimates from this disaggregated analysis are used to decompose the differences in the occupational distributions across age groups using the methodology proposed by Farber (1990). This allows the difference in the representations in occupations for two age groups to be decomposed into a component that can be explained by the different characteristics of the groups, and a part that is unexplained. When samples of males and females are compared using this methodology, it is conventional to term the unexplained or differences in treatments effect a gender effect, as it arises simply because the second group of workers are females. Analogous to this terminology, in the comparison of the difference in occupational outcomes between the two groups analysed here, the component due to differences is the way the characteristics of the workers in the two age groups are linked to occupational outcomes can be termed an age impact. In this decomposition the gender or age effect can arise due to differences between gender/age groups in either the slope coefficients or in the constant terms. Where the difference is due to constant terms, it can be termed a pure or direct gender or age effect. 6 The basis of this decomposition is the average predicted probability, given as: (5) where F( ) is the logistic distribution function, X ji is a vector of the characteristics of the i th individual in the specific age sample j, is the vector of logit parameters estimated from the data, and n j is the number of individuals in the j th sample. Using this notation, the difference between the actual representations in any specific occupation of workers in the two age groups, 1 and 2, namely, can be decomposed as: 7 The first term in square brackets on the right-hand side of this decomposition is the explained or characteristics effect. It provides an answer to the question, By how much would the representations of the two groups in a specific occupation differ because of their different characteristics if their characteristics were linked to occupational outcomes in exactly the same way? Differences of this nature may arise when comparing age groups with the model outlined above because older age groups have had the 5 Results from the multinomial logit models are available from the authors. 6 This is illustrated below with reference to the results presented in table 9. 7 For applications of this methodology, see Miller (1994) on gender differences in the incidence of training, and Le and Miller (2004) on gender and cohort differences in high school completion rates. (6)

11 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 365 opportunity to acquire more education, and are likely to be distributed across marital states and home ownership types in ways that differ from the distributions for younger age groups. The second term in square brackets on the right-hand side is the unexplained effect. It provides an answer to the question, By how much do the representations of two groups in a particular occupation differ because of the different ways their characteristics are linked to occupational outcomes? In the absence of either discrimination on the basis of the characteristics used to partition the sample (here age) or occupational choices that vary across these characteristics, this component should be negligible. Where there are different links between particular characteristics and occupational outcomes for the various age groups then the estimates in the multinomial logit models will differ, and result in the size of this component increasing. If discrepancies between occupational distributions in the current application are a pure age effect, that is they do not arise because of different payoffs to education etc., then this final component should be due to differences in the constant terms in the multinomial logit models. There is one complication that needs to be addressed in the application of this methodology, which was developed for a binary dependent variable, to the polychotomous dependent variable in a multinomial logit model. And this is that the decomposition holds for each occupation, but as the components sum to zero across occupations, the aggregates of the decompositions have no meaning. Hence, the decomposition is used in two ways. First, equation (6) is computed for each of the nine occupations to inform on the relative importance of the explained and unexplained components in accounting for the different representations of the two groups in that particular occupation. This is in line with the Farber approach to a binary variable. Second, the occupational distributions computed in the first term in square brackets are compared using the Duncan index of dissimilarity to illustrate the overall role of different characteristics in accounting for occupational differences. Similarly, the occupational distributions computed in the second term in square brackets on the right-hand side of equation (6) are compared using the Duncan index of dissimilarity to illustrate the overall role of different treatments in accounting for occupational differences. 8 This is in line with studies such as Miller (1987), where there is an emphasis on the overall occupational distributions rather than on the causes of under- or over-representation within a particular occupation. Table 6 illustrates these computations with reference to and year old males. While models were estimated for each of the 10-year age groups, given the emphasis on the role of entry-level jobs in generating 8 The values for the Duncan index computed for the two comparisons in square brackets on the right-hand side of equation (6) need not sum to the value for the comparison on the left-hand side.

12 366 Australian Journal of Labour Economics, September 2004 occupational segregation on the basis of gender, the discussion can be limited without any loss of content to just the results for and year olds. Table 6 lists the actual occupational distributions for and year old male workers in the first two columns, and the differences between these distributions in the third column. It is seen that year old males are less likely than year old males to be employed as Tradespersons and Related Workers, Elementary Clerical and Service Workers, and Labourers and Related Workers, and are more likely to be in the first three occupations listed in the table. These changes provide information on the typical career paths for males, with the occupational distributions for year olds representing entry-level positions and the differences between and year olds representing occupational mobility (from lowskilled to high-skilled jobs). Comparison of the occupational distributions using the Duncan index shows that 18 per cent of year (or year) old males would need to shift across occupations for the occupational distributions of the two groups to be congruent. The final two columns of figures in table 6 show the parts of the differences in occupational outcomes of the two groups due to differences in characteristics and treatments, respectively. About 40 per cent 9 of the difference in occupational distributions is due to differences in characteristics: the remainder is due to differences in the way these characteristics are linked to occupational outcomes. As noted above, this latter effect can be interpreted as an age or experience effect. The analyses for and year old females are presented in table 7. The data for the actual occupational distributions listed in this table reveal that typical career paths for female workers commence in Elementary and Intermediate Clerical and Service Workers occupations and lead to employment in the first three occupations listed in the table. Thus, the career paths for female workers have different starting points compared to males (namely, relatively fewer entry level positions as Tradespersons and Related Workers or as Labourers and Related Workers and more entrylevel positions as Intermediate Clerical and Service Workers ), but similar end points, in general. Older females are more likely than younger females to be found in higher skilled occupations, in part (30 per cent 10 ) because they have superior levels of characteristics that are associated with these occupations, and in larger part because of age or experience effects per se. Hence, age is associated with greater representation of both males and females in the higher skilled occupations. For both groups of workers a substantial part of this change in occupational outcomes arises due to changes in the circumstances of the workers (e.g., changes in education level, marital status, home ownership). However, the major component (63 per cent and 68 per cent for males and females, respectively) of the occupational change is revealed in tables 6 and 7 to be an age (or age-related treatments) effect. What are the implications of these changes for the occupational segregation on the basis of gender among younger and older workers documented in table 4? 9 7.4/( ) x /( ) x 100.

13 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 367 Table 6 Actual Distributions Across Occupations, Duncan Indices of Dissimilarity and Equal Treatments and Equal Characteristics Occupational Distributions of Year Old Males and Year Old Males, 1996 Census of Population and Housing Occupation Actual Distributions Difference in Equal Treatments Difference in Equal Characteristics Difference Distributions (Explained Component) Distributions (Unexplained Component) Males, Males, in Actual Distributions Managers and Administrators Professionals Associate Professionals Tradespersons and Related Workers Advanced Clerical and Service Workers Intermediate Clerical and Service Workers Intermediate Production and Transport Workers Elementary Clerical and Service Workers Labourers and Related Workers Total (a) Index of Dissimilarity (a)due to rounding, total may not sum to 100 for the first two columns, and to 0 for the last three columns. Table 7 Actual Distributions Across Occupations, Duncan Indices of Dissimilarity and Equal Treatments and Equal Characteristics Occupational Distributions of Year Old Females and Year Old Females, 1996 Census of Population and Housing Occupation Actual Distributions Difference in Equal Treatments Difference in Equal Characteristics Difference Distributions (Explained Component) Distributions (Unexplained Component) Females, Females, in Actual Distributions Managers and Administrators Professionals Associate Professionals Tradespersons and Related Workers Advanced Clerical and Service Workers Intermediate Clerical and Service Workers Intermediate Production and Transport Workers Elementary Clerical and Service Workers Labourers and Related Workers Total (a) Index of Dissimilarity (a)due to rounding, total may not sum to 100 for the first two columns, and to 0 for the last three columns.

14 368 Australian Journal of Labour Economics, September 2004 Table 8 illustrates these computations with reference to year old males and females. It lists information on the actual distributions of male and female workers across the nine major group occupations in the first two columns. These occupational distributions will largely reflect entry-level occupations. As with the previous two tables, the differences between the occupational distributions are presented in the third column, along with the Duncan index of dissimilarity, of The large value of this index results mainly from the considerable differences in the representations of males and females in the Tradespersons and Related Workers, Intermediate Production and Transport Workers and Labourers and Related Workers occupations (males are over-represented) and in the Professionals and all Clerical and Service Worker occupations (where females are over-represented). The fourth and fifth columns of table 8 list information on the differences in the equal treatments and equal characteristics occupational distributions of year old males and females. Examination of this information reveals that, with the possible exception of the second occupational group, Professionals, the considerable differences between the occupational distributions of year old males and females would be removed under the equal treatments scenario. Indeed, at the aggregate level, as revealed by the Duncan index of dissimilarity, only 4 per cent of males and females would have to shift across occupations for their occupational distributions to be the same. In other words, when year old males and females are being compared, the different occupational distributions arise because of differences in the way the characteristics of males and females are linked to occupational outcomes. This is illustrated in the final column of the table. As noted above, the component that is attributed to the differences between males and females in the estimated coefficients in the model of occupational outcomes could be due to differences in the slope coefficients (e.g., weaker links between educational attainment and occupational prestige for females than for males) or due to differences in the constant terms (a pure gender effect). This issue is canvassed further in the discussion of the results for year olds. A similar set of findings emerges when the occupational distributions of year old males and females are analysed in this way see table 9. In this instance the Duncan index of dissimilarity is 40, and the lower value compared to that for year olds is due mainly to smaller discrepancies between the older males and females in the Intermediate and Elementary Clerical and Service Workers and Labourer and Related Workers occupations. In other words, the lower degree of occupational segregation on the basis of gender among year olds compared to year olds can be traced back to occupational mobility that reduces the gender differences in the relatively low-skilled entry-level jobs more than it increases any gender differences in the higher-skilled jobs. The information provided in the final two columns of table 9 shows that the differences in the representation of males and females in the various occupations is due (again, with the possible exception of the Professionals occupation) to differences in the way that the characteristics of male and female workers

15 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 369 Table 8 Actual Distributions Across Occupations, Duncan Indices of Dissimilarity and Equal Treatments and Equal Characteristics Occupational Distributions of Year Old Males and Females, 1996 Census of Population and Housing Occupation Actual Distributions Difference in Equal Treatments Difference in Equal Characteristics Difference Distributions (Explained Component) Distributions (Unexplained Component) Males, Females, in Actual Distributions Managers and Administrators Professionals Associate Professionals Tradespersons and Related Workers Advanced Clerical and Service Workers Intermediate Clerical and Service Workers Intermediate Production and Transport Workers Elementary Clerical and Service Workers Labourers and Related Workers Total (a) Index of Dissimilarity (a)due to rounding, total may not sum to 100 for the first two columns, and to 0 for the last three columns. Table 9 Actual Distributions Across Occupations, Duncan Indices of Dissimilarity and Equal Treatments and Equal Characteristics Occupational Distributions of Year Old Males and Females, 1996 Census of Population and Housing Occupation Actual Distributions Difference in Equal Treatments Difference in Equal Characteristics Difference Distributions (Explained Component) Distributions (Unexplained Component) Males, Females, in Actual Distributions Managers and Administrators Professionals Associate Professionals Tradespersons and Related Workers Advanced Clerical and Service Workers Intermediate Clerical and Service Workers Intermediate Production and Transport Workers Elementary Clerical and Service Workers Labourers and Related Workers Total (a) Index of Dissimilarity (a)due to rounding, total may not sum to 100 for the first two columns, and to 0 for the last three columns.

16 370 Australian Journal of Labour Economics, September 2004 are linked to occupations. If the characteristics of males and females were linked to occupations in the same way, there would only be a minor difference in their occupational distributions. The Duncan index of dissimilarity for the Equal Treatments occupational distribution is only 5.3. There are essentially three broad sets of slope coefficients in the multinomial logit models estimated for each age group, for educational attainment, birthplace and home ownership/children. 11 The calculations in table 9 were repeated using only subsets of in the computation of. In the first experiment, the set of coefficients used for birthplace and home ownership/children, but for educational attainment. 12 The Duncan index of dissimilarity under the equal treatments scenario suggests that about 11 per cent of males and females would have to shift across occupations for their occupational distributions to be the same. This compares with the 5 per cent figure computed for the Difference in equal treatments distributions in table 9, and the 40 per cent figure for the actual occupational distributions of year old males and females. This information suggests, therefore, that only a very minor part of the differences in the occupational distributions of year old males and females is due to the differences in occupational advances associated with educational attainment. In the second experiment, the set of coefficients used for educational attainment and home ownership/children, but for birthplace. The Duncan index of dissimilarity under the equal treatments scenario for this set of calculations is only 6 per cent, which is the same order of magnitude as reported in table 9. This suggests that the different links between occupational outcomes and birthplace for males and females is not a major contributor to the occupational segregation on the basis of gender that characterises the Australian labour market. In the third experiment, the set of coefficients used for educational attainment and birthplace, but for home ownership/children. The Duncan index of dissimilarity for the equal treatments scenario for this experiment is only 6 per cent. Again, as this figure differs by only a small amount from that reported in table 9, the implication is that gender differences in the way that home ownership/children are linked to occupational outcomes have little influence on the extent of occupational segregation on the basis of gender. The results from these three experiments therefore show that while the links between occupational outcomes and each of the three broad sets of characteristics, educational attainment, birthplace and home ownership/ children, differ for year old males and females, the quite considerable difference in occupational distributions of males and females in this age group does not have its origin in any of these differences. Instead, it is the different constant terms in the models estimated separately for males and 11 As mentioned above, as the sample is disaggregated by age groups, neither age nor experience variables are entered into the estimating equation. 12 The results are available from the authors.

17 Lee & Miller: Occupational Segregation on the Basis of Gender: the Role of Entry-level Jobs 371 females that can be linked to the disparities in their occupational outcomes. This is confirmed by the computation of hypothetical occupational distributions based on the set of coefficients being for all slope coefficients and for the constant term. That is, the occupational segregation on the basis of gender among year olds that table 9 reveals as due to different treatments rather than differences in characteristics is due predominately to a pure gender effect rather than being reflected in different links between characteristics such as educational attainment and birthplace and occupational outcomes Summary and Conclusion Almost twenty years after the introduction of the Sex Discrimination Act of 1984 and the Affirmative Action (Equal Employment Opportunity for Women) Act of 1986, there remains significant occupational segregation on the basis of gender in the Australian labour market. Female workers are over-represented in the Clerical and Service Workers occupations, and are under-represented in the Managers and Administrators, Tradespersons and Related Workers and Intermediate Production and Transport Workers occupations. For occupational distributions to be congruent, about onehalf of women would have to change their occupation. Comparisons of the occupational distributions of males and females across age groups indicate greater dissimilarity for younger workers compared to older workers. The detailed analyses of this pattern suggest that the occupational segregation on the basis of gender that is such a striking characteristic of the Australian labour market is linked more to different entry-level jobs for males and females than to different rates of occupational advancement. Individuals are often argued to accumulate information on the labour market by experiencing a range of jobs. General labour market skills, and also job specific skills, may also be developed in this way. Accumulating information and skills will arguably be more important among younger workers than among older workers. But there seems little, if any, reason why young females should have to acquire skills and information on the labour market through employment in jobs that differ so much from those where young men accumulate information and skills. Indeed, in a situation where gender oriented barriers are supposed to be dismantled, young females should place a premium on securing employment in the same types of jobs as young males, and hence developing the same information and skill sets. Occupational advancement reduces the gender differences in the relatively low-skilled entry-level jobs more than it increases any gender differences in the high-skilled jobs. Nevertheless, imposing the male patterns of either occupational attainment or advancement on females will not necessarily advantage females in terms of earnings. From this perspective, it might be questioned whether there is any gain to pursuing equality of occupational 13 Calculations using the Treiman and Hartmann (1981) methodology outlined in equation (1) shows that imposing the Equal Treatments occupational distribution on females would not necessarily advantage them in terms of earnings.