UNFPA COUNTRY SUPPORT TEAM
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The UNFPA Country Support Team for the South Pacific, based in Suva, Fiji, is one of eight regional technical support teams established by the United Nations Population Fund to provide countries with technical backstopping to meet country needs in the population field. In fulfilling this function, apart from field missions, the Country Support Team aims to provide active and close backstopping to the local pool of national experts to promote a more holistic approach to population programmes.
This Discussion Papers series has been initiated by the CST (Suva) in an attempt to establish a dialogue among national population programme personnel on the integrated and coordinated multidisciplinary approach to population.
The present paper was originally prepared for presentation at a Regional Training of Trainers' Workshop organised by the UNIFEM Mainstreaming Project based in Suva. It has been extensively revised for distribution to a wider audience of developme nt planners and population programme managers in the South Pacific region. As the title suggests, Mr William House examines the need for Pacific Island countries to investigate and be concerned about gender inequalities in modern sector employment. The current relative position o f men and women within the sector is an important determinant of how the development process will unfold and affect the future status of women, which will help to determine changes in their demographic behaviour.
The development process in most countries involves a restructuring of the labour force from traditional and subsistence - type rural employment to modern, organised urban employment. For women, this restructuring may increase their disadvantage, t he modern sector takes over economic activities which were traditionally the means by which self-employed women earned their income, such as food processing, retail trade, making of clothes etc., while, at the same time, modern sector employers are said t o favour men as employees. The objective of this paper is to illustrate the need for development planners in Pacific Island Countries (PICs) to investigate and be concerned about gender inequalities in modern sector employment. Some of the issues which need to be investigated, researched and explained are:
Although the modern non-agricultural sector is still relatively small in most PICs, it will be targeted to be the fastest growing sector as development proceeds. The current relative position of men and women within this sector is an important determinant of how the development process will unfold and affect the future status of women which, in turn, will have a profound effect on demographic behaviour and change. It is also a key area where planners can design policies aimed at reducing sex inequalities, since the formal organisations in the modern sector are much more amenable to policy measures than are disparate entities outside of the organised sector, in the subsistence farm sector and in the urban informal sector.
This paper calls for structured, in-depth empirical investigations of labour market processes and practices in the Pacific Island Countries. It suggests that special sample surveys be undertaken of both employees and employers in the PICs. The objective is to obtain data and information on how firms behave with respect to hiring, promoting, training and firing. It would also be of relevance for the researcher to explore differences in turnover and absenteeism rates, pay differentials by sex, and the within-firm occupational segregation of women. To illustrate such an exercise draft questionnaires are presented which might be used in a survey of employers and male and female employees.
These questionnaires would allow the researcher to document the differences in work histories between married women, single women and men. The extent of absenteeism could be measured and some indication of the reasons for breaks would be gauged. Married women's continuity in labour market experience could be measured from these responses. In addition to problems of discrimination within the work environment, the dual roles in the home and at work can often impose severe time constraints on women. The questionnaire explores these issues and the degree of satisfaction with existing working hours. Inequalities between men and women in their access to training would be estimated in the analysis of respondents' answers. Do women receive job and pay promotion less frequently than men, and in what kind of jobs? Is pay discrimination widespread for similar kinds of work?
For policy makers the analysis of this data set could be used to suggest certain changes which would help to correct the severely disadvantaged position of Pacific Island women. It might be expected to shed light on how best to construct a social and economic environment in which married women would find wage employment and domestic responsibilities to be reasonably compatible. The results of such a study would allow some assessment of the use and adequacy of existing child-care facilities and, if necessary, to identify what changes need to be made. Are trade unions hindering or promoting the cause of women in PICs? And how widespread are maternity leave guarantees, particularly outside of the public sector? Is the length of existing maternity leave adequate?
Evidently, the answer to these and many other questions could be revealed by analysis of these illustrative questionnaires. Above all else, they would allow us to go beyond merely describing gender concentration and move forward by helping us understand the social and labour market processes at work. From such an assessment, remedial policy measures might be forthcoming.
The development process in most countries involves a restructuring of the labour force from traditional and subsistence - type rural employment to modern, organised urban employment. For women, this restructuring may increase their disadvantage; the modern sector takes over economic activities which were traditionally the means by which self-employed women earned their income, such as food processing, retail trade, making of clothes etc., while, at the same time, modern sector employers are said to favour men as employees.
The objective of this paper is to illustrate the need for development planners in Pacific Island countries (PICs) to investigate and be concerned about gender inequalities in modern sector employment. Some of the issues which need to be investigated, researched and explained are:
Although the modern non-agricultural sector is still relatively small in most PICs, it will be targeted to be the fastest growing sector as development proceeds. The current relative position of men and women within this sector is an important determinant of how the development process will unfold and affect the future status of women which, in turn, will have a profound effect on demographic behaviour and change. It is also a key area where planners can design policies aimed at reducing sex inequalities, since the formal organisations in the modern sector are much more amenable to policy measures than are disparate entities outside of the organised sector, in the subsistence farm sector and in the urban informal sector.
Such an approach would be in full accord with the recommendations of the 1994 International Conference on Population and Development (ICPD) which, in its Programme of Action, encourages countries to act to empower women and to take steps to eliminate inequalities between men and women. In addition, the ICPD resolved:In the PICs there is a dearth of data and studies concerning the employment situation of women. Recent statistical profiles of men and women in four countries, Tuvalu, Papua New Guinea, Marshall Islands and Cook Islands, generated from the UNIFEM Mainstreaming project, have probably stretched the limit of our knowledge (Booth n.d., a, b, c, d). Evidently, as a basic minimum, other countries need to assemble, collate and interpret data of this nature.
To recapitulate on Booth's findings, supplemented with recent census data, she showed a much lower rate of female labour force participation and a lower mean earnings level than for men. As an example, let us examine data from Tuvalu and PNG. From Tuvalu's most recent Census of Population, while the overall labour force participation rates (LFPRs) of women compare favourably with men, the gender distribution of urban economic activity is markedly different as reflected in Figures 1 and 2 . While 61% of men work for wages or salary in Funafuti, only 32% of women do so. Alternatively, 63% of women do housework, which is included, rather unusually, as an economic activity, over twice the proportion of men. Given the dominance of public sector employment in Tuvalu, the distribution of employees by grade and sex is strategic in gender analysis. Booth (n.d.) shows that all posts in grades 1-4 are occupied by men. In grade 10 (at the bottom), females dominate with 83% of all posts.
As Booth concludes:
| "The average grade for males is 7.5 compared to 8.8 for females. It has been suggested that women are concentrated in the lower grades because they leave government employment before they reach the higher grades. It has also been suggested that even in situations where male applicants have less experience and/or inferior academic qualification, they still manage more frequent promotions". |
While the overall distribution of income by sex in Tuvalu is unknown, mean earnings for males must be significantly higher than for females because of their dominance of senior public sector positions.
In PNG, the situation is illustrated in Figure 3 from the most recent Census of Population of 1990.
The starkest contrast in the gender differential is in urban PNG. In rural areas, the female LFPR is relatively high, since so many women are engaged in the production of primary products, especially foodstuffs, for their own consumption and this qualifies them for inclusion in the labour force. Booth's report (n.d.) shows that, in 1980, only 4% of economically active females were in wage employment, compared to 24% of economically active males. In contrast, 91% of economically active females were engaged in farming and fishing compared to only 66% of economically active males; and females accounted for 54% of those employed in agriculture. Amongst this group, 44% of females earned some cash compared to 49% of males. Just over half of the female employed population was engaged solely in subsistence agriculture, compared to only a third of the male employed population.
The occupational and industrial distributions of those earning money in the urban sector, either from formal employment or from agriculture, show that a quarter of females were in professional or technical occupations, although they were outnumbered by males by more than 2 to 1, and that females occupy only 8% of executive and managerial positions. The largest occupational group amongst females was clerical, accounting for a third of females, although again there were twice as many males. In agriculture, 42% of those earning cash were employed in community and social services, where they accounted for one fifth of all employees. Females accounted for about a third of employees in finance and business and about two-fifths of those earning cash in agriculture.
By 1990, according to a revised (1988) version of the International Standard Classification of Occupations (ISCO), excluding North Solomons Province, only 16% of urban female workers were classified as professionals and technicians and were outnumb ered by over 3:1 males in this occupational group. Only 11% of legislators, senior officials and managers were women. The largest female occupational group in 1990, other than the 'not stated', were clerks, accounting for over 20% of women workers. Wom en made up 48% of this occupation, reflecting an overall slightly improved gender balance in occupations between 1980 and 1990. No doubt little has changed in the intervening years such that mean earnings of females lie well both those of males.
THEORIES OF SEX INEQUALITIES IN THE LABOUR MARKETNeo-classical theories emphasise sex differences in variables which affect labour productivity and labour supply such as family responsibilities, physical strength, education, training, hours of work, absenteeism and turnover in order to explain why women earn less than men. A basic assumption of neo-classical economics is that workers in conditions of competition are paid the value of their marginal product; it then follows from this assumption that observed male-female differentials in earnings are either due to the lower productivity of women or to market imperfections. A separate assumption of the neo-classical 'new home economics' is that families allocate their resources (time and money) among family members in a rational manner which causes females to receive less human capital investments when young and to stay home to take care of the children when older.
Neo-classical theory suggests that women earn less than men because they have lower levels of human capital - mainly education, training and on-the-job experience - and therefore lower labour productivity. For example, because some women interrupt their employment to marry, and then bear and rear children, employers are said to be reluctant to invest in the training of women. Also, for parents and the women themselves there is said to be less incentive to invest in education and training. Periods of withdrawal from the labour force mean that women accumulate less work experience than men and that their skills tend to depreciate more.
According to the human capital approach, discrimination would be said to occur if employers pay different wages to persons with the same stock of human capital. Sex discrimination can then be measured by the amount of the wage gap between men and women which remains unexplained by male-female differences in human capital - mainly education, training and experience.
A hypothesis which is useful in explaining why so much of the male-female wage gap cannot be explained by human capital differences between men and women is that of statistical discrimination - where average differences between population subgroups are used as a basis for discriminating against all members in that subgroup. If the employer believes that women are on average more likely than men to be unstable, unreliable, etc. then he will tend to discriminate against women even though he knows he may be wrong in certain individual cases. The cost of obtaining detailed information on job candidates leads to the use of screening devices such as candidates' sex. In this way, negative stereotypes will penalise all women.
The neo-classical approach has been very important in pointing out some of the productivity-related differences between men and women which account, at least partially, for the fact that men earn more than women. Indeed, policies to improve the labour market position of women are often based on the need to improve women's educational levels and training - that is, their human capital. In developing countries - particularly in Africa, the Middle East and Asia - women are generally less well educated than men and this factor may, accordingly, be more important in these countries than in others. In the PICs recent cohorts of women have gained much greater access to educational opportunities, although they remain under-represented at the tertiary level, a factor which will continue to retard their progress to the top echelons of the occupational ladder.
While it is clear that lower levels of education and skill can be an important handicap for women in the labour market, the human capital approach is based on several assumptions which have been questioned, notably by labour market segmentation and gender theorists. Two of these questionable implicit assumptions are:
However, unlike pregnancy and breast feeding, there is no biological reason why the child rearing role must be performed uniquely by the mother.
This assumption ignores the sex segmentation of the labour market - which cannot be explained simply by sex differences in human capital. Evidence in the United States suggests that the rising educational levels of women are not related to any substantial decline in the extent of sex segregation of occupations.
Institutional theories of labour market segmentation, which are discussed next, emphasise factors related to the structure of the labour market and how men and women get slotted into separate segments of the labour market in explaining sex inequalities.
THEORIES OF LABOUR MARKET SEGMENTATIONTheories of labour market segmentation can be considered as refinements of neo-classical theories, in that they view the labour market as stratified or segmented by institutional barriers. Within each segment, neo-classical principles are generally still assumed to be relevant.
One of the best known theories of labour market segmentation is the dual labour market theory which distinguishes between two types of jobs: primary sector jobs which are relatively good in terms of pay, security and opportunities for advancement, and secondary sector jobs with low pay, low security and opportunities for advancement. Primary sector jobs are those where workers' skills tend to be firm-specific and the resulting employers' need for stability in this part of the workforce leads them to offer high wages and good prospects for advancement. For jobs in the primary sector, worker stability is important for employers and the perceived high turnover of women means that they are more likely to be relegated to secondary jobs. Thus, even with equal pre-entry qualifications, men (being perceived as more stable) would be more likely than women to be recruited for primary jobs where their chances for subsequent progress within the firm in terms of wages, training and promotions would be greater.
The main contribution of the dual labour market approach is that it emphasises the existence of segmented labour markets and analyses the different ways in which different labour market segments operate, thereby providing a refined alternative to the open competition between individuals assumed in neo-classical models. Two particularly important insights coming from this segmentation approach can be mentioned. First, it stresses the importance of the initial entry position into an organisation for determining future possibilities to acquire human capital (in terms of on-the-job training and experience) and for promotion. Second, it points out that worker behaviour is related to the characteristics of the jobs workers hold. Since absenteeism and turnover tend to be greater in low-level, dead-end jobs where women tend to be concentrated, reputedly higher turnover and absenteeism among women may be explained, at least partially, by sex differences in type of occupation rather than by inherent characteristics of women.
While the dual labour market approach helps to explain the occupational distribution of men and women, it does not explain the sex segregation which occurs within the primary and secondary sectors. There are both male and female occupations which involve lengthy schooling, such as engineers and nurses. There are female occupations which require firm-specific training such as executive secretaries and, at the same time, there are male occupations requiring relatively few skills and where stability is not an important factor, such as janitor or truck driver.
The sex segregation of occupations within both the primary and secondary sectors has led some writers to suggest that sex also needs to be considered as one of the dimensions on which the labour market is segmented. The existence of two relatively separate labour markets for men and women is seen by some as an important determinant of the lower earnings of women. To the extent that women's occupational choices are restricted and there is an oversupply of candidates for women's jobs, women can be considered as 'crowded' into these occupations. According to such an 'overcrowding model', wages are lower for occupations which are highly feminised, since women must compete against themselves for relatively few jobs in what is essentially an artificially restricted, overcrowded segment of labour market. Similarly, women do not compete with men for a large number of jobs considered to be 'male' jobs, which helps maintain the higher wages of these jobs.
Although theories of labour market segmentation provide insights into sex inequalities, they still do not adequately explain why sex is such a persistent and important dimension for labour market segmentation. The basic cause is probably outside the economic realm and so it is not surprising that economic variables cannot explain it.
THE NEED FOR EMPIRICAL INVESTIGATIONIn order to prepare the ground to ensure that development planners and national policy-makers are gender-sensitive it is essential for a number of key activities to be undertaken:
Segregation refers to the extent to which a pattern occurs in which men and women are separated from each other in the overall occupational structure. Total segregation would exist when all occupations are staffed exclusively by one sex, with no occupation containing both men and women. A situation without segregation would be evident when the mix of men and women is the same in each occupation.
One widely used measure of female concentration in labour market studies is the percentage of workers in an occupation who are women; male concentration would be reflected in the percentage of all workers in an occupation who are 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:
One of the principal objectives of this paper is to conceptualise such gender issues relating to labour market analysis in the context of Pacific Island countries. However, it is also important to briefly mention related questions of data quality and consistency. The usefulness of these exercises in measuring segregation and concentration will be found in the ability of the researcher to make comparisons and identify changes over time across countries or across different sections of the labour force in a particular country. As in all such analytical work, consistent and complete data on employment participation and occupational distributions are essential. Attention must always be paid to three areas of definition and classification when making comparisons over time. It will be necessary to make explicit how the results of such exercises may be tempered by the incomparability of the data over time. These areas of concern relate to definitions of the labour force, those that distinguish forms of employment, and the nature, quality and consistency of the occupational classification schemes over time. As in most empirically-based exercises of this nature, the quality of the final analysis will depend on the quality of the data set used and the skill with which its virtues are exploited and deficiencies acknowledged.
This illustrative example of the various ways of examining the level of concentration of the male and female labour forces is taken from Fiji, using the results of the 1986 Census of Population, the most recent year available with this kind of data. The emphasis is on the production of visual displays of concentration patterns to help identify the broader features of the data reported in table 1.
The sex composition of an occupation can be can be measured as the percentage of the total number of workers in an occupation who are female, or the percentage of the total workers in the occupation who are male, or the ratio of males to females. These are all essentially equivalent in terms of the information that they convey.
| ISCO 1968 | Occupation | Total (1) |
% (2) | Males (3) | % (4) |
Females (5) | % (6) |
Female Share % (7) | |
| 06/07/13 | Medical workers & teachers | 10,343 | 4.3 | 4,366 | 2.3 | 5,977 | 11.8 | 57.8 | |
| 0-1 | Other professionals | 7,431 | 3.1 | 6,359 | 3.4 | 1,072 | 2.1 | 14.4 | |
| 2 | Administrative and Managerial | 2,766 | 1.2 | 2,515 | 1.3 | 251 | 0.4 | 9.1 | |
| 3 | Clerical & Related Workers | 15,569 | 6.5 | 8,282 | 4.4 | 7,327 | 14.4 | 47.1 | |
| 4 | Sales Workers | 14,861 | 6.2 | 10,540 | 5.6 | 4,321 | 8.5 | 29.1 | |
| 5 | Service Workers | 15,422 | 6.4 | 7,979 | 4.2 | 7,443 | 14.7 | 48.3 | |
| 6 | Agriculture etc. Workers | 105,924 | 44.2 | 93,925 | 49.8 | 11,999 | 23.6 | 11.3 | |
| 7-9 | Production and Related Workers | 49,000 | 20.5 | 44,439 | 23.6 | 4,561 | 9.0 | 9.3 | |
| 998 | Seeking Employment | 18,182 | 7.6 | 10,331 | 5.4 | 7,851 | 15.5 | 43.2 | |
| TOTAL | 239,948 | 100.00 | 188,696 | 100.0 | 50,802 | 100.0 | 21.2 |
Table 1 and Figure 4 display an overview of the distribution of the sexes in the labour force. The structuring by gender is prominent, with male-dominated occupations on the left side of the figure and female-dominated occupations on the right. Interestingly, the female-dominated occupations all have larger representation of men in them, as compared with the share of women in male-dominated occupations. Perhaps various cultural, social and political processes of employment underlie this pattern. Yet, since there are more men in the Fiji labour force, it is statistically more probable that men will be more widely distributed throughout the occupational structure. Or, since female-dominated occupations are broader than male-dominated ones, they may contain pockets of 'male' jobs.
Another important feature of Figure 4 is that, of the two largest occupations - Agricultural workers (with 106 thousand workers, or 44% of the total) and Production and Related Workers (with 49 thousand workers, or 21% of the total) - both are at the male-dominated end of the spectrum. It is hardly surprising that women make up only one-in-five of the labour force when they are largely excluded (constituting only 11%) of the two largest occupations with 65% of the Fiji work force. To explore the reasons for such extreme gender concentration, and the labour market practices producing such a scenario, calls for a major research effort. With a greater understanding of the mechanisms at work, policy measures to remedy this enigma would be forthcoming. An example of the kind of labour market research which the author has in mind is explored in a subsequent section of the paper.
An alternative way of examining male and female concentration is to present the distribution of employment by sex across occupations to identify its variability. This is calculated as the percentage of the male and female labour forces in each occupation. Figure 5 illustrates how women are grossly under represented in the highly-paid, decision-making occupations - Administrators and Managers, and Other Professionals, with only 2% or less of all women in these categories. Women are better represented as Production workers, Sales workers, Clerical workers and Service workers, which contain between 9% and 15% of all female workers. Twelve percent of all women work in the traditional female occupations as paramedics and teachers, while figure 5 shows that almost 1 in 4 female workers are engaged in agriculture and fishing.
Such an illustration is extremely useful for identifying those occupations where women are found, and are not found, and for estimating their relative sizes.
An alternative method of presenting the material is portrayed in Figure 6 where all the occupations are grouped by their percentage female and then plotted against the number of workers to be found in each occupation. On the horizontal axis, occupations are plotted according to their percentage female. The minimum is 9.1% (Administrative and Managerial) and the maximum is 57.8% (Medical workers and Teachers). Thus, we see that for occupations that are between 0 and 10% female, there are almost 5,000 workers; for occupations with between 41% and 50% female there are almost 23 thousand female workers. Thus, most female workers are employed in occupations where the female share exceeds 40%.
The extreme ends of the distribution of these data will now be examined for additional insights on gender concentration. What proportion of women work in almost totally female occupations? What proportion of women are engaged in occupations almost totally dominated by men? Clearly, the level of detail in the occupational scheme will be a major influence on the results. In this example, 2-digit ISCO-1968 occupations have been used, which raises the likelihood of occupations being dominated by one sex or the other as compared with the broader groups of occupations used above.
| ISCO 0-15% Female | ISCO 70%+ Female | ||
| 01 Physical scientists | (34) | 06/07 Medical, dental, vet | (1838) |
| 02/03 Architects, engineers | (70) | 32 Stenographers | (2991) |
| 04 Aircraft, ships officers | (5) | 34 Computing mch. oper. | (249) |
| 05 Life scientists | (68) | 54 Maids | (4637) |
| 11 Accountants | (178) | 75 Spinners, weavers | (321) |
| 12 Jurists | (11) | 79 Tailors, sewers | (1845) |
| 16 Creative artists | (19) | 91 Paper product makers | (208) |
| 17 Composers, performers | (54) | 94 Prod. workers n.e.c | (229) |
| 18 Athletes, sportsmen | (5) | TOTAL | (12318) |
| 20 Legislative officials | (40) | ||
| 21 Managers | (211) | ||
| 31 Gov. executive officials | (129) | ||
| 35 Transport/comm. superv. | (34) | ||
| 36 Transport conductors | (10) | ||
| 37 Mail distrib. clerks | (45) | ||
| 40 Managers in trade | (87) | ||
| 41 Working propr. trade | (241) | ||
| 43 Technical salesmen | (25) | ||
| 44 Insurance salesmen | (43) | ||
| 49 Other slaes workers | (18) | ||
| 58 Protective services | (117) | ||
| 60 Farm managers | (22) | ||
| 61 Farmers | (8657) | ||
| 62 Farm workers | (2077) | ||
| 63 Forestry workers | (16) | ||
| 70 Production superv. | (109) | ||
| 71 Miners quarrymen | (17) | ||
| 72 Metal processors | (2) | ||
| 73 Wood prep. paper makers | (7) | ||
| 80 Shoe makers, leatherwork | (13) | ||
| 81 Cabinet makers, woodwork | (31) | ||
| 82 Stone cutters | (0) | ||
| 83 Blacksmiths, toolmakers | (50) | ||
| 84 Machine fitters | (56) | ||
| 85 Electrical fitters | (35) | ||
| 86 Sound equip. op. | (9) | ||
| 87 Plumbers, wleders | (22) | ||
| 90 Rubber, plastic products | (17) | ||
| 92 Printers | (82) | ||
| 93 Painters, construction | (5) | ||
| 95 Bricklayers, carpenters | (55) | ||
| 96 Stationary engine ops. | (12) | ||
| 97 Material handling | (469) | ||
| 98 Transport equip. oper. | (68) | ||
| 990 Other labourers | (313) | ||
| 991 Armed forces | (11) | ||
| TOTAL | (13599) |
Table 2 shows that there are 46-2 digit occupations, out of a total of 82, where women constitute only 15% or less of the workforce in those occupations. Almost 27% of all female workers are found in these overwhelmingly male dominated jobs. On the other hand, there are only 8 occupations which are dominated with women, with more than 70% of employment being women. The patterns of concentration are fairly predictable, with the large number of male-dominated jobs being either high-skilled, or managerial/supervisory, or requiring artisan skills or a capacity to undertake heavy labouring work. The female-dominated jobs are traditional 'women's' occupations, making up 24% of all their employment opportunities.
On this evidence, one-half of all employed women in Fiji work in an occupation heavily dominated by one or other of the sexes.
In the Western world much recent policy has been directed at creating integrated occupations in terms of their gender composition. In Fiji, which occupations are currently in this category and, therefore, in the middle of the range of concentrati on? Our approach is to examine what proportion of the labour force is in occupations where the female percentage deviates very little - say + 5% - from the overall female percentage of the total employed labour force. That is, occupations are selected w here the gender mix is close to the mix for the labour force as a whole, which might be regarded as the policy ideal.
According to this criterion all 2 digit ISCO occupations are divided into three groups: those that are strongly male-dominated (78.8% + 5%), those that are strongly female-dominated (21.2% + 5%), and those that are mixed, not particularly dominated by either sex. As we see in Figure 7, the great majority of all workers are engaged in male-dominated occupations, with a sizeable minority employed in female-dominated occupations. Only 1% of all workers are employed in a truly mixed fashion, where the female: male balance is similar to the gender balance in the overall labour force. This extreme situation is illustrated in Table 3 which shows that there is no difference between the sexes in their distributions.
Table 3| Strongly dominated | % | Mixed | % | Total | % | |
| Men | 187,160 | (98.9) | 2,027 | (1.1) | 189,187 | (100.0) |
| Women | 46,479 | (98.9) | 512 | (1.1) | 46,990 | (100.0) |
| Total | 233,638 | (98.9) | 2,539 | (1.1) | 236,177 | (100.0) |
The next step will be in introduce the overall sex composition of the labour force into the analysis. It would be unreasonable to believe that there could be a 50-50 gender distribution in each occupation when women only comprise 21% of the Fiji workforce. It is more practical, therefore, to assess the equality or inequality in the gender distribution of occupations using the sex composition of the overall labour force. Thus, occupations comprising more than 21% women are considered to be female dominated; those with less than 21% women are male-dominated.
In Figure 8, zero point on the vertical axis is where the percentage female of the occupation is 21%, equal to the percentage female in the total labour force. Female dominated occupations are plotted above the horizontal axis, those with male dominance lie below the horizontal axis. In Figure 8, the occupations are presented from the most male-dominated (Administrative and Managerial Workers) to the most female-dominated (Medicals and Teachers).
The results of the analysis of the distribution of gender by occupations in Fiji has shown that it is extremely concentrated. In the following section, a methodology is suggested for exploring some of the possible reasons which have produced this scenario.
LABOUR MARKET PROCESSES THAT EXPLAIN SEGREGATION AND CONCENTRATIONHaving determined the extent of gender segregation and concentration by occupation, and illustrated these results in a clear and concise fashion to policy-makers and planners, the investigation would be incomplete without the researcher addressing the reasons which might help to explain this situation. To explain the real extent of gender inequalities and the labour market processes which generate them, it is incumbent on researchers to go to the level of the individual firm, establishment, trade union or government department to explore personnel policies and practices and uncover the reasons which are used to justify them. Clearly, what is called for in the countries of the Pacific is the collection of data at both the worker and enterprise level, including interviews of employers and male and female employees. In this way, it would be possible to examine the processes at work behind the kinds of labour force statistics portrayed above to investigate such issues as:
Before initiating such an empirical investigation, it is important to consider a set of theories or hypotheses which the researcher would wish to investigate or test. A brief synopsis of the main conventional explanations for the disadvantaged position of women in the labour market has been presented above. More country-specific and situation - specific hypotheses would need to be formulated as a perquisite to designing the survey instrument or questionnaire to be used.
Resulting from such an investigation effective policies will need to proceed on several fronts, and the relative weight given to each will depend on the outcome of the empirical assessment of the strength of the problems to which they are addressed.
Effective policies are likely to encompass reforms in the areas of laws, education, attitudes, economic structures and social policies, and to involve national as well as local governments, trade unions, employers, women's organisations, and religious and cultural groups. Key areas of action might well include:
Since women's biological role as bearers of children and their cultural role as rearers of children in most societies constitute major handicaps in the labour market, the possibility for controlling pregnancy through effective use of contraception becomes a central concern and an area where policy intervention could have enormous effect. Much has been written on the relationship of women's employment outside the home to fertility; the main point to be emphasised is that knowledge of and access to family planning methods may be one important means by which women's position in the labour market can be improved.
Greater dissemination of information about women's work behaviour and skills and their contribution to family income might help dispel various myths and stereotypes which jeopardise their position in the labour market. In particular, information which demonstrates women's need for income and their role in supporting families would help give social reality to the fact that the earnings of women often provide for the basic economic needs of their families. Indeed, until international agencies, national governments, trade unions, employers and society in general come to recognise the role of women as breadwinners, policies aimed at reducing sex inequalities in the labour market are likely to be half-hearted.
This paper has called for structured, in-depth empirical investigations of labour market processes and practices in the Pacific Island Countries. To demonstrate how such an exercise might proceed and how the requisite data could be collected, the following is meant to be illustrative.
It is suggested that special sample surveys be undertaken of both employees and employers in the PICs. The objective is to obtain data and information on how firms behave with respect to hiring, promoting, training and firing. It would also be of relevance for the researcher to explore differences in turnover and absenteeism rates, pay differentials by sex, and the within-firm occupational segregation of women.
In the Fiji data analyzed above, and likely a similar conclusion would be drawn throughout the Pacific, the great majority of employees are located in relatively low-skilled jobs. A totally random sample of employees would produce too few women in professional occupations to allow much analysis of them. This would require the selection of a sample which includes an adequate number of women in top-level jobs to allow meaningful analysis.
A sample of employers should be selected, perhaps represented by owners/managers or personnel or general managers. One of the hypotheses which should be tested relates to the role of the employer in segregating the sexes into certain clearly demarcated job types. Respondents might be asked to focus on the following few job types:
At the commencement of the interview the respondent might be handed a sheet of paper containing a short description of the characteristics of these four job types.
The kinds of questionnaire which might be utilised are meant to be illustrative and are reproduced in Appendix A.
These questionnaires would allow the researcher to document the differences in work histories between married women, single women and men. The extent of absenteeism could be measured and some indication of the reasons for breaks would be gauged. Married women's continuity in labour market experience could be measured from these responses.
In addition to problems of discrimination within the work environment, the dual roles in the home and at work can often impose severe time constraints on women. The questionnaire explores these issues and the degree of satisfaction with existing working hours.
Inequalities between men and women in their access to training would be estimated in the analysis of respondents' answers. Do women receive job and pay promotion less frequently than men, and in what kind of jobs? Is pay discrimination widesprea d for similar kinds of work?
CONCLUSIONSFor policy makers the analysis of this data set could be used to suggest certain changes which would help to correct the severely disadvantaged position of Fiji women, and women in other countries in the Pacific. It might be expected to shed light on how best to construct a social and economic environment in which married women would find wage employment and domestic responsibilities to be reasonably compatible. Is equal opportuni ty legislation required in PICs? The results of such a study would allow some assessment of the use and adequacy of existing child-care facilities and, if necessary, to identify what changes need to be made. Are trade unions hindering or promoting the cause of women in PICs? And how wi despread are maternity leave guarantees, particularly outside of the public sector? Is the length of existing maternity leave adequate?
Evidently, the answer to these and many other questions could be revealed by analysis of these illustrative questionnaires. Above all else, they would allow us to go beyond merely describing the kind of gender concentration reported earlier in Fi ji, and move forward by helping us understand the social and labour market processes at work. From such an assessment, remedial policy measures might be forthcoming.
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