The Contribution of the Self-Employed to Employment in the EU. Report to the Small Business Service. Marc Cowling. summary. January 2003 URN 03/539

Transcription

1 The Contribution of the Self-Employed to Employment in the EU Report to the Small Business Service Marc Cowling summary January 2003 URN 03/539

2 executive summary Key Findings The UK self-employment rate is similar to that in Austria, France, Germany, Luxembourg and Ireland, and is in the mid-range for the EU. In total, the self-employed in the UK employ a further 2.6 million people. The UK is one of only six EU countries in which males have a higher probability of self-employment (holding factors such as age, education, occupation and industry sector constant). The norm in the EU is for individuals with high human capital to become self-employed. This is not true in the UK where selfemployment is most prevalent in craft related occupations. This is important as increasing levels of education are associated with higher employment levels in self-employed businesses. In terms of employing additional workers, the UK self-employed compare relatively unfavourably to other EU countries. For example, in Germany 51% of the self-employed have employees. In Austria this figure is 47.5%, in Denmark 45.8%, and in Ireland 40.6%. This compares to only 29.1% in the UK. The evidence shows that in terms of average number of jobs in selfemployed businesses, the UK is below the EU average which is one additional job per business, with a figure of 0.7 jobs per business. For the UK to improve the employment contribution of its selfemployed sector we would need to improve the average level of education of the self-employed (particularly at the graduate level). The experience of Germany might provide useful information concerning stimulating graduate self-employment. If we wish to promote self-employment for women, we would do well to look at the case of Finland where women (holding other factors constant) have higher self-employment entry probabilities than men. Further, both France, Netherlands and Austria have high rates of self-employment amongst women with young children. Examples of innovative policy-making in this area might help inform UK policy in this key area. 2

3 Aims Employment is a key political and economic issue of our time. Yet it is also only recently that research attention has begun to focus specifically on the role of the self-employed as creators of additional employment opportunities (Carroll et al, 2000, Cowling and Taylor, 2001). Unfortunately, both of these studies were based on single country data (the former for the US and the latter for the UK) so we are unable to draw any meaningful conclusions as to why the self-employed in some countries have a greater propensity to employ additional workers. This is a significant gap in our knowledge and one which, when addressed, can provide policy-makers with important information to support the policy process, particularly in the area of employment where smaller firms are perceived to make a significant contribution. Cross-country studies not only highlight key differences in the composition of the self-employed stock, but can provide benchmarks against which UK performance can be compared, and, if necessary policy action implemented to stimulate improved performance. Overall, the ability of the self-employed to provide employment is not trivial. For example, in the UK there are around 3.7 million self-employed. Our evidence suggests that they account for a further 2.6 million jobs in the economy out of a total employment level of 26.2 million. It is the explicit intention of this study to fill this research gap by analysing an EU wide, individual level, data set containing responses from in excess of 15,800 workers (waged and self-employed) spread across the fifteen EU member states. executive summary Thus for the purposes of this study, the self-employment rate is simply: self-employed / (self-employed + waged employed). In addition, we use the term job creators to define the self-employed who employ additional labour. This is not the same as the term job creation used in studies of job creation and destruction to calculate the relative contribution of new firms and existing firms to net new jobs in an economy (see for example, Birch,1979). It refers to that subset of the self-employed who actually employ additional labour. We use the term job creators as previous research strongly suggests that most business start-ups are a single person entity. Observing employment at a later date implies that jobs have been created at some point in the life-cycle of that business. This research initially poses one question: What key features distinguish the self-employed from the waged employed? It then goes on to address two important issues: How many of the self-employed employ others? How many jobs do they account for? 3

4 executive summary It also identifies: The personal characteristics of job creators The particular characteristics of the business set up In this aspect our work is separate from, but related to, the expanding volume of work analysing patterns of gross and net job creation/destruction at the level of the firm stimulated by the seminal contribution of David Birch (1979) (see Dale and Morgan, 2001, for recent UK evidence). To quote from Dale et al (2001, p.1), it is now well established that new and small firms are very important sources of job (and wealth) creation and further that controversy now exists only over the extent of the contribution. Thus, our work here is complementary to dynamic studies of job creation and destruction in that we explore the factors most closely associated with the probability of the self-employed employing additional labour. Our focus of attention is on the individual, although we consider firm level effects too. Further, we are building upon a small but important body of work in the UK and US which strongly supports the view that for vast majority of self-employed businesses start off, and remain, a one person entity. Data and Methodology The second European Survey on working conditions took place in January 1996 and collated the views of 15,800 workers from all over Europe. One thousand workers in each Member State of the European Union (15 Member States in 1996) were questioned simultaneously about their working conditions. This sample is representative of the working population (employees and the self-employed). It is a questionnaire based survey, involving face-to-face interviews conducted outside the workplace. The questionnaire covers all aspects of working conditions: physical environment, workplace design, working hours, work organisation and social relationships at the workplace. This dataset is ideally suited to the issues to be investigated in this report given its full EU coverage, its representative sampling distributions, its sheer size, and the fact that for each individual surveyed we have information on personal characteristics, job characteristics and firm characteristics. In total we have 2,904 self-employed individuals in this dataset. This is a very large (>15,000 individual data points) dataset, which is genuinely representative of the working population in each country, and one which covers fifteen EU member states. This is where the added value of our study lies. 4 In our methodological approach, we deliberately chose to replicate the analytical techniques used by Caroll et al in the US and Burke et al and Cowling, Mitchell and Taylor in the UK for the econometric part of this study. There are, of course, several potential weaknesses in our data. For example, we have no data on income. We also have no data on motivations

5 which new research suggests plays a key role in the decision to grow a business or not (see for example, Westall and Cowling, 1999; Meager, Cowling and Bates, 2001). Our data, as indicated previously, is crosssectional, thus we cannot capture the dynamics of labour market transitions over time. We analyse the data using econometric techniques, such as binary probit and ordered probit, to reflect the nature of the variable under investigation (e.g self-employed versus waged employed). General Findings Here we present a summary of the basic statistical evidence on the relationships between labour market status and personal, occupational and firm level characteristics. The key findings are as follows; 18.3% of the total and 16.3% of the non-agricultural working population of the EU-15 are self-employed. The EU can be split, as far as self-employment is concerned, into three categories: low (defined as a self-employment rate of less than 10%): medium (a self-employment rate of between 10% and 20%): and high (a self-employment rate of more than 20%). Using this simplistic banded classification for the non-agricultural self-employed we end up with Denmark, Sweden, Netherlands and Finland in the LOW band, Austria, France, Germany, UK, Luxembourg and Ireland in the MEDIUM band and Belgium, Spain, Portugal, Italy and Greece in the HIGH band. We observe that in the EU as a whole, 36% of the self-employed are job creators (JCs). Cross-country differences are however large. For example, in Germany 51% of the self-employed are job creators. This compares to only 27.3% in Greece. Do countries with high rates of self-employment have low job creation rates? The rate of job creating self-employment is diminishing, at an increasing rate, in the rate of self-employment. However, this factor alone can only explain 12 per cent of the variation across countries in self-employed job creation rates. From our simple equation where we explain job creating self-employment rates by the self-employment rate, allowing for non-linearities in the relationship, we observe the following pattern: 5executive summary

6 executive summary Fig.1 Estimated and Observed Self-Employment and Job Creating Self-Employment Rates Personal Characteristics Job creating self-employment is very much dominated by males. In the EU-15 as a whole, for example, the male share of waged employment is 55.3%, of individual self-employment (ISE) 61.8% and of job creating self-employment (JC) 75.9%. France is an exception to this general pattern. In the EU-15 the self-employed are older than the waged employed. On average, the difference is nearly five years, although in some countries, notably Ireland, this is as high as seven and a half years. For the full EU-15, the educational distribution of the self-employed is to the left of the waged employed. That is to say they are less well educated in general. Regression Results 6 We are interested in the fundamental determinants of individuals being in a particular labour market state at a point in time. In particular, we are concerned with the probability of observing an individual in waged employment (W), individual self-employment (ISE) or job creating selfemployment (JC). Given the decision process through which individuals are likely to go through (Cowling and Taylor, 2001), our estimation procedure takes the form of two probit regressions. The first estimates the probability of observing an individual in self-employment of either form (ISE or JC) rather than W, and the second the probability of observing an individual as a JC given that they are already in self-employment. Thus we are seeking to identify relationships between personal, job and firm characteristics and the probability of being self-employed and then having

7 employees. A separate model is estimated for the full EU-15 and for each separate country in both cases. Summary of Self-Employed versus Waged-Employed Results Men are more likely to be self-employed than women in the EU-15. The scale of the difference is of the order of 4.0%. This is the pure gender effect and it is much smaller than the overall difference between the male share of waged versus self-employment which is 11.5%. In short, this means that it is factors unrelated to gender per se that are causing the majority of the observed discrepancy in male and female selfemployment rates in the EU-15. At the level of individual countries, we observe that males in six countries have a higher probability of self-employment than women. The countries and effects are as follows; Spain (+7.8%), Ireland (+6.4%), Italy (+10.8%), UK (+5.3%), Austria (+4.4%) and Sweden (+5.8%). executive summary In Greece, Spain, Italy, UK and Sweden we observe that the probability of self-employment initially increases with age. At some point, and this varies across countries, the propensity peaks, then tails off. From our UK model (for men and women) we estimate the peak age to be fifty years old. The presence of young children generally reduces the incidence of selfemployment for men but increases it for women. For men, this is consistent with the desire to reduce the riskiness of ones income stream by seeking out employment in the waged sector (i.e a steady job). For women, it may be indicative of the need to balance work and childcare commitments. In this case it is the relative flexibility that self-employment offers that is attractive. Occupation is the key area distinguishing the self-employed from the waged employed in the sense that individuals across the range of occupations have significantly higher/lower probabilities of being selfemployed. Perhaps the most interesting feature is how different the UK is from all the other fourteen EU countries. In the UK, only craft related occupations were found to be significant in determining the selfemployment decision. Here we observe a fairly substantial (+11.7%) effect over all other occupations, on the probability of being selfemployed. The norm in the EU is for individuals from high human capital (managerial and professional) and skilled manual occupations to become self-employed. The former is not true for the UK where occupational differences between waged and self-employment are minimal. 7

8 executive summary For the EU-15, we observe high concentrations of self-employed in agriculture, retail, catering and real estate. We also observe low concentrations in manufacturing, utilities, transport, financial services and public services. Broadly speaking, the self-employed tend to operate in service sectors characterised by low start-up costs and accordingly low minimum efficient scales of operation. Job Creation and the Size Distribution of Businesses Having previously estimated the probabilities of choosing selfemployment over waged employment, we explore the size (measured by employment) distribution of self-employed businesses. Table 1 reports these results. As we observe, it is hard to create additional employment in large numbers. In the EU-15, for example, only 4.1% of self-employed businesses were larger than micro 1. The vast majority of those who create additional jobs create less than six. Table 1: Self-Employed Firm Size Distribution (Employees) Total % Belgium Denmark Germany Greece Spain France Ireland Italy Lux Netherlands Portugal UK Austria Sweden Finland EU A micro business has less than ten employees.

9 In the EU-15, only 4.1% of self-employed businesses active in 1996 were larger than micro (more than nine employees). The vast majority who employed additional labour had less than six employees. Overall, the ability of the self-employed to generate additional employment is not trivial. For example, in the UK there are around 3.7 million self-employed. This evidence suggests that they account for a further 2.6 million jobs in the economy out of a total employment level of 26.2 million. In terms of average job creation rates per self-employed business, the male figure is 1.1 and the female figure is 0.5. Education Fig.1: Education and Employment Size Distribution EU-15 Are we maximising the job creating potential of our self-employed sector? If we initially assume that the ability to create jobs is a good indicator of the quality of our self-employed population then we can begin to address the question of whether the right kinds of people are entering selfemployment or whether there is scope for policy intervention to promote more better quality self -employment. Our Approach The way we tackle this question is by separating out the results from the self-employed versus waged employed decision and then comparing them with the equivalent results from our business size estimation. This type of methodology accords with the important empirical work of Burke et al (2000). In effect we are questioning whether the characteristics of individuals who choose to enter self-employment in greater numbers are 9executive summary

10 executive summary the same as those associated with creating larger businesses and hence more jobs. It is, however, important to remember that this framework very explicitly assumes that job creation by the self-employed is the key policy goal and that all policy intervention in this area is focused on generating more job creating self-employed. As such it takes no account of other policy objectives or benefits that the economy and individuals derive from being self-employed. In addition it takes no account of job displacement when a self-employed business expands or any potential economic multiplier effects arising from new economic activity. The evidence for the EU-15 in total shows that male entry into selfemployment is higher than female entry, and that this leads to more job creation. Increasing age is associated with higher rates of self-employment and job creation. The higher an individuals level of formal education the more jobs they create. For the UK Self-employment entry per se is unaffected by education, but job creation is found to increase significantly in the level of education. Whilst male self-employment entry is high, this has no discernible effect on the job creating potential of the self-employed. That is to say that we are currently deriving no job creation benefits from having disproportionately high levels of male self-employment. Conclusions This study has analysed data on 15,800 workers spread across 15 EU member states in relation to their labour market status. Specifically, we have sought to provide detailed evidence regarding the determinants of self-employment in the first instance, and the nature and extent of additional employment in the second. From our more sophisticated employment status estimates, we observe a number of important areas of distinction between the self-employed and waged employed. Specifically, we observe that males (holding other personal characteristics, occupational status and industry sector constant) are more likely to be self-employed. Yet we suggest that the majority of the observed difference in male and female rates of selfemployment is unrelated to gender per se, but reflects occupational and sectoral differences in their employment patterns. 10 Given the underlying shift in the age demographics of the population in many countries, the economic and social impact of ageing has been debated at many levels. As far as self-employment is concerned, the

11 general pattern is that the older an individual is the more likely they are to become self-employed. In a smaller subset of countries, including the UK, the propensity to become self-employed initially increases in age then tails off in ones later working life. Household composition, in particular the presence of young children, significantly affects ones choice of labour market status. Yet there are opposing effects for men and women. For women, it appears that selfemployment is viewed as a means of achieving the flexibility required to balance work and childcare commitments. But men appear to shy away from the perceived riskiness of self-employment when they have family responsibilities. On balance, however, our results strongly suggest that occupation is the key area distinguishing the self-employed from the waged employed. To this end, any underlying shift in the occupational structure of a country, in particular an upward shift (more professionals and skilled manual workers) would be likely to raise the self-employment rate. Policy Implications Our findings suggest that in order to promote sustainable selfemployment and higher employment levels in this section of the labour force the SBS should work with its partners to; Raise educational attainment of the population as a whole Promote self-employment opportunities for women with children Promote vocational skills in the education system Promote gender equality in the labour market (increase female participation rates in employment more generally) Explore ways in which women with young children can be supported into self-employment However, there are some issues which must be considered before policy action is developed. Notably, some account needs to be taken of potential displacement by self-employed start-ups. Further, estimates of any potential economic multiplier effects arising from new activity would be apposite. References Birch, D (1979) The Job Generation Process. MIT. Burke, A, Fitzroy, F, Nolan, M (2000). When Less is More; Distinguishing between Entrepreneurial Choice and Performance. Oxford Bulletin of Economics and Statistics, Vol. 62 (5) December. Cowling, M, Taylor, M (2001). Entrepreneurial Women and Men: Two Different Species? Small Business Economics, Vol. 16 (3) May. Cowling, M, Taylor, M, Mitchell, P (2001) Job Creators. Research Centre for Industrial Strategy, The Birmingham Business School. University of Birmingham. Dale, I, Morgan, A (2001) Job Creation The Role of New and Small Firms. Small Business Service. Meager, N, Cowling, M, Bates, P (2001) Long-term Evaluation of Self-employment Assistance Provided by the Prince s Trust: First Interim Report. Department for Education and Skills, Research Report RR292. Westall, A, Cowling, M (1999) Agenda for Growth. Institute for Public Policy Research. London. 11executive summary

12 Marc Cowling Foundation for Entrepreneurial Management London Business School Regents Park London NW1 4SA Tel: +44 (0) Further copies of this publication can be obtained from: SBS Research & Evaluation Level 2 St. Mary s House c/o Moorfoot Sheffield S1 4PQ Or by phone through SBS Research Tel: Reports are available electronically via the SBS Website: URN 03/539