So where does this leave the debate? Psacharopoulos(1996b) resorts to a "third party" neither pro nor anti the RoR approach but one who "is a government official in the Ministry of Education of a country with a per capita income of less than $1000". The country faces a series of educational crises as documented in reports in the daily press, such as teacher's strikes, student unrest, low primary school attendance among girls in rural areas, inadequate physical facilities in the cities, low secondary achievement scores by international standards and insufficient university places to accommodate all those who want to pursue university degrees. Because of the many demands on the limited state budget, the government has not been able to increase the real amount of public resources devoted to education, so the educational crises has been lurking for the last two decades. What should he or she do? Resorting to human capital theory, Psacharopoulos places his bets on the discipline of economics so that "even if the costs and benefits cannot be satisfactorily quantified and measured, empty statements such as 'the country needs 10,000 engineers by the year 2005' are ruled out. It is the process of thinking about the costs and the potential benefits of education that really matters". A more realistic view is that of Lauglo (1996), who states that RoR is a useful technique but has limitations. "This technique is controversial. To give guidance for present decisions, one needs what is never available: information on future earnings associated with different types of education. Data from the past are the best we can do, and reliable estimates of lifetime income streams are only available for those educated many years ago. The problem is that labor markets and the supply of educated persons to those markets can change so as to make past income streams poor predictors of future ones. Take the example of primary education. Rate of return analysis is used as a rationale for giving priority to it, for the rate of primary education is said to be typically higher than for secondary or higher education. But the calculation of rates is based on data for cohorts that received their schooling many years ago, when primary education was much less scarce than it is today."
Large scale statistical systems with regular (monthly or quarterly) surveys of labour market phenomena are essential for LMIS's. Except in a very few rich countries such systems hardly exist in the world and this is particularly so for the developing world. Richter (1982) has suggested an innovative scheme to provide labour market information through the use of key informants. The idea is to collect selected communitylevel data through key informants using the local knowledge of particular categories of respondents public officials, teachers, businessmen, large farmers etc. This is much simpler and cheaper than a household survey and vastly cheaper than a census. Once reliable key informants have been identified the informants are questioned on a panel basis. The information includes both generalpurpose questions about the overall situation and longerterm trends of labour markets plus specific questions related to shortterm movements and fluctuations in labour supply and demand.
The active population is defined to be those aged 10 to 65 and, as we have seen, the model uses a concept called 'labour pool' which is those people who are in the work ages 10 to 65 and not in school. This is disaggregated into four education levels, incomplete primary education, incomplete secondary, completed secondary and with 'O' level pass and higher education including those with 'A' levels, dropouts from higher education as well as university graduates.
A full description of the equations and structure of the MACBETH model was given in the previous chapter. In Sri Lanka to obtain labour supply, a population model projects population by sex and age using the component projection method. The change in population arises from functions for fertility, mortality and external migration. The population is tracked as those of age five, or thereabouts , enter into school for the first time. Students can attend five years of primary school, then enter six years of secondary school at which point they take the 'O' level examinations. Those who succeed then pass into two years of further secondary school to take the 'A' level exam and then those who pass continue into three years of higher and university education. The possibility of working and then entering or receiving skill training is not modeled. At each level of education students can repeat a grade, graduate, dropout or die.
Ninety percent of children enter the education system at the age of five (90%). The model also allows children to enter the first grade at the ages of six (7%), seven (2%) and eight (1%) - the percentages in brackets give the percent spread of ages as children enter the first grade. No data are available for this spread and 'judgement' was used to obtain the data. Sixteen grades of education were considered and dropouts and leavers who neither die nor migrate but were at least ten years old went into four 'labour pools' - incomplete primary, incomplete secondary, completed secondary with 'O' level pass and higher education which includes 'A' level passes, university dropouts as well as university graduates. The term labour pool is not generally found in the international literature but is used here to denote those of working age not at school.
When asked in your opinion, what is the most important quality you are looking for when you hire a worker, the highest positive response came for 'worker attitude' with 65% of respondents saying that his was very important, next in the list was having a particular skill (51.6% felt that this was very important) followed by practical training received in-school (39% felt that this was very important). Surprisingly, practical training that had been received in-plant was not perceived as important as the other categories such as work attitude with only 19% believing that this was very important and, equally surprising, was that a high level of general education came last on the list of importance with only 16% of respondents thinking that general education was very important. Nevertheless, the list of attributes were all considered at least a little appropriate (with one exception). (Table 6(vi)).
The income range of the traced students ranged from VND200,000 per month to more than VND2mn with the mode in the range 800,000 to 1mn ($US57 to $US71) and average of VND687,500 per month(Table 2, Appendix II). The lowest paid can be found in the repair and installation of equipment (one reply); car, motorbike repair and in excavator work. While the highest paid are in crane operating, industrial garment (but only one reply),and industrial electrical repair (Table 3, Appendix II). Finally, attendance at Dong Nai Technical school in the south will mean significantly higher average monthly pay (751,700VND/m) compared with Viet-Xo in the north (623,300VND), while employment prospects in the north and south are very much the same (Tables 4 and 5, Appendix II).
Respondents were highly satisfied with their VTE experience, reporting 75% to 90% satisfaction rates with every aspect of their programs. This result is quite encouraging, until it is compared with other data emerging from the study. When asked how their schooling could be improved, respondents said that it is "important" or "most important" to:
There is no systematic method in Viet Nam to trace students one year after they have left the training institutions. The method adopted was as follows. First two Key Schools were selected as trials for the testing of the questionnaire methodology - resources were not available to carry out the survey for all Key Schools given that local costs (interviewing, tracing of students, transport, accommodation, travel) for the two Key Schools only just fell inside the available budget. Second, the Key Schools were chosen to respect the geography of the country and so one Key School from the north and one from the south was chosen. Third, a list of all graduates from long-term courses in 1997 and all short-term graduates from mid-98 to end 98 was drawn up for each Key School. The lists were in course order and so a random sample drawn from these lists enabled all courses to be represented in proportion to their importance in terms of number of students. A 5% random sample was taken so that 30 students from each school were required.
In these latter graphs the question was asked about existing courses in the schools and whether graduates eventually will receive excellent wages (graph 4.1) and very poor wages (graph 4.2). It is obvious to state this but the reason that these questions are asked, in true labor market signalling fashion, is to elicit an approximate rate of return on training invested. If a course was appropriate one would expect students both to obtain jobs easily and to gain high wages. This provides a guide to future investment in courses whereby a highly paid job should attract the greatest investment in a new course. However, this cannot be taken too far since if all investment was allocated to only those, possibly, few courses that provided the highest rate of return the increase in supply in technically qualified graduates would begin to depress expected wages and job availability. How to resolve this dilemma? This is not easy, but it requires balanced decision making across the board whereby identified new courses, or those scheduled for reduction in capacity or even elimination are carefully considered in the light of all available evidence. This is best done by a board for each school at the decentralized level who benefit from labor market information studies, such as this one, but take a balanced view based upon their own experience and hunches concerning future developments. Decisions are improved through good labor market information but should not be dominated by it.
Some of the most crucial questions required to identify training needs were raised in the next suite of questions. First, was asked whether there are specific courses that are not being taught that should be taught in the future. 72.6% of respondents felt that this was the case. Next the respondents were asked to name two courses that should be included in future programs given that they had responded positively to the previous question. Before analyzing these responses, the unclassified replies had to be put into a classification scheme which turned out to be a lengthy process. The list of possible responses are given in Table 9, Appendix I where can also be found the replies to the first course that came to mind. Of the 251 replies, food industry course had the most positive replies with 31 suggesting more courses in this area. However, a closer examination of the results showed that the responses came from the agriculture technical school which suggest a certain unanimity of opinion which one would hesitate to label as regimented! Next in the list came electrical refrigeration (20 replies), informatics (13), electronics (13) and industrial machine operator (11). Physicians with 11 replies came high on the list but these came from the La Chau Health Care Secondary School and one can remain impressed with the unity of purpose of that school! Their course request also seems somewhat out of kilter with the other courses in the list.