Education, Training and Applied Economics: A Digest
The relationship between education and economics is an area of wide scope and considerable complexity which merits an in-depth analysis, however this is a brief look at key points of interest.
Here we only consider those aspects of education and training particularly relevant to the debate on market versus non-market means of resource allocation. Benefits of education and training Additional education or training can clearly be regarded, in part, as yielding consumption benefits e.g. positive utility or pleasure to those directly involved in the process of gaining knowledge or acquiring skills.
However the main motivation to both suppliers and purchasers of education and training is likely to take the form of securing investment benefits. In other words, the use of scarce resources by suppliers (labour, capital) and purchasers (time, energy, money, income expended, income foregone, etc.) to yield higher future returns. At the micro level this return on ‘human capital investment’ may originate from a rightward shift in the Marginal Revenue Product Curve.
This rightward shift raises the value of labour input to both the firm and (via higher wages) the individual undertaking the education or training. At the macro level such investment is seen as shifting the production possibilities frontier for the economy to the right, i.e. raising what is often referred to as ‘sustainable growth’. This is growth which can be attained without running into capacity constraints, causing inflationary or balance of payments pressures.
The term ‘endogenous growth’ is also sometimes given to investment in education or training; ‘endogenous’ meaning here ‘growth which develops from within’. Research by Robert Barro, for example, suggests that a 10% increase in educational attainment increases growth by 0.2% per year. To what extent is the market capable of providing an appropriate level of education or training? What role might there be for an internal or quasi-market in this area? Before seeking to address these issues it may be useful to review the circumstances under which the purchaser or provider, respectively, might be induced to invest in a given amount of education or training.
Research by London Economics (Chote 1994) for the Committee of Vice Chancellors and Principals shows how attractive an investment in higher education can be. For example, a woman graduate of equivalent profile to a non-graduate in other respects, would by the age of 49 years be taking home some 70% more pay than her non-graduate counterpart. In similar vein, Schmitt (1995) reports that a male graduate in the UK can expect to receive a weekly wage some 90% in excess of that for a non-graduate of otherwise similar profile.
Perhaps the most comprehensive survey of this issue (IFS 1998) has involved the life profiles of 2,500 individuals born in a particular week in March 1958. The study concludes that male graduates in their early 30s earned 15 to 20% more than similar aged men who completed A levels but did not undertake higher education. For women the average gain due to a degree was even higher, around 35%.
Cream Skimming and Adverse Selection
As with health care, the extent of efficiency gains from quasi-markets depends upon providers being unable to ‘cream skim’ by taking only those most likely to succeed. If certain schools are able to do this, other (non opted-out) schools will have a progressively adverse pool from which to draw their pupils, and disparities between schools will widen rather than narrow.
There is evidence to suggest that a quasi-market in which only a minority of schools opt out is indeed likely to result in elements of selection. For example detailed studies such as Mortimore et al (1988) found that only about 5% of what actually takes place in schools affects ‘outcomes’ (e.g. exam results). Over 64% of the variance in pupil achievements could be explained by initial attainment and social background.
Poor Information and Externalities
A selective system might create rather misleading information and a variety of negative externalities as compared to an initially non-selective system. This is in line with the point that in quasi-markets there needs to be symmetry of information between providers and purchasers. In a competitive and selective system, educational providers may have incentives not to reveal relevant factors, and parents (purchasers) may be unable to extract that information.
Of course such ‘secrecy’ may also be the case in non-selective schools as a defence mechanism for teachers. Selectivity may also lead to certain negative externalities. For example, the loss of local community ties fostered by non-selective local schools; the loss of improved educational outcomes attributed to average and below average children mixing together in non-selective schooling.
A quasi-market will lead to ‘winners’ and ‘losers’, with some schools expanding and others contracting. The corollary of this is that there will be more frequent entry to, and exit from, individual schools by children. Sunk costs are costs which cannot be recovered on exit from a market, and arguably these will be high in an educational context where schools have become part of a community. Any closure of the less successful schools will then incur substantial sunk costs. Similarly the disruption costs to children from frequent upheaval will be considerable.
As in the case of health care, there can be no presumption that quasi-markets in education and training must of necessity create greater efficiency and choice. The issues are complex and require thorough analysis and empirical investigation. Certainly the momentum is towards extending such quasi-markets with reports such as Social Justice (1994) recommending the creation of individual learning accounts (i.e. transferable vouchers) for both pupils and employees.
However the Social Justice Report recognized that pure market solutions will not work in the case of training, so it also proposes that minimum standards be set for all employers. For instance all employers, whether providing training or not, might be required to set aside up to 2% of payroll, reclaimable in part or full depending on the amount of training they themselves actually provide. Employers unable or unwilling to provide that level of training themselves would be required to put the difference into their employees’ individual learning accounts or to pay the TECs to reimburse companies who do provide such training.
Again the aim is to use quasi-markets rather than ‘pure’ markets to redress a situation in which nearly two-thirds of UK employers invest less than 2% of payroll costs in training, whereas three-quarters of French employers invest more than 2% and in Germany an average of 3.5% of payroll is given towards training related programmes (Social Justice 1994).
Productivity and Labour Skills
The above account points to the importance of capital intensity in enhancing productivity. Of course the productivity of a nation also depends on the skills of its management and workforce in making the best use of whatever resources are available. Management is responsible for selecting projects, organizing the flow of work and the utilization of resources, so that effective management is a ‘necessary’ condition for good productivity performance.
It is not, however, ‘sufficient’ since a labour force which possesses inappropriate skills, or which refuses to adapt its work practices and manning levels to new technology will prevent advances in productivity, whatever the merits of management. A major issue in many industries is workers’ lack of flexibility between tasks, resulting in overmanning and also acting as a disincentive to innovation.
Lack of flexibility can result from union restrictive practices, but is also caused by badly trained workers and managers who are unable to cope with change. There is evidence of lower standards in UK education which mean that many school leavers are ill-equipped for the growing complexity of work. Throughout British industry there is less emphasis on training than in other countries. Only around 52% of 18 year olds in the UK were in full time or part time education or training in 1999, much less than the 80% figure for Germany, France Netherlands and Belgium, suggesting that young people as a group in the UK are among the least educated and trained in Europe.
When considering the whole labour force, that is the stock of human capital rather than the flow, the situation is probably even worse. Davies and Caves (1987) had pointed out that British managers were only marginally better qualified than the population at large: for example very few production managers were graduate engineers.
Amongst production workers only a quarter in Britain had completed an apprenticeship compared with about half in Germany. Very few British foremen had formal qualifications for their job but in Germany foremen were trained as craftsmen and then took the further qualification of Meister. In fact only 14% of UK technicians and 3% of UK foremen possessed higher intermediate qualifications, compared to 36% of German technicians and 64% of German foremen (Steedman et al. 1991).
The study by O’Mahony (1998) also confirms the UK’s skills deficit against Germany at the intermediate level. Higher levels of skill have arguably enabled German firms to make better use of their capital equipment and to adapt to change of all kinds, so investment in human capital raises productivity of both physical capital and labour, whilst also achieving consistently higher quality of output. Overall some progress has been made in narrowing the productivity gaps previously identified for the UK vis-a-vis its major competitors.
However, the UK is still at a considerable productivity disadvantage in terms of many of its competitors. A similar picture emerges from our review of capital intensity and the quality of the work force. Nevertheless it is important to remember that the whole question of productivity differences is much more complex than might at first appear. For example, a NIESR research project investigated the reasons for observed differences in productivity between the US and Europe in two quite different sectors, namely the biscuit sector and the precision industry sector (Mason and Finegold 1997).
The survey did find that some of the reasons for the higher US productivity could be related to higher physical capital investment per worker in these sectors in the US as compared to Europe. However, the most important factor underlying the productivity gap was found to be the greater economies of scale available in the US sectors compared to the European sectors, a factor which is often overlooked in studies comparing productivity performances.
That the reasons for productivity differences are complex is apparent from comparisons in 1998 between Nissan’s Sunderland plant, which produced 98 cars per employee per year, and the former Rover Group plant at Longbridge, which produced 33 cars per employee per year. Investigations revealed that as compared to Nissan’s Sunderland plant, the Longbridge plant was older, had a more complex layout, and suffered from a lower demand for its product range, suggesting that simplistic conclusions from productivity comparisons must be treated with some caution.
Certainly the existence of relatively inefficient car plants is by no means a British phenomenon. For example, the Renault plant at Sandouville, France, produced only 36 cars per employee per year and the Volkswagen plant at Emden, Germany, produced only 28 cars per employee per year. A more detailed comparison of labour productivity and capital utilization in six manufacturing and service industries across the five leading industrialized countries has been undertaken by the McKinsey Global Institute (McKinsey Report 1998). These comparisons covered car assembly, food processing, food retailing, hotels, telecommunications and software production.
This McKinsey study reinforces some of our earlier data, revealing a clear productivity gap between the UK and its major competitors in both labour productivity and TFP, though not in capital productivity as compared with France and Germany. Labour productivity across these six sectors is 37% higher in the US than in the UK, and 26% higher in both Germany and France than in the UK.
Similar, if less substantial, productivity gaps are revealed using the TFP measure, but UK capital productivity is only behind the US (by 10%), being ahead of France (by 8%) and Germany (by 7%). However, capital investment per hour worked is some way behind all our competitors. In seeking to probe behind these productivity differences, the McKinsey Report concludes that ‘UK management often fails to adopt global best practices even when in some cases these are readilv understandable and achievable’.
Urban Policy In The UK
Since the Second World War government policy towards the plight of urban and inner-city areas can be divided into four phases: 1945-65, 1965-77, 1977-88 and 1988-2000. 1945-65 During this phase the government’s policy was to limit the growth of major conurbations in an attempt to solve some of the pressing problems of urban congestion. Green Belts were established around major conurbations to prevent their expansion, and New Towns were built outside the major conurbations to take up any urban overspill.
After 1947 the use of IDCs further restricted the growth of industries in the urban areas, whilst the Location of Offices Bureau sought to redistribute office work away from the conurbations, especially London.
1965-77 From the middle of the 1960s the government’s attitude towards the innercity problem began to change. Attention began to be drawn to the fact that the UK non-white population had grown to some half a million and was largely concentrated in cities. Fears were expressed that race riots similar to those of the US in 1967-68 might occur in the UK and this helped focus government attention on the urban problem.
This emphasis was strengthened by the findings of the Plowden Report on children’s education in 1967. This report identified deprived areas in inner cities which needed special help and led to the setting-up of Educational Priority Areas (EPAs) in 1969.
In 1968 an Urban Programme was also established which, under the Local Government Grants (Social Needs) Act of 1969, was provided with a fund of £20-25m over four years. The aim of the Urban Programme was to provide resources for capital projects and educational schemes, such as pre-school playgroups, in order to raise the level of social services in areas of acute social need. In 1969 the Community Development Project was established to research into new ways of solving social deprivation in large urban communities.
Total managed expenditure (TME) by programme In 2000-01, central and local government intended to spend approximately £371bn, divided between twenty programmes (roughly equivalent to the responsibilities of the main government departments).
Health, Personal Social Services and Social Security plans dominate expenditure by programme, with 42.2% of the total managed expenditure in 2000/01. The next most important item is Education with 12.3% of the total, followed by Defence with 6.2% of total managed expenditure. Law and order accounts for 5.4%, Housing, Heritage and the Environment 3.8% and Transport less than 3%. Debt interest can be seen to take a sizeable portion of total managed expenditure (7.5%). Although this has been falling as the government has been using some of its revenue to repay debt.
A large proportion of the expenditure in areas such as transport, law and order and education is carried out by the local authorities. However, central government expenditure is increasing its share of total spending on areas such as education (opted-out schools, FE colleges and sixth forms centrally funded) and housing (grants to housing associations).
These programmes include capital spending (e.g. school buildings) and current spending on both goods (e.g. school books) and services (e.g. wages of policemen/nurses/teachers). The expenditure plans revealed in July 2000 showed that increased resources had been made available for health, housing, transport and education. The above plans for public spending are an integral part of the government’s medium-term financial strategy aimed at reducing inflation and maintaining the conditions for sustained growth, the creation of jobs and higher living standards.
Qualifications and Unemployment
The demand for low-skilled and poorly educated workers has been declining since the early 1980s, whereas the demand for skilled workers has outstripped the supply. The overall result is that the employment prospects and wages of poorly educated and unskilled workers have deteriorated relative to those for better educated and skilled workers.
For example 33% of men aged 25-49 years with no qualifications are currently out of work, but this is true of only 5% of such men with a university degree. Similarly the wage gap between men aged 25-49 years with no qualifications and those with a university degree was 61% in 1979, but this gap had increased to 89% in 1998.
The development of European social policy has involved both the operation of the European Social Fund (ESF) and developments in the ‘Social Chapter’ of the Maastricht Treaty. The European Social Fund is designed to develop human resources and improve the workings of the labour market throughout the EU. Expenditure is concentrated in those regions of the EU which are suffering from high unemployment and is designed to help with retraining initiatives, improving skills and providing educational opportunities in order to make the labour force more flexible.
In March 1998 the European Commission formally adopted a series of draft regulations which will form the backbone of the ESF’s plans for the 2000-2006 period. At the centre of these plans is the new European Employment Strategy (EES) which stipulates that each member state must submit an annual employment plan directed towards raising ’employability, entrepreneurship, labour force adaptability and equal opportunities’. A sum of 210bn euro was allocated to achieving these goals by 2006.
As far as the ‘Social Chapter’ of the Maastricht Treaty is concerned, the UK had been opposed to many of the regulations and directives associated with the Social Chapter, with successive Conservative governments arguing that attempting to impose regulations in such areas as works councils, maternity/paternity rights, equal pay, part-time workers issues, etc merely increased labour costs and decreased UK competitiveness. For example German social/labour market policies have been criticized for making labour too expensive to employ!
In the area of training, one of the greatest problems for the UK has involved a dearth of vocational skills at the intermediate level. A recent study by O’Mahony (1998) confirms this situation, showing the UK to have only 36.2% of its workforce with intermediate skills qualifications compared to 63.2% for Germany. The study also found the UK to have 54.4% of its manufacturing workforce in the ‘low skill’ category, compared to only 29.5% for Germany.
A new revised National Curriculum was introduced in September 2000 designed to make more explicit the links between education, employment and enterprise. Meanwhile, in the first few years of the new millennium, various strategies were to be introduced to provide a better quality of work experience for pupils (Education-Business links); to encourage more entrepreneurial attitudes (National Enterprise Campaign); to improve management expertise (Council for Excellence in Management and Leadership); and to increase training initiatives (New Deal). All these policies were designed to focus on increasing the UK’s stock of ‘human capital’.
Oulton compared the UK’s cyclical performance with that of a number of other main industrial countries since 1970 and found that boom periods were shorter and recessions longer in the UK than in the other industrial countries. For these and other reasons (differing accounting conventions, economic and social practices, etc.) there has been a move towards the use of indicators other than the GNP per capita figure to reflect the ‘true’ standard of living in various countries, using various ‘quality of life’ indicators such as life expectancy, medical provision, educational opportunities, etc.
The United Nations has published a Human Development Report since 1990 in which two new methods of classification are presented, one involving a Human Development Index and the other a Human Poverty Index. We now consider each of these in turn.
Human Development Index (HDI) classification An interesting issue is whether the conventional GNP per capita figure can be merged with ‘quality of life’ indicators to give an overall index of economic wellbeing. A first step in this direction has in fact been made with the publication of the United Nations’ Human Development Index (HDI).
The Human Development Index (HDI) is based on three indicators:
- Standard of living, as measured by real GNP per capita (PPP$)
- Life expectancy at birth, in years
- Educational attainment, as measured by a weighted average of adult literacy (two-thirds weight) and enrolment ratio (one-third weight).
Each of these three indicators is then expressed in index form, with a scale set between a minimum value (index = 0) and a maximum value (index = 1) for each indicator.
- Standard of living: $100 real GNP per capita (PPPS) is the minimum value (index = 0) and $40,000 is the maximum value (index =1).
- Life expectancy at birth: 25 years is the minimum value (index = 0) and 85 years is the maximum value (index = 1).
- Educational attainment: 0% for both adult literacy and enrolment ratios are the minimum values used for calculating the weighted average (index = 0) and 100% for both adult literacy and enrolment ratios are the maximum values used for calculating the weighted average (index =1).
An index is then calculated for each of these three indicators, and the average of these three index numbers is then calculated. This average of the three separate index numbers is the Human Development Index (HDI). The closer to 1 is the value of the HDI, the closer the country is to achieving the maximum values defined for each of the three indicators.
Using a GNP per head indicator, even adjusted for purchasing power parities, gives a different ranking for countries than using the HDI index which brings quality of life aspects into the equation. The HDI, by bringing together both economic and quality of life indicators, suggests a greater degree of underdevelopment for some countries than is indicated by economic data alone.
For example Nigeria is 137th out of 174 countries when the GNP per head data is used for ranking but falls a further 23 places to 160th when the HDI is used for ranking. For Nigeria it would seem that the relatively disappointing enrolment ratio into education has helped depress the ‘education’ index and with it the overall HDI.
On the other hand, the HDI suggests a smaller degree of underdevelopment for some countries than is indicated by economic data alone. For example Uruguay is 52nd out of 174 countries when the GNP per head data is used for ranking but rises by 14 places to 38th when the HDI is used for ranking. For Uruguay it would seem that the relatively high adult literacy and enrolment ratios have helped increase the ‘education’ index which, together with encouraging life expectancy data, have raised the overall HDI.
Although only in its infancy, it may be that classifications of countries based on indices such as the HDI which bring together both economic and quality of life data, may give a more accurate picture of the level of development.
Countries with similar levels of real income per head can have markedly different scores in terms of the Human Development Index. For example both Paraguay and Morocco have real GDP per head of around $3,500, but the better quality of life indicators for Paraguay give it a much higher HDI than do similar quality of life indicators for Morocco.
Human Poverty Index (HPI) A further step in this direction has been taken by the UN in 1997 with its introduction of the Human Poverty Index (HPI). This seeks to measure the extent of deprivation in terms of the percentages of people not expected to attain specified target levels for various economic and quality of life indicators. In this sense the HPI measures the proportion of people who are ‘left outside’ certain minimum standards for a country or community.
This is a digest of Alan Griffiths and Stuart Wall’s work. They were Reader in Economics at Anglia Polytechnic University, and Principal Lecturer of Economics at Anglia Polytechnic University, visiting lecturer, supervisor and examiner at Cambridge University respectively [Applied Economics; 9th Edition Edited by Alan Griffiths and Stuart Wall, Pearson Education Limited Copyright Pearson Educaiton 1984, 2001 ISBN 0273-65152-8 Page 324]