Ageing and entrepreneurship

Ageing and entrepreneurship An empirical study of the relationship between entrepreneurial age and objectives, strategy and performance Arjan Ruis Gerard Scholman Zoetermeer, June 2012

This research has been financed by SCALES, SCientific Analysis of Entrepreneurship and SMEs (www.entrepreneurship-sme.eu) EIM Research Reports reference number H201206 publication June 2012 number of pages 54 email address corresponding author address EIM Bredewater 26 P.O. box 7001 2701 AA Zoetermeer The Netherlands Phone: +31(0)79 322 22 00 All the EIM research reports are available on the website www.entrepreneurship-sme.eu. The responsibility for the contents of this report lies with EIM bv. Quoting numbers or text in papers, essays and books is permitted only when the source is clearly mentioned. No part of this publication may be copied and/or published in any form or by any means, or stored in a retrieval system, without the prior written permission of EIM bv. EIM bv does not accept responsibility for printing errors and/or other imperfections.

Ageing and Entrepreneurship Arjan Ruis a and Gerard Scholman a,b a Panteia/EIM, Zoetermeer, the Netherlands b Erasmus School of Accounting & Assurance, Rotterdam, the Netherlands Abstract This study examines whether the age of the entrepreneur is related to the objectives, strategy and performance of the firm. The study is primarily an explorative empirical study. We combine a descriptive analysis with econometric estimation techniques in an attempt to explain different outcomes concerning the aforementioned three variables of interest. In each analysis, age of the entrepreneur is the key explanatory variable. Possible presence of nonlinear effects of age is tested. Comparing the outcomes concerning objectives for different age cohorts, only small differences are visible between younger and older entrepreneurs regarding their indication of what is the most important objective of the firm. For the competitive strategy, younger entrepreneurs more often pursue an innovation or marketing strategy, while older entrepreneurs more often tend to practice a price discounting strategy. Moreover, by using econometric techniques, we find a strong negative relation between age and the innovation strategy. Looking at the performance of the firm, younger entrepreneurs perform better than older ones regarding turnover and investments, in the sense that that these performance measures more often increase in the former group, while older entrepreneurs do better on making profit. However, no evidence is found that such a relation exists for the firm's profit. For employment growth the variation between different age cohorts is small. Keywords: entrepreneurship, age, objective, strategy, performance JEL-codes: L26, J14, L21, L10 Version: June 2012 Contact: a.ruis@eim.panteia.nl, EIM Business and Policy Research, P.O. Box 7001, 2701 AA Zoetermeer, The Netherlands Document: Ageing and entrepreneurship.doc 3

Contents 1 Introduction 7 2 Why age might matter 9 3 Data collection and descriptive statistics 11 4 Objectives, strategy and performance for different age cohorts 15 4.1 Objectives 15 4.2 Strategy 16 4.3 Performance 17 5 Model and results 21 5.1 Objectives 21 5.2 Strategy 23 5.3 Performance 27 6 Limitations and recommendations 35 6.1 Limitations 35 6.2 Recommendations for further research 35 7 Conclusions 37 References 39 Annexes I Details on the logit models 41 II Results excluded from the main report 45 5

1 Introduction The ageing population increasingly becomes a challenge for policy makers. Possible consequences of the ageing population on, e.g. the size of the labour force (relative to the population) and the sustainability of national pension systems have already received much attention. Less attention has been given to the consequences that the ageing of the population may have for entrepreneurship. The ageing of the population may affect both the number and the performance of enterprises. Entrepreneurship is an important factor in the economy, and a changing population size and composition can have major consequences for e.g. the number of start-ups and established businesses. Recent studies show, for instance that the number of entrepreneurs in a country is associated with the growth of national income (Praag and Van Stel, 2011), that start-ups are important in creating new jobs (Bangma, Snel and Bakker, 2010), and that older starters less often employ staff than younger starters (De Kok, Ichou and Verheul, 2010). This study focuses on the effect of the ageing population, by examining whether the age of the entrepreneur (at a certain point in time) is related to the objectives, strategy and performance of the firm. The study is primarily an explorative empirical study. Organization of the paper In Chapter 2 we will briefly elaborate why age may affect objectives, strategy and performance. Chapter 3 presents the method of the data collection, an explanation of the variables and some descriptive statistics. Chapter 4 provides an overview of the three main variables (i.e. objectives, strategy and performance) for different age cohorts. The conceptual framework and the model results are presented in chapter 5. Chapter 6 covers the limitations of this study and suggestions for further research. Finally, chapter 7 concludes. 7

2 Why age might matter Braaksma, Gibus and De Kok (2012) conclude that ageing barely affects the size and composition of the entrepreneurial population. Nevertheless, ageing may affect the performance of companies. Do older entrepreneurs perform worse or better than younger entrepreneurs? A distinction can be made between two different age effects. The first effect is concerned with the entrepreneur's age at the start of the enterprise. Suppose that the start-up age of the entrepreneur is negatively correlated with her time horizon. Entrepreneurs who start their enterprise at the age of, say, 55 may then be less likely to actively pursue a growth strategy than entrepreneurs starting at the age of 35. According to a recent EIM study on start-ups, entrepreneurs who start at older age are less likely to work fulltime in their new venture, are less willing to take risks and have a lower perception of their entrepreneurial skills. Each of these factors has, in turn, a positive impact on the probability of employing personnel (De Kok, Ichou and Verheul, 2010). The second effect is concerned with the current age of the entrepreneur. At any given moment in time, older entrepreneurs in established enterprises may benefit from their experience and larger networks, but could for example also be less innovative. This could result in age-related differences in the growth rate of firms. As mentioned in the introduction, this study focuses on the relation between the age of the entrepreneur and objectives, strategy and performance. Theoretically, objectives and strategy may affect performance. However, we have chosen to investigate each of the relations separately. We do so, because the data we use in this study is not suitable to examine the interdependence. For example, we only have information on the type of the competitive strategy and we lack information on the extend in which firms are able to practise this strategy. In addition, the strategy variables are constructed using data of the 2006 edition of the SME Policy Panel, while the data concerning the performance variables come from the 2011 edition (see also chapter 3). 9

3 Data collection and descriptive statistics This chapter gives an overview of the data that is used in the attempt to explain the relationship between entrepreneurial age and objectives, strategy and performance (see chapter 5). The SME Policy Panel For this study we made use of the EIM SME Policy Panel (in Dutch: MKB Beleidspanel). This panel consists of around 2,000 SMEs 1, which are interviewed several times a year about different subjects regarding entrepreneurship and policy. This dataset not only contains information on performance, objectives and strategy, but also on the characteristics of the respondent (age, gender, educational level, position in the enterprise, etc). In the sample, we selected only those respondents that are either owner or manager of the firm (so no employees for example). This reduces the sample a little bit, resulting in 1,676 respondents. Table 1 shows the share of different age groups in the sample. The age groups are defined in such a way that each group has a considerable share in the sample. The 'younger' entrepreneurs (younger than 45 years) count for 22% of the total group, while the 'middle-aged' (45-55 years) have the largest share with 38%. The 'older' entrepreneurs are represented by the latter three groups (55-60 years; 60-65 years and 65 years and older). Although the retirement age is 65 in the Netherlands, a relatively large part of the entrepreneurs is active beyond that age. Table 1 Entrepreneurs by age Age of the entrepreneur Share in sample < 45 yr 22% 45-55 yr 38% 55-60 yr 18% 60-65 yr 14% >= 65 yr 8% Total 100% Source: EIM, SME Policy Panel 2011. Gender Figure 1 shows the division of the sample by gender for the five different age groups and the total sample. Four out of five of the respondents are male. Amongst the older entrepreneurs the share of female entrepreneurs is relatively low, especially for the oldest group. 1 SMEs according to the following definition: small companies (1-9 working persons) and medium sized companies (10-99 working persons). 11

Figure 1 Gender of entrepreneurs, by age < 45 yr 78% 22% 45-55 yr 81% 19% 55-60 yr 87% 13% 60-65 yr 85% 15% >= 65 yr 89% 11% Total 83% 17% 0% 100% male female Source: EIM, SME Policy Panel 2011. Educational level The educational level is distinguished into three categories; low, medium and high. 1 The medium and higher educated entrepreneurs are the two largest groups with a share of 42% and 45% respectively. Within the oldest group, the share of entrepreneurs with a higher education is relatively large compared to the other groups. The differences between the other age groups are smaller. The share of low educated entrepreneurs is relatively low for the youngest group. Figure 2 Educational level of entrepreneurs, by age < 45 yr 10% 43% 47% 45-55 yr 14% 45% 41% 55-60 yr 14% 39% 47% 60-65 yr 14% 41% 45% >= 65 yr 15% 32% 53% Total 13% 42% 45% 0% 100% low medium high Source: EIM, SME Policy Panel 2011. Sector In this study we distinguish eight different sectors. The agricultural sector as well as mining and quarrying, electricity companies, the public sector (public administration and services and education), and health and social work activities are not represented in the sample. The relatively high share of the oldest entrepreneurs working in the construction and transportation sector is notable. Furthermore, entrepreneurs in this age group do not very often work in the sectors manufacturing, accommodation and food service and other service activities. The low share of the entrepreneurs aged 55-60 in the construction sector and other 1 Definition of the educational levels according to the International Standard Classification of Education (ISCED). This classification is widely used as a framework to determine the educational level of a population. The ISCED classification covers six educational levels. These six levels can be further reduced into the following three main categories: Low (pre-primary, primary and lower secondary education (ISCED 0-2)); Medium (secondary and post-secondary non-tertiary education (ISCED 3-4)) and High (tertiary education (ISCED 5-6). 12

service activities is worth mentioning too. Only a few entrepreneurs in the age group 60-65 work in the accommodation and food service, while the share of them working in the business services is relatively high. Table 2 Entrepreneurs by sector and age Age of the entrepreneur Manufacturing Construction Wholesale and retail trade Accommodation and food service Transportation Financial institutions Business services Other service activities Total < 45 yr 14% 11% 16% 10% 9% 8% 17% 14% 100% 45-55 yr 15% 15% 17% 10% 8% 8% 16% 11% 100% 55-60 yr 16% 9% 21% 10% 8% 7% 20% 8% 100% 60-65 yr 14% 12% 18% 5% 8% 6% 26% 11% 100% >= 65 yr 10% 17% 19% 7% 11% 8% 22% 6% 100% Total 14% 13% 18% 9% 9% 8% 19% 11% 100% Source: EIM, SME Policy Panel 2011. Size class Table 3 shows the division of the entrepreneurs across the different size classes. The results herein show that older entrepreneurs do work more than average in single person enterprises and less often in larger enterprises. This holds especially for the oldest group. Table 3 Entrepreneurs by size class and age Age of the 1 working 2-9 working 10-99 working Total entrepreneur person persons persons < 45 yr 17% 46% 38% 100% 45-55 yr 17% 46% 36% 100% 55-60 yr 19% 46% 34% 100% 60-65 yr 19% 48% 32% 100% >= 65 yr 28% 46% 27% 100% Total 19% 46% 35% 100% Source: EIM, SME Policy Panel 2011. Note that size class is defined by looking at the number of persons working in the firm at the moment the questionnaire was administered. Imagine a firm with 12 employees at t and 8 employees at t-1. Defining size class at t, the firm is classified as a medium sized enterprise, while in fact it was a small firm at t-1. In this study, the employment growth is assigned to a medium sized enterprise and we ignore the so called dynamic classification method (De Kok and De Wit, 2012). As a result, single person enterprises at t can by definition not have 13

grown (in terms of employment) compared to t-1. This has to be kept in mind while interpreting the results of the investigated relation between age and employment growth (an indicator of performance, see chapters 4 and 5) later on. 14

4 Objectives, strategy and performance for different age cohorts This chapter presents the variables of interest, i.e. objectives (section 4.1), strategy (section 4.2) and performance (section 4.3). A breakdown by age is made, such that a first insight in the relation between age and the different variables of interest becomes clear. The results presented in the various tables and figures in this chapter are weighted by sector and size class. 4.1 Objectives Respondents were asked what they consider to be the most important objective for their company. Continuity of the enterprise was answered most (62%, see figure 3 below), followed by independence (18%), making profit (13%) and achieving growth (7%). They were also asked what they consider to be the second most important objective. Making profit and independence were frequently indicated in this case. Achieving growth does not appear to be an important objective for most of the entrepreneurs. Figure 3 Main objectives of entrepreneurs, by age of the entrepreneur continuity of the enterprise independence < 45 yr 45-55 yr 55-60 yr 60-65 yr >= 65 yr Total 16% 23% 23% 20% 17% 21% 61% 61% 62% 63% 68% 62% < 45 yr 45-55 yr 55-60 yr 60-65 yr >= 65 yr Total 16% 24% 18% 25% 20% 26% 19% 33% 17% 38% 18% 27% 0% 80% most important goal second most important goal making profit 0% 80% most important goal second most important goal achieving growth < 45 yr 45-55 yr 55-60 yr 60-65 yr >= 65 yr Total 14% 15% 11% 11% 9% 13% 39% 41% 40% 37% 34% 39% < 45 yr 45-55 yr 55-60 yr 60-65 yr >= 65 yr Total 8% 20% 6% 12% 8% 10% 7% 10% 4% 10% 7% 13% 0% 80% most important goal second most important goal 0% 80% most important goal second most important goal Source: EIM, SME Policy Panel 2011. Older and younger entrepreneurs do not differ a lot in their objectives, although older entrepreneurs have slightly more preference for continuity and independence, while younger entrepreneurs somewhat more often say their main objective is either making profit or achieving growth. However, there is no statistical evidence for significant 1 differences between the age groups. 1 Significant if p<0,05. 15

4.2 Strategy The strategy variables used in this study are developed based on Porter's interpretation. Strategy is modeled through the following variables: innovation orientation strategy, marketing orientation strategy, price discounting strategy and service orientation strategy. Innovation orientation strategy and marketing orientation strategy variables were created using Principal Components Analysis. For innovation strategy this resulted in a variable composed of seven items, including items on attitude towards innovation of products, services or production processes and expected investments in innovations. The marketing orientation strategy variable is composed of four items, including items on marketing activities and competitors. CATPCA was used to create scales. Single item variables were used for the other strategy variables. 1 The service orientation strategy variable was created by using information on the extent to which firms say they focus on providing (excellent) service to their customers. Almost all respondents indicated they did, so as a result there is no difference between entrepreneurs in different age groups. Therefore, we decided not to use this variable in our analysis. Table 4 Competitive strategy, by age of the entrepreneur Age of the entrepreneur Innovation Marketing Price Relative score* % yes < 45 yr 2,86 1,50 75% 45-55 yr 0,94 0,97 75% 55-60 yr 0,71 0,90 77% 60-65 yr 0,51 0,64 80% >= 65 yr 0,29 0,38 88% Total 1,00 1,00 76% * The factor score of the total group is set to unity, the scores of the different age groups are related to this score. Source: EIM, SME Policy Panel 2006. The results in table 4 show that younger entrepreneurs have higher scores on the innovation and marketing strategy variables than the older entrepreneurs. A higher score means that the company is focused more on the concerning competitive strategy. Older entrepreneurs on the other hand, follow more often a price discounting strategy than the younger ones. 1 These strategy indicators/variables are based on Zhou, Tan and Uhlaner (2007). 16

4.3 Performance To measure the performance of enterprises we use four different indicators: Turnover. Respondents were asked whether the turnover of their company has increased, remained unchanged or has decreased compared to previous year. Employment. Respondents were asked whether the employment in their company has increased, remained unchanged or has decreased compared to previous year. Profit. Respondents were asked whether the profit of their company has increased, remained unchanged or has decreased compared to previous year. Investments. Respondents were asked whether the investments of their company have increased, remained unchanged or have decreased compared to previous year. The SME Policy Panel also provides information on the level of the turnover, employment, profit and investments of most of the enterprises. Since it is a panel, we could have also used the growth of the different variables by using the values of the measurement of the last two years. We have tried this possibility, but the resulting data proved to be unsuitable 1 for further analysis. Furthermore, we would have lost some data, since not all respondents answer the questions about the level of the different variables (especially the question about profit) and because not all of them participated in the 2010 as well as the 2011 questionnaire. Therefore we decided to use the indicators described above. Below we will briefly describe the results for the four indicators. Turnover The results in figure 4 show that the older entrepreneurs in comparison with the younger ones, less often reported an increase of the turnover. More often the turnover in their company remained unchanged or decreased. Figure 4 Turnover 2010 compared to 2009, by age of the entrepreneur < 45 yr 41% 26% 34% 45-55 yr 28% 32% 39% 55-60 yr 29% 26% 45% 60-65 yr 20% 34% 46% >= 65 yr 21% 38% 41% Total 29% 30% 41% 0% 100% increased unchanged decreased Source: EIM, SME Policy Panel 2011. Employment The larger part of the respondents (83%, see figure 5) answered that the employment in their company remained unchanged. An explanation for this fact is the large amount of single person enterprises (see table 3 before). A large part of the entrepreneurs of those enterprises typically does not have the ambition to 1 In section 6.1 we elucidate the problem further and discuss it as one of the limitations. 17

grow in terms of employment and have deliberately chosen not to employ staff (see also Stam et al, 2012). Excluding those companies results in a somewhat lower share (71%) of entrepreneurs, that state that employment has neither increased nor decreased. Figure 5 Employment 2010 compared to 2009, by age of the entrepreneur, for a) all companies and b) companies with 2-99 working persons a) all companies (1-99 working persons) < 45 yr 8% 83% 9% 45-55 yr 6% 81% 13% 55-60 yr 5% 85% 10% 60-65 yr 7% 79% 14% >= 65 yr 4% 91% 5% Total 6% 83% 11% 0% 100% increased unchanged decreased b) companies with 2-99 working persons < 45 yr 14% 71% 15% 45-55 yr 12% 68% 20% 55-60 yr 10% 72% 17% 60-65 yr 13% 67% 21% >= 65 yr 5% 84% 11% Total 12% 71% 18% 0% 100% increased unchanged decreased Source: EIM, SME Policy Panel 2011. The results vary for the different age groups. The oldest group has the lowest share of increase in employment, but also has the lowest share of entrepreneurs that indicated that employment decreased in their company. The differences between the other age groups are small, although the youngest group seems to perform somewhat better. Note that, although employment has not changed for a large part of the firms, turnover has. Profit The third indicator for performance is profit. A majority of the entrepreneurs indicated that profits were lower compared to profits the year before. This is probably due to the financial and economic crisis, which started in 2008. Although the downfall was particularly large in 2009, the effect of the crisis on SMEs was not only felt in 2009 but also in 2010 (see also De Kok et al, 2011). In this case, the older entrepreneurs except for the oldest group perform better than their younger colleagues/competitors (see figure 6 below). 18

Figure 6 Profit 2010 compared to 2009, by age of the entrepreneur < 45 yr 13% 6% 70% 10% 45-55 yr 14% 12% 67% 6% 55-60 yr 20% 18% 50% 12% 60-65 yr 17% 19% 58% 6% >= 65 yr 10% 10% 77% 4% Total 15% 13% 64% 8% 0% 100% increased unchanged decreased don't know/won't tell Source: EIM, SME Policy Panel 2011. Investments Figure 7 at last shows the development of investment for the different age groups. Approximately one third of the entrepreneurs answered that investments have increased compared to last year. For the younger entrepreneurs this share is considerably higher than for the older entrepreneurs. However, the oldest two groups of entrepreneurs also report less often a decrease of investments. Remarkably though, is the relatively large amount of entrepreneurs aged 55-60 that indicate a decrease of investments. Figure 7 Investments 2010 compared to 2009, by age of the entrepreneur < 45 yr 43% 35% 23% 45-55 yr 38% 35% 27% 55-60 yr 27% 40% 34% 60-65 yr 23% 60% 17% >= 65 yr 29% 50% 21% Total 35% 40% 26% 0% 100% increased unchanged decreased Source: EIM, SME Policy Panel 2011. 19

5 Model and results In this section we describe various models and results in an attempt to explain different outcomes concerning performance, objectives and strategy of a firm. In all these models, we include as exogenous variable the present age of the entrepreneur. Also, the possible presence of nonlinear effects of age is tested. In all upcoming sections we show results of logit or OLS estimation. When discussing results of logit estimation, we combine significance of parameter estimates with implications that follow from graphed odds ratios, in an attempt to describe a, if any, relation between the age of the entrepreneur and the probability of a success (or a certain category, in the multinomial setup) in an endogenous variable. When discussing OLS results, we use significance of parameter estimates to describe a, if any, relation. We will also briefly discuss statistical fit of each model. Details of the models can be found in the annex. We will now discuss the models for each of the outcomes separately. 1 5.1 Objectives The SME policy panel contains data regarding the objectives of a firm. First, let us list the four objectives that are distinguished throughout this section. 1. Growth 2. Independence 3. Making profit 4. Continuity Each of these objectives can be expressed as a binary variable, having success value 1 if the objective is deemed important by the entrepreneur. We will use a logistic and a multinomial setup to model this variable. 5.1.1 Growth The first objective we consider is growth. We did not find a strong relation between the age of the entrepreneur and the probability of pursuing a growth strategy. We show the parameter estimates in Annex II (table 13). In the highest age interval (65+), we do find a negative effect at 10% significance level. Combining this weak significance with the predictive power of the hit-rate relative to q and the low pseudo R 2, we conclude that the model is not capable of fitting the data and refrain from drawing any conclusions concerning a link between age and having growth as a primary objective. 5.1.2 Independence To the objective independence a similar reasoning as seen with growth applies. No improvement of the hit-rate relative to q was visible and no relation between 1 We will report the most striking results in the body text and the appendices. Other results can be provided upon request. Since binary logit has an easy interpretation, compared to multinomial logit, we will report these results in the text and use the latter to check results for robustness. 21

age and the likelihood of being independent as primary goal of the entrepreneur becomes clear from the results (see table 14 in Annex II). 5.1.3 Making profit The third objective we discuss is the profit. Again, we find a bad statistical fit judging the diagnostics of the logistic setup. Results can be found in Annex II (table 15). 5.1.4 Continuity The final objective we discuss is continuity. We report both the parameter estimates and their implication via odds-ratios, plotted against the age of the entrepreneur. In the logit estimation, we include age as a continuous variable. Replacing age with dummies for different age intervals yields similar implications. Table 5 Parameter estimates of binary logit model: P[continuity=1] Group Variables Estimated Standard error Significance level Constant -.198.334.553 Gender Man -.215.130.100 Size class Solo self-employed -.261.104.012 Medium.504.199.011 Sector Manufacturing -.331.240.166 Construction.337.211.110 wholesale and retail trade Accommodation and food service -.172.186.354.094.264.723 Transportation.542.315.086 Financial institutions.358.354.312 Business services.228.185.218 Age` Continuous age.016.005.004 Educational level High.010.159.950 Medium.180.152.237 R 2 Nagelkerke 0.031 Hit-rate 60.6% Nr obs. 1,597 q 53.1% Source: Panteia/EIM, SME Policy Panel 2011. 22

Figure 8 Scatter plot of Odds Ratio (growth) vs. age, objective is continuity 25.000 20.000 Odds Ratio 15.000 10.000 5.000 0.000 0 10 20 30 40 50 60 70 80 90 Age Source: Panteia/EIM, SME Policy Panel 2011. The pseudo R 2 is not very high, but the hit-rate of this model seems to suggest that the explanatory variables add substantial predictive power regarding the explaining of the probability of having continuity as a primary objective. The scatter plot of the odds ratios shows that this effect does not disappear in terms of estimated relative probabilities. On the contrary, it shows that as the age of the entrepreneur increases, since the odds ratio can climb up towards 20. Besides the age effect, being solo self-employed makes it more unlikely to pursue a continuity objective, while being a large firm makes it more likely (both compared to an averaged size firm). 5.1.5 Summary This section briefly summarizes the results found for the objectives. Chapter 4 suggested that there is no clear sign of a relation between entrepreneurial age and the main objective of a firm. In this section, the objectives growth, independence and making profit do not seem to be related to age. Model diagnostics show signs of data not well fitted by the model. Hence, older entrepreneurs do not less often have growth as their main objective than younger entrepreneurs. Though, for continuity, we find that as the age of the entrepreneur increases, it becomes more likely to have continuity as the most important objective. 5.2 Strategy This section deals with the relation between the age of the entrepreneur and each of the three following competitive strategies based on Porter's typology: 1. Innovation 2. Marketing 3. Price Discounting As explained earlier, the first two strategies are extracted via Principal Component Analysis (PCA), providing a specific score for every firm on each strategy. 23

We will make use of continuity in these measures by performing OLS procedures whilst treating each strategy as endogenous. We use a list of explanatory variables similar to the previous section concerning objectives. Since the price discounting strategy variable is based on one survey question, we model this question, similar to previous sections, with a binary logistic setup. The question that was asked is as follows: Does the company emphasize on costs optimization? The binary logistic setup therefore models the probability that an entrepreneur emphasizes on costs optimization. 5.2.1 Innovation The first strategy we analyze is innovation. As noted before, we perform OLS regressions by setting the score for a particular strategy as dependent variable and we include similar explanatory variables as in previous sections. The results are displayed in table 6. Table 6 OLS parameter estimates, dependent strategy measure is innovation Group Variables Estimated Standard error Significance level Constant.378.191.048 Gender Man.306.071.000 Size class Solo self-employed -.310.066.000 Medium.423.071.000 Sector Manufacturing.094.115.413 Construction -.475.117.000 wholesale and retail trade Accommodation and food service -.066.104.530 -.278.133.037 Transportation -.381.131.004 Financial institutions -.144.142.310 Business services -.109.108.314 Age Continuous age -.014.003.000 Educational level High.480.078.000 R 2 adjusted 0.160 Nr obs. 1180 Medium.172.074.021 Source: Panteia/EIM, SME Policy Panel 2011. Judging the R 2 adjusted, quite some of the variation can be explained by this model. Among the strongly significant parameters are gender (positive), size class (negative and positive effect for solo self-employed and medium size employers, respectively) and the educational level (higher innovation score as educational level rises). We find that including age as dummies or as continuous delivers 24

similar results, in the sense that the fitted scores are the same. For that reason we include the actual age of the entrepreneur. As can be seen from table 6, age has a strongly significant negative effect on the innovation score. The results are clearly in line with what we have presented in chapter 4 (table 4). The result is also comparable with the conclusions from Pleijster et al (2010). They found that (for some specific sectors) older entrepreneurs are on average less innovative than younger entrepreneurs. In Annex II we included the parameter estimates for a model that includes age dummies (see table 16). 5.2.2 Marketing The next strategy we discuss is marketing. The results of the OLS regression are presented in table 7. Table 7 OLS parameter estimates, dependent strategy measure is marketing Group Variables Estimated Standard error Significance level Constant -.225.122.064 Gender Man.204.069.003 Size class Solo self-employed -.565.064.000 Medium.490.069.000 Sector Manufacturing.068.111.539 Construction -.301.114.008 wholesale and retail trade Accommodation and food service.273.102.007 -.063.129.624 Transportation -.112.128.379 Financial institutions.239.138.083 Business services -.055.105.599 Age 45 55 -.204.076.008 55 60.068.074.362 60 65.037.079.641 65 + -.110.088.210 Educational level High.377.076.000 R 2 adjusted 0.209 Nr obs. 1,180 Medium.220.072.002 Source: Panteia/EIM, SME Policy Panel 2011. The R 2 adjusted indicates that quite some of the variation can be explained. Similar as in the innovation analysis, being a man, having a medium sized firm or having a higher educational level positively influences the average score on marketing. Age, in this model, has a negative effect in the interval 45 55. All other age parameters are insignificant. Moreover, including or excluding age parameters does not change the R 2 adjusted, implying that there is no explanatory power in the age of the entrepreneur for the marketing score. 25

5.2.3 Price Discounting The price discounting variable is a binary variable, therefore estimated parameters should be considered within the appropriate non-linear logistic relationship with explanatory variables. Results are shown in table 8. Table 8 Parameter estimates of binary logit model: P[costs optimization=yes] Group Variables Estimated Standard error Significance level Constant 1.126.250.000 Gender Man.071.134.594 Size class Solo self-employed -.766.141.000 Medium.529.149.000 Sector Manufacturing.320.260.218 Construction.143.258.579 wholesale and retail trade Accommodation and food service.492.253.052 -.137.275.618 Transportation.458.316.147 Financial institutions.511.321.111 Business services -.243.232.296 Age 45 55 -.201.160.210 55 60.354.178.046 60 65.223.180.216 65 +.453.212.033 Educational level High -.019.159.903 Medium.215.161.183 R 2 Nagelkerke 0.092 Hit-rate 79.5% Nr obs. 1,948 q 67.0% Source: Panteia/EIM, SME Policy Panel 2011. The results in table 8 show that the hit-rate relative to q is quite high, indicating that the included explanatory variables add substantial predictive power. However, this power seems to come mostly from the size class, judging the corresponding p-values and estimated coefficients. It is on average harder for smaller companies to compete on price, since economies of scale are smaller compared to larger companies (see for instance Chandler, 1990). No obvious relation becomes clear from the output between age and pursuing (or not pursuing) a price discounting strategy. This is emphasized by the fact that the hit-rate of a model without age as explanatory has a hit-rate of 79.2%. 5.2.4 Summary In this summary we discuss the main findings of our attempt to describe the relation between entrepreneurial age and competitive strategy. We have found a negative relation between age and the score on innovation. For the marketing 26

strategy and the price discounting strategy no obvious relationship with age became clear. 5.3 Performance The performance of firms is measured in four dimensions which are listed below. 1. Turnover 2. Employment 3. Profit 4. Investments In respective order: the turnover (in Euros), employment (in number of persons), profit (in Euros) and investments (in Euros). For each of these performance measures there are 3 possible outcomes: the measure has increased, decreased or remained unchanged. Aggregating 2 of 3 possible outcomes enables a binary logistic, whereas all three possible outcomes can be modeled using a multinomial setup. Aggregating the categories decreased and remained unchanged will be denoted by 'growth', whereas aggregating the categories increased and remained unchanged will be denoted by 'growth and equal'. See the annex for details. 5.3.1 Turnover The performance measure we examine first is the turnover. The dependent variable in this context is the answer to the question: Has the turnover in 2010 compared to 2009 increased, decreased or has it remained unchanged? For the turnover, we choose to report the estimates of a binary logit model explaining the probability of an increase in the turnover (therefore indicated by 'growth'), with age as a continuous variable. 1 Table 9 shows the results. In figure 9 we show corresponding odds ratios, plotting the likelihood of an increased turnover relative to either a decreased or an unchanged turnover, against age in a scatter plot. 1 Testing for nonlinearity by replacing age with group-dummy variables of age showed no sign of a nonlinear relation in the odds ratio of Figure 9. Therefore in this particular case we include age as a continuous variable. 27

Figure 9 Scatter plot of Odds Ratio (growth) vs. age, measure is turnover 3.500 3.000 2.500 Odds Ratio 2.000 1.500 1.000 0.500 0.000 0 10 20 30 40 50 60 70 80 90 Age Source: Panteia/EIM, SME Policy Panel 2011. Table 9 Parameter estimates of binary logit model: P[turnover growth = 1] Group Variables Estimated Standard error Significance level Constant.898.361.013 Gender Man.340.144.018 Size class Solo self-employed -.316.112.005 Medium.381.184.038 Sector Manufacturing.119.265.654 Construction -.165.234.480 wholesale and retail trade Accommodation and food service.256.207.217 -.032.286.910 Transportation.556.306.069 Financial institutions.649.355.068 Business services.101.206.625 Age Continuous age -.038.006.000 Educational level High -.039.171.819 Medium -.134.163.413 R 2 Nagelkerke 0.062 Hit-rate 60.2% Nr obs. 1,590 q 58.5% Source: Panteia/EIM, SME Policy Panel 2011. Evaluating the R 2 Nagelkerke, we consider the model to reasonably fit the data. The overall hit-rate, which is higher than q, of about 60% seems to emphasize this. We find that being solo self-employed negatively influences the probability of a rise in the turnover, while larger firms seem to be more likely to grow in this measure. Age is strongly negatively related to the probability that the turnover 28

has risen in the past year. The scatter plot in figure 9 shows the odds ratio plotted against age. From this graph, the downward sloping relation is clearly present. This result confirms the outcomes presented in chapter 4 (see figure 4). What about the relation between age and decreasing turnover? In the appendix we show parameter estimates of a growth and equal model (table 17) and its corresponding odds-ratios (figure 12). This can be viewed as modeling the complement of the probability of a decreased turnover Also in this model, the hitrate is higher than q, and the continuous age variable has a significant negative slope. This implies that as age increases, entrepreneurs will not only have a decreasing probability of a growing turnover, but also an increasing probability of a decreasing turnover. Theoretically, it is possible that the age effect we have found here is in fact an indirect effect. In section 5.2.1 we saw that older entrepreneurs are less innovative. This might result in a decreasing turnover. To examine this, additional research is required. 5.3.2 Profit The next measure we will discuss is profit. In this section, we attempt to model the answer to the question: Have the profits in 2010 compared to 2009 improved, deteriorated or remained unchanged? We will first show results of a 'growth and equal' setup. This can be viewed as modeling the complement of the probability of a decreased profit. Afterwards, we will discuss its relation to a 'growth' model. Table 10 yields the 'growth and equal' results and figure 10 displays the corresponding odds ratios. 29

Table 10 Parameter estimates of binary logit model: P[profit growth&equal = 1] Group Variables Estimated Standard error Significance level Constant -.924.383.016 Gender Man -.458.213.032 Size class Solo self-employed.151.177.394 Medium -.379.294.198 Sector Manufacturing -.118.407.772 Construction.312.347.369 wholesale and retail trade Accommodation and food service -.068.314.829.363.413.380 Transportation.698.447.118 Financial institutions.303.497.542 Business services -.598.325.065 Age 45 55.338.228.139 55 60 1.170.258.000 60 65.951.267.000 65 +.225.362.533 Educational level High -.293.274.285 Medium.113.260.664 R 2 Nagelkerke 0.115 Hit-rate 66.0% Nr obs. 791 q 58.5% Source: Panteia/EIM, SME Policy Panel 2011. Figure 10 Scatter plot of Odds Ratio (growth & equal) vs. age, measure is profit 3.500 3.000 2.500 Odds Ratio 2.000 1.500 1.000 0.500 0.000 0 10 20 30 40 50 60 70 80 90 Age Source: Panteia/EIM, SME Policy Panel 2011. 30

Combining the value of the Nagelkerke likelihood ratio and the hit-rate relative to q, we consider the data to be fitted quite accurately by our model. We included 4 groups of dummy variables representing the age categories of the entrepreneur. Notice that our setup implicitly defines the youngest entrepreneurs to be a base group (reference). Since only the middle two groups of age included in the analysis, read age 55-65, are significant, there seems to be a non-linear relation between age and the dependent variable. Figure 10 shows that this relation is somewhat inversely U-shaped. Apparently, probabilities of not residing in the 'decreased profit' category over the past year, are on average the highest in this particular age interval. This confirms the results presented in figure 6 of chapter 4. So, in which category do these entrepreneurs reside? Do they have higher probabilities of making profit? Closer examination of the 'growth' model parameters shown in Annex II (see table 18) provides an answer. In this model setup, the age parameter estimates are no longer significant and the statistical fit has decreased substantially by judging the overall hit-rate relative to q. Combining both models implies that the aforementioned entrepreneurs from the age category 55 65 that fall out of the category 'decreased profit' do not necessarily make more profits. The results of the two models suggest that on average it is more likely that these entrepreneurs will reside in the 'equal' group. Also worth noting, the age group that is outside the '55 65' interval, does not seem to have a bigger probability of having increased nor decreased profits. Besides this age effect, being a man and working in the rental sector seems to decrease the likelihood of increasing profits. 5.3.3 Employment The next measure of performance we discuss is employment. Underlying the analysis is the following question: Has the number of employees in your company in 2010 compared to 2009 increased, decreased or remained unchanged? This measure of employment does not seem to be related to the age of the entrepreneur. In chapter 2 we also concluded that there are no big differences between the different age groups regarding the development of employment. We show parameter estimates of a 'growth' model in this section and explain our findings statistically. 31

Table 11 Parameter estimates of binary logit model: P[employment growth = 1] Group Variables Estimated Standard error Significance level Constant -2.958.564.000 Gender Man -.007.289.981 Size class Solo self-employed -2.066.340.000 Medium 1.519.230.000 Sector Manufacturing.516.566.362 Construction.202.561.718 wholesale and retail.716.475.132 trade Accommodation and.809.550.141 food service Transportation.658.647.309 Financial institutions.843.755.264 Business services.888.482.065 Age 45 55 -.306.265.250 55 60 -.579.330.079 60 65 -.183.325.574 65 + -.594.457.194 Educational level High.182.360.614 Medium.149.354.675 R 2 Nagelkerke 0.220 Hit-rate 65.5% Nr obs. 1,599 q 88.7% Source: Panteia/EIM, SME Policy Panel 2011. Viewing the model diagnostics, we come to the conclusion that this model is not reliable. Even though the Nagelkerke measure is quite high, random predictive power is much stronger than that of this model (because the difference between the hit-rate and q is negative), suggesting that this model does not add any predictive power. On the contrary, it gets worse relative to random prediction. 1 5.3.4 Investments In this section we attempt to describe answers of respondents to the following question: Have investments in 2010 compared to 2009 increased, decreased or remained unchanged? Similar to the 'turnover' analysis we find no sign of a nonlinear relation between age and investments. Therefore, we choose to report estimates of a model that 1 The same results are found in and a similar reasoning applies to an employment growth&equal model. The results of this model are shown in the Annex II. 32

includes age as a continuous exogenous variable. The results are displayed in table 12 and figure 11. Table 12 Parameter estimates of binary logit model: P[investment growth = 1] Group Variables Estimated Standard error Significance level Constant.562.537.295 Gender Man.973.238.000 Size class Solo self-employed.181.170.287 Medium.312.230.175 Sector Manufacturing -.982.384.011 Construction -.875.323.007 wholesale and retail trade Accommodation and food service -.260.286.362.007.381.985 Transportation -.875.535.102 Financial institutions -.178.528.737 Business services -.785.285.006 Age` Continuous age -.039.009.000 Educational level High.483.281.086 Medium.300.268.263 R 2 Nagelkerke 0.88 Hit-rate 59.9% Nr obs. 883 q 58.5% Source: Panteia/EIM, SME Policy Panel 2011. Figure 11 Scatter plot of Odds Ratio (growth) vs. age, measure is investment 3.000 2.500 2.000 Odds Ratio 1.500 1.000 0.500 0.000 0 10 20 30 40 50 60 70 80 90 Age Source: Panteia/EIM, SME Policy Panel 2011. 33

Figure 11 shows a clear downward sloping curve in the odds ratios implying that as the age of the entrepreneur increases, the likelihood of a rise in investments over the past year decreases. The fact that less entrepreneurs will invest more relative to last year as they become older, does not necessarily imply that they will invest less. More specific, table 20 in Annex II, displaying parameter estimates of a 'growth & equal model', shows that only in the category of age 55-60, entrepreneurs are significantly more likely to decrease the amount invested. So combining these two models, it appears that there is a certain turning point (at the age 55 60), at which most entrepreneurs will lower investments. Outside this interval and as the age of the entrepreneur increases, on average, the likelihood of rising investments will decrease. The results are completely in line with what we have found earlier in chapter 4 (see figure 7). Besides the 'age effect', being a man or possessing a high educational level has a positive effect on the likelihood of increasing investments, while the rental, building and industrial sector all have a negative impact. 5.3.5 Summary A few interesting findings are provided by the results concerning performance. In this section we summarize them briefly. The first is that as the age of the entrepreneur increases, the turnover seems to become more likely to stop growing and start decreasing. As for the profits, the non-linear relation seems to suggest that in the age interval of 55 65, entrepreneurs are most likely not to have decreased profits over the past year. Moreover, this does not imply that they start making profit. On average, they mostly fell into the 'equal' group that had neither growth nor decreased profits. For investments, we found a similar relation as the turnover; downward sloping as the age increases. Age and employment did not appear to have any relation. 34

6 Limitations and recommendations This chapter presents some limitations of this study (section 6.1) and recommendations for further research (section 6.2). 6.1 Limitations In the section regarding performance, we evaluated model parameters that were based on either binary or ordinal choices. In our dataset, there is information available on the level of the variables (all performance measures, given by the entrepreneur). On top of that, it is even possible to merge questionnaires of several (consecutive) years to construct a dynamic panel about for instance the turnover. However, we already found that the variation in these data could not be explained by the explanatory variables incorporated in the SME policy panel (for instance the sector, age and educational level of the entrepreneur). In this context, we consider these data to not be applicable for determining a possible relation between actual values of a performance measure (and derivatives hereof that can be calculated by constructing a panel, such as year-on-year growth) and the age of the entrepreneur. Possible explanations can be found in the environment of the survey. Respondents were asked many questions by phone, which might not have been answered precisely. Some entrepreneurs could, for instance, have estimated certain amounts asked, leading to a loss in efficiency and creating a bias in the estimated parameters of our analysis. This inability to explain actual values of performance is the major limitation of our research. Considering the strategies of section 3.3, innovation and marketing were both extracted via PCA and subsequently used as a dependent variable in OLS regressions. These two estimation steps both have a degree of uncertainty, imposing a restriction on the accurateness of our estimation results. We consider this to be a limitation. Outliers are possibly a problem in some of the models used in this report. Even though more advanced 'outlier analysis' lies outside the scope of this research, in some way it limits robustness of the results. 6.2 Recommendations for further research An interesting way to continue this research is to use other, perhaps more reliable, data. For instance the National Bureau for Statistics in the Netherlands (Centraal Bureau voor de Statistiek, CBS) offers a survey, the so called production statistics, which covers statistics regarding employment and financial information in various sectors. In general, any dataset that contains firm details such as performance measures, age, education and gender of the entrepreneur can be used for this type of analysis. Having that said, it is also worth noting that we only used the years 2006 and 2010 of the SME policy panel. Obviously other years can provide data for a robustness check. In general, this research report focuses on the link between the age of the entrepreneur and objectives, strategies and certain performance measures. Outside 35

the scope of this work but possibly highly relevant for policymaking is the question how these variables are related to each other. Do firms that concern their most important objective to be making profit actually perform better (on profit) than firms that do not indicate this objective as most important? In general, are objectives and strategies related to performance and could there be an indirect effect of age on performance? Also it is interesting to investigate whether the years of working experience as an entrepreneur affects any of the three variables of interest noted earlier. In addition, it is also worth examining whether there is an effect of specific age cohorts (e.g. all entrepreneurs born in the sixties). Furthermore, if information is available on the reason(s) to become an entrepreneur in first place, this could be used as one of the explanatory variables. One can roughly distinguish two types of entrepreneurs, necessity and opportunity entrepreneurs (see for instance Reynolds et al, 2002 and Thurik et al., 2008). The first type concerns entrepreneurs that became entrepreneur because, for example, they lost their job and are not successful in finding a new job (this holds especially for older and low educated people). The second type of entrepreneur deliberately chooses to become an entrepreneur. This may affect the performance of the firm. Finally, the aforementioned possible presence of outliers and overall robustness can be tested using for instance recursive residuals. Kianifard and Swallow (1996) emphasize in their review that many model validation tests 1 can be derived from this estimation technique. 1 Such as test statistics for outliers, but also for serial correlation, heteroscedasticity, functional misspecification and structural change. 36

7 Conclusions In this study we investigate the relationship between the age of the entrepreneur and objectives, strategy and performance of the firm. In this final section we present our main findings. Concerning the objective of the firm, we did not find a clear sign of an age effect. One might expect in advance that older entrepreneurs less often have growth as their main objective compared to younger entrepreneurs. However, we did not find any evidence for that. The same holds for the objectives independence and making profit. Though, for continuity we found that as the age of the entrepreneur increases, it becomes more likely to have continuity as the most important objective. In addition, continuity appears to be the most important objective for all age cohorts, while achieving growth is only indicated as most important objective by a relatively small group of entrepreneurs. Regarding the competitive strategy of the firm, we found a strong negative relation between age and the innovation strategy, indicating that older entrepreneurs are less innovative than younger entrepreneurs. Though, for the marketing strategy and the price discounting strategy no obvious relationship with age became clear. A few interesting findings are provided by the results concerning performance. The first is that as the age of the entrepreneur increases, the turnover seems to become more likely to stop growing. In addition, as age increases, entrepreneurs will not only have a decreasing probability of a growing turnover, but also an increasing probability of a decreasing turnover. For investments, we found a link similar to the turnover; there is a negative relation with age. For profits, we did not find a clear co-movement with age. The results suggest that in the age interval of 55 65, entrepreneurs are most likely not to have decreased profits over the past year. However, this does not imply that they start making profit. On average, they mostly had neither growth nor decreased profits. Age and employment do not appear to be related. The present study provides a first insight in the relationship between age of the entrepreneur and objectives, strategy and performance of the firm. The results show that age negatively affects innovation (strategy), investments and turnover. However, there seems to be no relation between age and objectives, marketing and price discounting strategy, and employment growth. Further research, on especially the extent in which age affects performance, is necessary to provide more evidence on the exact relationship between the age of the entrepreneur and the performance of the firm. 37

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ANNEX I Details on the logit models Objectives The four objectives that are distinguished are: 1. Growth (G) 2. Independence (I ) 3. Making profit (P) 4. Continuity (C) Every objective o O = { G, I, P, C} can be modeled as a binary choice variable: P[ o i ' ( xi β e ) = 1] = ' ( xi β 1+ e ) o O, i = 1K,, n (1) With corresponding odds-ratios: P oi P[ o [ i i = 1] ' ( x β = e ) = 0] o O, i =1K,, n (2) The odds ratio for the logit model calculates the chance of 'success' relative to its complement (no success). We also calculate hit-rates and the probability of making a correct prediction when guessing randomly, say q. The difference between the hit-rate and q is used as a quality measure for each model. Finally, we 2 will report the pseudo R of Nagelkerke: L 1 L int ercept 2 / n 2 full R nagel ker ke = (3) 2 / n 1 Lint ercept Where L denotes the function value of the underlying maximum likelihood procedure. The subscripts intercept and full indicate the function value from a model containing only an intercept and a model containing all exogenous variables, respectively. This pseudo R (as apposed to for instance the Cox & Snell pseudo 2 measure) can take on the value 1, making it more realistic compared to the OLS- R 2. 1 Strategy We use the following three strategies in our study: 1. Innovation (In) 2. Marketing (Ma) 1 See Nagelkerke (1991) for more properties of this pseudo-r 2. 41

3. Price Discounting (Pd) We perform OLS regressions on each of the first two strategies, say s S =, s i x i { In Ma} i. The regression can then be formulated as: = α + β + ε s i S, i 1K,, n = (4) Where x i is a set of explanatory variables and s i denotes the score on that particular strategy. Since the Price Discounting variable is based on one survey question, we model this question with a binary logistic setup. If we let f i denote the answer to that question then: f i 1 = 0 if answered yes else (5) This enables us to estimate parameters for effects of x i on f i : P[ f i ' ( xi β e ) = 1] = ' ( xi β 1+ e ) i = 1, Kn (6) Again we calculate odds-ratios, the hit-rate, q, and R 2 Nagelkerke. Performance The performance of firms is measured in four dimensions: in terms of the turnover in Euros, employment in number of persons, profit in Euros and investments in Euros. 1. Turnover (T ) 3. Employment ( E ) 3. Profit ( P ) 4. Investment ( I ) For each of these performance measures m M = { T, E, P, I} there are 3 possible outcomes. If we let the subscript of m denote the year to which the performance measure is related, we can define m as follows: 1 m = 0 1 if m if m if m 2010 2009 2010 > m = m < m 2009 2010 2009 (7) Starting from this abstraction we can formulate both a binary logit and a multinomial logit model that models the expected value of m. In the scenario of a logit setup we first define two binary variables denoted as event growth and e growth& equal : e 42

e growth 1 = 0 if m =1 else (8) e growth&equal 1 = 0 if m 0 else If we now let event e E = {, } setup for P [ e i =1] as follows: e growth e growth & equal (9), we can formulate a binary logit P[ e i ' ( xi β e ) = 1] = ' ( xiβ 1+ e ) e i E, i = 1, Kn (10) Where the subscript i denotes the i th firm owner, ' x i is a row vector containing explanatory variables and β is a vector containing parameters. Note that the e on the right hand side of the equation denotes the base of the natural logarithm, whereas e on the left side denotes an element of a set of possible events. In a similar way, for m, we can formulate a multinomial logistic setup for P[ m i = j] as follows: 1+ P[ mi = j] = 1+ h h e β0, j + β1, j xi { reference class} 1 { reference class} e e β0, h + β1, hxi β0, h + β1, hxi and { 1,0,1 } if j and = 0 j reference class if j reference class (11) For m M, i = 1, Kn j is for obvious reasons taken as the reference class. In order to measure the effect of age on P [ e i = 1] or [ m j] P i =, after including age in x, we can calculate the model's corresponding odds-ratios for the n individuals: [ e 1] ' i = i P P[ e i P[ mi P[ m = e = 0] i = = j] = k] x β ( β 0, j β 0, k ) + ( β1, j β1, k ) x i e E, i =1K,, n m M, (12) i =1K,, n (13) In a multinomial setup the odds ratio of option j is measured relative to option k. We also calculate hit-rates and q and report the pseudo R 2 of Nagelkerke. 43

ANNEX II Results excluded from the main report Table 13 Parameter estimates of binary logit model: P[growth=1] Group Variables Estimated Standard error Significance level Constant -1.644.406.000 Gender Man -.396.226.079 Type of entrepreneur Sector dummy Solo self-employed -.634.204.002 Medium -.109.325.739 Manufacturing -.685.462.139 Construction -1.144.443.010 wholesale and retail trade Accommodation and food service -.217.303.473 -.108.413.793 Transportation -.575.557.302 Financial institutions -.198.609.745 Business services -.616.319.053 Age 45 55 -.378.247.125 55 60 -.020.271.940 60 65 -.338.314.281 65 + -.756.448.092 Education High.219.339.518 Medium.395.323.221 R 2 Nagelkerke 0.055 Hit-rate 62.6% Nr obs. 1,597 q 87.5% Source: Panteia/EIM, SME Policy Panel 2011. 45

Table 14 Parameter estimates of binary logit model: P[independence=1] Group Variables Estimated Standard error Significance level Constant -1.713.289.000 Gender Man -.112.163.492 Type of entrepreneur Sector dummy Solo self-employed.650.136.000 Medium -1.449.436.001 Manufacturing.733.290.011 Construction.110.267.681 wholesale and retail trade Accommodation and food service -.064.245.795 -.268.379.480 Transportation -.750.483.120 Financial institutions -.251.473.596 Business services.044.234.851 Age 45 55.059.175.736 55 60.203.196.301 60 65.056.215.795 65 + -.034.250.892 Education High -.037.201.852 Medium -.328.194.091 R 2 Nagelkerke 0.070 Hit-rate 60.4% Nr obs. 1,597 q 70.7% Source: Panteia/EIM, SME Policy Panel 2011. 46

Table 15 Parameter estimates of binary logit model: P[making profit=1] Group Variables Estimated Standard error Significance level Constant -2.549.361.000 Gender Man 1.028.234.000 Type of entrepreneur Sector dummy Solo self-employed.029.151.847 Medium.028.252.911 Manufacturing.140.374.708 Construction -.222.336.509 wholesale and retail trade Accommodation and food service.568.296.055.139.404.730 Transportation.069.454.879 Financial institutions -.191.555.730 Business services -.116.305.705 Age 45 55 -.023.184.900 55 60 -.582.229.011 60 65 -.355.243.144 65 + -.775.302.010 Education High -.100.221.651 Medium -.200.211.345 R 2 Nagelkerke 0.054 Hit-rate 56.2% Nr obs. 1,597 q 77.4% Source: Panteia/EIM, SME Policy Panel 2011. 47

Table 16 OLS parameter estimates, dependent strategy measure is innovation (In) Group Variables Estimated Standard error Significance level Constant -.182.126.000 Gender Man.310.072.000 Size class Solo self-employed -.307.066.000 Medium.421.071.000 Sector Manufacturing.101.115.381 Construction -.464.117.000 wholesale and retail trade Accommodation and food service -.058.105.579 -.268.133.044 Transportation -.373.132.005 Financial institutions -.137.142.338 Business services -.106.108.326 Age 45 55 -.174.079.027 55 60 -.087.077.258 60 65 -.124.081.126 65 + -.276.091.003 Educational level High.485.078.000 Medium.176.075.018 R 2 adjusted 0.157 Nr obs. 1,180 Source: Panteia/EIM, SME Policy Panel 2011. 48

Table 17 Parameter estimates of binary logit model: P[Turnover growth&equal=1] Group Variables Estimated Standard error Significance level Constant 1.728.341.000 Gender Man.272.128.034 Size class Solo self-employed.021.103.842 Medium.071.184.699 Sector Manufacturing -.608.243.012 Construction -.801.211.000 wholesale and retail trade Accommodation and food service -.249.192.195 -.407.260.118 Transportation -.182.301.545 Financial institutions.745.410.069 Business services -.502.188.008 Age Continuous age -.023.005.000 Educational level High -.108.159.496 Medium.124.152.414 R 2 Nagelkerke 0.041 Hit-rate 55.9% Nr obs. 1590 q 51.7% Source: Panteia/EIM, SME Policy Panel 2011. Figure 12 Scatter plot of Odds Ratio (growth & equal) vs. age, measure is turnover 9 8 7 6 Odds-Ratio 5 4 3 2 1 0 0 10 20 30 40 50 60 70 80 90 Age Source: Panteia/EIM, SME Policy Panel 2011. 49

Table 18 Parameter estimates of binary logit model: P[profit growth=1] Group Variables Estimated Standard error Significance level Constant -3.078.600.000 Gender Man -.421.255.099 Size class Solo self-employed.073.215.735 Medium.016.341.962 Sector Manufacturing -.298.650.647 Construction 1.233.462.008 wholesale and retail trade Accommodation and food service.602.436.168.644.575.263 Transportation.530.611.385 Financial institutions -.414.841.622 Business services.651.430.130 Age 45 55 -.140.268.603 55 60.449.293.125 60 65.140.315.656 65 + -.313.455.492 Educational level High 1.211.465.009 Medium 1.158.457.011 R 2 Nagelkerke 0.069 Hit-rate 57.8% Nr obs. 791 q 72.8% Source: Panteia/EIM, SME Policy Panel 2011. 50

Table 19 Parameter estimates of binary logit model: P[employment growth&equal=1] Group Variables Estimated Standard error Significance level Constant 2.089.372.000 Gender Man -.616.241.011 Size class Solo self-employed 1.357.187.000 Medium -.787.210.000 Sector Manufacturing.355.361.325 Construction -.095.304.754 wholesale and retail trade Accommodation and food service.613.290.034.808.407.047 Transportation.620.468.185 Financial institutions 1.060.657.107 Business services.692.297.020 Age 45 55 -.495.223.026 55 60 -.225.265.396 60 65 -.666.259.010 65 +.560.412.174 Educational level High.061.255.810 Medium -.100.241.677 R 2 Nagelkerke 0.149 Hit-rate 63.2% Nr obs. 1,599 q 80.8% Source: Panteia/EIM, SME Policy Panel 2011. 51

Table 20 Parameter estimates of binary logit model: P[investment growth&equal=1] Group Variables Estimated Standard error Significance level Constant 1.839.444.000 Gender Man.263.224.242 Type of entrepreneur Sector dummy Solo self-employed.218.181.230 Medium.403.264.127 Manufacturing -.555.397.162 Construction -.340.360.344 wholesale and retail trade Accommodation and food service -.289.325.375.250.480.603 Transportation -.491.529.353 Financial institutions -.038.607.950 Business services -.447.311.151 Age 45 55 -.287.214.181 55 60 -.601.246.014 60 65.239.319.454 65 +.122.401.760 Education High -.813.347.019 Medium -.542.339.110 R 2 Nagelkerke 0.051 Hit-rate 56.7% Nr obs. 883 q 61.9% Source: Panteia/EIM, SME Policy Panel 2011. 52

The results of EIM's Research Programme on SMEs and Entrepreneurship are published in the following series: Research Reports and Publieksrapportages. The most recent publications of both series may be downloaded at: www.entrepreneurship-sme.eu. Recent Research Reports and Scales Papers H201205 H201204 H201203 H201202 H201201 H201119 H201118 H201117 H201116 H201115 H201114 H201113 H201112 H201111 H201110 H201109 H201108 H201107 H201106 H201105 H201104 H201103 H201102 21-6-2012 Innoveren in het consumentgerichte bedrijfsleven 16-2-2012 Time series for main variables on the performance of Dutch SMEs 1-2-2012 Do Small Businesses Create More Jobs? New Evidence for Europe 19-1-2012 Trends in entrepreneurial Activity in Central and East European Transition Economies 9-1-2012 Globalization, entrepreneurship and the region 2-1-2012 The risk of growing fast 22-12-2011 Beyond Size: Predicting engagement in environmental management practices of Dutch SMEs 22-12-2011 A Policy Theory Evaluation of the Dutch SME and Entrepreneurship Policy Program between 1982 and 2003 20-12-2011 Entrepreneurial exits, ability and engagement across countries in different stages of development 20-12-2011 Innovation barriers for small biotech, ICT and clean tech firms: Coping with knowledge leakage and legitimacy deficits 20-12-2011 A conceptual overview of what we know about social entrepreneurship 20-12-2011 Unraveling the Shift to the Entrepreneurial Economy 24-11-2011 Bedrijfscriminaliteit 25-8-2011 The networks of the solo self-employed and their success 23-6-2011 Social and commercial entrepreneurship: Exploring individual and organizational characteristics 9-5-2011 The relationship between firm size and economic development: The Lucas hypothesis revisited 22-3-2011 Corporate Entrepreneurship at the Individual Level: Measurement and Determinants 30-01-2011 Determinants of high-growth firms 13-1-2011 Determinants of job satisfaction across the EU-15: A comparison of self-employed and paid employees 13-1-2011 Gender, risk aversion and remuneration policies of entrepreneurs 11-1-2011 The relationship between start-ups, market mobility and employment growth: An empirical analysis for Dutch regions 6-1-2011 The value of an educated population for an individual's entrepreneurship success 6-1-2011 Understanding the Drivers of an 'Entrepreneurial' 53

H201101 H201022 H201021 H201019 H201018 H201016 H201015 H201014 H201013 H201012 H201011 H201010 H201009 H201008 H201006 H201005 H201004 H201003 H201002 H201001 H200911 H200910 H200909 H200908 H200907 H200906 Economy: Lessons from Japan and the Netherlands 4-1-2011 Environmental sustainability and financial performance of SMEs 16-11-2010 Prevalence and Determinants of Social Entrepreneurship at the Macro-level 20-10-2010 What determines the volume of informal venture finance investment and does it vary by gender? 9-8-2010 Measuring Business Ownership Across Countries and Over Time: Extending the COMPENDIA Data Base 6-7-2010 Modelling the Determinants of Job Creation: Microeconometric Models Accounting for Latent Entrepreneurial Ability 29-4-2010 The Nature and Prevalence of Inter-Organizational Project Ventures: Evidence from a large scale Field Study in the Netherlands 2006-2009 27-4-2010 New Firm Performance: Does the Age of Founders Affect Employment Creation? 1-4-2010 Van defensief MKB-beleid naar offensief ondernemerschapsbeleid 16-3-2010 Human capital and start-up succes of nascent entrepreneurs 16-3-2010 New business creation in the Netherlands 16-3-2010 Factors influencing the entrepreneurial engagement of opportunity and necessity entrepreneurs 16-3-2010 The more business owners the merrier? The role of tertiary education 2-3-2010 Open, distributed and user-centered: Towards a paradigm shift in innovation policy 1-2-2010 Geographical distance of innovation collaborations 21-1-2010 Family ownership, innovation and other context variables as determinants of sustainable entrepreneurship in SMEs: An empirical research study 12-1-2010 Intrapreneurship An international study 12-1-2010 Schumpeter versus Kirzner: An empirical investigation of opportunity types 7-1-2010 Institutions and entrepreneurship: The role of the rule of law 5-1-2010 Patterns of innovation networking in Dutch small firms 4-1-2010 Administratieve lasten en ondernemerschap 29-1-2010 Ambitious entrepreneurship, high-growth firms and macroeconomic growth 4-2-2010 Entrepreneurial exit and entrepreneurial engagement 10-3-2009 Entrepreneurship Education Monitor (EEM) 3-3-2009 Internationale samenwerking door het Nederlandse MKB 2-3-2009 The Dynamics of Entry and Exit 2-3-2009 Bedrijfsgrootteverdelingen in Nederland 54