by
William J House
Adviser on Population Policies & Development Strategies
UNFPA Country Support Team, Suva


The views and opinions contained in this Report
have not been officially cleared and thus do not
necessarily represent the position of the
United Nations Population Fund

December 1999

Preface

Executive Summary

1. Introduction

2. The Demographic and Economic Context

3. The Characteristics of the Formal Sector Labour Market

4. The Structure of Earnings

5. Vocational Training

6. Education, Vocational Training, Work Experience and Earnings

7. Occupational Filtering Down and Returns to Eduction

8. Conclusions

9. References

Preface

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 assistance and information to meet country needs in the population field. In fulfilling this function, apart from field missions, the Country Support Teams also try to foster active communication and open discussion with 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 co-ordinated multidisciplinary approach to population. Hence, CST Discussion Papers are not particularly addressed to academic audiences but to practitioners.

In this paper the author utilises the data collected from a sample of over 8,000 formal sector workers in Fiji to explore the underlying operation of the labour market. The analytical results will be of direct interest to human resources and employment planners in Fiji in particular, and to officials in the other countries of the South Pacific who may wish to undertake similar analyses in their countries.

During 1995-1997, UNFPA funded a project in the National Planning Office entitled: "Assistance in Population, Workforce Planning and Human Resources Development Planning". Since that time, UNDP has taken over the funding of the project which is now entitled: "Strategic Human Resources Planning in Fiji". Both phases of the project have been executed by the International Labour Organization. UNFPA technical assistance has been provided throughout the project via advice and assistance given by the UNFPA Country Support Team based in Suva. This consisted of advice in the design of this study and the construction of the sample survey and, over the last 12 months, the detailed analysis of the survey which forms the basis of this report.

Fiji has the largest and most developed economy of all the South Pacific island countries, with a per capita GDP of about US$2,300 in 1996. The country is favourably endowed with natural resources, a high level of human resource development, a dynamic class of entrepreneurs, and a strategic geographic situation at the hub of economic activities in the region. Furthermore, Fiji’s social indicators – life expectancy, infant mortality, adult literacy and school enrolment – reflect the country’s relatively high income and development status. According to the latest 1998 UNDP Human Development report, Fiji ranks 44^th out of the 174 countries in its human development index. The closest South Pacific island country was Samoa, which ranks 94^th, followed by Solomon Islands and Vanuatu at 123^rd and 124^th respectively.

Despite this favourable portrait, the economy relies on a very narrow base and is over-dependent of the tourism and sugar sectors. Perennially, the economy remains extremely vulnerable to dramatic changes in climatic conditions. Following the economic decline in the period after the events of 1987, a series of economic reforms induced a rise in the economic growth rate, ranging between 1 and 5% per annum during the 1990s. In 1996 per capita GDP was 18% higher than in 1990.

Despite these improvements, the generation of formal sector employment opportunities has failed to meet the needs of a burgeoning supply of new job-seekers. With a cap imposed on public sector employment, the private sector has been capable of absorbing no more than one-half of the new labour market entrants each year. This imbalance in the labour market – the gap between the number of new job seekers coming out of the school and vocational training institutions and the number of new job opportunities opening up – remains one of the major social and economic problems yet to be adequately addressed.

In order to contribute to a possible solution of this national issue, this paper undertakes to provide an in-depth analysis of the working of Fiji’s formal sector labour market. Very little analytical work has been undertaken previously, largely because of the dearth of relevant data. On the basis of a relatively large sample of formal sector workers, the paper provides a comprehensive analysis of some attributes of the functioning of Fiji’s formal labour market. It is anticipated that the results of the analysis will be used as inputs into the national Human Resources Development Plan and Programme to address one of the priority social and economic problems facing the country.

In order to redress the deficiency in relevant labour market information and data in Fiji a survey of formal sector employees was undertaken in 1997 with financial support from the United Nations Population Fund (UNFPA). The survey exercise was undertaken by a researcher and a team of student enumerators from the University of the South Pacific in Suva. Analysis of the data they collected forms the basis of what follows in this paper.

The topics covered in this paper are wide-ranging and each requires much more in-depth analysis before any definitive conclusions can be drawn. However, the initial analysis does suggest areas of policy concern where some tentative assertions can be made.

The background and context of the analysis is one where the size of the labour force has been growing much faster than the rate of growth of formal sector jobs. Since unemployment has not increased dramatically, much of the increased supply has been absorbed into the rural economy and urban informal sector which are not investigated here.

Meanwhile, the formal sector remains the major attraction for the majority of school-leavers. From the survey data analysed here, they can observe a market where male earnings are consistently higher than female earnings, as are earnings in the public and semi-public sector compared with the private sector.

Our estimated rate of return to an additional year of schooling of 17.4 per cent is well in excess of the mean rate for countries at Fiji’s level of development, suggesting that the market may be signaling scarcities in the supply of highly educated and skilled labour, particularly since the level of earnings for this kind of labour is greater in the private than public sector.

Yet, because of an expansion in the supply of educated labour, it appears that a filtering-down process has occurred whereby more recently trained job-recruits filter into occupations formerly reserved for less well-educated and less well-trained workers. This preliminary analysis also suggests considerable earnings differences between ethnic groups and between the public, semi-public and private sectors. Apart from the professional and artisan groups, the public sector appears to pay a premium to its employees compared with the private sector.

An important policy finding is that there is a significant positive return to those who have undergone vocational training, as high as 17 per cent for artisans.

Attendance at the Fiji National Training Council (FNTC) for artisan and other blue-collar workers proves to be significantly remunerative. Many of the other institutions provide training for narrowly-based occupations which show up as being rewarding; for example, teachers do significantly better than other middle-level professionals from attending teachers’ college; attendance at the Fiji Agriculture College rewards middle professionals in an agricultural-related occupation; attendance at the police college raises the earnings of policemen in the Service Workers groups; and attendance at one of the number of miscellaneous vocational colleges has a significant impact in a number of occupational groups. Interestingly, Clerical workers who have attended FIT earn significantly more than those who have not done so; the 117 Clerical workers who have attended a commercial college do significantly worse.

Preliminary analysis of occupational assignment and pay suggests that gender differentials are extremely important in Fiji. Male-female pay differences are significant after controlling for broad occupational groupings despite women in the formal labour market having a slight advantage in terms of their mean years of education. Rather than suggest that male and female employees receive different rewards from performing the same job side-by-size, it is much more likely that more specific gender-based occupational assignments explain much of these pay differences. And an earlier contribution to this Discussion Paper series has demonstrated severe occupational segmentation in Fiji which needs further explanation.

While this paper has covered much ground in exploring the operations of Fiji’s formal sector labour market, additional tasks need to be performed. More in-depth econometric analysis is required to help to explain sex and race differences in earnings and job assignments while the extent of the public and semi-public sectors v. the private sector differences in pay for various kinds of occupations need further investigation. Is the public sector the "wage leader" for certain kinds of occupations?

The exploration of these and other labour market operational issues, including issues relating to gender discrimination in occupational assignments, will be taken up in subsequent papers.

During 1995-1997, UNFPA funded a project in the National Planning Office entitled: "Assistance in Population, Workforce Planning and Human Resources Development Planning". Since that time, UNDP has taken over the funding of the project which is now entitled: "Strategic Human Resources Planning in Fiji". Both phases of the project have been executed by the International Labour Organization.

Fiji has the largest and most developed economy of all the South Pacific island countries, with a per capita GDP of about US$2,300 in 1996 (UNDP, 1998). The country is favourably endowed with natural resources, a high level of human resource development, a dynamic class of entrepreneurs, and a strategic geographic situation at the hub of economic activities in the region. Furthermore, Fiji’s social indicators – life expectancy, infant mortality, adult literacy and school enrolment – reflect the country’s relatively high income and development status. According to the latest 1998 UNDP Human Development report, Fiji ranks 44^th out of the 174 countries in its human development index. The closest South Pacific island country was Samoa, which ranks 94^th, followed by Solomon Islands and Vanuatu at 123^rd and 124^th respectively.

Despite this favourable portrait, the economy relies on a very narrow base and is over-dependent of the tourism and sugar sectors. Perennially, the economy remains extremely vulnerable to dramatic changes in climatic conditions. Following the economic decline in the period after the events of 1987, a series of economic reforms induced a rise in the economic growth rate, ranging between 1 and 5% per annum during the 1990s. In 1996 per capita GDP was 18% higher than in 1990.

Despite these improvements, the generation of formal sector employment opportunities has failed to meet the needs of a burgeoning supply of new job-seekers. With a cap imposed on public sector employment, the private sector has been capable of absorbing no more than one-half of the new labour market entrants each year. This imbalance in the labour market – the gap between the number of new job seekers coming out of the school and vocational training institutions and the number of new job opportunities opening up – remains one of the major social and economic problems yet to be adequately addressed.

In order to contribute to a possible solution of this national issue, this paper undertakes to provide an in-depth analysis of the working of Fiji’s formal sector labour market. Very little analytical work has been undertaken previously, largely because of the dearth of relevant data. On the basis of a relatively large sample of formal sector workers, the paper provides a comprehensive analysis of some attributes of the functioning of Fiji’s formal labour market. It is anticipated that the results of the analysis will be used as inputs into the national Human Resources Development Plan and Programme to address one of the priority social and economic problems facing the country.

At the time of the 1996 Census the total enumerated population of Fiji was 775,077 compared with 715,375 ten years earlier. The annual inter-censal growth rate was 0.8% which is significantly less than the 1.97% p.a. increase recorded over the 1976-86 decade. This fall reflects the continuing decline in fertility apparent in earlier years – for Fijians a fall in the TFR from 5.6 in 1966 to 3.9 in 1986; for Indians from 5.4 to 2.6 – and the massive emigration, largely of the Indian ethnic group, soon after the 1987 military coups. While emigration levels have tended to stabilize, the country continues to suffer from the past and current loss of some of its more educated and trained citizens.

The percentage distribution of the population by broad age groups and ethnic origin is portrayed in table 1 for the period 1966 – 1996. Evidently, Fiji is slowly departing from its very youthful status in which, in 1976, 41% of the population was under 15 years old; by 1996 this had declined to 35%. Over the same period the share of the 65 years and over had grown from 2.4% to 3.2%. As a result, the share of those of working age and potential labour force members rose from 51% in 1966 to over 61% by 1996, a "demographic bonus" to the nation only if most are able to find productive employment.

The changing age and ethnic structures¹ of Fiji’s population will impact on patterns of consumption, rates of labour force participation, dependency rates, and demands on the social services, including health and education, as well as impose intense pressure on remunerative employment opportunities.

Among the most important challenges facing the government will be the task of identifying development strategies which can generate new employment and income opportunities and reduce underemployment and unemployment. The urgent need to create employment opportunities is underscored by the higher rate of labour force growth than population growth, resulting from a period when fertility was much higher than today, and from recent increases in the rate of female labour force participation. Recent slow economic growth and consequent retarded growth in job opportunities, particularly in the public sector, have forced many frustrated school-leavers into marginal activities in small-scale agriculture and the urban informal economy.

As noted earlier, Fiji’s population is still relatively young, with 35% of the total in 1996 less than 15 years of age. With the annual population growth rate of 0.8%, projections suggest that the total population will exceed 800 thousand by the end of this century, an increase of almost 13% over one and a half decades. The outcome will depend on the scale of out-migration, which will be partly a function of the future performance of the economy.

In the meantime, the size of the labour force has been growing much faster than the overall growth rate of the population, as reflected in table 2. Over the 20 year period 1976-1996, the labour force grew at an annual average rate of 2.7% while the population rose at a rate of 1.4%. A major contributing factor has been the apparent and quite remarkable increase in the labour force participation rate of females, particularly in the most recent inter-censal period, rising from less than one-quarter to almost 40% overall. No doubt, some part of this rise is attributable to a change in definition between censuses.

It is also of interest to note the marginal decline in the age-specific labour force participation rates of males. While the fall in these rates for men aged 15-24 might be attributable to greater educational and training opportunities, some older men may have been pushed out of the labour force by younger newcomers as well as the large registered influx of women competing for similar jobs.

The reported rates of unemployment in table 3 have tended to contract over time, particularly those for females since 1986. Thus, despite a rapid growth of labour supply, rates of unemployment appear to have fallen, indicating successful labour absorption into productive employment. On further examination in table 4, however, we find that much of the growth in labour supply over the period 1986-1996 failed to be absorbed into the cash economy. While total employment grew at an annual rate of 2.3% over the 1986-96 period, just exceeding the 2.1% average annual growth in labour supply, there was a marked difference in the labour market treatment of males and females. Male cash employment expanded at a rate (1.1% p.a.) which exceeded the growth in male labour supply (0.5% p.a.) with the result that the share of non-cash employment in total male employment shrank from 17% to 13%. In contrast, while female cash employment growth was impressive (4.2% p.a.), it was exceeded by the annual growth in female labour supply (6.7% p.a.) with the result that, as overall female unemployment fell from 15.3% in 1986 to 7.8% in 1996, the share of non-cash employment in total female employment expanded from 19% to 41% over the decade.

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

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 remain 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. Furthermore, their presence as a potential labour supply to the cash economy has, no doubt, served to dampen wage increases in the low-wage segments of the labour market.

Employment in the formal sector of the economy, comprising public and private establishments offering continuous wage and salaried employment, is estimated to have grown at an annual rate of 2.2% over the period 1993-1996 (Fiji, 1997, p.47). If this expansion were to continue, this suggests an annual net increase of perhaps 2,500 new net openings, together with additional vacancies opening up resulting from job turnover and retirements. Yet, the net annual addition to labour supply exceeds 6,000, assuming unchanged labour force participation rates. The fundamental policy question remains: How can the economy generate even further formal employment opportunities to satisfy the rising aspirations of an increasingly better educated crop of school leavers? On recent past experience, the informal and subsistence sectors will be called upon to serve as employers of last resort, a situation which will become increasingly intolerable as pressure on land and other scarce resources intensifies.

Another perennial problem relates to skill shortages in the Fiji labour market. Emigration, primarily of persons of Indian descent, which has been biased towards skilled individuals, together with the development of new industries and tourism facilities, has resulted in a shortage of particular categories of skilled labour. It is estimated that 42 percent of those in administrative and managerial positions prior to 1987 have emigrated, together with 17 percent of clerical workers, 14 percent of sales workers and 13 percent of professional and technical workers. To place the loss in perspective, the annual emigration of professional and technical workers averaged 550 per year, more than twice the total output of all Fijians (250 persons) per year from the University of South Pacific (USP). Over the period 1987-1990, 61 per cent of all accountants, over 30 per cent of all typists and computer operators, 20 per cent of all doctors, 10 per cent of skilled tradesmen, and 25 per cent of toolmakers and fitters left Fiji. More generally, about 35 per cent of all Fijians attaining a post-secondary degree are now employed overseas. Given obvious capacity constraints in the domestic education system, both the Government and the private sector have resorted to a mixture of in-service training and hiring of expatriates. In 1991, for example, there were 225 expatriates employed in the civil service and 664 expatriates employed in the private sector, for an average of about 2 percent of the domestic work force. The small share of expatriates employed in the Fiji labour force is surprising, coming on the tail of a large-scale upsurge in out-migration of skilled workers. This, however, can best be explained by the strong base of general skills in the labour force and the rapidity with which on-the-job training has been able to overcome the loss of trained personnel.

Very little recent analytical work has been undertaken on the operation of the labour market in Fiji² . If Fiji is to absorb a greater share of its burgeoning newcomers into productive employment, a greater understanding by planners and policy makers of how labour markets operate in the country is essential. Factors which may serve to segment labour markets, to inhibit labour force absorption and to slow down growth in productivity and output, need to be identified. Especially important will be measures to redress the disadvantaged position of women, the poor and other vulnerable groups in the country’s labour market. At the same time a delicate balance needs to be struck between interventions which aim to protect women and other vulnerable groups and those which effectively inhibit their enhanced absorption into productive employment.

Table 5 portrays the sectoral distribution of the total labour force for the most recent census year of 1996. The data confirm the concentration of economic enterprises in rural Fiji and in agricultural – related activities. Since the bulk of those classified in "subsistence" are, no doubt, engaged in agricultural-related activities, perhaps as many as 45% of the labour force are involved in the broad sector of agriculture, forestry and fishing. Even then, of the 219,314 employed for money, only 122,985 (or 41% of the total labour force) were employed for at least 5 days, perhaps constituting an estimate of the "formal sector"³ . The government accounts for roughly 40% of total formal employment via central and local governments. Manufacturing employment is still responsible for less than 10% of the total labour force while trade and services, including government services, employ over 27% of the labour force. It is also revealing to note how over 1 in 5 of the labour force is still engaged in subsistence activities in Fiji.

Men contribute two-thirds of the total labour force and tend to be concentrated in formal agriculture, construction and transport; women are over-represented in garment manufacturing, trade, services and the subsistence sector.

The occupational distribution of those in the labour force is portrayed in table 6. Fiji’s dependence on the primary sector is again apparent with 21% of the paid labour force classified as agricultural and fisheries workers. Only 17% of the paid labour force are found in the top three occupational groups, of Legislators, Senior Officials and Managers; Professionals; and Technicians and Associates. Men dominate the senior occupations although women are well represented as Professionals in traditional 'female' occupations. An in-depth analysis of the structure of occupations by sex follows in a later section of the paper.

The Fiji Employment and Development Mission Report of 1984 distinguished four separate "spheres" in Fiji’s labour market: the "formal", largely urban, wage labour market; the sugar economy; the diversified agricultural economy which largely supplies urban markets; and the coconut economy. While the relative size of each may have changed somewhat, this description is still likely to be reasonably valid. Each "sphere" continues to have a geographic concentration and they overlap extensively due to a relatively mobile labour force. However, the primarily urban, "formal" labour market generates relatively high incomes and is the centre of gravity around which the others revolve, in the sense that most of the labour force represents a potential supply to that market’s better paid, regular jobs.

The 1984 report concluded that wage and salary levels in Fiji were responsive to changes in the patterns of supply and demand and the net result was a labour market relatively free of distortions and functioning efficiently. Until 1991, however, the formal "sphere" or segment was considered to have been highly regulated and characterized by centralized wage bargaining (AusAID, 1995, p.7). In that year, labour market reforms removed statutory guidelines that had set wages on the basis of movements in the Consumer Price Index, economic growth and changes in the terms of trade. The reforms were aimed at making the labour market more "flexible", providing employers and trade unions with the freedom to negotiate wages and other conditions of employment.

Source: Population Census, 1996, Preliminary Results

While the new system saw average real wages continue to increase in most industries over the 1990s, the level of real wages are still below their mid-1980 levels, especially in the light of the recent 1998 currency devaluation and consequent price increases. For example, in 1985, the mean daily wage in all industries was F$12 (1985 prices). In 1991 this had declined to F$9.54; by 1994 the mean wage level had increased somewhat to F$9.90 (AusAID, 1995, Table A11).

Yet, our current knowledge of how the formal sector labour market operates in Fiji is still severely limited. Little analytical work has been undertaken on such issues as: the nature and determinants of personal earnings differentials, including the returns to various human resource characteristics such as formal education, informal and formal training and labour market experience; the extent to which the formal sector labour market is segmented according to certain institutional (public v. private) and personal (race, sex) characteristics; and the impact of past educational expansion and the manner in which younger, more educated recruits are absorbed into the labour market.

Using data collected from a relatively large sample of employees in the formal sector, this paper initiates the process of exploration by examining some of these labour market issues, including the relevance of the human capital approach to earnings differentials, focusing on the role of formal education, vocational training, on-the-job labour market experience and various other kinds of institutional factors. The paper examines the gender distribution of occupations and its changes in recent years, as well as estimates the extent of pay discrimination against women in the formal labour market in Fiji.

3.    THE CHARACTERISTICS OF THE FORMAL SECTOR LABOUR MARKET

The data utilized in the remainder of this paper derive from a specially designed survey initiated by the Ministry of National Planning and Information of the Government of Fiji and financed under a UNFPA-funded project: "Assistance in Population, Workforce and Human Resources Development Planning" (FIJ/95/P02) and executed by the ILO. The survey data collection exercise was sub-contracted to a researcher from the School of Social and Economic Development of the University of the South Pacific (USP) the UNFPA Country Support Team, Suva, provided overall guidance and technical advice. The survey was conducted in April 1997.

The overall rationale for the establishment-based survey was to collect scarce information on Fiji’s formal sector labour market, including its gender dimensions, which could be analyzed and used in the preparation of a Strategic Human Resources Development Plan for the years through 2001 and beyond. Information collected included the establishment name, its location, and the public-private-parastatal nature of its operations. From each individual employee respondent, the information collected included: ethnicity, marital status, age, sex, highest level of formal education attained, type of vocational training undertaken, if any, its duration, hours of work in the last week and gross wages/salary, value of fringe benefits in past 12 months, occupation, years in the occupation, years of tenure with current employer and previous number of employers.

According to the list of establishments of the Fiji National Provident Fund (FNPF), there were approximately 4221 firms and establishments paying FNPF contributions for their employees in 1997. The total number of employee contributors amounted to 110,091. While the payment of contributions by employers for those employees who work for more than two weeks is a legal requirement, it is believed that there are some firms in Fiji who are not included in the list because they are not registered with the FNPF.

Aside from this unavoidable omission, this FNPF source is the most comprehensive list of firms and establishments in Fiji and, for this reason, this listing was used as the frame for selecting the sample of firms and their employees for this exercise.

A total of 320 establishments (7.6% of the total) were selected for enumeration using a stratified random sampling technique. It was necessary that the sample of establishments was selected from a reasonable representation of the main economic activities and, in addition, that small, medium and large establishments were represented in the sample. Because of the concentration of formal economic activities on the two main islands of Viti Levu and Vanua Levu, and to minimise the costs of conducting the survey, only establishments located in the main urban areas were selected.

Each of the selected establishments, including Government and semi-government or parastatal organisations, was visited by a trained enumerator from the USP. Contact with a senior administration officer had taken place in advance of the enumerator’s visit and the purpose of the exercise explained. From those establishments employing less than 200 employees, information was collected from each individual worker. For those with 200 or more employees, the enumerator made a stratified random selection of 200 names from a complete listing of employees provided by the firm.

As a result, a total of 8153 individual employees were interviewed, representing a sample of just over 7% of the formal sector workforce.

Preliminary results of the 1996 Population Census of Fiji report the age distribution of all those working for cash, which would include workers in the informal sector, as well as those working for cash in the urban areas.

These Census tabulations are summarised in Table 7.

Source: Census of Population of Fiji, 1996, Preliminary Report

Table 8 presents a profile of the sampled formal sector workers, according to various personal and other characteristics. Since we do not have a complete profile of the population of formal sector workers, it is difficult to gauge how well the characteristics of our sample of employees fit those of workers in the formal sector.

The age distributions from the 1996 Population Census in table 7 are reasonably close to those of our survey in table 8, particularly that representing cash workers in the urban economy, a concept coming closest to that of the formal sector as represented in our survey. According to this criterion, that is, the age distribution of workers in our sample, we can be reasonably satisfied that our sample of workers is very similar to the age distribution of formal sector workers in Fiji.

In addition, the public sector, including public corporations or parastatals, is believed to account for roughly 40% of total formal sector employment; our survey contains a total of 38.6% of public and parastatal employees.

It is revealing to note how older workers, those 45 and over, are relatively underrepresented in our sample of formal, mainly urban, employees compared with their share of all workers in the total labour force and in cash work of various kinds. It could well be that their generally lower educational attainments and their greater propensity to remain in rural areas help to explain their lower representation in formal employment⁴ .

Because of well-defined promotion ladders in the public and parastatal sectors, their more formalised recruitment procedures, as well as more recent constraints placed on new recruitment by these sectors, table 8 demonstrates that their employees have a much older age structure than the private sector.

The educational attainments of the formal sector workforce in Fiji are quite impressive, with only 17.8% of respondents having completed primary education or less. The mean years of education of formal sector employees exceeds 10 years. In 1986 27% of the total economically active population had only a primary education or less, indicating how the formal sector uses education as a screening device. Meanwhile, in 1986, 17% of the non-agricultural labour force – approximating the formal sector workforce – had only primary education or less. Thus, during the most recent inter-censal period, the average educational attainments of the formal sector labour force may not have improved much, despite expansion in the education sector, because of the significant loss of the more educated citizens through overseas migration.

Table 9 shows the distribution of human resources in the formal sector by major occupational group and public/private sector of activity. In general, for the range of occupations from Senior Professionals to Sales and Service workers, the public and parastatal sectors tend to attract workers who are better educated than those in comparable occupations in the private sector. ‘Other blue collar’ workers in the private sector, on average, have more years of education.

While only 1 in 3 employees is a woman in the survey, they have, on average, one-half of a year more education than men. While there is a slight educational advantage of men over women in terms of years of education in Senior and Middle Professional occupations, women in Sales, Service, Artisan and Blue-Collar work have a slight advantage. The conclusion is that women working alongside men in similarly classified broad occupation groups are often better schooled. This may be an established requirement on their part in order to overcome possible traditional sex discrimination practices of male employers against women⁵ .

There have been significant gains in school attainments for both male and female formal sector workers in Fiji over the long term. For example, respondents in our sample aged 55 and over had an average of 7.1 years of education (7.5 for women and 7.1 for men); those aged 35-44 have 10.4 years (10.4 for both men and women; and those aged 20-24 mean years of education is 11.7 (11.9 for women and 11.6 for men).

In 1986, Indo-Fijians made up 53% of the urban non-agricultural labour force, the latter coming close to an approximation of the definition of the formal sector; Fijians constituted 37% and other races 10% (Population Census, 1986, Vol.3). The indigenous Fijian population, with its greater access to land, is overly concentrated in rural areas. It is hardly surprising, therefore, that 60% of our survey respondent in 1997 were Indo-Fijians. Nor is it surprising that they are over-represented in the private sector, given their dominance of private business and their obvious entrepreneurial talent and, no doubt, an inclination to hire members of their own ethnic group. Equally unsurprising is the over-representation of Fijians in the public and parastatal sectors where they have traditionally had access to jobs in the civil service bureaucracy.

As noted earlier, females have become much more likely to join the labour force in the past decade or so. In 1986, they constituted only 21.2% of the national labour force, 19.2% of the employed labour force working for money, 27.9% of the total urban labour force and 26.5% of the urban workforce engaged in the money economy. By 1996, their shares had grown to 32.8% of the total labour force and 24.2% of the labour force working for money; in the urban economy their share of the total labour force had grown to 34.5% but their share of the labour force working for cash had fallen to 20.5%.

Women’s share of employment in our survey of the formal sector amounted to 32.4% which is a reasonable reflection of their share in the underlying total labour force. They are well represented in the public (36.5%) and private (34.1%) sectors but under-represented in the parastatal sector (only 19.6% of the total).

With greater security of employment, better pay, at least in some occupations, and other non-monetary advantages, it might be expected that workers stay with their public and parastatal employers longer than in the private sector. These expectations are confirmed in table 8 where over 80% of public and parastatal employees have had only the previous or the current employer. Almost 1 in 3 private sector employees have had at least 2 previous employers.

Because women’s commitment to continuous employment is often felt to be suspect, because of their traditional domestic and biological duties to bear and rear children, it might be expected that they have lower levels of job tenure than men. These hypotheses are only partly confirmed in table 10 which shows that, after controlling for date of entry into the labour market, women’s tenure with their current employer is sometimes less than that of men in all three sectors. No doubt, this situation puts women at a disadvantage when it comes to prospects for pay and job promotion.

If a worker’s productivity increases with job tenure, and employers compensate employees correspondingly, it might be anticipated that employees stay with those employers whose whole package of remuneration is better than average; hence the greater job tenure in the public and parastatal sector which contributes to, and partially results from, their above average pay levels.

Preliminary results of the 1996 Population Census of Fiji indicate that Senior and Middle Level Professionals constituted about 17% of the total paid labour force and 21% of the non-agricultural paid labour force. These relative shares are well reflected in our sample survey where over 17% of the total are classified as professionals. As might be expected, employees classified as professionals are much more likely to be found in the public and parastatal or semi-public sectors while sales workers and artisans are more numerous in the private sector and service workers in the public sector.

There is no known independent assessment of the distribution of non-agricultural formal sector employees by location but the distribution of workers in the sample, with just over 50% in the nation’s capital of Suva, seems reasonable. As expected, public and parastatal workers are heavily concentrated in Suva in Table 8.

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

The following section examines the structure of earnings from our survey according to various personal human capital and other institutional characteristics.

Table 11 and figure 1 report the mean level of weekly earnings, including overtime and annual fringe benefits converted to a weekly basis, by age group, sector and sex. They demonstrate 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.

Note: + less than 20 cases
Source: Fiji Employment Survey, 1997

(a) By sector: Males

Earnings are higher for younger, new recruits in the public and parastatal sectors and rise fairly consistently in all sectors with age, proxying for labour market experience, up to age 55, after which earnings tend to decline. The earnings profiles are much steeper in the public and parastatal sectors, perhaps reflecting the structured internal labour market in these sectors and the formalised procedures allowing for job promotion along with lengthening job tenure.

It is also revealing to see the much flatter profile of female earnings as age increases, particularly in the private sector, perhaps reflecting the kinds of occupations to which they are assigned and the lack of opportunity for much on-the-job skill acquisition and productivity and pay advance. It may also reflect, to some extent, the effect of certain discontinuities in women’s attachment to the labour market and the loss of productivity and pay-enhancing continuous work experience.

Note: + less than 20 cases
Source: Fiji Employment Survey, 1997

Mean weekly earnings in table 12 rise consistently with school attainments, but especially after completing at least 8 years of education (see Figure 2). The public and parastatal sectors pay a significant premium to workers with schooling up to the completed secondary level, for both men and women, and thus appear as wage leaders in the market for the lower skilled. The private sector is much more competitive in its earnings scale in the market for the most educated labour and exceeds the mean pay of the public sector for university level males and both the public and parastatal sectors for males with a post-secondary certificate or diploma.

Male-female earnings differentials are significant in all three sectors in table 12 and generally take a U-shape, being wider at lower and higher levels of education compared with completed secondary education. This is also the case in the private sector where the U-shape pattern is exaggerated. No doubt, one possible explanation would be that there are differences in the occupational assignment of males and females at lower and higher levels of education. For example, very few females with higher education attain the very senior executive positions with correspondingly high pay. These positions remain almost exclusively male preserves.

The same is also true in the public and parastatal sectors except that the male-female pay differences are not nearly so marked as in the private sector and the pattern is one of low pay differences in line with the lower the level of education. It would appear that the public and parastatal sectors are much less discriminating in their treatment of the sexes. Even then, male university graduates earn about one-third more than female graduates, so complacency on the part of public sector officials is not warranted.

Figure 2: Mean Weekly Earnings by Education – All Workers

Source: Fiji Employment Survey, 1997

These patterns are more clearly demonstrated in figures 3 to 11. Figure 3 demonstrates how the earnings profile of more educated males has a much steeper slope than for the less educated whose earnings profile is quite flat as age progresses; the pattern is broadly similar for females in figure 4. The less educated seem to be assigned to occupations where opportunities for productivity–enhancing, on-the-job skill acquisition are severely limited.

Figures 5 to 11 demonstrate these patterns quite clearly. At all levels of education women earn less than men and the rewards to experience, as proxied by age, are lower for women. Again, occupational assignments may well explain much of these differences which are explored in more detail below.

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Source: Fiji Employment Survey, 1997

Note: + Less than 20 cases
Source: Fiji Employment Survey, 1997

Table 13 demonstrates that occupational pay differences are large in Fiji, with earnings being five times greater for the highest paying occupation group (Senior Professionals) over the lowest (Garment Workers). Senior professionals in the parastatal and private sectors earn more than their peers in the public sector; lower skilled workers in the public and parastatal sectors have a pay advantage over those in the private sector. For example, male blue-collar public sector workers receive a 31% premium over their counterparts in the private sector; female public sector workers in this broad occupational category have an 18% advantage over their private sector equivalents.

Male-female pay differentials are significant after controlling for these 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 investigated in much more detail using multivariate techniques later in this paper.

It is often reported from other countries that the size of firm has an independent effect on the level of pay. Particularly in the sectors of manufacturing, construction and trade, small size is often associated with a certain degree of informality in the labour market, with the inability of the authorities to enforce minimum wage laws and other workers’ protection measures. Larger size firms are often believed to be capital intensive in their production techniques and to pay above average earnings to attract better quality and more competent workers who will use, and be protective of, the expensive machinery.

Note: *Significant at the 5% level
Source: Fiji Employment Survey, 1997

The pattern of earnings change across the size classes of private firms in table 14 has an inverted U shape, for both males and females. The smallest firms pay the least, earnings peak in the 100-249 class (25-49 for males) and then fall away in the largest size class. Controlling for size of firm, male-female earnings differences are all large and highly significant.

Table 15 reports the mean level of weekly earnings by industrial sector and sex. Earnings are highest in the Finance and Insurance and Utilities sectors. The lowest mean pay arises in the sectors of Agriculture, Manufacturing – especially garment making – and Trade and Hotels, all traditionally employers of relatively low-skilled, cheap labour. Predictably, in most of the sectors, average female earnings are exceeded by male earnings, with the exception of Construction and Transport and Communications, where perhaps, women are performing office work and men are engaged in more manual tasks. However, only in a few cases is the overall significant earnings difference between the sexes replicated at the sectoral level.

Finally, to endorse the portrait of relationships between mean earnings and certain human capital factors, Table 16 demonstrates the distribution of weekly earnings by age, years of schooling and sex. Predictably, earnings rise with educational achievements and age (proxying for labour market experience). Almost one in four women, but only 1 in 8 men are located in the lowest 20% of the earnings distribution. In contrast, men are over-represented in the top 20% of the distribution.

The events of 1987 led to massive emigration of many skilled individuals and families from Fiji, particularly those of Indian descent. The consequence has been a severe shortage of particular categories of skilled labour which has continued to the present time. As a result, increased emphasis in public policy has been placed on expanding the quality and quantity of the country’s post-secondary institutions and their graduates.

Post-secondary vocational education and training in Fiji is undertaken by both public and non-Government institutions, with the bulk of students taught in Government centres which include the largest, the Fiji Institute of Technology (FIT)⁶ as well as the Lautoka Teachers’ College, the Police Academy, the Fiji School of Nursing, the Fiji College of Agriculture and the Telecom Training Centre. The Hotel and Catering School has branches in Suva and Nadi while the Fiji National Training Council (FNTC) offers short-term courses to meet the needs of employers to have their employees’ skills up-graded. FNTC offers courses in secretarial studies, computer and business subjects as well as a range of courses in such artisan skills as electrical, mechanical, electronics, plumbing, carpentry, etc. Non-government institutions include Montfort Boys’ Town, Fulton College, Navuso Agricultural School and Corpus Christi Teachers’ College. Various private commercial schools are operating to provide courses in stenography, computing and business and commercial studies.

The World Bank’s "Pacific Regional Post-Secondary Education Study" (1993) noted that systematic mechanisms to evaluate the nature and success of the post-secondary programmes being offered and their compatibility with the requirements of the labour market are either extremely weak or non-existent. Few institutions keep records of the placement and progress of their graduates through such exercises as tracer studies and no attempt seems to have been made to assess the relative success of differently trained individuals in the labour market.

The Fiji Employment Survey of 1997 allows us to explore, in a very limited way, the nature, extent and impact of vocational training in the national labour market, to assess the level of training for various broad occupational groups and to estimate, later in the paper, whether vocational training has any positive return, in the form of increased earnings, for the recipients of such training.

Source: Fiji Employment Survey, 1997

Roughly one-third of all respondents in the survey claim to have received some kind of vocational training. However, there are significant differences in both the proportion receiving training and the length of such training according to the individual socio-economic characteristics of employees included in table 17. For example, a larger proportion of the small number of employees engaged by foreign owned firms have received training, albeit for a much shorter period, than those in locally owned firms. Workers in Suva, Ba and Nausori have a better chance of undergoing training than those engaged elsewhere and, as expected, so do salaried employees relative to wage earners. Younger workers, except for the most recent recruits to the labour market whose opportunity for training will not arise immediately on joining, have the best chance of training while those joining pre-1961 would have had the least. Interestingly, a higher proportion of females overall have experienced some form of vocational training compared with men and the differences are significant for the age groups 20-24, 25-34 and 45-54.

Employees in the private sector are least likely to have undergone training while over one-half of all those engaged by the public sector have had some chance to upgrade their skills. These results are reflected in the lower than average proportion of skills training in the manufacturing, trade and hotels sector and the above average share in utilities and services. Skilled white-collar workers are more likely to have been sent for training and blue-collar and sales workers the least likely. Similarly, the more educated, who may have been recruited to certain occupations on the basis of their formal schooling, have greater likelihood of undergoing training. Rotumans and Fijians, who it may be recalled are over-represented in the public and parastatal sectors, have a greater chance of training than Indians who dominate the private sector.

In addition to having a greater opportunity to access training, employees who are salaried in the public and parastatal sectors, working for utilities, and are classified as professionals with above average formal education, all undergo a longer period of training than others.

Note: *Significant at least at 5% level
Source: Fiji Employment Survey, 1997

6.    EDUCATION, VOCATIONAL TRAINING, WORK EXPERIENCE AND EARNINGS

The contribution of education to improving economic output and labour productivity, and to lowering income inequality and promoting growth and development, has become widely recognised in a vast body of literature of the theory of human capital investment. The Government of Fiji fully recognises these linkages and asserts in its recent "Development Strategy for Fiji":

An important feature of the education system in Fiji is the manner in which non-Government and community organisations operate the large network of primary and secondary schools. The high rate of primary level enrolment for classes 1-9 of 98 percent has been achieved largely because of this strong community participation. Intermediate education consists of Senior Primary school (classes 7 and 8) or Junior Secondary School (Forms 1 and 2). Secondary education commences in Form 3 and continues through to Form 7. The enrolment rate for the 12-17 age group was recently estimated to be 68 per cent, with the absolute number of students increasing significantly in the recent past (World Bank, 1993).

Primary education is neither free nor compulsory although the Government covers the tuition fees of all students. However, schools charge an assortment of fees to cover administration, building, maintenance and other costs. The result is that many parents are unable to bear the burden of these charges and many students drop out along the way. For 1994, it has been estimated that 12 per cent of students had dropped out by the end of Class 6 and one-quarter had left by the end of Class 8 or Form 2 (Bartsch 1996).

There is considerable attrition at the secondary level due to examination failure and financial difficulties on the part of parents. The 1996 Census shows that, by the age of 18 years, about 40% of this age group are still at school. Fijian boys appear to have the highest rate of drop-out and Indo-Fijian girls the least.

At the tertiary level, a range of institutions include four teachers colleges, the Fiji Institute of Technology, the Fiji School of Medicine, the Fiji College of Nursing and the University of the South Pacific. The Fiji National Training Council (FNTC) provides a wide array of in-service vocational training courses while numerous commercial colleges and schools offers secretarial and business courses. Other institutions include, inter alia, the Police Academy and Montfort Boys Town.

Human capital theory provides the standard framework for examining the relationship between earnings and schooling (Becker, 1975; Mincer 1974; and most recently, Chiswick, 1997), where educational and on-the-job training are viewed as capital investments in an individual. In the simplest "schooling" model, only schooling raises workers’ productivity, and the possibility of accumulating post-schooling human capital through on-the-job training is temporarily ignored. Education is expensive, however, because of direct monetary outlays and because students forego labour market earnings to attend school. Firms benefit from high productivity from their educated employees and therefore offer such workers sufficiently higher lifetime earnings. Individuals must choose among different levels of schooling, each associated with different costs and a different expected earnings stream. In long run competitive equilibrium, each worker is satisfied with his/her level of schooling, and each firm’s demand for workers at each schooling level is satisfied. More general formulations of human capital theory allow for the possibility of heterogeneous ability among individuals and post-schooling formation of human capital on the job.

Mincer introduced the possibility of post-schooling investment in human capital in his empirical work by including a quadratic term in experience. The standard form of the earnings function that is usually estimated is therefore:

                                ln Y = b0 + b1 SCH + b2 EXP + b3 EXPSQ + e (1)

where 1n Y represents the natural logarithm of earnings, SCH represents completed years of formal education, EXP represents potential labour market experience, EXPSQ is the quadratic of EXP and e is a well-behaved error term which captures other unobserved factors that are important contributors to labour earnings. Note the influence of schooling is modelled here as separable from the influence of experience, b1 captures returns to schooling, while b2 and b3 are intended to capture returns to on-the-job training which are assumed to be non-linear because of diminishing marginal returns to increased on-the-job training and rising marginal cost of further training over time. Hence, it is expected that b0 > 0, b1 > 0, b2 > 0, and b3 < 0.

A number of econometric problems plague the basic model and have fuelled an enormous debate over the degree to which the "schooling coefficient" (b1) accurately reflects the private rate of return to investment. Many of the issues can be traced back to the validity of the initial set of simplifying assumptions which were introduced to lend mathematical tractability to the problem. These issue include, inter alia, (1) there is no control in the model for the quality of schooling; (2) all results refer only to wage and salaried employees; (3) the market should not be assumed to be in long-run equilibrium; (4) there is no correction for unobserved ability bias that is correlated with school attainment; (5) the amount of schooling may be measured with error; and (6) there are no controls for background variables such as parental education.

As is often the case in the social sciences, the theoretical debate is advanced to a point where the requirements for adequately testing such models are considerable and far exceed the available data. This is certainly true in Fiji and other countries in the South Pacific where any micro-level data on employment and earnings are rare. Given the innovative nature of this analysis, it was decided to follow a well-established empirical literature by estimating equation (1) and its variants using ordinary least squares. Further justification for this approach comes from Willis (1986) who has reviewed the evidence of the effect of "ability bias" (i.e. the effect of omitting measures of ability from the model) on estimates of the return to schooling and concludes that:

While there is obviously no evidence to either support or refute this hypothesis for Fiji, the straight forward approach produces results that are comparable to other empirical studies, such as those compiled earlier by Psacharopoulos (1985, 1994).

The common theme among all of the above critiques is that the schooling coefficient may be biased in one direction or another. However, it is also worth noting that no-one questions whether schooling affects productivity, even though virtually no studies contain any direct measure of output. A completely different debate centres around the interpretation of the schooling coefficient. Some authors argue that formal education, per se, does little to increase productivity. Rather, most improvements are acquired through on-the-job training. Under this interpretation, the observed correlation between schooling and earnings reflects the fact that education is used by employers as a device for screening applicants for pre-existing ability. One implication is that, if workers compete for the most lucrative jobs by collecting credentials merely to signal their abilities to employers, they have an incentive to over-educate themselves and exaggerate their true abilities. Consequently, the private rate of return to education will exceed the social rate of return (Layard and Psacharopoulos, 1974). The strategy adopted below is first to estimate the standard human capital model and then to test for the importance of credentials.

The results of estimating the basic schooling and schooling-experience equations are reported in table 18. The dependent variable is the natural logarithm of hourly earnings, including cash payments for overtime and the imputed value of food, housing and other benefits {Ln(Hrl Pay)}. This is our measure of worker productivity. The upper part of the table relates to all workers, while the two lower parts examine the model for male and female workers separately. Two variants of the basic semi-logarithmic model are presented in table 20 and all co-efficients take the expected signs and are significant at the 1 per cent level.

The basic schooling model in equations (1), (3) and (5) explains 12-19 per cent of the logarithm of hourly earnings. Potential labour market experience is approximated as Age less Years of Education less 6, since most children start school at 6 years of age, and it is included, together with its quadratic, in equations (2), (4) and (6). This inclusion raises the explanatory power of the model, as reflected in the adjusted R² , to 38-41 per cent. The average private rate of return to an additional year of schooling for all workers in Fiji increases from 9.0 per cent in equation (1) to 17.4 per cent in equation (2); for males, from 8.1 per cent to 16.1 per cent in equations (3) and (4); and for females, from 13.0 per cent to 21.1 per cent in equations (5) and (6). The rate of return to an additional year of potential labour market experience is very much lower at 4.3 per cent for all workers (equation 2) and, in contrast to the rate of return to education, is higher for males than females (4.7 per cent v. 3.6 per cent).

Note: * Denotes significance at the 1% level. Numbers in parentheses are Student=s >t= statistics

How do these results for Fiji compare with other countries at various stages of development? In his recent review of the profitability of investment in education at the global level, Psacharopoulos (1994) confirms the patterns he uncovered in earlier reviews, namely that primary education remains the investment priority in developing countries; the returns decline along with the level of schooling and the country’s per capita income; and investment in women’s education is generally more profitable than that for men. The Mincerian mean rate of return to an additional year of schooling was estimated to be 11.2 per cent for low income countries (income per capita of US$610 or less); 11.7 per cent for lower middle income countries (to US$2,449) and then fell to 7.8 per cent for upper middle income countries (to US$7,619) and to 6.6 per cent for high income countries (US$7,620 or more). The mean rate of return was highest in Sub-Saharan Africa at 13.4 per cent and lowest in OECD countries at 6.8 per cent.

Therefore, our estimated rate of return in Fiji of 17.4 per cent is well in excess of the mean rate for countries at its level of development (11.7 per cent for lower middle income countries) and even exceeds that for Sub-Saharan Africa^7. We shall explore possible reasons for this surprising result later in the paper.

The inclusion of a measure of labour market experience is intended to track the productivity enhancing effect of on-the-job training over the life-cycle. It also serves as a proxy for seniority which may, in itself, lead to higher earnings, but is not necessarily a guarantee of higher productivity. Training is assumed to be most cost effective early in the working life of a labour market participant and, typically, earnings rise during the initial thirty years of experience and subsequently decline. Equations (2), (4) and (6) in table 20 suggest that each additional year of labour market experience is worth about 4-5 per cent of additional income with the return never falling to zero within the normal active life of the worker. In Fiji, therefore, earnings continue to rise over the working lives of the great majority of persons engaged in the formal economy.

It is of interest to note that in all the earnings equations, the constant term is greater for men than for women, perhaps indicating that females face disadvantages in the Fiji formal sector labour market that need to be explored.

The relationship between earnings and years of labour market experience is brought out further in table 19 where separate earnings functions are reported for each level of education for all workers, for males only and for females only. The results highlight the interaction between education and potential labour market experience, with the most educated workers tending to have the steepest earnings profiles as experience rises. While the pattern is similar for females the steepness of their increase in earnings along with experience is not as pronounced as for males, perhaps reflecting the kinds of occupations to which they are assigned which do not offer an opportunity to acquire as much on-the-job human capital as males. It is also of interest to note that, given the education level, the intercept term for men is often greater than for women, suggesting that newly recruited men are paid more than women despite offering comparable educational attainments.

Note: EDO = No formal education; ED1-3 = 1-3 years of education; ED4-6 = 4-6 years of education; ED7-9 = 7-9 years of education;
ED10-12 = 10-12 years of education; ED13-15 = 13-15 years of education; ED16+ = 16 years and over of education

It is of additional interest to note that the return to an extra year of on-the-job training exhibits diminishing returns (especially for men) with the coefficient of EXPSQ being negative and usually significant.

Human capital theory asserts that both schooling and training are productivity-enhancing activities but this kind of interaction is not predicted by it nor by the "screening" hypothesis. The latter maintains that the main function of formal education is not to augment productivity but merely to filter or label the existing productivity – boosting characteristics of individuals. According to the screening hypothesis, schools, and the formal education system generally, exist to administer tests with the intention of separating individuals with above average ability from the rest. Grades are assigned and diplomas awarded to label candidates for prospective employers who, in turn, may be willing to pay an earnings premium to properly screened, high-calibre workers.

A consensus view is that education serves both as a productivity-enhancing and labeling function, with the relative importance of each varying with the level of education and type of curriculum followed. A human capital interpretation is that unmeasured ability is correlated with school achievement and more educated, gifted workers my develop enhanced work skills once they are on-the-job. Alternatively, workers with better credentials, since they have a greater potential for training, will be selected by employers for jobs that require more instruction.

An alternative explanation has been offered by Knight and Sabot (1981) who emphasize the changing and growing supply of educated workers over time. Past population growth and educational expansion may lead to a discrepancy between the size of each incoming cohort and the number of available jobs. As labour market conditions become more difficult for those entering the labour market, the structure of employment changes as more educated workers are forced to "filter-down" into less-skilled jobs, implying that the labour market is not in the kind of static equilibrium postulated by the basic "schooling" model.

In this section of our analysis of the relationship between schooling and earnings, a set of dummy variables are introduced to represent the different levels of education. This analysis tests whether the rate of return to education is simply a linear function of the level of formal schooling, as suggested by the basic model, or whether it is a non-linear function of education as suggested by the findings reported in Table 19.

The results of the exercise are reported in table 20. The estimated rate of return to an additional year of schooling is now calculated by dividing the difference between the coefficients of adjacent education levels by their difference in years of schooling. The results of this exercise are reported in table 21 and demonstrate that substantial returns to education only materialize after workers have attained 7-9 years of education. During this period women perform better than men in achieving higher returns to senior secondary over junior secondary and to certificate/diploma education over senior secondary schooling. At the highest level of university education, the returns compared to the next lower level are slightly higher for men, perhaps reflecting lower occupational attainments of highly educated women.

Table 22 introduces an alternative formulation of our basic human capital equation by disaggregating the total length of potential labour market experience into the actual amount of time spent with the current employer (YRSEMP) and the residual potential length of time spent with all other employers (OTHJOBEX). For male, female and all workers the return to years of current job tenure (YRSEMP) is far greater (6.6%, 9.2% and 7.5% respectively) than for other potential job experience, confirming that current job tenure is the most important work experience segment in the determination of present earnings. It is revealing to note that the coefficient of YRSEMP is larger for females than males while the coefficient on other potential experience is very small and insignificant for female workers.

Note: EDO is the excluded class. * Denotes significance at the 1% level. Numbers in parentheses are Student=s >t= statistics.

Note: Estimated rates of return to an additional year of schooling are obtained by dividing the difference
between the two adjacent coefficients by the difference in the mean years of education of the schooling

Note: * significant at the 5% level; ** significant at the 10% level

This result may well reflect the fact that this segment of labour market experience for females is, indeed, only "potential" experience, and is not a good indicator of the actual time they have spent in the labour market because they may have dropped out to cater for their other childbearing and household activities.

It is significant that the constant term in the equation for males (0.371) is again greater than for females (0.230), suggesting that other earnings-determining factors are helping to being about the higher realized earnings for males. Table 23 examines the relative importance of these labour market segments by age group, education and sex. Predictably, job tenure with the current employer (YRSEMP) lengthens with the age of the worker; there is a tendency for it to be longer for more educated workers. The length of tenure for younger, less educated women is slightly lower than for men and this gap tends to widen with age, indicating more frequent job changes for women. Although few women remain in the formal sector workforce after the age of 44, those that stay, particularly the more educated, have comparable job tenure with men.

Conversely, because female current job tenure is less than for males, their potential other labour market experience (OTHJOBEX) is often greater than that for males.

Note: * Less than 10 cases

Labour markets are continually responding to desequilibria created by the demand and supply of educated workers altering over the course of development. On the demand side, the development process in most countries, including Fiji, begins with the emergence of a large, centralized public sector which provides the majority of formal sector employment opportunities. Once infrastructure and private markets emerge, the importance of the public sector as an employer diminishes. This change in the economy leads to a change in the occupational structure – a proportionally lower demand for new administrative and clerical workers and an increase in the demand for skilled artisan and semi-skilled blue collar workers, particularly in the manufacturing and service sectors. Partly generating the above changes and partly as a result of these changes, the creation of a capital market leads to an expansion of the school system altering the composition of labour supply. Each entering cohort of workers becomes more educated than the last. More important still, past rapid population growth often leads to a large discrepancy between the size of each incoming cohort relative to the number of new jobs created within the formal sector. The result is a growing disparity between the structure of the labour force and the structure of employment opportunities and the consequent ‘filtering-down’ of educated workers into less skilled tasks. Consequently, a number of authors have recently argued that occupation may play a more central role in the formal sector wage determination process than previously thought (Knight, 1979; de Beyer and Knight, 1989; Knight and Sabot, 1990; Cohen and House, 1994).

The purpose of this section is to explore whether such a process has been operating in Fiji and to examine the consequences for the labour market and the implications for education and employment policy.

Occupation is not usually conceived of as an important determinant of wages. Under a neo-classical model with perfect mobility of labour, an equilibrium condition is that the return to education is equal in all occupations. Consequently, explanations of wage differentials among workers focus more on individual characteristics than the nature of the jobs they perform. In contrast, de Beyer and Knight (1989), building on the earlier theoretical contribution of Knight (1979), argue that individual and job characteristics interact and wage determination models are improved by explicitly accounting for occupational choice.

De Beyer and Knight employ the concept of the "occupation production function", an idea introduced by Knight (1979), whose point of departure was to recognise the occupational specific relationship between a worker’s skills and higher productivity. Productivity within any occupation can be expressed as a function of the individual’s skills as represented by cognitive skills, obtained through formal education, vocational skills acquired on the job, and innate ability. Rates of return to these factors differ across occupations for a number of reasons. First, different occupations require different sets of skills. Second, skill levels vary across jobs. Third, prior skills obtained (e.g. cognitive skills from formal education) can affect the rate of vocational skills acquisition once on the job. Fourth, different rates of return can offset differences in non-pecuniary compensation. Finally, certain occupations may be able to capture benefits depending on the strength of institutional factors and/or occupationally-based bargaining groups.

The supply of educated workers has increased significantly in Fiji in past years as the education system expanded while the rate of formal sector employment expansion has been depressed. What are the implications of this expansion for productivity wage levels and the returns of education?

First, consider the neoclassical model, in which wage flexibility is permitted. An increase in the supply of well-educated workers lowers the wage to skilled professionals. If the wage falls below the wage for semi-skilled occupations, well-educated workers will leave the skilled professions and ‘filter-down’ into less skilled occupations. This effectively lowers the wage rate for semi-skilled occupations and, in turn, forces a portion of less educated workers to lower their expectations and move down into less skilled tasks.

Second, consider the case of "job competition" a concept adopted as an alternative to the neoclassical wage competition model. In this model, on-the-job training is the primary means of cognitive skill acquisition, and therefore the primary determinant of productivity. Prior education does little to augment productivity per se; however, if there were an interaction between background variables and skills acquisition, then more highly educated workers would require less on the job training to achieve a given level of productivity. For this reason, employers use education as a screening device and prefer to employ the more educated who have lower expected training costs.

Assuming the supply of educated workers grows faster than the number of newly created formal sector jobs, highly educated workers will filter down into occupations which require fewer skills. Here, however, the wage rate is determined by the job, not by the level of formal education. Under this scenario, if there is an increase in the supply of educated workers, new entrants into the labour market are likely to feel the major impact since cognitive skills are only acquired with work experience. Employed workers have already undergone a certain amount of their training. Employers will, however, attempt to reduce their training costs by favouring workers with the highest levels of formal education. Depending on the relative elasticity of supply of labour, employers if they so desire, may be able to practice discriminatory hiring against certain socio-economic groups based on non-economic factors such as ethnic group and sex.

In both scenarios educated workers filter down into low-skilled jobs. In the wage competition model, workers with the same level of schooling will receive identical entry-level wages. This may not be true under the job competition model in which new workers enter lower paying positions. Furthermore, in the event of an expansion of the school system, standard rate of return analyses, as illustrated above, may over-estimate the true rate of return to education, by failing to appreciate that it is falling over time. Inferences concerning the true rate of return that are based on cross-sectional data are valid only when the period accurately represents a synthetic cohort. Put differently, the implicit assumption in rate-of-return analysis based on period data is that today’s school leavers have the same scarcity value and access to occupations that previous cohorts with this level of education enjoyed. If this were not the case, for instance, if the most lucrative occupations were satiated, the rate of return to education would be overstated. For example, today’s school leaver, entering the labour market as a semi-skilled blue collar workers, will never aspire to be a bank manager since the position is now reserved for university graduates.

To what extent has this process of filtering down taken place in Fiji? What role does occupation have in the wage determination process?

In our cross-sectional data set, the age of a worker measures two separate effects. First, it provides a crude measure of potential labour market experience which serves as a proxy measure of the extent of skills acquisition. Second, it defines the year of labour market entry. This is not a serious drawback in a competitive labour market which is virtually static over time. However, if the supply of educated workers increases over time, the return to experience is more likely to reflect the scarcity value of labour at the time of entry into the labour market and the scarcity value over the life-cycle of being in a small cohort than it is to reflect the gains to productivity acquired through labour market experience. Evidence of the dramatic expansion of Fiji’s school system is displayed in Table 24 which shows the mean years of schooling for formal sector workers by date of joining the labour force.

Table 24 clearly demonstrates the results of an expanding educational system. Workers joining the labour force pre-1961 averaged 6.1 years of education. This grew to 9.1 between 1961-70 and upwards to 12.1 in the most recent 7 years between 1990 and 1997 when our survey was undertaken. This means the average worker recently entered the formal sector labour market with slightly better than Form 6 school attainments. It is also of interest to note how the variation in school attainments, as expressed in the standard deviation of years of education, has declined over the years, reflecting the move to greater equity in access to education and, perhaps, the higher level of schooling considered essential before obtaining employment. The increase in education over time follows the same pattern for males and females with some indication that the differential within the sexes narrowed more for women than men.

Between 1976 and 1986, the population of Fiji grew at an annual rate of 1.98%; the labour force grew at 3.2%. In the most inter-censal period 1986-96, the population grew at 0.8% and the labour increased at 2.1% p.a. How has the changing demographic composition and rising school attainments of the labour force impacted on the operation of the formal sector labour market in Fiji? Has the hypothesized filtering down of school attainments and qualifications into lesser jobs taken place? For example, where in earlier times a secondary school certificate may have sufficed to secure employment in a professional or semi-professional occupation, now employers may have raised the minimum certification to a tertiary level certificate or university degree. Perhaps this process of filtering down helps to explain the results reported earlier that the rate of return to education, particularly for males, tends to rise with labour market experience. The better educated, earlier entrants to the labour market faced less competition and acquired employment high on the scale of remuneration. Later entrants faced greater competition and had to settle for lower-status occupations with commensurate lower pay.

Table 25 portrays this phenomenon and how the distribution of school attainments within each formal sector occupational group has changed over time. Occupations are classified into eight skill-based groups, labelled as Senior Professionals (SENPROF), Middle Professionals (MIDPROF), Clerical/Office Workers (CLERICS), Sales Workers (SALES), Service Workers (SERVICE), Artisans (ARTISANS) Garment Workers (GARMENTS) and Other Blue Collar Workers (BLUECOLL)⁸. They are constructed to rank occupations on the basis of skill, and to limit possible movement between occupations. In table 25, all occupation groups display a pattern of increasing educational attainment for the most recent recruits to the formal labour market. For example, of those senior professionals joining during the 1960s, 34% had completed primary education or less. Of those recruited since 1991 no-one with this level of education could enter such jobs. Similarly, further down the scale of occupation skills, during the 1960s 57% of artisans had completed primary education or less; since 1991 only 7% of those entering these kinds of jobs had such education. In contrast, while only 8% of artisans joining the labour market in the 1960s had form 6-7 education, by the 1990s fully 54% of newly recruited artisans had attained this level of education.

Table 26 reports the mean weekly earnings for each occupation group by sex. Senior Professionals (SENPROF) are by far the highest paid with Garment workers (GARMENT), Sales workers (SALES) and Blue Collar workers (BLUECOLL) the lowest paid. Women earn less than men in all occupation groups while the differential is greatest in the higher skilled occupations⁹. Results of unreported multivariate regression analysis indicate that 39% of the variation in the natural logarithm of hourly earnings is explained alone by dummy variables representing the occupational groups. For men alone, occupation explains 35% of the variation in the natural logarithm of hourly earnings; for women alone this rises to 50%.

To what extent do the pay differences by occupational groups merely reflect differences in years of schooling, and tenure with the current employer? As demonstrated above, and in table 25, years of schooling tend to correlate well with the skill-intensity of occupations and the corresponding pay levels achieved. However, sales workers attain relatively low pay despite having above average years of education; but this advantage is offset by their relatively higher propensity to change jobs more often and thus have below average levels of tenure with their current employer. Garment workers earn much less than other blue-collar workers despite being better educated but, again, their job tenure is significantly lower.

Women’s receipt of lower pay than men in all occupations cannot be attributed to their inferior schooling since they have more years of education, on average. However, their years of tenure with their current employer are almost one-third less than for men, perhaps, partly explaining their inferior pay levels. This is likely to be attributable to their childbearing and other domestic duties which often lead to their sometimes intermittent participation in the labour market.

The following exercise explores the effect of occupational assignment in the wage determination process controlling for an extended number of other factors that could be expected to influence hourly earnings. The results are reported in Table 27.

The use of the ordinary least squares (OLS) regression techniques to estimate the wage functions in table 27 encounters the problem of self-selection bias or endogeneity of a number of the explanatory variables included in the equations. For example, while some of the explanatory variables included in table 29 are unambiguously exogenous, for example, sex, education and ethnic group, others, such as sector of employment (DVPUBLIC; DVPRIVATE; DVSTAT) and occupation depend on the choice of the individual worker or employer and may be conceived as endogenous to the model. For example, the higher paying occupations or sectors of employment may be able to choose from the applicant pool of workers those with the most desirable characteristics, only some of which are included in our equations. Similarly, only certain individuals with particular characteristics feature in our sample of formal sector workers while many others, according to the selection process, are in the informal or rural sectors. If these latter persons were engaged in the formal sector, or certain of its occupations, it is unlikely that their earnings would have been comparable to those reported here. In this case, the OLS estimates reported in table 27 will contain selection bias and will require more sophisticated econometric methods to address the problem.

For the moment, as a first approximation, we examine the results generated from OLS estimation and reported in table 27. It is of interest to note the sharp increase in the explanatory power of the equations in table 27, varying between 67%-59% of the variation of the natural logarithm of hourly earnings. Of particular interest are the significant coefficients on the Indian dummy variable (DVINDIAN), indicating that Indian workers earn about 8% less than Fijian workers with similar characteristics; on the positive coefficients on public (DVPUBLIC) and public corporation employment (DVSTAT) and on the sizeable negative coefficients of the occupation variables, all relative to senior professionals. It is also revealing to note that men earn a 15% premium compared with women with comparable characteristics.

*, ** Significant at 5% and 10% respectively. Figures in parentheses are students’ ‘t’ statistics

Comparing the equations for male and female workers, it is of interest to note the greater public sector differential for females while their return to occupational training (DVCTRAIN) is less than one-half that of men. This result suggests the need for greater in-depth analysis of the kind of vocational training undertaken, a subject which is explored below. Occupational pay differentials are also much wider for women compared with men, a subject which is taken up in the following section.

To further our understanding of the effect of occupation on the wage determination process, earnings functions have been estimated for each occupation group and the results are reported in table 28. Again, the chosen earnings function is an extended variant of Mincer’s (1974) basic formulation. The results are very successful with the proportion of the variance of the natural logarithm of hourly earnings (ln Hrly Pay) varying between 51% (Artisans) and 28% (Garment workers). Most of the explanatory variables bear the expected sign and are often significant according to the standard criteria. Each individual regression equation is compared in turn with every other on a pairwise bias using a standard F-test first suggested by Chow (1960). Specification test statistics corresponding to these pairwise comparisons are presented in table 29. All pairwise specification tests are significant at the one percent level, confirming that each occupational group can be conceived of as having its own production function.

How do the variable coefficients compare across the eight equations? Formal education enters as a set of dummy variables with EDO, no schooling, used as the numeraire or excluded class. Professionals and other white-collar workers receive the highest returns to education as well as to job tenure with the current employer and previous potential experience. For service and garment workers the returns to the various forms of experience are among the lowest. The steepest rewards for experience accrue to the relative skill-intensive occupations – Senior and Middle Professionals, Clerical, Sales Workers and Artisans – and the lowest to Service and Garment workers. This result may reflect the greater opportunity for upward job mobility in the former occupation groups.

Apart from the Professional and Clerical groups of occupations the coefficient on sex (males=1) is significant and positive, indicating positive discrimination in favour of men. The differential varies from 31% for Artisans to 9% for service workers.

Indians receive a premium as Middle Professionals and Garment workers but in most other occupations they receive significantly less (15-6%) than the excluded Fijian group. Rotumans and other races seem to gain favourable treatment, especially in the Professional and other white-collar occupations.

Apart from the Professional and Artisan groups the Public sector appears to pay a premium to its employees compared with the Private sector, ranging from 26% for Service workers, 16% for Other Blue Collar workers and 5% for Clerical staff. Yet, its Senior Professionals receive 15% less pay than comparable staff in the Private sector. The largest pay differential across all occupational groups favours employees in the Statutory Bodies where the premium over the Private Sector is 58% for Sales workers to a low of 29% for Senior Professionals.

An important finding is the positive return to those who have undergone vocational training in all occupational groups except Senior Professionals. The return is as high as 17% for Artisans with the low at 5% for Clerical workers.

*, ** Significant at 5% and 10% respectively. Figures in parentheses are students’ ‘t’ statistics