GENDER ISSUES IN
EMPLOYMENT, UNDEREMPLOYMENT
AND INCOMES IN FIJI
Dr Wadan Narsey
Vanuavou Publications
USP Library Cataloguing-in-Publication Data
Narsey, Wadan
Gender issues in employment, underemployment and incomes in
Fiji:/ Wadan Narsey. – Suva, Fiji : Vanuavou Publications, 2007.
xiii, p. 149; 30 cm.
At head of cover title: Fiji Islands Bureau of Statistics.
ISBN 978- 982-9092-10-6
1. Women employees—Fiji 2. Women—Employment—Fiji 3.
Wages—Women—Fiji 4. Underemployment—Fiji I. Fiji. Bureau of
Statistics II. Title.
331.4
HD6220.6.A6N37 2007
©
Wadan Narsey (Vanuavou Publications)
Production
Vanuavou Publications
Printing
Star Printery
Inquiries
Fiji Islands Bureau of Statistics
Ratu Sukuna House, Mac Arthur Street, Victoria Parade, Suva,
Fiji Islands
P O Box 2221
Government Buildings
Suva
FIJI
Telephone: [679] 3315822
Fax No: [679] 3303656
E-mail:
info@statsfiji.gov.fj
Website:
www.statsfiji.gov.fj
or
Dr Wadan Narsey, Box 524, Suva, Fiji
wlnarsey@connect.com.fj
Ph: 3384158 or 9910564
Disclaimer: The views in this publication are those of the authors and not necessarily those of the
Australian Agency for International Development.
ii
Contents
page
Preface by Mr Tim Bainimarama, Government Statistician
iv
Foreword by AusAID
v
Acknowledgements
vi
Acronyms and Glossary
vii
Executive Summary
ix
Recommendations
xii
Chapter 1
Introduction
1
Chapter 2
The Female Population, Economically Active and Labour Force
9
Chapter 3
Schooling and Education Attainment
23
Chapter 4
Paid Time Worked, Employment, Effective Underemployment
and Effective Unemployment
31
Chapter 5
Unpaid Household Work
49
Chapter 6
Total Time Worked, Including Household Work:
62
Chapter 7
Incomes Earned (Over Previous 12 Months)
73
Chapter 8
Income Earned for Equal Time (Over Previous 12 Months)
95
Chapter 9
Incidence of Poverty Amongst Income Earners (L7D data)
106
Chapter 10
Changes in Economically Active and Inactive 1982 to 2004-05
118
Chapter 11
Conclusions and Recommendations
126
Annex A
Paid Time Worked L7D: Underemployment and Unemployment 131
Annex B
Gender-Neutral ERoUnder and ERoU for Last 7 Days
141
Annex C
Estimated Mid-point of Top Income Bracket $150,000+
144
Annex D
FIBoS Note on EUS Methodology
145
References
149
iii
Preface by Government Statistician (FIBoS)
The Bureau normally conducts an Annual Employment Survey (AES) with employers
who are on the Bureau’s Business Register. This Survey tends to focus on formal
sector employment, with little coverage of the extremely large informal sectors, and
those who are unemployed. To also cover the latter, the Bureau has occasionally
conducted a number of national surveys usually in response to special requests from
other arms of government, such as the Ministry of Planning.
Thus the first major employment and unemployment survey was conducted in 1973,
as a response to a request from the then Prime Minister’s Working Party on
Unemployment.1 Then in 1982, an Employment and Unemployment Survey (EUS)
was conducted by the Bureau in response to the needs of the Fiji Employment and
Development Mission.2 The report was published in 1985.3
The most recent survey has been the 2004-05 Survey on Employment and
Unemployment a Report on which, also authored by Dr Wadan Narsey, was published
in May 2007. The Report presented basic tables on national employment,
unemployment, and under-employment conditions by a number of useful
disaggregations: rural/urban, gender, divisions, ethnicity, age, industries and
occupations.4
This monograph is particularly focused on gender issues arising out of the 2004-05
Employment and Unemployment Survey. The work done for this study, and its
publication are in keeping with the Bureau’s objective of maximising the use of
relatively expensive national surveys of this nature, for the benefit of all our
stakeholders. The Bureau hopes to repeat such surveys every five years, funds
permitting.
I am grateful to AusAID for funding the analysis, the writing and publication of this
Report.
I am particularly grateful to the author, Dr. Wadan Narsey. The Bureau is fortunate to
have secured his services. Users of the report should find the tables and analysis in
this report easy to read, extremely illuminating and useful.
Timoci I Bainimarama
Government Statistician
___________________________________________________________________________
1
This resulted in the Report on Employment and Unemployment. Government of Fiji. 1973.
Final Report to the Government of Fiji by the Fiji Employment and Development Mission.
Parliamentary Paper No.66 of 1984.
3
A Report on the Fiji Employment/Unemployment Survey of 1982. Fiji Bureau of Statistics, June 1985.
4
Report on the 2004-05 Employment and Unemployment Survey, FIBoS, Dr Wadan Narsey, May 2007.
2
iv
Foreword (AusAID)
It is tempting to think that there is no time to waste in gathering and analysing data
when the key task for developing countries is to get on and address poverty, educate
children, deliver services and grow the economy. On reflection, though, it is clear
that, without solid information, none of these tasks will be achieved and policy
makers will be working in the dark. Evidence is crucial to public policy and good
governance. We need to know who is poor, where and why? Which children are in
school and which are not? Who is able to access services and who is not? Gender is
one of the key considerations in answering these questions. This is one of the reasons
gender equality is an overarching principle of Australia’s development program.
If governments are to support their people to increase economic engagement, improve
productivity and share the benefits of economic growth, it is necessary to understand
how men and women across the country use their time in economic, productive and
domestic activity. The analysis in this report, “Gender Issues in Employment,
Unemployment and Incomes in Fiji”, reveals the range of activities that occupy
women and men in Fiji and sheds light on barriers to economic engagement. In
integrating unpaid household work into economic analysis, Dr Narsey has handed us a
powerful policy and planning tool.
Women make a tremendous contribution to economic and domestic life but women’s
role is often to support men’s economic activity. Women are often so closely
associated in our minds with care of family and home that we can forget that the way
women use their time is work, and that women do a lot of it. This report clearly points
out the time burdens women struggle with, that interfere with their ability to
contribute to economic activity. It emphasises that women suffer financially for the
domestic and caring work they do. For example, women do 52 percent of total work
in the economy, but receive only 27 per cent of the total income.
The report indicates that the relations between work and remuneration for men and
women are not straightforward - outcomes are mixed and there are many complexities
beneath the headline results. Yet overall, there is clear evidence that some of the
inequalities between women and men are being addressed. The data demonstrate the
powerful effect of education in reducing gender based income gaps.
This report, in highlighting a number of uncomfortable truths, lays down a challenge
for policy makers and development practitioners, indeed for the whole society. Are
these arrangements fair? Are they making it difficult for women to contribute equally
to economic growth and poverty reduction in Fiji? How can men and women be better
supported to use their skills and talents to help the development of their nation?
What we do not measure we cannot value. This report equips us to understand and
value women’s and men’s contribution to Fijian society and will help to ensure that
development in Fiji is both fair and harnesses the skills, talents and contributions of
all men and women.
Sally Moyle
Gender Adviser
v
Acknowledgements
I am grateful to the Fiji Islands Bureau of Statistics for allowing the use of their 200405 Employment and Unemployment Survey data, for this important gender analysis.
Thanks are due to AusAID for very willingly funding this study. I am grateful to
Richelle Tickle (First Secretary, Development Cooperation, AusAID) for her keen
facilitation of the study and detailed comments on two drafts.
It is an odd coincidence that almost thirty years ago, I was the subject of wry humour
from my Economics Department colleagues because I insisted on including gender
economics as an essential part of my Microeconomics course. Gender issues are now
central in any global discussion of development.
With my wife first starting work at the YWCA some three decades ago, there have
been many friends and associates who worked towards the empowerment of women:
the old pioneers including the late Amelia Rokotuivuna (former Director of the
YWCA), Ruth Lechte, Anne Walker, Taufa Vakatale, Esiteri Kamikamica, Suliana
Siwatibau; the current seniors including Claire Slatter, Vanessa Griffin, Shamima Ali,
and Imrana Jalal; and a new younger generation including Virisila Buadromo and
Sharon Bhagwan-Rolls. Some proudly advocate their feminism, while others, simply
live the spirit of feminism through their personal lives.
Gender equality cannot become reality unless ordinary women are able to empower
themselves. My mother (Maniben Narsey) and mother-in-law (Yee Lum Po Yang)
both for decades did “two working shifts”, caring for their respective broods of eight
children, while also working in their husband’s business. My wife Joan Yee (USP
Librarian), like many professional women today, has with great determination,
balanced family responsibilities with a successful career. Our respective sisters
(Padma, Champa, Mangi, Saras, Beena Chauhan and Sin Ling, Virginia, Corinne,
Juliet, and Pamela) have all been strong career women who have battled to empower
themselves, while being corner-stones of their families. While men no doubt also
play their part, it is the women in our societies who bear the brunt of work at social
gatherings for births, marriages, deaths, and social and religious events in general.
The women also play a bigger part in the care of aging parents.
To change a nation’s attitudes towards gender equality for women requires
fundamental changes to male attitudes within each and every family. Many of the
men in the lives of these empowered women have also shared equally in household
responsibilities. Not only has this assisted the careers of the women, but I believe the
men have enjoyed richer relationships with those around them. Their daughters tend
to have a “can do anything attitude” while most of their boys also tend to share in
household responsibilities. Gender equality is not just good for women and girls, it is
very good for men and boys, whose real everyday commitment to gender equality can
do so much to advance the cause.
Dr Wadan L. Narsey
Vanuavou Publications
vi
Acronyms and Glossary
Adult Equivalent
A United Nations method of estimating household size: A child 14
and under = half an adult. Those 15 and over are regarded as 1 adult.
AES
Annual Employment Survey (of the FIBoS)
CPI
Consumer Prices Index
EA
Enumeration Area
Economically Active
All persons employed for pay, profit, or family gain in the production
of goods and services (Wage Earners, Salary Earners, Employers,
Self-employed, Family Workers, or Community Workers).
Economically Inactive All persons not Economically Active.
Effective Under-Employed The number of equivalent full-time working persons (working
Standard Years) represented by the under-employed.
Effective Un-Employed The number of persons Formally Unemployed plus the number
of Effective Under-employed.
ERoUnder
Effective Under-employed as a percentage of the Economically
Active.
ERoU
Effective Un-Employed as a percentage of the Labour Force.
EUS
Employment and Unemployment Survey
FIBoS
Fiji Islands Bureau of Statistics
FNPF
Fiji National Provident Fund.
Formally Unemployed: Persons “Unemployed, and looking
“Unemployed and given up looking.
for
employment”,
or
% Gender Gap (% GG) The difference (F-M) between the value (F) for Females and the
value (M) for Males, expressed as a percentage of the Male value.
i.e. % GG = %(F-M)/M.
GN-ERoUnder
Gender-Neutral Effective Rate of Under-employment, which takes
into account, the Household Work done by the Economically Active.
GN-ERoU
Gender-Neutral Effective Rate of Unemployment, which takes into
account, the Household Work done by the Economically Active.
HH
Household
HIES
Household Income and Expenditure Survey
J/NAW
Had a Job but was Not At Work
vii
Labour Force
Those persons Economically Active and the Unemployed. i.e those
available as the “labour supply”.
L7D
Last 7 Days (as in work done over the “Last 7 Days”)
MDG
NDG
Millenium Development Goal
National Development Goal
na
NAS
pa
pAE
pc
pm
pSY
pw
Not applicable
Not At School
per annum
per Adult Equivalent
per capita
per month
per Standard Year
per week
Paid Work
Work done by the Economically Active
P12M
Previous 12 months
RoFU
Rate of Formal Unemployment: the
Unemployed as a % of the Labour Force.
Standard Day
Standard Week
Standard Year
Standard Period
An 8 hour working day.
A 40 hour working week.
240 Standard Days
Standard Day, Standard Week or Standard Year.
Unpaid Work
Usually refers to household work.
Wage Earners
Wage earners are those paid on a daily or weekly basis, while Salary
Earners are those paid on a fortnightly or monthly basis.
Weight
Each person in the EUS is allocated a “weight” (e.g. 23 or 55) which
is inverse to the probability of that person being selected in the
random sample for the EUS. The sum of all the weights for the EUS
sample approximates the population of Fiji.
Weighted Average
For observations X1, X2, X3 etc to Xn , A “simple average”
= (X1+X2+X3+... Xn)/n.]
number
of
Formally
A weighted average effectively uses the weights for each observation
as a “rating up” factor for that observation, to get national totals.
The larger the weight, the larger is that obsevation’s influence in
group averages or totals. Thus for observations X1, X2, X3, etc with
corresponding weights w1, w2, w3 etc, the weighted average
= [(w1*X1) + (w2*X2) + (w3*X3) ....etc ]
(w1+w2+w3+ ...etc)
Working Poor
All working persons earning less than $60 per week.
viii
Executive Summary
Executive Summary
Educational Profile, Labour Force and Economically Active
1.
The overall educational profiles of the Economically Active Females and
Males are quite similar in terms of the proportions of their groups with highest
educational attainment, although younger working Females have a
significantly better educational profile than older working Females.
2.
Females are only 31% of those regarded as “Economically Active”. Fiji
Female Labour Force Participation Rate of 37% compares unfavourably with
46% for Mauritius, 51% for Trinidad and Tobago, and 67% for Australia.
3.
Females are 99% of Household Workers, whose number is larger than the
number of Female Economically Active.
4.
Female Economically Active are 26% of the total Female population
(compared to 54% for Males), but the Female ratio is a major improvement
from the 13% existing in 1982. For every one Economically Active Females,
there are two Economically Active Males.
5.
Females comprise a large 51% of Family Workers and 77% of Community
Workers, two groups who receive low incomes, and are very much underemployed.
Employment, Under-employment and Unemployment
6.
7.
“Economically Active” Females, working fewer hours in the day and fewer
days in the year, are far more under-employed and effectively unemployed
than Males.
(a)
The rates of Formal Unemployment are fairly low for both Females
(6.5%) and Males (3.5%).
(b)
But Females have a higher “Effective Rate of Under-Employment”
(ERoUnder) (31%) than Males (19%).
(c)
Hence, combining the two effects, the Effective Rate of
Unemployment for Females (ERoU) is a high 35% compared to the
22% for Male Economically Active.
(d)
Female Family Workers, Self-Employed and Community Workers
have such high rates of Effective Unemployment, as to be virtually
disguised unemployment.
Economically Active Females on average appear to work between 10% (L7D
data) and 18% (P12M data) less than Economically Active Males.
ix
Executive Summary
But With Inclusion of Household Work
8.
But when Household Work by the Economically Active persons is also taken
into account, then Females are shown to work between 26% and 31% more
than Males.
9.
Female Economically Active persons also have a lower “Gender-Neutral
Effective Rate of Unemployment” (11%) than Males (14%).
10.
While Females contributed only 27% of the total time worked by the
Economically Active, they contributed 76% of the time devoted to Household
Work, and hence 52% of all time worked in the economy (inclusive of
household work).
Incomes
11.
There is a sizeable negative gender gap of -19% in average incomes earned by
Economically Active persons:
Females: $7,600
Males: $9,393
Gender Gap
- 19%
12.
When the actual time worked is also taken into account, then the Gender Gap
in Average Income per Standard Year is reversed, becoming a positive 8% in
favour of Females.
13.
If the qualifications and occupations of the Economically Active persons are
taken into account, then the Gender Gap in Average Income per Standard Year
14.
(a)
for the better educated tends to be either statistically insignificant, or
even positive in favour of Females (especially for those with
Certificate or Diplomas);
(b)
for the less qualified, and occupations/industries not requiring higher
qualifications (such as in Agriculture or Craft work) generally are
shown to be large and statistically significant.
Despite doing 52% of all time work done in the economy, Females received
only 27% of all income earned in the economy.
Workers in Poverty
15.
Using $60 per week as a standard for the incidence of poverty for an incomeearning individual, the percentages of the Labour Force earning less than that
(i.e. “in poverty”) were:
Females: 44%
Males: 32%
16.
The incidence of poverty for those Not Paying FNPF was higher, and much
higher for Females than Males.
x
Executive Summary
(a)
50% for Female Wage Earners (38% for Males)
(b)
10% for Female Salary Earners (2% for Males)
(c)
67% for Females in aggregate (48% for Males in aggregate)
17.
The incidence of poverty for Economically Active Females drops sharply with
rising educational attainment: 78% for those with only Primary Education,
53% for Junior Secondary, 29% for Senior Secondary, 14% for those with
Certificates or Diplomas, and 6% for those with Degrees.
18.
Economically Active Females tend to push their households into the higher
deciles ranked by Household Income per Adult Equivalent: the Bottom 3
deciles (containing the poorest 30% of the population) contains only 14% of
Economically Active Females (but 23% of the Economically Active Males);
while the Top 3 deciles (containing the top 30% of the population) contains
47% of the Economically Active Females (and 35% of the Economically
Active Males). Female household workers do not push the families up the
deciles (because there is no value given to the services provided by the
household workers).
Value of Household Work
19.
With a moderate price ($30 per week) placed on full-time Household Work,
then
(a)
Household Work contributes an extra $478 millions of income to the
economy (more than the GDP contributed by either tourism or sugar)
(b)
16% is added to the monetary value of the total income
of the Economically Active, as recorded by the EUS.
(c)
the monetary value of Female contribution to the economy rises by
47%, and the Female share of the monetary contribution rises from
27% to 34%.
Unfair Sharing of Household Work
20.
While Economically Active persons did an average of 14 hours of household
work per week, the average for Females was 26 hours, while for Males was 9
hours. This indicates an unfair burden of Household Work falling on Females,
implying serious time constraints on Females’ ability to devote time and effort
to personal development (careers, leisure, etc).
21.
If the Economically Active Males (who are twice the number of Female
Economically Active) were to do on average an extra 5 hours per week, the
Economically Active Females would then on average have an extra 12 hours
to devote to personal development. (The average for all Economically Active
would remain at 14 hours per week).
xi
Recommendations
Recommendations
1.
2.
Statistical surveys by FIBoS and methodology
(a)
The Bureau be encouraged and given financial assistance by the
appropriate stakeholders, to implement a national EUS every five
years, with questions on household work being more rigorously
defined as to time period referred to.
(b)
Information be sought on
household.
(c)
The top bracket values for answers to questions on hours worked, days
worked, and incomes received be revised upwards to ensure that
reasonably accurate averages may be estimated.
(d)
Questions on current job satisfaction, alternative employment search,
alternative self-employment attempted, etc be applied to all
respondents (including those stated to be household workers), and not
just to those categorised as “unemployed”.
(e)
The EUS questionnaire attempt rigorous categorisation of Family
Workers, Self-Employed, and Household Workers, based on the
relative amounts of paid work and household work actually being
done.
(f)
The Bureau undertake a national “time use” survey so as to get a
complete profile of how the population uses its time in economic,
social, sport, leisure and other activities (such as reading, video/TV,
grog bowls).
(g)
Stakeholders encourage surveys to obtain reliable data on how incomes
are shared within families, between Females and Males, and how
wealth is passed on through inheritance practices, to Females and
Males.
“paid domestic workers” within the
Public Sector Incomes Policy
Government examine the incomes gender gap findings of this study with a
view to encouraging the restructuring of public sector income policies so that
Females’ pay scales across industries and occupations are commensurate with
Male pay scales for work of equal productivity.
xii
Recommendations
3.
Government’s Wages Councils Structure and Operations
Given that the incidence of poverty for Females (and Males) is far greater in
the informal sector, then Government strengthen its Wages Council
mechanisms to ensure that workers not covered by unions are given
appropriate and timely wages adjustments which maintain the real value of
their incomes over the long term.
4.
5.
Stakeholders consider setting gender-specific National Development Goals
(NDGs) (as a further development of the MDGs) relating to Gender Gaps in
employment, incomes and household work. The indicators would include:
(a)
Females as a proportion of Economically Active
(b)
Gender Gaps in Effective Rates of Unemployment
(c)
Gender Gaps in Average Total Income per Standard Year
(d)
Gender Gaps in Average Total Hours of Household Work by the
Economically Active
(e)
Proportions of Female Employees covered by the FNPF.
National campaigns by stakeholders
The relevant stakeholders mount national campaigns to
(a)
encourage men and boys to do their fair share of household work.
(b)
encourage Females to maximise their higher education.
(c)
encourage Females to focus on occupations and industries which fairly
reward their qualifications.
(d)
encourage private sector employers to reward Female and Male
employees equally for work of equal productivity.
(e)
encourage households to share incomes equally between Females and
Males
(f)
encourage Males and Females to treat Female and Male children
equally when they bequeath their wealth.
xiii
Chapter 1 Introduction
Chapter 1
Introduction
The United Nations General Assembly strongly reiterated in 2000, that that one of the
more powerful forces for sustainable growth and development, good governance, and
the reduction of poverty, is gender equality and the empowerment of women. 5 That
sentiment, however, has periodically been reaffirmed by most nations, international
and regional organisations, and donor agencies, for more than five decades.
Thus gender equality was an essential part of the United Nation’s Universal
Declaration of Human Rights in 1948. Article 23 stated that “everyone has the right
to work, to free choice of employment, to just and favourable conditions of work and
to protection against unemployment.”6 It also stated that “everyone, without any
discrimination, has the right to equal pay for equal work”.
The International Labour Organisation approved an Equal Remuneration Convention
of 1951, in which Article 2 whereby each Member State agreed to “ensure the
application to all workers of the principle of equal remuneration for men and women
workers for work of equal value”.
Probably best known internationally is the Convention for the Elimination of All
Forms of Discrimination Against Women (CEDAW) which was adopted in 1979 as
the “most comprehensive treaty specifically on the human rights of women”.7
CEDAW not only defines what constitutes discrimination against women, but sets up
international and national agenda for action to end such discrimination.
Article 11 required states to ensure that women had the right to the same employment
opportunities, free choice of profession, the right to promotion, job security and
training, and the right to equal remuneration in respect of work of equal value,
amongst other employment benefits. CEDAW recognised that both men and women
would need to change what was regarded as their traditional role, if there was to be
genuine gender equality (Article 4).
Countries which ratified CEDAW are “legally bound to put its provisions into
practice, and are also committed to submit national reports, at least every four years”
on the progress made in the relevant areas where gender equality was being sought.
Another milestone was the Beijing Declaration and the Platform for Action that
emanated from the Fourth World Conference on Women held in 1995. That meeting
resulted in wide ranging but quite specific articulation of objectives and policy
___________________________________________________________________________
5
The Millenium Declaration by the United Nation’s General Assembly, of September 2000.
Cotter, Anne-Marie Mooney Gender Injustice. An International Comparative Analysis of Equality
in Employment. Ashgate. 2004. pp 42-43. This section relies heavily on this excellent survey of the
literature.
7
Cotter (2004), pp 53-54.
6
1
Chapter 1 Introduction
measures required from signatory governments. As part of Strategic Objective 1 to
promote women’s economic rights governments agreed, amongst other measures to8
“(a) enact and enforce legislation to guarantee the rights of women and men to
equal pay for equal work of equal value”
“(g) seek to develop a more comprehensive knowledge of work and
employment through efforts to measure and better understand the type, extent
and distribution of unremunerated work, particularly work in caring for
dependents and unremunerated work done for family farms or businesses”
and
“encourage the sharing and dissemination of information on studies and
experience in this field, including the development of methods for assessing its
value in quantitative terms, for possible reflection in accounts that may be
produced separately from, but consistent with core national accounts”.
As part of their Strategic Objective 5 to eliminate occupational segregation and all
forms of employment discrimination, governments and employers were required to9
“(g)
eliminate occupational segregation, especially by promoting the
participation of women in highly skilled jobs and senior management
positions...”
“(k)
increase efforts to close the gap between women’s and men’s pay...”.
“(o)
review, analyze and, where appropriate, reformulate the wage
structures in female-dominated professions such as teaching, nursing
and child-care, with a view to raising their low status and earnings”.
As part of Strategic Objective 6 to promote harmonization of work and family
responsibilities for women and men, governments were required to10
“(d) Develop policies in education to change attitudes that reinforce the
division of labor based on gender in order to promote the concept of shared
family responsibility for work in the home....”.
These sections from the Beijing Declaration are fully quoted, given that the necessity
for all such policies and actions, and their assessment of any progress in the future,
depend crucially on countries having the necessary data and statistics that establish
first of all whether there is any such gender inequality and issues requiring remedial
action. This study provides fundamental data that can help in the assessment of
progress towards these objectives.
The Asian Development Bank, in its recent review of its Gender and Development
Policy in the Asia Pacific region, noted the considerable progress over the last
___________________________________________________________________________
8
Cotter (2004, pp 69-70).
Cotter (2004, pp 76-78).
10
Cotter (2004, p.78).
9
2
Chapter 1 Introduction
decade.11 It noted that most countries had ratified CEDAW and amended or enacted
laws promoting gender equality and that virtually all countries had designated national
focal points for gender policies, and developed national action plans to implement the
Beijing Plan of Action.
The Pacific Ministerial Meeting on Women (August 2004) produced a policy platform
for action on gender equality for Pacific Women.12 Amongst the numerous
recommendations for policies for fostering gender equality, were ones to ensure that
at national (and regional) levels, the necessary gender-disaggregated data was readily
available through the national statistics offices, to monitor progress on the indicators
agreed upon (including the MDGs).
In the section on the Economic Empowerment of Women, it was noted that while
labour force data was often available with gender disaggregations, women’s
contributions in the informal sector was seldom recognised, while very basic
methodological issues such as how “work” was defined in surveys, impacted on the
results, often to the detriment of women.
One of the goals set by the Ministerial Meeting was to increase the participation of
women in the formal economy, and to recognise and support women’s contributions
to the informal sector, especially as it related to food security and contribution in
agriculture and fisheries.
The Conference called on governments to address the root causes of poverty as it
related to women. It asked that governments ensure that Female and Male wage
earners were both covered by a social insurance fund or similar scheme. While
Objective 2.4.2 called on men and women to “equally share the commitment,
responsibilities, decision-making and duties of raising a family”, the indicator to be
monitored for this objective was rather odd.13 Another Objective (3.1.1) included the
promotion of gender equity in employment, but there were no specific employment
related strategies or indicators given in the document.
The Ministerial Conference called on regional governments to adapt the Millennium
Development Goals to make them more appropriate for Pacific Island countries,
establish appropriate base lines, formulate indicators, and set targets.
The most recent gender policy document from AusAID, a major donor in the Pacific
(and Fiji) argues that “employment of women has done more to encourage growth
than increases in capital investment and productivity improvements”.14 AusAID not
only sees gender equality as “essential to reducing poverty and increasing the
effectiveness of aid”, but also a “critical development goal in its own right”.
___________________________________________________________________________
11
Implementation Review of the Policy on Gender and Development, November 2006. Asian
Development Bank.
12
Secretariat of the Pacific Community Revised Pacific Platform for Action on Advancement of Women
and Gender Equality 2005 to 2015. A Regional Charter. 2005.
13
The indicator was a reduction in the number of divorces and martial [sic] separations and martial
[sic] conflict” (presumably “marital”). This study suggests alternative indicators such as “Gender Gaps
in average hours of household work done by Economically Active Males and Females”.
14
Gender equality in Australia’s aid program- how and why. AusAID, March 2007.
3
Chapter 1 Introduction
Amongst the key outcomes sought by AusAID’s aid program are “improved
economic status of women” and “equal participation in decision-making and
leadership”. AusAID’s operating principles in using aid to foster gender equality
also include the need to “collect and analyse information to improve gender equality
results”.
The Fiji Government’s Strategic Development Plan 2007-2011 stated clearly
Government’s commitment to “enable women to participate fully in the
socioeconomic development of the country”.15 It was noted that Government had
given its commitment to eight major international programmes of action on gender
equality and the advancement of women including: CEDAW, Beijing Platform of
Action, Millennium Development Goals (MDGs), UN Security Council Resolution
1325 titled Women, Peace and Security; Commonwealth Plan of Action; and Pacific
Plan of Action.
The Strategic Development Plan noted that despite Government’s commitment to
gender equality, “occupational discrimination and gender segregation are strong and
persistent”, women’s share of formal employment was significantly lower than men’s,
women were under-represented in the managerial positions, and tended to be
concentrated in the low pay end of the labour market. Many worked in selfemployment, and semi-subsistence types of economic activity. Women faced a higher
risk of poverty.
This study should assist Fiji stakeholders to address a number of the above concerns
and calls for action, and specifically the commonly held objective on the “collection
and analysis of gender-sensitive data” on employment, unemployment and incomes.
Included in this study is a whole range of statistics, integrating household work with
“paid ” work by the Economically Active.
The Need for Gender-Sensitive Data
Gender equality may be encouraged at a wide variety of inter-related levels: access to
education, resources, employment opportunities at all levels, remuneration for equal
work done, and key positions requiring decision-making, to name just a few.
One of the weaknesses of the Millennium Development Goals is that the employment
related targets (“wage employment in the non-agricultural sector” and “gender
balance in representation in Parliament”), while a start, are extremely limited as
indicators of progress on the wide number of fronts that concern gender stakeholders.
This study will suggest more specific and relevant employment-related targets for this
extremely important area of women’s development for Fiji, other Pacific island
countries, and developing countries in general.
Policies to encourage gender equality in any of these areas require accurate data and
statistics which clearly outline the nature of gender inequality and how the situation
has changed over time.
___________________________________________________________________________
15
Strategic Development Plan, 2007 to 2011, Ministry of Finance and National Planning, November
2006. See section on Gender Equality and Women in Development, pp.27-29 .
4
Chapter 1 Introduction
In respect of employment and incomes in the Fiji labour market in general, there have
been various ad hoc sample surveys over the years, as well as regular censuses that
collect information on the whole population, albeit of a more limited kind.
The Fiji Islands Bureau of Statistics attempts to annually conduct an employment
survey of establishments on their business register. This survey unfortunately has an
important limitation in that it is focused on the formal sector organisations. It does
not therefore cover the large number of persons engaged in the informal sector, or the
large numbers who are self-employed.
A national Employment and Unemployment Survey does cover the entire labour
market. Although the one immediately prior to 2004-05 was way back in 1982 the
Report (published in 1985)16 did not give extensive sex-disaggregated tables.
Nevertheless, a few comparisons with the 2004 data are possible and will be given in
Chapter 10.
The 2004-05 Survey on Employment and Unemployment
The basic Report on the 2004-05 EUS survey was published in May 2007 and should
be referred to for the background on survey methodology and logistics, and general
findings.17
That Report contained comprehensive statistical data on the economically active
population, the unemployed as well as on the inactive population. There were national
estimates of the employed by employment status, unemployed, incomes, hours and
days worked, major activities, industries, occupations, qualifications, mode of
transport, distance traveled, and time taken to reach place of employment.18
The statistics were presented according to a number of useful disaggregations:
rural/urban, gender, divisions, ethnicity, and age, although of necessity there could not
be presented all the detail that might have been wished.
Women comprise half of Fiji’s population, but stakeholders interested in the gender
issues in employment and unemployment are often limited in their policy analysis
because of the paucity of accurate statistics specifically by sex.
The 2004-05 EUS did attempt to obtain comprehensive sex-disaggregated data on
employment, unemployment and incomes, including for the first time, data on
“household chores”. This study is focused particularly on gender issues arising out of
The 2004-05 Household Survey of Employment and Unemployment.
___________________________________________________________________________
16
A Report on the 1982 Fiji Employment and Unemployment Survey. Bureau of Statistics, June 1985.
Report on the 2004-05 Employment and Unemployment Survey. Dr Wadan Narsey. May 2007.
18
Some of the numbers in the tables in this Report may not match exactly the numbers in the earlier
Report. As this analysis was conducted, some revisions of variables became possible as different fields
were cross-checked - for instance the numbers of “Handicapped” were revised, as were the “Other
reason for Inactive” who were found to be Over 55, pensionable, and hence probably Retired. Further,
as more complex analysis was conducted, a number of records were found to have incomplete fields or
inconsistent data which were corrected.
17
5
Chapter 1 Introduction
This study will not attempt to duplicate statistics in areas where the 2007 Census is
likely to produce far more accurate national data than any sample survey could
achieve.19 But the 2007 Census naturally has a different focus, and the two data
sources should be complementary in many ways.20 Note that the Census results will
not have data on incomes21 of persons or time worked (paid or unpaid), two critical
focal points for this study.
Critical Importance of Household Work
One of the limitations of traditional labour market analyses is that the category
“Economically Active” typically does not include household work: full-time
household workers are included as part of the “Economically Inactive”.
If data on household work is available, analysis of that work is usually conducted
separately from the analysis of work done by the “Economically Active”. From a
gender point of view, this places fundamental limitations on the analysis and the
conclusions reached, since a very large part of women’s work (roughly a half) is
abstracted away from “economic activity”.
Pichio (2003) describes the full range of critically important functions performed by
unpaid domestic work as “labour involved in maintaining living spaces, buying and
transforming the commodities used in the family, supplementing services provided to
family members by the public and private sectors (e.g. health, education, transport,
administration), and managing social and personal relationships” as well as “care of
people”.22
Pichio argued that if unpaid work was fully quantified, it would reveal “(1) the extent
and persistence of major inequalities between men and women in the distribution of
time, activities, economic resources and social responsibilities; and (2) a necessary
and dynamic component of the economic system represented by the process of social
reproduction of the population and of the working population in particular”.
Pichio and her fellow authors introduced new concepts such as “total work” (the sum
of paid and unpaid work) and “extended income” (sum of money income and the
value of services derived from unpaid work). These concepts will be used in very
simple form in this study.
The 2004-05 EUS, for the first time in a Bureau survey on employment, had questions
on each person’s time spent on “household chores”. The term “chore” itself has an
implication of relatively unimportant work, but this study shows beyond all doubt the
critical importance of this work, not just for Females, but the economy as a whole. It
is therefore important to give full respectability to this economic activity, by referring
to it as “Household Work” deserving of the same importance as the work of those
normally classified as “Economically Active”.
___________________________________________________________________________
19
The 2007 Census date is 16th September.
The 2004-05 EUS results may be used to estimate national aggregates for important parameters.
21
The one exception is a set of questions on remittances received.
22
Picchio, Antonella (ed) Unpaid Work and the Economy. Routledge. 2003. p.1.
20
6
Chapter 1 Introduction
This study not only attempts to go deeper into gender disaggregations of work done
by the Economically Active, but also makes a first attempt to integrate the analysis of
household work with work done by the Economically Active. The method of analysis
may be controversial23, but the findings are interesting if not startling.24 The results
will hopefully stimulate further discussion and debate amongst the stakeholders, and
continued and more intensive analysis of women’s work in the economy.25
This report also hopes to provide stakeholders with enough material to formulate
specific employment-related targets that may in future be used to measure Fiji’s
progress in achieving gender equality in the economy, including as part of the MDG
process.
Outline of Chapters
This Report begins with a chapter on the Female population and its relative size in so
far as the Economically Active and Labour Force are concerned. There follows a
chapter outlining the gender aspects of school attendance and highest educational
attainment, allied with other dis-aggregations such as ethnicity, rural/urban, division
and age.
The two chapters reveal that while there is very little gender difference in the
educational profile of the population or Economically Active, there is serious gender
imbalance in the numbers of Economically Active Females and Males. The major
contributory factor is the relatively larger number of Females who are in full-time
unpaid Household Work whose monetary value is not quantified, nor usually
acknowledged.
Given that the integration of unpaid household work with that of the “economically
active” may be a controversial process, this study first analyses the time worked and
income earned by the “Economically Active” in separate chapters. The integration
with unpaid household work is conducted in separate chapters.
Chapter 4 covers paid time worked, employment, effective under-employment and
effective unemployment. The bulk of the analysis uses the Previous 12 Months
dataset which is more comprehensive of all work done over the previous year. The
results indicate that Economically Active Females are far more “under-employed” and
“effectively unemployed” than Economically Active Males. Part of the explanation
for this may be that Females are much less likely to engage in secondary or tertiary
income earning activities.
Chapter 5 then outlines in detail, the gender disparities in Household Work, especially
for those regarded as Economically Active. There is also a simple attempt at
___________________________________________________________________________
23
Simply aggregating the hours of household work with paid work may be disputed is there are
significant productivity differences between the two kinds of work.
24
Many men will find quite surprising, the results on household work by the Economically Active,
although most women will probably not.
25
The collection of articles in Pichio (2003) provides an excellent introduction to the variety of
political economy gender issues that surround household work.
7
Chapter 1 Introduction
estimating the macroeconomic monetary implications if household work is given
appropriate but still conservative dollar values.
Chapter 6 then provides a “gender-neutral” analysis of “total time worked” by
Females and Males in the economy, fully taking into account not just the paid work,
but also the unpaid household work that is also done by the Economically Active.
Contrary to the results of Chapter 4, a conclusion from Chapter 6 is that Economically
Active Females are less under-employed and less unemployed than Males, and hence
do significantly more work than Economically Active Males.
Chapter 7 then gives a variety of gender perspectives on average total incomes earned
over the Previous 12 Months, and the gender gaps, by a number of other disaggregations: educational attainment, age, employment status, industry, occupation.
Given the evidence for significant gender differences in total time worked over the
Previous 12 Months, Chapter 8 presents statistics on “income earned per standard
time period” as possible indications of gender inequality and discrimination. There is
the interesting finding that the aggregate Gender Gap is in fact positive in favour of
Females. There is an attempt to identify where the negative gender differences may
be statistically significant and where the data indicates there is virtually no gender
gap, or where the gender gap is positive in favour of Females.
Chapter 9 addresses the issue of poverty through gender perspectives on the
individual incomes earned by the Economically Active, in relation to a very
conservative poverty line for workers. Especially vulnerable groups are identified by
industry, occupation, formal/informal sector, and other disaggregations.
Chapter 10 is a brief attempt to outline the broad changes in numbers of Economically
Active, which appear to have taken place between the time of the 1982 EUS and the
2004-05 EUS, with a special focus on the trends in numbers of those designated as
household workers.
The concluding chapter gives a summary of the findings and Recommendations for
stakeholders to consider for implementation.
A number of annexes provide supplementary results: employment, underemployment, unemployment statistics on paid time worked using the Last 7 Days
dataset (Annex A), similar “gender-neutral” statistics (inclusive of household work)
using the Last 7 Days dataset (Annex B), a small note on the method of estimating the
mid-point of the top income bracket (Annex C), and a note by the FIBoS Household
Unit on the EUS methodology (Annex D).
8
Chapter 2
The Female Population, Labour Force and Economically Active
Chapter 2
The Female Population, Economically Active
and the Labour Force
Based on the EUS weights, the projected population for Fiji, by gender and other
characteristics are given in Table 2.1. The estimated totals for Females comprise
roughly half of all the population sub-groups.
Table 2.1
Basic Distribution of Females and Male Population (2004-05 EUS)
Numbers
% Fem
Vertical Percentages
Female
Male
All
Female
Male
All
Age Groups
< 15
115282 125787 241069
48
29
30
29
15 to 54
240285 250819 491104
49
60
60
60
Over 54
44214 41564 85778
52
11
10
10
Rural/Urban
Rural
197684 212971 410655
48
49
51
50
Urban
202097 205199 407297
50
51
49
50
By Divisions
Central
162317 168060 330377
49
41
40
40
Eastern
18394 19832 38226
48
5
5
5
Northern
63915 69457 133372
48
16
17
16
Western
155156 160821 315977
49
39
38
39
By Ethnicity
Fijian
205907 225846 431753
48
52
54
53
Indo-Fijian
174153 172078 346231
50
44
41
42
Others
14668 14599 29267
50
4
3
4
Rotuman
5054
5648 10702
47
1
1
1
By Schooling status
NAS/Underage 37452 42042 79493
47
9
10
10
At School
113858 119509 233368
49
29
29
29
NAS/Of age
8740 12003 20743
42
2
3
3
Not at School
239731 244617 484348
49
60
59
59
ALL FIJI
399781 418170 817952
49
100
100
100
The preliminary 2007 Census results suggest that ethnic Indo-Fijians are slightly overrepresented in the 2004-05 EUS while ethnic Fijians are under-represented.26
The distribution of Females amongst the Age Groups, Rural/Urban, Divisions,
Ethnicity and Schooling Status follows pretty well the distribution of the general
___________________________________________________________________________
26
The 2007 Census proportions are 57% for indigenous Fijians and 38% for Indo-Fijians as opposed to
53% and 42% indicated by the EUS respectively. With the EUS sampling frame using modified 1996
Census data, the more urbanised Indo-Fijians would have been selected more easily than the more rural
indigenous Fijians. One consequence is that in the EUS national estimates of values, Fijian values will
be slightly under-estimated and Indo-Fijian values over-estimated.
9
Chapter 2
The Female Population, Labour Force and Economically Active
population and that of
Males with the expected
small differences. Thus
Females are a slightly
higher proportion of the
Over 54 (given their
higher life expectancy);
slightly lower proportion
of the “underage” Not At
School.
However Females are a
significantly
lower
proportion of those Not
At School/Of Age group
(42%) suggesting that
Females tend to drop out
of school somewhat less
than Males.
Table 2.2 The Female Population (by ethnicity)
Ethnicity
< 15
15 to 54 Over 54
All
Number
Fijian
68244
118089
19574
205907
Indo-Fijian 41588
113148
19416
174153
Others
3754
9435
1479
14668
Rotuman
1696
2846
512
5054
All
115282
243518
40981
399781
Females as Perc. of Each Ethnic Sub-group
Fijian
46.2
48.2
49.8
47.7
Indo-Fij
50.4
49.7
53.8
50.3
Others
47.3
51.8
47.3
50.1
Rotuman
56.5
44.8
37.9
47.2
All
47.8
49.0
51.3
48.9
Age structure of females
Fijian
33
57
10
100
Indo-Fij
24
65
11
100
Others
26
64
10
100
Rotuman
34
56
10
100
All
29
61
10
100
While Females are just
about a half of the IndoFijian
and
Other
population, they are a slightly lower 48% of Fijians and 47% of Rotumans.
There are quite significant ethnic differences in the age structure- with Fijian and
Rotuman “14 and Under” age group comprising just about a third of the total Female
population, while for Indo-Fijians it is a much lower 24% and Others 26%. These
proportions (which mirror the age ratios of the Male and the total population) are
largely a reflection of the demographic fertility differences between the different
ethnic groups.27
The Labour Force and Economically Active
The 2004-05 EUS collected data on the work status over two time periods- the
Previous 12 Months, and over the Last 7 Days: on employment status, time worked,
incomes earned, unemployment and other labour market characteristics for both time
periods. There was also data collected for both time periods on why the person was
“inactive”.
From these sets of questions, it is possible to create two useful parameters that
describe all persons in the economy “Usual Activity over P12M” and “Usual Activity
L7D”. It is thought that data on the economic activities over the previous 7 days is
usually of greater accuracy than data obtained by recollection of activities over the
previous 12 months.28 However, both sets of statistics have been found to be useful
for this study, as quite different questions were asked for the two time periods, and the
data on time worked and incomes earned were differently collected, with different
relevance for different issues being discussed.
___________________________________________________________________________
27
28
Fijians, for decades have had a much higher fertility rate than Indo-Fijians.
The Labour Force is usually defined with reference to the status of the workers over the Last 7 Days.
10
Chapter 2
The Female Population, Labour Force and Economically Active
It was possible for two parallel sets of statistics to be calculated throughout the study
for average time worked, incomes earned, rates of under-employment and underemployment etc., and these were indeed generated in earlier drafts. However, it was
suggested that the parallel statistics would be confusing to the average reader and
stakeholder, while in most cases not adding significantly to a better understanding of
the issues involved, except to the very technical-minded persons.29
It was therefore decided to eliminate most of the parallel statistics, and retain that
analysis most suitable for the topic under discussion. The Box on the next page
clarifies some of the differences in the two data sets, and where each is used in this
study. For most of the analyses (except of the incidence of poverty), the P12M dataset will be used.
Table 2.3 gives a summary of the Usual Activity of Females and Males over the
Previous 12 Months.30
Table 2.3 Usual Activity over Previous 12 Months (by gender)
UsActP12M
Female
Male
All % Fem % of Fem % of Mal
A Wages
39263 106835 146098
27
10
26
B Salary
18303 30581 48884
37
5
7
C Employer
617
2647
3263
19
0
1
D Self-employed
23105 68713 91818
25
6
16
E Family Workers
19486 18470 37957
51
5
4
F Community Workers
1730
505
2235
77
0
0
H Retired/Over 54
11155 11483 22638
49
3
3
I Handicapped
1992
2107
4099
49
0
1
J Other/Inactive
2159
2937
5096
42
1
1
L Household Work
120855
1642 122497
99
30
0
M NAS/Underage
37452 42042 79493
47
9
10
N FT student
113858 119509 233368
49
28
29
T NAS/school age
2680
2344
5025
53
1
1
U Unemployed
7127
8355 15483
46
2
2
All
399781 418170 817952
49
100
100
Econ. Active (A to F) 102504 227751 330255
31
26
54
___________________________________________________________________________
29
Experts in the field will find some of the differences in results interesting and worth exploring.
Some of the numbers in this table for the Economically Inactive are different from that given in the
earlier published Report on the 2004 Employment and Unemployment Survey. Those designated as
“Handicapped” in the P12M and the L7D data have been reconciled; while those previously classified
as “Other Reason for Inactive” were reclassified with “Retired/Over 54” if their ages were over 54.
30
11
Chapter 2
The Female Population, Labour Force and Economically Active
Differences in data sets for Last 7 Days and Previous 12 Months
The EUS obtained data on economic activities of each person Over the Previous 12 Months,
and over the Last 7 Days. While both data sets may be used to derive averages for “time
worked” or “incomes earned”, the structure in which data was recorded implies strengths or
weaknesses depending on the problem being focused on.
The questions on economic activities over the Previous 12 Months (P12M) requested
information on the main activity- Activity 1, as well as Activity 2 and Activity 3 (if
additional activities were engaged in). For each activity, information was obtained in
ranges:
* days worked (in ranges : <50, 50-99, 100-149, ...., 300 days or more)
* hours worked per day (in ranges 1-2, 3-4, ..., 9+.)
* gross annual income (in ranges $0-2999, $3000-4999, .... $150,000+)
The questions on economic activity over the Last 7 Days sought information only on
work done over the previous 7 days, and it is not clear if all activities were covered.
Information was sought for
* hours worked over L7D (in ranges: <10 hrs, 10-19, 20-29, 30-39, 40+)
* gross weekly income or value of goods and services
(in ranges: $0-29, $30-59, .... $300+ )
On Household work, the EUS only asked how many hours per week was spent on
various “chores”. It is unclear whether the respondents referred to just the previous
seven days, or the typical time spent per week over the Previous 12 Months.
Generally, it seems:
(1)
The P12M data is a more comprehensive representation of all work done over the
whole year. Estimates of total work done by the Economically Active using the
P12M data set gives a value some 10% higher than that obtained from the L7D data
set on hours of work done. This study therefore focuses on the P12M data set for
estimates of total work done, under-employment and effective unemployment.
(Some parallel tables using the L7D data-set are placed in Annex A).
(2)
As the highest bracket for income over the L7D kicks in at only $300 pw or
$15,600 per year while the P12M data set has $150,000 per year at the highest
level, the P12M data-set is far more useful and accurate for overall estimates (and
comparisons) of average incomes and gender gaps (for which the P12M data set is
therefore used). No parallel estimates of average incomes are provided with the
L7D data-set.
(3)
Because the “total income” estimated from the P12M data is calculated as the sum
of mid-points of ranges for Activities 1, 2 and 3, this data is not useful for analysis
of the low income groups and poverty incidence. For the latter, the Last 7 Days
data-set is far more applicable and is therefore used in the chapter on the analysis of
the incidence of poverty. (This is explained further in the relevant chapter)
(4)
The analysis of “total work done” including Household (HH) Work may use both
data-sets and both are used where appropriate.
12
Chapter 2
The Female Population, Labour Force and Economically Active
With some 30% of all Females (and a very large 121 thousands) classified as being on
Full-time Domestic Work (of which category Females comprised 99%), Females are
generally under-represented in the categories usually classified as “economically
active”: Wage Earners (only 27%)31, Salary Earners (a bit higher 37%) and
Employers (19%); Self-employed (25%).
Conversely, while only 26% of the Females are narrowly construed to be
economically active (as opposed to 54% of the Males), the percentage for Females
would be 56% (26% + 30%) if Household Work was included as an economic
activity.
The only category of Economically Active persons in which Females are more than
proportionately represented were Family and Community Workers, which later
sections show to be extremely poorly paid, and very significantly underemployed.
Labour Force and Formal Unemployment P12M
Table 2.4 gives a
summary of the
Economically
Active and the
Labour
Force,
which
includes
those
declaring
themselves
Unemployed.
Table 2.4 Ec. Active and Labour Force Previous 12 Months
% Fem
Female Male
All
Economically Active
102504 227751 330255
31
Unemployed
7127
8355 15483
46
Labour Force
109631 236106 345738
32
Rate of Formal Unemp.
6.5
3.5
4.5
The Rates of Formal Unemployment32 appear to be low for both sexes, although the
Female rate (6.5%) is roughly twice the Male rate (3.5%).
These numbers refer to economic activity (or lack of it for the whole year). The Last
7 Days data gives a picture of somewhat lower employment, lower labour force, and
higher numbers of short-term unemployed.
Differences from Labour Force data from the Last 7 Days dataset
Because of the slightly different way in which questions were asked on actual activity
over the previous 7 days, the Last 7 Days dataset generates a slightly different
perspective on what people were doing over the “short term” (the previous 7 days) as
opposed to the previous 12 months. It needs to be kept in mind that the persons were
being surveyed in batches throughout the whole year, so that even the “Last 7 Days”
___________________________________________________________________________
31
According to the classification by the FIBoS, Wage Earners are those usually paid on a daily or
weekly basis, while Salary earners are paid on a fortnightly or monthly basis.
32
The term “formal unemployment” is used to describe those who declared themselves to be
unemployed. The chapter below on time worked indicates that large numbers of persons (especially
the categories Family Workers and Self-employed) are extremely under-employed and are therefore
probably “disguised unemployment”.
13
Chapter 2
The Female Population, Labour Force and Economically Active
data refers to the “immediate” or “short term” activity of persons during the whole
year.33
Table 2.5
State of the Labour Force Last 7 Days
Hor %
Persons
Vertical Perc.
Labour Force L7D
Female
Male
All % Fem Female Male All
A Working L7D
91353 216062 307416
30
89
93
92
B Job/NAW L7D
2963
5760
8723
34
2.9
2.5
2.6
C Working Soon L7D
2755
1476
4231
65
2.7
0.6
1.3
D Unemployed L7D
6084
9437 15521
39
5.9
4.1
4.6
All
103155 232735 335890
31
100
100 100
Table A.1 in Annex A gives the “usual activity” of all persons over the Last 7 Days.
Table 2.5 gives the L7D Labour Force summary, with two new categories: those who
had a job but were not at work (Job/NAW) and those who stated they expected to be
“Working Soon”.
While Females were 31% of
Table 2.6 Why Not At Work for Those With Job
the L7D Labour Force, they
Why NAW L7D
Fem Mal
All % Fem
were 34% of Job/NAW, and a
A Sickness/Injury 358 1634 1992
18
large 65% of those expecting
B Lay-off/Temp.
297 680 977
30
to be “Working Soon”. It is
C Leave
1058 1669 2727
39
not clear whether these
D Other
1250 1777 3027
41
persons had firm job offers or
All
2963 5760 8723
34
were simply optimistic of
working soon.
Crosschecking with the Previous 12 Months data indicates a large proportion were
unemployed or Household Workers.34 They should be considered as part of the
short-term unemployed.
It is useful to examine further the category Job/NAW (Table 2.6). There were a
further 977 workers who stated that they had been laid off or temporarily laid off.
Again, while it is unclear whether they would be re-employed soon, they also need to
be classified as “unemployed”.
Of note is that Females were a relatively low 18% of those who stated sickness or
injury as the reason- suggesting that Females are more reliable as workers.
Table 2.7 gives the state of the Labour Force in terms of their immediate activity over
the Last 7 Days. The numbers of Economically Active and the Labour Force are
generally lower, and those immediately unemployed are higher. The “short-term”
rate of Unemployment is 8.9% for Females and 6.2% for Males. These are much
higher than the Rates of Formal Unemployment given by the P12M data.
___________________________________________________________________________
33
Refer to Annex D on the methodology of the EUS. By taking batches of households throughout the
year, the survey attempted to minimise seasonal bias.
34
According to the P12M categories, out of these 4,200 workers, some 1,200 were defined as HH
Workers, while another 2,000 were Unemployed who had supposedly stopped looking for work.
14
Chapter 2
The Female Population, Labour Force and Economically Active
Table 2.7 Labour Force Status Over last 7 Days
Female
Male
All
Economically Active
94019
221142
315161
Probably Unemployed
9136
11593
20729
Labour Force L7D
103155
232735
335890
Short-Term Rate of Unempl. L7D
8.9
5.0
6.2
% Fem
30
44
31
Table 2.8 gives the percentage differences from the Labour Force status over the
Previous 12 Months, and the immediate Last 7 Days. It is fairly clear that Females
tend to drop out more from the Economically Active (8% lower for Females
compared to 3% lower for Males)
Table 2.8 Perc. Differences (L7D-P12M)
and from the Labour Force (6%
Fem Male All
lower for Females compared to 1%
Economically Active
-8
-3
-5
lower for Males).
This may
Labour
Force
-6
-1
-3
suggest that Females are generally
Unemployed
28
39
34
more vulnerable in the formal
labour
market
to
seasonal
economic contraction.
What are the short-term changes
apparently taking place in the
categorisation of workers? Table 2.9
indicates that the largest percentage
movement (-33% for Females) is
from
those
categorised
as
Community Workers followed by
“Self-employed” (-20%) and Salary
Workers (-13%).
Some 6% of
Female Wage Earners also became
economically inactive, moving to
Household Work, and Retired/Over
54.
Table 2.9 Perc. Diff: (L7D - P12M)
Usual Activity
Female Male All
A Wage Earner
-6
-5
-6
B Salary Earner
-13
-11 -12
C Employer
67
-11
4
D Self-empl.
-20
-15 -16
E Family Wrk.
-9
31
10
F Community Wrk
-33
367 57
H Retired/Ov.54
4
13
9
L Household Work
4
38
5
Other Inactive
26
24
25
A Gender-Neutral Labour Force?
Fundamentally, with only 109 thousand Females in the formal Labour Force, and 121
thousand doing “Household Work”, more than a half of Women’s work is being
defined away as “Economically Inactive” because it is in the household (and unpaid).
However, if those on
Table 2.10 Females and Gender-Neutral Labour Force
Full-Time Household
P12M
Work
were
also
Female Male
All
% Fem
included,
then fully
Labour Force (LF) 109631 236106 345737
32
56% of Females would
incl. HH Work
230486 237748 468234
49
be considered to be
Economically Active,
and they would be 49% of the Economically Active (Table 2.10).
15
Chapter 2
The Female Population, Labour Force and Economically Active
Were those doing Household Work also included in the tables on Employment Status
(as for instance in Table 2.8) then the proportions of Females who are Wage and
Salary Earners, Employers, Self-employed etc, would all drop to proportions
significantly lower than those for Males.
This would strengthen even further the general conclusion that Females are relatively
more marginalised from the formal Labour Force than Males.
Employment Status of Economically Active (Previous 12 Months)
Table 2.11 gives the major employment status of Females and Males over the
Previous 12 Months.
With
Females
comprising some
31%
of
the
economically
active,
a
somewhat larger
proportion
of
Family Workers
were
Females
(51%), as well as
Salary
Earners
(37%).
Table 2.11
Employment Status P12M (by gender)
Female Male
All
% Fem
A Wage earner
39263 106835 146098
27
B Salary earner
18303 30581 48884
37
C Employer
617
2647
3263
19
D Self-employed
23105 68713 91818
25
E Family worker
19486 18470 37957
51
F Community Worker
1730
505
2235
77
All
102504 227751 330255
31
Vertical %
A Wage earner
38
47
44
B Salary earner
18
13
15
C Employer
1
1
1
D Self-employed
23
30
28
E Family worker
19
8
11
F Community Worker
2
0
1
All
100
100
100
Consequently
slightly
higher
proportions
of
Females
were
Family Workers
(19%) compared
to the 8% for Males. This will have a strong bearing on gender comparisons of
incomes and under-employment.
Table 2.12
indicates that there
are several ethnic
differences in the
employment status
of
women:
a
disproportionately
large 50% of IndoFijian women are
Wage
Earners,
compared to 32% of
the Fijian women,
while much larger
Table 2.12 Distrib. of Females by Employment Status P12M
(by ethnicity)
Fijian Indo-F Others Rotuman All
A Wage earner
32
50
28
25
38
B Salary earner
16
18
37
34
18
C Employer
0
1
3
0
1
D Self-employed
27
16
18
13
23
E Family worker
23
13
12
28
19
F Comm.Worker
2
1
3
0
2
All
100
100
100
100
100
16
Chapter 2
The Female Population, Labour Force and Economically Active
proportions of Fijian women are Self-employed (27%) and Family Workers (23%)
than for the other ethnic groups.
Quite high proportions of Other and Rotuman women are Salary Earners. The above
factors will have a strong bearing on the earnings of these groups of women.
Labour Force Participation Rates
To enable international comparisons, this section gives some statistics on Labour
Force Participation Rates (LFPR) as
Table 2.13 LFPR L7D (by age and gender)
internationally defined- in reference to
Fem Male All % GG
those aged 15 to 64, and for youth
0 to 14
0.1
0.6
0.4
-81
employment, to those aged 15 to 24.
15 to 24
25 to 34
35 to 44
45 to 54
55 to 64
> 64
15-64
The upper boundary of 64 is somewhat
incongruous for Fiji given that the
official retirement age at which
contributors can begin to collect
pensions from the Fiji Provident Fund
is 55, while the current Interim
Government has also decreed a
compulsory retirement age of 60.
24
42
46
41
34
17
37
50
95
98
96
78
51
81
38
69
72
69
56
33
59
-52
-56
-53
-57
-57
-66
-55
It may be seen from Table 2.13 and Graph 2.1 that the there is a very large Gender
Gap of 55% in aggregate and for all the age groups. The LFPR is quite low (24%) for
the 15 to 24 (youth) age group.
Graph 2.1 LFPR (by age groups and gender)
Labour Force Participation Rate
(Males and Females)
Percentage
It is interesting, given the
retirement age of 55, that
the LFPR for the 55-64 age
group is 34%, only slightly
lower than the 41% for the
45 to 54 age group.
100
80
60
40
20
0
Male
64
>
64
to
55
to
54
44
45
to
35
24
to
25
14
to
to
15
0
34
Fem
Table 2.14 indicates that
Fiji’s Female LFPR (37%)
as indicated by the 2004-05
EUS is the lowest of the
Age Groups
three developing countries
(including Trinidad and
Mauritius), and much lower
than the 67% of Australia and 71% of NZ.35 Fiji also has the highest Gender Gap of 55% (using the 2004-05 EUS result).
___________________________________________________________________________
35
The World Bank’s WDI database gives a somewhat high 2004 value of 54% for Fiji’s Female LFPR.
This was possibly estimated from the 1996 Census, which may have used a different methodology from
the 2004-05 EUS.
17
Chapter 2
The Female Population, Labour Force and Economically Active
There are some ethnic
differences in Female
LFPRs which help to
give rise to the lower
aggregate Female LFPR
for Fiji.
Table 2.15 indicates that
while Fijian Females
had a LFPR of 44% (for
those aged 15 to 64), IndoFijian Females had a
considerably lower 29%,
thus also having the largest
Gender Gap with IndoFijian Males.
Rotuman
Females also have an
extremely low LFPR of
28%.36
Table 2.14 LFPR (Fiji and comparators) (2004)
Country Name
Fem Males All % GG
Australia
67
81
74
-17
New Zealand
71
83
77
-15
Trinidad & Tobago
51
82
67
-38
Mauritius
46
84
65
-45
Fiji 2004-05 EUS
37
81
59
-55
Table 2.15 LFPR (by ethnicity and gender)
Fijians Indo-F Others Rotuman
Females
0 to 14
0.2
0.0
0.0
0.0
15 to 24
24
24
23
16
25 to 34
46
38
50
34
35 to 44
56
33
58
50
45 to 54
60
26
54
20
55 to 64
50
20
29
25
> 64
23
8
4
0
15-64
44
29
43
28
Males
0 to 14
0.8
0.5
0.0
0.0
15 to 24
50
52
47
30
25 to 34
94
97
92
84
35 to 44
97
98
98
100
45 to 54
97
95
94
74
55 to 64
82
73
89
83
> 64
57
40
65
26
15-64
80
82
79
66
80
Fij Mal
60
Ind Mal
40
20
Fij Fem
0
64
64
>
54
to
55
44
to
45
34
to
35
to
25
to
to
14
24
Ind Fem
15
0
It is quite likely that the
extremely low LFPR for
older Indo-Fijian Females
is the result of cultural
mores which tended to
keep Indo-Fijian Females
at home, even if they
were
educationally
qualified
to
become
Percentages
Comparing the two major
ethnic groups, Graph 2.2
shows clearly that while
Male Fijian and Indo-Fijian
LFPR curves pretty well
track each other, the Female
ones don’t. In particular,
while the Female IndoFijian LFPR follows the
Female Fijian one for the
younger age groups, the
Indo-Fijian one peaks at the
age group 25-34 (at 38%) and then dips downwards from there on, while the Fijian
Female LFPR keeps
Graph 2.2 LFPR P12M (by ethnicity, age groups)
rising until it peaks at
Labour Force Participation Rates
60% for the 45 to 54
(by ethnicity and gender)
group.
100
Age Groups
___________________________________________________________________________
36
It is also unusual that the Rotuman Male LFPR is also low, 66%. This may be a result of inadequate
sampling of households in Rotuma. Rotumans who reside in Viti Levu may have a higher proportion
of inactive persons (students and retirees) residing with the Economically Active.
18
Chapter 2
The Female Population, Labour Force and Economically Active
Economically Active in the Labour Force. Those cultural limitations are much less
evident for younger perhaps more modern Indo-Fijian Females.
Table 2.16 gives the rural/urban dimension of LFPRs. The Gender Gaps are present
in both Rural and
Urban areas, with the
Table 2.16 LFPR P12M (by rural:urban and age groups)
Rural Gender Gap (Rural
Urban
59%) being slightly
wider than the Urban
Fem Mal % GG Fem Mal % GG
Gender Gap (51%).
0 to 14
0.1
1.0
-87
0.1
0.3
-59
It may be noted
15 to 24
26
55
-53
22
46
-52
though that the Rural
25 to 34
34
96
-65
49
94
-48
Female LFPR is
35 to 44
40
98
-59
51
97
-47
lower (34%) than the
45 to 54
44
97
-54
39
95
-59
39%
for
Urban
55 to 64
33
85
-62
35
70
-51
Females, the Rural
> 64
17
56
-69
12
42
-71
Male LFPR of 83% is
15-64
34
83
-59
39
78
-51
higher than the 78%
for Urban Males.
Distribution by Main Industry Groups
Table 2.17 indicates that while Females are 31% of the formally defined
Economically Active, they are quite under-represented in some industrial groupings:
only 4% in Construction, 5% in Mining and Quarrying, 6% in Electricity and Water
and 12% in Transport, Storage and Communications. They are relatively overrepresented in Hotel, Retail and Restaurants (44%) and Community, Social and
Personal Services (43%).
Table 2.17 Distribution of Economically Active P12M by Industry Grouping and Gender
% of % of % of
Industrial Group P12M
Female
Male
All % Fem Fem Mal All
1 Agric.Forestry & Fishing
20491 69810 90301
23
20
31
27
2 Mining&Quarrying
176
3291
3467
5
0
1
1
3 Manufacturing
16056 30782 46839
34
16
13
14
4 Electricity & Water
167
2552
2719
6
0
1
1
5 Construction
617 17016 17634
4
1
7
5
6 Hotel, Retail, Restaurants
31817 41240 73057
44
31
18
22
7 Transp.Stor.Communic.
2888 20218 23106
12
3
9
7
8 Fin. Real Est. Busi
3726
7118 10844
34
4
3
3
9 Comm. Soc.& Pers. Serv. 26695 36038 62733
43
26
16
19
All
102632 228067 330699
31
100 100 100
Table 2.18 indicates that in those industries in which Females tend to be concentrated,
they tend to be in Wage Earners, Self-Employed, or Family Workers, typically low
19
Chapter 2
The Female Population, Labour Force and Economically Active
paying. Thus 69% of those in Agriculture, Forestry and Fisheries are in Family
Workers, 56% of those in Manufacturing are Wage Earners and 23% Self-employed.
Table 2.18
Distribution of Female Econom.Active by Industry and Employment Status (P12M)
Self- Family Comm.
Wrk
Industry Grouping
Wages Salary Empl. empl. Wrk.
All
Nos
AgForFishing
7
1
0
23
69
0
100 20491
Mining&Quarrying
85
15
0
0
0
0
100
176
Manufacturing
56
4
2
23
14
0
100 16056
Elect & Water
51
49
0
0
0
0
100
167
Construction
74
16
0
10
0
0
100
617
Hotel, Retail, Rest.
42
6
1
43
8
0
100 31817
Transp.Stor.Comm.
54
42
1
1
2
0
100
2888
Fin. Real Est. Business
33
50
0
13
4
0
100
3726
Comm. Soc.& Pers. Serv.
46
46
0
1
1
6
100 26695
All
38
18
1
22
19
2
100 102632
In Hotel, Retail and Restaurants, 42% are Wage Earners and 43% Self-employed.
Only in Community, Social and Personal Services, are a large percentage (46%) in
Salaried work. Only 6% of those in Hotel, Retail and Restaurants are in Salaried
work, somewhat surprising given usual Female employment affinity for this industry.
Distribution by Main Occupation Groups
Table 2.19 indicates that Females are significantly over-represented amongst Clerks
(58%), but also amongst Professionals (44%). They are under-represented amongst
Senior Officials and Managers (26%) and more so amongst Plant and Machine
Operators and Assemblers.
Table 2.19
Distrib. of Economically Active P12M by Main Occupation Groups and Gender
% of % of % of
Male
All % Fem Fem Mal All
Occupation Group P12M
Female
1 Sen. Officials & Manag.
4526 12711 17237
26
4
6
5
2 Professionals
9350 11811 21161
44
9
5
6
3 Tech. & Assoc Prof.
6546 11462 18008
36
6
5
5
4 Clerks
12827
9288 22115
58
12
4
7
5 Service, Shop, MktSales
15201 21039 36240
42
15
9
11
6 Sk.Agr.& Fishery
18677 65687 84364
22
18
29
26
7 Craft & Related
10616 34229 44845
24
10
15
14
8 Pl. & Mac.Oper.&Assemblers
4493 21716 26209
17
4
10
8
9 Elementary Occupations
20396 40125 60521
34
20
18
18
All
102632 228067 330699
31
100 100 100
20
Chapter 2
The Female Population, Labour Force and Economically Active
Table 2.20 indicates that in some occupations, Females are extremely highly
concentrated as typically low income Wage Earners: Service, Shop, Sales (71%),
Clerks (59%), Plant and Machine Operators and Assemblers (96%) and Elementary
Occupations (51%).
In others, they are largely Self-Employed: Craft and Related (58%), Senior Managers
and Officials (42%) and Elementary Occupations (39%). Females in Skilled
Agriculture and Fisheries, have a very high 72% engaged as Family Workerstypically under-employed, and lowly paid.
Table 2.20
Perc. Dist. of Female Econom.Active by Occupation and Employment Status
Self- Fam Comm.
Wrk
Occupation Activity 1 Wages Salary Empl. empl. Wrk.
All
Nos
Sen. Off. & Manag.
20
17
8
42
13
100
4526
Professionals
22
74
1
0
3
100
9350
Tech. & Assoc Prof.
23
58
3
1
16
100
6546
Clerks
59
37
1
2
0
100 12827
Service, Shop, Sales
71
9
0
13
6
1
100 15201
Sk.Agr.& Fishery
1
1
1
24
72
100 18677
Craft & Related
14
1
58
26
2
100 10616
Pl. & Mac.Oper.&Ass
96
1
3
100
4493
Elementary Occup.
51
2
39
7
1
100 20396
All
38
18
1
22
19
2
100 102632
FNPF Payment (Formal/Informal sectors)
One of the indicators of employees and self-employed persons not being in the
“formal” economy is non-contribution to the Fiji National Provident Fund, the
principal pension fund for workers in Fiji. Table 2.21 gives the proportions of the
workers in different categories who stated that they did not pay FNPF with few
gender differences evident.
Some 57% of the Female
Economically Active did
not pay FNPF (59% of
Males).
Some 36% of Female Wage
Earners did not do so, some
97% of the Self-employed
and Family Workers (two
employment
categories
dominated by Females) and
87% of the Community
Workers were not covered
by FNPF.
Table 2.21 Perc. Of Econ. Active P12M
Not Paying FNPF
Female Male
All
A Wage earner
36
42
40
B Salary earner
7
9
8
C Employer
60
69
67
D Self-employed
97
96
96
E Family worker
97
97
97
F Community Worker
87
100
90
All
57
59
58
21
Chapter 2
The Female Population, Labour Force and Economically Active
This table is a very powerful indication of the lack of pension security for these
workers in their old age. As well, the probable informal nature of their work
environment would be generally associated with an absence of benefits normally
accruing to more organised workers.
22
Chapter 3
Schooling and Educational Attainment
Chapter 3
Schooling and Educational Attainment
Educational attainment is one of the primary means by which the work-force is able to
increase its productivity, obtain decent jobs, promotions and incomes. Since the first
coup in 1987, the Fiji workforce has suffered extremely high rates of emigration of
skilled and qualified human resources, of both Females and Males.
It is important therefore that the educational profiles of the Economically Active be
fully understood, before
Table 3.1 Percent of Age Group for All Persons in School
their work and income
School Age Group
Fem Mal All % GG
patterns are examined.
At School
B 2 to 5 (pre-school)
C 6 to 11 (primary)
D 12 to 15 (junior secondary)
E 16 to 18 (senior secondary)
F 19 to 22 (tertiary)
G 23 to 34 (early career)
H > 34 (late career)
All
12
95
97
78
33
3
0
28
11
96
93
70
34
3
0
29
11
95
95
74
33
3
0
29
8
-1
4
11
-4
7
117
0
Table 3.1 indicates that
while on average, there
is
virtually
no
difference
in
the
aggregate proportion of
Females and Males who
were at school (around 29%), there are quite significant differences at different age
groups (which have been defined to roughly correspond to the age groups associated
with different stages at school and stages in work careers).
Thus Female school attendance at pre-school ages (2 to 5) is some 8% higher than for
males, about the same at primary school age (6 to 11), slightly higher by 4% at junior
secondary age (12 to 15) and 11 percent higher at senior secondary age (16 to 18).
At typical
Table 3.2
Perc. of Age Group Attending School (by gender and
tertiary
ethnicity)
ages (19 to
Female
Male
21) there is
Age Group
Fij Ind Oth Rot Fij Ind Oth Rot
a slightly
lower
B 2 to 5 (pre-school)
12 11 16
0 11 12
0
0
percentage
C 6 to 11 (primary)
95 96 82 95 95 96 98 100
at school (D 12 to 15 (junior sec)
95 99 100 100 94 94 86 88
4 percent).
E 16 to 18 (senior sec)
75 80 89 100 70 70 69 81
However
F 19 to 22 (tertiary)
29 35 45 37 33 34 46 48
Female
G
23
to
34
(early
career)
3
3
3
0
3
3
4 12
enrolment
H > 34 (late career)
0
0
1
0
0
0
0
0
is
some
All
30 27 27 27 31 26 29 33
7% higher
than males
at ages 23
to 34, usually the ages of young career development. What is evident is the very high
23
Chapter 3
Schooling and Educational Attainment
drop-out/push-out for all ethnic groups by the time children reach senior secondary
ages.
Tables 3.2 and 3.3 indicate that there are a few differences when comparing Females
and Males within ethnic groups, and comparing Females across ethnic groups. Thus
Fijian Females have a lower (75%) attendance ratio at ages 16 to 18 (senior
secondary) compared to Indo-Fijian Females (80%). They also have a lower
proportion (29%) at ages 19 to 22 (tertiary education) compared to 35% for IndoFijians.
Table 3.3 indicates that
there are ethnic differences
in Female-Male disparities
in enrolment ratios. Thus
Indo-Fijian Females have a
superior school attendance
than Indo-Fijian Males at
both junior and senior
secondary school ages (by
5% and 14% respectively).
Table 3.3 Perc. Gender Gap in Perc. At School
(by ethnicity)
Sch Age Grp Fijian Indo-Fij Others Rotuman
B 2 to 5
7
-6
C 6 to 11
-1
0
-16
-5
D 12 to 15
1
5
17
14
E 16 to 18
8
14
30
23
F 19 to 22
-10
3
-3
-23
G 23 to 34
10
18
-17
-100
H > 34
9
All
-4
7
-6
-19
While Fijian Females are at
a 10% disadvantage to Fijian males at tertiary ages, Indo-Fijian Females have a 3%
advantage. Both Fijian and Indo-Fijian Females have an advantage at the career
development ages of 23 to 34, with the latter group having a large 18% advantage.
Table 3.4 suggests that gender differences are somewhat different between rural and
urban areas. In rural areas, Females have a higher participation rate at junior
secondary (12 to 15) and senior secondary ages (16 to 18) but considerably lower at
tertiary levels (by 27%) and at young adult levels (23 to 34) by 16%. In Urban areas,
however, Females have an advantage over Males at all age levels.
The
urban
enrolments at
the pre-school
ages (2 to 5)
for both Males
and Females
are double that
in the rural
areas.
Table 3.4 Perc. At School: Gender Differences (by rural/urban)
Rural
Urban
Sch Age Grp
Fem Mal % GG Fem Mal % GG
B 2 to 5
8
7
17
16
15
8
C 6 to 11
94
96
-2
95
95
0
D 12 to 15
97
92
6
97
95
2
E 16 to 18
71
65
9
84
74
13
F 19 to 22
20
28
-27
43
40
7
G 23 to 34
1
2
-16
5
4
10
All
28
27
0
29
30
-1
The enrolment
at
senior
secondary ages
(16 to 18) are also much higher in the urban areas, for both Females and Males. Part
of this difference may be due to rural children studying in urban areas, where schools
are generally better.
24
Chapter 3
Schooling and Educational Attainment
Highest Educational Attainment37
Table 3.5 gives the highest
educational attainment by gender.
This would indicate that a slightly
higher percentage of Females had
No Schooling compared to Males
(difference of 8%) but also higher
Senior
Secondary
attainment
(difference of 17%).
Females
however
lagged
behind
in
Certificates and Diplomas and in
Degrees.
Table 3.5 Highest Educat. Attainment (by
gender)
Vert %
Highest Educ.
Fem Mal % GG
A No Schooling
13
12
8
B Primary (C1-C7)
26
29
-8
C Junior Sec (F2-F4) 40
39
1
D Senior Secondary
14
12
17
E Cert/Diploma
6
7
-11
F Degree/PG
1
1
-14
Grand Total
100 100
0
It is useful to disaggregate by age
groups, as is done in Table 3.6.
Here, Females and Males in the older generation (aged over 34) are compared with
the younger generation (aged 23 to 34). This Table may be used to give a number of
useful
comparisons.
Table 3.6 Highest Educational Attainment (by gender and age groups)
Firstly, note
Ed Summary
the very much
A No Schooling
improved
B Primary (C1-C7)
position
of
C Junior Sec (F2-F4)
Females in the
D Senior Secondary
23 to 34 age
group,
E Cert/Diploma
compared to
F Degree/PG
the state of the
All
Over 34 age
group:
the
percentage with No Schooling declines
Education declines from 26.1% to 4.3%.
Ages > 34
Fem Mal % GG
7.8 3.2
146
27.1 26.8
1
52.5 52.6
0
6.8 7.7
-12
4.8 7.3
-34
0.9 2.1
-57
100 100
Ages 23 to 34
Fem Mal % GG
0.3 0.4
-27
4.3 10.4
-58
45.1 46.0
-2
30.7 25.2
22
16.0 15.6
3
3.6 2.4
51
100 100
from 7.8% to 0.3%, with only Primary
On the other hand, the percentage with Senior Secondary rises from 6.8% to 30.7%;
with Certificate and Diploma rises from 4.8% to 16%; while those with Degree and
Post Graduate rises from 0.9% to 3.6%. Note that these improvements are evident
despite the fact of heavy emigration in the last twenty years of the higher skilled
persons.
Secondly, the percentage gap with the Males has moved in progressive38 directions for
Females: the gap for No Schooling has gone from +146% to -27%; that for Primary
Education has gone from +1% to -58%.
___________________________________________________________________________
37
The EUS questionnaire had boxes for Classes 1 to 3, and 4 to 7 (not including Class 8). So primary
here refers to Classes 1 to 7; and Junior Secondary refers to Class 8, and Forms 3 and 4.
38
“Positive” developments for females would be associated with negative changes at the lower
educational levels, and positive changes at the higher educational levels. The changes in proportions at
the middle levels are more complex to interpret.
25
Chapter 3
At the senior levels; the
gap for Senior Secondary
has gone from -12% to
22%; and Certificate or
Diploma from -34% to
+3%, while that in
Degrees/PG degrees has
gone from -57% to +51%.
Schooling and Educational Attainment
Table 3.7
Highest Ed. Attainment of Females
(rural/urban)
Rural Urban %(Rur-Urb)
A No Schooling
15.2
11.0
38
B Primary (C1-C7)
29.8
23.0
30
C Junior Sec (F2-F4) 40.8
38.7
5
D Senior Secondary 10.2
16.8
-39
E Cert/Diploma
3.5
8.7
-59
F Degree/PG
0.5
1.9
-73
All
100
100
0
With dis-aggregation by
rural/urban, one gets the
generally expected result
that Females in rural areas
tend to have higher proportions of those with lower educational attainment, while
those in the urban areas have higher educational attainment (Table 3.7). Partly, this
may be a result of the more educated women finding employment in urban areas, with
relatively lower opportunities in rural areas for better educated women.
Table 3.8 Highest Educ. Attainment for Females (by ethnicity)
Table
3.8
indicates that as of
Fijian Indo-Fij Others Rotuman
the time of the
A No Schooling
13
13
11
16
2004-05
EUS,
B Primary (C1-C7)
27
27
21
24
Fijian and IndoC Junior Sec (F2-F4)
41
38
39
36
Fijian
Females
D Senior Secondary
13
14
15
18
had a similar
E Cert/Diploma
6
7
10
5
profile of highest
F
Degree/PG
1
2
5
2
educational
All
100
100
100
100
attainment
for
virtually all the
categories.
It
needs to be kept in mind that these are the results after two decades of emigration of
Indo-Fijian skilled persons. Without that heavy emigration, there would have been
much higher percentages of Indo-Fijians at the higher qualification levels.
Table 3.9 suggests that there are some ethnic differences in the Female/Male gaps in
highest educational attainment. The gap on No-Schooling is much larger for IndoFijians Females (+35%)
Table 3.9 % GG in Highest Ed. Attainment (by ethnicity)
while Fijian Females
Fijians Indo-F Others Rotum
have a lower proportion
of those with No
A No Schooling
-6
35
-5
130
Schooling (-6%). Fijian
B Primary
-10
-6
-2
-7
Females have 20%
C Junior Secondary
7
-5
-7
-4
higher proportion of
D Senior Secondary
20
11
42
5
those
with
senior
E Cert/Diploma
-6
-15
-2
-61
secondary,
compared
F Degree/PG
-35
-8
6
89
with 11% difference for
Indo-Fijian Females.
On the other hand, at the degree level, Fijian Females lag further behind the Males
(-35% difference) compared to the -8% lag for Indo-Fijian Females.
26
Chapter 3
Schooling and Educational Attainment
Usual Activity P12M and Highest Educational Attainment
Table 3.10 gives the highest educational attainments of Females and Males by their
“Usual Activity” over the previous 12 months. The picture is generally one of
Females being more qualified than Males in every employment category.
Amongst Wage Earners, a significantly higher 28% percent of Females had attained
Senior Secondary compared to only 18% of Males, while 17% of Males had No
Schooling, compared to 8% of Females.
Amongst Salary Earners, a higher 49% of Females had attained Certificate or
Diplomas, compared to 39 percent of Males. Of Males, some 35% had attained
Junior Secondary or less, compared to only 13% of Females. It could be interesting to
investigate whether Females are generally more qualified than Males, for the positions
they hold.
Of note is that some 11% of Female Employers had had No Schooling, compared to 0
percent of Males, while 24% had Certificates or Diplomas, compared to only 13% for
Males.
The educational attainment profiles for Females and Males were quite similar when it
came to the Self-employed, with only 11% of both groups having attained Senior
Secondary or higher.
A very similar picture also existed for the Family Workers with only 15% of Females
and 13% of males having Senior Secondary or higher; and for Community Workers,
23% of both Females and males having Senior Secondary or higher.
Of those who classified themselves as Handicapped, a very large 49% of Females had
no schooling at all, while another 39% had only primary schooling. Only 10% of
Females had attained Junior Secondary compared to 25% of Male Handicapped. The
picture is generally one of extremely low educational attainment by the
“Handicapped” with Females having a relatively lower attainment than Males. The
bulk of the Handicapped for both Females and Males were 15 years and over.
Of those on Full-Time Household Work, only 18% of Females had attained Senior
Secondary or higher (just slightly lower than the 20% of males), although 58% of
Females had attained Junior Secondary compared to 42% of the males.
Worth noting is that Females on Household Work have a slightly better educational
profile than the Females who are Self-employed. Were those on Household Work to
be self-employed, ceteris paribus, there would be no reason to suppose that their
average incomes would be any worse than those Self-employed.
27
Chapter 3
Schooling and Educational Attainment
Table 3.10
Highest Educational Attainment (by Usual Activity and gender)
No
Primary
Jun.Sec.
Senior
Cert/
Degree
Schooling C1-C7
F2-F4
Second
Diploma
/PG
Sex
A Wage earner
Female
1
8
51
28
11
1
Male
1
17
54
18
10
0
All
1
14
53
21
10
1
B Salary earner
Female
0
1
12
23
49
16
Male
0
3
32
23
30
12
All
0
2
24
23
37
13
C Employer
Female
11
24
20
20
24
0
Male
0
20
44
17
13
6
All
2
21
40
18
15
5
D Self-employed
Female
2
22
65
8
3
0
Male
2
27
60
8
3
0
All
2
26
61
8
3
0
E Family worker
Female
4
23
58
11
4
0
Male
2
30
54
9
4
0
All
3
26
56
10
4
0
F Community worker
Female
4
2
70
16
5
2
Male
10
0
67
17
6
0
All
6
2
69
16
6
2
I Handicapped
Female
49
39
10
2
0
0
Male
32
39
25
4
0
0
All
40
39
17
3
0
0
L FT Household Work
Female
5
20
58
14
4
0
Male
5
32
42
16
4
0
All
5
20
57
14
4
0
Unemployed
Female
1
8
48
31
9
2
Male
1
10
52
27
8
2
All
1
9
48
30
10
2
Fiji
13
28
40
13
7
1
28
All
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Chapter 3
Schooling and Educational Attainment
Poverty deciles and educational attainment
It may be expected that the lower
the educational attainment of a
person, the lower would tend to
be the general poverty status of
his/her household.
One way of designating the
standard of living of households
is by ranking households by
Income per Adult Equivalent39
and allocating the households to
the Bottom 3 deciles containing
the bottom 30% of the population
(i.e. really the people in the
“poor” households); the Middle 4
deciles containing the middle
40% of the population (i.e. the
“middle classes”) and the Top 3
Deciles containing the top 30%
of the population (i.e. the “upper
classes”).
Table 3.11 gives the proportions
of Males, Females and All
persons in the Bottom 3, Middle
4 and Top 3 deciles by
educational attainment.
For those without any schooling,
a slightly higher proportion of
Females are in the Bottom 3
deciles (43%) compared to 34%
of Males with No Schooling.
Similarly for those with only
Primary Education.
Table 3.11 Educational Attainment and Decile
Class
Dec Class
Female Male All %GG
No Schooling
Bottom 3
43
34
43
27
Middle 4
38
46
39
-16
Top 3
19
20
18
-8
100
100
100
Primary Education
Bottom 3
39
36
39
8
Middle 4
40
44
39
-9
Top 3
21
20
22
5
100
100
100
Junior Secondary
Bottom 3
30
28
30
7
Middle 4
42
45
44
-7
Top 3
28
27
26
4
100
100
100
Senior Secondary
Bottom 3
20
16
19
24
Middle 4
39
37
38
4
Top 3
42
47
43
-11
100
100
100
Certificate/Diploma
Bottom 3
9
10
10
-11
Middle 4
22
26
24
-15
Top 3
69
64
66
8
100
100
100
Degree/PG
Bottom 3
4
6
6
-33
Middle 4
8
13
13
-37
Top 3
88
81
82
8
100
100
100
For both Females and Males,
there is a trend of higher
proportions being in the Bottom
3 or Middle 4 deciles the lower is the educational attainment. And conversely, the
higher the proportions in the Top 3 deciles, given higher educational attainment.
Thus with No Schooling, or Primary Education only, only around 20 percent could be
in the Top 3 deciles- for both Females and Males. However, the proportions in the
___________________________________________________________________________
39
The households are ranked by Total Household Income per Adult Equivalent, and not that of the
individual persons. Thus a person with a high educational attainment may be residing in a household
with lower overall income status depending on others’ income and the size of the household.
29
Chapter 3
Schooling and Educational Attainment
Top 3 deciles rise to 26% (Junior Secondary), 43% (with Senior Secondary), 66%
(Certificate/Diploma) and 82% with a Degree or PG qualification.
And at the higher educational levels, Females have a higher proportion than Males in
the Top 3 deciles: the Female proportion in the Top 3 Deciles is 8% higher than that
for Males, for both those with Certificates/Diplomas and those with Degrees.
For Females, not having higher education is more likely to put them into
households which are in the lower deciles than it does Males. And conversely,
having higher education, is more likely to put Females into households which
are in the upper deciles than it does Males. Higher Education is good for
Females
Not at School/Of School Age
This
is
an
Table 3.12 NAS/Of School Age (gender and Poverty Class)
interesting group
Fem Male
All
Fem
Male
Vert %
as there are some
Bot 3
909
729
1638
55
45
33
five thousand of
Mid 4
1343 907
2250
60
40
45
them,
with
a
Top 3
429
708
1137
38
62
23
slightly
higher
All
2680 2344 5025
53
47
100
proportion (53%)
of them being
Females. Some 68 percent of them are six years old.
Table 3.13 indicates that 79% of them had “No schooling”, so the bulk had never been
to school. Nevertheless there were some 21% who had been to school and obviously
dropped out or been pushed out. While this latter group had not responded to the
EUS as “unemployed”, they clearly may be in that category.
It is also
Table 3.13 NAS/Of School Age (Educ. attainment and ethnicity)
unclear
to
Ver %
Ed
Attain.
Fijian Ind-F Others Rotum
All
what extent
A
No
Sch.
2771
915
261
37
3983
79
the six year
B C1 to C3
266
261
27
554
11
olds who had
C
C4
to
C7
47
105
152
3
never been
D
C8
to
F3
37
253
45
335
7
to
school
All
3122
1534
333
37
5025
100
might also
62
31
7
1
100
never go to
school and
therefore
some proportion of them may also be classified as potentially unemployed. This
group are slightly more represented in the Bottom 3 and Middle 4 deciles, than in the
Top 3 deciles.
***
30
Chapter 4
Paid Time Worked by the Economically Active
Chapter 4
Time Worked: Employment, Under-employment and
Effective Unemployment
One of the paradoxes of statistics on Fiji’s labour force is the apparently low rate of
unemployment previously reported and also recorded by the 2004-05 EUS- some
4.5% nationally (here referred to as “Rate of Formal Unemployment” (RoFU). Yet
even a cursory examination of the large numbers of apparently job-less persons in Fiji
would suggest that the real extent of unemployment is far higher.
Indeed, the figures for wage and salaried employment obtained through the Annual
Employment Surveys of the FIBoS and usually published annually, reveal an
extremely low growth rate over the last three decades, totally unable to absorb the
numbers of school leavers every year.
The totals recorded by the AES are well below the size of the Labour Force in the
country, as recorded by the 2004-05 EUS. The AES typically reports about 70
thousand Wage Earners and about 47 thousand Salary Earners. The 2004-05 EUS
estimates a total of 146 thousand Wage Earners and 49 thousand Salary Earners. The
AES therefore captures Salary Earners fairly well, but only about half of the Wage
Earners.40 Total employees captured by the AES is about 147 thousand, while the
EUS covers in excess of 330 thousand Economically Active persons, of which about
130 thousand are Self-Employed, Family Workers, Employers and Community
Workers. In addition, the EUS also covers some 15 thousand unemployed.
The AES is unable to establish where the large numbers of school leavers end up: the
surveys generally cover persons employed by establishments who are listed in the
Business Register maintained by the Bureau, and would generally be considered to be
in the formal sector. These surveys do not cover employment in the informal sector
of the economy which employs a large portion of the Economically Active in the
country.
Critically for gender analysis, the annual employment surveys also have no
information on the large amounts of household work done, both by the Economically
Active (both Males and Females), as well as those who classify themselves as
engaged in full-time household work (mostly women).
The 2004-05 EUS gives the first opportunity for stakeholders to obtain a full picture
of all work (including household work) done by all persons in the economy, including
the fulltime Household Worker who are usually excluded from the definition of
“Economically Active”.
It is clear from the EUS results that the apparently low rates of unemployment are
largely a definitional problem. According to the EUS, the “unemployed” are the
persons who reported themselves as “Unemployed/Looking for Work” or
___________________________________________________________________________
40
These would be usually earning relatively higher wages.
31
Chapter 4
Paid Time Worked by the Economically Active
“Unemployed/Not Looking for Work”. This category of unemployed workers will be
referred to as “Formal Unemployment”.
However, one of the key findings of this study is that there is a very large extent of
under-employment amongst those who are normally classified as Economically
Active. An under-employed person is defined as anyone who works less than 240
Standard Days (i.e. of 8 hours per day) per year. In particular, those who are
classified as “Self-Employed”, “Family Workers” and “Community Workers” suffer
from very high levels of under-employment, in terms of hours worked per day or per
week, and/or days worked per year.
Some countries use minimum cut-off points for time worked, below which the person
would be classified as unemployed. This however still introduces a degree of
arbitrariness which can hide the true extent of unemployment. The EUS data on
actual times worked by each Economically Active person, enables us to far more
accurately quantify the true extent of “Under-employment” and “Effective
Unemployment” in the economy. These statistics can be generated not just nationally,
but also by gender, and other useful disaggregates such as ethnicity, rural/urban,
industry, occupation group.
To this end, this study estimates two useful statistics. The first is the Effective Rate
of Under-employment (ERoUnder) (of the Economically Active persons). These
rates completely dwarf the Rates of Formal Unemployment discussed earlier.
The second is the Effective Rate of Unemployment (ERoU) of the Labour Force in
totality, which takes account of both the Formally Unemployed and the UnderEmployed. The results indicate the effective unemployment in the country is far
more than indicated by the Rates of Formal Unemployment.
The methodology of calculating the ERoUnder and ERoU is explained in a Box at the
appropriate point in this chapter.
Working in Activities 1, 2 and 3 over the Previous 12 Months
The information provided for work done during the Previous 12 Months covered the
primary activity (Activity 1) as well as any additional two activities. Thus this data is
expected to be more comprehensive of total work done during the year, than the
information on work done over
the Last 7 Days.
Table 4.1 Persons Engaged in Activities 1,2,and 3
Table 4.1 indicates that 29% of
the
Economically
Active
Females also engaged in
Activity 2, and 12% engaged in
a further Activity 3. Some
43% of the Economically
Active Males engaged in
Activity 2 and 18% engaged in
Activity 3. Males are therefore
far more likely to be engaged in
Activity 1
Female
Male
All
Female
Male
All
32
Activity 2 Activity 3
Persons
102447
30103
12264
227637
97878
39892
330084
127982
52156
Percent of Numbers in Activity 1
100
29
12
100
43
18
100
39
16
Chapter 4
Paid Time Worked by the Economically Active
secondary and tertiary economic activities.
Table 4.2a gives in one table, the entire distribution matrix of percentages of the
Economically Active Females and Males working in each Activity. It is worth
spending some time to understand this table because it gives the complete picture of
the time spent by the Economically Active Females and Males in each of the three
main activities.
The data for the Females are in three blocks on the left hand side, Activity 1, Activity
2 and Activity 3 (going down). Each block gives the matrix of the percentages of all
Females (or Males in the right hand blocks) working combinations of particular hours
per day and days per year. The numbers in each individual block add up to 100%.
Similarly, the data for Males are in the three blocks on the right hand side of the table.
It would be useful to just examine some of the comparable data for Females and
Males to understand what the matrix is saying about the degree of under-employment
of Economically Active persons.
The reader should verify from the table, that for Activity 1 (compare the numbers in
the top left hand block for Females and top right hand block for Males)
*
3 percent of Females worked 1-2 hours per day and less than 50 days per year,
compared to 1 percent of Males working the same
*
4% of Females worked more than 8 hours per day and more than 299 days per
year, compared to 7% of Males
*
25% of Females worked (7 to 8) hours per day, for (250 to 299) days per year,
compared to 21% of Males
*
altogether, 11% of all Females in Activity 1, worked only (1-2) hours per day
(last row in block) compared to 4% of Males
*
altogether, 8% of all Females worked less than 50 days per year (last column
in left block) compared to 4% of Males (last column in right block).
It is evident that in Activity 1, a higher percentage of Females worked shorter hours,
and fewer days than Males, and a lower percentage of Females worked longer hours
and higher numbers of days, than Males. Economically Active Females are more
under-employed than Economically Active Males.
A similar conclusion can be drawn by examining Activity 2. Thus 16% of Females
worked between 1 and 2 hours per day and less than 50 days per year, compared to
11% of the Males. And in Activity 3, the corresponding percentages are 27% and
14%.
33
Chapter 4
Paid Time Worked by the Economically Active
In general it may be seen that for Activity 2 and Activity 3, larger proportions of the
Economically Active persons are working shorter hours, and fewer days.
Table 4.2a Perc. of Persons Working P12M in Activities 1, 2 and 3
(by hours and days worked)
Hours Worked Per Day
1 to
2
Days pa
< 50
50 to 99
100 to 149
150 to 199
200 to 249
250 to 299
> 299
All
3 to
4
5 to
6
7 to
8
>8
Hours Worked Per Day
All
1 to
2
3 to
4
5 to
6
7 to
8
>8
All
% of EcAc Females in Activity 1 % of EcAc Males in Activity 1
3
3
2
0
1
1
1
11
2
4
5
4
1
2
1
19
1
2
2
3
2
2
1
12
1
2
1
1
6
25
9
44
1
0
0
1
1
7
4
14
8
11
9
9
10
36
16
100
1
1
1
1
0
0
1
4
1
2
3
3
3
1
1
14
0
1
2
6
4
2
1
17
1
1
2
5
7
21
10
46
0
1
1
1
2
8
7
20
4
5
8
16
16
31
19
100
% of EcAc Females in Activity 2 % of EcAc Males in Activity 2
< 50
50 to 99
100 to 149
150 to 199
200 to 249
250 to 299
> 299
All
16
22
4
1
0
0
1
45
9
14
6
2
0
1
0
33
4
2
3
1
0
0
0
10
4
1
1
1
0
0
0
8
1
0
1
1
0
1
1
4
34
39
15
6
1
2
2
100
11
8
4
3
0
0
1
28
8
14
10
5
2
1
0
39
2
4
6
4
1
1
0
18
2
2
3
2
1
0
1
11
1
1
1
1
0
0
0
4
24
30
23
15
4
2
2
100
% of EcAc Females in Activity 3 % of EcAc Males in Activity 3
< 50
50 to 99
100 to 149
150 to 199
200 to 249
250 to 299
> 299
All
27
16
4
3
2
0
0
51
12
15
4
0
0
0
0
32
4
2
3
0
0
0
0
9
3
2
0
0
0
0
0
6
2
0
0
0
0
0
0
2
49
35
11
3
2
0
1
100
14
12
6
1
0
1
1
34
17
13
4
3
1
1
0
39
6
6
2
1
0
0
0
15
3
3
2
0
0
0
0
8
2
1
0
0
0
0
0
4
42
35
15
4
2
2
1
100
Table 4.2b simplifies the data in Table 4.2a showing in a smaller table, how Females
were more likely to be
under-employed both Table 4.2b Perc. of Persons Engaged in Activities 1, 2 and 3
in terms of hours
Working
Activity 1 Activity 2 Activity 3
worked per day, and
< 5 hrs
Females
31
78
83
days worked per year,
< 5 hrs
Males
17
67
73
in each of the three
<
100
days
Females
19
73
83
activities. Thus 31%
< 100 days Males
9
54
76
of Females worked
less than 5 hours in
34
Chapter 4
Paid Time Worked by the Economically Active
Activity 1 (compared to 17% of the males), 78% in Activity 2 (compared to 67% of
the Males) and 83% in Activity 3 (compared to 73% of Males).
Similarly, in Activity 1, 19% of Females worked less than 100 days per year
(compared to 9% of males), 73% in Activity 2 (compared to 54% of Males) and 83%
in Activity 3 (compared to 76% of Males).
These numbers indicate very significant gender differences in the degree of underemployment, especially in Activity 1, but also to some extent in Activity 2 and
Activity 3. With so much under-employment, crude aggregated gross numbers of
persons employed will not give an accurate perspective on the total amount of work
being done by the Economically Active in the economy, over the whole year.
It is important to therefore accurately measure the total amount of work done by
Females and Males over the full 12 Months period, in each activity, using a common
standard.
This section attempts to analyse the total effective work done over the Previous 12
Months, in all three activities, taking full account of the hours per day and the days
per year worked in each of the three activities by the Economically Active persons.
For each person therefore, the amount of work done over the P12M in each activity is
converted into hours, aggregated for all three activities, and then converted into
“Standard Years” (assuming an 8 hour working day, and 240 working days per year).
The Relative Importance of Activities 1, 2 and 3
Table 4.3 presents the aggregate amount of work done by Females and Males in
Activities 1, 2 and 3 in “Persons Years”.41 First, Activity 1 contributed 89% of all
the work done, with 9% contributed by Activity 2 and only 2% of the aggregate total,
by Activity 3.
Table 4.3
Data
Activity 1
Activity 2
Activity 3
All
Total Work Done in Activities 1,2 and 3 over Previous 12 Months
Standard Years
Percentages
Female
Male
All % F % of F % of M
% of All
77616 197705 275321 28
93
87
89
5017
23604
28621 18
6
10
9
1195
5702
6897 17
1
3
2
83828 227011 310840 27
100
100
100
Females contributed 28% of the work done in Activity 1, but a lower 18% to Activity
2 and 17% to Activity 3. Females were somewhat less likely to do paid secondary
and tertiary activities.
___________________________________________________________________________
41
Some of these numbers here are slightly different from that given in the earlier EUS Report, because
the estimated mid-point of top bracket for time worked per day was changed from 9 to 11 hours (see
the note and explanation in the Box on the Methodology of Averages).
35
Chapter 4
Paid Time Worked by the Economically Active
Females did 93% of their total work through their main activity (Activity 1) and only
7% through Activity
2 and 3. Males on
Table 4.4 Aggr. Standard Years in All Activities
the other hand did
Female Male
All
87% through Activity
A Standard Years Act.1, 2, 3 83828 227011 310840
1 and 13% through
B Labour Force
109631 236106 345737
the secondary and
A as percentage of B
76
96
90
tertiary work.42
Table 4.4 gives another perspective on the “Aggregate Standard Years” devoted to
Activities 1, 2 and 3, relative to the gross numbers present in the Labour Force If the
work done by the Economically Active is reduced to a common standard of
“Standard Years”, then the total work done by Females amounts to only 76% of the
Female Labour Force, while that done by the Males amount to 96% of the Male
Labour Force.
While it might be tempting to use the statistics in this table as an indication of the
greater under-employment of Females, this would not be correct methodologically,
since such estimates are likely to be biased downwards, because there are significant
numbers of economically active persons indicated to be working more than 365 days
in the year.
Table 4.5 indicates that when all the work of the Economically Active in Activities 1,
2 and 3 are aggregated together in “Standard Working Days”, some 53% are indicated
to be working over 240 days per year (47% of Females and 56% of Males). Hence
the positive effect of the “extra” work done by the “over-working” proportion of the
labour force would statistically neutralise part of the impact of the “under-employed”
persons.
Table 4.5 Economically Active Persons Working in Activities 1, 2 and 3
(by Standard Working Days)
Effective
Persons
Vertical Percentages
W/Days
Female
Male
All
% Fem Female
Male
All
<100
32431
33153
65584
49
32
15
20
100-200
12654
43878
56531
22
12
19
17
200-219
7443
17241
24683
30
7
8
7
220-239
1797
5112
6910
26
2
2
2
240-259
22071
38613
60684
36
22
17
18
260-279
3519
12696
16215
22
3
6
5
280-299
9032
25469
34501
26
9
11
10
300-365
11651
41845
53496
22
11
18
16
> 365
1905
9687
11593
16
2
4
4
All
102504 227694 330197
31
100
100
100
___________________________________________________________________________
42
With Females are more heavily involved in household work, it would be inevitable that their
secondary income-earning activities would be curtailed.
36
Chapter 4
Paid Time Worked by the Economically Active
Overall, 32% of all economically active Females worked less than a hundred days,
compared to only 15% for Males. Females comprised 49% of all those that worked
less than a 100 days, while they were a much lower 31% of the Economically Active.
Generally a higher percentage of women worked fewer days.
All Fiji Effective Rates of Under-employment and Unemployment
For all Economically Active persons, the extent of under-employment (“deficits”) are
aggregated and converted into “Standard Years” of “effective unemployment” which
is then divided by the number of Economically Active persons to obtain the “Effective
Rate of Under-employment”. A detailed explanation of the methodology is given in
Box following.
Table 4.6 indicates that while the national Rate of Formal Unemployment was only
4.5%, the Effective Rate of Under-employment was 23%, for Females 31% and 19%
for Males. Graph 4.1 indicates the extent to which the “Under-employment” effect is
far greater than the Formal Unemployment.
In common sense terms, while there may have been 102 thousand Economically
Active Females, some 31% of their time was un-utilised in the work for which they
were defined as Economically Active. For Males the comparable figure was 19%.
When the number of persons “Effectively Under-Employed” (D) is combined with the
number of persons Formally Unemployed (U), then the total number of persons
“Effectively Unemployed” (EU), as a proportion of the Labour Force, gives the
“Effective Rate of Unemployment”- the true measure of overall unemployment of the
Labour Force in activities which define the persons as “economically active”.
Table 4.6
Effective Rates of Under-employment and Unemployment
Fem
Mal
All % GG
Rate of Formal Unemployment (RoFU)
6.5
3.5
4.5
84
ERoUnder-employment (ERoUnder)
31
19
23
63
ERoUnemployment (ERoU)
35
22
26
62
Nationally, this ERoU was 26%, with a much higher 35% for Females and slightly
lower 22% for Males. These are horrendous rates of effective unemployment of the
Economically Active. It would suggest that there is a considerable amount of
disguised unemployment.
More than a third of the Female Labour Force and more than a fifth of
the Male Labour Force are effectively un-utilised in paid work, because
of the under-employment of the Economically Active.
37
Chapter 4
Paid Time Worked by the Economically Active
Calculating Effective Rates of Under-employment and Unemployment for P12M
For Economically Active persons, it is possible to derive statistics which measure the
true extent of under-employment, and effective unemployment for the economy.
(1) The Labour Force = (Economically Active persons) + (the Unemployed)
LF = EA + U
(2) The Rate of Formal Unemployment = (the number of Unemployed)/(Labour Force)
= U/LF
In the P12M section, for each of the 3 economic activities, the EUS asked for the number
of hours worked per day, and the number of days worked over the previous year.
The hours per day were given in ranges (1-2) (3-4) (5-6) (7-8) (9+).
The mid-points used were 1.5, 3.5, 5.5, 7.5, and 11.
For each activity, the mid-points were multiplied by the number of days worked, to
obtain total hours worked for Activities 1, 2 and 3.
The sum of these was then divided by 8 to obtain the effective Standard Days worked by
each person.
The deficit with 240 days is then assumed to the amount of “under-employment”.
e.g. if a person worked 210 Standard Days, then he/she was underemployed by 30 days
= 240 – 210.
The aggregate sum of all the deficits was then divided by 240 to obtain an estimated total
value of “Standard Years” representing the full extent of the under-employment (call it
D).
Hence the Effective Rate of Under-Employment (ERoUnder) = D/(EA)
Since the Formally Unemployed persons = U, then the total number of “Effectively
Unemployed” persons = EU = U + D.
Hence the Effective Rate of Unemployment (ERoU) = (U + D)/(LF)
The real effective rate of unemployment therefore aggregates the effects of the formally
unemployed and the equivalent of those who are under-employed.
Note: The rates of Formal Unemployment and the Effective Rate of Underemployment
are not additive, because the denominators are different. The denominator for the
ERoUnder is the number of Economically Active persons (E), while the denominator for
both the Rate of Formal Unemployment and the Effective Rate of Unemployment is the
Labour Force (LF), which is the sum of the Economically Active and the Unemployed.
(LF = E + U).
38
Chapter 4
Paid Time Worked by the Economically Active
Importance of Employment Status
It is useful to first note (Table 4.7) that the under-employment of the Labour Force is
more
associated
with
Graph 4.1 Rates of Formal Unemployment,
Community Workers, Family
Under-Employment and Effective Unemployment
Workers, and Self-Employed,
which had extremely high
40
35
Effective Rates of Under31
Employment of 66%, 59% and
Females
30
34% respectively.
Males
22
19
20
Female Community Workers and
Family Workers had even higher
ERoUnder of 67%, 66% and
51%.
10
6.5
3.5
0
RoFU
ERoUnder
ERoU
In simple language, one could say
that because of their underemployment, the Female Family Workers and Community Workers were doing the
same amount of work as a only a third of their gross numbers, and for Female SelfEmployed, only a half of their gross numbers. Male under-employment in these
categories was not too
Table 4.7 ERoUnder (by Employ. Status P12M and gender)
far behind either. But
Usual Activity 12m
Female Male All % Diff
for all categories
A
Wage
earner
13
11
12 (F-M)/M
14
Females had a much
B Salary earner
4
4
4
19
higher ERoUnder than
C Employer
28
13
16
107
males.
D Self-employed
E Family worker
F Community worker
All
51
66
67
31
29
52
64
19
Note that Female
Employers also had
high
rates
of
ERoUnder with 28%
of Female employers
being effectively unemployed, 107% higher than the rate for Males.
34
59
66
23
77
26
4
63
In aggregate, it is sobering that of those persons regarded as Economically Active,
such large proportions are underemployed to the extent of an “effective underemployment” rate of 23%. For Females the ERoUnder is a high 31%, some 63%
higher than the rate of 19% for Males.
It is not possible to calculate Effective Rates of Unemployment by Employment
Status, as the Formally Unemployed did not have any Employment Status43 although
the ERoU will probably be just a few percentage points higher than the ERoUnder.
___________________________________________________________________________
43
The EUS questionnaire did not ask the unemployed respondents to give any previous Employment
Status.
39
Chapter 4
Paid Time Worked by the Economically Active
By Ethnicity
Table 4.8 indicates
that Females have
much
higher
ERoUnder for all
ethnic groups, but the
highest ERoUnder is
for Fijian Females
(with 36%), with
Rotuman
Females
having 34% and IndoFijian Females having
21%.
Table 4.8 ERoUnder and ERoU (by ethnicty)
%(F-M)/
Ethnicity
Female
Male
All
M
Rate of Formal Unemployment
%(F-M)/M
Fijian
5
3
4
36
Indo-Fij
10
4
5
167
Others
5
5
5
12
Rotuman
7
2
3
311
Eff. Rate of Underemployment
Fijian
36
24
28
49
Indo-Fij
24
14
16
69
Others
25
11
16
121
Rotuman
34
27
29
25
Effective Rate of Unemployment
Fijian
39
27
31
46
Indo-Fij
31
17
21
81
Others
29
15
20
87
Rotuman
38
28
31
37
Overall, the same
relativities are there in
the full Effective Rate
of
Unemployment,
with Fijian Females
having the highest
ERoU
of
39%
compared to 31% for Indo-Fijians. The gender gap is however larger for Indo-Fijians
(81%) than for Fijians (46%).
For both sexes, while Fijians had a slightly lower Rate of Formal Unemployment than
Indo-Fijians, Fijians have a significantly higher (by more than 50%) Effective rate of
Unemployment (31%) compared to Indo-Fijians (21%).
By Rural/Urban
Table 4.9 indicates that while the
Rates of Formal Unemployment are
higher for urban Females than rural
Females, there are large opposite
disparities in under-employment,
with Rural Females having the
highest degree of under-employment
(ERoUnder = 42%).
The impact is high enough to ensure
that Rural Females end up with a
total
Effective
Rate
of
Unemployment of 45% - some 75%
higher than Rural Males, and 65%
higher than Urban Females.
`Table 4.9
Region
ERoUnder and ERoU (by region)
Fem Mal All % Diff
(F-M)/M
RoFU
Rural
6
2
3
179
Urban
7
5
6
38
%(R-U)/U -19 -60 -47
ERoUnder
Rural
42
24 29
73
Urban
22
13 16
66
%(R-U)/U 91
84 80
ERoU
Rural
45
26 31
75
Urban
28
18 21
56
%(R-U)/U 65
47 49
Urban Females however also have a significantly higher Effective Rate of
Unemployment of 28%, some 56% higher than the Urban Male rate of 18%.
40
Chapter 4
Paid Time Worked by the Economically Active
By Division
Table 4.10 indicates that Females in
the Northern Division had the highest
ERoUnder of 47% compared to 18%
in the Western division.
Indeed, the Western division had the
lowest ERoUnder of all the divisions
and for both Males and Females.
Tourism, in this division,
is a
powerful employment provider for
both Females and Males.
Aggregating the impact of Formal
Unemployment
and
Underemployment,
Females in
the
Northern Division end up with the
highest ERoU of all, some 49%.
Almost a half of all Economically
Active Females in the Northern
divisions are effectively unemployed.
The gender gap is also the
largest for Northern Females
(at 91%) although the Central
division gap is also somewhat
high at 68%.
The Western
Division Females also had the
lowest
ERoU
of
the
Economically Active, at 28%,
while the Western Division
Males similarly had a low of
20%, comparable to the same
rate for Central Division Males.
Age Groups and
Employment
Unemployment
Underand
Table 4.11 gives the Rates of
Formal Unemployment, Underemployment and Effective
Unemployment of the Labour
Force, age group and gender.
These
age
groups
corresponding roughly to stages
Table 4.10 ERoUnder and ERoU (by division)
Division
Fem
Mal All % Diff
RoFU
Central
5
3
4
61
Eastern
0
1
1
-78
Northern
4
3
3
39
Western
13
5
7
167
ERoUnder
Division Female Male All
Central
31
18
23
73
Eastern
41
33
36
25
Northern
47
24
31
100
Western
18
16
17
11
ERoU
Central
34
20
26
68
Eastern
41
34
37
22
Northern
49
26
33
91
Western
28
20
22
40
All
35
22
26
62
Table 4.11 RoFU, ERoUnder, ERoU (by age groups)
% Diff
Age Group Female Male All
(F-M)/M)
RoFU
A < 20 yrs
B 20 to 29
C 30 to 39
D 40 to 54
E Over 54
All
35
12
3
2
1
6.5
A < 20 yrs
B 20 to 29
C 30 to 39
D 40 to 54
E Over 54
All
49
20
29
33
53
31
A < 20 yrs
B 20 to 29
C 30 to 39
D 40 to 54
E Over 54
All
67
30
31
34
53
35
41
11
18
6
8
3
3
1
2
1
1
3.5 4.5
ERoUnder
37
40
17
18
13
18
17
22
34
39
19
23
ERoU
45
50
22
25
15
20
18
23
34
40
22
26
211
89
20
28
46
84
31
18
122
96
57
63
51
33
103
89
56
62
Chapter 4
Paid Time Worked by the Economically Active
in working life- teens, young entrant, middle experience, senior experience, and
retirement age.44
By all criteria (RoFU, ERoUnder, and ERoU) and for all age groups, the rates for
Females are significantly higher than the rates for Males (all the numbers in the last
column are positive and large. Females have in aggregate twice the RoFU (6.5%) as
Males (3.5%), but the rates are higher, the younger is the age group, with the Female
teenagers having the highest rate of 35%, declining to 12% for the twenties group, and
3% for the thirties.
Female teenagers also have the highest rate of Under-employment (at 49%) compared
to 37% for Males. However, the rates of Under-employment drops to 20% for the
twenties group, and then gradually increases up the age scale, with 29% for the
thirties, 33% for the mature age workers, and 53% for the retirement age group.
Overall, the Effective Rate of Unemployment (ERoU) (which shows the aggregate
effect of both the Formal Unemployment and the Under-employment) is higher for
Females than for Males by some 62% in aggregate. For Females, the ERoU is highest
for the teenagers (at 67%).
Then the rate drops to 30% for those in the twenties,
after which it slowly rises up the age scale. The pattern for Males is slightly different
with the ERoU dropping from 45% to 22% for the twenties, falling further to 15% for
the thirties, before rising from thereon to reach 34% for the over 54s.
By Industry
Stakeholders interested in productivity and economic efficiency would be interested
in examining where the highest rates of under-employment are, by industry. Table
4.12 indicates that Agriculture, Forestry and Fishing had the highest proportion of
Females (74%) who worked less than a hundred days compared to only 27% of
Males. The Female percentages were also higher for Manufacturing (27%) and Hotel,
Retail and Restaurants (28%) than for Males (16% and 13% respectively).
Table 4.12 Standard Working Days Worked over P12M (by industry)
Female
Male
100 to
100 to
No. of
239 > 239 All < 100
Industry Act 1
< 100
239 > 239 All Workers % F
AgForFishing
74
20
6
100
27
47
27
100
90301 23
Mining&Quarrying
0
0
100 100
2
6
91
100
3467
5
Manufacturing
27
23
49
100
16
30
54
100
46839 34
Elect & Water
0
0
100 100
1
4
95
100
2719
6
Construction
0
13
87
100
7
25
69
100
17634
4
Hotel, Retail, Rest.
28
22
50
100
13
22
65
100
73057 44
Transp.Stor.Comm.
3
18
78
100
4
15
81
100
23106 12
Fin. Real Est. Bus
11
19
70
100
6
13
82
100
10844 34
Comm. Soc.& Pers.
13
22
65
100
6
20
74
100
62733 43
All
32
22
47
100
15
29
56
100 330699 31
___________________________________________________________________________
44
Fiji workers are able to begin their FNPF pensions at the age of 55.
42
Chapter 4
Paid Time Worked by the Economically Active
These relativities are the same for all industrial groups except for Mining &
Quarrying, Electricity & Water and Construction- all industries where there were very
few Females employed.
The degree of underemployment is summarised
by the Effective Rates of
Under-Employment (Table
4.13). The overall Female
ERoUnder is 31% compared
to a much lower 19% for
Males.
Table 4.13 ERoUnder (by Industry)
Industry
Fem Mal All % Dif
AgForFishing
67
34
41
95
Mining&Quarrying
0
2
2
Manufacturing
28
20
23
37
Elect & Water
0
2
2
Construction
2
11
11
-86
Hotel, Retail, Rest.
28
15
21
84
Transp.Stor.Comm.
7
7
7
4
Fin. Real Est. Bus
12
7
9
66
Comm. Soc.& Pers.
15
9
12
70
All
31
19
23
63
By industry, Agriculture,
Forestry and Fisheries has the
highest
Female
underemployment
with
an
ERoUnder of 67% compared
to 34% for Males. Much of
this under-employment would probably be associated with subsistence work in the
informal sector, rather than formal employment.
In contrast, the Female ERoUnder of 28% for each of Manufacturing and Hotel,
Retail and Restaurants suggest a large degree of part-time and possibly casual work
associated with these industries.
While the Female ERoUnder were higher than Male ERoUnder in both Finance, Real
Estate & Business and in Community, Social and Personal Services, the rates were
relatively low, although the gender difference was still high at 66% and 70%
respectively.
Note that the ERoUnder by Industry (or by Occupation Groups) cannot be aggregated
with the Formally Unemployed because the unemployed are not categorized by
Industry or Occupation. Hence it is not possible to give ERoUs by Industry or
Occupation groupings.
By Major Occupation Groups
By major occupation groups, Females in Skilled Agriculture and Fisheries, Craft and
Related and Elementary Occupations tended to have more persons under-employed
(Table 4.14).
43
Chapter 4
Paid Time Worked by the Economically Active
Table 4.14 Distr. of Workers by Standard Days Worked P12M (by Major Occupation Group
Occupation
< 100
Female
100 to
239 > 239
All
< 100
Male
100 to
239 > 239
All
No. of
Workers
%F
Sen. Officials & Manag.
8
26
66
100
7
15
78
100
17237
26
Professionals
Tech. & Assoc Prof.
5
18
23
10
71
71
100
100
7
6
21
14
72
79
100
100
21161
18008
44
36
Clerks
Service, Shop, MktSales
Sk.Agr.& Fishery
Craft & Related
Pl& Mac.Oper.&Assemb.
Elementary Occupations
6
12
78
49
9
38
13
19
19
33
16
28
82
69
3
18
75
34
100
100
100
100
100
100
3
4
30
6
4
18
16
12
44
24
14
41
81
84
26
70
82
42
100
100
100
100
100
100
22115
36240
84364
44845
26209
60521
58
42
22
24
17
34
Grand Total
32
22
47
100
15
29
56
100
330699
31
Table 4.15 summarises the under-employment with highest ERoUnders for Skilled
Agriculture and Fishery of 70% (double the 36% for Males), while Females in Craft
and Related had an ERoUnder of 51% contrasting with only 10% for Males.
There was a high rate of
Table 4.15 ERoUnder (by Occupation Group and Gender)
37% for Females in
Occupation
Fem Mal All % (F-M)/
Elementary Occupation
M
Sen. Officials & Manag.
12
9
9
38
as well.
The only occupation
group where Males had a
slightly higher ERoUnder
was in Professionals (a
small difference of 9%).
The lowest rates were for
Clerks (6%) and Plant
and Machine Operators
and Assemblers (9%).
Professionals
Tech. & Assoc Prof.
Clerks
Service, Shop, MktSales
Sk.Agr.& Fishery
Craft & Related
Pl& Mac.Oper.&Assemb.
Elementary Occupations
Grand Total
9
16
6
14
70
51
9
37
31
10
8
5
6
36
10
7
24
19
9
11
6
9
44
20
7
29
23
-9
110
23
135
92
389
41
54
63
Average Time Worked
There are some methodological difficulties in working out averages, whether for
“average time” worked (hours per day, hours per week or days per year) or “average
income” earned (per hour, day, per week or per year).
The problem exists for both the Last 7 Days data and the Previous 12 Months data
(see Box following for a detailed explanation).
Nevertheless, some reasonably
accurate estimates may be made with both datasets, for selected variables.
44
Chapter 4
Paid Time Worked by the Economically Active
Methodology Note on “Averages”
Some statistics presented in this Report on “averages” (whether for time worked, or
incomes received) suffer from a small methodological difficulty, possibly producing
small biases in the results.
The 2004-05 EUS recorded time worked or incomes earned in ranges of values, where
the top range of necessity had to be left open-ended, but for some variables, were
unfortunately done so at relatively low values. For example, the top range for
The Last 7 Days data
Hours worked over the Last 7 Days was “40+” hours
Gross weekly income was “$300 and over”.
Previous 12 Months data
Hours worked per day was “9+”,
Days worked per year was “300 or more”
Gross annual income was “$150,000 and over”
To calculate averages for time worked, arbitrary “mid-point” values had to be given to
the top ranges for time worked. The earlier EUS Report had used a value of 40 for the
40+ top bracket in the Last 7 Days data-set. This gave much lower averages for Wage
Earners and Salary Earners (around 34 hours) than is indicated for these two categories
from the Annual Employment Survey (around 45 hours).
In an attempt to match the L7D averages for Wage Earners and Salary Earners in the
EUS with that derived from the AES, the mid-point for 40+ was set at 55 hours.
Hence the mid-point for the “9+” hours per day in the P12M data set was also set at 11
hours, to correspond to a 5 day week. For days worked (300 and over) the minimum
of 300 was used.
Given that Males tend to work longer hours and more days than Females, there may
still be a tendency to under-estimate Male averages relative to Female average, both for
average time worked and average income earned. Hence the estimated Gender Gaps
(%(F-M)/M) may still be slightly under-estimated.
Exception:
The time spent on Household Work was not given in ranges but actual
hours. Hence total hours and averages for various categories of
Household Work do not suffer from the above limitation.
There is a further methodological note on the estimation of incomes at the higher
bracket levels.
45
Chapter 4
Paid Time Worked by the Economically Active
Average Days Worked P12M
The hours and days spent in each of the three activities worked in over the P12M may
be aggregated and converted into “Standard Days”.
Table 4.16 gives the “Average
Standard Days” worked over the
P12M, by Employment Status.
Those cells with averages higher
than the national average of 226
are indicated in bold italics.
Females on average worked 196
days, while Males on average
worked 239 days.
Table 4.16 Average Standard Days Worked P12M
Employment
Status
Fem Mal All % GG
A Wages
-7
252 271 266
B Salary
-6
275 292 286
C Employer
-13
242 279 272
D Self-empl.
135 197 182
-32
E Family Wrk.
90 123 106
-27
F Commun.Wrk 81
86
82
-6
All
196 239 226
-18
The overall national Gender Gap is -18%.
For “paid work” Economically Active Females work
some 18% less than Males (P12M data).
The averages shown in Graph 4.2 makes clear that there are there are roughly two
groups of Employment
Graph 4.2 Average Standard Days Worked P12M
Status
with
Wage
(by Employment Status)
Earners, Salary Earners,
Employers all working
Average Standard Days Worked
far more than the SelfEmployed,
Family
F Community Wrk
Male
Workers
and
E Family Wrk.
Female
Community Workers.
D Self-empl.
All
categories
of
Employment
Status
however have negative
gender gaps, with the
smallest being for
Wage Earners and
Salary Earners (and
Community Workers).
C Employer
B Salary
A Wages
0
46
100
200
300
Chapter 4
Paid Time Worked by the Economically Active
Rural/Urban Differenecs
It is useful to have a
rural/urban disaggregation
as there are very significant
differences.
In general, the Average
Standard Days worked in
Urban areas are generally
higher than in the Rural
areas, for both Females and
Males. This with averages
higher than the national
average are indicated by
bold italics.
The Gender Gap in rural
areas is much larger, -26%
than it is in Urban areas
(-15%).45
Table 4.17 Average Standard Days P12M (rural:urban)
Female Male All % GG
R Wages
-2
250
254 253
R Salary
-8
274
298 288
R Employer
93
215 195
-57
R Self-empl.
129
189 176
-32
R Family Wrk.
87
128 106
-32
R Community Wrk
56
14
53
299
All Rural
158
213 198
-26
U Wages
273
-11
253
283
U Salary
-5
275
291 285
U Employer
-5
293
307 304
U Self-empl.
146
-37
231 200
U Family Wrk.
99
111 105
-10
U Community Wrk
94
94
94
0
All Urban
227
268 254
-15
196
239 226
-18
Note that not only is the gender gap larger in the rural areas, but there are also
Rural/Urban gaps for Females, as well as for Males.
Table 4.18 indicates that the gap
between Rural Females and Urban
Males is a larger -30% than the
-21% gap between Rural and
Urban Males.
The rural/urban gap is not
particularly significant for Female
Wage Earners or Salary Earners.
There is however a -10% gap
between Rural Males and Urban
Males.
Table 4.18 Rural/Urban % Gaps
Female Male
Wages
-1
-10
Salary
-1
2
Employer
-68
-30
Self-employed
-12
-18
Family Workers.
-12
15
Community Work
-40
-85
Total Rur:Urb Gap
-30
-21
All
-7
1
-36
-12
1
-44
-22
The rural/urban gaps for Employers, Self-Employed, Family Workers, and
Community Workers are all large, for both Females and Males. The one unusual gap
is the +15% gap between Rural Males and Urban Male Family Workers.
___________________________________________________________________________
45
The results for Female Rural Employers and Community Workers may not be reliable as the number
of observations in those cells were rather small.
47
Chapter 4
Paid Time Worked by the Economically Active
Average Hours Worked L7D
While other tables on
time worked over Last 7
Days are given in Annex
A, the data in Table 4.19
is given here as it will be
useful when Total Time
Worked
(including
Household Work) is
being estimated.
Table 4.19
Average Hours Worked Per Week L7D
(by Employment Status)
Empl.Status L7D
Female Male All % GG
A Wages
42
43
43
-3
B Salary
46
46
46
1
C Employer
38
44
42
-15
D Self-employed
27
32
31
-17
E Family Work
15
20
18
-21
F Community Work
21
29
26
-29
All
33
37
36
-10
Table 4.19 indicates that
the overall Gender Gap
for average hours worked
over the Last 7 Days is a somewhat smaller -10%, compared to the Previous 12
Months gap (-18%).
This is to be expected: as explained earlier, the Previous 12 Months data is more
comprehensive since it includes also Activities 2 and 3, in addition to the main
Activity 1.
As with the P12M data, the first three employment categories of Wage Earners, Salary
Earners and Employers work much longer hours on average (43, 46 and 42 hours
respectively) than the bottom three categories of Self-Employed, Family Workers and
Community Workers (31, 18, 26).
Graph 4.3 Relation between Average Hrs L7D
and Average Standard Days P12M
Average Standard Days P12M
The gender gaps are quite large for
Employers (-15%), the Selfemployed (-17%), Family Workers
(-21%) and Community Workers
(29%).
The Gender gap is a small -3% for
Wage Earners and +1% for Salary
Earners.
350
250
150
50
10
20
30
40
50
While there are reservations about
Average
Hours
L7D
the adequacy of the L7D data to
fully reflect the work done over the
year, there is in fact a very strong
linear
relationship
(Correlation
Coefficient R2 = 0.97) between the Average Hours Worked L7D for Females and
Males in Table 4.19 and the Average Standard Days Worked P12M in Table 4.16
(see Graph 4.3).46
___________________________________________________________________________
46
Average Standard Days Worked P12M = (6.14) * (Average Hours Worked L7D). Outliers for
Community Work were excluded from the regression because of the small numbers of observations for
this Employment Status for both Females and Males.
48
Chapter 5
The Unpaid Household Work
Chapter 5
The Unpaid Household Work
Household work has historically been one of the neglected areas in labour market
analyses of the Fiji economy. This study provides comprehensive data on this
important subject area.
The statistics in this chapter are derived from a very simple question (Q1.13) in the
2004-05 EUS asking for data for every person, on the number of hours of “household
chores” per week spent on cooking, washing clothes, child care, compound work,
other household chores.47
Semantics in discussions of gender issues can be quite important for the slant or
biases that can be portrayed simply because of the terms that are used. The term
“chores” itself is a value-laden expression which conjures images of unpleasant tasks
that have to be done in the household, instead of referring to it as “household work”
that is not only totally essential to the running of households and society, but
absolutely essential for the perpetuation of the work-force. The irony is that
household work is typically unpaid unless performed by “domestic help”.
It is not easy to find a term that differentiates between the work done by the
economically active and household work. The term “economically active” is of
course already a value-laden term which implies that household work is somehow not
“economic” work. Other words that come to mind are “normal” to describe the work
by the Economically Active, but that would imply that household work was somehow
“not normal”, which it not only is, but is also essential.
Rather than create other terms such as “Recognised” work of the Economically Active
and “Unrecognised” work of those doing household work, this study will simply use
“paid work” to describe that of the Economically Active. It should be noted that
“paid work” also covers those who work for subsistence. Note also that many Family
Workers and Community Workers often are not paid money wages but in kind.
Household Work (not done by paid domestic workers) can also be referred to as
“Unpaid Household Work”.
A methodological warning: simply taking national averages of household work done
by all Females and Males, will not give fair gender comparisons as 99% of full-time
household workers are Females, hence the averages for Females will be pulled
upwards. To make fair comparisons, it is important to separate out the household
work done by the Economically Active. Of course there is also a need to document
all household work done by the Economically Active and others, including full-time
Household Workers. This chapter will first give the analysis of household work done
by all persons.
___________________________________________________________________________
47
One weakness of the EUS questionnaire was that households were not asked if they employed fulltime or part-time workers to do the household work.
49
Chapter 5
The Unpaid Household Work
Household Work by All Persons
Table 5.1 indicates that in aggregate, some 11 million hours of household work is
done per week, or roughly 71 hours per household per week.48
Fully a third (34%) of
Household Work is
devoted to Cooking (24
hours per household).
Of the others, Child
Care took 23%, while
Washing Clothes and
Compound Work took
another 16% and 15%.
Table 5.1 Aggregate Data on Household Work (by types)
Total Hrs per HH Standard Vert
pw (000)
pw
Years
perc.
Cooking
3861
24
104567
34
Washing Clothes
1838
11
49791
16
Child care
2577
16
69793
23
Compound
1718
11
46519
15
Other Chores
1330
8
36024
12
Total Chores
11324
71
306694 100
The overall importance
of this household work can only be gauged if the total hours were converted into
equivalent “Standard Years”.49 The third column of Table 5.1 estimates the number
of persons (Standard Years) who would have been required had they to do the same
amount of household work on a fullTable 5.2 Share of Total Household Work
time basis. A remarkable 307 thousand
Female Male All
persons would have been needed to do
Cooking
87
13
100
the household work that is currently
Washing
Cl.
86
14
100
being done to serve the total EUS
Child care
79
21
100
population of some 817 thousand
persons.
Compound
36
64
100
Other
75
25
100
Some 104 thousand would have been
Total HH Work
76
24
100
required to do the cooking, 70 thousand
to care for children, 50 thousand to wash
clothes, and 46 thousand to do compound work. These are extremely large numbers,
given that there are less than 200 thousand Wages and Salary Earners estimated by the
EUS for 2004-05.
These 306 thousand Standard Years required to do HH Work amounts to some 93%
of the 330 thousand persons normally considered “economically active”.
The
macroeconomic monetary importance of this household work is estimated below.
The Gender Division of HH Work
How is the burden of all the household work shared between Females and Males?
Table 5.2 indicates that Females did some 76% of all the Household Work in Total,
but over 86% of Cooking and Washing Clothes and 79% of Child Care. Only in
Compound Work did Males do a higher proportion (64%) than Females (36%).
___________________________________________________________________________
48
A rough figure of 160 thousand households was used. The estimate derived from the 2002-03 HIES
was 156 thousand households.
49
A Standard Year consists of 240 days per year, with 8 hours per day.
50
Chapter 5
The Unpaid Household Work
Age Profile of Household Work
In terms of Total Household Work done,
Females aged between 20 and 39 do the
largest portion, 41%, while Males in that
comparable age group do 12%.
Table 5.3 Percentage of Total HH Work
Age
Female Male All % Diff
0 to 9
0
0
1
42
10 to 19
5
3
8
84
20 to 39
41
12
53
249
40 to 59
25
7
32
238
Over 59
5
2
7
138
All
76
24
100
215
Interestingly, the 10 to 19 age group do
some 8% of all Household Work, with
Females in that group, already doing
84% more work than their Male
counterparts. There is a clear age profile
of household work, with the average hours per person peaking in the thirties, for both
Females and Males (Table 10.4). For that age group, the difference in the average
hours is a very large 27 hours
Table 5.4 Av. Hours of Total Household Work pw
per week.
The gender
Age
Female Male All Diff Hrs % Diff
difference is more than 20
0-9
1
0
1
0
56
hours for all ages between 20
10-19
7
4
5
4
99
and 60.
Females in the sixties are still
doing an average of 24 hours
of household work pw, while
those in the seventies do an
average of 14 hours per week.
The national averages of 14
hours for all persons (22
hours for Females and 7
hours for Males) need to be
treated cautiously because of
a methodological issue.
20-29
30-39
40-49
50-59
60-69
70-79
80-89
90-99
All
> 9 yrs
> 19
33
38
34
31
24
14
6
0
22
26
32
8
11
10
9
9
7
7
5
7
8
9
21
24
22
20
17
10
6
3
14
17
21
24
27
24
22
14
7
-1
-5
15
18
23
291
234
247
253
148
104
-20
-100
230
At what age may persons be regarded as “potentially responsible” for household
work, when calculating average hours of household work for each category? This
question is obviously relevant for “child care” as those themselves requiring child
care should not be part of the denominator when
calculating average hours of child care.50 Table 5.5 Av. Hours of HH Work
Similarly, one cannot expect young children to be pw (of those below 18 yrs of age)
undertaking potentially dangerous cooking duties,
Age
Female Male All
although they may well be expected to clean up
0-1
0.3
0.1 0.2
the compound, as many indeed do in Fiji.
2-3
0.1
0.0 0.0
Table 5.5 indicates the average hours of Total
Household Work for those aged under 18. It may
be seen that already by the age of 8-9, children are
doing more than an hour’s household work per
week, rising to 2.3 hours by the ages of 10 and 11.
4-5
6-7
8-9
10-11
12-13
14-15
16-17
0.3
0.8
2.0
2.9
4.3
5.2
9.9
0.0
0.4
1.6
1.7
2.3
3.7
4.7
0.1
0.6
1.7
2.3
3.3
4.4
7.4
___________________________________________________________________________
50
I.e. they should not be expected to be caring for themselves.
51
Chapter 5
The Unpaid Household Work
Children up to the age of 9 (who comprise some 19% of the total population of Fiji)
did only 1% of Total Household Work done nationally (Table 5.3). Excluding these
children from the analysis of responsibility for household work, would therefore be
disregarding less than 1 percent of the Household Work done, while excluding a very
large 19% of the population from the denominator.
Table 5.4 (second last row) indicates that excluding those below 10 years of age,
results in a national average of 17 hours of household work per person, 26 for
Females and 8 for Males. If only those over 19 are taken into account, then the
average for Females becomes 32 hours per week, and Males 9 hours per week. These
may be compared with some international averages: Bangladesh (31 and 5); India (34,
10); Nepal (42, 15).51
Economies of Scale in Household Work
Table 5.6 and Graph 5.1 gives the average time (simple averages) spent per week on
various household work, by household size. There is a clear pattern of some
economies of scale.
Overall, the average time spent by each person on all Household Work declines from
18.2 hours per week for a household size of 1, to 12.5 for household of size 6, and
Table 5.6
Average Hours of Household Work per occupant per week (by household size and Work)
Household Size
Work
Av
1
2
3
4
5
6
7
8
9
10
11
12
Cooking
4.7
9.2
8.3
6.4
5.3
4.7
4.2
3.9
3.7
3.6
3.2
3.0 2.3
Washing
2.2
3.9
3.2
2.9
2.4
2.2
2.1
2.1
1.9
2.1
1.8
1.9 1.4
Childcare
3.2
0.0
0.6
2.8
3.4
3.2
2.9
3.2
3.0
3.0
4.1
3.1 2.1
Compound 2.1
2.7
3.1
2.5
2.2
1.9
1.7
1.8
1.6
1.7
1.6
1.2 1.4
Others
1.6
2.4
2.3
2.0
1.9
1.7
1.6
1.6
1.5
1.4
1.3
1.1 1.3
All
13.8 18.2 17.5 16.5 15.2 13.6 12.5 12.5 11.7 11.7 12.1 10.3 8.4
further to 8.4 hours for a
household of size 12.
Graph 5.1 Economies of Scale in HH Work
Economies of Scale in Household Work
10
Average Hours Per Week
The bulk of the economies are
to be found in Cooking and to a
lesser extent in Washing
Clothes. Only for Child-care,
as would be expected, do
increases in HH size require
higher average amounts of time
(the number of infants also
increase on average with HH
size).
Cooking
Washing Clothes
Childcare
Compound
Others
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10 11 12
Household Size
It is important to keep these
economies of scale differences
___________________________________________________________________________
51
Quoted in Beneria (2003) pp 146-148.
52
Chapter 5
The Unpaid Household Work
in mind when examining HH work by other characteristics such as ethnicity,
Rural/Urban, or Qualifications. There may be systematic differences in household
size by these characteristics, which may have a bearing on differences in average
hours of household work done, purely because of household size alone.
Division between Economically Active and Others
The analysis of household work is made more complex because of the reality that
those who are typically regarded as Economically Active (in other than household
work) also do a very large share of all household work. To a certain extent, this is
driven by economic circumstances, as those who are affluent would by and large tend
to employ full-time or part-time domestic workers. Those who are not economically
affluent no doubt feel that there is a financial saving to doing this work, as the
opportunity cost of the time devoted may be considered by them to be insignificant.
Table 5.7 indicates that some 45% of all the household work is done by those
normally considered to be Economically Active (the Wage and Salary Earners, the
Employers, the Self-Employed, the Family Workers, and the Community Workers).
Of that 45%, the Female Economically Active did 57%, but Male Economically
Active (who number twice as many as Female Economically Active) also did a quite
large 43%. Some 43% is done by those on Full-time Household Work, of which 99%
is by Females.
Some 7% of the HH Work
is done by FT Students
(two thirds by Female
students), and only 4% by
Others
(retired,
handicapped,
unemployed,
otherwise
inactive).
Table 5.7 Proportion of Total Household Work Done by
Activity P12M
Female Male All % Fem
Economically Active
26
20
45
57
FT Household Work
42
0
43
99
FT Students
5
3
7
64
Others
3
1
4
66
All
76
24
100
By Usual Activity P12M
The importance of differentiating between the different categories of those who are
Economically Active, may be gauged from Table 5.8. This suggests that there are
roughly five different grouping of persons with similar HH work characteristics.
The highest average number of hours per week (40), as may be expected, is done by
those who had designated themselves as being on Full-time Household Work. It is an
interesting coincidence that the average hours done by full-time Household Workers
is exactly the same as the number of hours (40) that is being assumed to be the length
of the Standard Week (of work).
However, there is a whole category of Female Economically Active workers (Selfemployed, Family Workers, and Community Workers) who on average did almost as
much (35 hours per week) as that done by the FT Household Workers. This should
not be surprising since we earlier documented that these three categories of workers
were also significantly under-employed in so far as the normal Economically Active
53
Chapter 5
work
was
concerned. Females
in these categories
did an extra 20
hours extra per
week
or
more
compared to their
Male counterparts.
This is analysed a
little further below.
The Unpaid Household Work
Table 5.8 Av. Hours of Total HH Work pw
(by Usual Activity P12M)
Fem Mal All (F-M) % GG
A Wages
24
8
12
16
191
B Salary
22
10
14
13
134
C Employer
21
7
10
14
196
D Self-employed
35
11
17
24
219
E Family Workers
35
14
25
21
156
F Commun. Wrks.
35
13
30
22
162
H Retired/Ov.54
10
7
9
3
44
I Handicapped
2
4
3
-2
-44
J Other/Inactive
18
4
10
13
306
U Unemployed
24
8
15
16
197
L Household Wrk
40
30
40
10
32
M NAS/Underage
0
0
0
0
418
N FT student
5
2
4
2
89
T NAS/school age
0
1
1
0
-40
All Fiji
22
7
14
15
230
What is of some
significance is that
Female
Wage
Earners,
Salary
Earners
and
Employers
also
worked more than
20 hours per week
on
average
on
Household work. They also did more than 13 hours extra per week compared to their
Male counter-parts.
The number of hours in the week available for work, study, leisure and other activities
is limited by physiological factors. For Economically Active Females to work 16
hours per week more than Males, must have a major impact on the ability of Females
to devote time to study and professional development, and leisure activities. The
unequal balance in housework has to be seen as a critical factor explaining why
Females lag behind Males in career development as well as personal development.
Also somewhat unusual is that the Unemployed on average did as much work as the
first three categories of Economically Active. As with other categories, the Female
Unemployed also did some 16 hours per week more than the Male Unemployed.
Full-time Female students did some 89% more household work than their Male
counterparts, amounting to an extra 2 hours per week.
Distribution of Economically Active Time On HH Work
Table 5.9
indicates the
distribution of
Economically
Active
persons
doing
different
hours
of
household work
per week.
Table 5.9 Distribution of Economically Active Persons
by Hours of HH Work pw (percentages)
0-9 10-19 20-29 30-39 > 39 All < 20 > 19
Female 15
22
23
17
23 100 37
63
Male
66
20
7
4
3
100 86
14
54
While
63%
of
Female
Economically Active did more
than 19 hours per week, only
14% of Male Economically
Active did the same. Some 66%
of the Economically Active
Males did less than 10 hours of
HH
work
per
week.
The Unpaid Household Work
Graph 5.2 Percentage of Economically Active
Persons Doing HH Work pw
80
Percentages
Chapter 5
Female
60
Male
40
20
0
Graph 5.2 makes the difference
0-9
10-19 20-29
30-39
> 39
between Female Economically
Active and Male Economically
Hours of HH Work pw
Active quite clear. However, the
aggregate gender differences hide
interesting differences
between
some Table 5.10 Distribution of Wage Earners, Salary Earners and
categories
of
Employers according to HH Work done pw
Employment Status.
0-9 10-19 20-29 30-39 > 39 All
Females
20
27
24
15
14
100
It
is
useful
to
Males
70
18
6
3
2
100
distinguish between the
two
groups
of
Employment Status as indicated above. Wage Earners, Salary Earners and
Employers, not only have very similar patterns of paid work, but also similar patterns
of Household Work (Table 5.10). Only 5% of Males do more than 30 hours of HH
Work per week (as opposed
to 29% of Females in these
Graph 5.3 HH Work done by Female HH Workers and
categories).
Econ.Act. Fem. Self-Empl., Family, Community Workers
60
However, Female SelfEmployed, Family Workers
Fem HH
and Community Workers
40
have virtually the same
Fem SE/Fam/Com
profile of HH Work per
week as do full-time
20
Household Workers (Table
5.11 and Graph 5.3). This
similarity of HH Work
0
0-9
10-19
20-29
30-39
> 39
profiles
raises
many
interesting questions. Are
these
“Economically
Active” Females
(SelfEmployed, Family Workers, Community Workers) doing “two shifts”, given that they
are doing about as
much Household Table 5.11 Distrib. of Female HH Workers and Econ.Active Female
Work as those Self-Employed, Family and Community Workers (by HH Work pw)
0-9 10-19 20-29 30-39 > 39 All
regarded as fulltime Household
Fem HH Workers
4
10
20
26
40 100
Workers?
Fem SE/Fam/Com 7
15
22
19
36 100
55
Chapter 5
The Unpaid Household Work
Alternatively, given that these Economically Active Females are doing significant
amounts of “paid work” (e.g. 27, 15, 21 hours per week respectively), why are the
Household Workers not doing similar amounts of paid work?
If part of the answer is that there is simply no work available, then to what extent are
those persons categorised as full-time household workers simply not stating that they
are unemployed? (i.e. would like to work but there is no work available).
Ethnicity and Household Work
Table 5.12 gives the ethnic differences in Gender Gaps in Average Total Household
Work done- the percentages by which the Females of each group, exceeded the
respective Males.
Overall,
Indo-Fijians
have
the
highest gap.
Indo-Fijian
Females do
330% more
household
chores than
Males,
compared to
the
182%
difference
for Fijians
and
158%
for
others
(last row).
Table 5.12 Gender Gaps Average Household Work
(Perc. by which Female Averages are Higher than Male Averages)
Usual Activity
Fijians Indo-Fij Others Rotumans
A Wage earner
124
266
128
128
B Salary earner
104
213
195
53
C Employer
301
228
-20
D Self-employed
171
361
234
E Family worker
149
201
41
171
F Community worker
83
347
767
H Retired/pensioner
75
110
-100
L FT Household Work
22
46
62
N Full-time student
71
134
87
72
U Unemp./looking
166
200
232
V Unemp/Stopped looking
308
318
34
70
All
182
330
158
211
These ethnic relativities are present for all categories of “Usual Activity”, except
“Employers” where the Fijian Gender Gap of 301% is higher than the 228% for IndoFijians.
Thus the Female/Male difference for Indo-Fijian Wage Earners is 266% compared to
124% for Fijians; the corresponding percentage are 213% and 104% for Salary
Earners. Perhaps the lowest differences are for those on Full-time Household Work
where the Indo-Fijian differential is only 46% compared to the 22% for Fijians.
It may be noted that the differential for Indo-Fijian full-time students (134%) is
around twice that for Fijians (71%), Others (87%) and Rotumans (72%).
The
patterns of uneven burdens of household chores clearly begin at the school-ages, and
continue into adult-hood.52
___________________________________________________________________________
52
The differences in overall averages of time spent are not given as Indo-Fijians have a relatively
higher proportion as full-time Household Workers, compared to the other ethnic groups and hence
there would be biases in the differences. Also, Indo-Fijian households tend to be smaller.
56
Chapter 5
The Unpaid Household Work
Qualifications and Gender Gaps in Household Work done by Economically
Active
It might be thought that as Females
and
Males
become
more
“educated”53, the Gender Gaps in
Household Work should decrease.
Table 5.13 Av. Total Hours of HH Work pw done
by Wage Earners, Salary Earners and Employers
Fem Mal All % Diff
No Schooling 24.6 9.1 13.2
170
Primary
26.2 8.0 10.9
226
Junior Sec
24.2 8.8 12.6
174
Senior Sec
22.1 7.8 13.1
184
Cert/Diploma 24.8 9.1 15.4
173
Degree/PG
15.2 6.5 10.2
133
All
23.4 8.5 12.9
176
We focus here only on those who
are Wage Earners, Salary Earners
and Employers since the other
categories of Economically Active
are
relatively
more
underemployed, and do relatively more
household work. Table 5.13 indicates that for both Females and Males there is no
strong pattern of reducing gender gaps, except right at Degree/PG level where the gap
drops to 133%, from the more than 170% gaps for the lower qualifications.
Some of the decline in the absolute numbers of hours done may well be associated
with the greater employment of house-help, by those who are better qualified, as for
instance by those with Degree/PG qualifications.
It also seems that the average size of
Table 5.14 Av. Size of HH by Qualification
households declines with the
Ed Summary
Female Male All
improvement in qualifications (Table
A No Schooling
6.2
6.4 6.3
5.14).
Thus a person with No
B Primary
5.9
6.0 6.0
Schooling is on average in a
C Junior Secondary
5.8
5.9 5.9
household of size 6.3, some 30%
D Senior Secondary
5.7
5.7 5.7
larger than the household associated
E Cert/Diploma
5.3
5.4 5.3
with a person with a Degree/PG (size
F Degree/PG
4.7
4.9 4.8
4.8).54 One would therefore expect
All
5.8
5.9 5.9
from our economies of scale
statistics that the smaller household
sizes
would
require
somewhat more HH work.
Table 5.15 Av. Hours of HH Work per Occupant pw
Ed Summary
Table
5.15
therefore
A No Schooling
estimates the Average Total
B Primary
Hours of Household Work
C Junior Secondary
done
per
household
D Senior Secondary
occupant.
There is no
E Cert/Diploma
particular
pattern
of
F Degree/PG
reducing gender gaps, even
All
though on average, Females
do more than three times
the amount of work done by Males.
Female Male All % Dif
4.0
1.4 2.1 177
4.4
1.3 1.8 233
4.2
1.5 2.1 179
3.9
1.4 2.3 183
4.7
1.7 2.9 178
3.2
1.3 2.1 142
4.0
1.4 2.2 180
___________________________________________________________________________
53
The assumption would be that better educated Males would also be gender sensitised to their
Females partners’ needs, and/or that more educated Females would demand greater equality in
household work.
54
These are average household sizes for the whole sample covered by the EUS.
57
Chapter 5
The Unpaid Household Work
The one exception is that those with Degree/PG qualifications, still have the smallest
gender gap- of 142% (still large).55
Is there a Trade-off Between Paid Work and Household Work?
We have earlier documented that in general, Females tended to do fewer hours of paid
work per week than
Males. It may be thought
Table 5.16 Average Hrs of HH Work pw
therefore, that the lower
Hours of Paid Work L7D
household work done by
5
15
25
35 45 All
Males may be purely the
Female
32
35
28
24 23
26
result of them doing more
Male
10
12
12
9
8
9
hours of the other
All
18
19
17
14 12
14
“normal” work done by
the Economically Active
(called here Paid Work), i.e is there a trade-off?
Average HH Work pw
Table 5.16 (and graph 5.2) gives the average hours of household work done for each
group of Hours of Paid Work L7D. There seems to be a clear trade-off for Females,
but a not particularly
Graph 5.4 Av. Hrs of HH Work and Paid Work L7D
strong one for Males.
Best-fit
regression
56
lines may be fitted
40
y = -2.9022x + 37.08
to the two sets of
2
57
R = 0.7816
Both
data.
30
regression lines show
Female
20
the expected inverse
relationship- i.e. as
Male
10
the hours of “Paid
Work” increases, so
y = -0.574x + 11.809
0
the average hours of
2
R = 0.3298
5
15
25
35
45
Household
Work
Paid Work L7D (PW)
decreases (Graph 5.4).
However, the Female
line is significantly higher than the Male line for all hours of normal economic work
done, with the gap decreasing from 24 hours at the left side, to about 15 hours towards
the higher levels of normal work.
Moreover, the Female line is much steeper- suggesting that for Females at least, there
is a genuine “trade-off” in that those who devoted higher average times to normal
___________________________________________________________________________
55
Note the slight increasing trend in average hours per occupant for all persons, in going from 1.8
hours for Primary qualifications to 2.9 hours for those with Certificate/Diploma. This may well be
partly explained by the smaller household size of those with higher qualifications, requiring higher
amounts of household work, because of the economies of scale factor.
56
Note that these regression lines are being fitted to the group averages, not to the individual
observation points.
57
For Females, the line has the equation [HH Work = 37- (2.9 PW) with a correlation coefficient of
0.78, while for the Males the line has the equation [HH Work = 12 – (0.6 PW) with a correlation
coefficient of 0.33].
58
Chapter 5
The Unpaid Household Work
economic work, had to cut down on their household work. For every extra hour of
Paid Work, Females do 3 hours less of Household Work. For Males, every extra hour
of Paid Work, results in only 0.6 hours less of HH Work. Effectively, with a fairly
flat regression line for Males, the amount of Paid Work done has little bearing on
Household Work they do.
Overall, women who do the highest amounts of normal work, still end up doing on
average far more household work, than Males who do the least amount of Normal
work.
In aggregate, we have documented earlier, that on average, Females do significantly
higher amounts of total work than Males, taking into account both the paid work and
Household Work.
For nearly all this household work, there is no particular reason why Females would
be more suited than Males, although a case may be made for child-care.58
There would seem to be ample scope for Males to do a much bigger share of
household work. There is an interesting arithmetical result because there are twice as
many Economically Active Males as Females. Economically Active persons do an
average of 14 hours of household work per week (Females 26 and Males 9). If the
Males did an extra 5 hours on average, the Economically Active Females would then
on average have an extra 12 hours to devote to personal development (an all would
then be doing an average of 14 hours per week).
The Macro-economic Importance of Household Work
This section assesses the macro-economic importance of household work to the
economy, firstly in terms of unweighted aggregate time. Table 5.17 indicates the
overall importance of household work, in relation to the aggregate amount of work
done by those normally classified as Economically Active, both converted to Total
Standard Years.59
Table 5.17 Time Worked by All Persons in the Economy (Econ. Active and HH Work)
Female Male
All
Perc. Fem
Total Standard Years Economically Active 80860 217718 298579
27
Total Standard Years Household Work
232821 73872 306694
76
Total Standard Years All Work
313682 291590 605272
52
Perc. HH Work
74
25
51
___________________________________________________________________________
58
Infants requiring breast-feeding might require women to be at home, although women have been
known to express milk to be fed their infants by child minders.
59
A Standard Year is defined as a person working for 8 hours per day, for 240 days per year.
59
Chapter 5
The Unpaid Household Work
Household Work comprised some 51% of Total Time worked, a result which is in
keeping with international studies.60
Females did some 27% of the work done by the Economically Active, but 76% of all
Household work. Altogether, judged purely by the amount of time spent, Household
Work comprised 51% of All Work done in the economy, 74% for Females, but only
25% for Males61.
Calculating the monetary value of the household work will always be problematic.
Beneria (2003: 143) identified three ways of doing so: (a) a “global substitute”
method which simply uses the cost of a hired domestic worker who would be needed
to do all that work (b) “specialised substitute” method using the average wage of a
specialist with skills for each specific household task and (c) the opportunity cost
method based on the wage that the person doing household work would receive in the
market.
In an economy such as Fiji where there is substantial unemployment and underemployment of the work-force, the opportunity cost of much of the labour devoted to
household work (i.e. alternative income earning activities) may well be very lowcertainly much lower than the normal price of unskilled labour. Some might even
argue that the opportunity cost is close to zero.
However, it is noted in Chapter 7 that Female Subsistence farmers earned on average
around $1400 per annum, while Male Subsistence Farmers earned $1741, giving a
national average of $1658 (earlier chapter). Presumably, some portion of household
workers (especially in the rural areas) can always revert to subsistence work.
The “lack of domestic jobs” argument is also becoming less and less tenable with the
increasing international labour mobility of Fiji women as caregivers, and fruit-pickers,
whose potential incomes abroad are far in excess of the salaries of Fiji university
graduates.
Rather than engage in a fruitless debate trying to set one monetary value for
household work, this study uses a range of possible values, and gives the monetary
implications of the value of this household work, relative to the value indicated by the
EUS for the work of the “Economically Active”.
The Average Annual Income indicated by the EUS data for the Previous 12 Months
for the occupation “Domestic Workers” was some $3318 per year- roughly about $60
per week. This is probably on the high side, and is unlikely to be replicated across the
economy.
Ad hoc information suggests that Domestic Workers hired by middle income and low
income families are paid around $30 per week (or $1560 per year – which is close to
the average subsistence income per person per year), while those in the upper income
households often receive in excess of $60 per week plus food and lodging.
___________________________________________________________________________
60
Hugh Davies et al “How do couples spend their time? House of market and domestic work time in
British partnerships” quoting the UNDP Human Development Report 1995. In Gustafsson (2000,
p226).
61
There is no account taken of productivity of the different kinds of work.
60
Chapter 5
The Unpaid Household Work
Two values are therefore used in the following table- $30 per Standard Week for a
moderate estimate, and $60 per standard week for an upper estimate. At $30 per
week, Total Income as measured by the EUS would need to be adjusted upwards by
16% of the Income of the Economically Active nationally, 5% for Males, but by 47%
for Females (Table 5.18). Thus, even going by the moderate price of $30 per week
for Household Work, the significance of Household Work cannot be doubted.
Valued at $60 per week, Total Income as measured by the EUS would need to be
adjusted upwards by 34% of the income of the Economically Active nationally, 11%
for Males, but an extremely large 93% for Females.
Table 5.18 Value of Household Work Relative to That of the Econ. Active
Incomes Previous 12 Months
Female Male
All
Perc. Fem
Tot. Income Econ. Active P12M ($m)
780
2143 2924
27
HH work valued at $30 pw ($1560 pa)
Econ. Value of HH Work P12M ($m)
363
115
478
76
Total Adjusted GN-Income ($m)
1144
2258 3402
34
Perc. Adjustment Required
47
5
16
HH work valued at $60 pw ($3120 pa)
Econ. Value of HH Work P12M ($m)
726
230
957
76
Total Value Produced ($m)
1507
2374 3881
39
Perc. Adjustment Required
93
11
33
In debates about the methodology of estimating national income, any factor which has
the capacity to change the total value by 16%, would be given utmost attention by
those responsible for the national accounts. Yet household work rarely receives the
attention it deserves, and the 16% required adjustment may well be a lower estimate.
In an earlier chapter we have documented that those doing full-time household work,
seem to have roughly the same educational attainment profile of Wage Earners and
Self-Employed. Another issue therefore is the potential value of the work that
currently full-time household workers could be doing, were they to be employed as
Economically Active persons defined in the usual sense, given their educational
attainment.
The analysis would also need to take into account that if the household work being
done is absolutely necessary, than Males would need to take up the additional
household work that Females would not then be doing. That might well require that a
certain number of Males would lower productivity would need to leave the Labour
Force and become full-time Household Workers.
It would be an interesting exercise to develop a model of the likely behaviour of
individual members of a household, with the basic premise being that the household
members, given a reasonably accurate awareness of their market value in paid work,
co-operate in choosing particular combinations of “paid work” and household work,
with the ultimate objective being the maximisation of aggregate household income
(rather than the maximisation of each individual’s income).
61
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
Chapter 6
Total Time Worked by the Economically Active
(including Household Work)
Typically, analyses of labour markets exclude household work from the scope of work
done by the “Economically Active”. Ironically, producing goods (for instance food)
for household consumption is regarded as “economically active” work (and is called
subsistence) and the employment status of “self-employed” is usually allocated to it.
Yet household work is also vitally necessary in all economies and households, not
just to maintain the sustainability and living standards of families, but at the most
basic level, ensure the reproduction of the labour force including those labeled as
“economically active”. Just as with food, were these services not available
voluntarily through the labour of those designated to do “Household Work”, they
would have to be purchased by the household for a price, and these activities would
ironically then be included in the “Economically Active” category, and given a value
in the national accounts.
Chapter 5 has given convincing data to show that Household Work is a very large part
of total work done in the economy (some 51%) with Females doing some 76% of it.
It is critical therefore, that when labour market analysis of employment, underemployment, and unemployment of the economically active is conducted, household
work must be included as an integral part of the analysis and statistics.
Chapter 4, on time worked by the Economically Active revealed significant
differences between Females and Males, with Females appearing to work significantly
less than Males (on average by -18%) and having significantly higher levels of
“under-employment” and consequently Effective Unemployment, than Males.
Chapter 4 covered only the “paid work” by the Economically Active, without
including household work.
The EUS does not clarify whether the economically active persons are doing
household work as a “left-over” after they have been able to (or not able to) obtain
part-time paid work, or whether the part-time paid work is under-taken as an
additional activity, with the household work as the person’s core activity.
Given the importance of cash incomes to most families in Fiji, especially the lower
income ones, it is likely that the unpaid household work is the secondary activity
undertaken if paid activity is not available. Chapter 5 has also shown that Females in
some categories of Employment Status (Family Workers and Self-Employed) do
almost as much household work as full-time Household Workers.
In this chapter, household work is considered to be of economic value even if unpaid,
and even if performed as an additional activity. This chapter therefore integrates into
the statistical tables on the Economically Active, the work also done in the household,
by the Economically Active, using two approaches.
62
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
The first is to calculate, as before, the Effective Rates of Under-employment and
Effective Rates of Unemployment, but now also including Household Work. To
distinguish these rates from the ones in Chapter 4, these will be called “GenderNeutral Effective Rate of Under-employment” (GN-ERoUnder) and “Gender-Neutral
Effective Rate of Unemployment” (GN-ERoU) to denote that household Work is now
fully included.
These statistics will convey a sense of the extent to which there are gender differences
in unused human resources, when both the “Paid” work of the Economically Active
and the “Unpaid” Household Work of the Economically Active are taken into
account.
The second approach is to estimate “average total time worked”, taking into account
both kinds of work. Note that adding together the hours (or days) of “Paid” work by
the Economically Active to the hours of Household Work (or converted to Standard
Days) implicitly assumes that the two kinds of work are equivalent. This is in itself
an issue for those who wish to argue that household work is not as intense or “tiring”
as that of the “Paid” work, although women (and men who do substantial household
work) would probably differ.62
The values in Table 5.1 for the average hours per week spent per household on
cooking (24 hours), washing clothes (11 hours) etc., are not particularly unreasonable.
In the absence of any evidence to the contrary, it is here assumed that the hours spent
on “paid work” and Household Work may be added together.
We can recall that the averages also give full weight to those who do far more work
than the conservative standards we are using (8 hours per day, 240 days per year),
whereas the ERoUnder and ERoU only aggregate the extent of under-employment of
individuals. These latter statistics enable us to make strong, perhaps somewhat bold,
statements about who is doing more work on average (Females or Males) by whatever
other disaggregation we choose.
As before we can use the Last 7 Days data or the Previous 12 Months data. We recall
again that the Last 7 Days data and the Household Work data are probably more
compatible as far as the time periods are concerned, because they both cover a period
of 7 days. In contrast, the “Paid” work over the Previous 12 Months refers to the
whole year, and aggregating that with adjusted estimates derived from the 7 days
household work data may create a slightly upward bias.63
There is first calculated a “Gender Neutral Effective Rate of Under-employment”
(GN-ERoUnder) which is now inclusive of household work done by the Economically
Active.
The gender relativities in employment and under-employment change
drastically when household work is included.
Then, by combining with the data on the Formally Unemployed, there is calculated a
“Gender-Neutral Effective Rate of Unemployment” (GN-ERoU). This latter statistic
takes into account the under-employment of the Economically Active, the Formally
___________________________________________________________________________
62
Those who do both kinds of work will probably testify that household work is far more tiring and
mentally draining than the paid work of the Economically Active.
63
It is not clear, for instance, whether the time spent on household work would be lower, if secondary
and tertiary activities (of the P12M data) are being worked.
63
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
Unemployed, and the household work done by the Labour Force (including the
household work of the Formally Unemployed).
As before, the analysis is conducted using the data available on work done over the
Last 7 Days (which gives an “immediate” snapshot), as well as data on work done
over the Previous 12 Months.
The snapshot over the Last 7 Days has the advantage in that both sets of data refer to
the same period of 7 days, hence one obtains reasonably accurate statistics on total
work done over the Last 7 Days. The 7 day snapshot has the weakness in that it is
unlikely to include other secondary activities undertaken during the year.
The data on the work done over the Previous 12 Months may be more accurate in
terms of including all the major paid economic activities over the previous year. But
combining this data with household work has a methodologically weakness in that the
household work done may well have decreased if additional paid economic activities
were being undertaken by the working person.
To avoid confusing parallel statistics on Gender-Neutral Effective Rates of UnderEmployment and Unemployment, only the P12M data is used for that, while the
parallel statistics using the Last 7 Days data is given in Annex B.
Both data sets are however used to estimate “Average Total Time Worked” and the
Percentage Gender Gaps.
It will be seen that there is remarkable closeness between the percentage Gender Gaps
using the two data sets, and the overall “swing” going from “% GG Without
Household Work” to “% GG With Household Work”. This consistency would
suggest that the results derived from both datasets are quite reliable.
Gender-Neutral Effective Rates of Under-Employment and Unemployment
(including Household Work): Previous 12 Months Data
Table 6.1 indicates that while the Rates of Formal Unemployment for Females were
some 84% higher than that for Males, the relativities are reversed when household
work is taken into account.
The GN-ERoUnder for Females is only 5%, compared to the 11% for Males- with the
Gender Gap being -55%. Effectively, Females are only 5% under-employed after
their
household
Table 6.1 Gender-Neutral Rates of Under-Employment and
contribution is taken
Unemployment (including HH work)
into account, while
Female
Male
All
% GG
Males are 11%
RoFU
6.5
3.5
4.5
84
under-employed.
GN-ERoUnder
GN-ERoU
5
11
11
14
9
13
-55
-21
Taking into account
both
Formal
Unemployment and Under-Employment, leaves Females with GN-ERoU of 11%,
some 21% lower than the Male GN-ERoU of 14%.
64
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
Females are much less under-employed in the economy than Males,
once we take into account household work.
By Employment Status
Table 6.2 gives the Gender-Neutral Effective Rates of Under-employment by
Employment Status over the Previous 12 Months.
The figures in the last column indicate a sizeable negative gender gap for all
employment categories except that of Employers.
However Female Salary
Earners
and
Wage
Earners had the lowest
Gender
Neutral
Effective
Rates
of
Under-Employment- of
just 1% and 3%
respectively.
Table 6.2 GN-ERoUnder by Employment Status
Usual Activity
Female Male All % GG
A Wage earner
3
7
6
-58
B Salary earner
1
2
2
-37
C Employer
11
9
10
21
D Self-employed
7
17
14
-57
E Family worker
10
29
19
-67
F Community worker
10
39
16
-75
All
5
11
9
-55
On the other hand,
while
Female
Employers, Family Workers and Community Workers had moderate GN-ERoUnder
of around 10%, that for Males was considerable higher for Community Workers
(39%), Family Workers
Graph 6.1 GN-Effective Rates of Under-Employment
(29%) and Self-employed
(by Employment Status)
(19%).
These
are
quite
high
Effective
Rates
of
Unemployment, given that
we
are
including
all
household work done by
these Economically Active
persons.
ALL ECON. ACTIVE
Community worker
Family worker
Self-employed
Employer
Salary earner
Male
Female
Wage earner
We again recall that the
Standard Year of work is
0
10
20
30
40
Percent
quite moderately chosen- 8
hours per day, and 240 days
per year.
It is clear,
therefore, that there is considerable room for these categories of Male workers to
become more “economically active” (in terms of “paid work”) or to do more
household work. To some extent these categories of workers represent “disguised
unemployment”.
65
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
By Rural/Urban
Females in both Rural and Urban
areas have a much higher rate of
Formal Unemployment than Males,
with the gap being larger in the Rural
areas (by +179%).
However, the Gender Neutral
Effective Rate of Under-employment
is much lower for Females, in both
the Rural areas and Urban areas, with
the gap again being larger (-63%) in
the Rural areas.
Table 6.3 Gender Neutral Rates of UnderEmployment and Unemployment (including
HH work) (gender, and rural/urban)
Region Female Male All % GG
RoFU
Rural
6
2
3
179
Urban
7
5
6
39
GN-ERoUnder
Rural
5
14
11
-63
Urban
5
8
7
-39
GN-ERoU
Rural
11
15
14
-32
Urban
12
13
12
-9
Aggregating the two effects, leaves
Rural Females with a 32% lower Effective Rate of Unemployment (11%) compared
to the 15% for Rural Males. Urban Females also have a slightly lower ERoU of 12%
than Urban Males (13%) – a % GG of only 9%. The gap is therefore much larger in
the Rural areas.
Rural Females not only a less underemployed than rural Males, but also less
underemployed than Urban Females.
By Divisions
Females in the Western Division have the highest rate of Formal Unemployment
(13%) but the lowest rate of
Table 6.4 Gender Neutral Rates of Underunder-employment if household
employment and Unemployment (including HH
work is taken into account (3%)
work) (gender and division)
(Table 8.9).
Division
Female Male All % GG
The GN-ERoUnder are lower for
Females in all the divisions,
compared to that for Males.
Taking the two effects together
still leaves the Females in the
Western Division with the
highest ERoU of 15%, compared
to 11% in Central Division and
8% in the Northern Division.
The gender gap for the GNERoU is negative for all
divisions except the Western
Division.
Central
Eastern
Northern
Western
Central
Eastern
Northern
Western
Central
Eastern
Northern
Western
Formal Unemployment
5
3
4
61
0
1
1
-78
4
3
3
39
13
5
7
167
GN-ERoUnder
6
11
9
-45
6
18
13
-64
5
12
10
-63
3
9
8
-73
GN-ERoU
11
14
13
-23
7
19
14
-65
8
15
13
-43
15
14
14
8
The Male GN-ERoU are uniformly high in all the divisions, the highest being in the
Eastern Division (with 19%).
66
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
By Age Groups
Table 6.5 Gender-Neutral Effective Rates of
Under-employment and Unemployment
Age Group Female Male All % GG
RoFU
A < 20 yrs
35
11
18
211
B 20 to 29
12
6
8
89
C 30 to 39
3
3
3
20
D 40 to 54
2
1
2
28
E Over 54
1
1
1
46
All
6.5
4.5 3.5
84
GN-ERoUnder
A < 20 yrs
18
24
23
-24
B 20 to 29
4
10
8
-63
C 30 to 39
2
7
5
-62
D 40 to 54
3
9
7
-64
E Over 54
15
21
19
-30
All
5
11
9
-55
GN- ERoU
A < 20 yrs
47
32
36
45
B 20 to 29
15
16
16
-4
C 30 to 39
6
9
8
-38
D 40 to 54
5
11
9
-52
E Over 54
15
21
20
-28
All
11
14
13
-21
The results for Gender-Neutral
ERoU by age groups indicate the
generally lower rates for Female, at
all ages (Table 6.5).
Females throughout their working
lives (twenty to 54) have extremely
low rates of under-employment of
between 2% and 4%, compared to
the Males rates between 7% and
10%.64
As would be expected, those over
54 have the highest GN-ERoUnder
for both Females (15%) and Males
(21%).
Taking into account the formal
rates of unemployment, then raises
the GN-ERoU for all age groups,
and for both Females and Males,
with the Female rates still being
lower than the Male rates at all age
groups except for teenagers65 who
are Economically Active. This latter group has a high GN-ERoU of 47% and 32%
respectively for Females and Males.
However the much higher
rate
of
Formal
Unemployment for Females
in their twenties (12%),
ensures that the final GNERoU for Females and Males
leaves a -21% gender gap
with Females having a GNRRoU of 11% compared to
the 14% for Males.
Graph 6.2 Gender-Neutral Effective Rates of
Unemployment (by age groups)
50
40
Percentage
There is an interesting “U”
pattern to the GN-ERoU by
age groups, with the overall
rates of unemployment being
lowest in the middle ages,
and higher at both the lower
and upper age groups.
Females
Males
30
20
10
0
< 20 yrs 20 to 29 30 to 39 40 to 54 Over 54
Age groups
___________________________________________________________________________
64
The rate is lowest for women in their thirties, possibly because of work associated with children.
Note that teenagers are indicated to be a very small proportion of the Economically Active for both
Females and Males, in the EUS sample.
65
67
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
By Ethnicity
Table 6.6 indicates that while
Females of all ethnic groups had
higher
rates
of
Formal
Unemployment than Males, the
opposite was the case with GenderNeutral Effective Rates of Underemployment: the male GN-ERoU
were roughly twice that for Females.
The lowest GN-ERoUnder for
Females was uniformly low ranging
from the lowest Indo-Fijian Females
(at just 4%) and 6% for Fijian
Females.
The gender-gap for all ethnic groups
was fairly uniform at around -55%,
except for Others (at -10%).
Table 6.6 GN-ERoUnder and GN- ERoU
(by ethnicity)
Ethnicity Female Male All % GG
Formal Unemployment
Fijian
5
3
4
36
Indo-Fij
10
4
5
167
Others
5
5
5
12
Rotuman
7
2
3
311
All
6.5
3.5 4.5
84
GN-ERoUnder
Fijian
6
13
11
-58
Indo-Fij
4
9
8
-55
Others
5
6
6
-10
Rotuman
8
18
15
-58
All
5
11
9
-55
GN-ERoU
Fijian
10
16
14
-38
Indo-Fij
13
12
12
9
Others
10
10
10
0
Rotuman
14
20
18
-28
All
11
14
13
-21
Taking the two effects together,
Fijian Females had the widest gender
gap (of -38%) while Indo-Fijian Females had a positive gap of 9%, largely due to the
very high Rate of Formal Unemployment (10%) for Indo-Fijian Females .
Average Total Time Worked (including Household Work)
Possibly the more accurate estimate of Total Time Worked (the aggregate of Paid
Work and Household Work) is using the last 7 Days data Paid Work data for the
Economically Active, and combining it with the unpaid Household Work data. The
time periods for data on work done over the Last 7 Days, and household work done
over the last week, happen to coincide and therefore does not require extrapolation to
other time periods.
Average Total Time Worked Last 7 Days
The previous chapter had shown that the Self-employed, Family Workers and
Community Workers were quite significantly under-employed, relative to Wage
Earners, Salary Earners and Employers, but they also did more household work.
Table 6.7 indicates that once Household Work is included, Females on average
worked 61 hours per week in total, some 14 hours more than the 46 hours per week
done by Males.
68
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
Indeed, Females of all
Employment Status Table 6.7 Aver. Hours of Total Work Done Per Week Over L7D
by the Economically Active (including HH work)
worked some 13 to 17
(F-M)
hours extra compared
(hours)
Emp.Status
L7D
Female
Male
All
% GG
The
to Males.66
A Wage earner
65
51
55
14
26
averages for Females
B
Salary
earner
69
55
60
13
24
range from 50 hours
C Employer
65
52
56
13
25
per week (Family
D Self-employed
61
43
47
17
40
Workers) to 69 hours
per week (Salary
E Family worker
50
32
40
17
54
Workers), while that
F Comm. Worker
51
38
42
13
34
for Males range from
All Econ.Active
61
46
51
14
31
a mere 32 hours per
week
(Family
Workers) to 55 hours per week (Salary Workers).
Household work therefore converts the negative gap (-10%) earlier estimated of the
average hours worked by the Economically Active over L7D, into a positive +31%
gap in favour of Females.
Using Last 7 Days data and including Household Work,
Economically Active Females do 31% more work
than Economically Active Males.
An interesting perspective on how much total work is done by each sub-group is
obtained from Table 6.8 which gives the percentage difference of each group, from
the national average of 51 hours per week (derived from Table 6.7)67.
There are large positive differences for Females of all Employment Status (except
Family Workers and Community Workers).
The
“hardest
working”
Economically Active persons are
Female Salary earners, who on
average work some 35% more
hours per week than the national
average.
Female Wage Earners, Employers
and Self-Employed were not too
far behind, working 27%, 27% and
20% harder than the national
average.
Table 6.8 Perc. Diff. from National Av.(L7D)
Employment Status Female Male All
A Wages
27
0
8
B Salary
35
8
18
C Employer
27
2
10
D Self-empl.
20
-16
-8
E Family Wrk.
-2
-37 -22
F Community Wrk
0
-25 -18
All
20
-10
0
___________________________________________________________________________
66
The averages in this Table are somewhat higher than those given in the 2007 EUS Report, as a more
accurate mid-point (of 55 hours) is used for the top bracket of hours worked over the Last 7 Days (40
or more) rather than the value of 40 used previously.
67
This table is useful also because a comparison of relativities can be made with the Previous 12
Months data on average time worked over the Previous 12 Months.
69
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
Interestingly, Male Salary Earners seem to work only 8% more than the national
average, while the Male
Wage Earners are about
Table 6.9 Average Standard Days Worked P12M
the same as the national
Paid Work and Household Work
average.
Those who
UsActP12M revised Fem Mal All (F-M) % GG
work much less than the
A Wages
407 324 346
83
26
national average are
B Salary
421 355 379
66
19
Male Family Workers
C Employer
382 326 337
56
17
(-37%),
Male
D Self-empl.
365 269 293
95
35
Community Workers
E Family Wrk.
318 212 266 107
50
(-25%) and Male SelfF Commun. Wrk
307 172 277 135
78
Employed (-16%).
All
381 302 327
79
26
Previous 12 Months Data
Table 6.9 gives the Average Standard Days Worked in both Paid Work and
Household Work, over the Previous 12 Months. Some averages are over 365, partly
because the unit is a “Standard Day” of 8 hours per day68 and partly because of the
methodological difficulty of combining data from Paid Work over the P12M with
Household Work data for the week.69 Nevertheless the relativities are very consistent
with the Last 7 Days data (as we show below).
Using Previous 12 Months data and including Household Work,
Economically Active Females do 26% more work
than Economically Active Males.
Table 6.10 gives the percentage differences from the national average of 327 days. It
may be seen that the conclusions about which groups work more are pretty much the
same as that from the Last 7 Days data.
Indeed, if a simple linear regression exercise is done, there is found to be a fairly high
statistical correlation (R2 = 0.96) between the “Percent Difference from the National
Average from the L7D data” (Table 6.8) and the corresponding numbers from the
previous 12 Months data (Table 6.10) .70
Putting the two datasets together, it may therefore be quite reliably stated that
Economically Active Females on average do between 26% and 31% more work than
Economically Active Males.
___________________________________________________________________________
68
Most workers work longer than 8 hours per day.
To remind, the P12M data includes all activity over the year, while the Household Work probably
only refers to the immediate week. Hence the HH work is likely to be over-estimated if combined with
the P12M data. Less HH work is probably done when other activities are engaged in. Hence some of
the averages are estimated to be over 365 days.
70
The regression equation (% Diff from P12M Data) = (0.84)* (% Diff from the L7D data) has a
Correlation Coefficient (squared) R2 = 0.96.
69
70
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
Summary of Gender Gaps: Without HH Work and With HH Work
Table 6.11 indicates the fairly consistent picture given of the manner in which the
Gender Gaps71 change when Household Work is included.
In aggregate, using the L7D data, the -10% GG “Without HH Work” converts to
+31% GG “With Household Work” (a “swing”72 of 41 percentage points).
And, in aggregate, using the P12M data, the -18% GG “Without HH Work” converts
to +26% GG “With Household Work” (a “swing” of 41 percentage points).
Indeed, it may be seen
that the swings from both
data sets are pretty
consistent
for
each
Employment Status.
For both, the clear
implication
is
that
focusing only on the Paid
Work done by the
Economically
Active
gives very misleading
results on who work done
in the economy.
Table 6.11 % Gender Gaps Without HH Work and
With Household Work (L7D data and P12M data)
w/o HH With HH “Swing”
Last 7 Days Data
29
A Wage Earners
-3
26
25
B Salary Earners
1
24
40
C Employers
-15
25
57
D Self-employed
-17
40
75
E Family Work.
-21
54
63
F Community Wrk
-29
34
41
All
-10
31
Previous 12 Months data
33
A Wage Earners
-7
26
25
B Salary Earners
-6
19
30
C Employers
-13
17
67
D Self-employed
-32
35
77
E Family Work.
-27
50
84
F Community Wrk
-6
78
44
All
-18
26
Without
including
Household work, Females
would appear to be doing
between 10% and 18%
less work than Males (left
hand column).
If
Household Work is included, the evidence indicates that Females are doing between
26% and 31% more work on average, than Males.
Economically Active Women work
between 26% and 31% more
than Economically Active Men.
This is a major reversal of conclusion about the relative burdens of total work done in
the economy. Statistically ignoring Household Work in the economy, clearly
constitutes a grave economic injustice to all women in Fiji. The fact that the
Household Work is largely unpaid, makes the injustice even more untenable.
___________________________________________________________________________
71
72
Gender Gap = %(F-M)/M where F and M are values for average work done (L7D or P12M)
The “swing” is simply the (% GG using the P12M data - % GG using the L7D data).
71
Chapter 6
Total Time Worked by the Economically Active (including Household Work)
A “Gender-Neutral Labour Force” (including HH Workers)?
An additional interesting question arises: what would be the nature of the Gender
gaps, if Household Workers were included in the Labour Force e.g. in a “GenderNeutral Labour Force”?73
Recollect that compared to the work done by Economically Active Females, full-time
Household Workers, on average do less hours of work over the L7D: only 40 hours
L7D compared to the average of 61 for Economically Active Females.
Female Household Workers also do fewer Average Standard Days of work over the
Previous 12 Months:
only
259
days Table 6.12 Av Total Time Worked by Full-Time HH Workers
Fem Mal All % GG
compared
to
the
Av.Standard
Days
(P12M)
259 195 258
32
average of 381 for
Av. Hours L7D
40
32
40
25
Economically Active
Females (Table 6.12
and data in Tables 6.7 and 6.9).
With more some 121 thousand full-time Female Household Workers added to a
“gender-neutral labour force”, the quite low average time worked for Female
Household Workers can be expected to significantly bring down the overall average
for working Females. With very few Male Household Workers (only about 1600),
their average will barely change.
Table 6.13 gives the expected results that the average total time for Females has
significantly dropped, with the gender gap also declining, but still being a positive
4%. Interestingly, and Table 6.13 Av. Total Time Worked (including HH Workers
this is good evidence of
Fem Male All % GG
the consistency of the two
Average Standard Days pa 315 301 308
4
datasets, the % GG in
Average Hours L7D
47
45
46
4
average time worked by
Females and Males in this
imaginary “Gender-Neutral Labour Force” is virtually the same, using both the L7D
and P12M datasets.
The bottom line: even if Household Workers are included in a “Gender-Neutral
Labour Force”, Females on average still work some 4% more than Males.74
___________________________________________________________________________
73
Note that Household Workers are typically not included in a Labour Force because there is no
monetary income associated with this economic activity. This little exercise here merely examines the
average time worked by this “Gender-Neutral Labour Force”.
74
Recollect that large proportions of household workers are under-employed (for instance doing as
much household work as the Family Workers). Including HH Workers in a “gender-neutral” Labour
Force will also increase Effective Rates of Under-employment, and Effective Rates of Unemployment
for Females.
72
Chapter 7
Incomes Earned Over Previous 12 Months
Chapter 7
Incomes Earned Over Previous 12 Months
The 2004-05 EUS obtained incomes data for two periods: Income earned over Last 7
Days, and that earned over the Previous 12 Months. For meaningful interpretation of
the statistical results in this chapter, it is critical to understand the different
methodology by which these two sets of incomes data were derived, the differences
between the results, and their limitations.
The incomes data for both periods were obtained in ranges. While normally the midpoints of the ranges may be used to derive weighted averages and weighted average
and total incomes, this is an accurate procedure only for the P12M data, not the last 7
Days data (see Box). Overall, the incomes data for the P12M is more comprehensive
than that for the Last 7 Days, insofar as the focus is total incomes earned and work
done over the entire year.
Estimating Average Incomes
The income data for the L7D is given in ranges: $0 - $29, $30 - $59, etc to the
highest income bracket “$300+”. This top bracket converts effectively to a mere
$15,600 + annually.
Even though only 9 percent of income earners earned above this level, it would
be statistically inaccurate to attempt to estimate some mid-point for this top
bracket, for the purpose of calculating average incomes. The averages will
necessarily be biased against groups who earn relatively a lot more income than
other groups. Given that Males tend to have higher proportions at the highest
income ranges, the averages for Male incomes will tend to be under-estimated,
hence the gaps with Female average incomes will also be under-estimated.
The P12M data for each of the three activities (1, 2 and 3) were given in ranges:
$0-$2999, $3000-$4999, $5000-$6999, etc to $150,000 +. Only some 0.5% of
the Economically Active were in the top bracket.
It was therefore possible, using FIRCA data on taxable incomes, to roughly
estimate mid-points for the top-bracket $150,000 + and the frequencies obtained
from the EUS. The method of roughly estimating the different values for
Females and Males using data obtained from FIRCA, is given in Annex C.
By using the revised mid-points, the total overall reported income changed by a
mere 2%. The average incomes for Fijian Females rises by slightly more than
that for Fijian Males, while the opposite was the case for the other ethnic groups.
73
Chapter 7
Incomes Earned Over Previous 12 Months
The numbers of persons reporting incomes, and their total and average incomes for
the P12M period are as in Table 7.1. While Females were 31% of the income earners
over the Previous 12 Months, they earned a somewhat lower 27% of the total incomes
reported.
On average, Females appear to be earning some 19% less in terms of average total
income earned, through all three activities, over the Previous 12 Months. This chapter
will present several perspectives which may help to explain some of this 19% Gender
Gap.
First, a word of
caution
in
interpreting
the
many gender (or
other
variable)
differences
in
average incomes
indicated in this
section.
Table 7.1 Numbers reporting Incomes (Activities 1,2,3)
Total and Average Per Capita Incomes for P12M
Female
Male
All
% Fem
Numbers Reporting Income
Numbers
102688 228180 330869
31
Est.Tot. Income ($m)
$780
$2143
$2924
27
Average Income P12M $7600
$9393
$8836
Gender Gap (% GG)
-19%
The mere fact that some Average Female Income for a particular group is less than
that of a corresponding Male group, should not per se be taken as evidence of “gender
discrimination”. A whole heap of factors other than gender discrimination may
explain the difference, and some difference may be purely by chance and not
statistically significant at all. (See Box below for a more detailed explanation).
If fair gender comparisons of incomes are to be made (for instance to elucidate if
Females are being paid different incomes for the same amount of work being done),
then it is important to take into account the actual amount of time worked. It has been
shown earlier that Females do work significantly less in terms of time by any
definition. This exercise is attempted in the next chapter.
The P12M incomes data, when assessed by other characteristics such as
Qualifications, also need to be treated selectively for gender comparisons. Activities
2 and 3 may be engaged in by the person purely to obtain additional income, which
may have little relationship to the normal characteristics associated with that primary
income (such as qualifications and experience).
It was therefore thought advisable, that a focus on Activity 1 using the P12M data
may be more useful for analysis of “gender discrimination” than using the combined
income for all three activities.75 This analysis will be done in Chapter 8.
___________________________________________________________________________
75
It would be a useful research issue to find if rates of income for Activities 2 and 3 are significantly
different from the rates of income for Activity 1 for both Females and Males.
74
Chapter 7
Incomes Earned Over Previous 12 Months
A Negative Gender Gap in Average Incomes is not necessarily
sound statistical evidence of “gender discrimination”
It would be statistically wrong to immediately conclude that if the average
income for a particular Female group is less than the average for the
corresponding Male group, that this is clear evidence of gender discrimination
against Females.
Consider, for example, if mangoes are picked from the same tree and
randomly divided into two groups, and an average weight calculated for each
group. One average will certainly be higher than the other. Yet it would not
thereby be correct to conclude that the two groups of mangoes are
intrinsically “different”.
Incomes of working persons are determined by a whole host of factors
working together, such as overall time worked, qualifications, experience,
industry, occupation, and productivity, just to name a few.
There are
significant gender differences for all these criteria.
The variables themselves have methodological weaknesses. Age for instance
may be used as a proxy for experience. However, it is quite likely that for
many women who have had children and have had to stay home to mind the
children, there may well have been broken service and lost opportunities for
promotion, which may have been better utilized by men without child-rearing
responsibilities.
Many women may also have acquired qualifications somewhat later in life,
hence may have moved up the ladder (with higher incomes) relatively later in
life.
Most important, actual time worked may be an important factor in explaining
gender differences in income. Data in previous chapters has shown that
Economically Active Males tend to work in more activities than Females (and
the Previous 12 Months data covers Activities 1, 2 and 3), longer periods in
the day, and for more days in the year. A special chapter is devoted to
examining income earned over the same time periods.
Last, but not least, while the 2004-05 EUS had a pretty large number of
observations for the Economically Active (some 5641 altogether),
disaggregation by several variables (such as sex, qualifications, industry,
occupation) results in the numbers of observations falling to extremely small
values for sub-groups being compared, especially for Females. Gender gaps
in average incomes may be calculated and shown to be large, but at times, the
number of observations may be too small, and the standard deviations too
large, to enable the difference to be judged statistically significant. Subgroups where the numbers of observations for either Males or Females were
below 5, have usually been left out of the tables.
75
Chapter 7
Incomes Earned Over Previous 12 Months
The average annual income P12M for Females ($7600) is fairly close to the Basic
Need Poverty Line for Fiji in 2004.76 However, when it comes to discussions of
poverty, the data for total incomes over the Previous 12 Months has a methodological
weakness in that the summation of incomes when additional activities are involved,
requires the sum of the mid-points of ranges of income, because of the structure of the
questions in the EUS. It is therefore a difficult exercise to ascertain from the P12M
data exactly what percentage of workers may be earning below a precise value of
some Basic Needs Poverty Line expressed as $ per annum.
The L7D data is much more useful for that purpose. Chapter 9 will therefore use data
on Incomes over the Last 7 Days, to provide a gender perspective on the incidence of
poverty amongst the economically active.
This chapter therefore focuses on total incomes earned during the Previous 12
Months, regardless of the actual time periods that may have been worked.
Distribution of Total Incomes Over Previous 12 Months
Table 7.2 (and Graph 7.1) gives the P12M income distribution of Females and Males,
for all three activities, as in 2004-05. What stands out is that an extremely large 27%
of all Economically Active
Females were earning less than Table 7.2 Percentage Distributions of Income P12M
Income Ranges ($)
$3000 per year over the P12M.
77
(to mid-points)
Fem Mal All % GG
The % Gender Gap (% GG)
to
$1500
27
14
18
93
was some 93% higher than the
78
to mid-pt 4000
22
20
20
9
14% of Males.
The Gender Gap is negative
thereafter and significant (except
for one income range between
$16,000 and $20,000.79
Roughly some 49% earn below
$5000, while some 70% probably
earn below $7000 per year,
which is about the value of the
Basic Needs Poverty Line for a
family of five.80
to mid-pt 6000
to mid-pt 8000
to mid-pt 12000
to mid-pt 16000
to mid-pt 20000
to mid-pt 30000
to mid-pt 50000
to mid-pt 100000
> mid-pt 100,000
All
21
10
4
7
5
2
1.0
0.7
0.5
100
23
14
9
9
5
3
1.7
1.0
0.5
100
22
13
7
9
5
3
1.5
0.9
0.5
100
-7
-29
-56
-23
1
-26
-42
-32
-3
0
___________________________________________________________________________
76
The BNPL for Fiji in 2004 was roughly $135 per week, or about $7020 per year.
The Percentage Gender Gap (% GG) is defined as % (F-M)/M.
78
The numbers in the left hand column are to be used cautiously as they are not upper limits, but the
sum of mid-points of ranges of incomes for Activities 1, 2 and 3.
79
This probably relates to the Female dominance of very specific industries (such as teachers) who
earn incomes in these ranges..
80
Whether the person’s family is living below the poverty line would also depend on the total number
of income earners in the family.
77
76
Chapter 7
What needs to be kept in
mind is that extremely
large proportions of the
economically active are
earning below or just
around the values which
correspond to the Basic
Needs Poverty Line.
Incomes Earned Over Previous 12 Months
Graph 7.1 Percentage Distribution of Income P12M
Percentage of Income earners
30
25
20
Fem
15
Mal
10
By Employment Status
> 100,000
to 100000
to 50000
to 30000
to 20000
to 16000
to 12000
to 8000
to 6000
to 4000
< 3000
5
Some 70% of the
0
economically active earn
below
$12,000
per
annum. The incidence of
poverty
is
therefore
extremely sensitive to
small reductions in real
income, whether through nominal income reductions (such as Wage or Salary cuts) or
income reductions because of a down-turn in the economy.
Table 7.3 Average Incomes P12M (by Employment Status)
Emp. Status P12M
Female Male
All
% GG
A Wages
6157 7030 6795
-12
B Salary
18264 21021 19989
-13
C Employer
19401 39634 35810
-51
D Self-employed
5719 8357 7693 -32 **
E Family Workers
2825 3637 3221 -22 **
F Community Wrk
2876 3817 3088
-25
All
7600
9393 8836
-19 **
Table 7.3 gives the
average incomes over the
Previous 12 Months by
Employment
Status.81
Overall the Females
Average Income was
19% lower than the Male
Average Income.
The
Gender
Gaps
were
smaller, however, for Wage Earners, and Salary Earners, and much larger for other
categories.
The aggregate % GG (of -19%) and the GGs for Self-Employed and Family Workers
were
statistically Graph 7.2 Average Incomes P12M (by Employment Status)
significant
at
95%
confidence
levels
40000
(indicated by **) in the
30000
Female
Male
“one tailed test”.
20000
10000
Co
m
m
un
i
ty
W
rk
er
s
or
k
ily
W
pl
oy
ed
Fa
m
Se
lfe
m
pl
oy
er
Em
Sa
la
r
y
0
W
ag
es
The “one-tailed test” of
statistical significance of
the differences in two
means is explained in the
Box following.
Not too much should be
made of the statistical
___________________________________________________________________________
81
All averages in this chapter, unless otherwise stated, are “weighted averages” taking into account the
weights for each observation. “Simple averages” if used, will be specified.
77
Chapter 7
Incomes Earned Over Previous 12 Months
significance of these differences at this aggregate level, as there are a number of other
critical variables such as educational qualifications, industry and occupation that need
to be also taken into account.
The lowest averages are recorded for Female Family Workers and Community
Workers, although these two categories also have the lowest incomes for Males as
well.
** Simple “One Tailed” Test of Statistical Significance
If Average Male Income (M) is larger than the Average Female Income (F),
is the difference (M-F) “just by chance” (one has to be bigger than the other) or is the
difference “statistically significant” (from a strict mathematical point of view)? This is
what the “one tailed test” tries to establish, thus:
Males
Females
If
Average Income =
Number of observations =
Standard Deviation of Incomes =
F
f
SDf
M
m
SDm
Then the Standard Deviation of the “difference” (M-F) in Income = S
where S2 = [ (SDf)2/f) + (SDm)2/m) ]
In a “one tailed test”, the difference in average incomes (M-F) is judged to be
significant (for a larger numbers of observations f and m)
For 95% confidence level, if (M-F) > (1.64 * S)
For 90% confidence level, if (M-F) > (1.28 * S)
Note that a particular difference in average incomes may appear large, but could be
statistically insignificant if the numbers of observations are small.
For example in Table 7.3, the -51% GG for Employers, and the -25% GG for
Community Workers are both wider than the -22% GG for Family Workers. The
latter is statistically significant while those for Employers and Community Workers
are not. The numbers of observations for Employers and Community Workers are
much smaller than that for Family Workers.
Notes (1) Where there is considerable disaggregation, data is only given for cells
where there are 5 or more observations for both Females and Males.
(2) To accept a decision at 90% confidence level also means that in the long
term, the decision to accept is likely to be wrong 10% of the time. Whereas
accepting a decision with 95% confidence means that one may expect to be
wrong 5% of the time, in the long run.
78
Chapter 7
Incomes Earned Over Previous 12 Months
By Ethnicity
Table 7.4 indicates
that while Fijians
comprised 53% of
the
total
Economically
Active over the
Previous
12
Months,
IndoFijians 42% and
Others 4%, their
shares of Total
Income
reported
were 49%, 41% and
8%
respectively.
The Others group
enjoyed somewhat
higher proportion of
income
than
indicated by their
share of population.
The Female shares
of the Economically
Active were slightly
higher than average
for Fijians and
Others, and lower
for Indo-Fijians and
Rotumans.
Table 7.4 Ethnic Diff. in Incomes Incomes P12M
Ethnicity
Fem
Mal
All
Ver % % Fem
Numbers
Fijian
61766
114752
176517
53
35
Indo-Fij
35295
102677
137972
42
26
Others
4492
7863
12355
4
36
Rotuman
1136
2888
4024
1
28
All
102688
228180
330869
100
31
Total Income P12M ($m)
Fijian
441
987
1428
49
31
Indo-Fij
245
954
1199
41
20
Others
75
162
237
8
32
Rotuman
19
41
60
2
32
All
780
2143
2924
100
27
Average Incomes ($)
% GG
Fijian
7143
8598
8089
-17
Indo-Fij
6940
9290
8688
-25
Others
16782
20589
19205
-18
Rotuman
16644
14179
14875
17
All
7600
9393
8836
-19
Diff. from National Av. Income
Fijian
-19
-3
-8
Indo-Fij
-21
5
-2
Others
90
133
117
Rotuman
88
60
68
All
-14
6
0
The latter is probably a result of higher proportions of Indo-Fijian and Rotuman
Females being involved in full-time domestic work.
Note the large difference
in total incomes earned by
Fijian Females ($441m)
and that earned by IndoFijian Females ($245m).
Graph 7.3 Average Incomes P12M (by ethnicity)
25000
Female
20000
Male
15000
For a variety of factors (to
be examined in greater
detail below) the gender
gap between Female and
Male Indo-Fijian average
incomes was the highest
at -24%, with the gap for
Fijians being -18%.
10000
5000
0
Fijian
79
Indo-Fij
Others
Rotuman
Chapter 7
Incomes Earned Over Previous 12 Months
Largely because of the higher proportions on Full-time Household Duties, Indo-Fijian
Females also comprised a lower proportion of the Indo-Fijian Economically Active
(26%) compared to the 36% for Fijians. Given that Indo-Fijian Females have just as
good an education profile as Females of other ethnic groups, this disparity in
participation in the Labour Force deserves further investigation.
The gender gaps for Others and Rotumans were not only lower, but the averages were
considerably higher than for Fijians and Indo-Fijians, Males and Females.
Overall, Indo-Fijian Females had the lowest average incomes compared to the
national average income (gap of -21%) with Fijian Females not too far behind (gap of
-19%). Female Others and Rotumans has significantly higher average incomes than
the national average- the former by a high 94%.
As a consequence of the combination of the above factors, Indo-Fijian and Rotuman
Females had somewhat lower shares of total income over the P12M- 21% and 25%
compared to the national average of 27%, and 31% for Fijian Females and 33% for
Female Others.
By Region
Table 7.5 indicates that while the numbers of economically active were evenly
balanced between rural and urban areas, the Female share was higher in the urban
areas (34%) compared to the 28% in the rural areas.
The Gender Gap
in
Average
Incomes is also
larger in the
rural areas
(-26%)
compared
to
that
in
the
Urban areas
(-22%).
These
gender
differences are
however
dwarfed by the
extremely large
rural/urban
differences in
average
incomes- -51%
for Females and
-47% for Males.
Table 7.5 Distribution of Numbers, Total Incomes and
Gender Diff. in Average Income P12M (by region)
Ethnicity
Female
Male
All
Vert %
Numbers
% Fem
Rural
46344 120023 166367
50
28
Urban
56345 108157 164502
50
34
102688 228180 330869
100
31
Total Income P12M ($m)
Rural
225
790
1014
35
22
Urban
556
1353
1909
65
29
780
2143
2924
100
27
Average Incomes ($)
% GG
Rural
4845
6581
6097
-26
Urban
9865
12513
11606
-21
%(R-U)/U
-51
-47
-47
Diff from National Av. Income
Rural
-45
-26
-31
Urban
12
42
31
All
-14
6
0
80
Chapter 7
Incomes Earned Over Previous 12 Months
Overall, Rural Female average income was 45% lower than the national average, but
Rural Male average income was also significantly lower- by 26%.
Urban Female average income
was 12% higher than the
national average, while the
Urban Male average income
was 42% higher.
All told, the Rural Females are
the most disadvantaged subgroup as far as average
incomes of the Labour Force
are concerned.
Graph 7.4 Average Incomes P12M (by Rural:Urban)
15000
Female
Male
10000
5000
0
Rural
Urban
By Division
Central Division
had 43% of the
Economically
Active but a
higher 55% of
total income over
the
P12M.
Western Division
had 34% of the
Economically
Active but a
lower 28% of the
total
income
(Table 7.6).
The Female share
of total income is
the lowest in the
Western Division
(20%) reflecting
both the lower
share
of
the
Economically
Active
(24%),
and much lower
average incomes
of
Western
Females.
Table 7.6 Gender Diff. in Incomes P12M (by division)
Ethnicity
Female
Male
All
Vert %
%F
Numbers
Central
52248
91059
143307
43
36
Eastern
7736
11744
19480
6
40
Northern
16131
39053
55184
17
29
Western
26573
86325
112898
34
24
All
102688
228180 330869
100
31
Total Income P12M ($m)
Central
487
1125
1612
55
30
Eastern
49
82
131
4
38
Northern
80
290
371
13
22
Western
163
646
809
28
20
All
780
2143
2924
100
27
Average Incomes ($)
%GG
Central
9328
12353
11250
-24
Eastern
6387
6975
6741
-8
Northern
4985
7437
6720
-33
Western
6142
7484
7168
-18
All
7600
9393
8836
-19
Diff. from National Av.Inc.
Central
6
40
27
Eastern
-28
-21
-24
Northern
-44
-16
-24
Western
-30
-15
-19
All
-14
6
0
There are large gender gaps in average incomes in all divisions, with the largest gap
being in the Northern Division- at -33%.
81
Chapter 7
Overall,
however,
the
Central Division stands out
in having higher than the
national average income
very much so for Males
(+39%)
and
slightly
positive for Females as
well (+6%).
Incomes Earned Over Previous 12 Months
Graph 7.5 Average Incomes P12M (by Divisions)
15000
Female
Male
10000
5000
The average incomes in the
0
other
divisions
are
Central
Eastern
Northern
Western
significantly lower than the
national average for both
Females and Males, with
the Females being worst off: -44% for Northern Females, -28% for Eastern Females
and -30% for Western Females.
AND THE VALUE OF HOUSEWIVES’ WORK???
Darling, this
economist is using
a value of $30 per
week for a
housewife’s work.
Ha ha ha.
He must be joking. Can he add?
Cooking (21 meals)
Washing, ironing
Minding babies
Cleaning house
Sex when I am tired (twice pw)
Total
$123 per week
(Not to mention looking
after his parents)
82
$21
$14
$21
$7
$60
Chapter 7
Table 7.7 indicates
that a very small
percentage (1%)
of
the
Economically
Active over the
P12M had No
Schooling,
with
Females having a
slightly
higher
percentage.
What is salient is
that while Females
were
underrepresented
in
those with only
Primary Education
or
Junior
Secondary; they
were well and
truly
overrepresented
in
those with Senior
Secondary
(by
29%); Certificate
or Diploma (by
46%);
and
Degree/PG
(by
61%).
Table 7.7 Distribution of Economically Active (by qualifications)
and Average Incomes
Ed Summary
Female
Male
All
% Fem
Numbers
A No Schooling
1792
2769
4561
39
B Primary (C1-C7)
13188
43191
56379
23
C Junior Sec (C8-F4)
49680 120697 170377
29
D Senior Secondary
19604
33829
53433
37
E Cert/Diploma
15151
22983
38134
40
F Degree/PG
3231
4446
7677
42
G Other
43
266
309
14
All
102688 228180 330869
31
Vertical Percentages
% GG
A No Schooling
2
1
1
44
B Primary (C1-C7)
13
19
17
-32
C Junior Sec (C8-F4)
48
53
51
-9
D Senior Secondary
19
15
16
29
E Cert/Diploma
15
10
12
46
F Degree/PG
3
2
2
61
G Other
0
0
0
-64
All
100
100
100
Average Income
% GG
A No Schooling
2677
5908
4638
-55
B Primary (C1-C7)
5884
5520
5605
7
C Junior Sec (C8-F4)
4895
7553
6778
-35
D Senior Secondary
6925
10684
9305
-35
E Cert/Diploma
14088
16188
15354
-13
F Degree/PG
31689
44393
39047
-29
G Other
75000 172885 159182
-57
All
7600
9393
8836
-19
Despite
these
significant
advantages
at
the
senior
educational levels, the statistics on
average incomes indicate that
Female Average Incomes were less
than that of Males for all levels of
educational attainment except those
with Primary Education.
Graph 7.6 Qualifications and Average Incomes
50000
40000
30000
Female
Male
20000
10000
0
No
Sc
h
The Gender Gaps in Average Total
Income were quite wide at Junior
and Senior Secondary (-35% for
both); Degree/PG (by -29%); and
slightly less at Certificate or
Diploma levels (-13%).
oo
lin
g
Pr
im
ar
y
Ju
n
Se
io
rS
ni
or
ec
Se
co
nd
ar
Ce
y
r t/
Di
pl
om
a
De
gr
ee
/P
G
By Qualifications
Incomes Earned Over Previous 12 Months
83
Chapter 7
Incomes Earned Over Previous 12 Months
The graph makes clear how rapidly average incomes rise once the qualifications rise
above Senior Secondary, both for Females and Males. Even at the higher levels,
however, the Female average incomes lag considerably behind that for Males.
By Working Age Groups
Table 7.8 indicates that
for the Economically
Active in general, there
is a gender gap in
average incomes, that is
negative for all the age
groups except the 20 to
29 age group (where the
gap of +7% is in the
opposite
direction).
From 30 onwards, the
gender gap appears to
increase gradually.
Except for the jump in
average incomes from
the teenagers group to
the twenties, there is
virtually no upward
trend
for
Female
average incomes, as age
increases.
There is
however, a reasonably
large jump for Males,
from the twenties to the
thirties.
Table 7.8 Average Income over P12M (by Age Groups)
Female
Male
All
% Fem
Numbers
A <20 yrs
2917
11411
14328
20
B 20 to 29
29201
61232
90434
32
C 30 to 39
26319
57346
83665
31
D 40 to 54
31818
68991
100810
32
E Over 54
12433
29199
41632
30
All
102688 228180 330869
31
Vertical Percentages
A <20 yrs
3
5
4
B 20 to 29
28
27
27
C 30 to 39
26
25
25
D 40 to 54
31
30
30
E Over 54
12
13
13
All
100
100
100
Average Incomes
% GG
A <20 yrs
3006
3948
3756
-24
B 20 to 29
7189
6722
6873
7
C 30 to 39
7920
10134
9437
-22
D 40 to 54
8420
11786
10724
-29
E Over 54
6866
10012
9073
-31
All
7600
9393
8836
-19
Age may be expected not to be a
particularly important factor in
unskilled work requiring few
qualifications.
Graph 7.7 Aver. Tot Incomes (by working age)
12000
8000
Age
(and
the
associated
experience) may be expected to be
important in employment requiring
higher qualifications, with the
associated promotions into higher
positions and incomes. This is
evident from the next section.
4000
Male
Female
84
4
r5
Ov
e
54
40
to
39
30
to
29
to
20
<2
0
yr
s
0
Chapter 7
Incomes Earned Over Previous 12 Months
Qualifications /Age
For those with No
Schooling or just
Primary Schooling,
while
there
generally
is
a
gender gap at all
age levels, there is
no particular pattern
of the gender gap
increasing, which
might indicate that
Males
were
progressing faster
than Females with
age (Table 7.9).
Graph 7.7 Female Average Incomes (by Qualification and age)
70000
60000
50000
40000
Degree
30000
CertDip
20000
SenSec
Prim.Jun
10000
0
At Junior Secondary, there is a
distinct jump in the gender gap
from the 19% for the twenties,
to between 36% and 43% at the
upper income levels.
With
Senior
Secondary
qualifications, there is a trend
of an increase in the gender
gap from 11% to 52%.
At Certificate and Diploma
levels the gender gap is small
for the twenties (-4%) but
interestingly positive (+6%) for
the 40 to 54 age group.
Again
at
Degree/PG
qualifications, the gender gap
is positive and large (31%) for
the 40 to 54 age group, and
positive also (6%) for the
twenties group.
Higher academic qualifications
would seem to be a crucial
factor in reducing or reversing
the gender gaps in income.
What may also be seen is that
for each of the lower
qualifications, there is no great
positive trend of average
20 to 29
30 to 39
40 to 54
Over 54
Age Group
Table 7.9 Av. Incomes P12M, Qualifications and Age
Age Grp
Female Male
All % GG
No Schooling
B 20 to 29
1500 1500 1500
0
C 30 to 39
1500 7375 5783
-80
D 40 to 54
3542 5615 4648
-37
E Over 54
2230 6612 4924
-66
Primary (C1-C7)
B 20 to 29
4069 4903 4826
-17
C 30 to 39
3903 5246 4967
-26
D 40 to 54
8680 6052 6725
43
E Over 54
4114 5653 5206
-27
Junior Secondary
B 20 to 29
4351 5339 5090
-19
C 30 to 39
4427 7271 6435
-39
D 40 to 54
5101 9223 7875
-45
E Over 54
7493 10038 9318
-25
Senior Secondary
B 20 to 29
5957 6691 6394
-11
C 30 to 39
7411 12126 10562
-39
D 40 to 54
9410 20339 16244
-54
E Over 54
9294 19550 17172
-52
Certificate/Diploma
B 20 to 29 10323 10771 10588
-4
C 30 to 39 14643 19146 17257
-24
D 40 to 54 21755 20598 20981
6
E Over 54
16466 22616 19916
-27
Degree/PG
B 20 to 29 20491 19304 20024
6
C 30 to 39 34872 49056 42445
-29
D 40 to 54 65695 50202 53313
31
E Over 54
37064 68235 59665
-46
85
Chapter 7
Incomes Earned Over Previous 12 Months
incomes increasing with age group. At the higher qualifications (Certificate/Diploma
upwards), there is a strong trend of rising average incomes, with rising age group.
Put alternatively, higher qualifications places Females on a completely different tier
of average incomes, which do rise rapidly with rising age (Graph 7.7).
By Industry
Table 7.10 gives the Average Total Incomes over the Previous 12 Months by Industry
grouping and gender. For five of the industries, the differences are statistically
significant (denoted by **) - Agriculture, Manufacturing, Hotel, Retail and
Restaurants, Finance, Real Estate and Business, and Community, Social and Personal
Services. Those industries where the differences were not statistically significant,
comprised those where the numbers of Females were small.
It may be noted that there are significant differences between the industries in terms
of average incomes, with the last three and Electricity/Wager and Construction
generally having higher than average incomes for both Females and Males.
Table 7.10 Average Incomes over P12M (by Industry)
Female Male
All
Fem
Mal
% Diff. From
National Av.
Average Total Income
% GG
1 AgForFishing
3377 5619 5111 -40 **
-62
-36
2 Mining&Quarrying
6102 9184 9027
-34
-31
4
3 Manufacturing
4904 7698 6740 -36 **
-45
-13
4 Elect & Water
7977 10544 10387
-24
-10
19
5 Construction
20682 9055 9462
128
134
2
6 Hotel, Retail, Rest.
5662 8513 7271 -33 **
-36
-4
7 Transp.Stor.Comm.
26960 12321 14150
119
205
39
8 Fin. Real Est. Bus.
13737 26401 22050 -48 **
55
199
9 Comm. Soc.& Pers. Serv. 11540 14277 13112 -19 **
31
62
All Fiji
7603 9397 8840 -19 **
-14
6
Higher educational qualifications associated with these industries would be a primary
factor in the inter-industry differences. The education factor is taken into account in
the next section, with the inter-industry differences reducing considerably for those
with lower educational attainment.
By Qualification and Industry
Table 7.11 locates the gender differences in average total incomes for those with No
Schooling, by industry of employment for their main activity over the Previous 12
Months (Activity 1).
86
Chapter 7
Incomes Earned Over Previous 12 Months
Table 7.12 gives
Table 7.11 Av. Incomes P12M: No Schooling (by industry)
the
gender
Ind Act 1 Name
Fem Mal
All
% GG
differences
for
1 AgForFishing
1962 5796 4221
-66 **
those
with
3 Manufacturing
4038 6162 5389
-34
Primary Education
9 Comm. Soc.& Pers. Serv. 2162 2826 2484
-23
as the highest
All
2677 5908 4638
-55 **
educational
attainment.
The
gender gap is greater than 20% for all four industrial aggregations, with Community,
Social and Personal Services having the highest gap of -55%
Table 7.13 gives
the
gender
differences for
those with only
Junior
Secondary
Education,
nearly all being
wider than
-30%.
Table 7.12 Av. Incomes P12M: Only Primary (by industry)
Ind Act 1 Name
Fem Mal
All
% GG
1 AgForFishing
3534 4532 4333
-22 **
3 Manufacturing
3554 5035 4615
-29 **
6 Hotel, Retail, Rest.
4653 5933 5478
-22
9 Commun. Soc.& Pers. Serv. 3255 7289 5584
-55 **
All
5884 5520 5605
7
Table 7.13 Av. Incomes P12M: Junior Secondary (by industry)
It may be noted
Ind Act 1 Name
Fem
Mal
All
% GG
the significant
1
AgForFishing
3493
5753
5250
-39
**
differences
3
Manufacturing
4337
6878
5889
-37
**
between
6 Hotel, Retail, Rest.
5179 8157 6848 -37 **
industries, with
8 Fin. Real Est. Business
7253 12291 11168 -41 **
Agriculture,
9
Comm.
Soc.&
Pers.
Serv.
6454 10032 8799 -36 **
Forestry
and
All
4895
7553 6778
-35
Fisheries having
the
lowest
average incomes
for Females and Males, although the gap remains similar.
Table 7.14 gives
Table 7.14 Av. Incomes P12M of Senior Secondary (by industry)
the
gender
Ind Act 1 Name
Fem
Mal
All
% GG
differences for
1 AgForFishing
2508 6139 5140
-59 **
those
with
3 Manufacturing
5421 7956 7217
-32 **
Senior
6 Hotel, Retail, Rest.
5667 9668 7790
-41 **
Secondary
8 Fin. Real Est. Business
10834 13825 12700
-22
Education. The
9 Comm. Soc.& Pers. Serv. 9057 14784 12209 -39 **
gender gaps are
All Senior Secondary
6925 10684 9305
-35
wider than
-22%, with that
for Agriculture, Forestry and Fisheries being
-59%. The differences between industries are widening for both Females and Males.
87
Chapter 7
Incomes Earned Over Previous 12 Months
Table 7.15 gives the gender differences for those with Certificate or Diplomas. The
gender gaps are not so homogenous, with the gaps being statistically significant only
for Agriculture,
Forestry
and
Table 7.15 Av. Incomes P12M of Certificate/Diploma(by industry)
Fisheries and for
Ind Act 1 Name
Fem
Mal
All
% GG
Finance,
Real
1 AgForFishing
5384 11264 9583 -52 **
Estate
and
3 Manufacturing
14856 14795 14806
0
Business (both
6 Hotel, Retail, Rest.
8263 11869 10293
-30
being around 8 Fin. Real Est. Business
12489 24877 19055 -50 **
50%). The gap
9 Comm. Soc.& Pers. Serv. 14333 16093 15179
-11
for
All Certificate/Diploma
14088 16188 15354
-13
Manufacturing
is 0%, while that
for Community, Personal and Social Services is a much lower -11%.
Table 7.16 gives the gender differences for those with Degrees. While there are three
industries with negative gender gaps, none of them were statistically significant. On
the other hand, the gap for Hotel, Retail and Restaurants was a small but positive 3%.
Higher
Table 7.16 Av. Incomes P12M of those with Degrees (by industry)
Education
Ind Act 1 Name
Fem
Mal
All
% GG
would seem to
be eradicating
3 Manufacturing
23084 63273 45415
-64
the gender gaps
6 Hotel, Retail, Rest.
21602 21045 21365
3
in incomes for
8 Fin. Real Est. Business
39412 72760 61494
-46
these industries.
9 Comm. Soc.& Pers. Serv. 33247 37969 35854
-12
All Degree/PG
31689 44393 39047
-29
Occupations
Table 7.17 gives the Average Total Incomes by the main Occupation groups.
The largest
gap is in
Plant
and
Machine
Operators
and
Assemblers
(-55%),
Craft
and
Related
(-49%) and
Skilled
Agriculture
and Fisheries
(-43%).
Table 7.17 Average Incomes Over P12M (by Occupation Groups)
Occ Act1 Name
Female Male
All % GG
1 Sen. Officials & Manag.
17899 29261 26278 -39 **
2 Professionals
18473 22762 20867
-19
3 Tech. & Assoc Prof.
15319 16453 16041
-7
4 Clerks
9061 10863 9818 -17 **
5 Service, Shop, MktSales
4975 7818 6626 -36 **
6 Sk.Agr.& Fishery
3251 5719 5172 -43 **
7 Craft & Related
3779 7462 6590 -49 **
8 Pl. & Mac.Oper.&Assemblers
4050 8997 8149 -55 **
9 Elementary Occupations
5659 5530 5574
2
All
7603 9397 8840
-19
88
Chapter 7
Incomes Earned Over Previous 12 Months
Occupation Groups where the differences were not statistically significant were
Professionals, Technical and Associated Professionals, and Elementary Occupations.
The two former are also associated with higher qualifications, which we have seen
earlier to be associated with relatively better gender equality.
Table 7.17 also makes obvious the significant differences in average salary across the
major Occupation Groups, with the top group being almost five times the bottom
groups.
It is useful therefore to examine each Occupation Group across
Qualifications.
Occupations and Qualifications
Table 7.18 gives the average incomes of the first major Occupational Group (Senior
Officials
and
Table 7.18 Aver. Incomes P12M of
Managers)
by
Senior Officials and Managers (by Qual.)
Qualifications
for
Ed Summary
Female Male
All % GG
cells with at least 5
B Primary
8208 12360 10853
-34
observations
for
C Junior Secondary
11820 21014 18613
-44
both Females and
D Senior Secondary
9534 27177 21769
-65 **
Males, and asterisks
E Cert/Diploma
52685 38307 41225
38
denoting
those
F Degree/PG
38683 50105 47919
-23
which
are
All
17899 29261 26278
-39 **
statistically
significant.
There
are the usual negative gender gaps at all levels except for those with Certificate or
Diploma (where the gap is a large positive 38%).
Only at
Senior
Secondary levels are
Female
average
incomes
significantly lower
than Male average
income (by -55%).
Table 7.19 Av.Incomes P12M of Professionals (by Qual.)
Ed Summary
Female Male
All % GG
C Junior Secondary
9327
8549
8816
9
D Senior Secondary
11532 16389 14222
-30
E Cert/Diploma
15464 19134 17207
-19 **
F Degree/PG
37401 48239 43802
-22
All
18473 22762 20867
-19
While Male Average incomes seem to follow a smooth upward trend with rising
qualifications, Female Average Incomes barely rise for the first three categories,
peaks for those with Certificate/Diplomas, and then falls for those with
Degrees/Diplomas.
For Professionals,
there is only one
qualification
category where the
gender difference is
statistically
significant,
Certificate/Diploma.
Table 7.20 Av. Incomes P12M of Technical and
Associated Professionals (by Qual.)
Ed Summary
Female Male
All % GG
C Junior Secondary
12064 12468 12336
-3
D Senior Secondary
11246 18233 16311
-38
E Cert/Diploma
16605 15776 16141
5
F Degree/PG
24690 25918 25262
-5
All
15319 16453 16041
-7
89
Chapter 7
Incomes Earned Over Previous 12 Months
At Junior Secondary
Table 7.21 Av. Incomes P12M for Clerks (by Qualification)
level, the gender gap
Ed Summary
Female Male
All % GG
is positive in favour
C Junior Secondary
8642
9245
8993
-7
of Females.
For
D Senior Secondary
7674
9497
8404
-19
both Females and
E Cert/Diploma
10404 12038 10847
-14
Males, there are
F Degree/PG
20349 86814 39499
-77
rapid increases with
9061 10863
9818 -17 **
the improvement in
qualifications,
although the gender gap is maintained at moderate levels.
For Technical and Associated Professionals, at the Senior Secondary level, while the
gender difference is large, it is not statistically significant. At Certificate/Diploma
level, the gap is a small positive 5%, while at the Degree level, there is a negative but
low -5%. For both Females and Males, there exists a strong gradient with rising
qualifications.
For Clerks, while there were negative gender gaps for all qualification levels, none of
the individual gaps were statistically significant, although the aggregate result was.
Table 7.22 Av. Incomes P12M of Service, Shop, and Sales
For Service, Shop
Workers (by Qualification)
and Sales workers,
Ed Summary
Female Male
All % GG
there
were
B
Primary
4205
4682
4479
-10
statistically
C Junior Secondary
4312
7550 6231 -43 **
significant gaps for
D
Senior
Secondary
5656
8049 6880 -30 **
Junior Secondary,
E
Cert/Diploma
6580
10316
9326 -36 **
Senior
Secondary
All
4975
7818 6626 -36 **
and
Certificate/Diploma
levels, as well as at the aggregate level. The Female income/qualification gradient is
not as steep as that for Males.
Table 7.23 indicates Table 7.23 Av. Incomes P12M of Skilled Agriculture and Fisheries
that for Skilled
(by Qualification)
Agriculture
and
Ed Summary
Female Male
All % GG
Fisheries, the gender
A No Schooling
2114 6955 4724 -70 **
gaps are significant
B Primary
3508 4750 4510 -26 **
(and large) for all
C Junior Secondary
3423 5914 5367 -42 **
qualification levels.
D Senior Secondary
2236 6346 5241 -65 **
There is virtually no
E Cert/Diploma
3858 9599 8823 -60 **
salary gradient for
All
3251 5719 5172 -43 **
Females with rising
qualifications,
although there is a gradual moderate one for Males Average incomes are extremely
low for even the higher qualifications, with Female average incomes being way low at
around $3200.
A similar picture is painted for Craft and Related workers, by Table 7.24. The gender
gaps are statistically significant at all skill levels. Again, there is virtually no salary
90
Chapter 7
gradient for Females
with
rising
qualifications, while
there is a small
gradient for Males.
Incomes Earned Over Previous 12 Months
Table 7.24 Av. Incomes P12M of Craft and Related Workers
(by Qualification)
Ed Summary
Female Male
All % GG
B Primary
3539
6570
5559 -46 **
C Junior Secondary
3916
6834
6081 -43 **
D Senior Secondary
3620
7744
7153 -53 **
E Cert/Diploma
3233 11098 10483 -71 **
All
3779
7462
6590 -49 **
The Gender gap
there rises from
46% at Primary
levels, to 71% at
Certificate/Diploma level. Average incomes are extremely low for Females at all
qualification levels, while the Male average is twice as high as the Female average
income.
For
Plant
and
Machine Operators
and
Assemblers,
there is an unusual
picture
of
a
declining trend in
average incomes for
Females, with rising
qualifications.82
Table 7.25 Av. Incomes P12M of Plant and Machine Operators and
Assemblers (by Qual.)
Ed Summary
Female Male
All % GG
B Primary
4463 7729 7372 -42 **
C Junior Secondary
4120 9235 8202 -55 **
D Senior Secondary
3340 8382 7706 -60 **
All
4050 8997 8149 -55 **
The gender gaps are statistically significant at the three skill levels indicated and in
aggregate. The differences in average incomes are large- above 42% for each level
indicated. Average incomes are extremely low for Females at all qualification levels,
while the Male average is again twice as high as the Female average income.
A mixed picture is
Table 7.26 Av. Incomes P12M of Elementary Occupations
painted
for
(by Qualification)
Elementary
Ed Summary
Female Male
All % GG
Occupations, with
A No Schooling
3255 5370 4645
-39
there being positive
B
Primary
11122
4914
6402
126
gender gaps at two
C Junior Secondary
4334 5583 5115 -22 **
qualification levelsD Senior Secondary
6380 5981 6100
7
Primary
(+126%)
E
Cert/Diploma
4726
9880
7436
-52
and
Senior
All
5659 5530 5574
2
Secondary (+7%).
(Table 7.26). The
salary gradients are not pronounced for either Females or Males.
The statistics in this section appears to indicate that there is a greater prevalence of
statistically significant gender differences in average incomes, for Occupation groups
where there is no great need for higher qualifications, and where the income gradients
are low for both Females and Males, but especially for the former. For occupations
normally associated with qualifications, the picture is quite mixed.
___________________________________________________________________________
82
It is quite likely that the Female Machine Operators are in low wage industries such as the Garments
Industry, where Females predominate and wages have been extremely low for a variety of reasons.
Male Machine Operators would in heavy industry types usually attracting higher wage rates.
91
Chapter 7
Incomes Earned Over Previous 12 Months
Those “Paying FNPF” and those “Not Paying FNPF” (Formal/Informal)
While it is difficult to precisely identify those Economically Active who are in the
Formal Sector and those who are in the Informal Sector, payment towards the Fiji
National Provident Fund is a very useful criterion, especially for those in paid
employment.
Table 7.27 indicates the numbers of
persons involved in both categories, by
Occupation Group and Gender. For those
Not Paying FNPF, the extremely large
numbers (for both Females and Males) in
Skilled Agriculture and Fisheries (some 81
thousands altogether) and in Elementary
Occupation
(some
46
thousands
altogether) is to be noted.
Graph 7.10 Average Incomes for Females
by Occupation Groups and FNPF Payment
40000
FNP F
30000
No FNP F
20000
10000
0
1
2
3
4
5
6
7
8
9
O c c u p a tio n G ro u p
For both Females and Males, average
incomes for those paying FNPF are
roughly double those Not Paying FNPF.
This would suggest that the Formal/Informal dichotomy (insofar as it applies to paid
employees) is an extremely important distinction.
The Gender Gaps are negative for virtually all Occupation Groups for both those
Paying FNPF and those Not Paying FNPF, although the latter appear to have larger
gender gaps than those paying FNPF.83 In aggregate, the Gender Gap for those Not
Paying FNPF is -26% compared to -16% for those Paying FNPF. Females would
seem to be roughly twice as disadvantaged in earning total incomes in the Informal
Sector, compared to those in the Formal Sector.
Graph 7.8 Average Incomes by occupation
Groups (Paying FNPF)
Graph 7.9 Average Incomes by
occupation Groups (Not Paying FNPF)
40000
40000
Ma le s
30000
Ma le s
30000
Fe ma le s
Fe ma le s
20000
20000
10000
10000
0
0
1 2 3
4 5 6
7 8 9
1 2 3
Occupation Group
4 5 6
7 8 9
Occupation Group
___________________________________________________________________________
83
The asterisks indicate those Occupation Groups where the Gender Gap in Average Income is
statistically significant.
92
Chapter 7
Incomes Earned Over Previous 12 Months
Table 7.27 Numbers and Average Incomes by Occupation Group and FNPF Payment
Fem
Mal
All
Fem
Mal
All
Occupation Activity 1
Numbers
Average Income
% GG
Those Paying FNPF
1 Sen. Officials & Manag.
1088
7870
8958
21726 31706 30494
-31
2 Professionals
8260
8747
17007 20353 24158 22310
-16
3 Tech. & Assoc Prof.
5269
9711
14981 15838 17518 16927
-10
4 Clerks
11290
8392
19682
9732 11368 10429
-14
5 Service, Shop, MktSales
8588
15730
24318
6106
8835
7871
-31**
6 Sk.Agr.& Fishery
347
2401
2748
2653 11101 10033 -76**
7 Craft & Related
1255
20240
21495
4292
8199
7971
-48**
8 Pl. & Mac.Oper.&Assemb.
3696
10589
14285
4217
9036
7789
-53**
9 Elementary Occupations
4014
10144
14158
6093
7853
7354
-22**
All
43806 93825 137631 11045 13144 12476 -16**
Those Not Paying FNPF
1 Sen. Officials & Manag.
3438
4841
8279
16689 25287 21716
-34
2 Professionals
1091
3063
4154
4234 18775 14957 -77**
3 Tech. & Assoc Prof.
1276
1751
3027
13175 10552 11657
25
4 Clerks
1537
895
2432
4135
6131
4870
-33
5 Service, Shop, MktSales
6613
5309
11922
3508
4805
4085
-27**
6 Sk.Agr.& Fishery
18329 63160
81489
3263
5522
5014
-41**
7 Craft & Related
9361
13989
23350
3710
6397
5320
-42**
8 Pl. & Mac.Oper.&Assemb.
797
11127
11924
3279
8959
8580
-63
9 Elementary Occupations
16383 29981
46363
5552
4744
5030
17
All
58826 134115 192941 5040
6782
6251 -26 **
Table 7.28 presents data on two different sets of “gaps”: the usual Gender Gap (%
GG) comparing Females to Males, and the other comparing those “Paying FNPF”
with those “Not Paying FNPF” (NP FNPF).
Table 7.28 Gender Gaps, and FNPF Gaps in Average Incomes
(by Occupation Group and Gender)
% GG
% Gap (NP: Paying)
Occupation Activity 1
NP FNPF Paying FNPF
Fem
Mal
1 Sen. Officials & Manag.
-34
-31
-23
-20
2 Professionals
-77
-16
-79
-22
3 Tech. & Assoc Prof.
25
-10
-17
-40
4 Clerks
-33
-14
-58
-46
5 Service, Shop, MktSales
-27
-31
-43
-46
6 Sk.Agr.& Fishery
-41
-76
23
-50
7 Craft & Related
-42
-48
-14
-22
8 Pl. & Mac.Oper.&Assemblers
-63
-53
-22
-1
9 Elementary Occupations
17
-22
-9
-40
All
-26
-16
-54
-48
93
Chapter 7
Incomes Earned Over Previous 12 Months
On average, the Gender Gaps for those Not Paying FNPF is more than double that for
those Paying FNPF, although there are four groups (including the sizeable Skilled
Agriculture and Fisheries) where the Gender Gap is smaller in the informal sector.
Indeed, for Elementary Occupations, the Gender Gap is a positive +17% for those Not
Paying FNPF.
For Females in one group (Skilled Agriculture and Fisheries), the gap is a positive
23% in favour of the Females in the informal sector.84
It is quite clear therefore that just as Gender is an important variable explaining
differences in income, so also is the Formal/Informal dichotomy, with statistically
significant gender differences popping up everywhere.
Indeed, as far Average Total Incomes are concerned, statistically significant gender
differences have been shown to be quite common.
This will not be the case for Gender Gaps in “Average Incomes per Standard Year”,
which are estimated in the next chapter.
AND THE VALUE OF NURSES’ WORK?
You nurses should not
be demanding a pay rise
like other unions.
Nurses, like Florence
Nightingale, must follow
their true caring
vocation and just serve
the country, without
self-interest.
And why not use the
same argument for
accountants,
economists, lawyers,
judges, consultants,
businessmen, doctors,
electricians, plumbers,
and everybody else in
society?
___________________________________________________________________________
84
This needs to be treated cautiously as the numbers of workers in Skilled Agriculture and Fisheries
according to the EUS was relatively small.
94
Chapter 8
Income Earned for Equal Time
Chapter 8
Income Earned for Equal Time
The previous chapter on Average Total Incomes earned over the Previous 12 Months,
did not take into account that the persons may have been working for different periods
of time, and hence the average incomes calculated are not strictly comparable, for
instance for purposes of drawing conclusions about gender discrimination.
This section gives some statistical results which may enable more accurate estimates
to be made and conclusions drawn about gender disparities in incomes paid in the Fiji
labour market. The attempt is made to compare incomes for equal amounts of work
done adjusting for other critical criteria such as qualifications, industry, occupations,
employment status, and age.
For purposes of public policy, there is a need to focus on “paid incomes” of Wage
Earners and Salary Earners since these are the workers for whom stakeholders are
concerned that Females may not be receiving “equal pay for equal work”. Employers
and Self-employed persons are of course responsible for creating their own incomes.
And incomes received by community workers and family workers are not really
subject to public policy, as they are not employed in genuine private sector labour
markets subject to the usual forces of supply and demand.
While the work done for the Previous 12 Months asked for information on Activity 1,
2 and 3, it is possible that Activity 2 and 3 may have been engaged in purely for the
additional monetary income, without due regard to the person’s qualifications and
experience.
To enable a more reliable comparison therefore, the focus in this chapter will be on
income earned from, and time spent on the main activity- Activity 1- over the
Previous 12 Months. The methodology is as follows.
For each Economically Active person there is first calculated “Income per Standard
Year” (Income pSY) for Activity 1, which is equal to the
( Income earned for Activity 1 )
(Time worked in Standard Years85).
Then, for each group or sub-group, the Average Income per Standard Year is
estimated as the Sum of Weighted Incomes Per Standard Year for Activity 1, divided
by the Sum of Weights.86
This chapter first examines the overall gender gaps for all Economically Active
persons, by Employment Status, before focusing on Wage Earners and Salary Earners.
___________________________________________________________________________
85
Time worked in SY = (hours worked * days worked)/(8*240).
If individual Income pSY are Y1, Y2, Y3 etc to Yn, and weights are w1, w2, w3 ... wn, then
Average Income pSY = [(w1*Y1)+(w2*Y2)+....... + (wn*Yn)]/(w1+w2+... + wn).
86
95
Chapter 8
Income Earned for Equal Time
By Employment Status
It is important first of all to see the absolutely dramatic changes that do take place
when time worked is taken
Table 8.1 Av. Income in Activity 1, and Av. Income in
into account.
Activity 1 per Standard Year (by Employment Status)
Table 8.1 gives (by
Employment Status) the
normal Average Income
earned for Activity 1 in the
top third of the table (in
italics),
then
Average
Income per Standard Year
in the middle third, and the
percentage changes when
time worked is taken into
account.
In aggregate, the increase
for Females was 118%,
compared to the 69% for
Males, or roughly 50
percent more.87
Female Male
All
% GG
Average Income for Activity 1
Wages
4610
5354
5154
-14
Salary
14834 15706 15380
-6
Employers
18497 28627 26713
-35
Self-employed
3940
5909
5413
-33
Family Workers
1639
1744
1690
-6
Commun.Wrks.
1500
1500
1500
0
All
5751
6880
6530
-16
Aver. Income per Standard Year in Activity 1
Wages
6558
7028
6902
-7
Salary
14759 14723 14737
0
Employers
22816 41633 38077
-45
Self-employed
16727 16703 16709
0
Family Workers
17093 10248 13762
67
Commun.Wrks.
15359 11759 14546
31
All
12564 11654 11936
8
Perc. Difference Taking Time Worked into Account
Wages
42
31
34
Salary
-1
-6
-4
Employers
23
45
43
Self-employed
325
183
209
Family Workers
943
488
714
Commun Wrks.
924
684
870
All
118
69
83
This overall differential
result for Females and
Males should be expected
since our earlier chapter on
time worked revealed the
very considerable extent to
which Females worked
much fewer hours and
days, in order to earn their particular incomes. Thus while Females earned lower
incomes than Males, they worked disproportionately less, so that the overall impact
was a larger increase for Females than for Males.
The overall impact is that the Gender Gap goes from -16% (without taking time
worked into account) to + 8% (comparing incomes per Standard Year)- a swing of 24
percentage points in favour of Females.
Given that our earlier statistics on time worked showed extremely high rates of underemployment for the Self-Employed, Family Workers and Community Workers, it is
also quite interesting to note the changes in incomes and the Gender Gaps, for the
different employment categories.
___________________________________________________________________________
87
Note that the increases depend on the length of the “Standard Year” chosen – 8 hours per day and
240 days per year. One could have chosen a longer Standard Year to ensure that the overall aggregate
average did not change- in which case the change for Females would be around +20% while that for
Males would be around -7%.
96
Chapter 8
Income Earned for Equal Time
Indeed, the % GG for Wage Earners declines from -14% to -7%, for Salary Earners
from -6% to 0%, Self-Employed from -33% to 0%, Family Workers from -6% to
+67%, and Community Workers from 0% to +31%.
The general conclusion must be made that while Females may be earning less than
Males in Activity 1 (i.e. with a -16% Gender Gap), the time they worked must be
more than proportionately less, since the gender gaps are all reduced or reversed.
The only exception was for Employers.88
Note also that while the normal average incomes for Activity 1 for the SelfEmployed, Family Workers and Community Workers are extremely low (less than
$6000, $2000 and $2000 respectively), taking time worked into account gives
extremely high values of Income per Standard Year (all higher than $11000).
Indeed, the incomes per standard year come out to be twice that for Wage Earners.
These groups of workers may be earning little over the whole year, but they are not
being particularly hard done by, relative to the other workers.
The Wage Earners are in fact the poorest paid group of all,
taking time worked into account.
Distribution of Wage Earners and Salary Earners by Educational Attainment
Given that one of the focal points for this study is issue of Females being paid lower
remuneration than Males for equal work done, it is more relevant to focus on just the
Wage and Salary Earners.
It is useful at this point to understand
the percentage distribution of Female
and Male Wage Earners and Salary
Earners in terms of highest educational
attainment (the higher the percentage,
the greater will be the impact on the
overall aggregate averages).89
Thus for Wage Earners and Salary
Earners, more than seventy percent
have Secondary Education (Junior or
Senior Education) as their highest
attainment. Only 9% of Female Wage
Earners had No Education or only
Primary Education, compared to 18%
for Males. Around a tenth of both
Male and Female Wage Earners had
Certificates or Diplomas.
Table 8.2 Perc. With Educational Attainment
Education Attain. Female Male All
Wage Earners
A None/Primary
9
18
15
B Secondary
79
72
74
E Cert/Diploma
11
10
10
F Degree/PG
1
0
1
G Other
0
0
0
100
100 100
Salary Earners
A None/Primary
1
3
2
B Secondary
35
55
47
E Cert/Diploma
49
30
37
F Degree/PG
16
12
13
G Other
0
1
0
100
100 100
___________________________________________________________________________
88
This finding needs to be treated with caution as the number of Females Employers was somewhat
small.
89
Put alternatively, these percentages represent the percentage of total persons, and hence total
“weights” which have a fundamental impact on the “weighted averages” being calculated.
97
Chapter 8
Income Earned for Equal Time
A mere 1% had Degrees or Post Graduate qualifications (with a very small number of
observations in the EUS sample)90 and the results for them are usually statistically
insignificant. The “Other” category also has a very small number of observations for
both Wage earners and Salary Earners and results for them will therefore be discarded
in this chapter.
For Salary Earners, the numbers of those with No Education or Primary Education are
small. For Females, most influential is Certificate/Diplomas (with 49% compared to a
much lower 30% for Males), Secondary Education (30% for Females as opposed to
55% for Males) and Degrees (16% for Females compared to a lower 12% for Males).
It is important that these relativities are kept in mind when examining aggregate
results for Wage Earners and Salary Earners.
Wage and Salary Earners
It
was
originally
intended to pool Wage
Earners and Salary
Earners in order to
facilitate
tests
of
statistical significance.
However, Table 8.3
reveals very significant
differences in Average
Incomes pSY between
Wage Earners and
Salary earners.
First, for Wage earners,
the Gender Gap was
-7%, while that for
Salary Earners is 0%.
The overall GG for both
Wage Earners and
Salary
Earners
combined
was
a
positive 5%.91
Table 8.3 Average Income per Standard Year for Activity 1
For Wage and Salary Earners (by Qualification)
Educ. Attainment
Female Male
All
% GG
Wage Earners
None/Primary
10629 6273 6971
69
Secondary
5894
6437 6281
-8
Cert/Diploma
6718 12151 10515 -45 *
Degree/PG
17757 15526 16332
14
All Wages
6558
7028 6902
-7
Salary Earners
None/Primary
13121 5942 7344
121
Secondary
12858 12097 12306
6
Cert/Diploma
13700 14367 14039
-5
Degree/PG
22414 26808 24866 -16 *
All Salary
14759 14723 14737
0
All Wages/Salary 9166
8741 8866
5
% Diff (Salary-Wage)
None/Primary
23
-5
5
Secondary
118 ** 88 ** 96 **
Cert/Diploma
104 ** 18
34
Degree/PG
26
73 ** 52 **
For the largest groups
within Wage Earners, the Gender Gap was -8% for those with Secondary Education.
However, the % GG was +69% for those with No Education or Primary Education
and for that small group of Wage Earners with degrees the GG was also positive
(+14%).
___________________________________________________________________________
90
Only 6 for Female and 12 for Males.
While this overall result may seem strange (given that the other two averages are -7% and 0%), this
result has been checked to be statistically sound, a result of the relative sizes of the weights and
averages of the sub-groups of Males and Females.
91
98
Chapter 8
Income Earned for Equal Time
For Wage Earners with Certificates or Diplomas, the GG was a large -45% and
statistically significant at 90% confidence levels.92
The bottom third of the table indicates that for those with the same educational
attainment, it makes a difference whether they are employed as “Wage Earners” or
“Salary Earners”. For the important groups with large numbers, the differences were
extremely high for Female Salary Earners with Secondary Education (118%
compared to 88% for Males) and Female Salary Earners with Certificates or Diplomas
(104% compared to 18% for Males). Salaried Females with No Education or Primary
education, had an average Income per Standard Year which was 23% higher than
comparable Female Wage Earners.
The differences were statistically significant at 95% confidence levels, for those with
Secondary Education (Females, Males and All), and for those with Certificates or
Diplomas (significant for Females), and also significant for Males with Degrees/PG
qualifications.
It is important therefore, that further analysis of the data with Educational Attainment
as one of the variables examined, keeps Wage Earners separate from Salary Earners.
Educational Attainment and Age
Table 8.4 gives some
indication of how Age
affects the Gender
Gaps
for
Wage
Earners of various
qualifications
and
ages.
There
are
three
statistically
significant
Gender
Gaps, two at 95%
confidence levels:
-26% for those aged
40 to 54, with Senior
Secondary Education,
and -25% for those
aged 20 to 29, with
Certificates
or
Diplomas.
Table 8.4 Average Incomes per Standard Year for
Wage Earners (by Educational Attainment and Age)
Wrk Age Grp Female Male
All
M-F %GG
No Education/Primary
C 30 to 39
3462
4231 4079
768
-18
D 40 to 54
11404 6981 7869 -4423
63
E Over 54
6143
6328 6300
185
-3
Secondary Education
A <20 yrs
5082
7258 6771 2176
-30
B 20 to 29
5184
5254 5232
70
-1
C 30 to 39
7279
6737 6882 -542
8
D 40 to 54
5565
7503 6925 1939 -26 **
E Over 54
7643
6393 6654 -1251
20
Certificate/Diploma
B 20 to 29
5955
7896 7283 1941 -25 **
C 30 to 39
8276
8287 8283
11
0
D 40 to 54
6338 12169 10990 5831 -48 *
All Wages
6558
7028 6902
470
-7
There are a number of positive gender gaps: notably 8% for those aged 30 to 39 with
Secondary Education, and also a 0% for 30 to 39 with Certificates or Diplomas.
___________________________________________________________________________
92
Results statistically significant at 90% confidence levels will be indicated with one asterix while
those significant at 95% confidence levels will be indicated with two asterixes,
99
Chapter 8
Income Earned for Equal Time
There does not appear to be any significant age gradient for any qualification level for
Females.
Table 8.5 gives the Average
Income pSY for Salary
Earners.
For those with
Secondary Education there is
an overall +6% GG, with
those aged 20 to 29 having a
21% GG.
None of the
negative GGs are statistically
significant.
For those with Certificates or
Diplomas, the overall GG is a
low -5% and even lower -3%
for those aged 20 to 29, and
yet lower -1% for those aged
30 to 39. There is a positive
GG of 17% for the small
group of those aged Over 54.
Table 8.5 Average Incomes per Standard Year for
Salary Earners (Education and Age Group)
All
%GG
Age Grp.
Female Male
Secondary Education
20 to 29
8928
7405 8106
21
30 to 39
10597 11577 11342
-8
40 to 54
11122 13126 12710 -15
Over 54
63147 18794 28328 236
All Sec.
12858 12097 12306
6
Certificates/Diplomas
20 to 29
11131 11495 11295
-3
30 to 39
12832 12910 12873
-1
40 to 54
17533 18843 18307
-7
Over 54
20785 17760 19514
17
All Cert/Dip 13700 14367 14039
-5
Degrees/PG
20 to 29
16916 17002 16949
-1
30 to 39
24004 32973 28369 -27
40 to 54
39815 28975 31325
37
All Degree 22414 26808 24866 -16 *
For Salary Earners with
Degrees,
there
is
a
statistically significant -16%
in aggregate. The GG for the 20 to 29 group is a low -1%, while the 40 to 54 group
has a +37%.
Overall, there is no great picture of statistically significant negative Gender Gaps,
except for those with Degrees, in aggregate.
But there does seem to be a tendency for the youngest age group (20 to 29) who
comprise the entrants to the labour market for the previous decade, to have very low
or even positive Gender Gaps.
Given that the bulk of salary earners are working for public sector organisations or the
Civil Service, this may be evidence that the younger better educated Females are
receiving somewhere near to “equal pay for equal work”.
100
Chapter 8
Income Earned for Equal Time
By Industry and Qualifications
Table 8.7 indicates
that the Gender Gaps
for Wage Earners are
not uniform across the
industries and by
qualifications. Thus
there
are
large
positive Gender Gaps
in
Agriculture,
Forestry
and
Fisheries, with an
aggregate GG of
314%.
In Transport, Storage
and Communication
also, there is an
aggregate
positive
GG of 7%.
The other industries
have negative gender
gaps,
with
two
indicating
strong
statistical
significance.
Table 8.6 Av Incomes pSY for Wage Earners
by Industry and Qualification
Educ.Attainment Female
Male
All
%GG
1 AgForFishing
A None/Primary 31553
6714
10416
370
B Secondary
28543
7163
8865
298
All Agric.
30196
7296
9704
314
3 Manufacturing
A None/Primary
3417
7349
6751 -53 **
B Secondary
5456
6399
6080 -15
All Manuf.
5272
7102
6570 -26 **
6 Hotel, Retail, Rest.
A None/Primary
6241
5277
5403
18
B Secondary
5055
5471
5313
-8
E Cert/Diploma
5377
6094
5786
-12
All Hotel, etc
5321
5500
5435
-3
7 Transp.Stor.Comm.
B Secondary
6804
6296
6356
8
E Cert/Diploma
12973
40952
34443
-68
All Transport..
10027
9398
9472
7
8 Fin. Real Est. Business
B Secondary
6987
6806
6845
3
E Cert/Diploma
4635
5213
5005
-11
All Finance..
6258
6746
6630
-7
9 Comm. Soc.& Pers. Serv.
A None/Primary
3358
5582
4533 -40 **
B Secondary
5597
7918
6933 -29 **
E Cert/Diploma
7480
12778
10352 -41 *
All Community.
5822
8460
7321 -31 **
In Community, Social
and
Personal
Services, there is an
aggregate GG of All Wages
6558
7028
6902
-7
31% significant at
95%
confidence
levels, as also were the sub-groups with No Education or Primary Education (%GG of
-40%) and Secondary Education (%GG of -29%). The GG of -41% for those with
Certificates or Diplomas was significant at the 90% confidence level.
In Manufacturing, the aggregate GG of -26 is significant at 95% confidence level, as
also was the -53% GG for the sub-group with No Education/Primary Education.
101
Chapter 8
Table 8.7 gives the
Gender Gaps for Salary
Earners by Industry and
Qualifications.
Four industries have
negative Gender Gaps in
aggregate although there
are zero or positive
Gender Gaps within each
sub-group.
Salary
Earners
in
Manufacturing had a -8%
GG in aggregate not
statistically significant.
Then while those with
Secondary Education had
a statistically significant 55% GG, the GG had
declined to zero for those
with
Certificates
or
Diplomas.
Income Earned for Equal Time
Table 8.7 Average Income pSY for Salary Earners
by Industry and Qualification
Female
Male
% GG
3 Manufacturing
B Secondary
4967
11013
9818 -55 **
E Cert/Diploma
10513
10545
10535
0
All Manuf.
11786
12768
12532
-8
6 Hotel, Retail, Rest.
B Secondary
6050
8313
7629 -27 **
E Cert/Diploma
9636
7027
8225 37
All Hotel,..
7892
8543
8313 -8
7 Transp.Stor.Comm.
B Secondary
11999
19095
17162 -37
E Cert/Diploma
26529
12835
15434 107
F Degree/PG
18891
28252
25348 -33 **
All Transport...
16931
17963
17713 -6
8 Fin. Real Est. Business
B Secondary
14148
21887
19299 -35 *
E Cert/Diploma
15681
25251
20225 -38
F Degree/PG
30261
25018
27115
21
All Finance..
17661
26652
22876 -34
9 Comm. Soc.& Pers. Serv.
B Secondary
15743
11152
12496
41
E Cert/Diploma
13473
13741
13592
-2
F Degree/PG
21872
27266
24738 -20 *
All Community
15436
14202
14728
9
A very similar trend
existed for Hotel, Retail
and Restaurants: -8% in
aggregate
(not
All Salary Earn. 14759
14723
14737
0
statistically significant), a
statistically significant 27% for those with
Secondary Education, but a positive Gender Gap of 37% for those with Certificates or
Diplomas.
In Finance, Real Estate, Business also had the same trend: significant Gender gaps for
low qualifications (-35% for those with Secondary Education), but a positive 21% for
those with Degrees.
For these three industry groupings at least, the above results would suggest that
higher education has a very strong impact of either reducing gender gaps, or even
making them positive in favour of Females.
Table 8.7 also indicates that for Salary Earners in Community, Social and Personal
Services, the aggregate Gender gap is a +9%, a much larger 41% for those with
Secondary Education, but -20% for those with Degrees (significant at 90% level).
Overall, Female Wage Earners in Community, Personal and Social Services tend to
do relatively poorly compared to Salary Earners (comparing Tables 8.6 and 8.7).
102
Chapter 8
Table 8.8 indicates that
seven out of the nine
industry groupings have
negative Gender Gaps,
with two (Craft and
Related (-38%) and
Plant and Machine
Operators (-43%) being
statistically significant
at 95% confidence
levels.
The Gender Gap for
Clerks was 0% in
aggregate, while that
for
Elementary
Occupations
was
positive.
The general tendency
however seems to be
that the gender gaps for
the higher occupation
groups tend to be
statistically
not
significant.
One may note, for both
Females and males,
another general trend of
reducing
Average
Incomes pSY as one
goes down the major
occupation groups.
Income Earned for Equal Time
Table 8.8 Average Income pSY for Wage Earners
By Occupation and Qualifications
Qualification
Female Male
All
%GG
1 Sen. Officials & Manag.
B Secondary
8728
11759 10879
-26
E Cert/Diploma
9154
76076 58547
-88
All Senior...
8824
26159 21595
-66
2 Professionals
B Secondary
7746
9731
9050
-20
E Cert/Diploma
8882
10115 9581
-12
All Professional..
9324
9898
9701
-6
3 Tech. & Assoc Prof.
B Secondary
5874
7875
7420 -25 **
E Cert/Diploma
9642
7205
7922
34
7181
8398
8122
-14
4 Clerks
B Secondary
6592
6582
6588
0
E Cert/Diploma
5609
6556
5852
-14
6621
6630
6625
0
5 Service, Shop, MktSales
A None/Primary
3156
3680
3505
-14
B Secondary
4388
5469
4987 -20 **
E Cert/Diploma
4611
5002
4887
-8
4342
5258
4869
-17
7 Craft & Related
B Secondary
4035
5729
5628 -30 **
All Craft &...
3981
6409
6278 -38 **
Plant and Machine Oper.
A None/Primary
3148
8336
7644 -62 **
B Secondary
4053
6453
5867 -37 **
3927
6902
6262 -43 **
9 Elementary Occupations
A None/Primary 14500
6069
7639
139
B Secondary
7754
6327
6748
23
All Elementary
9130
6211
6973
47
All Wage Earners
6558
7028
6902
-7
103
Chapter 8
Income Earned for Equal Time
Table 8.9 indicates that for Salary Earners in aggregate and by several industry
groupings, the Gender Gaps are either small, or positive in favour of Females.
Thus there are positive
Gender Gaps for Senior
Officials and Managers
(+43%), Clerks (+2%),
and for Service, Shop,
Marketing and Sales
(1%).
There are very few
statistically significant
negative Gender Gaps -41% for Elementary
Occupations (at 95%
confidence levels), and
all Professionals (-13%)
but only at 90%
confidence level.
Most
industry
groupings
have
a
mixture of positive and
negative gender gaps,
not unexpected given
that
the
aggregate
Gender Gap for all
Salary Earners is zero.
As with Wage Earners,
there is a slight trend of
declining
average
Incomes pSY going
down the occupation
groups.
Table 8.9 Average Income pSY for Salary Earners
By Occupation and Qualifications
Females Males
All
% GG
1 Sen. Officials & Manag.
B Secondary
9123
11365 11170
-20
E Cert/Diploma
39522
16739 20423
136
F Degree/PG
27268
33696 32164
-19
All Senior...
24772
17365 18303
43
2 Professionals
B Secondary
11050
15936 13717 -31 *
E Cert/Diploma
13775
14617 14147
-6
F Degree/PG
21903
22960 22510
-5
All Professionals 15194
17475 16355 -13 *
3 Tech. & Assoc Prof.
B Secondary
30779
21933 23981
40
E Cert/Diploma
12832
18188 15365
-29
F Degree/PG
23581
25276 24303
-7
All Technical..
18970
20948 20129
-9
4 Clerks
B Secondary
11800
9306 10670
27
E Cert/Diploma
11754
9143 10999
29
All Clerks..
12181
11978 12104
2
5 Service, Shop, MktSales
B Secondary
7964
8100
8071
-2
E Cert/Diploma
9970
9106
9286
9
All Service..
8537
8438
8460
1
9 Elementary Occupations
B Secondary
4809
9797
8719 -51 **
All Elementary
5913
10077 9252 -41 **
All Salary Earn.
14759
14723 14737
0
Comparing Tables 8.8 and 8.9 it is evident that the kinds of jobs that are associated
with “salaries” are generally good for reducing gender gaps, while those associated
with “wages” are not.
104
Chapter 8
Income Earned for Equal Time
Paying FNPF and Not Paying FNPF
Table 8.10 gives the
somewhat unexpected
result that the Gender
Gap for Wage Earners
Not Paying FNPF was a
postive 8%, while that
for those Paying FNPF
was a negative 15%,
statistically significant
at 95% confidence
levels.
Table 8.10 Average Incomes pSY for Wage Earners
Paying FNPF and Not Paying FNPF
Qualifications
Female Male
All
%GG
Not Paying FNPF
A None/Primary 12740 5498 6857
132
B Secondary
6428
6512 6490
-1
E Cert/Diploma
4324 26081 19160
-83
Not Paying
7537
6990 7121
8
Paying FNPF
A None/Primary 4045
7567 7179
-47
B Secondary
5616
6385 6149
-12
E Cert/Diploma
7248
9346 8721 -22 **
F Degree/PG
17757 17184 17413
3
Paying FNPF
6008
7056 6753 -15 **
There is also the odd
result that for Females
Not Paying FNPF, there
is a inverse trend of
average incomes pSY falling with rising qualifications.
There is the expected strong upward trend for those Paying FNPF, of rising average
incomes pSY with rising qualifications- for both Females and Males.
Table 8.11 gives unusual
results for Salary Earners
Paying and Not Paying
FNPF.
For the group Not Paying
FNPF, the Gender Gaps
were all positive (36% in
aggregate), while for
those Paying FNPF, the
Gender Gap was -3% in
aggregate,
and
a
statistically significant 17% for those with
Degrees.
Table 8.11 Average Incomes pSY for Salary Earners
Paying FNPF and Not Paying FNPF
Qualifications
Female Male
All
%GG
Not Paying FNPF
A None/Primary 23808 16398 18922
45
B Secondary
35906 34523 34976
4
Not Paying
24944 18297 20416
36
Paying FNPF
B Secondary
11258 11652 11547
-3
E Cert/Diploma 12997 13063 13030
-1
F Degree/PG
22676 27229 25202 -17 **
Paying FNPF
13971 14362 14213
-3
For both groups, there is an upward trend of rising average incomes pSY with rising
qualifications.
It is somewhat odd that the Average Salaries per Standard Year for those Paying
FNPF are lower than those Not Paying FNPF. This may be partly explained by the
quite high incomes per time worked by Family Workers and Self-Employed.
105
Chapter 9
Incidence of Poverty Amongst Income Earners
Chapter 9
The Incidence of Poverty amongst Income Earners
The usual analysis of poverty is conducted at the level of households and not at the
level of the individual. This is because most workers’ incomes are usually devoted to
supporting some family, and in most families there is some degree of pooling of
incomes and a sharing of the benefits of expenditures.
What poverty standard: how derive a Basic Needs Poverty Line for workers?
In standard poverty analysis it is assumed that the “standard of living of a household”
derived from a particular level of total household income depends most directly on the
size of the household. The bigger the household, the more a particular level of
household income has to “stretch” to satisfy the needs of the household.
In poverty analysis, the measure that is used to reflect this standard of living, and used
to rank the households, is some version of “Income per capita”.93 However, it is
thought the simple “Income per capita” measure (total income divided by the number
of persons) is not appropriate as that would treat the needs of children the same as the
needs of adults, which is usually not the case.
The United Nations methodology, which is the one adopted here, treats each child (14
and under) as half an adult. All persons in the household are therefore converted to
“Adult Equivalents”. Then the total household income is divided by the number of
“Adult Equivalents” to obtain the measure “Income per Adult Equivalent” (Income
pAE) which is then used to rank the households from the lowest living standard to the
highest living standard.
It is also accepted that the incomes working people receive ought to be enough to
place the average household above the accepted social norm- which is reflected in the
value of some “Basic Needs Poverty Line” calculated per Adult Equivalent.
The normal process if to first estimate what income would be needed to ensure a
minimum living standard for a “standard household or family” comprising 2 adults
(assumed working) and 3 children (who are the equivalent of 2 adults), making a total
of 4 Adult Equivalents for the standard family.94 Once the Income per Adult
Equivalent is calculated for all households, it is then possible to find which percentage
of households (and hence population) have Incomes pAE which are below the
standard decided upon- ie below the Basic Needs Poverty Line, or “in poverty”.
___________________________________________________________________________
93
The WB methodology, not only treats the child (14 and under) as a half, but also treats the first adult
as 1, and subsequent adults as 0.75 of an adult.
94
This process includes first calculating the Food Poverty Line (which is the cost of basketof foods
necessary to provide the basic nutritional diet for the standard family) and a “Non-Food Poverty Line”
(which is the cost of basic non-food requirements such as housing, clothing, education, etc.).
106
Chapter 9
Incidence of Poverty Amongst Income Earners
Recent analysis of poverty using the 2002-03 HIES used a range of values between
$132 and $136 per week, for the Basic Need Poverty Line thought to be applicable
then to a family of 4 Adult Equivalents. Assuming roughly 2 working adults, this
would require an income per person of between $66 and $68 for 2002-03 or roughly,
$70 per working person in 2004-05, adjusting for inflation. For a conservative
analysis of poverty, this chapter uses $60 per week as the BNPL for an individual
worker.
As has been explained earlier, the incomes data derived from the Last 7 Days dataset
is more useful for the analysis of poverty. The ranges are such (see Table 9.1) that it
is relatively easy to estimate what proportions of persons are earning less than $60 pw
(or $70 per week). 95
The Incidence of Poverty Nationally
Table 9.1 and graph 9.1 give the
distribution of Female and Male
Economically Active over the Last 7
Days.
Females have much higher proportions
at the two lowest categories: 23% of
the Females earned less than $30 per
week compared to a much lower 15%
for Males.
Table 9.1 Distrib. of Income L7D (vert. %)
Gr Income L7D Female Male All
A 0 to 29
23
15
17
B 30 to 59
17
14
15
C 60 to 89
18
17
17
D 90 to 119
12
15
14
E 120 to 149
7
11
10
F 150 to 199
7
11
10
G 200 to 249
4
5
5
H 250 to 299
3
3
3
I 300 +
9
9
9
All
100
100 100
<$60 pw
40
29
32
< $70 pw
46
35
38
Males have much higher proportions in
the middle income brackets, while the
proportions generally equalise towards
the top bracket (which is the equivalent
of an extremely low
Graph 9.1 Distribution by Gross Income pw L7D
$16,000 annually).
Distribution of Income
30
Percentages
Consequently, it is not
surprising that some 40%
of
all
Female
Economically
Active
earned below $60 per
week, as opposed to 29%
of Males, and 32% of all
working persons. To the
Economically
Active
persons covered above,
would need to be added
the formally Unemployed
persons over the Last 7
Days- some 6513 Females
Male
20
Female
10
0
A 0 B C 60 D 90 E
to 29 30 to to 89 to 120
59
119 to
149
F
150
to
199
G
200
to
249
H
250
to
299
I
300
+
Income Range $pw
___________________________________________________________________________
95
In the L7D data set of distribution of income (for example as in Table 9.1), to obtain the percentage
of persons earning less than $70 pw, one simply sums the proportion earning less than $60 per week, to
a third of the proportion in the next income range $60 to $89.
107
Chapter 9
Incidence of Poverty Amongst Income Earners
and 9981 Males- who earn zero
incomes.
Table 9.2 Incidence of Poverty Labour Force L7D
Poor L7D
Female Male
All
Yes
43908 74272 118180
No
56485 157264 213749
Labour Force L7D 100393 231536 331929
Perc. Poor
44
32
36
Table 9.2 gives the “Incidence
of Poverty “ amongst the
Labour Force over the L7D.
Some 44% of the Females in
the Labour Force L7D would
be considered Poor (earning less than $60 per week), compared to 32% of the Males
(and 36% nationally).
The national figure derived from the 2002-03 HIES for the incidence of poverty was
between 34% and 36%, depending on the particular value used for the Basic Needs
Poverty Line for the standard household.96 The estimates in this section are therefore
consistent with the results of the 2002-03 HIES, assuming that the overall population
is evenly distributed with these Labour Force persons.
Of course, using the higher standard of $70 per week would give much higher
estimates for workers in poverty. This chapter will use the more conservative $60 per
week as the standard for incidence of poverty of individual workers.
A Gender-Neutral Incidence of Poverty?
The figures given above for the “Incidence of Poverty” used the normal definition of
Economically Active, not including Household Workers, the bulk of whom are Female.
Proponents of gender equality argue that Household Workers, even if they are unpaid,
should be included in the definition of the Economically Active. This would add another
128 thousand workers to the Labour Force (Last 7 Days data, different from the P12M
data), all of whom would fall below the poverty line given that they are unpaid. (We have
earlier used an imputed value of $30 per week as the value of work done by full-time
household workers). If HH workers are included in the “Gender Neutral Labour Force “,
then the incidence of poverty for Females becomes a considerably higher 75%.
Normal Labour Force Last 7 Days
Household Workers
“Gender-Neutral” Labour Force
Poor L7D
Total Poor (including Household workers)
“Gender Neutral” Incidence of Poverty
Female
100393
126143
226536
43908
170051
75%
Male
231536
2266
233802
74272
76538
33%
All
331929
128410
460339
118180
246590
54%
Of course, it is quite likely that there are a number of women on full-time household
work, who are there by choice, even if they could be earning incomes well above the
poverty line. The assumption may be that these women place a higher “value” on caring
for their children , than they do on the higher income available elsewhere.
___________________________________________________________________________
96
It should be noted though that the HIES results applied to the whole population (including nonearners and children) whereas the results here are applicable only to the income earners themselves.
108
Chapter 9
Incidence of Poverty Amongst Income Earners
The Box above suggests that if unpaid Household Workers are included in the Labour
Force, the incidence of poverty for Females would rise to 75%. Three quarters of all
working women, broadly defined, can be classified as “poor” from the point of view
of the income they receive personally.
Labour Force Status
Table 9.3 disaggregates
the Working Poor by
their Labour Force
Status.
For
virtually
all
categories (except those
With a Job But Not At
Work), Females had
higher incidence of
poverty.
What stands out is the
extremely high rates of
poverty amongst those
doing Community Work
and Family Work (over
90%) but also those
who
were
SelfEmployed (53%).
Table 9.3 Incidence of Poverty (by Labour Force Status L7D)
Labour Forces L7D
Female Male All % GG
Incidence of Poverty
A Wages
24
17
19
41
B Salary
1
0
1
136
C Employer
47
3
16 1363
D Self-employed
53
38
41
40
E Family Workers
91
86
88
7
F Community Wrk
99
93
95
6
G Job/Not At Work
27
29
28
-7
U Available/No work
100
100 100
0
All
44
32
36
36
Vertical Distribution of Poor
A Wages
20
23
22
B Salary
0
0
0
C Employer
1
0
0
D Self-employed
22
30
27
E Family Workers
37
28
31
F Community Work
3
3
3
G Job/Not At Work
2
2
2
U Available/No work
15
13
14
All
100
100 100
The 47% Incidence of
Poverty
amongst
Female “Employers” in sharp contrast to the 3% amongst Male Employers, may
deserve further study as to the cause of this massive difference.97
What was the composition of the Female poor? Some 37% of the Female Poor were
doing Family Work, 22% were Self-employed, and 20% were Wage Earners. The
other categories had negligible numbers.
Formal/Informal: Criterion of Payment of FNPF
The analysis of poverty using the 2002-03 HIES found it difficult to classify
“households” by formal/informal sectors, since income-earners in the same household
would not all necessarily be in the same category.
The income earners in the 2004-05 EUS may however be roughly classified thus by
the criterion of payment of FNPF, although many employers and self-employed
persons may not pay FNPF while being in the formal sector.98
___________________________________________________________________________
97
Note that the numbers of Female and Male Fijian employers in the sample were quite small.
109
Chapter 9
Incidence of Poverty Amongst Income Earners
Table 9.4 indicates firstly that the bulk of Female workers who did not pay FNPF
were Self-employed, Family Workers and Wage Earners, in that order. There were
also the same categories with high rates of poverty incidence.
Table 9.4 shows not only the great contrast between those who paid FNPF and those
who did not, but also that the Gender Gaps exists on both sides, even if the rates are
generally lower for those who did pay FNPF.
First, some 67% of Females who did not pay FNPF were in poverty, compared to 48%
of Males. For those who did pay FNPF, the corresponding figures were 8% and 5%
only.
The largest group of Female Poor were Family Workers, the Females amongst whom
had an incidence of poverty of 93%. This would be the largest group of vulnerable
Female workers in the labour force.
Table 9.4 Nos. of Persons & Incidence of Poverty (Labour Force Status and Payment of FNPF)
Not Paying FNPF
Paying FNPF
Fiji
Fem
Mal
All
Fem
Mal
All
Numbers of Persons in Labour Force L7D
A Wages
12557
42040
54597 23779 58990
82769 137366
B Salary
794
1608
2402 15128 25525
40653
43055
C Employer
740
1650
2390
289
715
1005
3395
D Self-employed
17409
55167
72576
572
2952
3524
76100
E Family Workers
16595
22909
39503
292
1234
1527
41030
F Community Wrk
973
2158
3131
75
105
181
3312
G Job/Not At Work
916
3209
4125
1960
2514
4474
8599
U Available/No work
739
1862
2602
302
1120
1422
4023
All
50723 130603 181326 42398 93156 135554 316880
Incidence of Poverty
A Wages
50
38
41
10
2
4
19
B Salary
10
2
5
1
0
0
1
C Employer
60
5
22
13
0
4
16
D Self-employed
53
38
42
38
25
27
41
E Family Workers
93
86
89
40
92
82
89
F Community Work
100
94
96
100
100
100
96
G Job/Not At Work
59
48
50
11
4
7
28
U Available/No work
100
100
100
100
100
100
100
All
67
48
53
8
5
6
33
Two categories worth noting are the Self-Employed and Wage Earners who we have
earlier noted were two of the largest categories of Female poor. Some 50% of all
Female Wage Earners who did not pay FNPF were in poverty compared to 38% of the
corresponding Male Wage Earners who did not pay FNPF (and compared to only 10%
of Females who paid FNPF).
98
The numbers for the aggregate incidence of poverty will not match the earlier estimates because the
Unemployed were not required to respond to the question on the payment of FNPF.
110
Chapter 9
Incidence of Poverty Amongst Income Earners
And some 53% of Female Self-Employed who did not pay FNPF were in poverty,
compared to 38% of the Males who did not pay FNPF (and compared to 38% of
Females who did pay FNPF).
Rural/Urban
Table 9.5 Incidence of Poverty in Labour Force L7D
(by Rural/Urban and Gender)
Region
Female Male All % GG
Rural
61
43
48
43
Urban
30
20
24
46
All
44
32
36
36
Rural:Urban Gap
105
110 103
Table 9.5 indicates the extent
to which the rural working
persons are twice as likely to
be Poor as the Urban working
persons. The Rural Incidence
of Poverty is 48% which is
twice that of the Urban incidence of poverty of 24%.
Amongst the rural persons, Rural Females had the highest incidence of poverty – at
61%, some 43% higher than the Male rate of 43%.
Note that while the Urban incidence of poverty is lower, the gender gap is about the
same: the Female Urban incidence of poverty of 30% is some 46% higher than the
20% rate for Urban Males.
By Division
Table 9.6 indicates that the Eastern Division (which had only 5% of the Labour Force)
had the highest incidence of poverty (70%). The gender gap was however negative.
The highest incidence of poverty
was for Female workers in the
Northern Division, with 68%,
which was some 28% higher than
the Male rate of 53%.
Table 9.6 Incidence of Poverty in Labour Force
L7D (by Rural/Urban and Gender)
Division Female Male All %GG
Central
37
26
30
42
Eastern
63
74
70
-15
Northern
68
53
57
28
Western
38
24
27
60
All
44
32
36
36
The other divisions also had the
usual higher incidence of poverty
for Females, with `the Western
Division (with 36% of the Labour
Force) having the highest Gender
Gap of 60%. The overall incidence of poverty for Females and Males for the Western
and Central Divisions were fairly similar.
111
Chapter 9
Incidence of Poverty Amongst Income Earners
By Educational Qualifications
Table 9.7 (and Graph 9.2)
gives the expected patterns
of reducing incidence of
poverty with improving
educational qualifications,
falling from a high of 67%
for
those
with
No
Schooling to 5% for those
with Degrees.
Table 9.7 Poverty Incidence by Educational Attainment
Female Male All % GG
A No Schooling
75
62
67
22
B Primary
78
49
56
58
C Junior Secondary
53
34
39
55
D Senior Secondary
29
20
23
49
E Cert/Diploma
14
12
13
19
F Degree/PG
6
5
5
26
All
44
32
36
36
Females with No Schooling
or only Primary education, had the highest rates of poverty incidence of all- 75% and
78% respectively.
At every qualification level, the Female incidence of poverty is significantly higher
than that of the Males- the
difference being 58%, 55%
Graph 9.2 Poverty Incidence by Educational Attainment
and 49% respectively for
Incidence of Poverty
those with Primary, Junior
(by educational attainment)
Secondary and Tertiary.
80
Female
Percentages
60
Not only does the incidence
Male
of poverty drop sharply
40
with the acquisition of
20
Certificates or Diplomas,
0
but the Gender Gap in the
a
g
G
ary
ary
ary
om
li n
e/P
nd
nd
oo
im
ipl
incidence of poverty also
o
o
r
h
D
c
c
c
gre
/
P
e
e
t
e
S
S
r
S
D
B
r
No
Ce
F
ior
drops to its lowest level
nio
E
A
Sen
Ju
C
D
(19%). This is generally in
keeping with our earlier
results
in
income
differences between Females and Males.
The numbers here emphasise once more that higher education has the most powerful
association with the reduction of poverty for Females, and the reduction of the gender
gap with males.
By Occupation Group
Table 9.8 gives the distribution of Poor Persons by major Occupational Groups.99
More than a half of all the Poor are in Skilled Agriculture and Fisheries (61% for
Males and 41% for Females). The next largest group are in Elementary Occupations,
some quarter of both Males and Females. For Females, there are also significant
proportions in Craft and Related (15%) and 9% in Service, Shop, Marketing, Sales.
___________________________________________________________________________
99
Some numbers here may not match the totals elsewhere in this Chapter as some persons did not have
their Occupation group identifiers, although incomes and periods worked were recorded.
112
Chapter 9
Incidence of Poverty Amongst Income Earners
Quite unusually, Clerks had the lowest incidence of poverty of all occupation groups,
with 3% for all, 5% for Females and 1% for Males.
Table 9.8 Distribution of Poor Persons (by Occupation Groups)
Occupation L7D name
Fem
Mal
All
Fem Mal All
Numbers
Vertical %
1 Sen. Officials & Manag.
1264
502
1766
3
1
2
2 Professionals
457
1141
1598
1
2
2
3 Tech. & Assoc Prof.
1028
2363
3391
3
4
3
4 Clerks
607
129
736
2
0
1
5 Service, Shop, MktSales
3363
1271
4634
9
2
5
6 Sk.Agr.& Fishery
15364 38935 54299
41
61
53
7 Craft & Related
5477
3203
8681
15
5
9
8 Pl. & Mac.Oper.&Assemblers
1567
911
2477
4
1
2
9 Elementary Occupations
8396 15836 24232
22
25
24
All
37523 64291 101814 100 100 100
Incidence of Poverty
% GG
1 Sen. Officials & Manag.
28
4
11
575
2 Professionals
5
10
8
-48
3 Tech. & Assoc Prof.
16
18
18
-10
4 Clerks
5
1
3
241
5 Service, Shop, MktSales
24
6
14
284
6 Sk.Agr.& Fishery
86
61
66
41
7 Craft & Related
73
10
22
636
8 Pl. & Mac.Oper.&Assemblers
36
4
10
735
9 Elementary Occupations
46
42
43
9
Interesting are the negative Gender Gaps for Professionals, and Technical and
Associated Professionals.
By and large, however, for nearly all the other Occupation Groups, the Females had
significantly higher rates of poverty than the Males. Of note is the rate of 86% for
Females (61% for Males) in Skilled Agriculture and Fisheries, and 73% for Females
in Craft and Related (a mere 10% for Males). For those classified as Plant and
Machine Operators and Assemblers, Females had a rate of 36% compared to a mere
4% for Males.
Only in Elementary Occupations, does the Gender Gap drop to 9%, with Females and
Males both having high rates of 46% and 42% respectively.
113
Chapter 9
Incidence of Poverty Amongst Income Earners
By Industry
Table 9.9 gives the incidence of poverty by major Industrial groupings. While some
61% of all the Poor in the Labour Force are in Agriculture, Forestry and Fisheries,
only 46% of the Female Poor are in that industry. The other Female Poor are roughly
distributed amongst only three other industrial groups- a surprisingly high 22% in
Hotel, Retail and Restaurants, 17% in Manufacturing (mostly in the Garments
industry), and 13% in Community, Social and Personal Services.
The highest incidence of poverty (86%) is for Females in Agriculture, Forestry and
Fisheries, followed by 46% in Manufacturing, and 30% in Hotel, Retail and
Restaurants.
In virtually every industry where there were significant Female and Male workers, the
Female incidence of poverty was significantly higher than for Males.
Table 9.9 Distribution of Poor Persons and Incidence of Poverty (by Industry Groups)
Occupation L7D name
Fem
Mal
All
Fem Mal All
Numbers
Vertical %
1 AgForFishing
17179 45406
62585 46
71
61
2 Mining&Quarrying
0
0
0
0
0
0
3 Manufacturing
6459
6080
12539 17
9
12
4 Elect & Water
44
0
44
0
0
0
5 Construction
0
979
979
0
2
1
6 Hotel, Retail, Rest.
8169
5786
13954 22
9
14
7 Transp.Stor.Comm.
245
790
1035
1
1
1
8 Fin. Real Est. Business
458
136
594
1
0
1
9 Commun. Soc.& Pers. Serv.
4969
5114
10083 13
8
10
All
37523 64291 101814 100 100 100
Incidence of Poverty
% GG
1 AgForFishing
86
65
70
32
2 Mining&Quarrying
0
0
0
na
3 Manufacturing
46
21
29
119
4 Elect & Water
26
0
2
na
5 Construction
0
6
6
na
6 Hotel, Retail, Rest.
30
15
21
103
7 Transp.Stor.Comm.
9
4
5
112
8 Fin. Real Est. Business
13
2
6
551
9 Comm. Soc.& Pers. Serv.
20
14
16
39
The high gender gaps in some industries would no doubt be partly attributable to
Females being in lowly paid occupations, usually requiring lower educational skills.
114
Chapter 9
Incidence of Poverty Amongst Income Earners
Industry and Education Qualifications
Table 9.10 examines the
incidence of poverty
within industries by the
highest
educational
qualifications
of
the
workers
(only
those
industries with adequate
numbers of observations
in each cell are given).
In
virtually
every
industry, and at every
qualification level, there
are large positive gender
gaps,
indicating
the
Females are far more
likely to be in the Poor
category, given the same
industry, and the same
qualification as the Males.
This should not be too
surprising
since
the
incidence of poverty
focuses on those with the
lower incomes, and we
have seen in the earlier
chapters
that
higher
proportions of Females
are generally to be found
in the lower income
ranges.
Table 9.10 Poverty Incidence (Industry and Qualification)
Fem Male All % GG
1 AgForFishing
A No Schooling
86
82
84
4
B Primary
89
69
73
29
C Junior Secondary
87
63
68
37
D Senior Secondary
83
59
65
40
E Cert/Diploma
61
83
76
-26
All AgForFishing
86
65
70
32
3 Manufacturing
A No Schooling
63
19
35
228
B Primary
77
38
49
106
C Junior Secondary
45
21
30
110
D Senior Secondary
22
10
13
112
E Cert/Diploma
17
3
6
454
All Manufacturing
46
21
29
119
6 Hotel, Retail, Rest.
A No Schooling
54
39
48
38
B Primary
60
37
44
62
C Junior Secondary
34
12
21
187
D Senior Secondary
17
6
11
169
E Cert/Diploma
11
8
9
29
All Hotel, Retail, Rest.
30
15
21
103
9 Comm. Soc.& Pers. Serv.
A No Schooling
81
51
63
58
B Primary
73
29
47
148
C Junior Secondary
35
20
25
76
D Senior Secondary
8
13
11
-40
E Cert/Diploma
5
2
3
138
F Degree/PG
4
2
2
139
All Commerce, Soc.Pers.
20
14
16
39
As would be expected, in
most industries, there is a
reduction in the incidence
of poverty with higher educational qualifications. But in general, for any educational
level, the incidence of poverty shows wide variation by industry.
The one exception is Agriculture, Forestry and Fisheries, where the rates of poverty
are not only higher than in the other industries (given the same qualification) but there
was no great improvement in poverty incidence until one reaches the levels of
Certificate/Diploma qualifications.
115
Chapter 9
Incidence of Poverty Amongst Income Earners
Economically Active Females, Household Workers, and Poor Households
In the analysis above, we have attempted to estimate the incidence of poverty amongst
the Economically Active
persons, and in particular,
Table 9.11 Numbers of Total Population and
the gender disparities.
Economically Active Persons in Poverty Deciles
However, poverty analysis
is usually conducted at the
household level and the
general finding is that
Females are usually around
their normal 50 percent in
every decile, from the
lowest to the highest.100
Total
Economically Active Persons
P Dec Population Females Males All EcAc
PD 1
81795
3181
12875
16056
PD 2
81795
4404
19385
23789
PD 3
81795
7135
19538
26673
PD 4
81795
7011
22659
29670
PD 5
81795
8717
23842
32559
PD 6
81795
11033
24524
35558
PD 7
81795
12987
25798
38785
PD 8
81795
13963
25290
39253
PD 9
81795
15348
26940
42287
PD top
81795
18909
27329
46238
817952
102688 228180 330869
An interesting question is:
where do the Economically
Active Females fall, as far
as the Poverty Deciles are
concerned, defined at the
household level. Table 9.11 gives the basic data with the households ranked by
Income per Adult Equivalent, and into deciles each containing a tenth of the total
population: i.e. these PD1, PD2, etc deciles of population (not households).
Table 9.12 indicates the extent to which Economically Active persons tend to be in
the higher deciles. Only
Table 9.12 Numbers of Total Population and
20% of Economically
Economically Active Persons in Poverty Deciles
Active persons were in the
Total
Economically Active Persons
Bottom 3 deciles (which
P
Dec
Population
Females Males All EcAc
contained 30% of the total
PD
1
10
3
6
5
population, of course).
There was an even lower
proportion of Economically
Active Females – 14%,
compared to 23% of Males.
At the other end of the
spectrum, some 47% of
Economically
Active
Females were in the Top 3
deciles, compared to 35%
of Economically Active
Males.
PD 2
PD 3
PD 4
PD 5
PD 6
PD 7
PD 8
PD 9
PD top
10
10
10
10
10
10
10
10
10
100
4
7
7
8
11
13
14
15
18
100
8
9
10
10
11
11
11
12
12
100
7
8
9
10
11
12
12
13
14
100
While a much higher proportion of Females were considered poor in the earlier
analysis, the more universal analysis of poverty incidence at the household level,
___________________________________________________________________________
100
See the findings in the Report on the 2002-03 Household Income and Expenditure Survey, by
Wadan Narsey. 2006.
116
Chapter 9
Incidence of Poverty Amongst Income Earners
indicates that Economically Active
Females generally tend to improve the
standards of living of the households they
are part of.
This follows simply because they are
income earning or producing goods or
services whose market value is estimated
by the EUS. In contrast, the services of
full-time Household Workers are not so
evaluated.
Table 9.13 Economically Active Females
and Female Household Workers as % of
Total Population in Poverty Deciles
P Dec EcAc HH Workers Both
PD 1
4
19
23
PD 2
5
18
23
PD 3
9
18
26
PD 4
9
17
25
PD 5
11
16
27
PD 6
13
15
28
PD 7
16
12
28
PD 8
17
12
29
PD 9
19
11
30
PD top
23
9
32
13
15
27
Table 9.13 gives another perspective on
this,
the
distributions
of
both
Economically Active persons, and
Household Workers.
Economically
Active Females are only 13% of the total
population, but 4% of the Bottom decile respectively. Full-time Female Household
Workers on the other hand, who are 15% of the whole population, are a much larger
19% of the Bottom decile.
Economically Active Females are seen to add income to the households and tend to
move their households up the poverty deciles. Household Workers, on the other hand,
add no income to the household, and hence their households tend to be on the lower
poverty deciles.
Ethnicity101 and Gender
The Fijian Labour Force has the highest incidence of poverty (41%) with Indo-Fijians
having a lower 28%, with the average pulled down by the low rate of 23% for IndoFijian Males.
Table 9.14 indicates that Fijian Females in the Labour Force have the highest
incidence of poverty (46%)
Table 9.14 Incidence of Poverty of Workers
followed by Indo-Fijian Females
by Ethnicity
with 42%.
Females Males All % GG
With Fijian Males having 39% and
Indo-Fijian Males having a much
lower 23% incidence of poverty,
the % GG is highest for Indo-Fijian
Females- with 80%.
Fijian
Indo-Fij
Others
Rotuman
All
46
42
36
12
44
39
23
42
31
32
41
28
40
27
36
17
80
-15
-62
Were full-time unpaid Household Workers to be added to the “Poor” category, then
Indo-Fijian Females would have the highest incidence of poverty of all the ethnic subgroups.
___________________________________________________________________________
101
The numbers of observations behind the statistics for Others and Rotumans are not high enough to
draw reliable conclusions.
117
Chapter 10
Changes in Economically Active 1982 to 2004-05
Chapter 10
Changes in Economically Active and Inactive
1982 to 2004-05
To date, there have been only two national Employment and Unemployment Surveys
conducted with similar methodology- the 2004-05 EUS, and that some twenty two
years earlier in 1982.102 The methodology of the 1982 EUS Survey seemed to have
been quite sound103, as was their processing of the data. Unfortunately, only a limited
number of tables were produced, and, somewhat a reflection of the period of that
survey, very few gender-disaggregated tables.
Over the period of the two surveys, some of the variables would have changed
considerably, for instance the urban/rural boundaries, and to a smaller extent, numbers
associated with ethnic identities.104 Nevertheless, it is possible to document some
broad changes taking place, in relation to gender.
Table 10.1 outlines probably the most fundamental changes taking place with respect
to the economic classification of Females in the Fiji economy. While the total
population of Females and Males changed between 1982 and 2004-5 by about 30%,
the Usually Active changed by 58% while the Usually Inactive changed by only 15%.
However, the number of Female Economically Active increased by 150%, compared
to only 35% for Males. Complementary to that, the Female Usually Inactive
increased by only 11% compared to 24% for Males.
Table 10.1
Numbers of Economically Active and Inactive (1982, 2004-05 and changes)
1982
2004
Perc. Ch. 1982-2004
Fem Mal All Fem Mal All Fem
Mal
All
Thousands of Persons (000)
Percent
Usually Active
41 169 210 103 228 331 150
35
58
Usually Inactive 268 154 422 297 190 487
11
24
15
All Fiji
309 322 632 400 418 818
29
30
30
Perc. Active
13
52
33
26
55
40
94
4
22
% Gender Gap
-75%
-53%
___________________________________________________________________________
102
A Report on the Fiji Employment/Unemployment Survey 1982. Bureau of Statistics, June 1985.
See the 1982 EUS Report (pp 4-5) for a detailed explanation of responses, and procedures for
dealing with non-response and incomplete questionnaires.
104
Following the 1987 coups when registration in the Vola Ni Kawa Bula become important as an
identifier for “Fijian”, many persons, for social or political reasons may have changed their
identification between “Fijian” and “Others” (in both directions). There has also been doubt about how
many Chinese there were in Fiji, because of the possibility of illegal migrants.
103
118
Chapter 10
Changes in Economically Active 1982 to 2004-05
The net overall result was that while in 1982, only 13% of Females were
Economically Active, the ratio had increased to 26% in 2004- an increase of 94%. In
that same period, Economically Active Males as a proportion of all Males increased
from 52% to 55%, a small 4% increase. The Gender Gap for the Percent
Economically Active declined from -75% to -53%.
Differential Changes in Household Workers
A key factor in the above changes in Economically Active and Inactive, were the
changes taking place in the numbers of Females and Males designated as Household
Workers.
Table 10.2 indicates that while the total population of both Females and Males
increased by some 30%, the total number of Household Workers decreased by 5%, the
same change in the number of Females designated as Household Workers.
As a
proportion of the total population, Household Workers declined from 20% in 1982 to
15% in 2004.
The largest part of this change is due to the fact Females Household Workers, who
were 41% of all Females in 1982, had declined to 30% in 2004-05, because of their
increased involvement as Economically Active persons.
Table 10.2
The Impact of Changes in Numbers of Household Workers 1982 to 2004-05
1982
2004
% Ch 1982 to 2004
Fem Mal All Fem Mal All Fem Mal All
Thousands of Persons
Percent
Tot. Population
309 322 632 400 418 818
29
30
30
FT Household Workers
128 1.1 129 121 1.6 122
-5
49
-5
HH Workers as % of Pop 41 0.34 20
30 0.39 15
-27
15
-26
While the number of Male Household Workers increased by an apparently large 49%,
this was from a tiny base. As a proportion of all Males, Male Household Workers
increased from a mere 0.34% in 1982 to just 0.39% in 2004-05.
It would seem that there has not been any major change taking place such as Males
replacing Females as Household Workers in the household, when Females joined the
Labour Force. The Females who joined the Economically Active probably continued
their shouldering of household duties, with Male Economically Active also
contributing.
The 1982 EUS did not have questions on the amount of Household Work that was
being done then by Females and Males in the economy, hence a comparison cannot be
made between the sharing of the household work burden in 1982 and in 2004.
But we have seen in an earlier chapter that in 2004-05, Economically Active Females
did some 13 to 15 hours more Household Work on average than Economically Active
Males. It is quite likely therefore that in converting from Household Workers to
119
Chapter 10
Changes in Economically Active 1982 to 2004-05
Economically Active persons, Females retained a large portion of the Household
Work that they had been previously doing, while earning the extra incomes for their
families.
It may be argued that with modernisation assisting women to increase the
participation of Females in the formal Labour Force, Females have had to work harder
than before, because of their continued responsibility for Household Work. It would
be an interesting research activity to estimate the extent to which home improvements
such as washing machines and gas stoves, may have led to savings in household work
time.
A contributory fact has to be seen as the failure of Economically Active Males to bear
their fair share of Household Work, alongside their Economically Active Females
members of the household.
By Rural/Urban
Table 10.3 indicates that both the Urban areas and Rural areas have seen significant
growth in the numbers of Economically Active, although the Urban increase (97%)
was three times as much as the Rural increase (32%)).
By gender, the highest increase (185%) was for Urban Females, more than twice that
of Urban Males (70%). Significantly, the Rural areas indicated a very large increase
for Females (of 119%) contrasting with only 14% for Rural Males, but the Female
increase is from a small base.
Table 10.3
Changes in Usually Active and Inactive (by Rural/Urban and Gender)
1982
2004
% Change 1982-2004
Fem Mal All Fem Mal All
Fem
Mal
All
Thousands of Persons (000)
Percent
Usually Active
41
169 210 103 228 331
150
35
58
Urban
20
64
84
56
108 165
185
70
97
Rural
21
105 126
46
120 166
119
14
32
Usually Inactive
268 154 422 297 190 487
11
24
15
Urban
101
61 163 146
97 243
44
59
49
Rural
168
92 260 151
93 244
-10
1
-6
All Fiji
309 322 632 400 418 818
29
30
30
Table 10.4 gives the overall Gender shares of the Usually Active in 1982 and 200405. Within the overall context of the Rural Share declining from 60% in 1982 to 50%
in 2004-05, Females increased their share both in the Urban areas (by 80%) and the
Rural areas (by 39%). Males increased their share by only 8% in Urban areas, while
declining in Rural areas by 28%.
Overall, while the Female share of the Economically Active in 2004-05 was only
31%, this was still 59% higher than the 20% in 1982.
120
Chapter 10
Changes in Economically Active 1982 to 2004-05
Table 10.4 Shares of Usually Active 1982 and 2004-05, and Percentage Changes
1982
2004
% Ch. 1982-2004
Fem
Mal
All
Fem
Mal
All
Fem
Mal
All
Urban
9
30
40
17
33
50
80
8
25
Rural
10
50
60
14
36
50
39
-28
-16
All
20
80
100
31
69
100
59
-14
0
It is not clear how much should be made of these differential changes indicated by the
data. Is there a methodological factor at work rather than any fundamental change?
Rural women may certainly be devoting themselves relatively more to the kinds of
economic activity (such as producing foodstuffs for sale or home consumption)
bringing them under the classification of Economically Active. There may also be a
greater understanding of the economic activities of Females, which may have led
them to not classify themselves as full-time Household Workers, which the same
people with the same mix of activities, may have done in 1982.
The Usually Inactive showed moderate increases in the Urban areas for both Females
and Males (44% and 59%), but in the Rural areas, there was a decline of 10% for
Females, and a very small increase for Males of 1%.
Table 10.5 makes clear the role of changes in the numbers of persons designated as
Household Workers, in a context where overall population in Urban areas has
increased by about 65% but a much lower 6% in Rural areas (roughly the same for
Females and Males).
Table 10.5
Changes in Numbers of Household Workers (Rural/Urban and Gender)
1982
2004
Perc. Ch. 1982-2004
Region
Female Male All Female Male All Female Male All
Numbers of Persons (000)
Percent
All
310
322 632
400
418 818
29
30
29
Urban
121
125 246
202
205 407
67
64
66
Rural
189
197 386
198
213 411
5
8
6
Household Work
128
1.1 129
121
1.6 122
-5
49
-5
Urban
46
0.5
47
58
0.5
58
25
5
25
Rural
81
0.6
82
63
1.1
64
-22
86
-22
HH Workers as % of Population
Household Work
41
0.3
20
30
0.4
15
-26
15
-26
Urban
38
0.4
19
29
0.3
14
-25
-36
-24
Rural
43
0.3
21
32
0.5
16
-26
72
-26
Rural:Urban Gap
13
-24
12
11
104
9
121
Chapter 10
Changes in Economically Active 1982 to 2004-05
Table 10.6 Usually Active and Inactive (by ethnicity and gender)
1982
2004
Perc. Change 1982-2004
Fem Mal All Fem Mal All
Fem
Mal
All
Usually Active
41
169 210 103 228 331
150
35
58
Fijians
23
77 100 62 115 176
167
48
76
Indo-Fijians
15
85 100 35 103 138
137
21
39
Others
3
7
10
6
11
16
88
58
67
Usually Inactive 268 154 422 297 190 487
11
24
15
Fijians
119
70 189 144 111 255
21
58
35
Indo-Fijians
138
76 214 139 69 208
0
-8
-3
Others
11
8
18
14
9
24
33
27
30
All Fiji
309 322 632 400 418 818
29
30
30
However, the number of Female HH Workers increased by 25% in Urban areas, and
decreased by 22% in Rural areas. The numbers of Male HH workers are too small to
have any significant impact.
The net result was that Female Household Workers, as a percentage of the total
Female population, declined by about 25% in both Urban and Rural areas, although
the rural percentage remains somewhat higher than the urban percentage.
There are some other small changes. The Rural/Urban Gap for Percentage of All
Persons doing Household Work declined from 13% in 1982 to 11% in 2004. The
Male gap (albeit for small numbers of persons) changed in the opposite direction: 13% in 1982 to +104% in 2004, suggesting a slightly greater willingness of Rural
Males to take on the designation of full-time Household Work.
Table 10.7 Perc. Shares of the Economically Active in 1982 and 2004-05, and Changes
1982 1982 1982 2004 2004 2004 % Change 1982-2004
Fem Mal
All Fem Mal
All
Fem
Mal
All
Fijians
11
37
48
19
35
53
69
-6
11
Indo-Fijians
7
40
47
11
31
42
50
-23
-12
Others
1
3
5
2
3
5
19
0
6
All Fiji
20
80
100
31
69
100
59
-14
0
By Ethnicity
Given the political sensitivity of ethnic balances in employment, Table 10.6 provides
evidence of significant changes taking place in employment in Fiji over the period.
While the numbers of Fijian Economically Active persons increased by 76%
compared to the 39% by Indo-Fijians, the two largest percentage increases were for
Female Fijians who increased by 167% (compared to 48% of their Males) and Female
Indo-Fijians who increased by 137% (compared to only 21% for their Male
counterparts). These large percentage changes for Females were however from small
bases.
122
Chapter 10
Changes in Economically Active 1982 to 2004-05
Table 10.7 gives the Ethnic/Gender shares of the Economically Active in 1982 and
2004-05 and the percentage changes in these shares. Thus while Fijian Females were
only 19% of the Economically Active in 2004-05 (compared to 35% for Fijian
Males), this was a 69% increase from their 11% share in 1982. In this period the
Fijian Male share had declined by 6%.
Similarly while Indo-Fijian Females were only 11% of the Economically Active in
2004-05 (compared to 31% for Indo-Fijian Males), this was a 50% increase from their
very small 7% share in 1982. In this period the Indo-Fijian Male share had declined
by 23%.
Table 10.8 gives the percentage shares of their own population/gender group.
Overall, the percentage of the total population Economically Active rose from around
33% to 40%. Females of all ethnic groups increased their proportion of Economically
Active, far more than did the Males.
Thus Fijian Females increased by 85% from 16% to 30%, while Fijian Economically
Active Males reduced their share from 52% to 51% (decline of 3%).
Table 10.8 Perc. of Ethnic Populations Ec. Active in 1982 and 2004-05
1982
2004
Perc. Change 1982-2004
Fem Mal All Fem Mal All
Fem
Mal
All
Fijians
16
52 35 30
51 41
85
-3
18
Indo-Fijians 10
53 32 20
60 40
109
13
26
Others
22
48 35 29
53 41
29
12
17
All
13
52 33 26
55 40
94
4
22
From a very low 10% being Economically Active in 1982, some 20% of Indo-Fijian
Females were Economically Active in 2004- an increase of 109%. The
corresponding change for Indo-Fijian Males was by 13% from 53% to 60%- the
highest of all the gender groups.
Table 10.9 gives the changes in the numbers of Household Workers in relation to the
Usually Active. For the two major ethnic groups, the numbers of Female Household
Workers decreased by 7% and 5% respectively, while that of the Male Household
workers increased from the previously miniscule numbers.
The bottom half of Table 10.9 gives the number of Household Workers as a
percentage of the Usually Active- effectively an indication of the “reservoir” of
potential economically active persons. While the percentage in aggregate declined by
40% from 61% to 37%, that for Females declined by 62% and for Males increased by
10%.
However the ethnic disaggregations reveals the extent to which Female HH Workers
still have a great capacity to swell the Female Economically Active. For Indo-Fijian
Females, from being 470% in 1982, the proportion had declined by 60% but only to
189% in 2004-05.
123
Chapter 10
Changes in Economically Active 1982 to 2004-05
Numerically, Indo-Fijian Females still represent the largest Female pool in the gender
sub-groups. For Fijian Females the proportion had declined from 229% in 1982 to
79% in 2004-05.
Table 10.9 Numbers of Household Workers and Perc. of Usually Active
1982
2004
% Ch. 1982-2004
Fem
Mal
All
Fem
Mal
All
Fem
Mal
All
Numbers of Household Workers (000)
All
128
1
129
121
2
122
-5
49
-5
Fijians
53
1
53
49
1
50
-7
60
-7
Indo-Fijians
70
1
71
67
1
67
-5
47
-4
Others
4
0
5
5
0
5
16
10
16
HH Workers as as % of Usually . Active
All
311
1
61
118
1
37
-62
10
-40
Fijians
229
1
53
79
1
28
-65
7
-47
Indo-Fijians
470
1
71
189
1
49
-60
21
-31
Others
147
1
46
91
1
32
-38
-30
-31
Changes in Educational Attainment105
Table 10.10 indicates the relatively superior improvement of Females in terms of the
educational status of the Economically Active, the Economically Inactive, and the
population as a whole. Altogether, Females with No Schooling had declined by 26%
compared to a lower decline of 16% for Males. Females with Senior Secondary or
higher attainment had increased by 262% compared to 176% for Males.
Table 10.10
Percentage Changes in Educational Attainment 1982 to 2004
Economically Active Economically Inactive
All
Educational Attain.
Fem Male All
Fem
Male
All Fem Male All
A No Schooling
-19
-65
-55
-26
-9
-19
-26
-16 -21
Fiji Junior and below 118
19
36
8
27
14
22
23
23
Senior Sec and above 280
160
195
249
237
245 262 176 214
All
150
35
57
11
24
16
29
30
30
The improvements for the Female Economically Active were even more emphasised.
Females with Fiji Junior and Below improved by 118% compared to 19% for Male
Economically Active. And the number of Females with Senior Secondary or above
had improved by 280%, compared to the 160% improvement for Males.
There is little doubt that this extremely significant improvement for Females would
have fed into corresponding improvements in salaries and positions in the Labour
Force.
___________________________________________________________________________
105
The 2007 Census will give far more accurate data on educational attainment and changes over the
last decade.
124
Chapter 10
Changes in Economically Active 1982 to 2004-05
Incomes of Wage Earners
While there was very little incomes data given in the 1982 EUS there were average
gross weekly incomes given for Wage Earners.106 These are compared in Table 10.11
with the estimated Gross Weekly Wages derived from P12M data.
It may first be noted Table 10.11 Numbers and Average Incomes of Wage Earners
Female
Male
All
that the numbers of
Numbers of Wage Earners
% Fem
Female Wage Earners
1982
8.2
47.0
55.2
15
have increased both
2004-05
39.3
106.8
146.1
27
absolutely and relatively
far more than that of
Gross Weekly Wages
% GG
Male Wage Earners.
1982
39.00
54.20
52.00
-28
The numbers have
2004-05
118.40
135.19
130.68
-12
increased five fold from
about 8 thousands in
1982 to 39 thousand in 2004-05.
The Female share of all wage earners increased from 15% in 1982 to 27% in 2004-05.
The incomes gap in that period also significantly declined from -28% to -12%.107
For Female Wage Earners at least, there has been some progress on both the
employment and incomes fronts.
___________________________________________________________________________
106
It was not stated whether these are true averages derived from actual weekly incomes, or estimates
derived from the ranges. The former is assumed.
107
These estimates of average incomes are not “average incomes for equal time”.
125
Chapter 11 Conclusions
Chapter 11
Conclusions
For stakeholders interested in fostering gender equality in the Fiji economy, a most
essential pre-requisite is having gender disaggregated data of good quality and
coverage.
The 2004-05 EUS has enabled this study to bring out an extensive set of relevant,
gender-disaggregated data on employment, under-employment, unemployment, and
incomes.
For the first time, there is solid data on Household Work, which has been analysed for
the whole population, including those regarded as Economically Active. Incomes
have also been analysed, in totality and in relation to actual time worked by Females
and Males.
The findings are mixed- some indicating the usual expected large negative gender
gaps, but some indicating virtual gender equality and evident progress from earlier
years. The picture on gender disparities in incomes, can be quite misleading, unless
actual time worked is taken into account.
What also stands out is that the usual examination of employment and unemployment
gives a completely misleading picture of gender disparities in under-employment and
unemployment, if household work is not taken into account.
The overall educational profiles of the Economically Active Females and Males are
quite similar in terms of the proportions of their groups with highest educational
attainment, although younger working Females have a significantly better educational
profile than older working Females. On a priori grounds there would appear to be no
“educational attainment” reason for the existence of gender disparities in incomes.
Without Household Work
As would be expected, with Females comprising almost of all the category of
household workers (whose number is larger than the Female Economically Active),
then Females comprise only 31% of those regarded as “Economically Active”. This
is still a major improvement from the situation prevailing in 1982.
Females however comprise a much larger proportion of Family Workers and
Community Workers, two groups who receive low incomes, and are very much
under-employed.
The actual time worked data (both the Last 7 Days and Previous 12 Months datasets)
worked by the Economically Active reveals that “Economically Active” Females, are
126
Chapter 11 Conclusions
working fewer hours in the day and fewer days in the year. They are therefore far
more under-employed and effectively unemployed than Males. Some groups (Female
Family Workers, Self-Employed and Community Workers) are so much underemployed as to be virtually disguised unemployment.
Overall, simply looking at “paid work) data would imply that Economically Active
Females work some 10% (L7D dataset) to 18% (P12M dataset) less than
Economically Active Males.
With Household Work
But when Household Work by the Economically Active persons is also taken into
account, then Females are shown to work between 26% and 31% more than Males.
And Female Economically Active persons also have a lower “Gender-Neutral
Effective Rate of Unemployment” (11%) than Males (14%)- a difference of 21%.
Overall, while the typical national accounts might indicate that Females contributed
only 27% of the total time worked by the Economically Active, the reality is that they
contributed 76% of the time devoted to Household Work, and hence 52% of all time
worked in the economy (inclusive of household work).
A major part of the problem of course is that household work is typically unpaid. If a
moderate price ($30 per week) were placed on full-time Household Work, then the
Household Work would contributes an extra $478 millions of income to the economy
which compares well with the GDP contributed by either tourism or sugar.
A very large 16% would be added to the monetary value of the total income of the
Economically Active, as recorded by the EUS. And the monetary value of Female
contribution to the economy would rise by 47%. The Female share of the total
monetary value would still however be a low 34%, given that $30 per week is a mere
half of what could be considered to be a “poverty line” for workers.
The bottom line remains after all and is said and done: despite doing 52% of all time
work done in the economy, Females receive only 27% of all income earned.
There is clearly much at fault with any system of national accounts which ignores an
element which clearly contributes a very significant part of the goods and services
being produced to sustain the economy.
With Female household workers virtually as educated as the Self-Employed, there is
no reason to suppose that they Females household workers could not earn more if they
did enter the Labour Force.
One of the key findings of this study is the unfair sharing of household work between
Economically Active Males and Females. While Economically Active persons did an
average of 14 hours of household work per week, the average for Females was 26
hours, while for Males was 9 hours. This unfair burden on Females cannot but imply
127
Chapter 11 Conclusions
serious time constraints on Females’ ability to devote time and effort to personal
development (careers, leisure, etc).
There is an interesting arithmetic twist to this which arises out of the fact that there
are twice as many Economically Active Males than Females. If all Economically
Active Males were to do on average an extra 5 hours per week, the Economically
Active Females would on average have an extra 12 hours to devote to personal
development. (The average for all Economically Active would remain at 14 hours per
week). What is asked of Males is not as much as it might seem.108
Incomes Received and Incomes Per Standard Time Worked
As would be expected, the study has revealed that on aggregate there is a sizeable
negative gender gap in average incomes earned by Economically Active persons with
Females earning some 19% less than Males.
However, the data shows than when the actual time worked is also taken into account,
then the Gender Gap in Average Income per Standard Year is reversed, becoming a
positive 8% in favour of Females.
If the qualifications and occupations of the Economically Active persons are taken
into account, then the Gender Gap in Average Income per Standard Year for the better
educated tends to be either statistically insignificant, or even positive in favour of
Females (especially for those with Certificate or Diplomas). Females in some
employment status (such as salaried persons) appear not to be disadvantaged at all.
There is considerable evidence that for the less qualified and occupations/industries
not requiring higher qualifications, Female Economically Active persons do have
large negative gender gaps, with many being statistically significant.
The data indicates that possibly the most vulnerable Females would be those who are
working in the informal sector (as identified with those who do not pay FNPF). There
may be room for State action to protect such disadvantaged workers.
Female Workers in Poverty
Using a conservative $60 per week as a standard for the incidence of poverty for an
income-earning individual, the data indicates that Female workers are far more
vulnerable to poverty than Males, with 44% earning below the poverty line, in
contrast to 32% of Male Economically Active.
The incidence of poverty for Females Not Paying FNPF was a much higher 67%,
compared to 48% of Males not paying FNPF.
___________________________________________________________________________
108
A superficial assessment might have been that if Males worked 5 hours extra then Females would
gain 5 hours. This would not be correct arithmetically.
128
Chapter 11 Conclusions
What is evident that the incidence of poverty for Economically Active Females drops
sharply with rising educational attainmen, declining from 78% for those with only
Primary Education to 6% for those with Degrees.
Females being Economically Active is very good for the households they are in,
tending to push their households into the higher deciles ranked by Household Income
per Adult Equivalent. Thus the Bottom 3 deciles (containing the poorest 30% of the
population) contains only 14% of Economically Active Females (but 23% of the
Economically Active Males); while the Top 3 deciles (containing the top 30% of the
population) contains 47% of the Economically Active Females (and 35% of the
Economically Active Males).
It may be noted that Female household workers do not push the families up the deciles
because there is no monetary value given to the services provided by the household
workers. There is a net monetary advantage to households if their Females are
Economically Active, in the usual sense of earning incomes, in cash or kind.
Future Action
This study points to a number of areas in which action can be taken by gender issues
stakeholders to further progress Fiji towards the objectives of gender equality as
articulated in the international agreements (like CEDAW) to which Fiji is party.
These are detailed on p.xii.
The Fiji Islands Bureau of Statistics may be assisted by stakeholders to do more
frequent employment and unemployment surveys, whose methodology is improved in
the areas suggested. The FIBoS can also undertake additional surveys such as “time
use” surveys which can provide additional information which enables us to
understand better how the entire society uses its available time for economic, social,
sports, and leizure activities.
The data on statistically significant negative gender gaps throughout the economy
would suggest that Government should examine what role it can play to reduce these
gender disparities through public sector incomes policy initiatives. The least that can
be done is to more efficiently use the mechanisms that already exist, such as the
Wages Councils, to improve the lot of Females (and Male) workers who are very
vulnerable in the informal sector, not protected by unions.
There is also considerable other research that needs to be undertaken. How do
Females and Males spend their available time in addition to paid and household work:
in sport, around the grog bowl, religious gatherings, video/TV, reading, other leizure
activities?
There is also no data on how incomes earned within the family are shared within the
family.109 Do full-time household workers get their fair share of income earned?
There is little data on how family wealth is passed on as inheritance to the children.
Are boys favoured over girls, as is generally the case in Indo-Fijian families?
___________________________________________________________________________
109
Sunil Kumar (USP Economics Faculty) is doing an interesting PhD in precisely this area.
129
Chapter 11 Conclusions
Ultimately, there is an urgent need to change attitudes of Males and Females in
society towards action that is necessary to progress society towards gender equality.
There is a role that private sector employers can play, in terms of considering “equal
pay for equal work” for Females in their employment.
There is room for public campaigns to encourage Male Economically Active persons
(men and boys) to do that little extra in household work that can take unfair burdens
of their Females partners in their households.
There is room for publicity campaigns to encourage Females to pursue higher
education, and to be selective towards labour market industries and occupations where
gender disparities are low. To some extent this is already occurring with Females
being a relatively higher proportion of the salaried persons.
Policy makers might wish to develop more gender-specific “National Development
Goals” or “NDGs” as part of their overall framework monitoring the MDGs. New
more relevant indicators are suggested by this study, which looks at gender gaps in a
variety of critical areas: proportions of economically active, rates of underemployment and unemployment, average incomes per standard period, hours of
household work done by the Economically Active, and proportions covered by social
security such as the FNPF.
While some indicators may be difficult to obtain data on annually, some base marks
have been set through this analysis of the 2004-05 EUS. It is certain that the problems
of gender inequality are not going to be eradicated over-night or even within the next
five years. Even five year assessments of the NDG indicators suggested here will be
useful in assessing the progress of our society towards genuine gender equality for
women.
The 2004-05 Employment and Unemployment Survey has made an excellent start and
provided many baseline indicators for the future.
Stakeholders in gender equality need to facilitate the efforts of FIBoS and other
researchers such as at the University of the South Pacific in this important area.
130
Annex A Paid Time Worked Over Last 7 Days
Annex A
Paid Time Worked Over the Last 7 Days
Employment, Under-employment and Effective Unemployment
Table A.1 gives
the Usual Activity
of
the
Economically
Active over the
Last 7 Days.
Different from the
P12M table on
“Usual Activity”
is that Table A.1
lists some 8599
workers who had
a job but were not
at work.
Also new is that
there were also
some
4314
persons
who
thought that they
would
be
“working
soon”.110
Table A.1 Usual Activity over Last 7 Days
Male
All % Fem
Activity L7D
Female
A Wages
36764 101295 138060
27
B Salary
15922 27226 43148
37
C Employer
1029
2365
3395
30
D Self-employed
18489 58321 76811
24
E Family Workers
17642 24267 41909
42
F Community Workers
1158
2357
3514
33
G Job/Not At Work
2876
5723
8599
33
G Working soon
2755
1559
4314
64
H Retired/Over 54
11645 12922 24567
47
I Handicapped
1852
2151
4002
46
J Other/Inactive
2727
3641
6368
43
L Household Work
126143
2266 128410
98
M NAS/Underage
36052 40763 76814
47
N Student
115109 120359 235468
49
T NAS/school age
2680
2344
5025
53
U Available/No work
6513
9981 16494
39
(blank)
425
630
1055
40
All
399781 418170 817952
49
Econ. Active A to G
93880 221554 315434
30
Hours Worked Last 7 Days
The L7D data set also
Table A.2 Hours worked L7D (by gender)
gives a good indication
All Working Persons
of the
nature
of
0 to 10 to 20 to 30 to
underemployment over
9
19
29
39 40 + All < 20
the Last 7 Days. Table
Female 20
10
11
18
41 100 29
A.2
gives
the
Male
14
9
11
19
48 100 22
distribution of Females
All
15
9
11
18
46 100 25
and males by the
number of hours worked
for pay by those At Work, over the Last 7 Days.111
The numbers in the first two columns indicate that while large proportions of both
Male and Female economically active do not work a full 40 hour week, a higher
___________________________________________________________________________
110
No further questions were asked, and it is unclear how realistic these expectations were.
The Annual Employment Survey of the FIBoS indicates that the average hours worked by wage
earners is generally much higher than 44 hours per week which would suggest that there are large
numbers of persons who work more than 50 hours per week. The EUS questionnaire therefore needs to
discriminate more at the upper end of the work scale.
111
131
Annex A Paid Time Worked Over Last 7 Days
proportion of Females work shorter hours per week than do Males. 29 percent of
Females worked less than 20 hours per week compared to 22 percent of males.
At the other end of the scale, 41% of Females worked more than 40 hours per week
compared to 49 percent of males.
Hours Worked L7D (by ethnicity)
Table A.3 indicates
that
there
are
significant
ethnic
differences for both
Females and Males.
Some 35% of Fijian
Females worked less
than 20 hours per
week compared to
20% of Indo-Fijian
Females.
There
were similar ethnic
differences
for
Males as well.
Table A.3 Hours worked L7D (gender and ethnicity) (%)
0 to 10 to 20 to 30 to
9
19
29
39 40 + All < 20
Ethnicity
Female
Fijian
23
12
12
16
37 100 35
Indo-Fij
15
5
9
23
47 100 20
Others
11
7
11
18
53 100 18
Rotuman 22
0
0
40
37 100 22
Male
Fijian
16
12
14
17
41 100 28
Indo-Fij
11
5
7
19
58 100 16
Others
11
5
16
23
44 100 17
Rotuman 13
16
11
15
44 100 30
To
understand
ethnic difference in Female hours worked , it is useful to first understand that there are
ethnic differences in percentages engaged in different kinds of employment. Table
A.4 indicates that there are much higher proportions of Fijian Females who are in
Self-employment (23%) and Family Work (24%) compared to 16% and 12%
respectively for Indo-Fijian Females.
On the other Table A.4 Perc. of Females (by ethnicity and Employment Status L7D)
hand,
some
Fijian
Indo-F Others Rotuman
52% of IndoA Wage earner
34
52
32
38
Fijian Females
B Salary earner
16
18
36
42
are
Wage
C Employers
1
2
4
0
Earners
D Self-employed
23
16
12
6
compared to
E Family worker
24
12
13
14
only 34% of
F Comm. Worker
1
1
3
0
Fijian
All
100
100
100
100
Females.
Wage Earners
(and Salary Earners) have much lower levels of under-employment than the other
categories.
It may also be noted that fully 80% of economically active Rotuman Females are
either Wage Earners or Salary Earners, 70% of Indo-Fijians, and 68% of Others.
132
Annex A Paid Time Worked Over Last 7 Days
Hours Worked L7D (by Employment Status)
Table A.5 indicates that for Wage Earners and Salary Earners, Females and Males
have a very similar profile of hours of work per week.
Thus 14% of both Female
and Male Wage Earners
worked less than 20 hours
per week (last column
Table 4.5). And 82% of
both Female and Male
Wage Earners worked 30
or more hours per week.
Amongst Salary earners
6% of Females and 9% of
Males worked less than
20 hours per week, while
65% of Females and 66%
of Males worked more
than 40 hours per week.
There are significant
gender differences for the
other
categories
in
working less than 20
hours per week: Female
Employers (21%) and
Male (12%), Female Selfemployed (42%) and
Male (28%);
Family
Workers (70% and 59%
respectively);
and
Community
Workers
(44%
and
36%
respectively).
Table A.5 Hours of Work L7D (by Employment Status)
(%)
0 to 10 to 20 to 30 to
9
19
29
39 40 + All < 20
Wage Earner
Fem 10
3
5
24
58 100 14
Mal 11
3
4
18
64 100 14
All
11
3
4
19
62 100 14
Salary Earner
Fem 5
1
1
28
65 100
6
Mal
7
2
2
23
66 100
9
All
6
2
2
25
66 100
8
Employer
Fem 21
0
10
17
52 100 21
Mal 11
1
8
10
69 100 12
All
14
1
9
12
64 100 15
Self-employed
Fem 23
19
22
13
23 100 42
Mal 13
14
21
22
29 100 28
All
16
15
21
20
28 100 31
Family Worker
Fem 48
22
20
4
6
100 70
Mal 31
27
23
10
9
100 59
All
38
25
22
7
7
100 63
Community Worker
Fem 41
4
30
18
8
100 44
Mal 30
6
27
3
34 100 36
All
34
5
28
8
25 100 39
133
Annex A Paid Time Worked Over Last 7 Days
Calculating Effective Rates of Under-employment and Unemployment for L7D
For Economically Active persons, both the data sets for work done during the L7D and P12M
may be analysed to derive statistics which measure the extent of under-employment, and
effective unemployment for the economy.
(1) The Labour Force = (Economically Active persons) + (the Unemployed)
LF
=
EcAc
+
U
(2) The Rate of Formal Unemployment = (the number of Unemployed)/(Labour Force)
= U/LF
In the L7D section, the EUS asked for the number of hours worked over the previous 7 days.
The answers were given in ranges (<10) (10-19) (20-29) (30-39) (40+). For anyone working
less than 40 hours, the mid-points of these ranges were: 5, 15, 25, 35.
Assuming that a full-time worker worked at least 40 hours per week, then anyone working in
the ranges below that would have a shortfall or “deficit” which reflects the degree of underemployment.
eg. anyone working <10 hours (=5) would have a deficit of 35 hours.
anyone working 20-29 hours (= 25) would have a deficit of 15 hours.
anyone working 30-39 hours (= 35) would have a deficit of 5 hours.
Only the deficits for all Economically Active persons working less than 40 hours is then
summed and divided by 40, to obtain the “Full Time Equivalent Persons” representing the full
extent of the under-employment (call it UE number of FTEPs)
The Effective Rate of Under-employment (ERoUnder) = UE/(EcAc)
But we already have U = Formally Unemployed persons
Hence the total effective number of unemployed persons = U + UE.
Hence the Effective Rate of Unemployment = (U + UE)/(LF)
134
Annex A Paid Time Worked Over Last 7 Days
Effective Rates of Under-employment and Unemployment L7D
If it is assumed that a full-time employed person works a minimum of 40 hours per
week, then it is possible to aggregate the “deficits” for each person into equivalent
under-employed and unemployed “full-time persons”.112
Table A.6 indicates that Females
are more unemployed by every
indicator- 66% higher rate of
Formal
Unemployment,
18%
higher Effective Rate of UnderEmployment, and overall, 22%
higher
Effective
Rate
of
Unemployment.113
Table A.6 ERoUnder and ERoU L7D
Female Male All % GG
RoFU
5.9
4.1
4.6
44
ERoUnder
27
23
24
18
ERoU
33
27
29
22
These are all extremely high rates of unemployment, largely driven by the very high
rates of Under-employment in some employment categories, with fully a third of
Economically Active Females effective unemployed (as indicated by the Last 7 Days
data).
Before one examines the unemployment disaggregations by other variables, it is
important to examine the patterns of under-employment associated with the
Employment Status of Females and Males, as there are significant differences.
By Employment Status
Table A.7 gives the
ERoUnder statistics by
the Employment Status
over the Last 7 Days,
by gender.
Table A.7 ERoUnder last 7 Days (by Empl.Status L7D)
Empt. Status L7D
Female Male All % GG
A Wage earner
16
15
16
4
B Salary earner
9
11
10
-19
C Employer
24
15
18
54
D Self-employed
42
31
34
32
E Family worker
64
54
58
16
F Community Worker
51
40
44
25
All
30
24
26
24
Except
for
Salary
Earners, Females have
a higher ERoUnder for
all
employment
categories with large
gender differences in all
except Wage Earners.
While the Male ERoUnder are also quite high, those for Females are even higher, the
difference in aggregate being 24%. For specific Employment Status, even higher:
64% for Female Family Workers; 51% for Community Workers; and 42% for the
Self-employed.
___________________________________________________________________________
112
Thus a person working 30 hours per week would have a deficit of 10 hours per week. All the
deficits are then aggregated, and divided by 40 to estimate the number of “effective full-time persons”
represented by the under-employment.
113
Note that the rates of formal unemployment (which is as % of the Labour Force) and rates of underemployment (% of the Economically Active excluding the unemployed) are not additive.
135
Annex A Paid Time Worked Over Last 7 Days
There are significant ethnic and regional differences in their internal distribution of
Employment Status categories.
By Ethnicity
Table A.8 indicates by and large for
all ethnic groups, Females have
higher RoFU, ERoUnder, and ERoU.
While the RoFUis highest for Female
Indo-Fijians (9%), ERoUnder is
highest for Female Fijians (at 35%).
Putting the two effects together,
Fijian Females end up having the
highest ERoU of 35%, compared to
29% for Female Indo-Fijians.
With Fijian males also having a very
high ERoUnder (29%) compared to
18% for Indo-Fijian males, the ERoU
gender gap is highest for Indo-Fijians
(at 36%) compared to 15% for
Fijians. The ERoUnder and ERoU
are high for all ethnic groups for both
Females and Males.
By Region and Gender
Table A.9 indicates the severity of the
under-employment and unemployment
in the rural sector – which has an
overall aggregate ERoUnder of 38%
and ERoU of 41%. Of the rural
Economically Active, Rural Females
have the highest rate of Underemployment (41%) compared to 29%
for Rural males- a gap of 38%.
Table A.8 Rates of Under-Employment and
Unemployment (Ethnicity and Gender)
Ethnicity Female Male All % GG
RoFU
Fijian
4
4
4
19
Indo-Fij
9
5
6
83
Others
6
3
4
126
Rotuman
0
3
2
All
5.9
4.1 4.6
44
ERoUnder-Employment
Fijian
35
29
31
18
Indo-Fij
23
18
19
31
Others
21
23
22
-7
Rotuman
27
26
26
4
All
30
24
26
26
ERoUnemployment
Fijian
35
31
32
15
Indo-Fij
29
21
23
36
Others
25
24
24
5
Rotuman
21
25
24
-15
All
32
26
28
24
Table A.9 ERoUnder and ERoU (by region)
Female Male All % GG
RoFU
Rural
5
2
3
152
Urban
6
6
6
3
ERoUnder-Employment
Rural
41
29
32
38
Urban
21
17
18
20
ERoUnemployment
Rural
43
30
34
41
Urban
24
22
23
12
The overall effect is that Rural Females have an Effective Rate of Unemployment of
43% compared to 30% for Males, and 24% for Urban Females.
136
Annex A Paid Time Worked Over Last 7 Days
Under-employment and Unemployment by Division and Gender
Table
A.10
indicates
some
interesting contrasts between the
four divisions with respect to Formal
Unemployment, Under-Employment
and Effective Unemployment.
Females in the Western Division
have the highest Rate of Formal
Unemployment (12%) with neglible
rates in Eastern and Northern
divisions.
However, Females in the Northern
division have the highest rate of
Under-employment (46%) followed
by Easter division Females with
38%. In contrast, Western Division
Females had a relatively low
ERoUnder of only 22%- almost
exactly the same as the Western
division males.
Table A.10 Rates of Under-Employment and
Unemployment (Ethnicity and Gender)
Female Male All % GG
RoFU
Central
4
4
4
2
Eastern
0
1
1
-54
Northern
2
3
3
-35
Western
12
5
7
149
ERoUnder-Employment
Central
27
20
23
35
Eastern
38
41
40
-7
Northern
46
30
35
55
Western
22
22
22
1
ERoU
Central
28
22
24
26
Eastern
36
39
38
-7
Northern
46
32
36
45
Western
31
26
27
22
Putting the two effects together, Northern division Females aggregated to the highest
ERoU of 46%, with a gender gap of 45% with the Males in Northern division. Eastern
division Females had an overall EroU of 36% (although the Eastern division males
had an even higher 38% ERoU.
Hours worked by Industry
Table A.11 gives the profile of hours worked, the ERoUnder, and the numbers
employed, by industry and gender. It may be noted at the outset that Casual Workers
are often part-time workers, and they are also the first to be laid off when an industry
has a down-turn, for instance due to events such as coups, or economic sanctions by
external sources. Hence the rates of under-employment and the numbers of workers
involved are important in understanding the possible extent of the impact of casual
and part-time workers being laid off, for instance, understanding any differential
gender impacts, if they exist.
Focusing only on industries with significant Female employment (last column of
Table A.11), it may be seen that the largest employer of Females (Hotel, Retail and
Restaurants with some 27000 Female workers) had a Female ERoUnder of 25%
compared to the Male ERoUnder of 19%.
137
Annex A Paid Time Worked Over Last 7 Days
Table A.11 Hours worked, ERoUnder and Numbers Employed (by Industry and Gender)
0 to 10 to
20 to
30 to
40 +
ERo
No
9
19
29
39
Sex
All Under % Diff LF L7D
1 AgForFishing
Fem
44
23
19
5
9
100
61
62
19414
Mal
17
18
22
23
20
100
37
68274
All
23
19
21
19
18
100
43
87687
2 Mining&Quarrying
Fem
0
0
0
0
100
100
0
na
176
Male 15
0
0
4
82
100
13
3046
All
14
0
0
4
83
100
12
3222
3 Manufacturing
Fem
15
7
13
20
45
100
25
-10
13697
Mal
24
5
5
13
53
100
28
28582
All
21
6
8
15
50
100
27
42279
4 Elect & Water
Fem
26
0
0
51
22
100
29
343
167
Mal
5
0
2
10
83
100
7
2341
All
7
0
2
13
79
100
8
2508
5 Construction
Fem
0
4
0
23
73
100
5
-61
808
Mal
9
5
3
14
70
100
14
15129
All
9
5
3
14
70
100
13
15938
6 Hotel, Retail, Rest.
Fem
15
9
12
15
50
100
25
32
26705
Mal
11
7
9
15
59
100
19
38294
All
13
8
10
15
55
100
21
65000
7 Transp.Stor.Comm.
Fem
5
8
1
32
54
100
14
42
2834
Mal
6
2
2
15
74
100
10
18964
All
6
3
2
17
71
100
10
21798
8 Fin. Real Est. Business
Fem
17
2
0
23
58
100
19
121
3392
Mal
6
1
3
16
74
100
9
6585
All
10
1
2
19
69
100
12
9977
9 Comm. Soc.& Pers. Serv.
Fem
11
3
6
30
50
100
18
12
24161
Mal
9
4
6
25
56
100
16
34749
All
10
4
6
27
54
100
16
58910
FIJI
15
9
11
18
46
100
26
307317
Agriculture, Forestry and Fisheries had the highest aggregate rate of underemployment (at 43%), with Females having a very large 67%, some 62% higher than
the Male rate of 37%.
138
Annex A Paid Time Worked Over Last 7 Days
While Manufacturing had the next highest aggregate ERoUnder of 27%, Females had
a slightly lower rate of 25% compared to the Male rate of 27%.
Commerce, Social and Personal Services (with some 24000 Female workers, had a
Female RoUnder of 18% compared to the Male rate of 16%.
By Occupation Groups
With Skilled Agriculture, Forestry & Fisheries workers having an aggregate
ERoUnder of 52%, Females had a much higher 62% compared to the 41% for Males
(Table A.12).
Females
are
more
under-employed than
Males in all the major
occupational
groups
except Professionals.
The gender gap is
widest in Craft and
Related Workers, where
Females
had
an
ERoUnder of 43%,
while Males had only
13%.
Table A.12 ERoUnder (by Major Occupational Groups)
Occupation L7D
Fem Mal All % GG
1 Senior Officials & Managers
17
13 14
25
2 Professionals
12
16 15
-26
3 Technicians&Ass. Professionals
18
18 18
4
4 Clerks
10
8
9
30
5 Service Work.Shop&Mkt Sales
16
10 13
60
6 Skilled Agr,&Fish. Workers
62
41 45
52
7 Craft & Related Workers
43
13 19
228
8 Plant&Machine Op. and Assemb. 11
11 11
-1
9 Elementary Occupations
36
29 31
23
All
30
24 26
25
Females in Elementary Occupations had an ERoUnder of 36% compared to the 29%
for Males.
Of interest is that Clerks (9%) and Plant and Machine Operators and Assemblers
(11%) had the lowest ERoUnder of all the occupation groups, while even Senior
Manager and Professionals had 14% and 15% under-employment respectively.
139
Annex A Paid Time Worked Over Last 7 Days
Comparison of ERoUnder and ERoU for L7D and P12M data
Table A.13 gives a comparison of the Under-Employment and Unemployment results
obtained from the data on work done over the Last 7 Days and the results from the
data on work done over the Previous 12 Months.
Overall, the P12M data indicates a larger gender gap for all three criteria. In going
from the L7D data to the P12M data, while the rates for Formal Unemployment
appear lower for both Females and Males, that for Under-employment is higher for
Females (27% and 31%), but lower for Males (23% and 19%). The gap is
consequently higher (18% and 63%).
This is likely to be a consequence of
the fact that Males have a higher
share of Economic Activities 2 and 3,
than Females. Data on the work done
over the Last 7 Days may not be
picking up work done in secondary
and tertiary activities, nor seasonal
work.
Table A.13 ERoUnder and ERoU:
comparison of results for L7D and P12M
Ethnicity
Female Male All % GG
Last 7 Days Data
Rof FU
5.9
4.1 4.6
44
ERoUnder
27
23
24
18
ERoU
33
27
29
22
Previous 12 Months Data
RoFU
6.5
3.5 4.5
84
ERoUnder
31
19
23
63
Full ERoU
35
22
26
62
Overall therefore the full Effective
Rate of Unemployment for Females is
a higher 35% (compared to 33% in
the L7D data) and a lower 22% for Males (compared to the 27% in the L7D data).
The gender gap is therefore much higher (62%) using the P12M data, compared to the
22% using the L7D data.
If the objective of the exercise is to obtain statistics on the full year of economic
activity, then the results using the P12M data are more accurate than the results using
the L7D data.
140
Annex B
Annex B
Gender-Neutral Effective Rates of Under-Employment and Unemployment
Gender-Neutral Effective Rates of Under-employment
and Unemployment (including Household Work)
The estimates provided in this Annex are somewhat higher than those provided using
the Previous 12 Months data.
Recollect that the P12M data gives higher estimates of work done by the
Economically Active (because it includes secondary and tertiary activities), than that
obtained from the L7D data. Hence the estimates of Effective Under-employment and
Effective Unemployment will be higher, using the L7D data (even when Household
Work is added).
Under-employment Over L7D
As before, the economically active
persons are considered to be underemployed if their total work done was
less than 40 hours per week.
Table B.1 GN-ERoUnder Over L7D
(by Gender and Employment Status)
Female Male All
Wage Earners
7
14
12
Salary Earners
4
9
7
Employers
12
11
11
Self-employed
9
21
18
Family Workers
11
31
21
Community W.
16
48
23
All
8
17
14
Table B.1 indicates that while the
aggregate ERoUnder due to underemployment is 14%, that for Females is
now only 8% while that for Males is
twice that at 17%.
While there is virtually no gender gap
for Employers, there are significant
differences for all other Employment Statuses, especially for the Self-employed,
Family Workers and Community Workers.
It must be noted therefore that the Female ERoU for the Economically Active is lower
for all categories (except Employers) when household work is taken into account.
This is a major reversal of the picture painted by the previous three chapters.
Gender-Neutral Effective Rates of Unemployment L7D (adjusting for HH Work)
To estimate the
Table B.2 Gender-Neutral Rates of Under-employment and
Total Rate of
Unemployment Over Last 7 Days
Unemployment,
%
the number of
Female Male All GG
equivalent persons
Rate of Formal Unemployment
5.9
4.1 4.6 31
unemployed due
GN-ERoUnder
8
17
14 -113
to the underGN-ERoU
14
21
19 -50
employment
is
added
to
the
numbers of persons formally unemployed.
141
Annex B
Gender-Neutral Effective Rates of Under-Employment and Unemployment
While Females may have had double the Male rate of formal unemployment, Table
7.3 indicates that if time spent on household work is also taken into account, and due
attention is paid to under-employment114, then it is Males who have a higher rate of
“effective unemployment”.
Therefore, using the data for work done over the Last 7 Days, the Gender-Neutral
Effective Rate of Unemployment (taking into account formal unemployment, underemployment and household work), is
14% for Females
21% for Males
19% for all persons in the Labour Force.
Comparison of GN-ERoUnder and GN-ERoU using L7D and P12M data
Table B.3 gives a comparison of the results obtained from the data on work done over
the Last 7 Days and the results from the data on work done over the Previous 12
Months. As previously, Females have a higher Rate of Formal Unemployment than
Males.
However, both the Gender-Neutral Effective Rates of Under-employment and Gender
Neutral Effective rates of Unemployment are lower for Females than for Males- by
both sets of data.
Table B.3
Gender Neutral GN-ERoUnder and GN-ERoU:
As previously
comparison of results for L7D and P12M
explained when
Ethnicity
Female Male All % GG
household
Last 7 Days Data
work was not
Rate of Formal Unemployment
5.9
4.1
4.6
40
being
taken
GN-ERoUnderEmployment
10
19
16
-54
into account,
GN-ERoUnemployment
16
23
21
-33
the Previous 12
Previous 12 Months Data
Months
data
Rate
of
Formal
Unemployment
6.5
3.5
4.5
84
may be taken
GN-ERoUnder
5
11
9
-55
as the lower
GN-EroU
11
14
13
-21
estimates
of
underemployment
and unemployment, as secondary activities of the economically active persons are
being taken into account- with Males being indicated as working more. The statistics
based on the Last 7 Days data may be taken as the upper limits of the rates of Underemployment and Unemployment.
The gender gap is smaller with the Previous 12 Months data, as secondary and tertiary
activities of Males (some possibly seasonal) are given full recognition, which may not
be the case with the L7D data.
___________________________________________________________________________
114
The Formal Unemployment rates (which have the Labour Force as the denominator) cannot be
simply added to the ERoUnder as the latter has the Economically Active as denominator.
142
Annex B
Gender-Neutral Effective Rates of Under-Employment and Unemployment
What is clear is that both sets of data indicate that when all work (including household
work) is taken into account for Males and Females, Females have lower GenderNeutral Effective Rates of Under-employment (by -54% or -55%).
Females also have lower Gender-Neutral Effective Rates of Unemployment (by
between -21% to -33%).
143
Annex C
Annex C
Estimated Income of Top Income Bracket ($150,000 +)
Estimated Mid-point of Top Income Bracket $150,000+
To estimate average incomes where incomes were given in ranges, it is necessary to
attribute a mid-point value which can be used with the frequency distributions.
The easiest estimation procedure is to use the mid-point of the range. This is difficult,
however, for the top bracket- for instance $150,000+ which is given for incomes
earned from Activity 1, 2 or 3 over the Previous 12 Months.
Use of a conservative value (for example $150,000) not only creates a down-ward
bias for all averages, but a greater bias for variables which are likely to have greater
numbers and/or proportions in the higher bracket. For instance, the reality for Fiji’s
income distribution is that a much higher number proportion of Males are likely to be
earning more than $150,000 than Females.
It is useful therefore to estimate more accurate mid-points to the top bracket, from an
independent source.
From the 2004-05 EUS, there were an estimated 326 individuals (38% Female)
earning more than $150,000. These numbers have to be taken as not too reliable as
they were based on a small number of observations.
FIRCA data for 2004 shows the following numbers for those earning more than
$120,000 per annum:
Females
Males:
Number of tax-payers filing
Average Income
154
9
$255,000
$227,000
(89% of Male value)
Using the frequency distribution of Males earning between $120,000 and $150,000 it
was estimated that the average income for Males earning over $150,000 was
$340,000.
Hence a rough estimate for the Female mid-point of the top bracket = $302,000
These are the values therefore used in the estimates of average annual incomes, using
the Previous 12 Months data.
144
Annex D
Annex D
FIBoS Note on the Methodology of the 2004-05 EUS
The Sampling Methodology, Data Processing and
Estimation Procedures for the 2004-05 EUS
This annex explains the sampling methodology and provide an indication of how
information gathered from the Employment/Unemployment Survey of 2004-2005 was
captured and processed prior to the output of the final data set for analysis.
Sampling Design
The survey included all householders in conventional dwellings distributed in
localities within the urban and rural sectors of the four administrative divisions
namely Central, Eastern, Western and Northern.
The target population were
Fiji Citizens and permit
holders
in
conventional
dwelling excluding those
found in households of nonFiji
citizens,
hospitals,
prisons, hotels, temporary
construction sites, boarding
schools
and
similar
institutions.
Table D.1 Distribution of EAs and households by Strata
Hhlds
%
Stratum
# EAs %
1 Central Urban 487 32.0 44156 28.4
2 Central Rural
133
8.7 15626 10.0
3 Eastern Urban
8
0.5
712
0.5
4 Eastern Rural
74
4.9
7182
4.6
5 West Urban
267 17.5 25898 16.6
6 West Rural
328 21.5 35741 23.0
7 North Urban
64
4.2
7281
4.7
8 North Rural
163 10.7 19116 12.3
Total
1524 100 155712 100
A sampling frame was
constructed using the count
of conventional households gathered from the listing stage for HIES 2002-2003 and
information gathered from updates to EAs identified to have had significant changes
in household numbers. In previous surveys the sample was drawn from a sampling
frame taken from the
immediate past census.
Table D.2
Selection of EAs and Households
No of
Hh in
EAs
Hh
This would not have
EAs
Frame Selected Selected
Stratum
been suitable for this
487
44156
1120
1 Central Urban
112
survey, as the last
133
15626
200
2 Central Rural
20
census
was
taken
8
712
30
3
3 Eastern Urban
almost 10 years ago.
74
7182
90
4 Eastern Rural
9
Since then, there has
267
25898
640
5 West Urban
64
328
35741
470
been
considerable
6 West Rural
47
64
7281
170
7 North Urban
17
rural/urban drift, while
163
19116
280
8 North Rural
28
the urban boundaries
1524 155712
3000
Total
300
have
extended
significantly in many
areas, for example, along the Nadi and Lautoka corridor.
Table 1 lists the stratified sampling frame from which a number of EAs, the Primary
Sampling Unit (PSU), were selected per stratum.
145
Annex D
FIBoS Note on the Methodology of the 2004-05 EUS
A sample of 3000 households was targeted using a two stage stratified systematic
sampling. The first stage involved the selection of 300 EAs in proportion to the
number of households in each stratum.
In the second stage, a random sample of 10 households within each identified EA was
selected. This sample, including a reserve pool, was drawn from a list of households
in the EA stratified by household size and ethnicity. Table 2 lists the distribution of
the selected EAs and Households per stratum and frame count.
Estimation Procedure
Based on the sampling design and the stratified two stage systematic sampling
procedure, the weights were calculated as follows. Let
Ni = Total number of Households in i th stratum in EUS Frame 2004
Nij = Total number of Households in i th stratum/j th EA in EUS Frame 2004
Hij = Total number of Households in i th stratum/j th EA during listing
hij115 = Actual number of households surveyed in i th stratum/j th EA
nij = Number of EAs selected in i th stratum
The probability of selection of the jth EA in the ith stratum is given by:
Nij x ni ……………………………………………..(1)
Ni
The probability for any household to be selected is given by:
hij
Hij
……………………………………………..(2)
Then the probability of selection of any household is obtained by multiplying (1) and
(2):
Nij x ni x hij
Ni x Hij
…………………………………..…(3)
The ‘weight’ is then given by the inverse of (3)
i.e.
Ni x Hij
Wij = Nij x ni x hij
Or
(No of Hhlds in i th Stratum j th EA - Frame) x (No. of Hhlds in EA @ Listing
)
(No of Hhlds in EA - Frame) x (No. of Hhlds Surveyed) x (No.of EAs in Stratum)
___________________________________________________________________________
115
This number may be less than the expected 10 per EA because of rejections and incomplete
returns.
146
Annex D
FIBoS Note on the Methodology of the 2004-05 EUS
Thus the Total Population Estimate becomes
Ŷ = ∑ Wij (y)
where
Wij = weight at ith stratum/j th EA for population (y)
Conduct of the Survey
The listing at the second stage of
the sample selection involved
enumerators
visiting
all
households in the selected PSU
(primary
Sampling
Unit)
gathering
information
on
household demographics and
some housing particulars.
Table D.3 Distrib. Of Responding Households
No of Resp.
No of
HH Hhlds
EAs
Stratum
%
37.9 1120 1100
1 Central Urban
112
6.5
200
2 Central Rural
20
190
1.0
30
3 East Urban
3
30
2.8
90
4 East Rural
9
80
22.0
640
5 West Urban
64
640
6 West Rural
47
15.6
470
453
6.2
170
7 North Urban
17
179
8.1
280
8 North Rural
28
234
100.0 3000 2906
Grand Total
300
From the list of households
collected above, a stratified
random sample of 10 households
were identified for enumerators
to administer the main questionnaire. In total there were 2906 households captured
from a list of 300 EAS selected and the distribution per stratum is as follows in Table
3.
Data Processing.
Generally data processing for EUS 2004-2005 started in the field with emphasis on
verifying the consistency of responses and making sure that data structure and counts
corresponded with expected numbers. Each of the four stations was required to
manage its own data collection through to data entry and editing phases before data
was sent to the central workstation in Suva for final checks and compilation of the
database.
Data Verification
Verification of information was done by enumerators on repeat household visits
during the week allocated for completion of the main questionnaire. Checks on age
and relationship of members of the household to the head were some of the initial
tasks in making sure the respondents provided information with a highest acceptable
degree of accuracy and consistency. For working employees, enumerators were able
to access statements of emoluments and at times balance sheets for those involved in
sale of goods and services.
Coding and Data Entry
Once the schedules were returned, coders tallied counts of population and households
by ethnicity. Written responses were standardised. These tasks include coding the
147
Annex D
FIBoS Note on the Methodology of the 2004-05 EUS
main occupation and industry of the employed and those involved in any economic
activity including responses of those not in the labour force.
Separate data entry screens were used for the Schedule 1 – Listing, and Schedule 2 Main schedule using CSPro, a survey data processing software. The data entry
screens had built-in skip patterns derived from the questionnaire, simplifying data
entry and editing.
Editing
Some editing were done in the field and verified at coding stages. However a more
thorough check involved printing all entered information and then verifying against
field records item by item. This ensured that data gathered from the field was not lost
in transition during data entry through to output. Consistency and structural checks
on the data were part of the tasks carried out at the compilation stages of the final
database. The calculated weight was assigned to each record at this edit stage.
Data frequencies on variables also provided an indication of the effectiveness of the
data collection exercise, particularly in checking the required number of households to
be visited per EA. Weighted frequencies further provided an indication of the
accuracy of the data collection and monitoring survey processes as a whole.
Tabulation
The estimates from the survey refer to population of Fiji Citizens and permit holders
of the targeted population indicated above who lived in conventional dwellings or
non-institutional households. Thus the population estimates will be lower than the
usual demographic estimates.
It should be noted that all the survey estimates will be subject to their own sampling
errors.
Given the limited resources, sample size and confidence in the sampling frame, the
Bureau is of the view that the lowest reporting levels (the strata), provide best
estimates where the expected variances of tabulated results are at acceptable levels of
consistency and accuracy.
Household Survey Unit
Fiji Islands Bureau of Statistics
148
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