Sex differences in occupational skill: Canada, 1961–1986

Sex differences in occupational skill: Canada, 1961-1986

MONICA BOYD Carleton University and Statistics Canada *

Cet etude, basee sur les donnees du recensement canadien, examine la segregation occupationelle et les differences dues au genre dans la distribution dhabilete professionnelle entre les annees 1961 et 1986. Lapprentissage a long terme a de niveaux superieurs dhabilete professionnelle caracterise autant le marche du travail ferninin que masculin. Cependant, la magnitude de cet apprentissage est plus grande pour les femmes que pour les hommes, notamment entre les annees 1981 et 1986 lorsque la distribution des habiletes professionnelles des hommes a changes tres peu. En depit de la direction de ces changements, il est peu probable que les femmes se retrouvent dans les occupations a niveaux superieurs dhabilete compare aux hommes. La main doeuvre feminine se retrouve donc aux niveaux moyens dhabilete professionnelle compare a la main doeuvre masculine, et ceci est en grande partie attribue a la concentration feminine dans les occupations de bureau inferieures. Quand la comparaison entre les femmes et les hommes se limite a linterieur des niveaux de travail comme le travail de bureau, le travail ouvrier et le travail dans les secteurs de service, la tendance de retrouver les femmes dans les niveaux inferieurs dhabilete professionnelle devient plus prononcee.

Using Canadian census data, this study examines occupational segregation and sex differences in the distribution of skill between 1961-1986. Skill upgrading over time characterizes both the female and male labour force. However, the magnitude of skill upgrading is larger for women than men, particularly between 1981 and 1986 when the skill distributions of men change very little. Despite

* This is a revision of a paper presented at the 1988 annual meeting of the Population Asso- ciation of America. The author conducted the research for this paper as a Visiting Fellow (1988-1989) a t the Social and Economic Studies Division, Statistics Canada. The analysis presented in this paper is the responsibility of the author, and it does not necessarily represent the views of Statistics Canada. John Myles was a collaborator in the initial stage of the project, and his input is gratefully acknowledged. The author also thanks Susan Lerow who analysed census data sets, Alfred Hunter and Michael Manley who provided skill scores, and Jennifer Quaile, Carleton University, who meticulously typed tables and text revisions. Two CRSA reviewers provided helpful comments on an earlier draft. This manuscript was received in May, 1989 and accepted in March, 1990.

Canad. Rev. SOC. & Anth. I Rev. canad. SOC. & Anth. 27(3) 1990

286 MONJCA BOYD

these upgrading trends, women are less likely to be in higher skilled occupations than are men. Instead, the female labour force more than the male labour force is found in the middle levels of skill, largely as the result of their concentration in lower white collar occupations. When comparisons are made within white collar, blue collar and service sectors, the employment of women in less skilled occupations becomes far more evident.

INTRODUCTION

During the 20th century, employment shifted away from farming occupations and extractive industries and into white collar occupations and service industries. Increased female labour force participation and expansion of female-typed jobs were part of this changing structure of Canadian employment. Labour force participation rates of women steadily increased throughout the century from one in six women in 1911 to one in two by 1981. As in the United States, rising female labour force participation reflected increased demand for female labour generated by the shift of employment to service industries (Connelly, 1978; Oppenheimer, 1970; Smith,1978). However, occupational sex stereotyping ensured that most women found employment in clerical, sales and service occupations and in selected health and education occupations, such as nursing and elementary school teaching.

Sex differences in occupations are linked to other labour market differences. Many studies note the association between sex segregation or sex typ- ing of occupations and the location of women in jobs characterized by low pay and/or low skill (Armstrong and Armstrong, 1983, 1984; England, Chassie and McCormack, 1982; England and McLaughlin, 1979; Fox and Fox, 1986; Gannage, 1986; Glass, Tienda, and Smith, 1988; McLaughlin, 1978). In recent years, interest in the work skills of women and men has increased for two additional reasons. First, sex specific analyses of skill trends refine contemporary debates over temporal trends in skill. Currently, two perspectives dominate this debate. The first envisions a deskilled work force, resulting from the sectoral shift of employment into services and the expansion of the new mass occupations of unskilled clerical, sales and service workers (Braverman, 1974). The second sees an increasingly skilled work force shaped by the technical knowledge base of a post-industrial society (Bell, 1973). Studies which address the debate by analysing trends for total labour force population risk aggregating substantial and/or divergent skill shifts by sex.

A second reason for the interest in skill differences between men and women arises from the debate over the effects of the microchip on the work performed by women. Despite considerable interest and numerous studies, firm conclusions do not yet exist regarding skill changes in those sectors where women predominate (Hartmann, Kraut and Tilly, 1986; Hartmann, 1987). The uncertainty arises partly because investigations into the effects of the microchip encounter the same difficulty as do studies on skill in the

287 SEX DIFFERENCES IN OCCUPATIONAL. SKILL

post-industrial society. Skill is not always easily measured, and shifts in the skill profile of any population can be the result of changes in the skill content of jobs and/or the result of alterations in the structure or composition of employment. Skill degrading occurs when activities performed on the job are restructured in a way that decreases the skill required on the job (content changes) and/or when employment shifts away from higher skilled jobs to lower skilled jobs (composition changes in employment). Conversely, skill upgrading can occur either because jobs are restructured to enhance the level of skill required and/or because employment shifts into higher skilled jobs when less skilled jobs are lost through automation.

Until recently, data limitations prevented any assessment of the distribution of skill for men and women in the entire Canadian labour force andlor in temporal changes in skill levels. In this paper, I compare skill distributions for women and men using the dimensions of skill developed by Hunter and Manley (1986) and appended to data from the 1961-1986 Canadian censuses of population. My purpose is threefold: 1/ to assess if women are employed in occupations with lower skill levels than are men, using the full range of census occupations and the entire labour force population; 21 to assess the changing gender gap in skill over time; 31 to evaluate if these temporal trends indicate skill upgrading or skill degrading for the female and male labour force.

I find that women are less likely to be in the very high skill or very low skilled occupations compared to men. However, the concentration of women in the middle levels of the skill distributions reflects their concentration in lower level white collar occupations. Substantial female-male skill differences exist within white collar, blue collar and service occupations. Over time, these gender gaps in skill narrow. The narrowing reflects differences between the female and male labour force in skill changes over time. Skill upgrading occurs over time for both sexes. But, because the magnitude of upgrading is not as great for men and virtually ceases between 1981-1986, the size of the gender gap diminishes over time.

CONCEPTUALIZING AND MEASURING SKILL

Many dimensions of gender inequality in the labour force (unemployment rates, wages, occupational and industrial differences) are well-documented. However, putting precise parameters on sex differences in skill is a less easy task. Studies vary in definitions and measures of skill, in data type (case study, or census or survey data), time span, population (total population, women only, or men only), number and types of jobs and occupations scrutinized. As a result, divergent and sometimes contradictory findings exist regarding sex differences in skill and temporal change in skill (Form, 1987; Hartmann, Kraut and Tilly, 1986; Hartmann, 1987; Spenner, 1982; 1983; 1988).

In popular usage, the meaning of the term skill is often elusive and im- precise. Spenner (1982; 1983) and Form (1987) review a number of pitfalls associated with the term, concluding that unidimensional, undefined con-

288 MONICA BOYD

cepts, nonmeasures and indirect measures of skill have not served us well (Spenner, 1983: 825). Strategies of measurement are central to this pro- nouncement. The non-measurement strategy applied to skill equates occupational groups with implicit skill levels, while the indirect measurement strategy takes schooling levels or wage rates of an occupational group as an indirect indication of the skill level of occupations or jobs. Given reliability and validity problems of the non-measurement and indirect measurement approaches to skill, Spenner (1983: 828) and others (Form, 1987) emphasize the need for direct measures of the components of skill for jobs or for workers in jobs.

Direct measures can be obtained through surveys in which respondents report on their job characteristics (Kalleberg and Leicht, 1986; Kohn and Schooler, 1983). They are also obtained from evaluations of the content of jobs or occupations, usually conducted when establishing an occupational classification system (Miller et al., 1980). Many United States investigations use worker trait data found in the Dictionary of Occupational Titles [DOT] (Cain and Treiman, 1981; Dubnoff, 1978; McLaughlin, 1978; Miller et al., 1980; Spaeth, 1979; Spenner, 1979; 1980). The Canadian Classification and Dictionary of Occupations 1971 [CCDO] (Canada. Manpower and Immigra- tion, 1974) is similar in design and intent to the DOT. It provides a total of 52 traits for several thousand different occupational titles. These measures refer to requirements of occupational positions rather than to characteristics of the incumbents.

By providing rich information about the characteristics of occupations, worker trait data provide an unusual opportunity to depict and study the skill dimensions of occupations (Miller et al., 1980; Spenner, 1980). Nonethe- less, worker trait measures of occupational skill do have limitations (Cain and Treiman, 1981; Spenner, 1982; 1988). One limitation is that trait data address only one component of change in skill. Temporal shifts in the skill distribution of employment can occur in two ways (Spenner, 1983). First, the actual content of work can change. Such alterations are central to many discussions about the proletarianization of the work force (Braverman, 1974) and the impact of the microchip in the workplace (Hartmann, 1987; Menzies, 1981). Second, change in the skill distribution of employment occurs because the distribution of people to jobs and occupations changes. Such composition induced shifts occur when employment shifts away from low skill occupations to higher skill occupations in tandem with industrial restructuring and/or technological change (Baran, 1987). Worker trait data are useful in tracing only those skill changes which result from shifts in the occupation distribution of workers (composition changes). They do not measure the changing skill requirements of individual occupations (content changes). Assessments of the changing skill content of occupations require new and independent evaluations of work performed (Spenner, 1983; 1988).

In addition, worker trait data are not necessarily unbiased measures of work requirements. As discussed in Boyd (19831, such measures are socially constructed and reflect values and perceptions about the nature of work (Braverman, 1974; Spenner, 1982). This social component of evaluations of

289 SEX DIFFERENCES IN OCCUPATIONAL SKILL

occupational characteristics has profound implications for attempts to measure the upgrading or downgrading of skill, since the measures them- selves may include changing evaluations (Spenner, 1982). In addition to the difficulties which this presents for measuring the changing content of work through technological change, the social construction of skill may bias evaluations of skill in female and male dominated occupations. Several researchers suggest that sexual stereotypes influenced the complexity ratings of jobs appearing in the third edition of the American DOT. Ratings on the extent to which a occupation required working with data, people or things were lower than warranted for certain female typed occupations, such as typist, nursery school teacher and practical nurse (Cain and Treiman, 1981; England, Chassie and McCormack, 1982; Witt and Naherny, 1975). This problem was reportedly removed in the fourth edition of the American DOT (Cain and Treiman, 1981). No published research exists on the possibility of sex biases in the scoring of occupations in the existing edition of the Canadian Classification and Dictionary of Occupations 1971, which was undertaken with the third edition of the United States DOT as a reference point.

Finally, although worker trait data represent evaluations of performance requirements of occupations, not all data represent skill (Form, 1987; Spenner, 1982; 1983; 1988). Some definitions of skill are very broad, including elements of the production task such as education, experience, responsibility, mental effort, dexterity, and extent of specialization and routinization (Form, 1987: 30; Kalleberg and Leicht, 1986: 274). Other definitions are more narrow, emphasizing the effective application of knowledge or job complexity rather than attributes such as dexterity which refer to the task requirements of jobs (Form, 1987: 30; Hunter, 1988: 757). Researchers increasingly emphasize a narrower definition which recognizes the multi- ple dimensions of skill (Form, 1987; Hunter, 1988; Kalleberg and Leicht, 1986; Spenner, 1982; 1983; 1988). For example, analysis of the United States DOT system generates two major components of skill: substantive complexity, or the level, scope, and integration of mental, interpersonal and manipulative tasks in a job and autonomy, or the amount of discretion which a person has within a job role to initiate and conclude action, to control the content, manner and speed with which a task is done (Spenner, 1983: 829).

Canadian research includes both broad and more restricted conceptualizations of skill. In the broad approach, two commonly used worker trait measures of skill are Specific Vocational Preparation (SVP) and General Ed- ucational Development (GED). The SVP scale ranks the requirements of an occupation in terms of training time requirements, ranging from a short demonstration to ten years or more. The GED ranks occupations on a six point scale on the basis of general reasoning, mathematical and language requirements (Canada. Department of Manpower and Immigration, 1974: vol. 1; Myles, 1988). Levels 4 and higher on the GED scale correspond to what would be considered skilled work (Myles, 1988).

Besides these two measures, 41 worker traits refer to the task require-

1

290 MONICA BOYD

ments of occupations. Hunter and Manleys (1986) factor analysis of the 43 traits, including SVP and GED, produced eight factors. Three of these factors are easily interpreted and generally correspond to the literature on skill, work and occupations (Hunter, 1988: 756-7; Hunter and Manley, 1986: 55- 57): cognitive complexity (the extent to which jobs require verbal, quanti- tative and related mental abilities or capacities); responsibility (the degree to which work involves the supervision, guidance or management of people and involves the direction, control and planning of tasks); and routine activity (the degree to which an occupation involves working under specific instruction and involves a small number of tasks performed repeatedly). In subsequent research, Hunter (1988) changes the label of the routine dimension of skill to task diversity.

Cognitive complexity and routine activityhask diversity share similari- ties with the skill dimension of substantive complexity, which is central in American conceptualizations of skill. Hunter (1988: 757) observes that the literature does not always distinguish between cognitive complexity and task complexity. He views the Canadian cognitive complexity scale as measuring the depth or intensity of skill and the routine/task diversity scale as tapping the breadth or extensivity of skill. Both are considered dimensions of skill in that they refer to performance on the job.

Hunter (1988: 757) notes that the conceptualization of autonomy or responsibility is an integral part of the literature on occupations and skill upgrading or skill degrading. However, given his definition of skill which excludes task requirements of occupations, Hunter (1988) does not consider the responsibility scale as an aspect of skill per se. A narrow definition of skill would also exclude GED and SVP which indicate educational requirements and training time requirements of occupations. However, GED, SVP, and the dimensions captured by the responsibility scale are found in many discussions of gender inequality. They are included in my analysis because they address important issues of whether, and to what extent, women and men hold occupations differing in educational and on the job training requirements and in supervision, management and guidance of self or others on the job. Throughout this paper, I refer to these three attributes, 0, sVP, and responsibility, as also representing skill dimensions of occupations. The more rigorous definition of skill (Hunter, 1988) would emphasize only cognitive complexity and routine activity.

DATA AND METHODS

Because they are based on the Canadian Classification and Dictionary of Occupations 1971 (Canada. Manpower and Employment, 1974) worker trait data and the scales developed by Hunter and Manley (1986) can be appended to any database which has occupations coded according to the 1971 census occupational classification (Hunter, 1988; Myles, 1988). In this study, the five measures of interest are added to 1961,1971,1981 and 1986 census data, all coded according to the 1971 census classification of occupations. By using a common metric over time, this approach permits assessing temporal


changes in the distribution of skill for the female and male labour force (composition changes). It does not address an equally important question which asks if the skill content of specific occupations changes over time.

The 1961 census data are a sample of the experienced labour force, drawn from the entire census. The data set consists of approximately 134,000 cases coded to the 1971 occupational classification as part of a special project conducted at Statistics Canada in the 1970s. The 1971-1986 data are from the entire census database. Skill distributions are analysed for two overlapping periods: 1961-1981 and 1971-1986. This break results from different labour force definitions used in different census data sets. The 1961 data are available only for the experienced labour force. The 1986 data are available only for the employed labour force population.2 The experienced labour force includes participants in the labour force at the time of the census as well as people who were in the labour force during the year prior to the census3 (Statistics Canada, 1982: 28, 31).

The study examines changing skill dimensions for the non-farm labour force. Changing definitions of unpaid family labour in the 1961-1986 censuses caused the deletion of farm occupations from the analysis. The 1961 and 1971 censuses exclude from the labour force domain those persons who are unpaid family workers or worked as farm labourers and did less than 20 hours of unpaid family work a week. The 1981 and 1986 censuses include these persons in the labour force population (Statistics Canada, 1972: 5; 1982: 12).

The sheer number of occupational titles in the census database prevents any meaningful ins ection of skill measures distributed over all 476 titles used in this study. SVP and GED scores have values ranging across fewer than ten integer categories and collapsed distributions can be easily produced (Myles, 1988: appendix A). However, most Hunter-Manley factor scores are unique to most occupational titles, generating almost as many values as occupational titles. Collapsed skill distributions for the cognitive complexity, routine activity and responsibility were calculated by ordering the 476 occupational titles from low to high on these factor scores. These occupational titles were then divided into five groups representing quintiles (Myles, 1988). By using occupational titles, unweighted by numbers of workers in each occupation, the resulting metric is uniform across time and permits the capture of shifts in the skill distributions of employment.

Skill dimensions are compared using indices of net difference and percentage distributions. The index of net difference has two uses in this analysis. It measures whether employment shifts over time toward higher or lower skilled occupations. It also shows whether women relative to men are likely to be in lower (or higher) skilled occupations at one point in time. In technical language, the index of net difference shows the difference and the direction between two populations in the probabilities of location along an ordinal distribution. It takes on values between -1.0 and + 1.0 (Lieberson, 1975). Calculated as the difference between P ( X > Y) and P f Y > X ) , the index measures the difference in the probability that a randomly selected individual from population X will have a higher score on a given skill scale

B

292 MONICA B O M

than a randomly selected individual from population Y, compared to the al- ternative probability that an individual from population Y will have a higher skill score than an individual from population X (Lieberson, 1975; Lieber- son and Waters, 1988: 26-27,110). In this study, X and Y represent women and men or time points.

OCCUPATIONAL STRUCTURE AND SKILL

The methodology employed in this study implies that skill distributions are sensitive to the distribution of employment across occupations. Canadas occupational structure is highly sex segregated (Armstrong, 1984; Armstrong and Armstrong, 1984; Boyd, 1982a; 1983; Connelly, 1978; Fox and Fox, 1987; Moore, 1985). Table I documents persistent sex segregation of occupations between 1961 and 1986 using the two digit grouping of the 1971 census classification of non-farm occupations. Although the two digit grouping loses information by aggregating the four digit census titles for 476 occupations, it permits a quick summary of Canadas sex segregated occupational structure.

Over time employment shifts away from service and blue collar occupations toward white collar work. Between 1961 and 1986, the share of female and male employment increases in management and administration, natu- ral sciences and mathematics, and social sciences occupations. The share of employment in clerical occupations also increases for women between 1961 and 1981, but declines slightly by 1986. Notwithstanding these temporal shifts, the occupational distributions of the female and male labour force differ. Throughout the 25 year period, between three and four out of ten women are employed in clerical occupations. In contrast, men are more dis- persed throughout the occupational structure. The following occupational groups - managerial and administrative, sales, service, product fabrication, and construction - each employ approximately one in ten men. Although employment increases in white collar occupations, men are still more likely than women to hold blue collar occupations in 1986. At the two digit level of aggregation, the degree of sex segregation of oc-

cupations remains virtually constant between 1961 and 1981 with a slight decrease for the 1986 employed labour force (Table I, last row). The index of dissimilarity indicates the percentage of one population which would have to shift occupational categories in order to have an occupational distribution identical to that of the second population. Thus, in 1986 over 44 per cent of the employed female labour force would have to move out of the existing occupational categories to have a occupational profile like that of the male population (or vice versa), down slightly from 49 per cent in 1971.

The index of dissimilarity is sensitive to the number of categories used in its calculation, generally increasing with more categories. With more detailed occupational data, Canadian and American studies show temporal declines in levels of segregation (Beller, 1985; Blau and Hendricks, 1979; England, 1981; Fox and Fox, 1987; but see Bielby and Baron, 1984). Such declines are also apparent from the 1961-1986 data used in this study. When

TAB

LE I

PERC

ENTA

GE

DIS

TRIB

UTI

ON

FOR M

AJO

R N

ON

-FA

RM O

CCU

PATI

ON

AL

CA

TEC

OR

IES(

P)

BY S

EX, C

ANAD

A, 19

61-1

986

Exp

erie

nced

Lab

our

Forc

e E

mpl

oyed

Labou

r Fo

rce

2 di

zit C

CDO

and

19

61

1971

19

81

1971

19

81

1986

de

scri

ptio

n F

emal

e M

ale

Fem

ale

Mal

e F

emal

e M

ale

Fem

ale

Mal

e F

emal

e M

ale

Fem

ale

Mal

e (1

) (2

) (3

) (4

) (5)

(6)

(7)

(a)

(9)

(10)

(1

1)

(12)

Tota

l 10

0.0

11 M

anag

eria

l and

A

dmin

istra

tive

1.4

21 N

atur

al S

cien

ce,

Engi

neer

ing,

Mat

h 0.

3 23

Soc

ial S

cien

ces

0.7

25 R

elig

ion

0.2

27 T

each

ing

7.1

31 M

edic

ine

and

Hea

lth

7.3

33 Art

s an

d R

ecre

atio

n 0.

7 41

Cle

rical

32

.4

51 S

ales

11

.9

61 S

ervi

ce

22.0

73

Fis

hing

, H

untin

g (C

)

75 F

ores

try

and

Logg

ing

(C)

77 M

inin

g (C

) 81

Pro

cess

ing,

M

etal

, C

hem

ical

0.

6

Wood, T

extil

e 3.

2 82

Pro

cess

ing,

Foo

d,

100.

0

4.4

3.5

0.6

0.4

1.3

1.5

1.1

8.3

12.2

10

.6

0.8

2.2

1.5

2.1

4.7

100.

0

2.3

0.7

1.2

0.1

8.3

9.6

0.9

37.1

9.

8 17

.7

(C) 0.

1 (C

) 0.3

2.0

100.

0

6.5

4.5

1.0

0.4

2.9

1.7

1.2

8.9

11.7

10

.8

0.6

1.4

1.2

1.9

3.8

100.

0

4.4 1.2

2.2

0.2

6.4

8.8

1.4

37.3

10

.2

16.4

(C) 0.1

(C) 0.

3

2.0

100.

0

9.4

5.3

1.4

0.4

3.0

1.0

1.5

7.5

10.4

10

.5

0.6

1.2

1.1

2.0

3.6

100.

0

2.4

0.7

1.2

0.1

8.7

9.9

0.8

37 3

3.'1

17

.3

0.0

0.1

0.0

0.3

1.9

100.

0

6.8

4.6

1.0

0.4

3.0

1.8

1.2

9.0

12.0

10

.7

0.5

1.2

1.2

1.9

3.7

100.

0

4.7

1.3

2.1

0.2

6.6

9.2

1.4

37.8

10

.2

15.6

0.0

0.1

0.0

0.3

1.7

100.

0

9.9

5.5

1.4

0.4

3.2

1.9

1.5

7.5

10.7

10

.2

0.5

0.9

1.1

2.1

3.5

100.

0

6.6 1.6

2.7

0.1

6.6

9.7

1.6

34.7

10

.5

16.1

0.1

0.1

0.3

0.3

1.6

100.

0

10.9

5.8

1.6

0.4

3.2

2.0

1.7

7.3

11.2

11

.1

0.5

0.9

0.8

1.6

3.3

~~

~~

TABL

E 1 c

ontd

. PE

RC

ENTA

GE

DIS

TRIB

UTI

ON

FOR

MA

JOR

NO

N-F

ARM

OC

CU

PATI

ON

AL

CA

TEG

OR

IES(

a) B

Y SE

X, C

ANAD

A, 1961-1986

Exp

erie

nced

Lab

our

Forc

e E

mpl

oyed

Lab

our

Forc

e 2 digit

ccw

and

19

61

1971

19

81

1971

19

81

1986

de

scri

ptio

n F

emal

e M

ale

Fem

ale

Mal

e F

emal

e M

ale

Fem

ale

Mal

e F

emal

e M

ale

Fem

ale

Mal

e (1

) (21

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1

1)

(12)

83 M

achi

ning

0.4

4.5

0.5

4.7

0.5

4.4

0.5

4.6

0.4

4.4

0.4

3.8

85 P

rodu

ct

fabr

icat

ion

8.2

9.9

5.9

10.0

5.0

10.8

5.7

10.1

4.8

10.9

4.2

10.8

87 C

onst

ruct

ion

0.2

13.9

0.2

11.6

0.3

11.6

0.2

11.2

0.3

11.1

0.3

10.0

91 T

rans

port

atio

n,

Equi

pmen

t 0.1

7.6

0.3

6.8

0.6

6.6

0.3

6.8

0.7

6.5

0.7

6.3

93 M

ater

ial

hand

ling 1.0

3.5

1.6

3.4

1.2

2.9

1.5

3.4

1.1

2.8

0.9

2.5

95 O

ther

Cra

fts

0.6

2.5

0.5

2.0

0.7

1.7

0.5

2.0

0.6

1.8

0.6

1.7

99 O

ccup

atio

ns,

N.E

.C.

0.7

2.7

0.9

3.0

0.7

2.3

0.8

2.9

0.6

2.1

0.8

2.6

Inde

x of

Dis

sim

ilarit

y (b

) 47.2

48.5

46.8

48.9

47.2

44.5

(a) O

ccup

atio

nal c

ateg

orie

s ar

e th

e tw

o di

git C

CDO

gro

upin

gs u

sing

the 1971 C

ensu

s cl

assi

ficat

ion of o

ccup

atio

ns fo

r all

year

s (b

) Ind

ices

are

cal

cula

ted

on d

istr

ibut

ions

car

ried

to

the

seco

nd d

ecim

al p

lace

(c

) Les

s th

an 0.051%


calculated over 476 non-farm occupations, indices of occupational dissimilarity for women and men decline from 66.5 to 61.3 and 57.5 in 1971, 1981 and 1986, respectively. However, despite these declines, the level of sex segregation remains high.

In the final section of this paper, I assess the implications of temporal trends in occupational distributions for sex differences in skill. However, I first consider a more immediate question: are men and women employed at different skill levels? As of the mid-l980s, Table I and the indices of dissimilarity still show substantial differences in the occupational location of men and women. Since different occupations have different skill profiles, the per- sistence of occupational segregation leads us to expect divergent skill profiles for the female and male labour force.

A MOMENT IN TIME: SKILL DIFFERENCES IN 1986

Do sex differences exist in the skill distribution of the labour force? And, as suggested by literature on women and wage work, is the female labour force found in the lower skill levels? The answers to these questions are found in Table 11 which provides the skill distributions and indices of net difference for men and women in the 1986 employed population.

Overall, the data in Table 11 provide mixed evidence for the argument that women are in low skill jobs. Indices of net difference indicate that women have lower probabilities than men of being employed at higher levels of SVP, GED, cognitive complexity and at lower levels of routine activity (or conversely, high levels of task d i ~ e r s i t y ) . ~ Sexual inequalities are largest for the skill measures of SVP and routine activities. Distributions also confirm that the female labour force is less likely to hold employment at the highest skill levels. However, with the exception of vocational training requirements (SVP) and routine activity, the female labour force also has lower percentages employed in the lowest rungs of the skill ladder. Generally, the 1986 female labour force is neither in the highest skill levels of employment nor in the lowest.

Given popular pronouncements that more women than men are at the bottom on the skill ladder, what are we to make of these findings? One possibility is that they are produced by the use of 1971 skill measures. If certain occupations commonly held by women became deskilled in content over the 15 year period while those held by men were either stable or undergo- ing skill upgrading, these changes would not be captured in an approach which uses the same classification of skill over time. There is in fact considerable discussion over the impact of technological change on the skills found in clerical and retail sales occupations, where women dominate. However, the amount and direction of change is far from clear (Hartmann, Kraut and Tilly, 1986; Labour Canada, 1982). While deskilling and polari- zation of skill can occur (Glenn and Feldberg, 1977; Menzies, 1981) the ef- fect of the micro-chip on changing the content of skills depends very much on the occupations and the industries involved (Baran, 1987; Ginzberg, Noy- elle and Stanback, 1986: chaps. 3 and 4; Noyelle, 1987).

TABL

E 11

PE

RC

ENTA

GE

DIS

TRIB

UTI

ON

S AN

D IN

DIC

ES O

F N

ET

DIF

FER

ENC

E FO

R SE

LEC

TED

SK

ILL

MEA

SUR

ES O

F O

CC

UPA

TIO

NS B

Y O

CC

UPA

TIO

NA

L. G

RO

UPS

AN

D S

EX

, EM

PLO

YED

LA

BOU

R F

OR

CE,

CA

NA

DA

1986

Tota

l Occ

upat

wns

W

hite

Col

lar

Blu

e C

olla

r Se

rvic

e F

emal

e M

ale

Fem

ale

Mal

e F

emal

e M

ale

Fem

ale

Mal

e (1

) (2)

(3)

(4)

15)

(6)

(7)

(8)

Perc

ent D

istr

ibut

wns

(a)

Spec

ific

Voc

atio

nal

Tra

inin

g 4 ye

ars

Gen

eral

Edu

catio

nal

Dev

elop

men

t 1

(low

) 2 3 4 5+ (

high

)

Cog

nitiv

e C

ompl

exity

1 (

low

) 2 3 4 5

(hig

h)

100.0

17.6

12.3

23.3

15.0

10.6

16.8

4.3

100.0

3.6

20.6

43.9

23.3

8.6

100.0

15.1

5.8

37.7

19.5

21.8

100.0

14.2

18.0

12.7

13.2

12.2

18.4

11.4

100.0

7.4

25.4

30.1

20.6

16.5

100.0

15.4

16.4

26.1

16.1

26.1

100.0

8.9

8.0

27.7

15.6

13.0

21.0

5.8

100.0

(b)

10.3

49.3

28.7

11.7

100.0

0.2

4.0

43.0

23.3

29.5

100.0

9.9

6.6

10.4

3.8

15.9

27.6

25.8

100.0

0.2

14.3

15.3

32.8

37.3

100.0

1.3

3.5

22.6

13.8

58.8

100.0

29.8

38.7

13.4

11.4

2.4

4.4

100.0

11.0

62.4

21.2

5.0

0.1

100.0

52.7

25.2

15.9

6.1

0.1

100.0

13.5

28.2

17.4

22.1

7.1

11.7

100.0

10.0

34.8

44.4

10.5

0.3

100.0

24.1

30.4

29.4

15.7

0.4

100.0

49.9

16.3

8.9

14.8

4.6

5.6

100.0

15.2

42.3

32.9

9.5

0.0

100.0

60.7

2.5

26.7

1d.l

0.0

100.0

34.5

22.0

2.9

14.1

17.8

8.7

100.0

25.0

31.3

30.9

12.9

0.0

100.0

36.1

10.9

26.5

26.5

0.0

~~~

~

TABL

E I1

con

td.

PER

CEN

TAG

E D

ISTR

IBU

TIO

NS

AN

D IN

DIC

ES O

F N

ET

DIF

FER

ENC

E FO

R S

ELEC

TED

SKIL

L M

EASU

RES

OF

OC

CU

PATI

ON

S BY

OC

CU

PATI

ON

AL

GR

OU

PS A

ND

SE

X,

EMPL

OY

ED LAB

OUR

FOR

CE,

CA

NA

DA

198

6

Tota

l O

ccup

atio

ns

Whi

te C

olla

r B

lue

Col

lar

Serv

ice

Fem

ale

Mul

e F

emal

e M

ale

Fem

ale

Mal

e F

emal

e M

ale

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Rou

tine

Act

ivity

10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 10

0.0

100.

0 1

(hig

h)

21.4

19

.3

11.1

12

.5

48.0

21

.7

52.3

37

.0

2 17

.8

17.5

17

.8

7.3

28.4

29

.3

11.3

10

.4

3 28

.1

20.8

30

.0

8.0

17.1

32

.0

26.2

26

.2

4 20

.2

18.6

24

.7

26.8

2.

8 8.

5 10

.1

26.5

5

(low

) 12

.5

23.8

16

.4

45.4

3.

7 8.

5 0.

0 0.

0

Res

pons

ibili

ty

1 (l

ow)

2 3 4 5 (h

igh)

Indi

ces

of N

et D

iffer

ence

SV

T G

ED

Cog

nitiv

e C

ompl

exity

R

outin

e A

ctiv

ity

Res

pons

ibili

ty

100.

0 10

0.0

6.6

12.6

16

.3

21.4

39

.3

25.4

18

.7

16.0

19

.1

24.5

-.09

8 -.0

04

-.025

-.

lo3

+ ,03

9

100.

0 10

0.0

3.6

1.5

14.5

4.

7 36

.8

26.0

22

.0

26.9

23

.1

40.9

-.305

-.3

10

-284

-.3

29

-.278

100.

0 10

0.0

40.2

26

.7

34.6

39

.8

20.1

21

.3

0.2

0.8

4.9

11.4

-.330

-.2

63

-.341

-.3

40

-.164

100.

0 10

0.0

0.0

0.0

13.3

13

.8

62.5

39

.2

14.6

34

.1

9.5

12.9

-.198

-.0

32

-.263

-.2

19

-.1a1

(a

) May

not

sum

to 1

00.0

bec

ause

of

roun

ding

(b

) Les

s th

an ,0

51 p

er c

ent

298 MONICA BOYD

In fact, another measurement issue is the more likely reason for the different distribution of skills among the female and male labour force. Re- flecting historical distinctions of headhand work, white collar occupations are considered to have greater social standing, more prestige and higher skill than blue collar occupations. Yet women more than men are employed in white collar occupations, and they are less prevalent in blue collar occupations. This can be seen in Table I. By 1986,45 per cent of the female labour force was employed in clerical or sales occupations versus under 25 per cent of the male labour force. Less than 10 per cent of the female labour force was employed in blue collar occupations (2-digit CCDO categories of 81-99), compared to over 40 per cent of the male labour force (Table I).

Higher skill scores attached to white collar occupations and the concentration of women in lower level white collar work create the situation in which the female labour force is found in the middle skill levels. This location and itsunderlying causes are similar to the concentration of women in the middle of the Blishen status scales (Boyd, 1982b; 1985: 250,286-7; 1986). Analytically it also produces much the same result as is observed in occupational status scores. Differences between men and women are less than might be expected given other well-known differences in income, in occupational segregation, and in power, authority and supervisory positions (Boyd, 1985; 1986; Boyd, Mulvihill and Myles, 1990).

Given the different location of women and men across the Canadian occupational structure, assessing skill differences within major occupational groupings makes more sense. Table I1 (columns 3 through 8) presents sex- specific skill distributions for Canadian white collar, blue collar and service occupations. Two digit CcDOs define these occupational sectors. Codes range from 11 to 51 for white collar occupations, and from 73 to 99 for blue collar occupations (Table I). Two reasons exist for leaving service occupations (CCDO grouping 61) as a separate category. First, the service category con- tains a mix of occupations ranging from the protective service occupations of police officers and fire fighters to waiters and chambermaids. Second, given this mix, it is not intuitively clear where the service category belongs in an occupational classification scheme which relies on a crude headhand organizing principle.

Earlier conclusions about sex distributions in skill are altered in two ways by the division of the employed labour force into three major occupational sectors. First, the magnitude of inequality, measured by indices of net differences, increases substantially across all sectors. For example, when the female and male labour force are compared, the index of net difference in cognitive complexity is -.025. This index reveals that the probability of a woman being employed at a higher skill level than a man is only 2.5 percentage points less than the reverse probability of a man being employed at a higher level of cognitive complexity compared to a women. The sex difference in relative probabilities rises to -.284, -.341, and -.263 when comparisons are made within white collar, blue collar and service occupational sectors. Overall, the gender gap in specific vocational training, cognitive complexity, and in routine activity is larger in the blue collar occupational


sector than in the white collar work world.6 Second, sector specific skill distributions accentuate the patterns of sex

differences in skill (Table 11). In white collar occupations, a substantially lower percentage of the female labour force is found at the highest levels of skill compared to the male labour force. A higher percentage of women than men is employed at the middle levels of the skill distributions for white collar work. In service occupations, the gap between the percentages of men and women who are employed at the higher levels also is large, and a larger percentage of the female labour force is employed in jobs with the lowest level of cognitive complexity and the highest level of routine activity. Dis- tributional differences are the most severe in the blue collar sector, where lower percentages of the female labour force are found in the middle to upper levels of skill and a substantially higher percentage are employed in jobs with the lowest level of SVP, GED, cognitive complexity, responsibility and task diversity (or high routine).

Empirically, the disaggregation of skill distributions indicates substantial sex differences in skill. These differences are consistent with existing characterizations of a disadvantaged female labour force. The tendency for women to hold occupations with no or short job training requirements (SVP) is extremely pronounced in the blue collar and service sectors although it also exists in white collar work. These sex differences in time requirements for job training no doubt are linked to the development of job related training programs within the high school system for female clerical and sales occupations and outside the school system for male blue collar jobs (Gaskell, 1982a, 198213). They also may reflect the exclusion of women from appren- ticeship programs or from work requiring investments in lengthy job training.

Compared to men, women are less likely to hold occupations which have high GED requirements. This is especially true in white collar work where one in ten women but four in ten men in 1986 are employed in occupations with GED scores of 5 + . White collar women also are conspicuously absent from occupations which require high cognitive complexity and responsibility and which are characterized by low levels of routine activity. Such absences are consistent with the male numerical dominance in managerial occupations and in many professional occupations such as school administration, university teaching, physicians and law.

In blue collar employment, women more than men remain in occupations with the lowest levels of vocational training requirements, general educational development, cognitive complexity, responsibility and the highest degree of routine tasks. Similarly, in service occupations women more than men are employed in low skill work. In 1986, for example, over half of the employed women in service occupations were in occupations which had the highest routine tasks, the lowest general educational requirements, and the lowest requirement for verbal and cognitive skills compared to 25-37 per cent of men in service occupations (Table 11).

Overall, the disaggregation exercise clarifies the earlier findings based on the total labour force population. Because of the concentration of women

300 MONICA BOYD

in clerical work and in white collar occupations more generally (Table I), the female labour force more than the male labour force is employed in jobs in the middle of the skill hierarchies (Table 11). This location dampens the magnitude of sex differences in skill for the total labour force population. However, when comparisons are restricted to employment within the white collar, blue collar and service work worlds, women are far more likely than men to be employed in less skilled occupations.

LOOKING BACK TEMPORAL CHANGE IN SKILL, 1961-1986

Data on the 1986 skill distributions of the female and male labour force are the most recent available. They also represent one point on a longer time frame. As Table I shows, employment shifts into white collar occupations during the 25 year period, 1961-1986. And, using 476 occupational categories, the magnitude of sex segregation in occupations declines somewhat. At the same time, women remain heavily concentrated in select white collar occupations, particularly in clerical occupations.

Contained in these temporal trends are several implications for sex differences in occupational skill. On the one hand, the movement of women into previously male dominated occupations could bring with it higher skill levels for the female labour force and/or convergence in the female-male skill distributions. On the other hand, continued or increased concentration of women in clerical work may stabilize skill levels for women compared to men, particularly within white collar work.

Given these possibilities, where should we situate the observed sex differences in skill for 1986? Are they part of a longer time series in which sex differences in skill have remained stable or even increased? Or do the 1986 data represent the end point in a trend of diminishing sex differences in the distribution of skill? Answering these questions links the analysis to the broader skill upgradinghkill degrading debate simply because temporal trends in skill differences between women and men are influenced by sex specific patterns of skill upgrading or skill degrading. For example, sex differences in skill could widen over time because skill upgrading occurred for the male labour force but not for the female labour force. Or, sex differences could narrow over time because the female labour force experienced more substantial skill upgrading from one decade to the next than did the male labour force.

In his analysis of census data between 1961 and 1981, Myles (1988) found that skill upgrading occurred rather than skill degrading. This upward movement in skill is consistent with the shift of employment from blue collar occupations to white collar work (Table I). With no change in the skill content of any occupational stratum, an expanding white collar sector will produce skill upgrading in the labour force (Form, 1987). However, Myles did not focus on sex differences in his study of changing skills. My analysis indicates that skill increases for the female labour force contribute to the general patterns of skill upgrading found in his study. Skill upgrading for women accounts for virtually all of the skill upgrading trend since 1981.


Indices of net differences show that skill upgrading occurs between 1961 and 1986 for the female and male labour force (Table III, panels 1 and 2). However, only between 1971 and 1981 is the magnitude of skill upgrading approximately the same for both sexes. With the exception of the responsibility dimension of skill, the magnitude of skill upgrading is greater for women than for men between 1961-1971 and 1981-1986. In fact, skill upgrading for the male labour force virtually ceases between 1981 and 1986. Conversely, given that 1981-1986 represents five years, it can be argued that the magnitude of upgrading for women accelerates during 1981-1986, representing changes comparable to those observed between 1961 and 1971.

These skill trends are consistent with research findings from the Social Change in Canada project, 1977-1981. Hunter (1988: Tables 5 and 6) focuses on the skill of entry level occupations over time. Strictly speaking, his results are not comparable to mine for two reasons. First, the focus is on entry jobs whereas this study examines skill for the current labour force. Sec- ond, Hunter makes statistical adjustments for the effects of temporal increases in education, which in turn are associated with skill upgrading in entry occupations over time. However, correlation matrices and standard deviations supplied by Hunter (1988: Tables 1 and 2) permit calculation of the gross relationship between skill of entry job and time. Two findings emerge: 1/ cognitive complexity, routine activity (task diversity) and responsibility dimensions of skill increase over time for entry level jobs (un- adjusted for rising educational levels of entry cohorts); 2/ the magnitude of the increase is larger for women than for men. The only caveat to these conclusions is that the level of responsibility is stable for first entry women.7

Inspection of the underlying and unpublished skill distributions shows that the patterns of skill upgrading over the 1961-1986 period in my study were produced primarily by the movement of the female labour force out of low skill occupations and into the middle of the skill distributions and by shifts of the male labour force away from low skill levels and into higher skill levels. As a result, between 1961 and 1986, the magnitude of sex differences decreases for the SVP, GED, and routine activity dimensions of skill (Table 111, panel 3). For the skill dimensions of cognitive complexity and responsibility, the gender ap narrows between 1961 and 1981, but increases between 1981 and 1986.

However, aggregate trends can conceal contradictory or offsetting trends within occupational and industry groupings (Attewell, 1987; Form, 1987; Hartmann, Kraut and Tilly, 1986: 138; Spenner, 1982; 1988). Spenner (1982) observes an offsetting pattern for men and women in his re-analysis of Dubnoffs (1978) research on shifts in skill between 1900 and 1970 in the United States. For the skill dimension of overall work complexity, Spenner finds skill u v a d i n g for women but skill downgrading for men in manual occupations. Such trends are offset by downgrading for women but skill upgrading for men in non-manual occupations. Dubnoff (1978) attributes the deskilling of women in non-manual occupations to the growth of employment in lower skilled clerical occupations over this 70 year period.

These empirical findings prompt a closer look at sex differences in skill

8

TABL

E I1

1 IN

DIC

ES O

F N

ET D

IFFE

REN

CE

FOR

SELE

CTE

D S

KIL

L M

EASU

RES

BY

SEX

AN

D O

VER

TIM

E, C

AN

AD

A,

1961

-198

6

Spec

ific

Gen

eral

V

ocat

iona

l E

duca

tiona

l C

ogni

tive

Rou

tine

Prep

arat

ion

Dev

elop

men

t C

ompl

exity

A

ctiv

ity

Res

pons

ibili

ty

(1)

(2)

(3)

(4)

(5)

Fem

ale

(t2)

vers

us F

emal

e (t

l)

Exp

erie

nced

Lab

our

Forc

e 19

61-1

971

1971

-198

1 E

mpl

oyed

Lab

our

Forc

e 19

71-1

981

1981

-198

6

Mal

e (8) ve

rsus

Mal

e (t'

) E

xper

ienc

ed L

abou

r Fo

rce

1961

-197

1 19

71-1

981

Em

ploy

ed L

abou

r Fo

rce

1971

-198

1 19

81-1

986

Fem

ale

Ver

sus M

ale

Exp

erie

nced

Lab

our

Forc

e 19

61

1971

19

81

1971

19

81

1986

Em

ploy

ed L

abou

r Fo

rce

.lo9

.048

.052

.0

50

.060

.0

47

.053

,0

06

-.179

-.1

34

-.133

-.138

-.1

40

-.098

,103

,0

42

,046

,0

42

,062

.0

35

,042

.0

08

-.02

5 -.

008

-.010

-.016

-.0

21

- ,004

.088

,037

,042

,0

48

.064

.0

34

.041

,0

16

-.017

-.006

-.006

-.001

-.002

-.025

.111

.0

50

,054

.0

49

.061

.0

35

.040

,0

03

-. 188

-.1

46

-.136

-.152

-.1

44

-.lo

3

,060

.020

,020

,0

51

.054

.0

17

.018

,0

21

,043

,0

33

,030

,027

.0

21

,039


distributions for Canadian white collar, blue collar and service occupations. This inspection produces two main conclusions, centring on sex specific trends in skill and changes in the magnitude of the gender gap in skill. First, modest skill upgrading occurs for the male and female labour force between 1961 and 1981 in white collar and blue collar sectors (Table IV). However, skill degrading occurs in service occupations for men between 1961 and 1971, to be replaced by extremely modest skill upgrading, or skill stability during the 1970s. Between 1981 and 1986, skill levels do not change for men across all occupational sectors." Thus, the trend noted earlier for the entire male labour force is not a function of changes in just one sector of the economy (Table 111, panel 2 versus Table IV, panel 2). In contrast, skill upgrading con- tinues to characterize the female labour force in all occupational sectors for all time periods. By 1981-1986, upward shifts in the skill levels of employment reflect skill upgrading within the female labour force.

Second, skill differences between men and women narrow in the blue collar and service occupational sectors over time (1961-1986). But in white collar occupations, where many women are employed, the gender gap in skill actually increases over time. The gap is largest in 1981. Indices of pet difference are slightly lower in 1986, but (excluding routine activity) they still remain above the values observed in the early 1960s (Table IV, panel 3).

Initially, these findings about the skill gap in white collar work appear paradoxical. Skill upgrading occurs for both the male and the female labour force in white collar occupations, yet the gap widens, particularly in 1981. This paradox is explained by sex differences in the magnitude of skill upgrading over time. The indices of net difference for change between 1971 and 1981 are larger for the male population than for the female population. The result is that skill distribution of the male population shifted upward to a greater extent, and widened the gender gap.

Sex differences in the magnitude of skill upgrading between 1971 and 1981 are related to sex specific employment structures within white collar work. Between 1971 and 1981, the percentage of females employed in clerical occupations did not alter very much, remaining at approximately half of the female white collar labour force (see also Table I). Although the percentages of women in management occupations rose, these gains in the 'higher skill' occupations were somewhat offset by declines in teaching and in nursing occupations. On the other hand, for men, changes in the employment structure of white collar jobs occurred largely because of decreasing percentages employed in sales and clerical occupations and increasing percentages in managerial, science and social science occupations (2 digit categories of 11, 21, and 23; see Table I).

By 1986, the gender gap in skills declined for white collar work. Once again, the share of employment held by the female clerical work force and related occupational shifts are important for understanding this declining skill gap. Unpublished data show that between 1981 and 1986, the percentages of women in white collar work employed in clerical occupations declined from 51.3 to 46.6 per cent (see also Table I ) . Conversely, between 1981 and 1986, the percentage of the white collar workers employed who held manage-

TABL

E IV

IND

ICES

OF

NE

T D

IFFE

REN

CE

BETW

EEN

WO

MEN

AN

D M

EN F

OR

SEL

ECTE

D S

KIL

L M

EASU

RES

WIT

HIN

WH

ITE

CO

LLA

R, B

LUE

CO

LLA

R A

ND

SER

VIC

E O

CC

UPA

TIO

NA

L G

RO

UPI

NG

S, C

AN

AD

A, 1961-1986

Spec

ific

Gen

eral

V

ocat

iona

l E

duca

tiona

l C

ogni

tive

Rou

tine

Prep

arat

ion

Dev

elop

men

t C

ompl

exity

A

ctiv

ity

Res

pons

ibili

ty

(1)

(2)

(3)

(4)

(5)

Fem

ale

(t')

vers

us F

emal

e (t'

) W

hite

Col

lar

Exp

erie

nced

Lab

our F

orce

1971-1961

1981-1971

Em

ploy

ed L

abou

r Fo

rce

1986-1981

Blu

e C

olla

r

1981-1971

Exp

erie

nced

Lab

our F

orce

1971-1961

1981-1971

Em

ploy

ed L

abou

r For

ce

1986-1981

1981-1971

,089

.079

,034

.089

.047

.043

.032

,051

.057

.043

.032

,050

,047

.042

.057

.051

,040

.040

.071

,065

,040

,013

,020

,029

.049

.021

,028

,038

.048

.037

.071

.071

Serv

ice

Exp

erie

nced

Lab

our

Forc

e 1971-1961

,034

-.014

,024

.033

1981-1971

,019

(a)

,010

,018

Em

ploy

ed L

abou

r For

ce

1981-1971

.021

(a)

.011

,020

1986-1981

,052

.062

,034

,054

,040

.013

.010

,047

,035

,023

.018

,070

-.001

.002

.003

.047

TABL

E R

con

td.

IND

ICES

OF

NE

T D

IFFE

REN

CE

BETW

EEN

WO

MEN

AN

D M

EN F

OR

SEL

ECTE

D S

KIL

L M

EASU

RES

WIT

HIN

WH

ITE

CO

LLA

R, B

LUE

CO

LLA

R A

ND

SER

VIC

E O

CC

UPA

TIO

NA

L G

RO

UPI

NG

S, C

AN

AD

A,

1961

-198

6

Spec

ific

Gen

eral

V

ocat

iona

l E

duca

tiona

l C

ogni

tive

Rou

tine

Prep

arat

ion

Dev

elop

men

t C

ompl

exity

A

ctiv

ity

Res

pons

ibili

ty

(1)

(21

(3)

(4)

(5)

Mal

e (t

) ue

mus

Mal

e (t

) W

hite

Col

lar

Exp

erie

nced

Lab

our

Forc

e 19

71-1

961

1981

-197

1 E

mpl

oyed

Lab

our

Forc

e

1986

-198

1

Blu

e C

olla

r

1981

-197

1

Exp

erie

nced

Lab

our

Forc

e 19

71-1

961

1981

-157

1 E

mpl

oyed

Lab

our

Forc

e

1986

-198

1 19

81-1

971

,080

.0

59

.068

,0

67

,070

,0

85

,053

,0

56

,053

,0

43

,090

,0

57

,054

,056

,043

,0

06

-.004

.0

05

-.00

4 ,006

.030

,0

33

.034

,0

36

-.001

,0

16

,008

.0

10

.017

-.0

15

,019

.0

12

,013

,0

21

-.017

-.0

11

-.004

-.

004

-.00

7 ,0

07

Serv

ice

Exp

erie

nced

Lab

our

Forc

e 19

71-1

961

-.067

-.0

75

-.070

-.0

69

-.045

19

81-1

971

,020

,0

25

-.005

-.

005

-.035

E

mpl

oyed

Lab

our

Forc

e

1986

-198

1 ,0

06

.016

,0

03

.002

.0

15

1981

-197

1 ,0

28

.031

.0

01

,002

-.0

29

TABL

E IV

cont

d.

Spec

ific

Gen

eral

V

ocat

iona

l E

duca

tiona

l C

ogni

tive

Rou

tine

Prep

arat

ion

Dev

elop

men

t C

ompl

exity

A

ctiv

ity

Res

pons

ibili

ty

(1)

(2)

(3)

(4)

(5)

Fem

ale

Ver

sus M

ale

Whi

te C

olla

r Ex

perie

nced

Lab

our

Forc

e 19

61

1971

19

81

Empl

oyed

Lab

our

Forc

e 19

71

1981

19

86

Blu

e C

olla

r Ex

perie

nced

Lab

our

Forc

e 19

61

1971

19

81

Empl

oyed

Lab

our

Forc

e 19

71

1981

19

86

Expe

rienc

ed L

abou

r Fo

rce

1961

19

71

1981

Em

ploy

ed L

abou

r Fo

rce

1971

19

81

1986

Service

Occ

upat

ions

-.278

-.3

06

-.342

-.311

-.3

51

-.305

-.400

-.4

00

-.388

-.403

-.3

91

-.330

-.315

-.2

23

-.226

-.229

-.2

35

-.198

-.294

-.3

23

-.343

-.329

-.3

54

-.310

-.301

-.3

02

-.301

-.314

-.3

11

-.26

3

-.029

-.0

40

-.009

-.032

-.006

-.032

-.254

-.2

95

-.325

-.299

-.3

32

-.284

-.437

-.4

11

-.409

-.421

-.4

16

-.341

-.363

-.2

90

-.280

-.296

-.2

90

-.263

-.336

-.3

51

-.372

-.356

-.3

81

-.329

-.426

-.4

07

-.407

-.417

-.4

14

-.340

-.344

-.2

61

-.246

-.270

-.2

58

-.219

-.262

-.2

77

-.311

-.279

-.3

16

-.278

-.309

-.2

67

-.22

4

-.268

-.2

26

-.164

-.318

-.2

79

- ,233

-.288

-.2

46

-.181

(a) Less

than

t .00050.


rial and administrative occupations rose from 6 per cent to 8 per cent for women and remained at 23 per cent for employed men.

CONCLUSION AND COMMENTARY

Three major conclusions follow from this analysis of female-male differences in skill measures between 1961 and 1986. First, relative to men, women are found in the middle of the skill distributions. This reflects Canada's sex segregated occupational structure, in which over half of the female labour force is employed in white collar jobs. Larger skill differences are observed when comparisons are made within broad occupational groups (white collar, blue collar and service). Within these three groups, the skill gap between men and women is much larger than in the labour force as a whole and women are employed in the less skilled occupations.

Second, between 1981 and 1986, skill upgrading occurs for women in all three occupational sectors but the skill distributions of men remained virtually unchanged. This stability in skill for men is a recent development. Prior to 1981, the skill levels of men in white collar work were rising. For men in blue collar occupations, stability in skill also follows trends of very modest skill upgrading prior to 1981. And, for men in service occupations, recent stability follows a deskilling trend between 1961 and 1971.

Third, across the entire occupational structure, there was a general decline in the magnitude of sex differences in skill between 1961 and 1986. This decline occurs within blue collar and service occupational sectors over the entire period. In white collar occupations, sex differences in skill rose between 1961 and 1981 and declined thereafter.

These results generate three types of observations. One set focuses on the broader implications of the findings for the position of women in the Canadian labour market. Here it is important to note that patterns of skill upgrading and decreasing gender differences may be imperfect indicators of improvements for women in other dimensions of labour market inequality. On the one hand, skill trends are consistent with modest declines in sex segregation of occupations, and with the shift of employment into white collar occupations between 1961 and 1986. But, changes in skill do not necessarily mean the eradication of traditional gender relations of authority and power in the workplace, or of gender differences in income. Many explanations exist for wage differentials (Gunderson, 1985). However, women workers have long argued that their wages do not reflect the growing demands and complexity of their jobs. Research also supports this claim (England, Chassie and McCormack, 1982; England and McLaughlin, 1979).

Furthermore, within broad occupational groups, gender differences in skill are still quite large. Despite the narrowing of sex differences in skill, men continue to dominate the higher skill occupations numerically and pro- portionately. This is evident in the management occupations, for example. A larger percentage of the female labour force is now employed in managerial and administrative occupations (Table I). But many of these women are found in the managerial and administrative support functions." These oc-

308 MONICA BOYD

cupations have higher skilled worker trait scores than many traditional female occupations. However, it is not clear that increased employment of women in higher skilled occupations has upset traditional gender relations of power, authority and decision-making (Boyd, Mulvihill and Myles, 1990).

A second set of observations emerge from the question What are likely future trends in sex differences in skill?. Here, empirical findings support both pessimistic and optimistic scenarios. Each scenario builds upon different aspects of sex differences in skill. Each is shaped by a vision of how employment for women and for men will change in the future.

One possibility is that skill differences between women and men will per- sist, if not widen. This view is fuelled by findings which show that sex differences in skill remain despite a narrowing of the gap. Across all occupational sectors women are less likely than men to be in higher skilled occupations. Within white collar work, the trend over time is one of fluctuation in the size of the gender gap, rather than its dissipation. And, despite declines in the gender gap in blue collar and service occupations, women more than men continue be employed in occupations with the lowest levels of SVP GED, cognitive complexity, responsibility and high levels of routine tasks.

However, the findings also support an optimistic outlook for future developments in sexual equality. Although women are less likely than men to hold higher skilled white collar, blue collar and service occupations, the magnitude of sexual inequality in skill declined between 1961 and 1986. In white collar occupations, where so much female employment is concentrated, employment in clerical work declined slightly and increased in the managerial and administrative occupations which have higher skill scores.

Which view best describes future trends depends heavily on the mix between trends in sex segregation and general shifts in Canadas occupational structure. Even if women were to move into male-typed occupations, and vice versa, the critical question is what occupations?. Movement can occur into both low skill and high skill occupations. Moreover, shifts in the employment 2tructure can enhance the impact of the entry of women (or men) into one cluster of occupations versus another. Skill trends in fact are composites of changes in the sex composition of occupations and/or changes in the distribution of employment across occ~pations.~ Thus, there is no easy answer to the question regarding future trends in the skill distributions of men and women. A pessimistic response would emphasize the continued high levels of sex segregation alongside expansion ofjobs in the low skill sector of the service economy, and increased feminization of these jobs. A more optimistic view of the continued eradication of gender differences in skill would point to temporal declines in the level of sex segregation, the expansion of employment in high skill service and white collar occupations, and increased movement of women into those jobs.

One final observation is motivated by the larger literature on skill upgrading versus skill degrading. If empirical results regarding sexual difference in skill defy easy projection into the future, they also attest to the ambiguous and complex nature of assessing evidence in the larger deskilling debate (Myles, 1988: 353). In his empirical analysis of the Braverman

19


thesis, Myles (1988: 343) argues that little support exists for the two argu- ments that I./ the new middle class has expanded at the expense of the (deskilling) working class and 2/ an ongoing decline of skill now exists for the new middle class. Myles based these conclusions on an analysis of 1961- 1981 census data, divided into four major occupational categories for the total labour force population (Myles, 1988: Table 5). Sex-specific analyses show that skill distributions for the total population can be the composite of different, indeed contradictory, trends in skill between sex and occupational groups. Between 1961 and 1971, the skills of service occupations held by men decline and stabilize thereafter. Shifts in skill for blue collar occupations are minimal after 1971 and virtually stable for men after 1981, as are skill shifts for men in white collar occupations. The patterns do not confirm Bravermans thesis, since he argues that deskilling rather than stability will be observed. But such patterns may be precursors of deskilling in as much as changes in direction (from skill upgrading to skill degrading) must pass through a zero point (skill stability). At the same time, such skill trends are not as evident for the female labour force, where skill upgrading has occurred, particularly between 1981 and 1986. Whether such sex- specific patterns continue remains an empirical question awaiting new data. But if these divergent patterns of male skill stability/deskilling and female skill upgrading continue, greater theoretical refinement incorporating gender will be essential in the skill upgradingbkill degrading debate.

NOTES

The comparisons made by United States researchers between female and male typed selected occupations cannot be done easily with the Canada CCDO, 1971 data because of country differences in occupational classification systems. However, my comparison of typists (CCDO = 4113) with typesetters (CCDO = 9511) indicated comparable evaluations of work traits except for SVP which was higher for the typesetting occupation. At the time this research was undertaken, the 1986 census data on economic variables were unavailable for general analysis within Statistics Canada. However, a data set con- taining the 1971 occupational codes was available for the employed labour force population. The experienced labour force is a preferred population for temporal analyses of labour force trends if different business cycles characterize different time periods. However, the recession of the early 1980s peaked between 1981 and 1986 and it would not substantially alter conclusions based on 1981 and 1986 data employed population. The 1971 occupational classification system has a total of 500 occupational titles. Dele- tion of farm occupations removes seven occupational titles. In the census data bases available to me, codes 1151 through 1158, representing eight titles, are collapsed to code 1149, other managers not elsewhere classified. Similarly, codes 9921 through 9926 are collapsed to code 9918, other labouring occupations not elsewhere classified. Codes 3131 and 3133 (graduate nurses excluding supervisors and nursing assistants) are combined, as are codes 5135 and 5137 (salesmen and sales persons, commodities not elsewhere classified and sales clerks, commodities not elsewhere classified) and codes 6130 and 6131 (supervisors: occupations, lodging and other accommodations; managers: hotels, motel and other accommodation). Negative signs indicate that the probability of a randomly selected woman holding a

310 MONICA BOYD

higher skilled occupation than a man is lower than the reverse probability of a randomly selected man holding a higher skilled occupation than a randomly selected woman. For example, the probability that a randomly chosen woman is located at a higher skill level of routine activity (signifying more task diversification) than a randomly chosen man is ,349. However, the reverse probability, that a randomly chosen man is located at a higher skill level for the routine activity measure than a woman, is .452. Overall, the probability that a woman will have a higher skill location is -.lo3 less than the reverse probability observed for the man. Like the index of dissimilarity, the magnitude of the index of net differences increases with the number of categories along which a variable is distributed. Consequently, com- paring the magnitudes of the indices of net differences in the service occupations to those calculated for the white collar and blue collar occupations is questionable given the absence of service occupations in the highest skill levels. This absence makes for re- duced variation in the skill distribution and could produce indices of gender inequality which are lower in magnitude than those observed in blue collar or white collar occupations. Calculations are based on the standard mathematical formula for the relationship between the standardized and unstandardized b. In a univariate analysis, the zero order correlation is identical to b*. For women, the zero order correlation between time and responsibility is -.OO (Hunter, 1988: Table 2). The indices of net difference for responsibility indicate that a randomly chosen woman is slightly more likely than a man to be employed at a higher level of responsibility, in which work involves the supervision, guidance or management of people and the direction, control and planning of tasks (Table III, panel 3). However, this reflects the greater employment of women in white collar work compared to men. Once the site of employment is held constant (Table IV), this positive differential becomes negative, indicating that a randomly chosen women is less likely to be employed at a higher level of responsibility than a man. Like this study, Spenner was examining the shift in skill distributions which occurs because of changes in the distribution of employment across occupational categories (shifts in composition). He was not examining shifts in the skill content of a given occupation.

10 The astute observer will notice that negative indices of net difference exist for some skill dimensions for the male population between 1981 and 1986. However, the magnitude of the indices of net difference are very small. The temptation to describe the trend as one of skill downgrading as opposed to skill stability should be resisted until 1991 census data more firmly establish the direction of skill changes. This occupational site increasingly may represent a new pink collar ghetto. Between 1981 and 1986, the number of males employed in managerial and administrative support occupations (CCDO = 117) actually declined from 218,000 to 186,000. The numbers rose for women from 79,500 to 127,000. The increasing percentages of women also indicate the feminization of this occupational group. In 1981,27% of workers in the support occupations (CCDO= 117) were female. In 1986 the percentage rose to 41%. In 1986, this category employed 45% of all women in management and administrative occupations (CCDO= 113, 114, 117) and 318 of the men. These calculations use the 1971 classification of occupations.

12 This reflects the stability of female employment in select blue collar occupations which are assigned low skill scores in the Canadian Classification and Dictionary of Occupa- Lions (Canada. Department of Manpower and Immigration, 1974). Women who are employed in blue collar occupations are concentrated in food and beverage processing occupations, in fabric and textile processing occupations (primarily as seamstresses), and in material handling occupations. In 1971, the percentages of employed women in

11


these three respective clusters of occupations represented 9.9, 32.6 and 12.5% of the female population employed in blue collar occupations. In 1986, the corresponding figures were 10.7, 28.0 and 10.2%.

13 An analysis of service occupations illustrate how skill trends are composites of changes in the sex composition of service occupations and/or changes in the distribution of employment across service occupations. For example, between 1971 and 1986, the share of employment in the higher skilled protective services declined from 22 to 1896, the share in food and beverage preparation rose from 28 to 37% and the percentage of employed workers in personal service occupations remained virtually stable at 16% in 1971 and 15% in 1986. But alongside these shifts in the distribution of employment were changes in the sex composition of various occupations. Occupations in the protective services as well as in personal services became substantially more feminized. The percentage of protective service occupations held by women rose from 4% in 1971 to 14% in 1986. The percentage of personal service occupations which were held by women rose from 78 to 86% between 1971 and 1986. As a result, the skill distribution of women in service occupations shifted slightly upward between 1971 and 1986, while at the same time a substantial percentage remained employed in low skill occupations.

REFERENCES

Armstrong, Pat 1984 Labour Pains. Toronto: The Womens Press Armstrong, Pat, and Hugh Armstrong 1983 A Working Majority: What Women Must do for Pay. Ottawa: Minister of

1984 The Double Ghetto (revised ed.). Toronto: McClelland and Stewart Attewell, Paul 1987 The Deskilling Controversy. Work and Occupations 14(August): 323-46 Baran, Barbara 1987 The Technological Transformation of White Collar Work: A Case Study of

Supply and Services, Catalogue No. LW31-lV1983E

the Insurance Industry. In Heidi Hartmann (ed.), Computer Chips and Pa- per Clips. Volume 11. Washington, DC: National Academy Press

Bell, Daniel 1973 The Coming of Post Industrial Society. New York: Basic Books, Inc. Beller, Andrea 1985 Changes in the Sex Composition of u.S. Occupations 1960- 1981. Journal of

Bielby, William T., and James Baron 1984 A Womans Place is with Other Women: Sex Segregation within Organiza-

Human Resources 20: 235-50

tions. Pp. 27-55 in Barbara Reskin (ed.), Sex Segregation in the Work- place: Trends, Explanations, Remedies. Washington, DC: National Academy Press

Blau, Francine, and Wallace Hendricks 1979 Occupational Segregation by Sex: Trends and Prospects. Journal of Hu-

man Resources 12: 197-210 Boyd, Monica 1982a Occupational Segregation: A Review. Pp. 66-92 in Sexual Equality in the

1982b Sex Differences in the Canadian Occupational Attainment Process. The Workplace. Ottawa: Minister of Supply and Services, Cat. No. L 36-38/19823

312 MONICA BOYD

Canadian Review of Sociology and Anthropology 19(1): 1-28

135-200 in Mary Kinnear and Greg Mason (eds.), Women and Work. Otta- wa: Social Sciences and Humanities Research Council

1985 Educational and Occupational Attainments of Native-born Canadian Men and Women. Pp. 229-96 in Monica Boyd, John Goyder, Frank E. Jones, Hugh A. McRoberts, Peter C. Pineo and John Porter, Ascription and Achie- vement. Ottawa: Carleton University Press

dian Review of Sociology and Anthropology 23(4):457-80

1983 Occupational Segregation: Research Issues and Data Requirements. Pp.

1986 Socioeconomic Indices and Sexual Inequality: A Tale of Scales. The Cana-

Boyd, Monica, Mary Ann Mulvihill, and John Myles 1990 Patriarchy and Postindustrialism: Women and Power in the Service Econo-

my. Departmental Working Paper 90-1. Department of Sociology and An- thropology, Carleton University, Ottawa

Braverman, Harry 1974 Labour and Monopoly Capitalism: The Degradation of Work in the Twen-

Cain, Pamela, and Donald Treiman 1981 The DOT as a Source

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Sex differences in occupational skill: Canada, 1961–1986

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