Technical problems
Political and structural problems
A framework for a labour market information system
Promoting the use of labour market signals and indicators
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A SIMPLE EXAMPLE OF WHAT CAN GO WRONG IN THE ABSENCE OF SUITABLE LMI ''During the 1980s a multi million dollar project in Bangladesh was designed to support the provision of very basic employable skills to out-of-work youth in the rural communities. The project revolved around the provision of sets of hand tools to rural schools, where special classes ere held to teach simple carpentry and metal working tasks. Because the financial allocation for hand tools was high, the funding agency's regulations clearly stated that purchasing could not be undertaken locally but had to go out for international competitive bidding. In the absence of appropriate information, the contract was won by a European country which in due course delivered the tool sets according to the limited available specifications. Unfortunately, many of these tools could not be used. In Bangladesh, village carpenters assemble furniture with pegs and dowels rather than screws; saws cut on the up-stroke rather than the down-stroke, etc. At the time of the evaluation, most of the relatively expensive tools, including screwdrivers, packets of screws, saws and the smaller sized drilling bits had rusted away.'' |
223. The basic finding from this research is that, while a wide variety of activities are undertaken which involve the collection and analysis of labour market information, the effects of this on the planning, organisation and delivery of TVET are minimal. This problem was succinctly expressed by Costa Rica's Deputy Secretary of Labour:
"We are very clear as to what the productive sector needs. What we do not have is a practical plan to bring all the information together and put it to use."
224. Our research suggests that the problems are even more far reaching than this. The information which is collected is partial and much of it is out-of-date or inaccurate. It concerns the needs of the formal sector rather than the informal sector, and its main impact is within the agencies responsible for collecting it. The LMI is used infrequently, therefore, in conjunction with data and market intelligence collected by other agencies. Once it has been collected, it presents technical problems of analysis and interpretation; and when findings are eventually produced, they are not acted upon, whether because they are politically inconvenient or because the TVET institutions are unable to translate LMI findings into specific changes in curriculum content, delivery and resourcing.
225. Furthermore, we found that traditional, heavily statistical data collection and analysis techniques are still used, for the purpose of medium to long term manpower needs forecasting, despite the very substantial body of international evidence which discredits these approaches. However, it is encouraging that, where simpler approaches, based on market intelligence, are being deployed, these are effective in tuning training provision to the real needs of local labour markets and employers.
226. This chapter examines these problems from a number of perspectives. It first examines the technical problems of data collection, including difficulties in accessing data, defining key concepts and processing the data collected. It then considers the agendas of the various parties involved in collecting and using LMI, and identifies some fundamental structural and political difficulties in seeking to improve the quality and effectiveness of labour market analysis. Finally, it proposes a framework for a labour market information system and for the effective use of specific labour market techniques.
227. One basic problem is the difficulty of distinguishing between some of the key concepts in labour market analysis. The central concepts of employment and unemployment present difficulties, which are not confined to developing countries. The United Kingdom government has been heavily criticised for the many changes which have been made in redefining "unemployment" over the past two decades. Where there are no unemployment benefits, under-employment is the normal alternative to employment. The latter is normally, though not exclusively, within the informal sector, while recorded employment levels are mainly within the formal sector. But the definition of these two sectors presents further problems, as they tend to shade into each other, rather than form quite distinct spheres of activity. The point at which a one-person business registers as a "formal" business, whether with the government, the chamber of commerce or a trade association, is determined by local factors such as tax advantages, available support structure and perceived status. The threshold between the two sectors is, therefore, blurred and information on the informal sector particularly difficult to extract.
228. Similar problems occur with other forms of data. Migration patterns are unclear where much of the migration is illegal. Population totals depend on the accuracy of decennial censuses and government estimates between censuses. School enrolments, literacy and numeracy rates and dropout rates all present data collection problems, particularly where funding regimes are based on student input data, with consequent built-in incentives to over-record enrolments and ignore dropouts. The work of the private training sector extends into the informal sector, with little record of numbers, achievements and particularly of those students combining learning with work through forms of informal apprenticeships. There is a strong data collection bias towards the formal sector. The problems of collecting data about the informal sector are considerable, and in consequence the sector's needs tend to be overlooked. This is particularly unfortunate where economic liberalisation policies are leading to extensive job-shedding in the formal sector and consequent job-seeking in the informal sector.
229. A rather different cluster of problems result from traditional emphases on quantitative data collection and analysis. These arise in part from the backgrounds and training of those responsible for the analysis. Careful adherence to best statistical practice is, oddly enough, itself a problem. Perfection becomes the enemy of the good, as it leads to evidence which does not become available until it is far too out-of-date to rely upon for short to medium term policy formulation. It also gives a spurious respectability to data whose origins are partial, inaccurate and in some cases deliberately misleading. A commitment to large samples, strict statistical tests, careful checks and re-checks and the confidentiality of such data is hardly compatible with the need for quick analyses and broad-brush approaches, which identify the main trends.
230. For LMI to be useful at local levels it needs to be disaggregated in order to identify local trends and needs. It also needs to be rapidly published, widely disseminated and action-oriented in its findings, so that local managers can respond to those needs. However, the clients for this data are normally in central government ministries or agencies, concerned only with the broad national trends - and also concerned to apply to such data the standard governmental constraints on confidentiality. These problems merge with the political and structural difficulties discussed below.
231. The economies of the case study countries are all currently affected by the world-wide processes which are liberalising the global economy. These are accompanied by greater regional and national labour movements, which makes it doubly difficult to forecast trends in employment opportunities and labour availability. Despite their otherwise enormous differences, Bangladesh, Costa Rica and Zimbabwe are all in the throes of economic liberalisation and "economic and structural adjustment" policies. Ministers and government officials interviewed were unclear as to the likely consequences of these trends, for which they had neither data nor previous experience. In Costa Rica, a government minister forecast that the free trade agreement with Mexico would wipe out sectors of the agricultural economy, with unknown effects on the labour force currently in those sectors. There are also fears that investment which previously had been targeted for Costa Rica would shift to areas of Mexico and Guatemala with lower labour costs. In Zimbabwe, South Africa will pose a formidable trade competitor, especially as the ambitious economic targets set within the Economic Structural Adjustment Programme have not been achieved and unemployment continues to rise at an alarming rate, compounded by uncertain climatic conditions.
232. Such uncertainties create problems for government departments and politicians, who are expected to provide policies and actions which control their consequences. This compounds the traditional rivalries and suspicions in any governmental structure, leading to the resistance, found in each of the case studies, to inter-departmental cooperation in the collection, analysis and use of LMI. The "practical plan" referred to in the quotation at the start of this chapter is not achievable unless the various departments involved in LMI collection and analysis pool their resources and share their findings. Without a conviction that such actions will produce tangible benefits, it is unlikely that cooperation can be achieved - and the poor track record of traditional manpower needs forecasting approaches over the last three decades offers little assurance that tangible benefits are there to be achieved. It is not surprising that the lessons drawn by politicians and others from investment programmes driven by the World Bank and International Monetary Fund's commitment to manpower needs forecasts include a belief that, as information-driven policies have not worked, politicians and government officials should stick to intuition and ideology as the safest basis for decision-making.
233. Political and structural difficulties are not confined to governmental levels. The status differences between technical 'education' (usually in institutions run by the Education Ministry) and vocational 'training' (in institutions run by the Labour Ministry) are significant throughout the world, even where both types of institutions provide virtually the same training experience (as in Costa Rica). These affect political attitudes to the two sectors: it is generally more acceptable to reform the low status training sector than tackle the more fundamental inefficiencies of the higher status post-school education sector.
234. One of the main components of current, worldwide structural adjustment policies is the aim that TVET systems should become more autonomous, so that they can respond to market forces and operate in a 'training market'. However, the managers and owners of both public and private sector institutions do not necessarily know how to deal with these changes. They need marketing skills and good antennae which enable them to understand and respond to the labour markets in which they operate. These needs are compounded by the tendency for employers to hire untrained employees and train them as required once in post. This again requires new responses by training organisations familiar only with the provision of full-time training programmes, to offer and support part-time and work-based training.
235. The framework below incorporates the main features of a comprehensive LMI system, combining socio-demographic, labour market and training information. Much of this information already exists, but needs to be assembled in one place. A large proportion is statistical and is already collected in many countries, whether by the national census, the Education Ministry when monitoring training institutions, the Labour Ministry in its employer surveys, or the Planning Ministry when examining industrial location and population trends. Other data is essentially qualitative. It might be collected by central agencies, e.g. from inspections of TVET institutions. But much this data is less likely to be available or to be amenable to quantitative approaches. Here the use of appropriate signals and indicators and judicious sampling can provide valuable evidence at relatively low costs which complements the statistical data. The areas where such signals and indicators might be used are indicated in italics in Figure Four below.
236. The definition of an LMIS in Chapter 1 is expanded here in terms of both the functions and the components of an LMIS.
LMIS functions: To define, collect, process and analyze labour market information and to disseminate it in usable formats with analysis and commentary to concerned and competent persons and institutions, where it can be used in the development and/or adaptation of human resource development (HRD) policies at both macro and micro levels. Also to promote, manage, evaluate and develop itself.
LMIS components:
i. Users - individuals and organisations (These are very important because if there is no demand for LMIS or understanding and appreciation of its uses and benefits, there is no purpose in constructing or maintaining it).ii. Sources of signals, indicators and intelligence
iii. System managers, data gatherers, operators and analysts
iv. Labour market information itself
v. Methodology of data collection and analysis
vi. Equipment-computers and other hardware
vii. Processing software
viii. Means of communication, including public media
ix. Financial resources
x. Sub-systems:
- training for system staff and end users
- feedback and evaluation
- research, development and publications.
237. A basic Labour Market Information System is likely to require the following input/output data.
Figure 4: a basic labour market information system (italics - data collected using labour market signals, indicators and sampling)
1. Socio-demographic Data
* Population over time by age and gender
* Population distribution by region
* Literacy rates by gender, age and region
* Migration patterns
* Main social/ethnic groupings by size and location.
Other data may be added as a more detailed system develops, particularly information relating to health, wealth and social groupings.
2. Educational Data
* Enrolments by gender in primary, secondary and tertiary levels
* Numbers completing each level by age and gender, and as a proportion of enrolments
* Educational attainments by qualification level and gender
* Regional variations of above
3. Employment and Labour Market Information
* Macro level economic trends showing projected growth of various sectors (inclusive of informal sector), disaggregated by region* Size of labour force and its projected growth, overall and by sectors
* Size of any migrant labour groups and their origins, by region
* Distribution of workers between modem wage employment (formal and informal) and its projected growth
* Growth or contraction of wage employment by occupational sector
* Skill level of workers by occupation sector
* Levels of unemployment and under-employment by age groupings and gender, by sector and educational/training attainment, disaggregated by region
* Levels of youth unemployment and under-employment by gender and region
* Identification of training needs as reported by sector.
4. Analysis of Labour Market Policies
* Identified sources and methods of collecting LMI* Policies on remuneration and social benefits in public/private sectors
* Government industrial investment and promotion policies and their effects on employment and the demand for skills, by sector and employment category
* Policies relating to economic liberalisation and the structural adjustment of the labour market
* Policies relating to technological development and their effect on employment by category and gender
* Policies relating to unemployment subsidies and work creation
* Policies to promote the employment of special groups (women, school dropouts, illiterates, unemployed youth, ethnic minorities, disabled)'
* Trends in the labour market and the impact on future demands for skills, knowledge and their effect on gender employment.
5. Analysis of Training Policies and Programmes
* The main features of the education and training system, its co-ordination, inter-relationships, and the balance of public and private sector provision* Details of staffing at training institutions - qualifications (technical, pedagogical, industrial experience), staff/trainee ratios, opportunities for staff development, staff turnover
* Training policies relating to equity, gender, role of private sector, and role of employers
* Training responsibility: numbers disaggregated by public/private institutions and in-plant training
* Apprenticeship, duration, numbers by sector, gender and region
* Available programmes by vocational fields/trades, duration, and qualification
* Capacity and numbers enroled in each category of training by gender and region
* Trainee performance, pass-rates, repetitions and drop-outs, by type of programme, region, and gender
* Provision of career guidance/counselling and job placement services
* Results of tracer studies showing training relevance to future employment
* Capacity of training systems to undertake evaluation and research
* Available evaluative studies of training system's effectiveness and responsiveness.
6. Content and Quality of Training Programmes and Facilities
* Subject distribution and balance of practical and theory elements* Contributions of government agencies, employers, labour organisations and training institutions to curriculum development
* Processes of curriculum review, evaluation and reform
* Quality of instructional methods, media, learning materials, assessment
* Quality of facilities and equipment in terms of new technologies, relevance to world of work, curriculum standards, safety
* Effectiveness of standard settings, testing and certification
7. Training System Costs and Financing
* Unit costs of formal training at all levels, by public sector, private institutions, and by private sector (in-plant)* Analysis of costs with regard to capital costs, other fixed costs (insurance, calculated interest opportunity costs), variable costs/recurrent expenditures, by sector and region
* Actual or potential income from income generating activities (training-cum-production) in public/private institutions
* Extent to which income from self-financing activities can be retained by training institutions.
* Sources of funding by type of establishment (government, student fees, enterprise levy, etc.).
* Scope for resource bargaining by training providers
* Planned future investment in training by sector.
8. Assessment of Training Response to Employment Needs
* Extent of fiscal contribution or other allocation by employers in support of training system* Recruitment of training graduates by employers, immediately after training, within six months, after one year
* Quality of graduates, training standards vs. employers' stated requirements
* Relevance of curriculum and/or standards as evidenced by extent of subsequent on-job training
* Formal linkages between employers and training system/institutions, including attendance of enterprise representatives on training committees, participation of employers in industry placements and curriculum review
* Numbers of training graduates entering self-employment, by gender and sector, and those successful after one year, three years
* Measurements of effectiveness (relationship of outputs to objectives): output of successful graduates by training programme numbers of graduates finding paid employment
- continuity of graduates in original employment and/or trade
- unemployed graduates compared with unemployed youth without training
- excess or shortfall of training places compared to demand
- numbers of graduates found jobs through institutional placement services
- extent of satisfaction expressed by employers* Measurements of efficiency (relationship of inputs to outputs):
- student teacher ratios- utilisation of facilities (equipment, workshops) by numbers and time
- scope for cost reduction (higher student staff ratios, better utilisation of facilities, economies of scale)
- ratio of qualifications achieved, completions, repetitions to enrolments.
238. The LMI requirements at institutional level depend crucially upon the extent to which training organisations fully appreciate their implications and have the autonomy and the capacity to use LMI in planning their recruitment strategies and curriculum programmes. Where this exists, institutions are likely to need a mix of:
a) statistical data, scaled down from the extensive catalogue of information needs listed above, but concentrating on:- population and population movement statistics
- employers, by size, location, job vacancies and labour force structure
- institutional performance data (efficiency and effectiveness indicators)
- student destination data;b) market intelligence, including reports from staff and students concerning employer trends, new technologies, new skill requirements, the performance of competitor organisations (in public and private sectors), and the political, economic and social concerns and interests of those most influential in shaping the institution's purposes, direction and strategies; and
c) a profile of training provision matched against perceptions of the skills needed in the informal and formal sectors of the labour market by skill levels, from craft and technical through to management skills.
239. The statistical requirements may already exist within or outside the institution, which needs to develop the skills (and possibly the computerised technologies) to re-focus and analyze data originally collected for other purposes. Much of the market intelligence is likely to be available within training institutions, but techniques are required to bring it together, make sense of it and relate it to the statistical data. The major need, however, is for the development of an awareness within the institution that such information, when encountered, is significant, so that, when fed into an LMI system, might help to promote the institution's success - and hence contributors' job security or continued availability of training opportunities.
240. Labour market signals and indicators include the output statistics from training systems. Information concerning the career progression of graduated students and trainees into work, whether in the formal or informal sectors, and of the relevance placed by employers upon the certification acquired as representations of particular skills learned, can reinforce the basic output data, to provide a useful source of information on the requirements of and changes within the labour market. Indicators which provide training institutions with maps showing the movement of trainees into the labour market, enable them to test their initial predictions concerning trainee destinations. Tracer studies and reverse tracer studies are valuable techniques in structuring the collection of such data. The consequent analysis should lead on to modifications in the provision of training, to match more closely the needs of trainees once in the workplace.
241. If providers are to improve their links with the labour market, they also need structured procedures for obtaining signals from employers, the communities they serve and their students/trainees. These should form important elements of the management information systems with which institutional managers can monitor their responsiveness, internally and externally. Sampling techniques which seek the opinions, attitudes, expectations and needs of internal and external clients and customers are increasingly used as part of the marketing and quality assurance systems of TVET institutions. They need to be accompanied by the internal mechanisms which not only analyze and digest such information but learn from the key messages thus acquired.
242. The range of 'labour market signalling' techniques which might be employed by training organisations colleges and vocational schools, (as well as by central planning units) is summarised below. As indicated throughout this report, it is just as important to be able to analyze the data and disseminate the key findings rapidly, while they are still relevant, as it is to collect the data in the first place. The emphasis within a TVET institution or a specialist research group is to match the analysis and publication resources to the effort expended in initial data collection. This requires careful sampling and an understanding of the weight which can be placed on evidence drawn from small samples.
243. The techniques include:
i. tracer studies, which:- follow students/trainees (including early leavers) after they move on from TVET institutions, whether into the labour market, other education and training or unemployment;- map the diverse routes from training to their destinations six months, one year or more later;
- identify the skills used in current jobs; and
- relate those skills to training programmes undertaken and skills acquired through them.
ii. reverse tracer studies, which:
- survey sample employee groups in a selected occupational sector;- map the diverse entry routes and qualifications into specific jobs within that sector;
- cost the various training routes; and
- identify cost-effective training strategies - while shifting public subsidies away from less efficient forms of training provision.
iii. wage level studies, tracked through:
- job advertisements in newspapers and journals;- employer wages surveys;
- identifying wage level shifts which indicate skills shortages;
- analyzing labour market segmentation created by restrictive entry practices and wage rigidities.
iv. skills analyses, which track:
- skills which require long lead times and long training programmes;- skills which can be acquired swiftly as needs arise;
- forthcoming skills needs (e.g. with planned industrial growth or new technologies) which are unlikely to be anticipated by the private training sector, and which, therefore, require public sector investment;
- analysis of entry qualifications for training programmes, distinguishing between those pre-entry skills required for successful skill acquisition on the programme, and those entry requirements which do not relate to training needs.
244. This approach places the emphasis on labour market analysis with individual institutions. This might seem idealistic, but in our view it is the necessary underpinning for a more decentralised - and hence more responsive - TVET system. Through their analysis of local labour market signals, training institutions will acquire the information which enables them to develop new courses and tune their programme provision to meet more closely the needs of local labour markets. It is only with appropriate LMI that the training 'markets' central to structural adjustment programmes can operate effectively. TVET institutions have most to gain from some investment in LMI. It provides the basis for marketing strategies which enhance their reputations and can increase their resources, whether in support and sponsorship from employers or in increased student numbers (in systems where resources follow student numbers).
245. This, in turn, leads to a re-examination of the roles of central support, redirecting the energies of central manpower planning teams (whether in Labour or Education ministries or central planning units) to:
- undertake national and regional employer surveys;
- analyze census and other population data;
- train staff in institutions and local manpower planning boards in relevant techniques; and
- publicise findings, specifying cost-effective training routes to students and employers.
246. A basic principle in such a reformed system should be that information is specially collected only if absolutely necessary. Wherever possible, existing data sources should be exploited, including government employment and social security records, census information and other household data. The labour market monitoring system should:
- identify occupational sectors which are growing and are declining;
- define qualifications needed to enter particular occupations;
- identify distortions arising from government policies; and
- assess the performance of education and training in achieving job placements.
Despite the need for relevant curricula, well-trained staff, students who are appropriately prepared at the start of their studies, and satisfactory facilities and equipment, none of these are sufficient if the local or national economy is unable to absorb the students when they complete their studies. Institutionally managed labour market signalling is not a panacea, but it is an important stage in the establishment of a more responsive education and training system. It is as relevant to the needs of the informal sector as it is to those of the formal sector. Central curriculum planning generally overlooks the 'multi-skilling' (including management skills) required for effective operation within the informal sector, and local labour market analysis which identifies the range of skills needed within the informal sector is a powerful counter to centrally directed curricula aimed at the needs of the formal sector. More ambitiously, TVET institutions working collectively might not only find ways to share costs and findings from their labour market analyses, but also exert collective pressures to obtain from the central agencies LMI (in digestible formats) whose acquisition lies beyond the capacity of local institutions.
