Gender Participation in Technical Training Institutions:An Assessment of the Kenyan Case
The paper focuses on skill training opportunities for females in Technical Education Programmes (TEP) in Kenya. In Africa, labour markets have become so competitive that females need to be assisted to enter such markets. Expanding skill-training opportunities for females in training institutions could meet this demand. Informal interviews and questionnaires were used to collect data that were analysed within the framework of human capital theory. Sex balance was lacking in TEP and most institutions were internally inefficient, with endogenous factors forcing trainees out of the training programmes.
I. Introduction
Resources used on education and training are investments in the socio-economic development of a nation. In developing countries, studies have shown that rates of return to expenditures on education and training are very high (Psacharopoulos 1995). In Kenya, such studies show that farmers and informal sector workers with primary education are one-third more productive than their counterparts without this basic education (Ndegwa 1991).
Human resource development is a critical factor for the success of an industrialisation process of any nation. It assures the supply of well-trained managers and skilled technicians at both shop-floor and supervisory levels. This calls for a collaborative effort between the training institutions and employers in order to narrow the gap between graduate outputs and skill demand.
In Kenya, it is estimated that 59 per cent of the population is below 20 years of age (Republic of Kenya 1996). The government of Kenya, through Sessional Paper No. 2 of 1996, observed that there existed a mismatch between demand and supply in the labour market. It continues to assert that the majority of the youth lack vocational and technical skills that are required in the labour market. This places those without work skills at a serious disadvantage.
In Africa, the majority of the population (with women comprising 52%) lives and works in the rural and urban informal sector (Kelly 1994). It has been observed that ideological beliefs about gender differences have [End Page 21] produced substantial constraints and biases affecting decision regarding women's productivity and investments in their education and welfare (Stromquist 1998). In Africa, women usually provide unpaid or underpaid family labour in the familial mode of production. Stromquist has observed that an increasing number of women are becoming heads of households in Africa. It is estimated that one-third of the households in Africa are headed by women. If unskilled, such women heads of households represent a vulnerable group. This calls for programmes to sensitise women and girls in Africa on the need for training in work skills necessary in life.
In Kenya, labour surveys have shown that women constitute over half of the labour force and predominate in the rural areas (Ndegwa 1991). Ndegwa observes that in the 1980's the unemployment rate among females was more than double (24.1 per cent) than that of males (11.7 per cent). This was partly attributed to lack of training among girls and women, especially in technical skills needed in the informal sector. Ndegwa (1991), Sessional Paper No. 2 of 1996, and Gatheru and Shaw (1998) concur that when gender inequities occur in education and the labour market, girls and women are the most disadvantaged. They continue to point out that the problem is complicated by the absence of reliable data needed for policy decisions.
A 1993 economic survey in Kenya estimated that the formal sector grew by 2.4 per cent while the informal sector grew by 14.7 per cent (Central Bureau of Statistics 1993). For girls and women to take advantage of this growing informal sector, they need to be trained in the relevant work skills. Such training will give females an added opportunity to contribute more to national development and family income. Sessional Paper No. 2 of 1996 recommended that girls should be encouraged and assisted to undertake training in non-traditional female occupations. The Sessional Paper partly bases its recommendation on the assumption that males and females are equally likely to go for technical training.
Stromquist (1998) pointed out that government reductions in support of education and training negatively affect poor families to a larger extent. He continued to assert that when this happens, it is the girls in the poor families that are affected most. Negative changes in an economy, especially in Sub-Saharan Africa, are likely to cause a disproportionate increase in the workload of women and the girl-children as they are required to participate more in earning an income for the household's survival. Jaquette (1997) observed that women presented the most persistent outcry against the costs and the assumptions of structural adjustments because of increased poverty and inequality of opportunities. Programmes aimed at encouraging and sensitising women and girls, especially those from marginal areas, on the need for training should be initiated. [End Page 22]
A report of a presidential committee on employment in Kenya recommended that efforts be made in technical training institutions to encourage girls to take up training skills that give them more options and opportunities for employment (Ndegwa 1991). The report noted that more girls than boys drop out from school, especially in marginal areas. In view of this, out-of-school education and training (informal) would increase literacy and offer work skills to girls who constitute the majority of dropouts.
Vocational education and training does not create jobs if no job exists (Oxtoby 1993). Training relies on the economy to create employment opportunities. Psacharopoulos (1995) argued that job-related skills are best taught by employers or private training providers. World Bank (1994) held the same view as Psacharopoulos. The Bank's experts argued that vocational and technical skills are best imparted in the work place after a general education. The experience of Psacharopoulos and World Bank seem to come from an environment where industry has a capacity for formal training. This is not always the case especially in the formal sector in Kenya.
In Kenya, the fast growing Jua Kali (self-employment) sector owes its stock of skills to the formal vocational and technical training institutions (Youth Polytechnic, Technical Training Institutions and Harambee Institutes of Technology). However, informal training in the Jua Kali sector takes place on a small scale in urban centres. In both formal and informal training, work skills, self-employment and entrepreneurial skills are emphasised. It has been observed that participation of girls in both formal and informal technical training (non-business) courses is below 20 per cent (Ngware, Wekesa, and Wasike 1999).
A study of factors that account for gender differences in access to post-secondary education in Uganda found out that the school system and household and labour market factors interact in a way that discourages girls' participation in training institutions (Kasente 1995). Kasente's findings show that females want to be trained; however, there are barriers that discourage them from going for training.
2. Theoretical Framework
The study was conducted within the human capital approach to education (Harbison and Myers 1964; Senanu 1996). Proponents of the human capital theory argue that education and training constitute an investment in human capital. Such an investment yields future returns in the form of income and earnings for the individual, and increased economic growth through enhanced productivity for the society. It is further argued that individuals, [End Page 23] being rational beings, always seek to maximise their utility and productive capacity through the acquisition of skills and knowledge necessary for economic growth (Psacharopoulos 1995; Senanu 1996). Cross-country studies have indicated the existence of a threshold of human capital accumulation after which a country may experience accelerated growth (World Bank 1994).
Critics of the human capital theory, while accepting the role of education in the acquisition of technical and vocational skills, argue that there exists no direct linkage between education, occupation, productivity and the commensurate income (Senanu 1996). Nevertheless, when combined with an Education Production Function (EPF), the human capital theory may help to explain the value of vocational and technical education to the society and individuals.
Lastly, a widely used approach to determine the distribution of educational opportunities is to compare it with a perfectly equal distribution. In such a case, the actual share of a group is compared with the amount that that group would receive if all groups under consideration were to receive equal shares.
3. Statement of the Problem
In a competitive environment, disadvantaged groups in a society will hardly be in a position to take advantages that come with liberalisation and a fast growing informal sector. Training potential and opportunities for women and girls in Kenya need to be explored with a view to expanding them. Further research on skill training needs for girls and women in developing countries, such as Kenya, is needed in order to provide reliable data for policy formulation. Vocational education and training of girls in Sub-Saharan Africa offer the society an opportunity to equate the distribution of resources, reduce absolute poverty and develop human resources. In this problem, it is assumed that if the society was unbiased, both male and females would equally go for training.
4. Purpose of the Study
The purpose of this study was to identify gender disparities that occur in technical training institutions. In this study, gender disparity was operationally defined as any difference between males and females that occurred as trainees passed through Technical Education Programmes (TEP). The research questions were:
i. Was female enrolment in TEP different from that of the male?
ii. Did participation in TEP differentiate trainees by geographical background? [End Page 24]
iii. Was there a difference in the examination performance of trainees by sex?
iv. To what extent did female wastage in TEP differ from that of males?
v. What were the causes of trainee wastage in TEP?
5. Methodology
The study used an ex-post facto design. This helped the researcher to relate an after-the-fact analysis to an outcome. This design was found appropriate for the study as it allowed the investigation of subsequent relationships between variables.
The population included technical training institutions that were initially sponsored by communities in Kenya as identified by the Ministry of Research, Technical Training and Technology annual returns (N=17). Sampling was done at two levels: institutions and trainees. A random sample of seven institutions was selected to represent the population's geographical distribution, institutional size and programme areas (technical and business-related courses). A random sample of 556 (N=6214) trainees was selected to represent the population's gender, geographical background, age distribution, year and course of study.
The TEP records proforma was developed to enter data on enrolments and examination performance for the period 1993/94 to 1996/97. The layout of the proforma was derived and constructed based on research studies and literature related to practical evaluations of technical and vocational training institutions.
The first section of the proforma contained enrolment entries by institution, gender, course and year of study. In the second section, data on examination performance and graduation by gender was recorded. To solicit information on the trainees' background and causes of wastage, a questionnaire, with closed and open-ended items was developed. The items sort demographic information that included gender, age and course of study. The item on geographical background required the subjects to state the nearest town (any urban centre that had a mayor) from their parents' place of residence while they were attending primary, secondary and post - (current) secondary education.
The item on the causes of wastage required the subject to respond on a five-point Likert scale items (i.e., Strongly Agree [5], Agree [4], Uncertain [3], Disagree [2] and Strongly Disagree [1]). The items were developed and validated based on the findings of earlier studies. A high rating for an item [End Page 25] indicated agreement with the statement while a low rating indicated disagreement with a statement.
The instruments were also pilot-tested with a small group (n=52) of TEP trainees. An internal consistency reliability estimate was calculated using Cronbach's Coefficient Alpha (a=0.93). The items were further revised using the data that had been collected during piloting. This ensured that the items were reliable in measuring the variables.
The researcher visited each of the seven institutions (in 1998) in order to enter data from records and to be available for consultation by subjects. The instruments were administered to the 556 subjects. The importance and purpose of the study was explained to the subjects. The researcher requested their assistance and co-operation. A total of 529 instruments were returned, and constituted a final response rate of 95%. However, institutions did not keep records on the number and background of trainees who dropped out.
Data were coded and analysed using Minitab (a computer statistical package). Descriptive statistics, including frequencies, means and per centages, were used to analyse the data in order to answer the research questions. The open-ended questions were qualitatively analysed and grouped into emerging categories (i.e., rural or urban). A chi-square (2) statistic and one-way analysis of variance (ANOVA) were used to determine the difference between sex and categories at a = 0.05.
6. Findings
6.1 Enrolments
Total enrolment by institution for the period under study ranged from 915 in Mathenge Technical Institute to 5228 in Rift Valley Institute of Science and Technology. In the same period, only two institutions (Mathenge and Kaimosi) recorded a higher female enrolment (58.4% and 56.3%, respectively) than that of males. It was noted that the two institutions had among the lowest total enrolments (915 and 1176, respectively). For the rest of the institutions in the sample, females took about one-third of the training places. The difference between male and female participation was tested to establish whether it was real or by chance using a chi-square test. The results of the test showed that a chi-square statistic (20.838) was statistically significant at a = 0.001 for 4 df. This meant that participation in technical training is not independent of gender. From these findings it was confirmed that:
i. Female participation in technical training is relatively low.
ii. The probability of access to a training institution for a female school leaver was less than a half. [End Page 26]
For all the years under consideration, female enrolment in technical training institutions ranged from 30% to about 35%. The rest of the training places were taken by their male counterparts. This confirmed that technical training institutions in Kenya are male dominated.
Data on course enrolment showed that females were concentrated in business-oriented programmes (accounting and secretarial studies). This was the case with Mathenge and Kaimosi that showed a relatively higher female enrolment. While male trainees registered evenly in manual skill-oriented courses (for instance, auto mechanics, electricity and building) the few females in such courses were concentrated (above 90%) in only two courses, namely, food and beverage and production services, and clothing and textiles. This confirmed that in Kenya, females are yet to penetrate the male-dominated courses offered in technical training institutions. A chi-square test of independence between admission to a training place and gender established that admission was not independent of gender. This suggested the presence of gender inequity in admission to a training place in favour of males.
Data on geographical background of the sampled students indicated that of the respondents with a rural background, 55% were males while 45% were females. On the other hand, among those with an urban geographical background, 62% were males and 38% were females. Thus in both (rural and urban) geographical backgrounds, males have a higher chance of participating in technical training programmes. Such a situation can be explained by the reluctance of girls with a rural background to enrol in institutions that have a rural location and, hence, restricting them to a rural life that has minimal self-employment opportunities. In the case of the urban areas, males enjoy two advantages. They have role models in the male-dominated and fast growing Jua Kali (self-employment) informal sub-sector in urban centres. Secondly, educating boys in Africa is seen as a better investment choice than sending girls to school (Todaro 1985).
Looking at males alone, those with an urban background (56%) were more represented in technical training institutions than those with a rural background (44%). In the case of females, those with an urban background constituted 57% while those with a rural one were 43%. The case of males could be explained by the presence of Jua Kali informal sub-sector mentioned earlier. In Kenya, it is likely that females with an urban background prefer to attend the mushrooming private and urban-based training institutions that offer short tailor-made courses. Such courses include hairdressing, tour and travel, computer applications, music, fashion and design, salesmanship and foreign languages. Except for computer applications that were offered in only three of the institutions visited, the rest of the courses were missing from the sample institutions' curriculum. [End Page 27]
6.2 Failure Rates
Subject failure and consequent repetition in technical training institutions was evident. This was more common with end of course examinations taken by final-year trainees (third years). It was revealed that students who failed in at least two subjects (papers), or in at least one trade practice paper in the examinations, made private arrangements to repeat the third (final) year of study and/or re-sit for the entire examination; otherwise the candidate was referred in the failed papers. As a result, failure rates in the sample institutions could be used as a proxy for repeater rates. Male failure rates in external examinations ranged between 14% and 30% while that of females were between 6% and 8%. This suggested that more males than females failed in their final examinations.
Students in years 1 and 2 of study sat for internal examinations at the end of every year. A trainee had to pass all the papers before progressing to the next year. An investigation into the proportion of trainee repeaters and non-repeaters showed that there was no significant difference between the number of males and females who repeated failed subjects in internal examinations.
In the analysis of external examinations done at the end of the final year (third year), performance was measured on four categories. The categories were: 'credit' with a score of 4, 'pass' with a score of 3, 'referral' with a score of 2, and 'fail' with a score of 1. The hypothesis test was to establish whether external examination results in technical training institutions differ on the two categories of gender (male and female). Table 1 shows the observed and expected cell counts (scores) for the two factors based on their respective categories for the period 1993/94 - 1996/97.
A chi-square statistic of 199.529 was found to be significant at a = 0.001 significance level. This meant that the observed differences in the cell counts were not by chance. This suggested that in technical training institutions, performance in external examinations differs significantly between males and females, and was hence discriminative. A higher percentage (71.4%) of females (compared to 39.6% of males) scored an overall grade of either 'credit' or 'pass' compared to a grade of either 'referral' or 'fail' (28.6% and 60.4% for males) in the final end of course examination for the period under study. [End Page 28]
Observed and expected cell counts for gender and examination performance in the sample institutions
6.3 Graduation Rates
Graduation rates by gender, 1993/94 - 1996/97
Table 2 shows the graduation rates by gender for the period 1993/94 to 1996/97. In Kenya, one of the criteria for employment is being a graduate. The certificate awarded to a trainee is evidence of being a graduate. Moreover, the pride of any educational institution is to have as many students as possible passing within a cohort. From such a background, graduation rates of between 39% and 77% observed in table 2 are far below the expectations of both the technical training institutions and the society. For all the years under consideration, female graduation rates were higher (69%) than those of males (46%). However, in absolute terms, more males than females graduated. This variation can be explained by the over-representation of males in technical training institutions. Table 2 also [End Page 29] shows that the combined (male and female) graduation rate was 51%. This suggests that almost half (49%) of the trainees in technical training institutions do not graduate (at least in their first attempt).
6.4 Drop-out Rates
In the analysis of drop-outs, it was assumed that those who could not be accounted for as a class proceeded from one academic year to the next constituted drop-outs. It was also assumed that transfer in and out of an institution would cancel out and hence make the effect of transfers negligible.
Wastage in terms of dropping out was a common phenomenon in technical training institutions. It was observed that between years 1 and 2, female dropout rates ranged between 14% and 27% while those of males were between 17% and 26%. Between years 2 and 3, the range for females was between 17% and 24% while that of males was between 16% and 22%. The observed dropout rates were high given the amount of material and human resources invested in technical training institutions that risked being under-utilised.
A closer look at the dropout phenomenon shows there existed a pattern that could be associated with gender. As students proceeded from the first year of study to the second, female drop-out rates were higher than males in only one out of four cases. This suggested that males have a higher probability of dropping out in the early stages of training than females. On the other hand, females' drop-out rates were higher than males' in three out of four cases between years 2 and 3. This suggested that more females tended to drop out in later stages of training.
In order to establish whether the observations differed significantly by sex, one-way analyses of variance (ANOVA) was done. The test of significance for equal means at a = 0.05 indicated that drop-out rates for males and females did not differ significantly in technical training institutions.
Analysis of cohort wastage for the 1993/94 and 1994/95 year 1 cohorts indicated that almost a third (33% and 34% for 1993/94 and 1994/9 cohorts, respectively) of the technical training institutions' trainees do not finish their training (table 3). This is a relatively high cohort wastage rate that portray technical training institutions in Kenya as internally inefficient.
Table 3 shows the cohort wastage rates by gender for the two cohorts. From the percentages, it was not easy to tell whether the rates in table 3 differ significantly. As a result, a chi-square test was done. The results of the test showed that cohort wastage rates for males and females differed significantly at a=0.05. From table 3it would seem from absolute values [End Page 30] that more males dropped out from the cohorts than females. However, in terms of percentages, the female cohort wastage rates were higher (36% on average) than those of males (33%). Since percentages are more reliable indicators of representation than absolute values, a higher proportion of females dropped out from the cohorts. Table 3 suggested that there existed inequalities in terms of retention power of technical training institutions along gender lines in favour of males.
Cohort wastage by gender
Table 4 shows the reasons given by drop-outs for leaving TEP. From the table, a large proportion (52 per cent) of trainees dropped out due to financial problems. Indiscipline among trainees ranked as the second (15 per cent) cause of dropping out. The background of drop-outs showed that while trainees from low social economic status (SES) dropped out due to financial problems, those who were expelled from training due to disciplinary cases were from middle and upper SES groups. Securing an employment (10 per cent) was also cited as a cause of leaving technical training. It was indicated that some trainees, while on field attachment, choose to work for the employer and earn an income rather than go back to complete their training.
High youth unemployment rate in Kenya tends to increase the opportunity cost of turning down an employment offer. Hence trainees, being rational, would prefer present employment rather than an expected future employment opportunity. Other notable causes of dropping out were joining alternative form of training (teaching and nursing), pregnancy and failing examinations. [End Page 31]
Reasons for dropping out as given by drop-outs, N=52
7. Conclusions
The investigation on gender participation in technical training institutions in Kenya has yielded information for planning purposes. The situation is quite discouraging with most institutions being male dominated. Nevertheless, the performance of girls in final examinations is encouraging. However, sex stereotype in course admissions is quite evidenced, with girls concentrating on traditional female areas that have to do with catering and secretarial work. The institutions involved need to consider broadening their range of courses. There are marketable courses available that would increase the participation of girls in TEP. There is also a need to introduce an admission policy that would encourage female participation in TEP. Such a policy would include setting aside a fixed number of training places for girls and especially in male-dominated courses.
It is clear that skill training is playing and will continue to play a vital and essential role in the growth of the informal sector in the 21st century. Informal sector labour market expects potential employees to have essential work skills. Those who do not have these skills will be seriously disadvantaged. It is suggested that policy makers in post-school training institutions place skill-training needs, especially for females, at the top of their funding list.
Acknowledgements
Acknowledgment
I would like to thank Dr. Gideon W. Webi of Laikipia Campus, Egerton University, and Dr. Wilson S. K. Wasike of Kenya Institute for Public Policy Research and Analysis, Nairobi, for their comments on the research from which this paper was written. [End Page 32]
