POPULATION, GENDER & DEVELOPMENT
by
William J. House
UNFPA Country Support Team, Suva
A Presentation at the
UNIFEM
UP-SKILLING OF GENDER
TRAINERS’ WORKSHOP
NADI, FIJI
22-29 MAY, 1999
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I. GENDER BALANCE AND
DEVELOPMENT PLANNING
• The principle of integrating women as well as
men into all phases of the development process
- as participants in policy-making and planning
and as beneficiaries - has become widely
accepted, as reflected in Beijing, Copenhagen
and Cairo conferences
• Yet, development efforts aimed at economic
growth maximization concentrates resources in
the industrialized and monetarized sectors,
spheres dominated by men. The informal and
subsistence sectors, where women’s
contributions are significant, have not received
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priority.
• Governments and international agencies
recognize women must be fully integrated
in the development process for reasons
of national progress as well as equity.
• For successful development planning,
research on women’s and men’s insertion
in the economy, as well as the collection
of relevant data, are essential tools.
This way, inequities in the distribution of
educational and employment opportunities
and of productive assets based on
gender can be corrected.
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• Governments and the international community
have embarked on collecting gender
disaggregated data, e.g.
– measures of family formation and dissolution
– child-bearing and household composition
– formal schooling & vocational training
– labour force participation
– time-use and household work
– health and nutrition
– internal and international migration, and
– participation in political & cultural life
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• Thus, emphasis on:
– Collection of relevant data
– Analysis in order to monitor progress
and identify problem areas
– Incorporate findings on gender
differences into planning and policymaking at local and national levels.
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• The use of biased economic
indicators - missing women in the
EAP, underestimates of value of
subsistence production - will lead to
distorted perceptions of the size
and nature of the economy, and the
stock of human resources.
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• In what follows an approach is presented
to assess women’s economic contributions
to development. I focus on methods of
measurement, data analysis, and on the
relevance of findings to national planning.
The intention is to demonstrate the
importance and usefulness of
incorporating an analysis of women’s and
men’s relative economic roles - and
analysis of prevailing constraints on their
economic productivity - into all aspects
of population, human resources and
development planning.
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II. WOMEN IN THE
LABOUR FORCE
• Women’s labour force participation
(LFP) is the most visible indicator
of their contribution to
development.
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• The “economically active population”
or “labour force” refers to the
“total number of persons available
for the production of economic
goods and services, corresponding
to the concept of income in national
income statistics”. It includes
employed and unemployed and those
seeking work for the first time.
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• How do the rates of LFP differ by
sex? What do they tell us about
differences between men and
women in their labour force profiles
over the life cycle? Fiji is used as
an illustration.
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Females
50
40
30
20
10
0
1976
1986
1996
15-19 20-24
25-29
30-39 40-49
50-59
60+
Males
120
1976
1986
100
1996
80
60
40
20
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0
15-19
20-24
25-29
30-39
40-49
11
50-59
60+
Male LF
1976
1986
1996
146,310
189,929
200,048
% Growth
Female LF
% Growth
2.6%
29,470
0.5%
51,231
5.7%
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97,723
6.7%
12
• The extent to which these dramatic changes in
the market for female labour are attributable
to improved enumeration of female participation
in non-cash economic activities in the most
recent census remains unknown. At face value,
however, it would appear that there has been a
significant increase in the supply of female
labour to the economy during a period when
economic growth was disappointing. While many
women have found low-wage employment in the
buoyant garments sector, many more have had
to be content with non-cash employment in the
rural and urban subsistence and informal
sectors. Their absorption in the subsistence
and informal sector has contributed to the
decline in their rate of unemployment.
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Employment for Cash and Non-Cash in Fiji,
1986 and 1996 by Sex
Cash
Employment
Non-Cash
Employment
Total
Employment
Males
1986
1996
% change per annum 1986-96
148,346
166,299
1.1
31,249
24,147
-3.5
179,595
190,446
0.6
Females
1986
1996
% change per annum 1986-96
35,278
53,015
4.2
8,098
37,045
16.4
43,376
90,060
7.6
Total
1986
1996
% change per annum 1986-96
183,624
219,314
1.8
39,347
61,192
4.2
222,971
280,506
2.3
Source: National Census Reports 1976 and 1986; Provisional Results of 1996 Census
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• The figures illustrate the critical
importance of women’s economic
contributions. Of every 10 EA
persons in Fiji in 1996, 3 are
women. Economic policies and
planned programmes affecting rural
and urban labour markets will
clearly have a direct impact on men
and women as well on women via an
indirect impact through male
workers in the household.
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• A key question is the extent to
which women workers earn lower
wages, on average, and lower
returns to human capital attributes
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• The key pieces of information are relevant
to planners:– The high female LFPR - 39.4% - reduces the
dependency burden on the economically active
population. For example, the dependency ratio
is:
D.R. = Dependents (<15; >65)/Working Age Adults 15-64
= 298,514 / 476,563
= 63 for every 100 working age adults
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• Incorporating economic activity
there are exactly 160 “inactive”
persons of all ages for every 100
EAP: i.e. every EAP supports an
average of 1.6 other person.
• What if the LFPR of females were
0? Then, there would be 287
“inactive” persons of all ages for
every 100 workers, a dependency
burden of almost 3 to one.
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• The implications of these differing
dependency ratios for the welfare of
Fiji’s population is clear - high rates of
FLFP reduce the ratio of dependents to
workers and raise per capita incomes.
For planning purposes it is important that
additional research specify those socioeconomic groups where the dependency
ratios are highest and how they can be
reduced via encouraging more women to
be EA and to have higher productivity
and earnings.
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– In 1996 there was an average of 1.5
children under the age of 15 for every
woman of childbearing age (15-44),
i.e. the child-woman ratio. But this
includes women who have not begun,
or are in the early stages of
childbearing, and older women. Still,
it shows the double burden of
production and reproduction carried by
most women. Planners should design
policies to alleviate it including the
availability of safe and effective
family planning services and the
provision of child care facilities.
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III. REGIONAL CONTRASTS IN
ACTIVITY PROFILES
100
FSM
90
PNG
80
Samoa
Tonga
70
60
50
40
30
20
10
0
15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+
Males
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100
90
FSM
80
PNG
Samoa
Tonga
70
60
50
40
30
20
10
0
10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75+
Females
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IV. OCCUPATIONAL
DISTRIBUTION BY GENDER
• The tendency for women to be
concentrated in particular occupations
and particular sectors of the economy is
universal. But the nature and degree of
occupational segregation based on gender
differ according to the economic, social
and demographic circumstances and to
the cultural sex stereotyping of
particular occupations. Again, let us
view the situation in Fiji.
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• One widely used measure of female concentration in labour
market studies is the percentage of workers in an
occupation who are women; male concentration would be
reflected in the percentage of all workers who are men.
Of course, the percentage of women in an occupation will
partly depend on the share of the labour force which is
female. The greater the female representation in the work
force the more women there are likely to be in any single
occupation. If female concentration were the same in all
occupations, it would be equal to the overall female share
of the total labour force. When attempting to compare
levels of concentration by occupation it is useful to relate
the gender composition of an occupation to the gender
composition of overall employment. Therefore, one widely
utilised ratio is the female percentage of a particular
occupation divided by the female share of the labour force.
A value greater than unity would signify over-representation
of women in this occupation; a value less than unity
indicates under-representation of women in the occupation.
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• This approach to the measurement and analysis of
concentration would answer some of the following types of
questions:
– Is a specific occupation, such as teaching, more likely
to be staffed by men or women? If so, to what
extent? (what percentage of teachers are female?)
– In which occupations are women more/less likely to be
employed?
– In which occupations are men more/less likely to be
employed?
– Is female employment well spread across the
occupational structure, or is it restricted to a limited
number of occupations?
– How well spread or restricted is male employment?
– In which occupations are women over-represented, and
in which are they under-represented?
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•The situation in Fiji in 1996 looks like this:
Sex composition of Occupations, Ordered by % Female
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Clearly, male-dominated occupations
are on the left; female-dominated
occupations on the right.
Interestingly, the femaledominated occupations all have
greater representation of men than
have women in male-dominated
ones. Occupation groups where
women have less than 33% - their
share in the total labour force indicate under-representation.
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• An alternative way of examining male and
female concentration is to present the
distribution of employment by sex across
occupations, indicated as the % of the male and
female labour forces in each occupation. Only
1% and 7% of women were found as Legislators,
Senior Officials, and Managers, and as
Professionals, respectively in 1996. Women are
a little better represented as Production
workers, Sales workers, Clerical workers and
Service workers, which contain between 8% 9% of women in 1996. 7% of all women work in
the traditional occupations as paramedics and
teachers.
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Distribution of Males and Females Across Occupations
32%
1996
Females
No. of Workers (thousands)
Males
43%
18 %
12 %
8%
7%
5%
3%
7%
4%
9%
4%
9%
8%
6%
3%
3%
1%
Le gis la tor s , Se nior
Of f ic ia ls , Ma na ge r s
P r of e s s iona ls
5%
8%
T e c hnic ia ns &
C le r ks
Se r vic e Wor ke r s
Agr ic ultur a l Wor ke r s
C r a f t Wor ke r s
P la nt &Ma c hine Ops .
E le me ta r y Oc c upa tions
Une mploye d
As s oc ia te s
Source: Censuses of Population Fiji, 1986 and 1996
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V. GENDER DIFFERENTIALS IN
EARNINGS
• Women’s earnings are inferior to men’s throughout the
world since average female-male pay ratios are roughly
70-75%, based on daily and weekly reference periods,
and 75-80% based on an hourly reference period. Ratios
are especially low in east and south-east Asian and some
OECD countries where, for all non-agricultural earnings,
the ratio for hourly pay is as low as 68% in Luxembourg
and Switzerland and as high as 88% in Australia and 91%
in Sri Lanka. On a daily or weekly basis the ratio is low
in Hong Kong (70%) and Cyprus (59%) and higher in Sri
Lanka (90%) and Turkey (85%). Unweighted world
averages are 77.8% on an hourly basis, 76.7% on a
daily/weekly basis and 71.6% on a monthly basis. In
Fiji, for the whole of the formal sector, the ratio is
78.9% on a weekly basis and 82.2% on an hourly basis.
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• We now turn to examine the extent of
‘discrimination’ against women in Fiji in
terms of job assignment and relative
pay. The table reports the mean level
of weekly earnings, including overtime
and annual fringe benefits converted to a
weekly basis, by age group, sector and
sex. It demonstrates that mean pay is
consistently higher for men compared
with women and is higher in the public
and parastatal sectors compared with
the private sector.
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Mean Weekly Earnings by Age Group, Sector and Sex (F$)
Public Sector
Age
Group
Males
Females
Parastatal
Sector
Males
Females
Private Sector
Males
Females
Total
Males
Females
14-19
112+
111+
141+
86+
63
58
67
61
20-24
140
135
169
149
99
87
110
104
25-34
174
156
224
208
142
117
161
136
35-44
206
183
245
214
163
121
194
146
45-54
247
225
221
251
192
137
219
185
55+
223
140+
214
102+
190
124+
206
131
Total
199
166
223
199
136
107
165
130
1147
638
1009
247
3279
1688
5435
2573
No. of
Obs.
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Mean Weekly Earnings by Occupation,
Sector and Sex (F$)
Public Sector
Age Group
Senior
Professionals
Middle
Professionals
Clerical
Sales
Service
Artisans
Garment Workers
Other Blue Collar
Total
No. of Obs.
Males
Parastatal
Sector
Females Males Females
Private Sector
Total
Males
Females
Males
Females
318
247
467
343+
380
300
374
283
234
195
293
304+
236
217
251
205
184
139
166
139
131
199
1147
145
190+
133
161+
110
166
638
208
213+
149
238
140
223
1009
185
201+
182+
243+
121+
199
247
164
112
107
109
75
100
136
3279
160
87
96
82
55
93
107
1688
179
118
133
130
75
117
165
5435
161
97
111
86
55
98
130
2573
Note: + Less than 20 cases
Source: Fiji Employment Survey, 1997
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• Male-female pay differentials are
significant after controlling for broad
occupational groupings, particularly at
the higher skill level. Rather than
suggest that male and female employees
receive different rewards from
performing the same job side-by-side, it
is much more likely that more specific
gender-based occupational assignments
explain much of these pay differences.
These issues are being investigated in
more detail using multivariate techniques.
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• The earnings differential is decomposed into (1) a portion
attributable to an “Endowments” effect and (2) a portion
attributable to structural differences in the two earnings
functions, labeled “Discrimination”. Approximately, 40% of the
difference in earnings can be attributed to an ‘endowments’
effect, while 60% can be attributed to ‘discrimination’. It
should be mentioned that no attempt was made to account for
workers’ innate ability, degree of motivation or commitment to
the labour force, quality of education, or union effects. The
advantage of male endowments in the total labour market
comes mainly in the form of greater labour market and firm
specific experience, being employed in the public sector and
being engaged in the high-wage industrial sectors. Men do not
have an advantage in formal education. With respect to the
cause of discrimination, the principal source lies in the size of
the differential in the intercept term in favour of men, which
accounts for 74% of the total hourly earnings differential
between the sexes. The returns to potential experience and
vocational training also favour men.
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