UNFPA COUNTRY SUPPORT TEAM
Office for the South Pacific
DISCUSSION PAPER NO. 23
Women’s Labour Market Status in Fiji:
Are They Subjected to Discrimination?
by
William J House
Adviser on Population Policies & Development Strategies
UNFPA/CST, Suva
14 January 2000
The views and opinions contained in this Paper
have not been officially cleared and thus do not
necessarily represent the position of the
United Nations Population Fund
Preface
The UNFPA Country Support Team for the South Pacific, based in Suva, Fiji, is one of eight
regional technical support teams established by the United Nations Population Fund to
provide countries with technical assistance and information to meet country needs in the
population field. In fulfilling this function, apart from field missions, the Country Support
Teams also try to foster active communication and open discussion with national experts to
promote a more holistic approach to population programmes.
This Discussion Papers series have been initiated by the CST (Suva) in an attempt to
establish a dialogue among national population programme personnel on the integrated and
co-ordinated multidisciplinary approach to population. Hence, CST Discussion Papers are
not particularly addressed to academic audiences but to practitioners.
This paper compares the status of women with that of men in Fiji's formal sector labour
market, using data collected from over 8,000 employees. This analysis examines gender
differences in relative earnings and occupational status as well as the returns to various kinds
of human resource endowments, such as education, job characteristics and tenure with
employer. It is anticipated that the results will be of practical interest to planners and policy
makers in both Fiji and other Pacific Island countries.
14 January 2000
William House
Officer-In-Charge
Table of Contents
EXECUTIVE SUMMARY .................................................................................................................................. I
1.
INTRODUCTION ....................................................................................................................................... 1
2.
THE COUNTRY CONTEXT .................................................................................................................... 2
LABOUR FORCE GROWTH AND THE LABOUR MARKET .................................................................................. 3
Males ............................................................................................................................................................. 4
Females ......................................................................................................................................................... 4
THE STRUCTURE OF EMPLOYMENT ................................................................................................................. 5
THE OCCUPATIONAL STRUCTURE OF EMPLOYMENT...................................................................................... 6
3.
THE STRUCTURE OF OCCUPATIONS IN FIJI BY GENDER ......................................................... 7
THEORIES OF SEX INEQUALITIES IN THE LABOUR MARKET........................................................................... 7
THEORIES OF LABOUR MARKET SEGMENTATION........................................................................................... 9
OCCUPATION SEGREGATION AND CONCENTRATION .................................................................................... 10
THE SITUATION IN FIJI ................................................................................................................................... 11
ALTERNATIVE PRESENTATIONS: DISTRIBUTION OF THE MALE AND FEMALE LABOUR FORCE ................. 13
ANALYSIS OF THE EXTREMES ......................................................................................................................... 15
4.
THE STRUCTURE OF EARNINGS BY SEX ....................................................................................... 20
THE SOURCE OF INFORMATION: THE FIJI EMPLOYMENT SURVEY .............................................................. 20
THE SURVEY METHODOLOGY ........................................................................................................................ 21
MALE-FEMALE PAY DIFFERENTIALS............................................................................................................. 22
5.
A DECOMPOSITION OF MALE-FEMALE EARNING DIFFERENTIALS ................................... 26
THE METHODOLOGY ...................................................................................................................................... 26
6.
CONCLUSIONS ....................................................................................................................................... 31
REFERENCES ................................................................................................................................................... 33
EXECUTIVE SUMMARY
The Programme of Action of the International Conference on Population and Development
(ICPD), held in Cairo in 1994, encourages countries to act to empower women and to take
steps to eliminate inequalities between men and women.
Unfortunately, in the Pacific Island countries, there is a dearth of information and data on the
relative labour market status of women vis a vis men on which to build policy reform. A
recent opportunity to break new ground in this area has come about through the activities of a
UNFPA-sponsored initiative in Fiji. During 1995-1997, UNFPA funded a project in the
National Planning Office entitled: “Assistance in Population, Workforce Planning and
Human Resources Development Planning”. Since that time, UNDP has taken over the
funding of the project, which is now, entitled: “Strategic Human Resources Planning in Fiji”.
Both phases of the project have been executed by the International Labour Organization and
received substantial technical input from the UNFPA Country Support Team (CST) based in
Suva.
One of the major activities of the project was to conduct a survey of employees in Fiji’s
formal sector labour market, documenting the human resource endowments and other socioeconomic characteristics of respondents, including their earnings and hours of work in a
recent reference period, and contributing to the design of policies to redress the seemingly
disadvantaged position of female workers. One primary concern of the survey was to
generate appropriate data from which to assess the relative labour market position of female
compared with male employees and to attribute such differences to human resource
endowments as well as to labour market “discrimination” against women.
This paper has demonstrated that there was an apparent significant increase in the labour
force participation of women in Fiji over the decade 1986-1996. The female share of the
total labour force rose from 21% to 33%, but a large proportion of these women failed to find
cash employment and large numbers entered the non-cash, subsistence, largely agricultural
economy. By the mid-1990s women’s share of the two largest occupational groupings
(agriculture and elementary occupations) had trebled to one-third and the gender
concentration of the labour force seems to have become somewhat less extreme. Yet, their
greater representation in the lowly paid occupations did little to raise women’s overall status
in Fiji’s labour market. Women have made little headway into male occupational preserves.
The analysis went on to examine how this extreme concentration has impacted on the relative
earnings of women compared with men in the formal sector of the labour market. Using data
collected from over 8,000 employees in a specially designed survey, analysis has shown that
mean pay is consistently higher for men compared with women while women’s earnings
profile across age groups is much flatter, especially in the private sector, perhaps reflecting
the kinds of jobs to which they are assigned, lacking opportunities for much on-the-job skill
acquisition and pay advance. This result may also reflect the effect of discontinuities in
women’s attachment to the labour market and the loss of productivity and pay-enhancing
continuous work experience.
Male-female earnings differentials take a U-shape across education levels, being wider at
lower and high levels of schooling. Again, since very few women with higher education
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i
attain the very senior, highly paid executive positions, gender differences in occupational
assignment contribute to these differences.
The paper concludes with a standard decomposition analysis of male/female pay differentials
which has been widely utilised around the world. The method decomposes the pay
differential between the sexes into a portion attributable to a composition effect –
“endowments” – and a portion attributable to structural differences, or “discrimination”,
reflecting differences in the returns to these endowment factors. The results show that
roughly 40% of the pay difference can be attributed to the composition effects, reflecting
differences between males and females in their education, general and firm experience, ethnic
group, sector of employment, training received and occupation. Some of these attributes,
including education and training, favour female workers.
The remaining 60% of the pay difference is attributable to differences in the returns to
various human capital and other characteristics of the workers. Again, some advantages in
these returns favour women. The principal source of “discrimination” lies in an unexplained
intercept term in the regression analysis, whereby mean male earnings lie above that of
females after controlling for many of these human capital attributes and various types of
insertion in the Fiji labour market.
The analysis leaves open the possibility that occupational sorting may play a major role in
explaining the differences in earnings between the sexes. By treating broad-based
occupational groups as a set of dummy variables, the analysis has discounted both the
possibility of different returns to human capital between occupations and the possibility that
the formation of the occupational distributions for men and women may, themselves, be
discriminatory.
This paper has elaborated on the methodology – the “expanded method” – that would allow
an estimate of occupational discrimination to be made, whereby women with the same human
capital and other attributes as men, do not have equal access to all occupations, including the
highly paid executive positions. A subsequent paper in this series will utilise the data
collected in the special survey to test this model.
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ii
1.
INTRODUCTION
The Programme of Action of the International Conference on Population and Development
(ICPD), held in Cairo in 1994, encourages countries to act to empower women and to take
steps to eliminate inequalities between men and women. The ICPD resolved:
“All countries should make greater efforts to promulgate, implement and enforce
national laws and international conventions to which they are party, such as the
Convention on the Elimination of All Forms of Discrimination Against Women, that
protect women from all types of economic discrimination. Governments and
employers are urged to eliminate gender discrimination in hiring, wages, benefits,
training and job security with a view to eliminating gender-based disparities in
income” (United Nations, 1994, para. 4.5; 4.7).
Unfortunately, in the Pacific Island countries, there is a dearth of information and data on the
relative labour market status of women vis a vis men on which to build policy reform. A
recent opportunity to break new ground in this area has come about through the activities of a
UNFPA-sponsored initiative in Fiji. During 1995-1997, UNFPA funded a project in the
National Planning Office entitled: “Assistance in Population, Workforce Planning and
Human Resources Development Planning”. Since that time, UNDP has taken over the
funding of the project which is now entitled: “Strategic Human Resources Planning in Fiji”.
Both phases of the project have been executed by the International Labour Organization and
received substantial technical input from the UNFPA Country Support Team (CST) based in
Suva.
One of the major activities of the project was to conduct a survey of employees in Fiji’s
formal sector labour market, documenting the human resource endowments and other socioeconomic characteristics of respondents, including their earnings and hours of work in a
recent reference period, and contributing to the design of policies to redress the seemingly
disadvantaged position of female workers. One primary concern of the survey was to
generate appropriate data from which to assess the relative labour market position of female
compared with male employees and to attribute such differences to human resource
endowments as well as to labour market “discrimination” against women.
The remainder of this paper utilises the results of recent Population Censuses in Fiji and the
1997 survey of employees to portray the changing position of females in the labour market
over time and to estimate the nature and causes of women’s relative position, as reflected in
their inferior pay and occupational assignment compared with male employees. Indeed, the
survey indicates that the average female formal sector employee receives only 79% of the
average weekly pay of males. In terms of the mean rate of hourly pay, the average woman
earns 81% of the hourly pay of the average man.
To what extent are these differences attributable to superior income-generating attributes of
men, such as education, vocational training and occupational assignment? And to what
extent are such differences in earnings attributable to “discrimination” against female
workers in one from or another? Indeed, is the occupation assignment mechanism itself
“discriminating” against female workers in its allocation of the most desired jobs? The
remainder of this paper undertakes to explore some of these issues and concludes with some
preliminary policy suggestions to redress women’s inferior labour market position in Fiji.
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2.
THE COUNTRY CONTEXT
Fiji has the largest and most developed economy of all the South Pacific island countries,
with a per capita GDP of about US$2,300 in 1996 (UNDP, 1998). The country is favourably
endowed with natural resources, a high level of human resource development, a dynamic
class of entrepreneurs, and a strategic geographic situation at the hub of economic activities
in the region. Furthermore, Fiji’s social indicators – life expectancy, infant mortality, adult
literacy and school enrolment – reflect the country’s relatively high income and development
status. According to the 1998 UNDP Human Development report, Fiji ranks 44th out of the
174 countries in its human development index. The closest South Pacific island country was
Samoa, which ranks 94th, followed by Solomon Islands and Vanuatu at 123rd and 124th
respectively.
Set against this favourable portrait, the economy relies on a very narrow base and is overdependent of the tourism and sugar sectors. Perennially, the economy remains extremely
vulnerable to dramatic changes in climatic conditions. Following the economic decline in the
period after the coups of 1987, a series of economic reforms induced a rise in the economic
growth rate, ranging between 1 and 5% per annum during the 1990s. In 1996 per capita GDP
was 18% higher than in 1990.
Despite these improvements, the generation of formal sector employment opportunities has
failed to meet the needs of a burgeoning supply of new job-seekers. With a cap imposed on
public sector employment, the private sector has been capable of absorbing no more than
one-half of the new labour market entrants each year. This imbalance in the labour market –
the gap between the number of new job seekers coming out of the school and vocational
training institutions and the number of new job opportunities opening up – remains one of the
major social and economic problems yet to be adequately addressed.
At the time of the 1996 Census the total enumerated population of Fiji was 775,077 compared
with 715,375 ten years earlier. The annual inter-censal growth rate was 0.8% which is
significantly less than the 1.97% p.a. increase recorded over the 1976-86 decade. This fall
reflects the continuing decline in fertility, for Fijians a fall in the total fertility rate (TFR)
from 5.6 in 1966 to 3.9 in 1986; for Indians from 5.4 to 2.6. By 1996, the TFR for all women
combined had fallen to 3.3. Another contributing factor to the decline in the rate of
population growth was massive emigration, largely of the Indian ethnic group, soon after the
1987 military coups. While emigration levels have tended to stabilize, the country continues
to suffer from the past and current loss of some of its more educated and trained citizens.
The distribution of the population by broad age groups suggests that Fiji is slowly departing
from its very youthful status in which, in 1976, 41% of the population was under 15 years
old; by 1996 this had declined to 35%. Over the same period the share of the 65 years and
over had grown from 2.4% to 3.2%. As a result, the share of those of working age and
potential labour force members rose from 51% in 1966 to over 61% by 1996, a "demographic
bonus" to the nation only if most are able to find productive employment.
Among the most important challenges facing the government is the task of identifying
development strategies which can generate new employment and income opportunities and
reduce underemployment and unemployment. The urgent need to create employment
opportunities is underscored by the higher rate of labour force growth than population
growth, resulting from a period when fertility was much higher than today, and from recent
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increases in the rate of female labour force participation. Recent slow economic growth and
consequent retarded growth in job opportunities, particularly in the public sector, have forced
many frustrated school-leavers into marginal activities in small-scale agriculture and the
urban informal economy.
As noted earlier, Fiji’s population is still relatively young, with 35% of the total in 1996 less
than 15 years of age. With the annual population growth rate of 0.8%, projections suggest
that the total population will exceed 800 thousand by the end of this century, an increase of
almost 13% over one and a half decades. The final outcome will depend on the scale of outmigration, which will be partly a function of the future performance of the economy.
Labour Force Growth and the Labour Market
In the meantime, the size of the labour force has been growing much faster than the overall
growth rate of the population, as reflected in table 1. Over the 20 year period 1976-1996, the
labour force grew at an annual average rate of 2.7% while the population rose at a rate of
1.4%. A major contributing factor has been the apparent and quite remarkable increase in the
labour force participation rate of females, particularly in the most recent inter-censal period,
rising from less than one-quarter to almost 40% overall. No doubt, some unknown part of this
rise is attributable to a change in definition between censuses and a more complete
enumeration of women’s economic contribution.
It is also of interest to note the marginal decline in the age-specific labour force participation
rates of males. While the fall in these rates for men aged 15-24 might be attributable to
greater educational and training opportunities, some older men may have been pushed out of
the labour force by younger newcomers as well as the large registered influx of women
competing for similar jobs.
The reported rates of unemployment in table 2 have tended to contract over time, particularly
those for females since 1986. Thus, despite a rapid growth of labour supply, rates of
unemployment appear to have fallen, indicating successful labour absorption into productive
employment. On further examination in table 3, however, we find that much of the growth in
labour supply over the period 1986-1996 failed to be absorbed into the cash economy. While
total employment grew at an annual rate of 2.3% over the 1986-96 period, just exceeding the
2.1% average annual growth in labour supply, there was a marked difference in the labour
market treatment of males and females. Male cash employment expanded at a rate (1.1%
p.a.) which exceeded the growth in male labour supply (0.5% p.a.) with the result that the
share of non-cash employment in total male employment shrank from 17% to 13%. In
contrast, while female cash employment growth was impressive (4.2% p.a.), it was exceeded
by the annual growth in female labour supply (6.7% p.a.) with the result that, as overall
female unemployment fell from 15.3% in 1986 to 7.8% in 1996, the share of non-cash
employment in total female employment expanded from 19% to 41% over the decade.
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Table 1
Rates of Labour Force Participation (%) by Age Group and Sex, 1976, 1986 and 1996
MALES
15-19
20-24
25-29
30-39
40-49
50-59
60+
Total
Total Labour Force
% Growth p.a. 1976-86 2.6
p.a. 1986-96 0.5
FEMALES
15-19
20-24
25-29
30-39
40-49
50-59
60+
Total
Total Labour Force
% Growth p.a. 1976-86
p.a.
1976
1986
1996
58.0
91.3
96.3
97.3
96.5
89.1
58.8
84.1
146,31
57.9
91.0
96.0
97.4
96.8
88.4
57.1
85.4
189,929
38.5
80.3
91.5
94.6
94.4
85.4
59.1
79.2
200,048
15.1
24.3
19.8
17.6
15.6
12.7
7.3
17.1
29,470
21.4
29.1
26.2
25.9
22.4
18.1
10.2
23.3
51,231
19.4
45.4
45.5
46.8
46.1
39.0
27.1
39.4
97,723
175,785
241,160
297,771
5.7
1986-96
6.7
National Labour Force (M&F)
% Growth p.a. 1976-86 3.2
1986-96 2.1
Source: National Census Reports 1976 and 1986; Provisional Results of 1996 Census
Table 2
Reported Rates of Unemployment by Sex, Selected Age Groups and Education
Completed for Census Years
Males
Total Population
Ages 15-19
20-24
25-29
Total
16.0
8.7
4.6
5.4
1976
Females
34.7
14.2
7.4
11.7
Total
20.5
10.1
5.2
6.6
Males
17.3
10.1
4.3
5.4
1986
Females
45.0
26.5
10.4
15.3
Total
24.7
14.1
5.6
7.5
Males
14.8
9.2
4.8
4.8
1996
Females
23.4
13.1
7.2
7.8
Total
17.6
10.5
5.6
5.8
Source: National Census Reports 1976 and 1986; Provisional Results of 1996 Census
The extent to which these dramatic changes in the market for female labour are attributable to
improved enumeration of female participation in non-cash economic activities in the most
recent census remains unknown. At face value, however, it would appear that there has been
a significant increase in the supply of female labour to the economy during a period when
economic growth was disappointing. While many women have found low-wage employment
in the buoyant garments sector, many more have had to be content with non-cash
employment in the rural and urban subsistence and informal sectors. Their absorption in the
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4
subsistence and informal sector has contributed to the decline in their rate of unemployment.
Furthermore, their presence as a potential labour supply to the cash economy has, no doubt,
served to dampen wage increases in the low-wage segments of the labour market.
Table 3
Employment for Cash and Non-Cash in Fiji 1986 and 1996 by Sex
Cash
Employment
Non-Cash
Employment
Total
Employment
Males
1986
1996
% change per annum 1986-96
148,346
166,299
1.1
31,249
24,147
-3.5
179,595
190,446
0.6
Females
1986
1996
% change per annum 1986-96
35,278
53,015
4.2
8,098
37,045
16.4
43,376
90,060
7.6
Total
1986
1996
% change per annum 1986-96
183,624
219,314
1.8
39,347
61,192
4.5
222,971
280,506
2.3
Source: National Census Reports 1976 and 1986; Provisional Results of 1996 Census
The Structure of Employment
Very little recent analytical work has been undertaken on the operation of the labour market
in Fiji1. If Fiji is to absorb a greater share of its burgeoning newcomers into productive
employment, a greater understanding by planners and policy makers of how labour markets
operate in the country is essential. Factors, which may serve to segment labour markets, to
inhibit labour force absorption and to slow down growth in productivity and output, need to
be identified. Especially important will be measures to redress the disadvantaged position of
women, the poor and other vulnerable groups in the country’s labour market. At the same
time a delicate balance needs to be struck between interventions which aim to protect women
and other vulnerable groups and those which effectively inhibit their enhanced absorption
into productive employment.
Table 4 portrays the sectoral distribution of the total labour force for the most recent census
year of 1996. The data confirm the concentration of economic enterprises in rural Fiji and in
agricultural – related activities. Since the bulk of those classified in “subsistence" are, no
doubt, engaged in agricultural-related activities, perhaps as many as 45% of the labour force
are involved in the broad sector of agriculture, forestry and fishing. Even then, of the
219,314 employed for money, only 122,985 (or 41% of the total labour force) were employed
for at least 5 days, perhaps constituting an estimate of the “formal sector”2. The government
accounts for roughly 40% of total formal employment via central and local governments.
1
An earlier analytical study in 1984 was by the Fiji Employment and Development Mission: "Work and Income for the People of Fiji: A
Strategy for More than Just Survival", which was financed by the then European Economic Community and commissioned to the Institute of
Development Studies (IDS) at the University of Sussex, England.
2
This would be slightly more than the figure used in the official document of the Ministry of National Planning: Development Strategy for
Fiji (1997). There, it is estimated that the formal sector, comprising “both the public and private establishments offering continuous wage
and salary employment”, employs 109 thousand, or 36.2% of the labour force.
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5
Manufacturing employment is still responsible for less than 10% of the total labour force
while trade and services, including government services, employ over 27% of the labour
force. It is also revealing to note how over 1 in 5 of the labour force is still engaged in
subsistence activities in Fiji.
Men contribute two-thirds of the total labour force and tend to be concentrated in formal
agriculture, construction and transport; women are over-represented in garment
manufacturing, trade, services and the subsistence sector.
Table 4
Sectoral Distribution of Employment 1996
Sector
Agriculture, Forestry
Fishing
Mining and Quarrying
Manufacturing
Food and Drinks
Clothes
Wood Products
Paper
Chemicals
Cement
Metals
Jewellery
Other man. nec.
Utilities
Construction
Trade
Wholesale & Retail
Restaurants
Hotels
Transport
Finance
Business Services
Services
Government
Other
Subsistence
Unemployed
Not Stated
Total
and
Males (%)
Females (%)
Total
32.1
1.2
8.9
3.9
1.3
1.7
1.0
0.3
0.2
0.7
0.1
0.1
1.0
5.2
10.2
7.3
0.5
2.4
7.5
1.0
1.4
13.8
3.6
10.2
12.1
4.8
0.8
200,052(100.0)
7.4
0.2
11.5
1.8
8.6
0.3
0.3
0.2
0.0
0.1
0.1
0.0
0.1
0.3
12.1
7.6
1.3
3.1
1.8
1.7
1.0
17.3
2.9
14.4
37.9
7.8
0.9
97,718(100.0)
71,485
2,507
29,043
9,583
11,005
3,772
1,441
835
424
1,603
218
162
2,107
10,639
32,175
18,616
2,327
7,785
16,722
3,900
3,912
44,620
10,080
34,540
61,191
17,265
2,204
297,770
(%)
24.0
0.8
9.8
3.2
3.7
1.3
0.5
0.3
0.1
0.5
0.1
0.1
0.7
3.6
10.8
6.3
0.8
2.6
5.6
1.3
1.3
15.0
3.4
11.6
20.5
5.8
0.8
100.0
Source: Population Census, 1996, preliminary results
The Occupational Structure of Employment
The occupational distribution of those in the labour force is portrayed in table 5. Fiji’s
dependence on the primary sector is again apparent with 21% of the paid labour force
classified as agricultural and fisheries workers. Only 17% of the paid labour force are found
in the top three occupational groups, of Legislators, Senior Officials and Managers;
Professionals; and Technicians and Associates. Men dominate the senior occupations
although women are well represented as Professionals in traditional 'female' occupations.
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Table 5
Occupational Distribution of Employment by Sex, Fiji, 1996
Occupation
Males
(%)
Females
Legislators, Senior Officials,
Managers
Professionals
Technicians & Associates
Clerks
Service Workers
Skilled Agric. & Fisheries
Craft & Related Trades
Plant & Machine Operators
Elementary Occupations
Unallocated occupations
Subsistence
Unemployed
Total
6,477
10,398
8,350
7,440
14,405
40,231
23,997
16,190
36,712
2,099
24,151
9,602
200,052
3.2
5.2
4.2
3.7
7.2
20.1
12.0
8.1
18.4
1.0
12.1
4.8
100.0
1,403
7,586
3,228
9,061
7,734
5,414
3,218
6,289
9,045
37
37,040
7,663
97,718
(%)
1.4
7.8
3.3
9.3
7.9
5.5
3.3
6.4
9.3
0.1
37.9
7.8
100.0
Total
7,880
17,984
11,578
16,501
22,139
45,645
27,215
22,479
45,757
2,136
61,191
17,265
297,770
(%)
2.6
6.0
3.9
5.5
7.4
15.3
9.1
7.5
15.4
0.7
20.5
5.8
100.0
Source: Population Census, 1996, Preliminary Results
3.
THE STRUCTURE OF OCCUPATIONS IN FIJI BY GENDER
Theories of Sex Inequalities in the Labour Market3
Neo-classical economic theories emphasize sex differences in variables which affect labour
productivity and labour supply such as family responsibilities, physical strength, education,
training, hours of work, absenteeism and turnover, in order to explain why women generally
earn less than men. A basic assumption of neo-classical economics is that workers in
conditions of competition are paid the value of their marginal product; it then follows from
this assumption that observed male-female differentials in earnings are either due to the lower
productivity of women or to market imperfections. A separate assumption of the neoclassical 'new home economics' is that families allocate their resources (time and money)
among family members in a rational manner which causes females to receive less human
capital investments when young and to stay home to take care of the children when older.
Neo-classical theory suggests that women earn less than men because they have lower levels
of human capital - mainly education, training and on-the-job experience - and therefore lower
labour productivity. For example, because some women interrupt their employment to
marry, and then bear and rear children, employers are said to be reluctant to invest in the
training of women. Also, for parents and the women themselves there is said to be less
incentive to invest in education and training. Periods of withdrawal from the labour force
mean that women accumulate less work experience than men and that their skills tend to
depreciate more.
According to the human capital approach, discrimination would be said to occur if employers
pay different wages to persons with the same stock of human capital. Sex discrimination can
3
This section summarizes arguments presented in Anker and Hein (1986) and Anker (1997).
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7
then be measured by the amount of the wage gap between men and women which remains
unexplained by male-female differences in human capital - mainly education, training and
experience.
A hypothesis which is useful in explaining why so much of the male-female wage gap cannot
be explained by human capital differences between men and women is that of statistical
discrimination - where average differences between population sub-groups are used as a basis
for discriminating against all members in that subgroup. If the employer believes that women
are on average more likely than men to be unstable, unreliable, etc. then he will tend to
discriminate against women even though he knows he may be wrong in certain individual
cases. The cost of obtaining detailed information on job candidates leads to the use of
screening devices such as candidates' sex. In this way, negative stereotypes will penalize all
women.
The neo-classical approach has been very important in pointing out some of the productivityrelated differences between men and women which account, at least partially, for the fact that
men earn more than women. Indeed, policies to improve the labour market position of
women are often based on the need to improve women's educational levels and training - that
is, their human capital. In developing countries - particularly in Africa, the Middle East and
Asia - women are generally less well educated than men and this factor may, accordingly, be
more important in these countries than in others. Recent cohorts of women in Fiji have
gained much greater access to educational opportunities, although they remain underrepresented at the tertiary level, a factor which will continue to retard their progress to the top
echelons of the occupational ladder.
While it is clear that lower levels of education and skill can be an important handicap for
women in the labour market, the human capital approach is based on several assumptions
which have been questioned, notably by labour market segmentation and gender theorists.
Two of these questionable implicit assumptions are:

Women's labour force participation is of necessity intermittent because of their 'natural'
child-rearing role.
However, unlike pregnancy and breast-feeding, there is no biological reason why the child
rearing role must be performed uniquely by the mother.

Another underlying assumption is that men and women have equal access to job
opportunities and compete on an equal basis in the labour market.
This assumption ignores the sex segmentation of the labour market - which cannot be
explained simply by sex differences in human capital. Evidence in the United States suggests
that the rising educational levels of women are not related to any substantial decline in the
extent of sex segregation of occupations.
Institutional theories of labour market segmentation, which are discussed next, emphasize
factors related to the structure of the labour market and how men and women get slotted into
separate segments of the labour market in explaining sex inequalities.
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8
Theories of Labour Market Segmentation
Theories of labour market segmentation can be considered as refinements of neo-classical
theories, in that they view the labour market as stratified or segmented by institutional
barriers. Within each segment, neo-classical principles are generally still assumed to be
relevant.
One of the best known theories of labour market segmentation is the dual labour market
theory which distinguishes between two types of jobs: primary sector jobs which are
relatively good in terms of pay, security and opportunities for advancement, and secondary
sector jobs with low pay, low security and few opportunities for advancement. Primary
sector jobs are those where workers' skills tend to be firm-specific and the resulting
employers' need for stability in this part of the workforce leads them to offer high wages and
good prospects for advancement. For jobs in the primary sector, worker stability is important
for employers and the perceived high turnover of women means that they are more likely to
be relegated to secondary jobs. Thus, even with equal pre-entry qualifications, men (being
perceived as more stable) would be more likely than women to be recruited for primary jobs
where their chances for subsequent progress within the firm in terms of wages, training and
promotions would be greater.
The main contribution of the dual labour market approach is that it emphasises the existence
of segmented labour markets and analyses the different ways in which different labour
market segments operate, thereby providing a refined alternative to the open competition
between individuals assumed in neo-classical models. Two particularly important insights
coming from this segmentation approach can be mentioned. First, it stresses the importance
of the initial entry position into an organisation for determining future possibilities to acquire
human capital (in terms of on-the-job training and experience) and for promotion. Second, it
points out that worker behaviour is related to the characteristics of the jobs workers hold.
Since absenteeism and turnover tend to be greater in low-level, dead-end jobs where women
tend to be concentrated, reputedly higher turnover and absenteeism among women may be
explained, at least partially, by sex differences in type of occupation rather than by inherent
characteristics of women.
While the dual labour market approach helps to explain the occupational distribution of men
and women, it does not explain the sex segregation, which occurs within the primary and
secondary sectors. There are both male and female occupations which involve lengthy
schooling, such as engineers and nurses. There are female occupations which require firmspecific training such as executive secretaries and, at the same time, there are male
occupations requiring relatively few skills and where stability is not an important factor, such
as janitor or truck driver.
The sex segregation of occupations within both the primary and secondary sectors has led
some writers to suggest that sex also needs to be considered as one of the dimensions on
which the labour market is segmented. The existence of two relatively separate labour
markets for men and women is seen by some as an important determinant of the lower
earnings of women. To the extent that women's occupational choices are restricted and there
is an oversupply of candidates for women's jobs, women can be considered as 'crowded' into
these occupations. According to such an 'overcrowding model', wages are lower for
occupations which are highly feminised, since women must compete against themselves for
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9
relatively few jobs in what is essentially an artificially restricted, overcrowded segment of the
labour market. Similarly, women do not compete with men for a large number of jobs
considered to be 'male' jobs, which helps maintain the higher wages of these jobs.
Although theories of labour market segmentation provide insights into sex inequalities, they
still do not adequately explain why sex is such a persistent and important dimension for
labour market segmentation. The basic cause is probably outside the economic realm and so
it is not surprising that economic variables cannot explain it.
Occupation Segregation and Concentration
"Segregation concerns the tendency for men and women to be employed in different
occupations from each other across the entire spectrum of occupations under
analysis. It is a concept which is inherently symmetrical; the relationship of women
workers to men workers is its key feature. In so far as women are separated from
men, so are men separated from women in the labour force under consideration. Men
can not be more 'segregated' than women, nor women more 'segregated' than men.
Women and men are segregated in relationship to one another and, therefore, each
are segregated to the same degree" (Siltanen, Jarman and Blackburn, 1993, p.4).
Segregation refers to the extent to which a pattern occurs in which men and women are
separated from each other in the overall occupational structure. Total segregation would exist
when all occupations are staffed exclusively by one sex, with no occupation containing both
men and women. A situation without segregation would be evident when the mix of men and
women is the same in each occupation.
“Concentration is concerned with the sex composition of the workforce in an
occupation or set of occupations. Whereas segregation refers to the separation of the
two sexes across occupations, concentration refers to the representation of one sex
within occupations” (Siltanen, Jarman and Blackburn, 1993, p.5).
One widely used measure of female concentration in labour market studies is the percentage
of workers in an occupation who are women; male concentration would be reflected in the
percentage of all workers in an occupation who are men4. Of course, the percentage of
women in an occupation will partly depend on the share of the labour force which is female.
The greater the female representation in the work force the more women there are likely to be
in any single occupation. If female concentration were the same in all occupations, it would
be equal to the overall female share of the total labour force. When attempting to compare
levels of concentration by occupation it is useful to relate the gender composition of an
occupation to the gender composition of overall employment. Therefore, one widely utilised
ratio is the female percentage of a particular occupation divided by the female share of the
labour force. A value greater than unity would signify over-representation of women in this
occupation; a value less than unity indicates under-representation of women in the
occupation.
This approach to the measurement and analysis of concentration would answer some of the
following types of questions:
4
Analogously, this concept of concentration can equally well apply to a group of occupations, an industry or a section of the labour force
with a particular work status, e.g. self-employed workers or part-time workers.
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10
*
Is a specific occupation, such as teaching, more likely to be staffed by men or women?
If so, to what extent? (what percentage of teachers are female?)
In which occupations are women more/less likely to be employed?
In which occupations are men more/less likely to be employed?
Is female employment well spread across the occupational structure, or is it restricted to
a limited number of occupations?
How well spread or restricted is male employment?
In which occupations are women over-represented, and in which are they underrepresented?
*
*
*
*
*
The Situation in Fiji
Attention now turns to examine the level of concentration of the male and female labour
forces in Fiji, using the results of the 1986 and 1996 Censuses of Population, the most recent
years available with this kind of data. The emphasis is on the production of visual displays of
concentration patterns to help identify the broader features of the data reported in table 6(a),
table 5 and table 6(b) and changes over time. Unfortunately, 1986 and 1996 are not directly
comparable because, in the earlier year, the authorities used ISCO-68 to classify occupations;
in 1996 they use ISCO-88.
The sex composition of an occupation can be measured as the percentage of the total number
of workers in an occupation who are female, or the percentage of the total workers in the
occupation who are male, or the ratio of males to females. These are all essentially
equivalent in terms of the information that they convey.
Table 6
Economically Active Population by Occupation and Sex, Fiji
(a)
1986
ISCO 1968
Occupation
Total
(1)
06/07/13
Medical Workers and
Teachers
Other Professionals
Administrative and
Managerial
Clerical and Related
Workers
Sales workers
Service Workers
Agriculture etc. Workers
Production and Related
Workers
Seeking Employment
10,343
4.3
4,366
2.2
5,977
11.8
Female
Share %
(7)
57.8
7,431
2,766
3.1
1.2
6,359
2,515
3.4
1.3
1,072
251
2.1
0.4
14.4
9.1
15,569
6.5
8,242
4.4
7,327
14.4
47.1
14,861
15,422
105,924
49,000
6.2
6.4
44.2
20.5
10,540
7,979
93,925
44,439
5.6
4.2
49.8
23.6
4,321
7,443
11,999
4,561
8.5
14.7
23.6
9.0
29.1
48.3
11.3
9.3
18,182
239,948
7.6
100.0
10,331
188,696
5.4
100.0
7,851
50,802
15.5
100.0
43.2
21.2
0-1
2
3
4
5
6
7-9
998
TOTAL
%
(2)
Males
(3)
%
(4)
Females
(5)
%
(6)
Source: Fiji (1988), Census of Population, 1986, Bureau of Statistics, Suva
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11
(b)
1996
1988
1
2
3
4
5
6
7
8
9
0
Occupation
Legislators, Senior Officials and Managers
Professionals
Technicians and Associate Professionals
Clerks
Service Workers and Shop and Market Sales Workers
Skilled Agricultural and Fishery Workers
Craft and Related Trades Workers
Plant and Machine Operators and Assemblers
Elementary Occupations
Unallocated
Subsistence
Unemployed
TOTAL
Source: Census of Population, 1996, Preliminary Results
Female
Share %
(7)
17.8
42.2
27.9
54.9
34.9
11.9
11.8
28.0
19.8
1.7
60.5
44.4
32.8
Figure 1: Sex Composition of Occupational Groups, Ordered by % Female
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12
Table 6 and Figure 1 display an overview of the distribution of the sexes in the labour force
in 1986 and 1996. Although data for these years are not strictly comparable because of the
changed occupational classifications, the structuring by gender is prominent in both years.
Male-dominated occupations are on the left side of the figures and female-dominated
occupations on the right. Interestingly, in both years, the female-dominated occupations all
have larger representation of men in them, as compared with the share of women in maledominated occupations. Perhaps various cultural, social and political processes of
employment underlie this pattern. Yet, since there are more men in the Fiji labour force, it is
statistically more probable that men will be more widely distributed throughout the
occupational structure. Or, since female-dominated occupations are broader than maledominated ones, they may contain pockets of 'male' jobs.
Another important feature of Figure 1 is that, of the two largest occupations in 1986 Agricultural workers (with 106 thousand workers, or 44% of the total) and Production and
Related Workers (with 49 thousand workers, or 21% of the 1986 total) - both are at the maledominated end of the spectrum. It is hardly surprising that women made up only one-in-five
of the labour force when they are largely excluded (constituting only 11%) of the two largest
occupations with 65% of the Fiji work force.
During the intervening decade between 1986 and 1996, however, the female labour force
participation rate rose significantly (from 23% to 39%) while the male rate declined (from
85% to 79%). The female share of the total labour force rose correspondingly, from 21% to
33%, but a large proportion of these women failed to find cash employment and large
numbers entered the non-cash, subsistence (largely agriculture) economy.
The result is reflected in the bottom part of figure 1 where Agriculture, including subsistence,
is placed towards the 'female-dominated' end of the scale of occupations as opposed to its
'male-dominated' position in 1986. By 1996, therefore, women's share of the two largest
occupations (Agricultural workers with 107 thousand workers or 36% of the total and
Elementary Occupations with 46 thousand workers or 15% of the total) had become 33%,
compared with 11% in 1986. Thus, the gender concentration of the labour force would
appear to have become less extreme, yet women's greater representation in these lowly paid
occupations would have done little to raise their overall status in Fiji's labour market.
Alternative Presentations: Distribution of the Male and Female Labour Force
An alternative way of examining male and female concentration is to present the distribution
of employment by sex across occupations to identify its variability. This is calculated as the
percentage of the male and female labour forces in each occupation5 Figure 2 illustrates how
women are grossly under represented in the highly-paid, decision-making occupations Administrators and Managers, and Other Professionals, with only 2% or less of all women in
these categories in 1986. With different occupational classifications, 1% and 7% of women
were found as Legislators, Senior officials and Managers, and as Professionals, respectively
in 1996. Women are a little better represented as Production workers, Sales workers, Clerical
workers and Service workers, which contain between 9% and 15% of all female workers in
1986 and between 8%-9% in 1996. Twelve percent of all women work in the traditional
female occupations as paramedics and teachers in 1986 and 7% in 1996, while figure 13
5
For example, (Number of women in occupation i/Number of women in the total labour force) *100.
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13
shows that almost 1 in 4 female workers were engaged in agriculture and fishing in 1986 but
this had risen to almost 1 in 2 by 1996.
Such an illustration is extremely useful for identifying those occupations where women are
found, and are not found, and for estimating their relative sizes.
Figure 2: Distribution of Males and Females Across Occupations
32%
1996
Females
No. of Workers (thousands)
Males
43%
18 %
12 %
8%
7%
5%
3%
7%
9%
4%
9%
8%
6%
P ro fe s s io n a ls
8%
5%
3%
3%
1%
Le g is la to rs ,
S e n io r
O ffic ia ls ,
Ma n a g e rs
4%
Te c h n ic ia n s &
As s o c ia te s
Cle rks
S e rvic e
Wo rke rs
Ag ric u ltu ra l
Wo rke rs
Cra ft Wo rke rs
P la n t &
Ma c h in e O p s .
E le me ta ry
O c c u p a tio n s
Un e mp lo ye d
Sourc e: Censuses of Population Fiji, 1986 and 1996
An alternative method of presenting the material is portrayed in Figure 3 where all the
occupations are grouped by their percentage female and then plotted against the total number
of workers to be found in each occupation.6 On the horizontal axis, occupations are plotted
according to their percentage female. In 1986, the minimum is 9.1% (Administrative and
6This
technique has the advantage of allowing the researcher to employ a larger number of more detailed occupations (say ISCO 2-or 3digits). The data reported in Figure 3 maintains the same broad categories used earlier.
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14
Managerial) and the maximum is 57.8% (Medical workers and Teachers). Thus, we see that,
in 1986, for occupations that are between 0 and 10% female, there are over 50 thousand
workers; for occupations with between 41% and 50% female there are almost 50 thousand
workers. Thus, most employment is found in occupations where the female share is less than
10% or exceeds 40%. In 1996, with different occupational classifications, the minimum is
11.8% (Craft workers) and the maximum is 54.9% (Clerks). We still find the great bulk of
total employment in occupations where women’s share is relatively small or relatively large.
Figure 3: Distribution of Workers in Occupations According to % Female
1986
Thousands
120
100
80
60
40
20
0
0-10
11-20
21-30
31-40
41-50
51-60
Percentage Points Female
Source: Censuses of Population – Fiji, 1986 & 1996
1996
140
120
100
80
60
40
20
0
0-10
11-20
21-30
31-40
41-50
51-60
Percentage Points Female
Source: Censuses of Population - Fiji, 1986 & 1996
Analysis of the Extremes
The extreme ends of the distribution of these data will now be examined for additional
insights on gender concentration. What proportion of women work in female dominated
occupations? What proportion of women are engaged in occupations dominated by men?
Clearly, the level of detail in the occupational scheme will be a major influence on the results.
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15
In this comparative analysis of the situation in Fiji in 1986 and 1996, 2-digit ISCO-1968
occupations have been used for the earlier year and 3-digit ISCO-1988 occupations for the
most recent year. This raises the likelihood of occupations being dominated by one sex or the
other in contrast to when the broader groups of occupations are used above.
Table 7 shows that, in 1986, there were 46 2-digit occupations, out of a total of 82, where
women constituted only 15% or less of the workforce in those occupations. Almost 27% of
all female workers were found in these overwhelmingly male-dominated jobs. On the other
hand, there were only 8 occupations which were dominated with women, with more than
70% of employment being women. The patterns of concentration are fairly predictable, with
the large number of male-dominated jobs being either high-skilled, or
managerial/supervisory, or requiring artisan skills or a capacity to undertake heavy labouring
work. The female-dominated jobs were traditional 'women's' occupations, making up 24% of
all their employment opportunities. On this evidence, in 1986 one-half of all employed
women in Fiji worked in an occupation heavily dominated by one or other of the sexes.
By 1996, using a different set of occupation codes (ISCO-88), there were 45 3-digit
occupations out of a total of 109 occupations where women made up 15% or less of the
workforce (see Table 7). About 8% of all female workers were located in these maledominated jobs. At the other extreme there were only 9 female-dominated occupations with
60% or more of employment being women7. They held 55% of all female employment.
Thus, in 1996, almost two-thirds of all employed women were engaged in an occupation
heavily dominated by one or other of the sexes. The patterns of concentration are similar to
those found in 1986.
While it is difficult to make comparisons between 1986 and 1996 because of the changes in
occupation codes, it seems reasonable to conclude that women made little headway in
diversifying into male preserves, despite their significant rise in labour force participation.
Figure 4 reflects the gender dominance of the distribution of occupations in both 1986 and
1996. In the earlier year, 80% of all workers were engaged in either male- or femaledominated occupations. Ten years later the relative situation had changed slightly whereby
72% of all workers were engaged in an occupation dominated by one sex or the other.
Significantly, the absolute number of workers in the 'mixed' category had almost doubled,
from 44 thousand to 83 thousand; the absolute number in strongly male-dominated
occupations had fallen from 163 thousand to 123 thousand; and the absolute number in
strongly female-dominated jobs had grown from 14 thousand to 92 thousand.
7
69% of total employment in these 9 occupations was found in one occupation, i.e. subsistence activities.
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16
Table 7
Occupations at the Extremes of the Distribution 1986-1996
1986
0-15% Female
ISCO-68
01
Physical Scientists
02/03 Architects, Engineers
04
Aircraft, Ships Officers
05
Life Scientists
11
Accountants
12
Jurists
16
Creative Artists
17
Composers, Performers
18
Athletes, Sportsmen
20
Legislative Officials
21
Managers
31
Gov. Executive Officials
35
Transport/Comm. Superv.
36
Transport Conductors
37
Mail Distrib. Clerks
40
Managers in Trade
41
Working Propr. Trade
43
Technical Salesmen
44
Insurance Salesmen
49
Other Sales Workers
58
Protective Services
60
Farm Managers
61
Farmers
62
Farm Workers
63
Forestry Workers
70
Production Superv.
71
Miners Quarrymen
72
Metal Processors
73
Wood Prep. Paper Makers
80
Shoemakers, Leatherwork
81
Cabinet Makers, Woodwork
82
Stone Cutters
83
Blacksmiths, Toolmakers
84
Machine Fitters
85
Electrical Fitters
86
Sound Equip. Op.
87
Plumbers, Welders
90
Rubber, Plastic Products
92
Printers
93
Painters, Construction
95
Bricklayers, Carpenters
96
Stationary Engine Ops.
97
Material Handling
98
Transport Equip. Oper.
990 Other Labourers
991 Armed Forces
TOTAL
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70% + Female
( 34 )
( 70 )
( 5 )
( 68 )
(178)
( 11 )
( 19 )
( 54 )
( 5 )
( 40 )
(211)
(129)
( 34 )
( 10 )
( 45 )
( 87 )
(241)
( 25 )
( 43 )
( 18 )
(117)
( 22 )
(8657)
(2077)
( 16 )
(109)
( 17 )
( 2 )
( 7 )
( 13 )
( 31 )
( 0 )
( 50 )
( 56 )
( 35)
( 9 )
( 22 )
( 17 )
( 82 )
( 5 )
( 55 )
( 12 )
(469)
( 68 )
(313)
( 11 )
(13599)
06/07 Medical, Dental, Veter
32
Stenographers
34
Computing Mch. Oper.
54
Maids
75
Spinners, Weavers
79 Tailors, Sewers
91
Paper Product Makers
94
Prod. Workers n.e.c.
TOTAL
(1838)
(2991)
( 249 )
(4637)
( 321 )
(1845)
( 208 )
( 229 )
(12318)
17
1996
0-15% Female
ISCO-88
111
Legislators & Senior Officials
113 Traditional Chiefs & Heads
211
Physicists, Chemists etc.
214
Architects
221
Life Science Professionals
246
Religious Professionals
311
Physical etc. Eng. Tech
314
Ship/Aircraft Controllers
321
Life Science Technicians
345
Police Inspectors
348
Religious Assoc. Profs.
516
Protective Service Workers
611
Market Gardeners
612
Animal Producers
614
Forestry Workers
711
Miners etc.
712
Building Trade Workers
713
Building Finishers
714
Painters etc
721
Metal Workers
722
Blacksmiths etc
723
Machinery Mechanics
724
Electrical Mechanics
732
Potters
811
Mining Plant Ops
812
Metal-Processing Ops
814 Wood Processing Ops
815 Chem-Plant Ops
816
Power Production Ops
817 Auto-Ass. Ops
822 Chem-Products Mach. Op.
824 Wood-Product Op.
825 Printing Op.
831 Logo-Engine Ops
832 M.V. Ops
833 Agric. Ops
834 Ships Deck Crews
912 Shoe Cleaning
914 Building Caretakers
915 Messengers etc.
916 Garbage Collectors
921 Agric. Labourers
931 Mining/Const. Labourers
933 Transport Labourers
Unallocated
TOTAL
60% + Female
(14)
(1)
(31)
(71)
(10)
(117)
(219)
(46)
(103)
(7)
(33)
(337)
(3,668)
(37)
(8)
(27)
(60)
(53)
(23)
(39)
(3)
(60)
(24)
(36)
(10)
(6)
(8)
(7)
(5)
(5)
(7)
(14)
(20)
(7)
(91)
(38)
(36)
(19)
(57)
(155)
(5)
(1,692)
(122)
(28)
(37)
(7,388)
223
243
323
411
422
513
621
826
913
Nursing Profs.
Archivists etc
Nursing Assoc. Profs
Secretaries
Client Info. Clerks
Personal Care Workers
Subsistence Workers
Textile Mach. Ops
Domestic Workers
(1,048)
(169)
(532)
(2,844)
(1,273)
(287)
(37,395)
5,598)
(4,967)
(54,113)
Note: Figures in parentheses are numbers of women in these occupations
Source: Fiji, 1988, Census of Population 1986 - and Fiji, 1988, Census of Population 1996, Bureau of Statistics, Suva
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18
Figure 4: Distribution of Gender Dominance of Occupations
Thousands of Workers
1986
200
150
100
50
0
Strongly Male Dominated
Mixed
Strongly Female
Dominated
1996
200
150
100
50
0
Strongly Male Dominated
Mixed
Strongly Female
Dominated
Source: Censuses of Population - Fiji 1986 & 1996
In the upper part of Figure 5, zero point on the vertical axis is where the percentage female of
the occupation is 21%, equal to the percentage female in the total labour force in 1986. The
most female-dominated occupations are plotted above the horizontal axis, those with the most
male dominance lie below the horizontal axis. In Figure 5, the occupations are presented
from the most male-dominated (Administrative and Managerial Workers) to the most femaledominated (Medics and Teachers) in 1986. In 1996, using 2-digit ISCO-88 occupations, the
most male-dominated are construction jobs, drivers, machine operators and science
professionals and managers. In contract but predictably, the most female-dominated jobs are
in teaching, nursing and clerical jobs.
The results of the analysis of the distribution of gender by occupations in Fiji confirms that it
is extremely concentrated and has remained so through time. How has this concentration
impacted on the relative earnings of women compared with men? It is to this question that
we now turn.
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19
Figure 5: Difference Between % Female in Occupation & In Total
1986
40
30
20
10
0
Admin. &
P roduc t ion
Agric ult ura l
Ma na ge ria l
Worke rs
Worke rs
Ot he r P rofs.
S a le s
S e e king
Cle ric a l
S e rvic e
Me dic s/ Te a c he rs
Employme nt
-10
-20
50
1996
40
30
20
10
0
-10
P hysic a l a nd
Drive rs &
Eng. S c i.
P la nt . Op.
Me t a l Tra de S t a t iona ry
Worke rs
P la nt Op.
Agric . La bs.
S c ie nc e
Ge ne ra l
Offic e
Te a c hing
S a le s &
S ubsist e nc e
Cust ome r
Life S c ie nc e
Ma c hine
P rofs.
Ma na ge rs
Cle rks
P rof.
S e rvic e
Fa rming
S e rvic e
& He a lt h
Ops.
Cle rks
P rofs.
Te c h
Ele m. Oc c .
-20
-30
-40
Source: Censuses of Population: Fiji, 1986 and 1996
4.
THE STRUCTURE OF EARNINGS BY SEX
The Source of Information: The Fiji Employment Survey
One of the principal aims of this paper is to examine the relative position of female workers
in Fiji’s formal sector labour market. After portraying the gender distribution of occupations
and changes in recent years, it now turns to estimating the extent of “discrimination” against
women in terms of their job assignments and relative pay.
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20
Most of the data in this particular analysis derive from a specially designed survey initiated
by the Ministry of National Planning and Information of the Government of Fiji and financed
under a UNFPA-funded project: “Assistance in Population, Workforce and Human
Resources Development Planning” (FIJ/95/P02) and executed by the ILO. The survey data
collection exercise was sub-contracted to a researcher from the School of Social and
Economic Development of the University of the South Pacific (USP) while the UNFPA
Country Support Team, Suva, provided overall guidance and technical advice. The survey
was conducted in April 1997.
The overall rationale for the establishment-based survey was to collect scarce information on
Fiji’s formal sector labour market, including its gender dimensions, which could be analyzed
and used in the preparation of a Strategic Human Resources Development Plan for the years
through 2001 and beyond. Information collected included the establishment name, its
location, and the public-private-parastatal nature of its operations. From each individual
employee respondent, the information collected included: ethnicity, marital status, age, sex,
highest level of formal education attained, type of vocational training undertaken, if any, its
duration, hours of work in the last week and gross wages/salary, value of fringe benefits in
past 12 months, occupation, years in the occupation, years of tenure with current employer
and previous number of employers.
The Survey Methodology
According to the list of establishments of the Fiji National Provident Fund (FNPF), there
were approximately 4221 firms and establishments paying FNPF contributions for their
employees in 1997. The total number of employee contributors amounted to 110,091. While
the payment of contributions by employers for those employees who work for more than two
weeks is a legal requirement, it is believed that there are some firms in Fiji who are not
included in the list because they are not registered with the FNPF.
Aside from this unavoidable omission, this FNPF source is the most comprehensive list of
firms and establishments in Fiji and, for this reason, this listing was used as the frame for
selecting the sample of firms and their employees for this exercise.
A total of 320 establishments (7.6% of the total) were selected for enumeration using a
stratified random sampling technique. It was necessary that the sample of establishments was
selected from a reasonable representation of the main economic activities and, in addition,
that small, medium and large establishments were represented in the sample. Because of the
concentration of formal economic activities on the two main islands of Viti Levu and Vanua
Levu, and to minimise the costs of conducting the survey, only establishments located in the
main urban areas were selected.
Each of the selected establishments, including Government and semi-government or
parastatal organisations, was visited by a trained enumerator from the USP. Contact with a
senior administration officer had taken place in advance of the enumerator’s visit and the
purpose of the exercise explained. From those establishments employing less than 200
employees, information was collected from each individual worker. For those with 200 or
more employees, the enumerator made a stratified random selection of 200 names from a
complete listing of employees provided by the firm.
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21
As a result, a total of 8153 individual employees were interviewed, representing a sample of
just over 7% of the formal sector workforce. Since we do not have a complete profile of the
population of formal sector workers, it is difficult to gauge how well the characteristics of our
sample of employees fit those of workers in the formal sector. However, the age distributions
from the 1996 Population Census are reasonably close to those of our survey, particularly that
representing cash workers in the urban economy, a concept coming closest to that of the
formal sector as represented in our survey. According to this criterion, that is, the age
distribution of workers in our sample, we can be reasonably satisfied that our sample of
workers is very similar to the age distribution of formal sector workers in Fiji. In addition,
the public sector, including public corporations or parastatals, is believed to account for
roughly 40% of total formal sector employment; our survey contains a total of 38.6% of
public and parastatal employees.
Male-Female Pay Differentials
The following section examines the structure of earnings from our employment survey
according to various personal human capital and other institutional characteristics. Table 8
reports the mean level of weekly earnings, including overtime and annual fringe benefits
converted to a weekly basis, by age group, sector and sex. It demonstrates that mean pay is
consistently higher for men compared with women and is higher in the public and parastatal
sectors compared with the private sector.
Table 8
Mean Weekly Earnings by Age Group, Sector and Sex (F$)
Age Group
14-19
20-24
25-34
35-44
45-54
55+
Total
No. of Obs.
Public Sector
Males Females
112+
140
174
206
247
223
199
1147
111+
135
156
183
225
140+
166
638
Parastatal Sector
Males Females
141+
169
224
245
221
214
223
1009
86+
149
208
214
251
102+
199
247
Private Sector
Males Females
63
99
142
163
192
190
136
3279
58
87
117
121
137
124+
107
1688
Males
Total
Females
67
110
161
194
219
206
165
5435
61
104
136
146
185
131
130
2573
Note: + less than 20 cases
Source: Fiji Employment Survey, 1997
Earnings are higher for younger, new recruits in the public and parastatal sectors and rise
fairly consistently in all sectors with age, proxying for labour market experience, up to age
55, after which earnings tend to decline. The earnings profiles are much steeper in the public
and parastatal sectors, perhaps reflecting the structured internal labour market in these sectors
and the formalised procedures allowing for job promotion along with lengthening job tenure.
It is also revealing to see the much flatter profile of female earnings as age increases,
particularly in the private sector, perhaps reflecting the kinds of occupations to which they
are assigned and the lack of opportunity for much on-the-job skill acquisition and
productivity and pay advance. It may also reflect, to some extent, the effect of certain
discontinuities in women’s attachment to the labour market and the loss of productivity and
pay-enhancing continuous work experience.
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22
Table 9
Mean Weekly Earnings by Educational Attainments, Sector, and Sex (F$)
Public Sector
Total
Males
Males
Education
None
Classes 1-6
Forms 1-2
Forms 3-5
Complete Sec. (F67)
Certificate/Diploma
University Degree
Total
No. of Obs.
153+
118
135
173
190
284
367
199
1147
Parastatal
Private Sector
Sector
Females Males Females Males Females
126+
121+
120+
152
158
212+
290
166
638
133+
147
152
213
248
355
456
223
1009
129+
131+
198
187
255+
341+
199
247
83
119
115
113
146
356
427
136
3279
48
59
72
86
135
223+
255
107
1688
Females
106
129
127
137
175
316
408
165
5435
66
66
78
105
148
222
292
130
2573
Note: + less than 20 cases
Source: Fiji Employment Survey, 1997
Mean weekly earnings in table 9 rise consistently with school attainments, but especially after
completing at least 8 years of education. The public and parastatal sectors pay a significant
premium to workers with schooling up to the completed secondary level, for both men and
women, and thus appear as wage leaders in the market for the lower skilled. The private
sector is much more competitive in its earnings scale in the market for the most educated
labour and exceeds the mean pay of the public sector for university level males and both the
public and parastatal sectors for males with a post-secondary certificate or diploma.
Male-female earnings differentials are significant in all three sectors in table 9 and generally
take a U-shape, being wider at lower and higher levels of education compared with
completed secondary education. This is also the case in the private sector where the U-shape
pattern is exaggerated. No doubt, one possible explanation would be that there are
differences in the occupational assignment of males and females at lower and higher levels of
education. For example, very few females with higher education attain the very senior
executive positions with correspondingly high pay. These positions remain almost
exclusively male preserves.
The same is also true in the public and parastatal sectors except that the male-female pay
differences are not nearly so marked as in the private sector and the pattern is one of low pay
differences in line with the lower the level of education. It would appear that the public and
parastatal sectors are much less discriminating in their treatment of the sexes. Even then,
male university graduates earn about one-third more than female graduates, so complacency
on the part of public sector officials is not warranted.
These patterns are more clearly demonstrated in figures 6 to 7. Figure 6 demonstrates how
the earnings profile of more educated males has a much steeper slope than for the less
educated whose earnings profile is quite flat as age progresses; the pattern is broadly similar
for females in figure 7. The less educated seem to be assigned to occupations where
opportunities for productivity–enhancing, on-the-job skill acquisition are severely limited.
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23
Figure 6: Mean Weekly Earnings by Education - Male Workers
800
600
400
None
Years 1-6
200
Forms 1-2
Forms 3-5
0
20-24
14-19
25-34
35-44
45-54
Forms 6-7
55+
Cert.
Age Group
University
Source: Fiji Employment Survey 1997
Figure 7: Mean Weekly Earnings by Education Female Workers
400
300
200
100
0
None
Years 1-6
Forms 1-2
Forms 3-5
Forms 6-7
14-19
20-24
25-34
35-44
45-54
Certificate
55+
University
Age Group
Source: Fiji Employment Survey 1997
Table 10
Mean Weekly Earnings by Occupation, Sector and Sex (F$)
Public Sector
Age Group
Males
Females
318
234
184
139
166
139
131
199
1147
247
195
145
190+
133
161+
110
166
638
Senior
Professionals
Middle
Professionals
Clerical
Sales
Service
Artisans
Garment Workers
Other Blue Collar
Total
No. of Obs.
Parastatal
Sector
Males Females
467
293
208
213+
149
238
140
223
1009
343+
304+
185
201+
182+
243+
121+
199
247
Private Sector
Males
380
236
164
112
107
109
75
100
136
3279
Females
300
217
160
87
96
82
55
93
107
1688
Total
Males
Females
374
251
179
118
133
130
75
117
165
5435
283
205
161
97
111
86
55
98
130
2573
Note: + Less than 20 cases
Source: Fiji Employment Survey, 1997
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24
Table 10 demonstrates that occupational pay differences are large in Fiji, with earnings being
five times greater for the highest paying occupation group (Senior Professionals) over the
lowest (Garment Workers). Senior professionals in the parastatal and private sectors earn
more than their peers in the public sector; lower skilled workers in the public and parastatal
sectors have a pay advantage over those in the private sector. For example, male blue-collar
public sector workers receive a 31% premium over their counterparts in the private sector;
female public sector workers in this broad occupational category have an 18% advantage
over their private sector equivalents.
Male-female pay differentials are significant after controlling for these broad occupational
groupings, particularly at the higher skill level. Rather than suggest that male and female
employees receive different rewards from performing the same job side-by-side, it is much
more likely that more specific gender-based occupational assignments explain much of these
pay differences. These issues will be investigated in more detail using multivariate techniques
in later papers.
Table 11 reports the mean level of weekly earnings by industrial sector and sex. Earnings are
highest in the Finance and Insurance and Utilities sectors. The lowest mean pay arises in the
sectors of Agriculture, Manufacturing – especially garment making – and Trade and Hotels,
all traditionally employers of relatively low-skilled, cheap labour. Predictably, in most of the
sectors, average female earnings are exceeded by male earnings, with the exception of
Construction and Transport and Communications, where perhaps, women are performing
office work and men are engaged in more manual tasks. However, only in a few cases is the
overall significant earnings difference between the sexes replicated at the sectoral level.
Table 11
Mean Weekly Earnings by Sector of Activity and Sex (F$)
Sector
Agriculture, For., Fishing
Food Manufacturing
Garments and footwear
Other Manufacturing
Electricity/Water
Construction
Trade & Hotels
Transport & Communications
Finance & Insurance
Services
Total
No. of Obs.
Males
Females
124
178
80
110
253
145
132
162
252
202
165
5435
144+
124
60
79
210
155
105
173
238
170
130
2573
Total
124
170
65
107
245
147
124
164
246
192
154
8008
Male/
Female
0.9
1.4*
1.3*
1.4
1.2
0.9
1.3*
0.9
1.1
1.2*
1.3*
Note: + less than 20 cases; * significant at the 5% level
Source: Fiji Employment Survey, 1997
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25
5.
A DECOMPOSITION OF MALE-FEMALE EARNING DIFFERENTIALS
Women’s earnings are inferior to men’s throughout the world where average female-male
pay ratios are roughly 70-75%, based on daily and weekly reference periods, and 75-80%
based on an hourly reference period (Anker, 1997). Ratios are especially low in east and
south-east Asian and some OECD countries where, for all non-agricultural earnings, the ratio
for hourly pay is as low as 68% in Luxembourg and Switzerland and as high as 88% in
Australia and 91% in Sri Lanka. On a daily or weekly basis the ratio is low in Hong Kong
(70%) and Cyprus (59%) and higher in Sri Lanka (90%) and Turkey (85%). Unweighted
world averages are 77.8% on an hourly basis, 76.7% on a daily/weekly basis and 71.6% on a
monthly basis (Anker, 1997). In Fiji, for the whole of the formal sector, the ratio is 78.9% on
a weekly basis and 82.2% on an hourly basis.
This section of the paper presents a description of the methodology to be employed to explain
these gender differences in pay in the Fiji formal sector labour market, followed by a report
of the in-depth analysis undertaken.
The Methodology
(a)
The Standard Method
The standard decomposition of male/female earnings differentials has been widely utilised in
many studies around the world. The method requires separating the male/female earnings
differential into two parts: the portion attributable to the ‘composition’ or ‘endowment’ effect
which accounts for the unequal distribution of human capital throughout the population, and
the portion reflecting ‘discrimination’ – indicating that women obtain a lower wage despite
comparable qualifications and potential productivity.
More formally, men and women are assumed to have separate earnings functions described
by:
Wim = Xijmßjm + Єim i = 1,. . .,Nm; j = 1,. . ., Nj
(1)
Wif = Xijfßjf + Єif i = 1, . . .,Nf; j = 1,. . .,Nj
(2)
Where Wim [Wif] denotes the earnings of the i-th male [or female] worker, Xim[Xif] is a 1xNj
row vector of j individual characteristics that impact the wage determination process, ßjm [ßjf]
is a set of regression coefficients, and Єim and Єif are well-behaved error terms with zero
means and constant variances. Recalling a property of ordinary least squares estimation, that
the fitted regression must pass through the vector of means, the difference between male and
female earnings can be expressed as:
m - f = j (ßjm – ßjf) jm + Σj ßjf (jm - jf)
(3)
where a bar over a variable denotes its mean value. This decomposition/standardisation of
earnings differentials is attributable to Blinder [1973] and Oaxaca [1973]. In equation (3) the
difference between male and female earnings has been divided into two components: (1) the
difference in wages which results from differences in male and female pay structures, {Σj
[ßjm-ßjf] jm} and (2) the difference which results from a compositional effect due to
differences in endowments, {Σj ßjf [jm-jf]}. The greater the first term, the higher the degree
of labour market discrimination.8 While this type of decomposition has been applied
8
Note that the decomposition can also proceed as:
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26
successfully in many previous studies, it has primarily been employed in the developed
country literature.9
(b)
The Expanded method
The above strategy is subject to the criticism that it does not distinguish adequately between
wage discrimination and occupational segregation; and we have observed earlier the
importance of the latter phenomenon in Fiji. Wage discrimination arises when workers with
exactly the same endowments are paid differently. Occupational segregation or job
discrimination arises when workers with the same endowments do not have equal access to
all occupations. We would suspect that differences in the occupational distributions of men
and women can play a major role in explaining differences in earnings between the sexes.
Brown et al. [1980] have proposed an alternative decomposition which can be thought of as a
straightforward extension of the basic methodology outlined above. The expected mean
wage (for either males or females, [m; f] can be thought of as the sum of each occupational
wage (hm; hf; h = 1, . . ., Nh) times the sex-specific probability of attaining that occupation
(Phm; Phf; h = 1, . . ., Nh). More formally:
m = h Phm hm (h = 1, . . ., Nh)
f = h Phf hf (h = 1, . . ., Nh)
h Phm = h Phf = 1
(4)
The probability of the I-th worker of either sex having occupation h, is a function of his/her
characteristics (Zikm; Zikf), written as:
Pihm = exp(Zihkm hkm)/t=1,.,h,.,Nh exp(Zitkm tkm) = Hhm(Zikm)
Pihf = exp(Zihkf hkf)/t=1,.,h,.,Nh exp(Zitkf) tkf) = Hhf(Zikf)
(5)
where Z is the vector of individual characteristics, and  is the set of (Njx(Nh-1) parameters
to be estimated. The proportion of men in occupation h is written:
Phm = Hhm (hm)
(6)
The proportion of women in occupation h, if their occupational attainment is determined the
same way as men, would be:
Phf = Hhm (hf)
(7)
By assuming that the errors in the occupational choice process are independent of the errors
in the wage equations within occupations, and using logic identical to that above, it is
possible to decompose the differences in mean earnings in the following manner:
m - f = h Phm hm - h Phm hm - h Phf hm + h Phf hm - h Phf hf
m - f = j (ßjm – ßjf) jm + Σj ßjf (jm - jf)
(3)
:reflecting the familiar ‘index-number’ problem whenever heterogeneous goods (X’s) can be aggregated using
two different sets of prices (ß’s) [Cain, 1986].
9
An exception is the application for Khartoum, Sudan by Cohen and House (1994).
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27
= (h (Phm – Phf) hm + h Phf (hm - hf)
(8)
The first term in equation (8) captures differences in earnings attributable to differences in the
distribution of jobs by sex, while the second captures differences in earnings attributable to
each occupation having a sex-specific wage. Both of these terms can be further decomposed.
Finally, we have:
m - f = h(Phm - Phf) hm + h Phf (hm - hf)
= h hm (Hhm(hf) – Phf) + h hm (Phm - Hhm(hf))
+ h Phf ((ßhm – ßhf)hm) + h Phf (ßhf (hm - hf)
(9)
All expressions are evaluated at their means. The first term represents occupational
segregation. It captures how differences in personal characteristics affect the probability of
attaining a given occupation. The second term represents differences in the occupation
distribution of men and women due to differences in their personal characteristics. Although,
if the entry criteria for certain jobs includes prior on-the-job or other firm specific training,
differences in personal characteristics can reflect unequal access to training opportunities.
The third term provides an alternative measure of wage discrimination than the one derived
from the standard method presented earlier. It isolates the effect of sex differences in wage
structures controlling for levels of endowment and occupation. The fourth term is the
proportion of differences in wages that can be explained by differences in worker
characteristics. Calculation of the extended model requires estimating a model of
occupational attainment as well as separate earnings equations for each sex-occupation cell.
Only the results of estimating the standard method are reported here; the empirical results of
the expanded method will be reported in a subsequent paper.
The Results
(a) The Standard Method
Earnings functions were estimated separately for males and females using a variant of the
model in equation (10) is derived from equations (1) and (2) above. The individuals and sex
subscripts have been dropped:
Ln(HRPAY) = o + u * EDu + 7 *EXP + 8EXP² + 9FIRMEXP + 10FIRMEXP²
+ v*ETHNICv + 14PUBLIC + 15PARSTAT + 16TRAIN
+ o*OCCUPo + s*SECTORs
(10)
Equation (10) contains a set of measures of formal school attainments (EDu; U = 1 …. 6)
years of potential labour market experience and its square (EXP, EXP²), years of firm
specific experience and its square (FIRMEXP, FIRMEXP²), ethnic group (ETHNICv; v = 11,
12, 13), variables denoting whether the respondent works in the public sector (PUBLIC) or
for a public corporation (PARSTAT), an indicator variable for whether the employee has ever
received formal vocational training (TRAIN), and additional control variables relating to the
respondent’s occupation (OCCUPo; o = 17 …23). The dependent variable, Ln(HRPAY) is
the natural logarithm of the hourly wage rate, which is believed to be a superior measure to
annual, monthly or weekly earnings, since it does not rely on a restricted and fixed quantity
of labour supply.
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28
The detailed equations, including variable definitions and mean values, are reported in the
Appendix. The model is very successful, explaining over 60% of the variance of the natural
logarithm of hourly earnings for males and over 70% for females. A Chow-test on the model
was performed to test for parameter or structural differences in the male and female
equations. The null hypothesis, that the slopes are equal, was rejected at the one percent
level. In other words, the labour market returns to the various human capital and other
attributes of male and female workers are significantly different.
Taken together, the dummy variables denoting educational attainments are highly significant
although positive returns do not appear until at least 9 years of education are attained.
Women perform better than men in achieving higher returns to senior secondary over junior
secondary education and to certificate/diploma-level education over senior secondary
schooling. At the highest level of university education, however, the returns relative to the
next lower level are slightly higher for men, perhaps reflecting the lower occupational
attainments, especially lower professional and managerial positions, of more educated
women. Men receive a higher return to general labour market experience than families; there
is a much higher rate of return to firm or current employer experience with a negligible
advantage to women.10
Using the ‘standard’ method described above, the earnings differential is decomposed in
Table 12 into (1) a portion attributable to a composition effect (the column labeled
‘Endowments’), and (2) a portion attributable to structural differences in the two earnings
functions, (the column labeled ‘Discrimination’). Referring to the summary at the bottom of
the table, approximately 40% of the difference in earnings can be attributed to a
compositional effect, while 60% can be attributed to differences in the structural parameters.
It should be mentioned that no attempt has been made to account for workers’ innate ability,
degree of motivation or commitment to the labour force, quality of education, or union
effects. As the first part of the table reveals, the advantage of male endowments in the total
labour market comes mainly in the form of greater labour market and firm specific
experience being employed in the public sector and being engaged in the high-wage
industrial sectors. As shown above, men do not have an advantage in formal education; this
is confirmed by a negative coefficient on education in the endowment and discrimination
columns. With respect to the cause of discrimination, the principal source lies in the size of
the differential in the intercept term in favour of men, which accounts for 74% of the total
hourly earnings differential between the sexes. The returns to potential experience and
vocational training also favour men.
Our measure of general experience is only potential experience (Age minus Years of Education-6) and does not control for protracted
absences from the labour market which may result in still obsolescence, obsences which are more common among women because of
their maternal and domestic duties.
10
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29
Table 12: Decomposition of Earnings Differentials between male and Female Formal
Sector Employees in Fiji
Explanatory Variable
Education
Potential Experience
Firm Experience
Ethnic Group
Public Sector Employee
Training Received
Occupation
Sector
Total
Summary of Results:
Contribution due to
different endowments
Contribution due to
differences in returns to
explanatory variables
Intercept differential
Total differential due to
structural parameters
Overall earnings
differential
(Difference in log
earnings)
bm(m-f)
‘Endowments’
-.016
.013
.048
-.007
.027
-.007
-.018
.045
.085
f(bm-bf)
‘Discrimination’
-.034
.068
-.009
.010
-.017
.021
-.007
-.062
-.030
bm(m-f)
‘Endowments’
-.077
.026
.080
.006
-.011
-.050
-.019
-.045
f(bm-bf)
‘Discrimination’
-.120
-.001
-.030
.003
.012
.021
-.021
-.136
bm(m-f)
‘Endowments’
.008
.007
.007
-.012
-.003
.006
.052
.065
f(bm-bf)
‘Discrimination’
-.024
.109
.004
.018
.009
-.041
-.028
.047
.085
(40.7%)
-.045
(-80.4%)
.065
(26.6%)
-.030
-.136
.047
.154
.124
(59.3%)
.237
.101
(180.4%)
.132
.179
(73.4%)
.209
(100.0%)
.056
(100.0%)
.244
(100.0%)
The combined effect of the occupational dummy variables is minimal even though, as
documented earlier, there is extreme occupational segregation. It is not possible, however, to
conclude from this analysis that occupational sorting does not play a major role in explaining
differences in mean earnings between men and women. By treating broad-based
occupational groups as a set of dummy variables the analysis has discounted implicitly both
the possibility of different returns to human capital between occupations and the possibility
that the formation of the occupational distributions for men and women may, themselves, be
discriminatory. The conclusion that occupational differences make little difference would,
therefore, be premature. More in-depth analysis of the expanded method, to be reported in a
later paper, will shed greater light on the extent of sex discrimination in access to
occupational assignments.
Earnings functions are decomposed separately for the combined public/parastatal or public
corporations sector and the private sector and the results are reported in table 12. In the
public sector the female-male pay differential (F$1.06) is relatively small compared with the
private sector (F$1.28). Overall endowments favour female workers in the public sector as
do the returns to these human capital attributes; but again the interecept term is relatively
large and in favour of men such that the net effect is a small male pay advantage.
In the private sector all the causal factors, endowments, returns to endowments and the
intercept term, all favour men. However, the intercept differential is the largest contributor to
the male pay advantage.11 Interestingly, the private sector appears to attract men with
Reflecting the importance of the intercept term as an indicator of discrimination, it is revealing to examine the male-female weekly pay
differential across the public-private sectors for one of the youngest age groups in table 8. The male advantage in pay is apparent in the
parastatal and private sectors for 20-24 year olds, where experience differences would be negligible. Also in this age group, males with
3-5 years of education receive a premium in the hourly rate of pay of 17% over similarly educated females; for those with university
education the premium is 24%. For those with completed secondary education there is no pay advantage for males. Again, it could well
be that discriminatory practices in occupational assignment may underlie these pay advantages of young males, which are then
perpetuated throughout their future working lives.
11
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30
superior human capital attributes – education, potential experience and firm experience – and
they are more likely to be located in the most desirable occupations and sectors. In addition,
the returns to these endowments very often favour male workers.
6.
CONCLUSIONS
This paper has demonstrated that there was an apparent significant increase in the labour
force participation of women in Fiji over the decade 1986-1996. The female share of the
total labour force rose from 21% to 33%, but a large proportion of these women failed to find
cash employment and large numbers entered the non-cash, subsistence, largely agricultural
economy. By the mid-1990s women’s share of the two largest occupational groupings
(agriculture and elementary occupations) had trebled to one-third and the gender
concentration of the labour force seems to have become somewhat less extreme. Yet, their
greater representation in the lowly paid occupations did little to raise women’s overall status
in Fiji’s labour market. Women have made little headway into male occupational preserves.
The analysis went on to examine how this extreme concentration has impacted on the relative
earnings of women compared with men in the formal sector of the labour market. Using data
collected from over 8,000 employees in a specially designed survey, analysis has shown that
mean pay is consistently higher for men compared with women while women’s earnings
profile across age groups is much flatter, especially in the private sector, perhaps reflecting
the kinds of jobs to which they are assigned, lacking opportunities for much on-the-job skill
acquisition and pay advance. This result may also reflect the effect of discontinuities in
women’s attachment to the labour market and the loss of productivity and pay-enhancing
continuous work experience.
Male-female earnings differentials take a U-shape across education levels, being wider at
lower and high levels of schooling. Again, since very few women with higher education
attain the very senior, highly paid executive positions, gender differences in occupational
assignment contribute to these differences.
The paper concludes with a standard decomposition analysis of male/female pay differentials
which has been widely utilised around the world. The method decomposes the pay
differential between the sexes into a portion attributable to a composition effect –
“endowments” – and a portion attributable to structural differences, or “discrimination”,
reflecting differences in the returns to these endowment factors. The results show that
roughly 40% of the pay difference can be attributed to the composition effects, reflecting
differences between males and females in their education, general and firm experience, ethnic
group, sector of employment, training received and occupation. Some of these attributes,
including education and training, favour female workers.
The remaining 60% of the pay difference is attributable to differences in the returns to
various human capital and other characteristics of the workers. Again, some advantages in
these returns favour women. The principal source of “discrimination” lies in an unexplained
intercept term in the regression analysis, whereby mean male earnings lie above that of
females after controlling for many of these human capital attributes and various types of
insertion in the Fiji labour market.
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31
The analysis leaves open the possibility that occupational sorting may play a major role in
explaining the differences in earnings between the sexes. By treating broad-based
occupational groups as a set of dummy variables, the analysis has discounted both the
possibility of different returns to human capital between occupations and the possibility that
the formation of the occupational distributions for men and women may, themselves, be
discriminatory.
This paper has elaborated on the methodology – the “expanded method” – that would allow
an estimate of occupational discrimination to be made, whereby women with the same human
capital and other attributes as men, do not have equal access to all occupations, including the
highly paid executive positions. A subsequent paper in this series will utilise the data
collected in the special survey to test this model.
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32
REFERENCES
Anker, R. (1997), “Theories of Occupational Segregation by Sex: An Overview”,
International Labour Review, Vol. 136, No. 3
Blinder, A.S. (1973), “Wage Discrimination: Reduced Form and Structural Estimates”,
Journal of Human Resources, Vol.8, No.4
Brown, R.S., Moon, M. and B.S. Zoloth (1980), “Incorporating Occupational Attainment in
Studies of Male-Female Earnings Differentials”, Journal of Human Resources, Vol.
15, No.1
Cain, G.G. (1986), “The Economic Analysis of Labour Market Discrimination: A Survey”, in
O. Ashenfelter and R. Layard (eds); Handbook of Labour Economics, Vol. 1,
Amsterdam; North Holland
Cohen, B. and House, W.J. (1994), “Education, Experience and Earnings in the Labour
Market of a Developing Country: The Case of Khartoum”, World Development, Vol.
22, No. 10
Fiji (1984), “Fiji Employment and Development Mission: Final Report to the Government of
Fiji”, Parliamentary Paper No. 66
Fiji, Censuses of Population, 1976, 1986 and 1996, Bureau of Statistics, Suva
Fiji, (1997), Development Strategy for Fiji: Policies and Programmes for Sustainable
Growth, Ministry of National Planning, Suva
Oaxaca, R.L. (1973), “Male-Female Wage Differentials in Urban Labour Markets”,
International Labour Review, Vol. 14, No. 3
Siltanen, J., Jarman, J. and Blackburn, R.M. (1993) Gender Inequality in the Labour Market:
Occupational Concentration and Segregation: A Manual on Methodology, Interdepartmental Project on Equality for Women in Employment, International Labour
Office, Geneva
United Nations (1994), Programme of Action Adopted at the International Conference on
Population and Development, New York
United Nations Development Programme (UNDP) (1998), Human Development Report,
1998, UNDP, New York
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33
APPENDIX
Table A1: Variable Definitions in Regression Analysis
Variable
Ln(HRPAY)
ED1
ED2
ED3
ED4
ED5
ED6
EXP
EXP²
FIRMEXP
FIRMEXP²
ETHNIC11
ETHNIC12
ETHNIC13
PUBLIC
PARSTAT
TRAIN
OCCUP17
OCCUP18
OCCUP19
OCCUP20
OCCUP21
OCCUP22
OCCUP23
SECTOR24
SECTOR25
SECTOR26
SECTOR27
SECTOR28
SECTOR29
SECTOR30
SECTOR31
SECTOR32
Definition
Natural logarithm of hourly pay
D.V: One if 1-3 years of schooling
D.V: One if 4-6 years of schooling
D.V: One if 7-9 years of schooling
D.V: One if 10-12 years of schooling
D.V: One if post-secondary schooling
(diploma/certificate)
D.V: One if university level education
Years of potential experience other than with current
employer
The square of EXP
Years with current employer
The square of FIRMEXP
D.V: One if respondent is Indian
D.V: One if respondent is Rotuman
D.V: One if respondent is not Fijian, Indian or
Rotuman
D.V: One if respondent works in the public sector
D.V: One if respondent works in a public corporation
D.V: One if respondent has received vocational
training
D.V: One if employed as a lower level professional
D.V: One if employed in a clerical occupation
D.V: One if employed in a sales occupation
D.V: One if employed in a service occupation
D.V: One if employed in an artisan occupation
D.V: One if employed as a garment worker
D.V: One if employed in any other blue collar
occupation
D.V: One if engaged in the agricultural sector
D.V: One if engaged in the utilities sector
D.V: One if engaged in the construction sector
D.V: One if engaged in the transport sector
D.V: One if engaged in the finance sector
D.V: One if engaged in the services sector
D.V: One if engaged in the food manufacturing sector
D.V: One if engaged in the garment manufacturing
sector
D.V: One if engaged in other sector
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Males
Mean
S.D.
1.128
0.637
0.017
0.131
0.038
0.191
0.210
0.408
0.640
0.480
0.105
0.307
Females
Mean
S.D.
0.920
0.689
0.007
0.081
0.024
0.153
0.119
0.324
0.728
0.445
0.114
0.318
0.049
8.714
0.217
8.669
0.031
7.725
0.175
8.096
151.076
8.218
138.216
0.647
0.012
0.032
272.654
8.408
247.654
0.478
0.110
0.177
125.189
6.344
87.533
0.516
0.016
0.049
228.250
6.878
178.598
0.500
0.125
0.216
0.211
0.186
0.307
0.408
0.389
0.461
0.248
0.096
0.370
0.432
0.295
0.483
0.112
0.162
0.098
0.136
0.240
0.025
0.154
0.315
0.368
0.297
0.342
0.427
0.157
0.361
0.110
0.356
0.092
0.069
0.116
0.156
0.071
0.313
0.479
0.289
0.253
0.321
0.363
0.256
0.027
0.037
0.094
0.132
0.052
0.255
0.072
0.050
0.161
0.188
0.292
0.339
0.222
0.436
0.259
0.218
0.001
0.017
0.045
0.068
0.094
0.246
0.028
0.272
0.028
0.130
0.208
0.251
0.292
0.431
0.164
0.445
0.043
0.203
0.007
0.086
34
Table A.2: Estimated Earnings Equations: Males and Females – All sectors
Independent
Dependent Variable = Ln(HRPAY)
Variable
Males
Females
(EDO) (None)
ED1 (1-3 years)
-.326(.049)*
-.100(.096)
ED2 (4-6 years)
-.239(.038)*
-.017(.060)
ED3 (7-9 years)
-.134(.022)*
-.060(.036)
ED4 (10-12 years)
-.100(.024)*
.129(.039)*
ED5 (13-15 years)
.266(.032)*
.257(.047)*
ED6 (16+ years)
.772(.038)*
.726(.059)*
EXP
.017(.002)*
.008(.003)*
EXP² x 10-4
-1.409(.541)
-1.352(.879)
FIRMEXP
.043(.002)*
.047(.003)*
FIRMEXP² x 10-4
-6.327(.673)*
-8.745(1.168)*
(ETHNICO) (Fijian)
ETHNIC11 (Indian)
-.029(.013)*
-.042(.016)*
ETHNIC12 (Rotuman)
.192(.050)*
.033(.057)
ETHNIC13 (Others)
.174(.032)
.168(.034)*
(PRIVATE)
PUBLIC
.047(.024)**
.098(.039)**
PARSTAT (Parastatal)
.323(.020)*
.369(.039)*
TRAIN (Vocational Training)
.112(.013)*
.055(.017)*
(OCCUPO)
OCCUP17 (Lower Prof.)
-.262(.026)*
-.267(.047)*
OCCUP18 (Clerical)
-.436(.025)*
-.406(.043)*
OCCUP19 (Sales)
-.614(.029)*
-.623(.049)*
OCCUP20 (Service)
-.704(.027)*
-.627(.050)*
OCCUP21 (Artisan)
-.628(.025)*
-.569(.052)*
OCCUP22 (Garments)
-.671(.045)*
-.252(.032)*
OCCUP23 (Other Blue Collar)
-.737(.027)*
-.762(.051)*
(SECTORO)
SECTOR24 (Agriculture)
.071(.036)
.048(.253)
SECTOR25 (Utilities)
.140(.034)*
.311(.065)*
SECTOR26 (Construction)
.010(.027)
.222(.052)*
SECTOR27 (Transport)
.031(.020)
.129(.041)*
SECTOR28 (Finance)
.441(.027)*
.648(.030)*
SECTOR29 (Services)
.102(.024)*
.199(.041)*
Constant
1.040
.885
Adjusted R
.620
.735
F-Statistics
278.0*
223.8
Sample Size
5433
2572
*, ** Significant at 1% and 5% respectively
Numbers in parentheses are standard errors
Source: Fiji Employment Survey
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35
Table A.2: Estimated Earnings Equations: Males and Females – Public & Parastatal
Sectors
Independent
Variable
(EDO) (None)
ED1 (1-3 years)
ED2 (4-6 years)
ED3 (7-9 years)
ED4 (10-12 years)
ED5 (13-15 years)
ED6 (16+ years)
EXP
EXP² x 10-4
FIRMEXP
FIRMEXP² x 10-4
(ETHNICO) (Fijian)
ETHNIC11 (Indian)
ETHNIC12 (Rotuman)
ETHNIC13 (Others)
(PRIVATE)
PUBLIC
PARSTAT (Parastatal)
TRAIN (Vocational Training)
(OCCUPO)
OCCUP17 (Lower Prof.)
OCCUP18 (Clerical)
OCCUP19 (Sales)
OCCUP20 (Service)
OCCUP21 (Artisan)
OCCUP22 (Garments)
OCCUP23 (Other Blue Collar)
(SECTORO)
SECTOR24 (Agriculture)
SECTOR25 (Utilities)
SECTOR26 (Construction)
SECTOR27 (Transport)
SECTOR28 (Finance)
SECTOR29 (Services)
Constant
Adjusted R
F-Statistics
Sample Size
Dependent Variable = Ln(HRPAY)
Males
Females
-.383(.065)*
-.266(.057)*
-.164(.037)*
-.181(.039)*
.288(.049)*
.727(.053)*
.009(.003)*
1.391(.901)
.021(.0030*
.001(.891)*
-.135(.276)
-.002(.180)
-.004(.097)
.298(.101)*
.389(.107)*
.878(.112)*
.015(.005)*
-2.495(1.860)
.033(.005)*
-5.125(1.682)*
.051(.017)*
.062(.069)
.196(.045)*
.052(.026)**
-.050(.084)
.171(.063)*
.068(.019)*
.047(.027)
-.228(.032)*
-.352(.034)*
-.496(.056)*
-.655(.035)*
-.431(.037)*
-.539(.039)*
-.273(.060)*
-.371(.057)*
-.324(.094)*
-.584(.072)*
-.292(.129)**
-.721(.084)*
.215(.141)
.207(.033)*
.-.235(.023)*
.162(.030)*
-.014(.083)
1.310
.540
98.2
2155
.251(.350)
.280(.060)*
-.034(.038)
.117(.038)*
-.029(.087)
1.073
.392
22.1
884
Source: Fiji Employment Survey
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36
Table A.2: Estimated Earnings Equations: Males and Females – Private Sector
Independent
Variable
(EDO) (None)
ED1 (1-3 years)
ED2 (4-6 years)
ED3 (7-9 years)
ED4 (10-12 years)
ED5 (13-15 years)
ED6 (16+ years)
EXP
EXP² x 10-4
FIRMEXP
FIRMEXP² x 10-4
(ETHNICO) (Fijian)
ETHNIC11 (Indian)
ETHNIC12 (Rotuman)
ETHNIC13 (Others)
(PRIVATE)
PUBLIC
PARSTAT (Parastatal)
TRAIN (Vocational Training)
(OCCUPO)
OCCUP17 (Lower Prof.)
OCCUP18 (Clerical)
OCCUP19 (Sales)
OCCUP20 (Service)
OCCUP21 (Artisan)
OCCUP22 (Garments)
OCCUP23 (Other Blue Collar)
(SECTORO)
SECTOR24 (Agriculture)
SECTOR25 (Utilities)
SECTOR26 (Construction)
SECTOR27 (Transport)
SECTOR28 (Finance)
SECTOR29 (Services)
Constant
Adjusted R
F-Statistics
Sample Size
Dependent Variable = Ln(HRPAY)
Males
Females
-.152(.079)
-.178(.050)*
-.102(.027)*
.065(.031)**
.256(.042)*
.863(.056)*
.021(.002)*
-2.845(.673)*
.055(.003)*
-10.011(1.048)
-.084(.102)
-.020(.064)
-.065(.038)
.087(.043)**
.229(.055)*
.771(.094)*
.005(.003)
-.759(.997)
.052(.004)*
-8.704(1.722)*
-.055(.018)*
.298(.070)*
.178(.044)*
-.078(.020)*
.125(.079)
.134(.041)*
.099(.018)*
.062(.022)*
-.309(.045)*
-.471(.037)*
-.655(.038)*
-.742(.040)*
-.675(.035)*
-.724(.051)*
-.796(.039)*
-.087(.084)
-.393(.062)*
-.627(.067)*
-.649(.072)*
-.586(.069)*
-.212(.033)*
-.784(.070)*
.033(.039)
.034(.062)
.029(.035)
.005(.020)
.442(.028)*
.013(.043)
1.062
.551
135.0*
3278
.298(.355)
.235(.182)
.052(.135)
.127(.051)**
.631(.032)*
-.038(.060)
.930
.711
139.5*
1688
Source: Fiji Employment Survey
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37