Fase 7. Conclusiones y recomendaciones Se plantean las conclusiones, limitaciones, alcances y recomendaciones producto del presente ejercicio
3.2. Capítulo II: Cátedra de la Paz, documentos de apoyo didáctico.
This brief survey of employment in SMEs is on much firmer ground than would have been possible only a few years ago, due to the great steps forward made by Member States in harmonising their statistical survey and compilation methods, particularly for the enterprises with no employees and the self-employed.
The picture it reveals is of significance for government policy making given the importance of employment among their social aims, the sheer numbers of small enterprises, and the vitality in job creation they display. The proportion of employment in the service sector has probably continued to grow and these activities are dominant in the lowest size classes, where the barriers to entry are lowest. It is likely that the lower proportion of SMEs in some of the northern Member States is due to the faster growth in the past in numbers of the largest firms, rather than recent faster formation of smallest SMEs in the southern countries.
It is to be expected that job stability is lower in the smallest units, as many of them are family businesses, and because close to a tenth of all firms cease to exist each year. Even in the largest firms average job stability will probably drop as they strip out non-core activities, adjust to shorter product cycles due to faster innovation, and strive to meet increasing global
competition. Even stable jobs do not translate into ‘good’ jobs, and there must be concern at the high proportion of workers in low-skilled occupations for which demand is declining — one of the underlying causes for the increasing income dispersion which is becoming another central concern for governments. There are signs of market failure in the provision of up-skilling and retraining for the large group of workers who individually bear the labour market risk because they are self-employed or in firms too small to provide it for them. This will be one of the factors leading to higher periods of unemployment for many in the EU who have not withdrawn from the job market. Non-capitalised intangible assets (which increase with ‘use’) are the key value generators for the future, and are created by the skill and experience of workers who are inherently mobile.
The proportion of women in the working population will tend, probably, to approach that of men, although they may bypass certain activities as a result of cultural and social attitudes to physically challenging production work and turn instead towards the attraction of the personal contact that many jobs in the service sector can provide. In four out of seven sectors the proportion of women with medium and low levels of education is higher (in both levels) than that for men, and higher in one or other in the three other sectors, presumably the result of proportionately fewer women in the past going to university. It can also be assumed that the proportion of women with part-time jobs is higher than for men in many sectors, if not all.
METHODOLOGY
Sources The main source used in this chapter is the SME database, a complete description of which is given in the methodology at the end of this publication, which provides in particular data on employment by enterprise size classes for the EU, the euro-zone, their Member States, Iceland, Norway and Switzerland (1). Owing to methodological changes in the data received from several Member States, notably Belgium, Denmark, Germany and Portugal, some data, concerning in particular the distribution by country of enterprises in the EU and the breakdown of total employment by size class, reproduced in the chapter, are not directly comparable with those published in the Fifth Report. Another source used is the Labour Force Survey which is presented below.
The Labour Force Survey database The data on employment conditions and education levels are taken from the Eurostat Labour Force Survey database annually, which contains information relating to about 700 000 households. These surveys obtain information on a number of characteristics of the population related to working conditions and levels of education.
These data relate to local units and the size classes are defined in terms of the number of persons working at the local unit.
Data on self-employment cover self-employed without employees and self-employed with employees. Self-employed persons with employees are defined as persons who work in their own business, professional practice or farm for the purpose of earning a profit, and who employ at least one other person.
The three educational levels used are a composite of school and professional educational achievements, where ‘high’ denotes a university degree (or equivalent level of professional or vocational education), ‘medium’ denotes completion of secondary schooling, and ‘low’ completion only of the first stage of secondary schooling or less. Eurostat requires the International Standard Classification of Education (ISCED 1997) of Unesco to be followed (although compilation of such figures is not compulsory for Member States) which provides for more than three levels, but due to the problems Member States have in allocating their national qualifications to this wider range, the present simplified set is used in this chapter. Further explanation can be found in the publication ‘Labour Force Survey — Methods and Definitions — 1992 Series’.
Data limitations As to the numbers engaged in the shadow or ‘black’ economy, this probably varies widely between Member States. This is a major source of uncertainty surrounding the employment picture.
Employment figures taken from surveys of enterprises do not of course show the changing picture for unemployment, for which figures are usually obtained from household surveys and administrative records, such as those kept by labour exchange offices.
(1) It should be noted that the unit taken into account is not the enterprise but the VAT unit in Belgium and the legal unit in Finland.
T
his chapter investigates the extent and nature of regional, rather than national, variations in business structures across the European Union. It examines regional differences in densities of micro, small and medium-sized, and large local units, in unit size structures by sector, and in the degree of specialisation within a region’s population of small and medium-sized local units upon manufacturing, high-technology industry, and financial and business services.A novel feature is its assessment of whether or not systematic variations exist in these indicators between groups of EU regions defined by their level of urbanisation (urban-rural) or recent economic growth (fast growth-slow growth).
Regional differences in the role and importance of small and large business units are of concern from the perspective of EU regional policy, and EU structural fund initiatives have often sought to assist small and medium-sized business units as one way of helping modernise and enhance the economic performance of lagging and less developed EU regions.
Numbers of SMEs, and of high technology and business service firms, appear to have been growing in the EU in the 1990s, and this chapter helps throw light on where such growth is likely to have been concentrated, as a broad context for EU regional policy action.
At the same time, it must be stressed that the EU’s regions are very diverse in socio-economic characteristics, such as level of urbanisation, market demand, natural resources, labour market attributes, supplier and subcontracting opportunities, and telecommunication and transport infrastructure. All these factors, as well as geographical location and cultural and institutional differences, affect regional variations in entrepreneurship, SME growth and large firm investment and disinvestment decisions.
The regional data and definitions used in this chapter are outlined in the ‘Methodology’ section at the end. Local units, which are examined in this analysis, are defined as enterprises or parts thereof situated in a geographically identified place (1).
x
There are significant regional variations within the European Union in the density of micro, small and medium-sized (SMU), and large local units. There are clear north-south divides in the density of micro units (units with no employees) and small and medium-sized units in several EU countries (France, Italy, the United Kingdom), and a striking European-wide centre-periphery pattern in large unit densities.x
High densities of small and medium-sized units are concentrated in northern Italy, Portugal and Spain, with very low densities in Finland, Sweden and in the UK. Italian, Spanish and to a lesser extent UK regions have high densities of micro units (units with no employees).x
There are marked regional variations in SMU specialisation on different sectors. Specialised small and medium-sized units manufacturing regions are most characteristic of northern Italy and parts of France, whereas specialised small and medium-sized units high-technology regions are focussed on central and southern Germany, northern Italy, and Europe’s major metropolitan regions.x
Business unit structures differ between Europe’s urban and rural regions. Europe’s urbanised regions have somewhat higher densities of smaller business units while rural regions exhibit greater manufacturing small and medium-sized unit specialisation.x
Business structures also differ between fast-growth and slow-growth regions. Europe’s fastest-growing regions tend to have relatively high densities of small and medium-sized units, but low densities of large units. In the 1990s, small and medium-sized unit specialisation on manufacturing has been associated with poor regional economic growth.(1) See the methodological box at the end of this chapter, pp. 125-126.
Wherever possible, the most recent regional local units data (for 1996) is used. It is important to note, however, that no data was available for Denmark, Greece, Ireland, Luxembourg and the Netherlands, and these countries had therefore to be omitted from the analysis. Lack of data also enforced omission of some other countries for particular analyses.
The regional data distinguish between three size-classes of local units:
x
micro (with no employees);x
small and medium-sized(with 1 to 99 employees, or ‘SMUs’) and
x
large (with 100 or more employees) (1).The particular sectoral groupings used in this chapter are outlined in the methodology box.