Who Creates the Better Jobs?

Transcription

1 The Rockwool Foundation Research Unit Study Paper No. 101 Entrepreneurs versus Incumbents: Who Creates the Better Jobs? Johan M. Kuhn, Nikolaj Malchow-Møller and Anders Sørensen University Press of Southern Denmark Odense 2015

2 Entrepreneurs versus Incumbents: Who Creates the Better Jobs? Study Paper No. 101 Published by: The Rockwool Foundation research Unit and University press of Southern Denmark Address: The Rockwool Foundation Research Unit Soelvgade 10, 2.tv. DK-1307 Copenhagen K Telephone forskningsenheden@rff.dk web site: ISBN ISSN November 2015

3 Entrepreneurs versus Incumbents: Who Create the Better Jobs? Johan M. Kuhn, Department of International Economics and Management, Copenhagen Business School, Porcelænshaven 16A, DK-2000 Frederiksberg, Denmark (tel: , Nikolaj Malchow-Møller, Department of Business and Economics, University of Southern Denmark, Campusvej 55, 5230 Odense M, Danmark (tel: , Anders Sørensen (corresponding author), Department of Economics, Copenhagen Business School, Porcelænshaven 16 Porcelænshaven 16A, DK-2000 Frederiksberg, Denmark (tel: , Abstract: What are the characteristics of jobs in entrepreneurial firms as compared to jobs in incumbent firms? Even though this question has been addressed by many researchers before us, we provide new evidence to the field since we measure the entrepreneur as the organic new firm. In the literature, the majority of studies have focused on entrepreneurs as measured by small or new firms. By organic new firm, we mean new firms that are not the result of restructurings or organising existing or additional activities in a formally new firm. Moreover, we distinguish entrepreneurial firms by different types and distinguish between growing and declining industry region clusters. Our results differ from the findings in the existing literature. Specifically, we find that compared to incumbents, entrepreneurial firms have higher total factor productivity, are more skill intensive, and pay higher wages. The differences are more pronounced in growing clusters. Moreover, the results show important differences between different types of entrepreneurial firms. Specifically, spin offs are found to enjoy the largest productivity advantage. The wage and skill premiums at the firm level disappear at the job level, as larger incumbents are both more skill intensive and pay higher wages than smaller incumbents. Keywords: Entrepreneurship, job quality, productivity JEL: L1 Acknowledgements: We are greatly indebted to the Rockwool Foundation for funding of this project, and Statistics for providing the data. Thanks to Jan Rose Skaksen, Søren Leth Petersen, Mette Ejrnæs, Pernille Bang and Zuzanna Tilewska for helpful comments. 1

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5 1. Introduction Entrepreneurs are often considered to play an important role as an engine for growth and prosperity. In the words of Schumpeter (1934, 1943), entrepreneurs create combinations of inputs and outputs to pioneer new activities, exploit new market opportunities and allocate labour to its most productive use. However, entrepreneurship is not always found to be good business for those involved in entrepreneurial projects. The majority of studies in the economic literature have found that entrepreneurs pay lower wages than other firms and hire employees with lower levels of human capital. Moreover, the productivity levels of entrepreneurs are found to be of similar magnitude or lower than in established firms. See Van Praag and Versloot (2007) for an overview of the literature. In this paper, we reconsider the characteristics of jobs in entrepreneurial firms as compared to jobs in established firms. Even though this question has been addressed by many researchers before us, we provide new evidence to the field since we measure the entrepreneur as the organic new firm. By organic new firm, we mean that the new firm is not a result of restructurings or a result of organising existing or additional activities in a formally new firm. Thereby, the organic new firm must not have existed previously under a different name, with a different owner, or in another legal form (personally owned, incorporated, etc.). Furthermore, the new firm must not have been started by persons who are already registered as business owners. Nor may the new firm be a re start of a business after closure, or a result of changes in the firm registration information. In sum, the set of new firms used in this paper is much more likely to reflect organic start ups than if we had simply used the set of all new or small firms, which have been common practice in the literature. In the literature, the majority of studies have focused on entrepreneurs as measured by small or new firms. Small firms are not necessarily start ups; it can also be old firms that have not grown large. In this way, old firms without employment growth are mixed up with entrepreneurial firms. This is unfortunately and may influence the established evidence for jobs created by entrepreneurs. Using new firms is in principle better, but still problematic because many of the formally new firms may be the result of restructurings or the result of organising existing or additional activities in formally new firms. New firms that are not organic new firms should not be included in empirical studies of the characteristics of jobs generated by entrepreneurs as they may influence the empirical findings. As our main purpose is to characterize jobs and to compare them across firm types, we compare the following variables across entrepreneurs and established firms: wages, skills, labour productivity as well as total factor productivity (TFP). The latter measure is not a job specific measure, however, it is partly 3

6 based on firms labour inputs and therefore indirectly includes important information on job characteristics. The following results are established in the literature that is based on the entrepreneur as measured by establishment/firm size or age: Brixy et al. (2007) and Koch and Späth (2009) both find that start ups or younger firms pay lower wages, whereas Nyström and Elvung (2014) find that there is a wage penalty associated with employment in a new firm for labor market entrants. In an earlier study, Brown and Medoff (2003) find that firms that have been in business for more years pay higher wages, but once they control for worker characteristics, the relationship becomes insignificant or negative. Kölling et al. (2002) find a similar result. A number of studies have also examined the relationship between firm size and wages, and here it is well established that smaller firms pay lower wages, see, e.g., Troske (1999). Just as small firms are found to hire less skilled workers, see Troske (1999), several studies also find that new firms hire less skilled workers. Koch and Späth (2009) thus find that the use of high qualified labor is lower for young firms than for incumbents, and Brown and Medoff (2003) also find that young firms employ workers with lower levels of education. Most studies show that new establishments or firms have lower levels of labor productivity, see, e.g., Brouwer et al. (2005), Jensen et al. (2001) and Foster et al. (2006), and also lower levels of TFP, see, e.g., Castany et al. (2005) and Brouwer et al. (2005). An exception is Disney et al. (2003) who find that labor productivity of new plants is relatively high, and also that TFP levels are higher for new plants than for old plants. Moreover, Huergo and Jaumandreu (2004) find that young firms have higher growth of productivity than old firms and that this difference can last for many years but also that productivity growth rates of surviving firms converge. As mentioned, we shed new light on the characteristics of jobs created by entrepreneurs as a consequence of the applied measure of entrepreneur; the organic new firm. In doing this, we further extend the existing literature in two important ways. First, we distinguish between firms located in growing industry region clusters and firms located in declining industry region clusters. The motivation behind distinguishing between growing and declining clusters is to take the business environment of the firms into account because a large share of entrepreneurial activity is not pulled by new market opportunities. Push factors like dissatisfaction with or separation from an earlier employment relationship or lack of alternative employment opportunities also play an important role, see, e.g., Amit and Muller (1995). As a consequence, a large share of the jobs created by entrepreneurs are not created 4

7 in business environments reflecting new market opportunities, but rather in stable or contracting markets, and this is likely to affect both the performance of the entrepreneurial venture and the quality and characteristics of the jobs created. Second, we distinguish between three types of entrepreneurs: spin offs, entrepreneurial start ups and other new firms. This is motivated by previous findings in the literature where spin offs have been found to be relatively successful in terms of survival and growth; see, e.g., Dahl and Reichstein (2007) and Eriksson and Kuhn (2006). Spinoffs are throughout defined as organic new firms where the founders have recent industry experience from the same industry as that of the entrepreneurial firm. From a policy perspective, the distinction between spin offs and other types of entrepreneurial start ups is also highly relevant given that most (OECD) countries are trying to figure out how to support entrepreneurship most effectively. Finally, other new firms are a residual category where it has not been impossible to identify the entrepreneur(s). We base our analysis on Danish worker firm register data for the time period The data are drawn from different Danish registers administered by Statistics Denmark and cover almost the entire private sector of the Danish economy for the period It contains detailed data on revenue, inputs, worker information, industry and regional location for all firms, including whether firms are organic new firms or not. The empirical methodology is quite simple. We regress the characteristics of the jobs and the performance measures that we wish to compare on a set of dummies for firm and cluster types. We also include a number of additional controls for other firm or worker characteristics, and we look at withincluster differences between firm types. Finally, we extend the definition of clusters to contain a third dimension the education of the worker. That is, a cluster is defined as jobs with certain skill contents in a given industry and region. We then compare jobs in entrepreneurial and incumbent firms within these education specific clusters. Throughout the paper, we consider job characteristics in the final year of our sample, i.e., 2010, and entrepreneurial firms are in most of the analysis defined as organic new firms established after That is, they can be up to nine years old. This choice involves a selection on relatively successful entrepreneurial firms as it includes surviving firms and excludes firms that have not survived up until E.g., entrepreneurial firms that are established in 2002 and have survived until 2010 are 8 years of age. These firms constitute a relatively selected group as compared to firms that are established in

8 and have closed down again before To check the robustness of our results to this assumption, we also look at alternative scenarios where organic new firms are firms established after 2005, i.e., they can be up to five years old. We find several interesting results. First, when comparing entrepreneurs and incumbents, without distinguishing between different types of entrepreneurs, we find that entrepreneurs have higher TFP (but lower labour productivity), are more skill intensive and tend to pay higher wages than incumbents. Furthermore, we find that these differences are more pronounced in growing industry region clusters. Second, distinguishing between different types of entrepreneurial firms, we find that spin offs enjoy the largest productivity advantage compared to old firms, whereas higher wages are mainly found within other new firms, although spin offs fare better than old firms when located in growing clusters. Third, the wage and skill premiums at the firm level disappear in the worker level regressions, which generally show lower wages in spin offs and entrepreneurial start ups than in old firms. The difference in results between the two approaches reflects that the larger old firms are given more weight in the worker level regressions, and these old firms are different from the smaller old firms. Fourth, when we look separately at manufacturing firms, we find more robust TFP advantages and higher average wages of entrepreneurial firms compared to old firms. Fifth, introducing the third dimension in our cluster does not change results qualitatively, and neither does defining new firms as those established after 2005 (instead of 2000). In the latter case, the only difference is that we now find that entrepreneurial firms are less productive than old firms. This lower productivity is driven by entrepreneurial firms in declining clusters, whereas entrepreneurial firms have productivity levels of similar magnitude to old firms in growing clusters. Moreover, spin offs still enjoy productivity advantages compared to old firms, especially in growing clusters, whereas entrepreneurial start ups and other new firms have similar or lower productivity levels. These results suggest that it takes time for organic new firms to catch up and exceed incumbents in terms of productivity as also found by Huergo and Jaumandreu (2004). The main contribution of this paper is to investigate the characteristics of jobs in entrepreneurial firms compared to established firms using a measure of the entrepreneur as the organic new firm. Our results differ from the findings in the existing literature. Specifically, we find that compared to incumbents, entrepreneurial firms have higher TFP (and lower labour productivity), are more skill intensive, and pay 6

9 higher wages. However, the wage and skill premiums at the firm level disappear at the worker level, as the larger old firms are both more skill intensive and pay higher wages than the smaller old firms. This latter result is more consistent with the existing literature. It should be emphasised that the purpose of this paper is not to present a causal relationship for job quality and firm types. The study is rather a study of facts for the raw and conditional differences between jobs in entrepreneurial and incumbent firms. It is therefore not clear whether the results are biased by for example selection of different types of individuals into different types of firms. We leave this important issue for future research. The rest of the paper is organised as follows. In Section 2, we describe the data and the definitions applied. Section 3 presents the empirical approach. In Section 4, we present our baseline results, where we compare new and old firms. In Section 5, we introduce our distinction between different types of new firms, and in Section 6, we consider the manufacturing sector separately. Sections 7 and 8 contain our robustness analyses. Section 9 concludes. 2. Data In the analyses below, we rely on matched worker firm data covering almost the entire private sector of the Danish economy for the period The data are drawn from different Danish registers administered by Statistics Denmark. First, we use the General Enterprise Statistics, which builds on the Central Business Register and contains annual information about all active firms in the Danish economy. From this database, we get information about, e.g., industry, region, sales, capital input, intermediate inputs and number of workers of all firms in the private sector. Second, we use the Statistics on New Enterprises, which identifies, for each year between 2001 and 2010, all the new start ups. This includes both personally owned and incorporated firms that fulfil a number of conditions that allow us to consider them as being organic new firms in a given year. We use this database to identify all the start ups among all the active firms in the General Enterprise Statistics. Note that Statistics Denmark has undertaken extensive efforts to identify these organic new firms. Many of the formally new firms may thus be the results of restructurings or the results of organising existing or additional activities in formally new enterprises. As a consequence, for firms to appear in the Statistics on New Enterprises, they must not only be newly registered at the business authorities for VAT taxation, it is also required that the firms must not have existed previously under a different name, with a 7

10 different owner, or in another legal form (personally owned, incorporated, etc.). Furthermore, they must not have been started by persons who are already registered as business owners at the VAT authorities. The data are also cleaned for re starts of businesses after closure, and changes in the firmregistration information; see Statistics Denmark (2002) for more details. Finally, we have removed an additional 0.2 per cent of observations in the Statistics on New Enterprises, where supplementary information in the General Enterprise Statistics suggests that the firm was established before In sum, the set of new firms used in this paper is much more likely to reflect organic start ups than if we had simply used the set of all new establishments or all new firms, which has been common practice in the literature. The Statistics on New Enterprises is restricted to industries that Statistics Denmark categorises as business related industries. This excludes the public sector and (most of) the primary sector, as well as industries with activities that are not liable to VAT, such as dentists, transportation of persons, banking etc. Furthermore, the Statistics on New Enterprises is restricted to firms with standard ownership types. To ensure valid comparisons, we impose the same sampling conditions on the General Enterprise Statistics, i.e., we exclude firms in non business related industries and firms with non standard ownership types. For the majority of the new firms in the Statistics on New Enterprises, Statistics Denmark has been able to identify the entrepreneurs behind the firms. For the personally owned firms identification is straightforward: The entrepreneur is simply identified as the owner of the firm. For incorporated firms, Statistics Denmark uses a prioritized list of criteria to identify the principal entrepreneur(s). 1 For 17 per cent of the cases, Statistics Denmark has not been able to identify the entrepreneur behind the firm. Third, we use the Firm Integrated Database (FIDA), which identifies all the individuals working in a given firm in the last week of November each year. From this database, we can get information about wages, education, age, and gender of all the workers in a given firm in a given year. Furthermore, as workers 1 If information about a founder is available from the registration information, this person is identified as the principal entrepreneur. In case of more than one founder, Statistics Denmark selects the one who has the highest salary or if none of the founders are employed in the firm, they pick the founder who appears first in the registration database. If information about founders is not available, they look for a member of the board (or the executive board) who is also employed in the firm. Again, they pick the one with the highest salary in the case where more than one board (or executive board) member is employed in the firm. If no board (or executive board) member is employed in the firm, they pick the board (or executive board) member who appears first in the registration database. 8

11 are linked to firms each year, we can identify all the individuals that leave (or join) a firm between two years. From the FIDA we construct firm level measures of the average age and wage of the workers, the gender composition and the share of workers who have completed tertiary education, i.e., individuals who have obtained at least a bachelor degree (we refer to these as highly educated ). We also use the FIDA to construct a worker level dataset where the information about the firm is linked to each worker. 2 Note that individuals can be associated with more than one firm in a given year as Statistics Denmark records both a primary and (potentially) a secondary job of each individual, where the distinction between the two is based on the wage income generated by the jobs. In the present paper, we discard information about secondary jobs. We deviate from this one job only rule in one case: for owners of personally owned firms with employees. These individuals can have two jobs: As an employer in their own firm and as a wage worker in another firm. However, any given individual can only have one job per firm. In the analyses of this paper, we use cross sections for 2010 at either the firm or the job level, but we use the historical information in the databases to identify the new firms and to construct our industryregion clusters. More about this below. In the main part of the analysis, we define the new firms as those established after 2000, i.e., new firms can be up to nine years old in 2010, whereas established/old firms are those already present in This leaves us with 108,322 old firms and 79,712 new firms. To check the robustness of our results to these assumptions, we also look at an alternative scenario where new firms are defined as firms established after 2005, which leaves us with 130,056 old firms and 57,988 new firms. We further divide the new firms into three types of new firms that are mutually exclusive: spin offs, entrepreneurial start ups, and other new firms. Spin offs are defined as new firms where at least one of the entrepreneurs has been employed in the same industry prior to establishing the firm. More precisely, we identify all (primary and secondary) jobs of the founders in the year prior to start up of the 2 Note that when we talk about workers, this may include both the employees and the owners of personallyowned firms. Technically speaking, the latter are not employed in their own firms. While wage information only exists for employees (and is only reliable for approximately 1.2 million employees corresponding to 75 per cent of the observations), information about age, gender and education is available for both types of workers. It should be mentioned, however, that the educational level is unknown for approximately 4 per cent of the observations. 9

12 new firm, and if any of these jobs were in the same 2 digit NACE industry as the new firm, the start up is categorised as a spin off. This leaves us with 24,017 spin offs among the 79,712 new firms. Entrepreneurial start ups, on the other hand, are new firms where none of the entrepreneurs have been employed in the same 2 digit NACE industry as the new firm in the year prior to starting up the new venture, i.e., these are firms that cannot be categorized as spin offs as defined above. This results in 46,637 entrepreneurial start ups. Finally, we are left with a residual category of 9,058 firms, where Statistics Denmark has been unable to identify the entrepreneur(s) behind the firms. We label this residual group other new firms. These new firms are all incorporated firms, and are thus likely to be larger than the average entrepreneurial firm. In the analysis, we divide the economy into a number of clusters to try to capture local market conditions. Specifically, we are interested in determining whether a given firm or job is in a declining or a growing cluster. We measure the local market conditions by the employment growth at the cluster level between 2005 and The clusters allow comparison of job characteristics not just by whether or not the given job is in a new or established firm, but also by whether or not the job is in a cluster with growing or declining economic activity as measured by changes in employment. We believe that this provides a more appropriate assessment of the role of new firms relative to old firms. The most obvious way of defining a cluster is according to industries (Goos and Manning, 2007), but also geography may be important for local producers and service industries. In the following, we therefore define clusters by the industry and region of the firm. For our baseline cluster definition, we use industries at the 3 digit NACE level, which gives us 233 different industries. Together with five different geographical regions, this results in 1,165 potential clusters of which 74 do not contain any firms in In Appendix D, we also consider the robustness of our results to changes in the number of clusters by using 77 industries at the 2 digit NACE level with the 5 regions. In Section 8, we also introduce the education level of the employee as a third dimension in the cluster definition. In this case, jobs are uniquely assigned to clusters, but a firm can then be located in more than one cluster with some of its jobs in one cluster and other jobs in another cluster. Before turning to the details of the estimation approach and our empirical findings, we present some descriptive statistics in Tables 2.1 to 2.4 below. In Table 2.1, we present the number of clusters, the 10

13 number of jobs and the number of firms applied in the analysis, broken down by the employment growth of the clusters. In total, the 1,081 clusters contain approximately 1.56 million jobs and 188,000 firms in Of the 1,081 clusters, more than 50 per cent (including new clusters) had negative or positive employment growth of more than 25 per cent during the period However, only around 24 per cent of the firms and 28 per cent of the jobs were located in these clusters. Furthermore, the large number of clusters with negative growth rates may in part reflect the outbreak of the financial crisis during this period. Less than 2.5 per cent of all clusters in 2010 were new, i.e., without any activity back in The shares of jobs and firms in these clusters in 2010 were only 0.05 and 0.02 per cent, respectively. [Table 2.1 around here] Table 2.2 splits up the firms on the four categories of firms used in this paper: Established firms, spinoffs, entrepreneurial start ups and other new firms. Among the 188,000 firms, 80,000 (42 per cent) are identified as new firms, i.e., established after 2000 and hence can be up to nine years old. Approximately 24,000 (13 per cent) are categorised as spin offs according to our definition, whereas 47,000 (25 per cent) are categorised as entrepreneurial start ups, leaving approximately 9,000 (5 per cent) in the residual category (other new firms). 4 From the lower part of the Table, we can see that while new firms constitute 42 per cent of the firms. They only represent around 16 per cent of the jobs. Most of the jobs (1.31 million) are found in established firms, whereas 114,000 jobs (7 per cent) are found in entrepreneurial start ups followed by spin offs with 82,000 jobs and other new firms with 56,000 jobs. Another observation from Table 2.2 is that there are relatively many entrepreneurial start ups and other new firms in clusters with more than 10 per cent employment growth. Somewhat surprisingly, spin off firms are overrepresented in clusters with more than 10 per cent negative employment growth. [Table 2.2 around here] 3 In the following tables new clusters are included in the 0.5+ interval as a consequence of confidentiality requirements when the number of observations in a cell is too low. 4 With the alternative definition of new firms where these are defined as firms established after 2005, 58,000 (31 per cent) firms are categorized as new firms. Approximately 17,000 (9 per cent) are categorised as spin offs according to our definition, whereas 35,000 (18 per cent) are categorised as entrepreneurial start ups, leaving approximately 6,000 (3 per cent) in the residual category (other new firms). 11

14 Table 2.3 presents the average firm size (as measured by jobs or workers per firm) for the four different firm types and by cluster growth intervals. Established firms have on average 12.1 jobs per firm. Among the new firms, the average other new firm is considerably larger than the two other types of new firms, where spin offs tend to be slightly larger than entrepreneurial start ups. However, there is considerable variation across clusters with different levels of employment growth, where there seems to be a tendency for most firm types to be larger in the more extreme growth intervals. [Table 2.3 around here] Table 2.4 summarizes some of the basic characteristics of the workers in the different firm types and by growing and declining clusters, respectively. It is seen that jobs in growing clusters have higher wages, have higher shares of women, and have higher shares of highly educated workers. Established firms and other new firms pay the highest salaries to their employees, while spinoffs in declining clusters pay the lowest. Spin offs in declining clusters are also characterized by the lowest shares of highly educated workers. [Table 2.4 around here] 3. Estimation Approach In the analysis, we apply simple linear regression models to compare the productivity levels and characteristics of jobs in new and old firms, and to determine how this depends on cluster growth. Specifically, we estimate a number of regressions of the following type at either the firm level or the job level: (1) where Y is the dependent variable, e.g., the average wage (or productivity level) in the firm, or the wage in the individual job if the estimation is at the job level. X is a vector of control variables, such as age and gender either at the firm or the job level. We operate with dummy variables for established firms and (different types of) new firms and interact them with dummy variables for whether the firm (or the job) is located in a growing or declining cluster. For ease of exposition, equation (1) only contains one dummy variable for the firm type, D NF, which equals one for new firms and zero for old firms. Later, D NF is split into three dummy variables for the 12

15 three different types of new firms. Similarly, the dummy variable, D GC, equals 1 for growing clusters and 0 for declining clusters. Note that we only distinguish between growing and declining clusters (not between more detailed growth intervals of the clusters) in the regression analysis. This reduces the number of coefficients to be estimated considerably and makes it easier to interpret the results. In equation (1), the parameter (the constant term) measures the average value of Y in the reference category, which is an old firm in a declining cluster. An old firm in a growing cluster on the other hand is measured by, i.e., measures the difference between an old firm in a growing cluster and an old firm in a declining cluster. The value of a new firm in a declining cluster is given by, i.e., measures the difference between new firms and old firms in a declining cluster. A new firm in a growing cluster is measured by, i.e., measures the difference between new firms and old firms in a growing cluster, whereas measures the difference between two differences, namely the difference between new and old firms in growing clusters and the difference between new and old firms in declining clusters. In the following Sections, we estimate (1) for a number of different dependent variables. Our main purpose is to characterize jobs and to compare them across firm types. To this end, we describe job characteristics using a number of measures; wages, skills, and productivity. In firm level regressions, we use the average hourly wage of the firm, the skill intensity of the firm measured as the share of employees with a tertiary education among all employees, as well as labour productivity as measured by firm sales per employee. For the latter measure, we also control for capital and intermediate inputs to get a measure of TFP. In job level regressions, we use the hourly wage and the education level of the individual that possesses the job. In each case, we present a number of different regressions. First, we present a regression that only distinguishes between old and new firms, i.e., we do not take differences between growing and declining clusters into account. This implies that we implicitly impose the restriction in equation (1). This is the approach that resembles the existing literature most closely. Second, we add the distinction between growing and declining clusters and hence estimate all four parameters ( to ). In neither of these regressions do we include additional control variables. In the third regression, we also include controls for the age, gender and education level of the worker(s) except in the case where the dependent variable is the education level of the worker(s) herself. By including this information, we take worker heterogeneity into account when comparing job 13

16 characteristics of different firms. If this is an important aspect, the estimated firm type dummies are expected to be of less importance after controlling for these worker characteristics. In the fourth regression, we also include industry and region dummies thereby taking (potentially unobserved) differences across industries and regions into account. If, e.g., certain industries or regions tend to have high employment growth, the dummy variable for growing clusters will become less important as part of the variation will now be picked up by the industry and region dummies. In the fifth regression, we take this one step further and include cluster dummies, implying that we control for observed and unobserved differences at the cluster level. This regression thereby yields within cluster differences between established and new firms. As a consequence, cannot be estimated in this regression. If the differences in job characteristics between new and old firms depend on different (unobserved) cluster characteristics, the estimated difference between new and old firms will become smaller in this case. Finally, in the job level regressions we re estimate the third fifth regressions taking firms size into account. This is motivated by the finding that firm size may play an important role, see, e.g., Troske (1999) who finds that smaller firms pay lower wages. I.e., in the worker level regressions we present 8 regressions per dependent variable. In Section 4, we only distinguish between new and old firms, whereas we apply the three different types of new firms (spin offs, entrepreneurial start ups, and other new firms) in Section 5. In Section 6 we consider the manufacturing and service sector separately, whereas we consider the case where new firms are only those established after 2005 in Section 7. Section 8 introduces education as a third dimension in our cluster definition. 4. New Firms vs. Old Firms In this Section, we investigate differences in the characteristics of jobs in new and old firms, taking the growth in employment in the industry region cluster into account. In Section 4.1, we use the firm level dataset to consider differences in average hourly wages, the proportion of workers with a tertiary education degree, sales per worker and TFP. In Section 4.2, we turn to our job level dataset, where we look at wages and education. 14

17 4.1 Firm level analysis As our first measure of job quality, we use the wage rate. Higher wages can reflect a number of effects but are thought of as being equal to the value of the marginal product of labour. In Table 4.1, we therefore present the results of estimating (1) using the log of the average hourly wage as the dependent variable. As some firms do not have employees, and since wage information is not reliable for all firms with employees, the sample is reduced to around 95,000 observations (firms) in this case (out of a total of 188,000 firms). 5 For the same reason, the number of clusters is reduced from 1,081 to 1,054. [Table 4.1 around here] In column 1, only the dummy for a new firm, D NF, is included. In this case, we find that new firms pay slightly higher average wages (around 2 per cent) than old firms. This result is somewhat surprising as entrepreneurs are typically found to pay lower wages than established firms in the literature. In column 2, we condition on the cluster growth by including the dummy for positive employment growth in the cluster, D GC, and its interaction with the new firm dummy, D NF. In this case, we can estimate all the four beta coefficients from equation (1). We find that average wages of old firms are higher in growing clusters than in declining clusters, as is significantly positive. The difference, however, is less than 2 per cent. New firms, on the other hand, now exhibit lower wages (1 per cent) than established firms in declining clusters ( is negative), but higher wages than established firms (around 7%) in growing clusters ( is positive). In other words, the local business environment does seem to make a difference for the relative performance of new vs. old firms. In the following columns, we check the robustness of these findings when including additional controls. In column 3, we control for the share of women among the workers, the share of workers with a tertiary education degree, and the average age among workers. The inclusion of these controls removes the difference in wages between old firms in declining and growing clusters, as is no longer statistically different from zero. The differences between new and old firms also change a bit. When we control for 5 Statistics Denmark s hourly wage variable is based on the available information from the wage income registers and different sources of information to determine working hours. The measurement of the latter can easily become highly uncertain, for which reason Statistics Denmark attaches a quality estimate to its hourly wage variable. If this estimate makes the wage information of a given individual too doubtful, the given individual s wage record is not considered for the analysis. 15

18 gender, education and age, new firms actually pay higher wages in both growing and declining clusters, but the difference is still largest in growing clusters, where it remains around 7 per cent ( ). Finally, Columns 4 and 5 include industry and region dummies and industry x region dummies, respectively, where the latter corresponds to fixed effects at the cluster level. In these cases, both and become insignificant. That is, we do not observe any differences between new and old firms when we control for (possibly unobserved) industry and region specific characteristics. In other words, within a cluster, we do not observe any (significant) differences between new and old firms when it comes to average wages. In Table 4.2, we consider the skill intensity in new vs. old firms, where we use the share of employees in the firm with a tertiary education degree as the dependent variable. Note that in this case, the sample size almost doubles compared to Table 4.1. [Table 4.2 around here] According to the first column of Table 4.2, the unconditional skill intensity is 2 percentage points higher in new firms than in old firms (the estimate of is both positive and significantly different from zero). When distinguishing between growing and declining clusters, it is found that the education intensity is around 10 percentage points higher in old firms in growing clusters compared to old firms in declining clusters, as the estimate of equals Moreover, new firms are found to be more educationintensive than old firms both in declining clusters (0.4 percentage points) and especially in growing clusters ( = 2.8 percentage points). Controlling for worker heterogeneity in column 3 does not change this picture qualitatively, and neither does the inclusion of region and industry dummies in column 4, although the difference between new and old firms in growing clusters become quantitatively less important. When looking at within cluster differences in column 5, we also still find that new firms have a higher skill intensity around 1 percentage point in declining clusters and around 1.5 percentage points in growing clusters but the difference between growing and declining clusters (i.e., ) is no longer significant. In Table 4.3, we present the results for the case where the dependent variable is the (log of) sales (or revenue) per worker. From column 1, we can see that sales per worker are, in general, lower in new firms (16 per cent). When we distinguish between growing and declining clusters (column 2), we still find that new firms have lower sales per worker and there is no statistically significant difference 16

19 between declining and growing clusters (the estimate of is insignificant). Controlling for worker heterogeneity and fixed effects (columns 3 5) does not alter this conclusion qualitatively. [Table 4.3 around here] Table 4.4 also has (the log of) sales per worker as the dependent variable, but includes (the log of) capital per worker, (the log of) intermediate inputs per worker and (the log of) number of workers as additional controls. In this case, the estimates of to can be interpreted as (differences in) total factor productivity. In column 1, we can see that this results in new firms having slightly higher TFP (1.5 percent) on average. When distinguishing between growing and declining clusters (column 2), it turns out that the advantage of new firms is found only within growing clusters as the estimated sum of and amounts to 5.5 per cent), whereas the estimate of is negative, implying that new firms in declining clusters have slightly lower TFP (1 per cent) than old firms in declining clusters. When we control for worker differences (column 3), however, new firms are found to have higher productivity in both growing and declining clusters, and the same holds when controlling for industry and region fixed effects (column 4). Even with cluster fixed effects (column 5), new firms are found to have a productivity advantage in growing clusters. 6 [Table 4.4 around here] Thereby, we find that new firms have lower sales per worker, but tend to have higher TFP (at least in growing clusters). This indicates that old firms are either more capital intensive or use more intermediate inputs per worker. In regressions not reported, we find that new firms are indeed less capital intensive and use less intermediate input per worker. 7 Before turning to our worker level regressions, we summarize the findings at the firm level: We find that new firms on average have higher wages, are more skill intensive, have lower sales per worker but have higher TFP. While the difference in wages disappears when controlling for industry and region (or cluster) characteristics, the findings that new firms are more skill intensive, have lower sales and higher TFP are more robust. Furthermore, we find evidence that the higher wages, skill intensity and 6 Note that throughout Table 4.4, the coefficients of capital per worker and input per worker are as expected, whereas the coefficient of (the log of) the number of workers is found to be significantly positive, which indicates increasing returns to scale. 7 Results are available upon request. 17

20 productivity of new firms are more pronounced in growing clusters. Even when we use only withincluster variation, we find differences between new and old firms. Specifically, we find that new firms are more skill intensive, have lower sales but higher TFP (in growing clusters). 4.2 Job level analysis Table 4.5 presents results where we use the (log of the individual) hourly wage as the dependent variable. The first five columns are comparable to the five columns in Table 4.1. Note that the results for individual wages are quite different from those presented for average firm wages in Table 4.1. Throughout the first five columns, we find that individual wages are lower in new firms than in old firms, but less so when we control for worker heterogeneity in column 3. When controlling for industry and regional fixed effects in column 4 (or cluster fixed effects in column 5), we even find that the difference is larger in growing clusters. Thus, in column 5, we find that wages in new firms in declining clusters are 3.7 per cent lower than in old firms in the same declining clusters, and that the difference is 4.7 per cent in growing clusters (the sum of and ). [Table 4.5 around here] The difference between the results in Table 4.1 and Table 4.5 can be explained by the fact that the regressions at the job level (Table 4.5) are weighted versions of the regressions at the firm level (Table 4.1), where the weights used are the numbers of jobs at the firm level. In other words, when large firms are weighted more than small firms (as it is the case in Table 4.5), we find that wages are higher in old firms. In light of this, it might be interesting to see how results are affected if we control directly for firm size. In the literature, it is thus a relatively robust finding that wages are increasing with firm size, see, e.g., Troske (1999) and Brown and Medoff (2003). The last three columns in Table 4.5 are therefore similar to columns 3 5 but include firm size (as measured by total employment) as an additional control. 8 When controlling for firm size, new firms are again found to pay higher wages, at least in declining clusters, where the difference is between 1.6 and 1.9 per cent. In growing clusters, on the other hand, there is only a minor wage difference between new and old firms when we control for firm size. Thus, 8 We apply a non parametric approach and include log(employment), log(employment) 2,.., and log(employment) 10 in regressions

21 individual wages are not necessarily higher in old firms because they are old, but perhaps because the old firms tend to be larger. In Table 4.6, we use a linear probability model where the dependent variable is a dummy for the individual having a tertiary education. That is, we estimate the probability that a given job is filled with an individual that has a tertiary education and how this probability is affected by whether the firm is new or old, and whether the firm is located in a growing or declining cluster. The results can thus be seen as job level versions of the estimations of skill intensities in Table When we do not distinguish between growing and declining clusters (column 1), we do not find any statistically significant difference between new and old firms. In column 2, however, we find that the probability of having completed tertiary education is marginally lower in new firms than in old firms in declining clusters, whereas the probability is slightly higher (but not significant) in growing clusters. These results do not change when we control for worker heterogeneity (column 3). When we add industry and region dummies (column 4) or cluster dummies (column 5), the positive effect of new firms in growing clusters disappears. [Table 4.6 around here] In sum, using our job level dataset, we find a slightly smaller tendency of the workers in new firms to have completed tertiary education. This result is in some contrast to the results at the firm level in Table 4.2, where it was found that new firms are more skill intensive than old firms. As in the case of Tables 4.1 and 4.5, this difference again reflects the different weighting of the observations in the worker level and firm level regressions. Thus, among the old firms, especially the larger firms tend to be more skill intensive, and these firms are given more weight in the worker level regressions. This is also evident from columns 6 8 where we control directly for firm size. In this case, the estimate of is no longer significant (although remains significantly negative but numerically small when controlling for cluster fixed effects in column 8). Thus, parallel to the case of Table 4.5, the higher probability of having a tertiary education in old firms may not be due to these firms being old but because old firms tend to be larger. 9 The reason for choosing a linear probability model is that it is difficult to estimate probit or logit models with several hundred industry, region and cluster dummies. In column 4 there are 233 industry dummies and five regional dummies, whereas there are more than 1,000 cluster dummies in column 5. 19

22 To summarize the findings at the job level, we find that individual wages are lower in new firms compared to old firms (as long as we do not control for firm size). In addition, we find hardly any differences across new and old firms in the probability that a job is occupied by an individual with tertiary education. Taken together, the findings in this section have shown that new firms are more productive and skill intensive and pay higher wages than old firms and these differences are typically more pronounced in growing clusters. However, the wage and skill premiums at the firm level disappear at the job level, as the larger old firms are both more skill intensive and pay higher wages than the smaller old firms. 5. Different Types of New Firms In this Section, we extend the analysis of Section 4, and distinguish between different types of new firms: spin offs, entrepreneurial start ups and other new firms. The results are organised in a parallel set of Tables to those in Section 4. Thus, Table 5.1 is comparable to Table 4.1 etc., with the only difference being that and are each split into three different dummies ( and ) representing the three different types of new firms. We will not go through all results in detail but will focus on the findings that differ from or qualify those of Section Firm level analysis In Table 5.1 we use the log of the average hourly firm wage as the dependent variable. Compared to Table 4.1, we find that it is the group of other new firms that drives the result of higher average wages in new firms (column 1). Thereby, the results in Column 1 partly confirm existing research findings of entrepreneurial jobs being relatively low paid. At least this is the case when we look at the average wages in entrepreneurial start ups, which are slightly lower (1.5 per cent) than in old firms. Average wages in other new firms are, on the other hand, 18 per cent higher than in old firms. [Table 5.1 around here] When we distinguish between growing and declining clusters (columns 2 and 3), entrepreneurial startups and spin offs seem to fare worse than or as well as old firms in declining clusters (as seen from the estimates of and ), but better than old firms in growing clusters (as seen from the estimates of and ). In this sense, entrepreneurial firms in growing clusters are generating highwage jobs. 20