While Many Women Have Found Low Wage Employment In The Buoyant Garments Sector

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A Presentation at the

UNIFEMUP-SKILLING OF GENDER TRAINERS’ WORKSHOP

NADI, FIJI22-29 MAY, 1999

POPULATION, GENDER & DEVELOPMENT

byWilliam J. House

UNFPA Country Support Team, Suva


I. GENDER BALANCE ANDDEVELOPMENT 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 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.


• 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


• 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 policy-making at local and national levels.


• 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.


• 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.


II. WOMEN IN THE LABOUR FORCE

• Women’s labour force participation (LFP) is the most visible indicator of their contribution to development.


• 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.


• 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.


Males

0

20

40

60

80

100

120

15-19 20-24 25-29 30-39 40-49 50-59 60+

1976

1986

1996

Females

0

10

20

30

40

50

15-19 20-24 25-29 30-39 40-49 50-59 60+

1976

1986

1996


1976 1986 1996

Male LF 146,310 189,929 200,048

% Growth 2.6% 0.5%

Female LF 29,470 51,231 97,723

% Growth 5.7% 6.7%


• 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.


Employment for Cash and Non- Cash in Fiji,1986 and 1996 by Sex

CashEmployment

Non- CashEmployment

TotalEmployment

Males19861996% change per annum 1986- 96

Females19861996% change per annum 1986- 96

Total19861996% change per annum 1986- 96

148,346166,299

1.1

35,27853,015

4.2

183,624219,314

1.8

31,24924,147

- 3.5

8,09837,045

16.4

39,34761,192

4.2

179,595190,446

0.6

43,37690,060

7.6

222,971280,506

2.3Source: National Census Reports 1976 and 1986; Provisional Results of 1996 Census


• 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.


• A key question is the extent to which women workers earn lower wages, on average, and lower returns to human capital attributes


• 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


• 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.


• 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 socio-economic 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.


– 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.


III. REGIONAL CONTRASTS IN ACTIVITY PROFILES

0

10

20

30

40

50

60

70

80

90

100

15-19Males

20-24 25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75+

FSM

PNG

Samoa

Tonga


0

10

20

30

40

50

60

70

80

90

100

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

FSM

PNG

Samoa

Tonga


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.


• 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.


• 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?


•The situation in Fiji in 1996 looks like this:

0

20

40

60

80

100

120

Unallocated Craf t & Related

Trades

Legislators, Senior

Offi cials &

Managers

Elementary

Occupations

Technicians &

Associates

Plant & Machine

Operators

Service Workers Skilled Agric.

Fisheries &

Subsistence

Professionals Unemployed Clerks

Females

Males

2%

12%

18%

20%

28%

28% 35%

40%

42% 44% 55%

1996

Sex composition of Occupations, Ordered by % Female


Clearly, male-dominated occupations are on the left; female-dominated occupations on the right. Interestingly, the female-dominated 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.


• 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.


Legislators, Senior

Offi cials, Managers

Professionals Technicians &

Associates

Clerks Service Workers Agricultural Workers Craf t Workers Plant & Machine Ops. Elemetary Occupations Unemployed

No.

of W

orke

rs (t

hous

ands

)

Females

Males

1996

1%

3%7%

5%

3%

4% 9% 4% 8%

7%

43%

32%

3%

12%

6%

8%

9%

18%

8%5%

Source: Censuses of Population Fiji, 1986 and 1996

Distribution of Males and Females Across Occupations


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.


• 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.


Mean Weekly Earnings by Age Group, Sector and Sex (F$)

Public Sector ParastatalSector

Private Sector Total

AgeGroup

Males Females Males Females Males Females 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

No. ofObs.

1147 638 1009 247 3279 1688 5435 2573


Mean Weekly Earnings by Occupation,Sector and Sex (F$)

Public Sector ParastatalSector

Private Sector Total

Age Group Males Females Males Females Males Females Males Females

Senior ProfessionalsMiddle ProfessionalsClericalSalesServiceArtisansGarment WorkersOther Blue CollarTotalNo. of Obs.

318

234

184139166139

-131199

1147

247

195

145190+

133161+

-110166638

467

293

208213+

149238

-140223

1009

343+

304+

185201+182+243+

-121+

199247

380

236

164112107109

75100136

3279

300

217

1608796825593

1071688

374

251

179118133130

75117165

5435

283

205

16197

111865598

1302573

Note: + Less than 20 casesSource: Fiji Employment Survey, 1997


• 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.


• 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.

Technology

While Many Women Have Found Low Wage Employment In The Buoyant Garments Sector