Presentation on theme: "G:\gen\house\unifem_upskilling1 A Presentation at the UNIFEM UP-SKILLING OF GENDER TRAINERS WORKSHOP NADI, FIJI 22-29 MAY, 1999 POPULATION, GENDER & DEVELOPMENT."— Presentation transcript:
g:\gen\house\unifem_upskilling1 A Presentation at the UNIFEM UP-SKILLING OF GENDER TRAINERS WORKSHOP NADI, FIJI 22-29 MAY, 1999 POPULATION, GENDER & DEVELOPMENT by William J. House UNFPA Country Support Team, Suva
g:\gen\house\unifem_upskilling2 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 womens contributions are significant, have not received priority.
g:\gen\house\unifem_upskilling3 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 womens and mens 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.
g:\gen\house\unifem_upskilling4 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
g:\gen\house\unifem_upskilling5 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.
g:\gen\house\unifem_upskilling6 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.
g:\gen\house\unifem_upskilling7 In what follows an approach is presented to assess womens 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 womens and mens relative economic roles - and analysis of prevailing constraints on their economic productivity - into all aspects of population, human resources and development planning.
g:\gen\house\unifem_upskilling8 II. WOMEN IN THE LABOUR FORCE Womens labour force participation (LFP) is the most visible indicator of their contribution to development.
g:\gen\house\unifem_upskilling9 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.
g:\gen\house\unifem_upskilling10 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.
g:\gen\house\unifem_upskilling13 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.
g:\gen\house\unifem_upskilling15 The figures illustrate the critical importance of womens 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.
g:\gen\house\unifem_upskilling16 A key question is the extent to which women workers earn lower wages, on average, and lower returns to human capital attributes
g:\gen\house\unifem_upskilling17 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 ( 65)/Working Age Adults 15-64 = 298,514 / 476,563 = 63 for every 100 working age adults
g:\gen\house\unifem_upskilling18 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.
g:\gen\house\unifem_upskilling19 The implications of these differing dependency ratios for the welfare of Fijis 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.
g:\gen\house\unifem_upskilling20 –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.
g:\gen\house\unifem_upskilling21 III. REGIONAL CONTRASTS IN ACTIVITY PROFILES
g:\gen\house\unifem_upskilling23 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.
g:\gen\house\unifem_upskilling24 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.
g:\gen\house\unifem_upskilling25 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?
g:\gen\house\unifem_upskilling26 The situation in Fiji in 1996 looks like this: Sex composition of Occupations, Ordered by % Female
g:\gen\house\unifem_upskilling27 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.
g:\gen\house\unifem_upskilling28 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.
g:\gen\house\unifem_upskilling29 Distribution of Males and Females Across Occupations
g:\gen\house\unifem_upskilling30 V. GENDER DIFFERENTIALS IN EARNINGS Womens earnings are inferior to mens 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.
g:\gen\house\unifem_upskilling31 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.
g:\gen\house\unifem_upskilling32 Mean Weekly Earnings by Age Group, Sector and Sex (F$)
g:\gen\house\unifem_upskilling33 Mean Weekly Earnings by Occupation, Sector and Sex (F$)
g:\gen\house\unifem_upskilling34 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.
g:\gen\house\unifem_upskilling35 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.