Family firms in clusters: an advantage or not?

Transcription

1 Fredrik Hoseth: Håvard Remøy: Family firms in clusters: an advantage or not? BI NORWEGIAN BUSINESS SCHOOL Master thesis Hand-in date: Supervisor: Øyvind Bøhren Campus: BI Oslo Examination code and name: GRA 1902 Master Thesis Program: Master of Science in Business and Economics This thesis is a part of the MSc programme at BI Norwegian Business School. The school takes no responsibility for the methods used, results found and conclusion drawn.

2 Abstract In this paper we examine the performance of family firms, firms in clusters and family firms in clusters. The main focus has been on the performance of family firms in clusters. To test the separate performances we ran three different regressions. Return on assets is used as a proxy for performance, and is the dependent variable in all our regressions. In order to test if there is a family effect, we use a dummy variable taking the value 1 if the firm is a family firm, 0 otherwise. We define a family firm as a firm where a blood-or-marriage-family owns more than 50% of the equity. A dummy variable is also used to indicate if the firm is located in a cluster. The cluster dummy variable takes the value 1 if the firm is located in the geographical area of a cluster, 0 otherwise. The family in a cluster dummy is the product of the family dummy and cluster dummy. Hence, the dummy takes the value 1 if the firm is a family firm located in a cluster, and 0 otherwise. After a thorough literature review and descriptive statistics, we ended up with firm size, firm age and firm risk as our control variables. All our data was collected from the Centre for Corporate Governance Research (CCGR) at BI Norwegian Business School. We found a significant effect on performance from being a family firm. However, we did not find any positive effect on performance from being in a cluster. When looking at family firms in clusters compared to family firms outside cluster we did not find any significant difference in performance. Hence, our paper finds no indication of an interaction effect from being a family firm in a cluster. Acknowledgement A special thanks to our supervisor Øyvind Bøhren for his input, comments and for being a good discussion partner throughout the year. We could not have done this with out him. We would also like to thank Menon Business Economics for helping us with the data extraction process. i

3 TABLE OF CONTENT 1. MOTIVATION THEORY EXISTING RESEARCH EXPECTED CONTRIBUTION RESEARCH QUESTION DATA DATABASE DATA FILTERS METHODOLOGY REGRESSION MODELS THERE IS A FAMILY EFFECT THERE IS A CLUSTER EFFECT THE FAMILY EFFECT AND CLUSTER EFFECTS IMPACT EACH OTHER ECONOMETRIC TECHNIQUES ENDOGENEITY PROBLEM VARIABLES DEPENDENT VARIABLE Performance INDEPENDENT VARIABLES Family dummy Family ownership stake The different clusters Møre og Romsdal Sørlandet Stavanger Bergen Comparable firms Family firm in cluster Family firms in Møre og Romsdal Family firms in Sørlandet Family firms in Stavanger Family firms in Bergen ii

4 6.2.5 Firm size Firm age Firm risk Board size MAIN RESULTS DESCRIPTIVE STATISTICS REGRESSION SUMMARY AND CONCLUSION LITTERATURE EXHIBITS iii

5 1. In the current Norwegian economy, family firms are the most common type of firms. When defining a family firm as a firm where a blood-or-marriage-family owns more than 50% of the equity, approximately 68% of all active firms in the Norwegian economy are family firms (Bøhren, 2011). Despite being such an important part of the economy, relatively little research has been conducted on family firms. And most of the research has been done using public companies with weaker family firms definitions than the one given above. Using the database of the Centre for Corporate Governance Research (CCGR) at BI Norwegian Business School, we have been able to perform analyses using the definition given above on both public and private firms. Another phenomenon that has received an increased focus the last years is cluster theory. In Norway a collaboration project between Innovasjon Norge, SIVA, and Forskningsrådet has been started to support the continued development of internationally competitive clusters. Michael Porter (1998) defines clusters as geographic concentrations of interconnected companies and institutions in a particular field. In the same paper he says that clusters affect competition by (i) increasing the productivity of companies in the cluster, (ii) driving the direction and pace of innovation, and (iii) stimulating the formation of new businesses. As outsourcing to low cost countries no longer is a competitive advantage in modern economies, but instead mitigates disadvantages. And accessing a competitive local cluster is a better solution compared to distant sourcing in terms of productivity and innovation. The most sustainable competitive advantage in the current global economy seems to be a local cluster (Porter, 2000). Given the realization of clusters as a possible source of competitive advantage and the large presence of family firms in the Norwegian economy, we find it interesting to investigate how family firms perform inside clusters. To our knowledge there has not been any previous study on this topic. Also, we both grew up in Sunnmøre. A region that in recent years has established a very strong maritime cluster comprised mostly of family firms. The cluster at Sunnmøre 1

6 contains ship-owners, shipbuilders and their subcontractors. Most of their activities are linked to the offshore industry. Often when you hear someone having a conversation about firms in the maritime industry from Sunnmøre, they are talking about the cluster effect and how strong an impact it has. Therefore we would like to investigate if the effect is really present. In addition we feel that writing a thesis on something we already have some knowledge on and find interesting is a good idea Existing research When looking at the area of corporate governance and family firms, the understanding of agency theory is fundamental. Agency theory has its roots in information asymmetry and the conflicts of interest that arise due to the division of ownership and management. The first agency problem (A1) is the conflict between owners and managers (Villalonga and Amit, 2006). According to corporate governance theory, A1 is supposed to be smaller in family firms than in non-family firms. This is because the family has a large portion of their wealth tied up in the company, which creates an incentive for them to monitor the management of the company more closely compared to smaller owners. In addition a large block holder has more power to exercise active ownership control. The family may also have representatives in the management of the company, and thereby minimizing the agency problem even more. This reduction should increase the performance of family firms compared to firms with a more scattered ownership structure. Despite the advantage of a lower A1, the second agency problem may be larger in family firms. According to Villalonga and Amit (2006) the second agency problem (A2) occurs when the large shareholder uses its controlling position in the firm to extract private benefits at the expense of the smaller shareholders. This may reduce the value of the company for the smaller shareholders. Earlier research finds that the seriousness of A2 is larger when the family owns just above 50%, compared to an ownership close to 100%. Since ownership stake is very high, the family carries most of the cost from A2 themselves (Bøhren 2011). 2

7 In finance literature support has been found for better performance in family firms compared to non-family firms. For instance Maurey (2006) concluded that family control could increase performance in western European firms. Anderson and Reeb (2003) investigate the relationship between founding-family ownership of S&P500 firms, and performance, measured by either return on assets (ROA) or Tobin`s q. Just as Maurey (2006), they find that family firms outperform, nonfamily firms. This is consistent with the theory explained above, and reinforces our confidence in family firm performance. However, Villalonga and Amit (2006) find that family ownership only creates value when the founder serves as CEO, or chairman of the board with a hired CEO. While Bennedsen (2007) finds that family succession has a negative impact on performance. These findings complicate the picture of family firm performance, indicating that a lot of different factors may be influential. In spite of this we expect family firms to have a stronger performance overall. The cluster effect is another phenomenon that might influence performance. Alfred Marshall (1890) was the first to acknowledge the concept of clusters in his. He used the term industrial districts and defined it as a centration of specialized industries in However, it was not until the beginning of the 1990`s that the topic became widely publicly recognized. M Competitive Advantage of N. In this book, Porter states that a cluster is formed by firms and industries linked through vertical or horizontal relationships, with the main players located in a single nation/state. Geographic concentration of rivals, customers and suppliers in a region will promote innovation and competitiveness in a cluster (Porter, 1990). In a more recent publication he revised the definition to geographic concentrations of interconnected companies and institutions in a particular field, linked by commonalities and complementarities (Porter 1998). According to Reve and Sasson (2012), clusters are known by a combination of cooperation and rivalry. This cooperation and the intense fight to always be the most innovative and profitable company inside the cluster, may drive the technology forward more rapidly inside clusters compared to outside. In general, cluster theory states that firms within a geographically defined region benefits 3

8 from participating and collaborating with each other and that these benefits makes economic growth in the region more likely. The benefits of being in a cluster comes as a result of lower input cost for firms and knowledge spill overs, which improve innovation and increases productivity. Firms inside clusters also gain directly from network effects, as the relationship between companies inside a cluster tends to be close. The rivalry amongst firms in a cluster is also stronger because of the ease of constant comparison and because local rivals have similar general circumstances (e.g., labour costs, local market access, utility costs). This leads to competition having to take place on other dimensions. Also pride and the desire to look good in the local community motivate firms to attempt to outdo each other (Porter, 2000). This strong rivalry within a cluster may also be a disadvantage, since it may push the profit margins down. However, even if this is the case, we expect the other positive effects to outweigh this. Accordingly, being in a cluster should lead to a better financial performance. We have not been able to find any literature testing the financial performance of firms in clusters. 2.2 Expected contribution We investigate if there is a family effect, if there is a cluster effect and, if both are present, how they interact. As mentioned above, the family effect and the cluster phenomenon have been looked at before to some extent, but always separately. The behaviour of family firms in clusters however has to our knowledge not been studied before. Therefore we are very interested in the behaviour of family firms in clusters. In other words, we want to investigate if there is a positive, negative or no interaction effect of being a family firm in a cluster setting. Our expectation is a positive effect from this interaction. Leaning on previous research on the two separate topics, common sense and conversations with people working in firms located in clusters we have derived a couple of reasons why this interaction effect should be positive. The main reason behind our expectation of a positive interaction effect is the ability of family firms to build long-term relationships. Taking this ability into a cluster setting should give family firms an advantage compared to non-family firms. In a cluster setting long-term relations create trust that again lowers the need for protection mechanisms, and the transaction costs between firms are 4

9 reduced (Jakobsen, 2008). As a clusters success depends both on cooperation and competition the ability to create and sustain long-term relationships should lead family firms to increased performance. There are several characteristics that make us believe that family firms are better at building long-term relationships. First of all, family firms often have a more long-term orientation than non-family firms. According to Kachaner, Stalk and Bloch (2012) family firms who has executives that are part of the family, often invest with a 10- or 20-year horizon, concentrating on what they can do now to benefit the next generation. Family firms are also characterized as the ultimate long-term investor (Dreux, 1990), and known for integrity and commitment to relationships (Lyman, 1991). This focus on the long-term performance and survival of the firm makes building long lasting relationships easier. In addition, external stake- holders are reportedly stronger and more value laden (Lyman, 1991). The fact that the families name and reputation is connected to the firm provides a strong social contract for the firm to over hold. In the sense that if they do something that is not appreciated in the local community the community may associate it with the family itself, and the family will loose social prestige. Similarities between family firms may be another factor that makes it easier for them to connect with each other and start long lasting relationships. After all they may have similar philosophies on how to run a firm, the owner families may be in a similar situation etc. Combining the arguments above with family firms often having a more locally entrenched philosophy, we think family firms are more likely to invest in longterm oriented projects inside the cluster. These kinds of investments will probably benefit both the firm and the cluster. The reason for the local entrenchment probably stems from that the family comes from a certain area and there is a lot of emotional connection to that particular area. The family may for instance have lived there over multiple generations, and the family firm is an important part of the local community. This makes the family firm more connected to that area compared to a non-family firm. The turnover of key personnel may also be lower as family members may fill many key positions. Which again could lead to more stable long-term relations. 5

10 Another argument is that family firms might help other family entrepreneurs to raise capital. Inside some clusters you can observe that different owners of a firm is related to each other, brothers, sisters, cousins etc. A good example of this is the Sævik and the Ulstein family from Sunnmøre. In these families, the different cousins own a lot of different firms. If you look at the ownership structure at their firms you frequently see that there is 1-3 big owners (brothers and sisters) that controls percent of the company, and their cousins own the remaining shares. In exhibit 1 and 2 in the appendix, one can see the most important firms these families have ownership in. This is a pattern you see in many of the firms in Sunnmøre. One can also observe that many newly founded AS is often owned by capital-intensive families that invest together. However, as the firm develops, the family with the highest ownership share will eventually buy a controlling share of the firm. Firms like Olympic shipping and REM shipping was founded in this way. Most recently World Wide Supply AS was founded like this (see Exhibit 3 in the appendix). These kinds of cooperation are a good example of how the close relationship between family firms/families inside clusters may give entrepreneurs an advantage when raising capital. Also, as the companies in a cluster all belong to the same industry, you get competent investors with vast knowledge about the industry. Which again may increase the success rate of the entrepreneurs, and work as a motivator for sharing important information across the cluster. The close relationship between family firms/families inside clusters and the industry knowledge they possess holds another benefit. It may limit the information asymmetry between the firm insiders and outside investors. A family investor may have knowledge of the process of starting up a similar company and also have more industry knowledge compared to an anonymous investor. This reduces the information asymmetry between the insiders and outsiders of the firm and makes attracting investments easier for the firm. One way to look at it is to compare the knowledge and expertise of the family investor to a venture capital investor when it comes to evaluating the firms. Metrick and Yasuda (2011) writes that venture capitalists works as informed screening agent limiting the information asymmetry between entrepreneurs and uninformed investors. Also venture 6

11 capitalist may provide valuable expertise to the entrepreneurs. Also, family investors in a cluster may limit the second agency problem. The reason for this is that if the majority shareholder takes advantage of the minority shareholders, it may hurt the relationship between them. This will again damage the cooperation within the cluster, and a clusters success depends on the interplay between the different firms. Navickas (2009) argues that the firms share technological know-how, knowledge skills, competencies and resources. Hence, majority shareholders in different firms may be sceptical of taking advantage of their minority shareholders, since in the long run they may end up hurting themselves. To summarize our two main arguments for why family firms should perform better inside clusters than other firms: 1. They are able to create more stable long-term relations that enhance the cooperation inside the cluster. 2. They may be an important source of financing for new entrepreneurs. In this paper, we are going to look at the performance of firms located in clusters, the performance of family firms, and more importantly the performance of family firms located in clusters. Since the major clusters in Norway are either in the maritime sector or the oil and gas sector we have chosen to only include firms from these sectors in the sample. This is done to limit the exposure to macroeconomic events that affect different sectors unevenly. A negative consequence of this is that we cannot generalise our results to the whole Norwegian economy. Our hypotheses are: (a) Family firms have higher performance than non-family firms. (b) Firms in clusters have higher performance than firms not in clusters. (c) There is a positive interaction effect on performance of being a family firm in a cluster. 7

12 Database We use data from Norwegian private and public firms in the period from Except from 2006 due to lack of data. Everything is collected from the Centre for Corporate Governance Research (CCGR) database. 4.2 Data filters In order to choose the firms most suitable for our thesis, we ran the dataset through a filter based on the Berzins, Bøhren and Rydland (2008) filter: Filter 1: Remove all firms who are not in the maritime or in the oil and gas sector. Filter 2: Remove all data from Filter 3: ROA between 1 and -1. Filter 4: Companies must have three years or more of data. Filter 5: Positive assets. Filter 6: Positive sales. Filter 1 ensures that the sample consist only of firms who are in the same industry. Filter 2 is necessary since CCGR have problems with the ownership data from Filter 3 is applied in order to remove outliers. Filter 4 is chosen to secure consistency, while filter 5 and 6 secures that the firm is active. This provided us with unique observations, and 3665 firms. We have not used a filter on firm employees. The filter was excluded since this variable contained a large number of missing observations. 5.1 Regression models In order to answer our research questions we needed to run three different regressions. We ran a pooled regression on all of our data, in addition to a regression for each year in our sample. Since we have observations on multiple firms at several points in time, we have a panel dataset. To take these two 8

13 dimensions into consideration, all our variables have the subscript i indicating the firm and t for the year the observation is from There is a family effect To test hypothesis (a) that family firms have higher performance than non-family firms we planned to run the following regression: ROA it + 1 (family dummy it )+ 2 (family ownership stake it ) + 11 ln(firm size it ) 12 ln(firm age it ) + 13 (firm risk it ) + 14 (board size it ) + it Where ROA is the return on assets. Family dummy is a dummy variable that takes the value 1 if a single family controls more than 50 percent of the shares, 0 otherwise. Ownership stake is the percentage of stock owned by the largest controlling family. And the variables firm size, firm age and board size are control variables, while are the error terms. All the variables will be discussed more closely in section 6. We expect the coefficients 1 and 2 to be positive and significant as argued in section 2 and shown by previous research (e.g. Villalonga and Amit 2006, Maury 2006) There is a cluster effect To test hypothesis (b) that firms in clusters have higher performance than firms not in clusters we ran the following regression: ROA it + 3 (cluster in Sunnmøre it 4 (cluster in Stavanger it ) + 5 (cluster in Sørlandet it ) + 6 (cluster in Bergen it ) + 11 ln(firm size it 12 ln(firm age it ) + 13 (firm risk it ) + 14 (board size it ) + it Where ROA is the return on assets. The variables (cluster in Sunnmøre), (cluster in Stavanger), (cluster in Sørlandet) and (cluster in Bergen) are dummy variables taking the value 1 if the firm is in the relevant cluster and 0 otherwise. The geographical area of the different clusters is defined in section 6. The base case will be firms that are not in any of the clusters. Firm size, firm age, firm risk and board size are control variables, while are the error terms. All the variables are defined more closely in section 6. 9

14 We expect the coefficients 3 to 6 to be positive significant due to the arguments given in section The family effect and cluster effects impact each other To test hypothesis (c) that there is a positive interaction effect on performance of being a family firm in a cluster we ran the following regression: ROA it 1 (family dummy it )+ 2 (family ownership stake it ) + 3 (cluster in Sunnmøre it 4 (cluster in Stavanger it ) + 5 (cluster in Sørlandet it ) + 6 (cluster in Bergen it 7 (cluster in Sunnmøre it )*(family dummy it ) + 8 (cluster in Stavanger it )*(family dummy it ) 9 (cluster in Sørlandet it )*(family dummy it ) + 10 (cluster in Bergen it )*(family dummy it ) + 11 ln(firm size it 12 ln(firm age it ) + 13 (firm risk it ) + 14 (board size it ) + it As before ROA is the dependent variable, family dummy is a dummy variable with value 1 if the firm is a family firm and 0 otherwise. Ownership stake is the percentage of stock owned by the largest family. The cluster effect will be measured using dummy variables in the same way as when we measured the cluster effect previously. To measure the interaction between family firms and the cluster effect we will multiply the family dummy with the cluster dummy, hence when the firm is both a family firm and in a cluster the value will be 1, otherwise it will be 0. The variables firm size, firm age, firm risk and board size are control variables, while are the error terms. All the variables are defined more closely in section 6. We expect the coefficients from 1 to 10 to be positive significant. These expectations are due to the arguments given in section Econometric techniques Since our data contains repeated observations of the same firms over multiple years, we have a panel of data. The advantages of using panel data compared to pure time series and pure cross sectional data alone is that one can address a broader range of issues and deal with more complex problems. Also panel data combines time series and cross-sectional data. One can therefore increase the number of degrees of freedom, and thus the power of the test, by employing 10

15 information on the dynamic behaviour of a large number of entities at the same time. The additional variation introduced by combining the data in this way can also help to mitigate problems of multicollinarity that may arise if time series are modelled individually. In our case multicollinarity may be a problem since some of our independent variables may be correlated. Lastly by structuring the model in an appropriate way, we can control for the impact of certain forms of omitted variables bias in regression results (Brooks, 2008). We use a random-effects model to control this. The main advantage of this model compared to a fixedeffects procedure is that one can estimate the effects of both time-constant and time-varying variables and, second, that it uses information on all individuals and all variables on each individual, even those that are constant over time (Petersen) For our year-to-year regressions we use a classical linear OLS regression. 5.3 Endogeneity problem Our study has a possible endogeneity problem. It has the possibility that performance is affected by other variables then the ones we have listed in our regressions. Also performance may be a determinant of for instance firm size and firm age. There is also the issue of multicollinearity. Using a random effects model will as mentioned in the previous section help mitigate a lot of these issues. In addition, we plan to run several robustness tests to deal with the issue. 6.1 Dependent Variable Performance In order to test firm performance we are using return on assets (ROA). Bodie, Kane and Marcus (2009) write that ROA measures the profitability for all contributors of capital. It is defined like this: =. The corporate tax rate in Norway is 28% (Ministry of finance, 2013). To mitigate the problem with the assets being measured at the start of the year, and EBIT being measured at the end of the year we calculate the real ROA. 11

16 This is done using the formula ROA real +1 = Where inflation (Statistics Norway 2013) is the percentage change in the consumer price index in the given year. This is calculated using the formula:. Where t indicates the year. All reference to ROA in the remainder of the thesis refers to the real ROA. ROA gives an indication on how good the company is at generating return from its assets. Evidence from previous research has indicated that family firms have a higher ROA due to minimization of the first agency problem. This was discussed earlier in the paper, as was the performance of firms in clusters. 6.2 Independent variables Family dummy To measure if the firm is a family firm we use a dummy variable taking the value 1 if the largest family owns above 50 percent of the stocks in the company, and 0 otherwise. We expect the family dummy to influence firm performance positively. The arguments for this view were given in section 2.1. However the main reason is the reduction in agency problem one Family ownership stake We will measure this by taking the total fraction of shares the controlling family owns of the company. According to Maury (2006), and Villalonga and Amit (2006) family firms tend to have a higher ROA, when the family has a large shareholding, higher m/b-ratio when the family has a moderate shareholding, higher m/b in countries with high minority protection, highest m/b when the founder is the CEO and chairman of the board. In short, family firms are more profitable than non-family firms, when the family has moderate control and there is good minority protection. As there is good minority protection in Norway we expect this variable to influence ROA positively until a certain level. The reason for this is that a higher fraction will reduce the first agency problem, since a family with high ownership stake will have a larger incentive to monitor the rage the biggest family in Norwegian family firms own 93 % of the shares, Bøhren (2009). Meaning that a lot of the family firms have an owner with super majority. From a family firm in a cluster perspective we expect the second agency problem to be smaller when the firm is part of a clusters than if 12

17 they are not. The reason for this is that the smaller owners in the company most likely would be friends or business partners from the same cluster, reducing the conflict level The different clusters In cooperation with the consultancy company Menon Business Economics and Øyvind Bøhren, we have selected four different clusters to use in our thesis. These four are Sunnmøre, Sørlandet, Stavanger and Bergen. We have used the same geographical cluster definitions as Menon when specifying our clusters. Menon has done a lot of research on clusters in Norway therefore we believe their definitions to be appropriate. Reve and Sasson (2012) says that in order to measure if a cluster is successful and attractive you need to see if it can entice international competence and international ownership. And at the same time have a strong concentration of national companies, competence and owners. This is something that describes all of our chosen clusters. For instance, in the Stavanger region there is a lot of international oil companies like Exxon and BP in addition to national oil companies. In Sunnmøre there are international companies like Rolls Royce and STX OSV and local companies like Olympic shipping and Farstad Shipping. All of our clusters are as mentioned in a broad way connected to the same industry, the oil and gas industry. Which is not surprising, since the Norwegian economy is dominated by the oil sector. But the clusters are not involved in exactly the same parts of the industry, and are geographically separated. In our opinion, the fact that we are focusing on clusters in the same industry strengthens our study. This is because we are using comparable firms that will be influenced by a lot of the same macro factors. All of the cluster variables are dummy variables taking the value one if the firm is located in the geographical area of the cluster. If the firm is not located in the relevant cluster the cluster variable takes the value zero. 13

18 Møre og Romsdal The cluster is a complete offshore oriented cluster with design and building of offshore vessels, specialized equipment producers, subcontractors and a lot of offshore vessels (Jakobsen, 2011). There are a lot of family firms in this cluster. The geographic area is defined by the whole Møre og Romsdal county Sørlandet The Agder counties have developed to become the global centre for drilling equipment. Additionally the region has several ship-owners, suppliers and service providers directed towards the offshore industry (Jakobsen, 2011). There are a lot of small family owned, oil related businesses in the cluster. Vest-Agder and Øst- Agder define the geographic area in this cluster Stavanger The area is one of the world centres for oil business, with oil companies and oil service companies (Jakobsen, 2011). Here the ownership structure is more dominated by foreign ownership, and indirect ownership. There are also a lot of start-ups, which owners build up and then sell. The cluster is based in the West of Rogaland and includes the communities Stavanger, Randaberg, Sola, Sandnes, Hå, Time, Gjesdal, Egersund, Sokndal, Haugesud, Karmøy Bergen The cluster in the Bergen area is a cluster that plays on many strings. It consists of many family firms that are involved in all types of shipping, shipyards and their suppliers. The cluster is well diversified within the maritime sector, and one of the oldest clusters we have in Norway. This cluster has affiliation in West Hordaland and consist of the communities Bergen, Askøy, Øygarden, Fjell, Sund, Austevoll, Stord, Os, Samnanger, fusa, Vaksdal, Austrheim, Fedje, Masfjorden, Meland, Radøy, Lindås, Modalen, Osterøy and Gulen, Comparable firms For comparison we use firms from all over the country. The firms are in the same industry, but are not defined as part of one of the four clusters we have mentioned. This is done to have comparable firms outside the clusters to the ones inside the clusters. 14

19 6.2.4 Family firm in cluster To measure if have a family firm in a cluster we take the family dummy variable and multiply it with the cluster variables. Hence, the variable takes the value 1 if the firm is both a family firm and in the relevant cluster. We expect the family firm in cluster variables to influence performance positively. The arguments for this interaction effect were given in section 2.2. Our different family firms in cluster dummy variables are given below Family firms in Møre og Romsdal This dummy variable is the product of the family dummy variable and the dummy variable for the Møre og Romsdal cluster Family firms in Sørlandet This dummy variable is the product of the family dummy variable and the dummy variable for the Sørlandet cluster Family firms in Stavanger This dummy variable is the product of the family dummy variable and the dummy variable for the Stavanger cluster Family firms in Bergen This dummy variable is the product of the family dummy variable and the dummy variable for the Bergen cluster Firm size We use firm size as one of the control variables for performance. Fama and French (1992) have argued that small firms tend to outperform large firms. Bøhren (2009) finds that non-family firms are on average seven times bigger than family firms, and that family firms vary much more in size. Hence, we expect that the average family firm in our sample will be smaller than the other firms. This Poza (2007) argues that the benefits of being a family firm declines as the firm increases, since it is easier for small firms to maintain a better interface between family and business. The Norwegian economy mostly consists of small family firms and when taking economies of scale into consideration, we believe that at least some size will increase performance. Accordingly, we are not sure how this 15

20 variable will affect performance. There may be a possible endogeneity problem with this variable since firm size may affect performance, and performance may affect firm size. Also as most family firms are small, the family variables may be correlated with firm size. How we deal with these issues was discussed in section 5.3. In order to measure the firm size, we will use the natural logarithm of revenues as a measurement. The reason for this is that we see revenues as a more reliable measurement than the firm`s assets. This comes from the fact that a firm can be misleading since some firms might have valuable assets without creating any value. Also the measure is independent of a firm s technology and capital structure Firm age Villalonga and Amit (2006) found that family firms are younger than non-family firms. Which is natural, since family firms as we define them would be diluted after some generations, due to the need for external capital to grow. Based on this, we expect family firms to be rela have an important role in the management team, most likely as the firm CEO or as chairman of the board. As younger firms tend to have comparatively better performance than older ones (Loderer and Waelchli, 2011), we expect this variable to influence firm performance negatively. Meaning that the older the firm gets, the lower we expect their ROA to be. This can come from both an entrepreneur effect and older firms being less risky than younger ones. Also according to lifecycle theories after a certain point in time firms reach the maturity and decline stage, with fewer opportunities in the market and lower performance. Endogeneity may also be an issue with firm age since a firm has to be profitable to survive, and the age of the firm affect performance. There is also the issue of multicollinearity since family firms tend to be young. As mentioned earlier, we explain how we deal with these issues in section 5.3. We will use the number of years the company has been operating as the 16

21 6.2.7 Firm risk The risk of the firm will also impact the return; a basic assumption in finance is after all that you should be rewarded for taking on extra risk. Since all the firms in our sample are from the same broad industry. They are influenced by a lot of the same macroeconomic factors, and we cannot use industry risk as a measure. Due to this we use firm risk as our measure. A major problem for family firms is that the owners normally have virtually all their capital tied to the firms. This means that they are undiversified and exposed to all the risk of the firm. Due to this high risk, they should demand a higher return, or else they should diversify their investments. Hence, we expect firm risk to influence the performance variable positively. Especially if the firm is a family firm, since the large ownership stake makes it unlikely that they are diversified. As a proxy for firm risk we use the absolute value of the three-year average volatility of return on assets (ROA) divided by the average ROA of that period. We divide by the three-year average ROA to get a risk measure that is independent of scale. ROA was defined in section As ROA ignores financial items the risk measure only reflects operating risk Board size Board size has been shown to have a negative correlation with performance, not only for large listed firms, but also for smaller firms. Eisenberg, Sundgren and Wells (1998) find a significant negative correlation between board size and profitability in a sample of small and midsize Finnish firms. Bøhren (2011) reaches the same conclusion using data on Norwegian companies. The negative correlation has two sources that are discussed in theory. First, increased problems of communication and coordination as group size increases, second, decreased ability of the board to control management thereby leading to agency problems stemming from the separation of management and control (Eisenberg, Sundgren and Wells, 1998). The board size variable may be correlated with firm size, and the family variables. Bigger firms often have bigger boards, and family firms tend to have smaller boards. How we plan to deal with these issues was discussed in section

22 Board size is measured as the number of people currently sitting on the board. All of our expectations are summarized in table 1. Table 1: Regression model predictions Theoretical variable Proxy CCGR data item Predicted sign on ROA Family firm Family owns > 50 percent of the firm Family ownership stake Largest family ultimate ownership Cluster Firm is in the geograpic area defined as a cluster Family firm in cluster Firm is both family firm and in a cluster 15302,503 + ln(firm size) Natural logarithm of sales /- ln(firm age) Natural logarithm of firm age Firm risk Std.dev of the firms ROA the last 3 years 19, 63, 78 + Board size Number of people on the board Descriptive statistics Table 2 shows the number of observations in each of the different subsamples. It also shows the mean values and standard errors of ROA, family ownership stake, and the control variables. The subsamples have been created to give a better overlook of the characteristics of firms in clusters and family firms. As table 2 shows, Stavanger is the largest cluster, with Bergen, Møre og Romsdal and Sørlandet in decreasing order. To compare the means of the different subsamples we have computed t-values on the means of ROA using the Welch`s formula, the results are shown in table 5. We used the Welch`s formula to account for the difference in variance between the samples. 18

23 Table 2: Descriptive statistics different samples The table shows the mean and standard deviation of our control variables for the different clusters we have defined, for the family firms in the different clusters, for firms not in a cluster and not a family firm, and for family firms that are not in clusters. It also shows the number of observations for each variable. M og Romsdal is all the firms in the M og Romsdal cluster. M og Romsdal family is all the family firms in the M og Romsdal cluster. M og Romsdal not family is all the firms M og Romsdal that is not family owned. S andet is all the firms from the S andet cluster. S andet family is all the family firms in the S andet cluster. S andet not family is all the firms in S andet that is not family owned. Stavanger is all the firms in the Stavanger cluster. Stavanger family is all the family firms in the Stavanger cluster. Stavanger not family is all the firms in Stavanger that is not family owned. Bergen is all the firms in the Bergen cluster. Bergen family is all the family firms in the Bergen cluster. Bergen not family is all the firms in Bergen that is not family owned. Cluster, is all the firms that are in a cluster. Not cluster, are all firms that are not in one of the clusters. Family, is all family firms in the sample. Not family, is all the firms that are not family owned. Family cluster, is family firms that are in a cluster. Not family, cluster, is firms in clusters that are not family firms. Family, not cluster, is family firms that are not inside a cluster Not family, not cluster is firms that is either family nor in a cluster. Standard deviations are given in grey. N ROA Family ownership stake Firm size Revenue Firm age Firm risk Board size M e og Romsdal ,0454 0,6757 6, ,3273 0,6749 3,0365 0,1489 0,3117 0, ,0717 1,1776 1,5739 M e og Romsdal family ,0491 0,8850 6, ,1430 0,6828 2,4734 0,1538 0,1628 0, ,3837 1,3006 1,3871 M e og Romsdal not family 820 0,0393 0,3258 7, ,6354 0,6618 3,9780 0,1402 0,1399 0, ,5348 0,9371 1,4097 S landet ,0455 0,7177 6, ,2205 0,6593 2,7551 0,1707 0,3078 0, ,3739 1,1419 1,5834 S landet family ,0403 0,8973 6, ,8264 0,6492 2,4045 0,1744 0,1577 0, ,8047 1,2444 1,5046 S landet not family 526 0,0568 0,3242 6, ,8935 0,6812 3,5228 0,1620 0,1487 0, ,2721 0,8771 1,4782 Stavanger ,0672 0,6379 7, ,4572 0,7915 2,7980 0,1686 0,3264 0, ,8863 1,4379 1,4149 Stavanger family ,0750 0,8811 6, ,3440 0,8300 2,2182 0,1718 0,1684 0, ,2930 1,5729 1,2541 Stavanger not family ,0565 0,3011 7, ,2298 0,7383 3,6005 0,1634 0,1395 0, ,4171 1,2252 1,2195 Bergen ,0579 0,6709 6, ,7529 0,7982 2,8484 0,1673 0,3110 0, ,9585 1,6443 1,4617 Bergen family ,0580 0,8717 6, ,1426 0,7245 2,4377 0,1724 0,1700 0, ,6138 1,0586 1,3518 Bergen not family ,0578 0,3177 7, ,0675 0,9277 3,5708 0,1581 0,1428 0, ,5293 2,3376 1,3652 Cluster ,0568 0,6672 6, ,5931 0,7489 2,8539 0,1650 0,3175 0, ,9022 1,4104 1,4913 Not cluster ,0505 0,6847 6, ,6578 0,7127 2,7286 0,1691 0,3202 0, ,8404 2,0849 1,5202 Family ,0550 0,8890 6, ,6234 0,7156 2,3049 0,1693 0,1635 0, ,9328 1,9717 1,3382 Not family ,0507 0,3143 7, ,3708 0,7535 3,6092 0,1635 0,1467 0, ,7516 1,4664 1,4235 Family cluster ,0589 0,8820 6, ,1422 0,7386 2,3651 0,1693 0,1661 0, ,3575 1,3316 1,3590 Not family, cluster ,0534 0,3135 7, ,6891 0,7659 3,6588 0,1575 0,1420 0, ,0625 1,5313 1,3431 Family not cluster ,0517 0,8950 6, ,0355 0,6959 2,2534 0,1692 0,1610 0, ,5439 2,3869 1,3179 Not family, not cluster ,0483 0,3150 7, ,9939 0,7423 3,5638 0,1689 0,1509 0, ,3211 1,4045 1,

24 Table 3: Welch`s test Testing the difference of the mean ROA of the different samples using a Welch`s test. This is to deal with the problem of the two samples having unequal variance. See table 4 for definitions of the variables. Significance levels of 10%, 5% and 1% are indicated by *, ** and *** respectively. M øre og Romsdal family M øre og Romsdal not family Sørlandet not family Stavange r not family Be rgen not family Cluste r Not cluste r Family Not family M øre og Romsdal Sørlandet Sørlandet family Stavange r Stavange r family Be rgen Be rgen family Family cluste r Not family, cluste r Møre og Romsdal. Møre og Romsdal family -0,7027. Møre og Romsdal not famil 1,0528 1,5300*. Sørlandet -0,0179 0,6088-0,9707. Sørlandet family 0,8358 1,3209-0,1544 0,7777. Sørlandet not family -1,4668* -0,9379-2,0374** -1,3739* -1,8792**. Stavange r -5,2295***-3,6627***-5,0026*** -4,3754*** -4,6299*** -1,3838*. Stavange r family -6,1379***-4,6997***-5,8667*** -5,3402*** -5,5086*** -2,2930** -1,7125**. Stavange r not family -2,1628** -1,2854* -2,7205*** -1,9001** -2,4744*** 0,0286 2,1994** 3,3969***. Be rgen -2,8369*** -1,7152** -3,2338*** -2,4041*** -2,9388*** -0,1517 2,2756** 3,5928*** -0,2761. Be rgen family -2,4838*** -1,5568* -2,9798*** -2,1777** -2,7232*** -0,1534 1,9259** 3,1644*** -0,2608-0,0148. Be rgen not family -2,1504** -1,3727* -2,7025*** -1,9336** -2,4796*** -0,1215 1,7088** 2,8514*** -0,2023 0,0228 0,0328. Cluste r -3,2047*** -1,7361** -3,4084*** -2,5341*** -3,0583*** -0,0052 3,3249*** 4,5967*** -0,0622 0,3274 0,2826 0,1975. Not cluste r -1,4435* -0,3203-2,1919** -1,1264-1,8926** 0,8674 5,3905*** 6,2331*** 1,3958* 2,1719** 1,7718** 1,4477* 2,8579***. Family -2,7687*** -1,3574* -3,0973*** -2,1679** -2,7559*** 0, ,004*** 5,1371*** 0,3501 0,8602 0,7088 0,5528 0,8331-2,1842**. Not family -1,4637* -0,3663-2,2043** -1,1562-1,9100** 0,8281 5,1033 6,0160*** 1,3128* 2,0333** 1,6784** 1,3789* 2,5461*** -0,1012 1,8989**. Family cluste r -3,5455*** -2,1075** -3,6882*** -2,8717*** -3,3412** -0,2849 2,4575*** 3,8634*** -0,5128-0,2513-0,1917-0,2023-0,7885-3,2499*** -1,5295** -2,9804***. Not family, cluste r -1,9878** -0,896-2,5809*** -1,6332* -2,2903** 0,4483 3,7736*** 4,9206*** 0,6585 1,1487 0,9858 0,8123 1,1537-1,0027 0,5704-0,8798 1,6904**. Family, not cluste r -1,7105** -0,5847-2,3775*** -1,3642* -2,0794** 0,6848 4,6729*** 5,6725*** 1,0677 1,7101** 1,4234* 1,1685 2,0275** -0,5131 1,3801* -0,3907 2,5198*** 0,5299. Not family, not cluste r -0,7093 0,1627-1,6384* -0,5722-1,3852* 1,1297 7,3508*** 6,0414*** 1,7273** 2,4257*** 2,0697** 1,7503** 2,8452*** 0,7466 2,3273*** 0,7909 3,2307*** 1,4472* 1,0912. Family Not family, not cluste r not cluste r 20

25 Table 3 shows that the mean ROA is significantly higher in our sample of family firms than in our sample of non-family firms at a 5% significance level. This is consistent with our expectations from hypothesis (a) that family firms have a higher performance than non-family firms. The mean ownership concentration in family firms is 88,9%, which is relatively high. The sample was also divided up into cluster and non-cluster firms. As predicted by hypothesis (b) it seems that cluster firms have a significantly higher mean ROA at a 1% significance level than firms not in clusters. However when comparing the individual clusters to the non-cluster sample the picture is not that clear. The Møre og Romsdal cluster have a lower performance significant at a 10% significance level, the Sørlandet clusters ROA is not significantly different, and the remaining clusters have a significantly higher ROA. This may be related to cluster size, because when comparing the different clusters, the bigger clusters seem to perform better than the smaller ones. The mean ROA in the Stavanger cluster is significantly higher than the ROA in Møre og Romsdal and Sørlandet at a 1% level. When it comes to the Bergen cluster it is significantly different at a 5% level. And the Bergen cluster has a significantly higher ROA than the two smaller clusters at a 1% level. The Bergen cluster is the second largest cluster by the number of firms in our sample. Hence, it may be that clusters need to be of a certain size in order to extract the positive effects from being in a cluster. Hypothesis (c) concerns the performance of family firms in clusters. The mean comparison here gives mixed indications concerning this hypothesis. In the Stavanger cluster family firms have a higher mean ROA at a 1% significance level. In Møre og Romsdal family firms have a higher mean ROA at a 10% level. However in the Bergen cluster there is no significant difference, and in Sørlandet family firms have a significantly lower mean ROA at a 5% level. When it comes to family firms inside a cluster compared to family firms that are not in a cluster the mean ROA is significantly larger for family firms in clusters. However when looking at the family firms of the different cluster separately the results are not that clear. Only family firms in Stavanger and Bergen had a significantly higher mean ROA at 1% and 5% level respectively compared to family firms outside the clusters 21

26 Real return on assets (ROA) is the dependent variable in all the regressions we plan to run. The mean value is 5,54%, since we have removed all companies with a ROA between 100% and -100% the maximum and minimum values are just below and above those numbers. The kurtosis indicates that some firms have ROA that is not distributed close to the mean. Our sample contains everything from companies with no large family owner, to companies where a family controls the whole company. This is indicated by the maximum and minimum values of the family ownership stake variable. However, the mean ownership stake in our sample is 66,6 percent, indicating high ownership concentration. The negative skewness suggests that most firms have a large family ownership stake. Table 4: Descriptive statistics for dependent variable and control variables Using revenue as a measure of size, we find a mean value of 32,9 Mill. NOK and median of 7,85 Mill. NOK. As can be seen from the difference in mean and median values the data contains some extreme values. However, taking the natural logarithm of the revenues in the regression will deal with this problem. Furthermore, the sample consists of firms with an average age of 13,28 years and median of 10 years. Extreme values of the variable are included in the sample, indicated by the very high kurtosis. The youngest firms in the sample are only two 22

27 years old while the oldest are 125 years. As seen by the positive skewness, most of the firms are young. To deal with this we take the natural logarithm of the firms age when running the regressions. The firm risk has a mean and median value of 67,65% and 44,49% respectively. The positive skewness indicates that most firms have lower risk than the mean value, while the high kurtosis is due to the extreme differences between firm risks. Finally, the mean and median board size in the firms is 2,8 and 3, indicating that most of the firms in the sample prefer small boards. Table 5: Descriptive statistics for dependent variable and control variables The table shows the yearly mean and standard deviation of our control variables for each year, as well as the number of observations. See table 2 for definiton of the variables. The standard errors are given in grey ROA 0,0554 0,0386 0,0812 0,0703 0,0990 0,0492 0,0394 0,0179 0,0379 0,1643 0,1600 0,1719 0,1639 0,1676 0,1644 0,1668 0,1653 0,1639 Family ownership stake 0,6660 0,6673 0,6648 0,6246 0,6858 0,6859 0,6839 0,6958 0,6975 0,3123 0,3121 0,3150 0,3426 0,3147 0,3195 0,3167 0,3172 0,3158 ln(firm size) 6,8321 6,7975 6,8099 6,8202 6,9257 6,9540 6,9083 6,8834 6,9245 0,7923 0,8022 0,8090 0,8505 0,8801 0,9125 0,9081 0,9040 0,9119 Firm Age 13, , , , , , , , , , , , , , , , , ,9102 Firm risk 0,6765 0,6719 0,7703 0,6741 0,7583 0,7267 0,7338 0,7650 0,7586 1,0285 0,3112 1,5181 0,9820 1,0484 1,7551 1,6711 3,5628 1,3441 Board size 2,8065 2,8110 2,7997 2,8542 2,7710 2,7875 2,7741 2,7559 2,7502 1,5361 1,5171 1,4811 1,5444 1,4890 1,5077 1,5002 1,5039 1,5038 Revenue N From table 5, we see that performance have more or less increased towards 2007, when it peaked and started decreasing. This is probably due to the financial crisis around The number of firms in our sample has also decreased after 2009, which could be connected to an increase in bankruptcies during the financial crisis. Another explanation is that newly established firms do not have enough accounting data to be included in the sample. This might be the case since the mean firm age has increased during the overall period. The firm risk seems to be higher from 2007/2008. Which is around the financial crisis. When it comes to firm size, board size and family ownership stake they are relatively stable throughout the sample. 23

28 Exhibit 4 in the appendix shows the correlations between our independent variables. The family dummy is highly correlated with the largest ownership variables, correlation of 0,87. This is very high, but not unexpected as the largest ownership variable is used as input when creating the family dummy variable. As the variables are highly correlated we remove the largest ownership variable from our regressions. This is done to limit the multicollinearity in our regression. A rule of thumb is that with correlation above 0,8 then multicollinearity is a serious problem (Gujarati and Porter, 2009). Also board size may have to be dropped from the regressions, as it is highly correlated with firm size, largest family ownership stake and the family dummy. The variable has a positive correlation with firm size indicating that larger firms have larger boards. This is connected to the fact that larger companies most often having more stakeholders and is more complicated to control. The board size variable is negatively correlated with the family ownership stake variable and the family dummy. Family firms prefer smaller boards, since they want to keep more control of the company. Also family firms are often smaller in size than non-family firms, limiting the need for large boards. Correlation may also be a problem between our family in cluster variables and the cluster and family variables. The reason for this is that the variable is a product of the cluster variables and the family variable. As can be seen from exhibit 5 in the appendix the correlation is strongest with the cluster variables. Due to this we may have to run the regression testing family performance in clusters without the cluster variables. Since all of the firms in our sample are in the same wide industry, namely the oil and gas industry or the maritime industry. A lot of the industry specific risk from the sample is eliminated. From the descriptive statistics we see that most of the firms are family firms. And the average ownership stake by the controlling family is large. The firms are mostly young and small, as can be seen from the median age of 10 years and median revenue of 7,85 Mill. NOK. When looking at the clusters individually we find some indication that firms in clusters performs better than other firms. However, this is only for the largest clusters, when taking all the clusters into consideration, our results are mixed. The results for family firms in clusters are also mixed. Hence we cannot conclude anything yet. Also the pooled 24

29 means does not take time and other variables that may influence performance into consideration. 7.2 Regression The results from our regression on the performance of family firms is given in table 6, the regression testing the performance of firms in clusters is given in table 7, and our main regression testing the performance of family firms in clusters is given in table 8. Due to high correlation with the family variable we had to drop the largest family ownership stake variables from our regressions. We mentioned in the descriptive statistics that the board size variable might have to be dropped due to high correlation with other variables. When running the family regression with the board size variable (exhibit 6 in the appendix) the family dummy was not significant. However when the board size variable was dropped the family dummy became significant. Dropping a variable from a model may lead to specification bias. Nevertheless, as economic theory does not state that the variable has to be in the model, we believe this problem to be of minor concern. All of the regressions are as mentioned previously done using a random effects model. When running the yearly regressions we are using the same variables, but instead of a random effects model we run a classical OLS regression. Table 6: Family model regression Panel data regression using a random effects model estimating the regression of the whole sample with ROA as the dependent variable. And a year-by-year regression model with ROA as the dependent variable. A dummy variable taking the value one if the firm is a family firm, zero otherwise. A firm is a family firm if the largest family owns more than 50% of the company. Firm size is measured by the natural logarithm of revenues, firm age measured by the natural logarithm of the number of years the firm has been operating. Firm risk is measured by the absolute value of the three year average standard deviation of the firms ROA divided by the average ROA of that period. Significance levels of 10%, 5% and 1% are indicated by, *, ** and ***. Standard errors are given in grey Intercept -0,2655*** -0,1001*** -0,1536*** -0,0829*** -0,1077*** -0,1143*** -0,1602*** -0,2329*** -0,2148*** -0,2193*** 0,0127 0,0325 0,0295 0,0305 0,0280 0,0262 0,0249 0,0240 0,0252 0,0249 Family dummy 0,0136*** 0,0213*** 0,0165** 0,0152** 0,0175*** 0,0146** 0, ,0251*** 0,0163** 0,0185*** 0,0030 0,0075 0,0069 0,0072 0,0067 0,0065 0,0064 0,0061 0,0065 0,0064 ln(firm size) 0,0457*** 0,0194*** 0,0261*** 0,0219*** 0,0212*** 0,0273*** 0,0285*** 0,0346*** 0,0316*** 0,0326*** 0,0018 0,0046 0,0042 0,0043 0,0039 0,0036 0,0034 0,0033 0,0034 0,0034 ln(firm age) -0,0010** -0,0009*** -0,0011*** -0,0011*** -0,0010*** -0,0009*** -0,0004 0, ,0002 0,0001 0,0003 0,0003 0,0003 0,0003 0,0002 0,0002 0,0002 0,0002 0,0002 Firm risk 0,0098*** 0,0308*** 0,0277*** 0,0277*** 0,0560*** 0,0365*** 0,0156*** 0,0227*** 0,0057*** 0,0292*** 0,0006 0,0035 0,0026 0,0023 0,0033 0,0030 0,0017 0,0017 0,0009 0,0022 N Number of groups 3665 R-squared 0,0541 0,0525 0,0707 0,0791 0,1337 0,0832 0,0556 0,0924 0,0455 0,0945 As can be seen from table 6 the family dummy is positive significant overall at a 1% significance level. This is consistent with earlier research, and what we 25

30 expected from hypothesis (a). However, for some reason it is not significant in The reason for this could be that the financial crisis affected all firms equally hard. In addition to the family dummy, the most important variable affecting performance seems to be firm size and firm risk. Both variables being significant at a 1% significance level. Firm age also seems to influence performance, and is significant at a 5% significance level. The larger the firm is the higher its ROA seems to be, and this is the case for all the years of our sample. The firm risk variable is also significant in all years. An explanation for this is that most of the firms in our sample have relatively large owners, which most likely are undiversified. Hence, as a compensation for bearing all the firm risk, the investors get a higher return. Firm age is not significant in all the separate years, consistent with life cycle theories stating that as the firm matures the investment opportunities of the firm decreases. Table 7: Cluster model regression Panel data regression using a random effects model for the pooled sample, and regressions for year-byyear regressions with ROA as the dependent variable. The independent variables are Stavanger, M og Romsdal, Bergen, S andet. Firm size is measured by the natural logarithm of revenues, firm age is measured by the number of years the firm has been operating. Firm risk is measured by the three year average standard deviation of the firms ROA. The first five independent variables are dummy variables given the value one of the firm is located in the geographic region of the given cluster and zero otherwise. Significance levels of 10%, 5% and 1% are indicated by, *, ** and ***. The reference category is firms that are not located in a cluster. Standard errors are given in grey Intercept -0,2232*** -0,0548* -0,1053*** -0,0362-0,0651** -0,0817*** -0,1427*** -0,2009*** -0,1915*** -0,1895*** 0, ,0316 0,0289 0,0301 0,0273 0,0269 0,0254 0,0238 0,0255 0,0258 M e og Romsdal -0,0119* 0,0242* -0,0042-0,0363*** 0,0086-0,0243** -0,0144 0,0055-0,0005-0,0176* 0,0063 0,0125 0,0115 0,0118 0,0114 0,0109 0,0106 0,0102 0,0107 0,0106 S landet -0,0035 0,0069 0,0252* 0,0123 0,0043-0,0199* -0,0278** -0,0077-0,0109-0,0192 0,007 0,0138 0,0129 0,0137 0,0126 0,0119 0,0119 0,0117 0,0119 0,0119 Stavanger -0,0014 0,01798* 0,0197** 0,0199** 0,012 0,0065 0,0099-0,0083-0,0049 0,0023 0,0049 0,0103 0,0093 0,0096 0,009 0,0087 0,0084 0,0081 0,0085 0,0085 Bergen -0,0025 0,0122 0,0209** 0,0156 0,0276*** -0,0122-0,0029-0,0106-0,012-0,0152* 0,0055 0,0112 0,0104 0,0107 0,0102 0,0096 0,0092 0,0089 0,0093 0,0092 ln(firm size) 0,0453*** 0,0162*** 0,0239*** 0,0189*** 0,0172*** 0,0258*** 0,0271*** 0,0320*** 0,0300*** 0,0308*** 0,0018 0,0046 0,0041 0,0043 0,0038 0,0036 0,0033 0,0032 0,0034 0,0033 ln(firm age) -0,0184*** -0,0126*** -0,0191*** -0,0152** -0,0112*** -0,0084* -0,0023 0,0022 0,0007-0,0012 0,002 0,0046 0,0043 0,0045 0,0043 0,0046 0,0044 0,0039 0,0043 0,0046 Firm risk 0,0098*** 0,0307*** 0,0273*** 0,0273*** 0,0559*** 0,0363*** 0,0154,0228*** 0,0057*** 0,0290*** 0,0006 0,0035 0,0026 0,0023 0,0033 0,0029 0,0017 0,0017 0,0009 0,0022 N Number of groups 3665 R squared 0,0527 0,0512 0,0753 0,0832 0,1312 0,0826 0,0576 0,0884 0,0442 0,0941 When running the panel data regression testing for a cluster effect, we are not able to show that the firms in the different clusters are performing better than firms that lie outside the clusters. Three out of four cluster variables are not significant, and the Møre og Romsdal variable have a significant negative impact at a 10% level. In the yearly regressions our two biggest clusters Stavanger and Bergen both have a significant positive influence at a 5% level in However, there seems to be no consistent positive influence on performance from the different cluster 26

31 variables through the years. The control variables are all significant at a 1% significance level. It seems like firm size, firm age and firm risk are variables that influence ROA, not if the firm is in a cluster or not. Overall, it seems like being in a cluster have no significant effect on firm performance. This is not consistent with hypothesis (b). As the results in table 7 did not provide the results we expected, we created a new cluster variable consisting of all the firms in the different clusters. Nevertheless, when pooling all the clusters into one variable, the variable is still not significant (exhibit 7, in the appendix). Table 8: Full model regression Panel data regression using a random effects model for estimating the pooled regression, and year-byyear regressions. ROA is the dependent variable. M og Romsdal family, S andet family, Stavanger family and Bergen family are dummy variables taking the value if the firm is a family firm located in a given cluster zero otherwise. See table 6 and 7 for a definition of the other variables. Significance levels of 10%, 5% and 1% are indicated by, *, ** and ***. Standard errors are given in grey. All years Intercept -0,2622*** -0,1017*** -0,1523*** -0,0788*** -0,1026*** -0,1135*** -0,1551*** -0,2353*** -0,2149*** -0,2154*** 0,0128 0,0328 0,0298 0,0307 0,0033 0,0265 0,0252 0,0242 0,0255 0,0253 Family dummy 0,0104** 0,0232** 0,0123 0,0146 0,0049 0,0160* 0,0053 0,0325*** 0,0162* 0,0137 0,0042 0,0100 0,0094 0,0097 0,0090 0,0089 0,0087 0,0084 0,0089 0,0088 M e og Romsdal -0,0197 0,0255-0,0266-0,0442** -0,0119-0,0238-0,022 0,0248 0,0135-0,0282 0,0093 0,0210 0,0193 0,0194 0,0176 0,0172 0,0169 0,0170 0,0182 0,0176 M e og Romsdal family 0,0122-0,0045 0,0337 0,012 0,0336 0,0015 0,0131-0,031-0,0217 0,0169 0,0108 0,0261 0,0240 0,0245 0,0230 0,0223 0,0216 0,0212 0,0224 0,0220 S landet 0,0048 0,0075 0,0252 0,0148 0,0105-0,0104-0,0249 0,0281 0,0373* -0,0027 0,0109 0,0244 0,0224 0,0226 0,0204 0,0226 0,0219 0,0204 0,0220 0,0215 S landet family -0,0136-0,0032-0,0014-0,0045-0,0107-0,0153-0,0049-0,0546** -0,0691*** -0,0244 0,0124 0,0295 0,0275 0,0283 0,0258 0,0267 0,0261 0,0248 0,0262 0,0258 Stavanger -0,0082 0,0124 0,0172 0,0197-0,0077 0,0104 0,0017-0,0123-0,0209-0,0028 0,0067 0,0162 0,0146 0,0150 0,0132 0,0136 0,0132 0,0126 0,0136 0,0135 Stavanger family 0,0138* 0,0112 0,0077 0,0007 0,0350** -0,0066 0,0129 0,0087 0,0275 0,0085 0,0080 0,0208 0,0187 0,0194 0,0179 0,0176 0,0170 0,0163 0,0173 0,0172 Bergen -0,004 0,0307 0,0263 0,0175 0,0093-0,0164-0,0105 0,0021-0,0208-0,0326** 0,0078 0,0190 0,0184 0,0182 0,0155 0,0158 0,0152 0,0146 0,0156 0,0154 Bergen family 0,0037-0,0279-0,0073-0,0028 0,032 0,0075 0,0119-0,0203 0,0139 0,0269 0,0089 0,0235 0,0223 0,0224 0,0204 0,0198 0,0190 0,0184 0,0194 0,0191 ln(firm size) 0,0458*** 0,0184*** 0,0252*** 0,0209*** 0,0206*** 0,0276*** 0,0284 0,0347*** 0,0321*** 0,0332*** 0,0018 0,0046 0,0042 0,0043 0,0039 0,0036 0,0034 0,0033 0,0034 0,0034 ln(firm age) -0,0010*** -0,0008*** -0,0010*** -0,0011*** -0,001*** -0,0008*** -0,0003*** 0,0000 0,0000-0,0002 0,0001 0,0003 0,0003 0,0003 0,0003 0,0002 0,0002 0,0002 0,0002 0,0002 Firm risk 0,0098*** 0,0307*** 0,0272*** 0,0271*** 0,0558*** 0,0364*** 0,0155*** 0,0226*** 0,0057*** 0,0291*** 0,0006 0,0035 0,0026 0,0023 0,0033 0,0030 0,0017 0,0017 0,0008 0,0022 N Number of groups 3665 R-squared 0,0555 0,0563 0,0764 0,0871 0,1395 0,0867 0,0593 0,0958 0,0506 0,0982 Our main regression is given in table 8. Here we combine our first two regressions. In addition we add four dummy variables taking the value one if a firm is in a cluster and if the firm is a family firm, zero otherwise. As found in the regression testing for family effect, the family dummy is positive significant. This is consistent with what we expected in hypothesis (a). When it 27

32 comes to the cluster variables none of them are significant. This is a bit different then what we got in the cluster regression. Since the Møre og Romsdal dummy was negative significant at a 10% significance level. A reason for this may be the strong correlation between the cluster dummies, and the family firm in cluster dummies. This issue was discussed in section 6, and will also be looked at later in the thesis. The most interesting variables in table 8 are the different family firm in cluster variables. Family firms in the Stavanger cluster have a positive effect on ROA, being significant at a 10% level. None of the other family in cluster variables is significant. This makes it difficult to draw a conclusion regarding the performance of clusters and family firms in clusters. The regression yields the same results as the previous regressions concerning firm size, firm age and firm risk being significant at a 1% level. As mentioned, the correlation between the cluster dummies, and the family in cluster dummies are strong. The family in cluster dummies are also correlated with the family dummy. This was discussed in section 6. To test if this has an impact on our regression, we run a regression where we leave out the family dummy and the cluster dummies (exhibit 8 in the appendix). In this regression the family firm Sørlandet variable is significant negative at a 1% level. None of the other family firm in cluster variables is significant. This result does not give support to any of our hypothesis. To get a clearer picture of the performance of family firms in clusters we created a new sample, including only cluster firms. The regression is given in exhibit 9 in the appendix. The family dummy is positively significant at a 1% level. Hence it seems that family firms performs better than non-family firms in clusters. Which means, a cluster setting does not seem to change the relationship between family firms and non-family firms. To turn things around we also created a sample consisting of only family firms. When running the same regression as in table 6 on this sample we got the same result as before (see exhibit 10 in the appendix). Hence, family firms in clusters do not seem to perform better than family firms outside clusters. We also pooled all the different clusters into one variable, and 28

33 ran a regression on the same dataset (exhibit 11 in the appendix). The result did not change. Consequently, being in a cluster does not seem to have an effect on family firm performance and vice versa. In summary our regressions have generated support for hypothesis (a), but not for hypothesis (b) and (c). This paper has investigated if firm performance is affected by being family owned, if being inside a cluster affects performance, and how these two effects interact. Where the main focus has been on the interaction of the two effects, that is, how family firms perform in a cluster setting. All of the firms observed are Norwegian and in either the oil and gas industry or the maritime industry. As previous research we find support for a stronger performance in family firms than in non-family firms. This result stands in all our different regressions. Hence, our paper supports hypothesis (a). Which is, family firms have higher performance than non-family firms. We suspect the main reason for this is the reduction of the first agency problem as was discussed in section 2.1. When running regressions on the cluster effect we were surprised by the results. We did not find any clear effect on performance by being in a cluster compared to not being in a cluster. Actually, three of four cluster variables were not significant when we ran them in our panel data regression. And the last cluster variable was significantly negative at a 10% level. This was not what we expected, and we therefore have to reject hypothesis (b). Hypothesis (b) was that firms in clusters have higher performance than firms not in clusters. There can be several reasons for this. Perhaps ROA is a bad measurement in order to examine the cluster effect, or that being in a cluster cannot be measured using a performance measure. But instead should be measured by for instance rate of innovation, mobility of the workforce or intensity of the rivalry in the area. Another possibility is that due to reap the benefits of being in a cluster. Perhaps Norway in itself can be seen as a cluster in regard of these industries. We did after all find an indication when comparing the mean ROAs that the performance in our biggest clusters is stronger than the 29

34 performance in the smaller ones. There could also be clusters present in our control sample, meaning that areas not defined as clusters by us have cluster effects. Our cluster definitions could be to wide or to narrow, including to many or to few firms. If that is the case getting a significantly different ROA of cluster firms compared to the control sample will be difficult. More controversial, maybe there is no performance effect from being in a cluster. ome firms year after year perform better than other firms just because of their location, the other firms will eventually move to that specific location. There would have to be large barrier of entry to prevent this relocation, and in the end all firms would be located in the same cluster. As we did not find any effect on performance from being in a cluster it was not very likely that there was a positive interaction effect between being a family firm and being in a cluster. This is confirmed when running the regression. We do not get any significant and consistent results. However, as previously, the family dummy is significant. When running a regression on just firms in clusters the family dummy is still significant. Hence, family firms in clusters perform better than non-family firms, but there does not seem to be any interaction effect between clusters and family firms. This leads us to reject hypothesis (c), that there is a positive interaction effect on performance of being a family firm in a cluster. In short we find support for superior performance of family firms, but no indication of increased performance for firms in clusters. This leads to family firms in clusters performing better than non-family firms, but not better than family firms outside clusters. 30

35 Anderson, Ronald C and David M. Reeb Founding-family ownership and Journal of Finance: 59: Benito, Gabriel. Berger, Eivind. de A International journal of transport management, 2003, Bennedsen, Morten. Meisner Nilsen, Kasper. Perez-Gonzalez, Francisco. Wolfenzon Succession Decisions and Performance. Quarterly Journal of Economics. Vol. 122, No.2, 2007, Berzina, Janis. Bøhren, Øyvind, and Rydland, Pål. Corporate finance and governance in firms with limited liability: Basic characteristics CCGR Research Report 1/2008, September, Accessed January 7 th, Bodie, Zvi, Kane, Alex and Marcus, Alan J Investments. 8 th edition. New York: McGraw Hill/Irwin Brooks, Chris Introductory Econometrics for Finance. 2 nd Cambridge, United Kingdom: Cambridge University Press edition. Bøhren, Øyvind Eierne, styret og ledelsen; Corporate governance i Norge. Bergen: Fagbokforlaget. Dreux, D. R Family Business Review, 3(3), Larger board size and decreasing fi Journal of Financial Economics, vol 48, issue 1,

36 Fama, Eugene F. French, Kenneth R The Cross-Section of Expected Stock Returns. Journal of Finance 47 (2): Gujarati, Damodar. Porter, Dawn Basic Econometrics. 5 th edition. New York: McGraw Hill/Irwin Hervik Arild. og Erik. W Jakobsen. 2001: Det regionale maritime Norge: En nasjonal næring med regional særpreg. BI Research report. Isaksen, Arne Innovation Dynamics of Global Competitive Regional Clusters: The Case of the Norwegian Centres of Expertise, Regional Studies, vol 43.9, Jakobsen, Erik.W En kunnskapsbasert maritim næring. Forskningsrapport 5/2011. Handelshøyskolen BI, institute for strategy and logistics. Accessed January 7 th, Kachaner, Nicolas. Stalk, Georg and Bloch, Alain What You Can Learn Harvard Business Review, Nov 2012, Vol 90 Issue 11, Loderer, Claudio. Waelchli Urs Firm age and governance. University of Bern, Working paper. Lyman, Amy. R Customer service: Does family ownership make a Family Business Review, 4(3),

37 Malakauskaite, Asta. Navickas, Valentinas The impact of clusterization. Journal of business and Economics Management, Marshall, Alfred Principles of Economics. 8 th Edition. Ed. Macmillan, London Maury, Benjamin evidence from Western European countries. Journal of Corporate Finance 12, Venture Capital and Other Private Equity: a Survey European Financial Management, Vol. 17, No. 4, Ministry of Finance. The corporate tax system and taxation of capital income. Accessed June 6, Petersen, Trond Analyzing Panel Data: Fixed- and Random-Effects Models. In Hardy, Melissa A. and Bryman, Alan (eds.) Handbook of Data Analysis. Sage, London. Pp Clusters and the new economics of competition. Harvard Business Review, Nov/Dec98, Vol. 76 Issue 6, p77 Porter, Michael. E The Competitive Advantage of Nations. Free Press, New York. Porter, Michael. E Locations, clusters and company strategy. In Clark, G., Feldman, M. and Gertler, M. The Oxford handbook of economic geography (p ). Oxford: Oxford University Press. 33

38 Porter, Michael E Location, competition and economic development: Economic Development Quarterly, Vol. 14 Issue 1, p15, 20p Poza, Ernesto. J Family business. New York: Thomson South-Western. Reve, Torger. Lensberg, Terje, & Grønhaug, Kjell Hva bestemmer konkurranseevnen?, Reve, Torger. Sasson, Amir Et kunnskapsbasert Norge. Universitetsforlaget. Statistics Norway Subject Retrieved June 6, Villalonga, Belen. and Raphael. Amit, How do family ownership, control, Journal of Financial Economics 80,

39 Exhibit 1 This picture shows many of the firms that are owned by the Ulstein family. 35

40 Exhibit 2 This picture shows many of the firms that the Sævik family owns. 36

41 Exhibit 3 The different shareholders in the newly founded company, World Wide Supply AS World Wide Supply Shareholder Number of shares Percentage Remøy Management AS ,68 % Nordvestor Aktiv As ,16 % Sydvestor AS ,18 % Frydenbø Investment AS ,24 % Hareid Elektriske AS ,24 % Gjerde Invest AS ,92 % Becker Invest AS ,26 % Soltun Invest AS ,63 % Jeasimo AS ,63 % Harald Invest AS ,63 % Duk Invest AS ,63 % Abri Invest AS ,63 % Inger M Kristensen AS ,63 % Stig Ulstein AS ,63 % Emar Invest AS ,63 % Ozo Invest AS ,32 % Evo Holding AS ,32 % Bjåstadbakken AS ,32 % Micosa AS ,66 % Haaset AS ,66 % Exhibit 4: Correlation matrix A matrix that describes the correlation between the family dummy, and the control variables. The definitions of the variables are given in table 6 and 7. Family dummy Family ownership stake ln(firm size)ln(firm age) Firm risk Board size Family dummy 1,0000 Family ownership stake 0,8696 1,0000 Firm size -0,2009-0,2500 1,0000 ln(firm age) 0,0469 0,0352 0,0537 1,0000 ln(firm risk) -0,0102-0,0203 0,0544 0,0537 1,0000 Board size -0,4175-0,5049 0,4242 0,0809 0,0099 1,

42 Exhibit 5: Correlation matrix Correlation matrix between all of the cluster dummy variables, the family in cluster dummies and the family dummy. For variable definition see table 7 and 8. Familie Family M e og Romsdal Family S landet Family Stavanger Family Bergen M e og Romsdal S landet Stavanger Bergen Familie 1 Family M e og Romsdal 0, Family S landet 0,1759-0, Family Stavanger 0,2528-0,0831-0, Family Bergen 0,23-0,0756-0,069-0, M e og Romsdal -0,003 0,7759-0,0745-0,1071-0, S landet 0,0327-0,0707 0,8185-0,0926-0,0842-0, Stavanger -0,0465-0,1137-0,1037 0,7313-0,1355-0,1465-0, Bergen 0,0058-0,0972-0,0887-0,1274 0,7777-0,1253-0,1083-0, Exhibit 6: Regression with board size variable Random effects regression where we use board size as an extra control variable. All other variables are defined in table 6. Standard errors are given in grey Inte rcept Family dummy ln(firm size) ln(firm age) Firm risk Boardsize Coefficient -0,2433*** 0,0000 0,0547*** 0,0173*** 0,0098*** -0,0174*** SE 0,0139 0,0032 0,0018 0,0019 0,0006 0,0011 Exhibit 7: Regression with pooled cluster variable A regression where we pooled all firms in cluster into one variable: Cluster=Møre og Romsdal+Sørlandet+Stavanger+Bergen. This is in order to see if there is an overall cluster effect. All other variables are defined in table 7. Standard errors are given in grey. Inte rcept Cluste r ln(firm size) ln(firm age) Firm risk Coefficient -0,2234*** -0,0041 0,0454*** -0,0186*** 0,0098*** SE 0,0127 0,0036 0,0018 0,0019 0,0006 Exhibit 8: Family firms in cluster regression Random effects regression without cluster variables and family dummy, but with family firms in cluster dummy. Done because of correlation between the family in cluster variables and the cluster variable. See table 8 for variable definitions. Standard errors given in grey. Intercept Family M e og Romsdal Family S landet Family Stavanger Family Bergen ln(firm size) ln(firm age) Firm risk Coefficient -0,2524*** 0,0036-0,0013*** 0,0157 0,0087 0,0447*** -0,0009*** 0,0098*** SE 0,0123 0,0070 0,0077 0,0053 0,0059 0,0018 0,0001 0,

43 Exhibit 9: Cluster regression on family firms Regression on a dataset consisting only of firms in clusters. Firms not located in a cluster have been removed. This is done to examine if there is a family effect in clusters. Variables are defined in table 6. Standard errors are given in grey. I ntercept Family dummy ln(firm size) ln(firm age) Firm risk Coefficient-0,2278*** 0,0159*** 0,0397*** -0,0013*** 0,0165*** SE 0,0180 0,0042 0,0025 0,0002 0,0010 Exhibit 10: Cluster regression on family firms sample Random effects regression on a dataset consisting only of family firms. Removing non-family firms to see of there is a cluster effect on family firms. See table 7 for variable definitions. Standard errors are given in grey. Exhibit 11: Cluster regression on family firms Regression on a dataset consisting only of family firms. Removing non-family firms to see if there is a cluster effect on family firms. See exhibit 7 for variable definitions. Standard errors are given in grey. 39

44 Fredrik Hoseth: Håvard Remøy: Family firms in clusters: an advantage or not? BI NORWEGIAN BUSINESS SCHOOL PRELIMINARY THESIS REPORT Hand-in date: Supervisor: Øyvind Bøhren Campus: BI Oslo Examination code and name: GRA 1902 Master Thesis Program: Master of Science in Business and Economics

45 Preliminary Thesis Report, GRA Page i

46 Preliminary Thesis Report, GRA Motivation Since we both grew up in Sunnmøre, we have always been fascinated by the different family firms in the area. In the latest years the region has developed a very strong maritime cluster, and many of the firms are family firms. The cluster contains ship-owners, shipbuilders and their subcontractors. Most of their activities are linked to the offshore industry. Often when you hear someone talking about firms in the maritime industry from Sunnmøre, you hear them talk about the cluster effect and how strong an impact it has. In addition we feel that writing a thesis on something we already have some knowledge on and find interesting would be a good idea. 2. Expected contribution to the topic We would like to investigate if there is a cluster effect, if there is a family effect and if both are present, how they interact. In finance literature it has been found that family firms often performs better than non-family firms. For instance Maurey (2006) concluded that family control could increase performance in western European firms. The cluster effect is another phenomenon that might influence performance. Michael Porter (1998) defines clusters as geographic concentrations of interconnected companies and institutions in a particular field. In the same paper he says that clusters affect competition in three ways, by increasing the productivity of companies in the cluster, driving the direction and pace of innovation and stimulating the formation of new businesses. According to Reve and Sasson (2012), clusters are known by a combination of cooperation and rivalry. This cooperation and the intense fight to always be the most innovative and profitable company inside the cluster, is what drives the technology forward at a more rapid speed inside clusters compared to outside. These two (the family and cluster) effects have been looked at extensively before. What we are most interested in looking at is how they affect each other. That is, if the effects are positive or negative multiplicative. We have not been able to find any previous literature on this topic. Page 1

47 Preliminary Thesis Report, GRA Variables Dependent Variable Performance In order to test firm performance we are thinking of using ROA. Bodie, Kane and Marcus (2009) say that ROA measures the profitability for all contributors of capital. It is defined as: - generating return from its assets. This measure gives an indication on how good management is at Independent variables Firm size as a measurement. The reason for this is that we see sales as a more reliable assets without creating any value. Fama and French (1992) have argued that small firms tend to outperform large firms. Bøhren (2009) finds that non-family firms are on average seven times bigger than family firms, and that family firms vary much more in size. Hence, we expect that the average family firm in our sample will be smaller than the other firms. This may be related to that when the firm being a family firm declines as the firm increases, since it is easier for small firms to maintain a better interface between family and business. Firm age We will use the number of years the company has been operating as the (2006) found that family firms are younger than non-family firms. Which is natural since family firms as we define them would be diluted after some generations. Based on this we expect family firms to be relatively young and that the firms founder will have an Page 2

48 Preliminary Thesis Report, GRA important role in the management team, most likely as the firm CEO or as chairman of the board. As younger firms tend to have comparatively better performance than older ones (Loderer and Waelchli, 2011), we expect this variable to influence firm performance negatively. Meaning that the older the firm gets, the lower we expect their ROA to be. This can come from both an entrepreneur effect and that older firms being less risky than younger ones. Family ownership stake We will measure this by taking the total fraction of shares the controlling family owns of the company. Our expectations are that the higher the fraction is, the better will the firms perform. The reason for this is that a higher fraction will reduce the first agency problem, due to that a family with high ownership stake (2006) refer to the first agency problem as the conflict of interest between professional managers and owners. On average the biggest family in Norwegian family firms own 93 % of the shares, Bøhren (2009). Meaning that a lot of the family firms have an owner with super majority. From a family firm in a cluster perspective we expect the second agency problem to be smaller when the firm is part of a cluster than if they are not. The reason for this is that the smaller owners in the company most likely would be friends or a business partner from the same cluster, reducing the conflict level. Firm risk The risk of the firm will also impact the return; a basic assumption in finance is after all that you should be rewarded for taking on extra risk. Due to that the firms in our sample all are from the same broad industry and hence are influenced by a lot of the same macroeconomic factors we cannot use industry risk as a measure. In addition most of the firms in our sample are not listed. We therefor want to use the balance sheet to measure risk. More specifically we want to use the five years average volatility of return on assets (ROA). The average volatility of equity (ROE) would be a better measure as it takes both sides of the balance sheet into consideration, but as the measurement of ROE normally involves a lot of noise we believe that it may be problematic to use. A major problem for family firms is that the owners normally have almost all their capital tied to the firms. This means that the owners are undiversified and carries more risk than they would if they Page 3

49 Preliminary Thesis Report, GRA invested their money in a diversified portfolio. This may be a reason why family firms tend to perform better compared to non-family firms. 4. Hypothesis There is a cluster effect: in Sunnmøre) + i(cluster in Stavanger) + j(cluster in Sørlandet) + k(cluster in Kongsberg) + Where the variables d to g is control variables, and h to k are dummy variables. The dummy variables will take the value 1 if the firm is in the relevant cluster and zero otherwise. The base case will be firms that are not in any of the clusters. We expect the variables h to k to be positive significant as shown by previous research. In the current globalized economy tangible resources are seldom the reason for competitive advantage. It is more likely that the intangible resources are the basis since they are much more difficult to imitate. In most cases a capabilities and resources. More formally competitive advantages are created in the interplay between company rivalry, factor conditions, demanding customers, and the quality of related and supporting sectors (Porter, 1990). These forces will increase the productivity of the firms in the cluster, which in turn will increase the profitability of firms inside the cluster compared to similar firms that are not part of the cluster. Reve and Sasson (2012) says that in order to measure if a cluster is successful and attractive you need to see if it can entice international competence and international ownership, and at the same time have a strong concentration of national companies, competence and owners. The clusters we will focus on are what Porter calls geographical clusters. There is a family effect: ake) + g(industry risk) + Where Z is a dummy variable that takes the value 1 if a single family controls more than 50 percent of the shares. Z is 0 otherwise. We expect b to be positive significant as shown by previous research (e.g. Villalonga and Amit 2006, Maury 2006). Øyvind Bøhren (2011) writes in his book that family firms tend have a higher ROA, when the family has a large shareholding, higher m/b-ratio when the family has a moderate shareholding, higher m/b in countries with high minority Page 4

50 Preliminary Thesis Report, GRA protection, highest m/b when the founder is the CEO and chairman of the board. In short, family firms are more profitable then non-family firms, when the family has moderate control and there is good minority protection. The reason for this may be that the incentives of the management and the owners are more closely aligned, that is agency problem 1 is smaller. For instance there may be more prestige/compensation for a manager when running a large company compared to a smaller one even though the solution is not the most profitable for the owners. According to Bøhren (2011) we can define a family firm as a firm where over 50% of the stocks are owned of people that are married, are in-laws or related to each other. The reason for this definition is that control of 50% of the stocks gives the family power to pick the board. Using this definition, 63 % of all Norwegian firms are defined as family firms, and 55 % of all firms are family firms with super-majority. The family effect and cluster effects impact each other: +h(cluster in Sunnmøre) + i(cluster in Stavanger) + j(cluster in Sørlandet) + k(cluster in Kongsberg) + l(cluster in Sunnmøre)*Z + m(cluster in Stavanger)*Z + n(cluster in Sørlandet)*Z + o(cluster in Kongsberg)*Z + Where Y is performance measured by ROA, Z is a dummy variable with value 1 if the firm is a family firm and zero otherwise. The variables size, firm age, ownership stake and industry risk is other variables that influence performance; a closer definition of those was given in section 3. The cluster effect will be measured using dummy variables in the same way as when we measured the cluster effect. To measure the interaction between family firms and the cluster effect we will multiply the family dummy with the cluster dummy, hence when the firm is both a family firm and in a cluster the value will be 1. We expect the variables from h to o to be positive significant. One of the reasons for this is that we expect the relationship between family firms that are part of a cluster to be stronger than for family firms that are not in a cluster. For instance it may be easier for two family firms inside a cluster to have a close and good relationship, compared to non-family firms where the ownership structure is more scattered. This comes from the fact that clusters are locally entrenched, meaning that the possibility of the owners to socialize with each other is high. In addition family firms have a more long-term orientation and a more locally entrenched philosophy Page 5

51 Preliminary Thesis Report, GRA than non-family firms. The turnover of key personnel may also be lower as family members fill many of the key positions. This again may lead to more stable longterm relations. Inside the clusters it is also normal that owners of the different family firms are related to each other, brothers, sisters, cousins etc. A good example of this is the Ulstein family from Sunnmøre. In this family, the different cousins own a bunch of companies. If you look at the ownership structure of the companies you often see that there is 1-3 big owners (brothers and sisters) that own about percent of the company and their cousins own the remaining shares. In exhibit 1 and 2 you can see the most important companies that the Ulstein and Sævik family have ownership in. This is a pattern you see in many of the companies in Sunnmøre. You can also often observe that many newly founded AS is owned by capitalintensive families that invest together. However, as the company develops, the family with the highest ownership share will eventually buy a controlling share of the company. Companies like Olympic shipping and REM shipping was founded in this way, and most recently World Wide Supply AS was founded in this way (see Exhibit 3). This kind of cooperation is a good example of how family firms inside clusters may have an advantage in raising capital in order to be an motivator for sharing important information across the cluster. Another benefit from this kind of financing is that the access to capital reduces the third agency problem, which is the problem between the firm and their creditors. In addition it may also limit the second agency problem. According to Villalonga and Amit (2006) the second agency problem is when the large shareholder uses its controlling position in the firm to extract private benefits at the expense of the small shareholders. The reason for this is that if the majority shareholder takes advantage of the smaller ones it may hurt the relationship between them, which again will hurt the cooperation within the cluster. A clusters success depends on the interplay between the different companies, Navickas (2009) argues that the companies share technological know-how, knowledge skills, competencies and resources. Hence majority shareholders in different companies may be skeptical of taking advantage of their minority shareholders, since in the long term they may end up hurting themselves. Page 6

52 Preliminary Thesis Report, GRA Regression Econometric technique Based on techniques that are used on similar papers like ours, we plan to run a time series regression using ordinary least squares (OLS) on our data. This is a technique, which minimizes the sum of squared distances in the dataset. In order to get an consistent OLS estimator we need to check if the repressors are exogenous, make sure that there are no perfect multicollinearity, and that the error terms are homoscedastic and serially uncorrelated. Multicollinarity may be a problem since some of our independent variables like firm risk, firm age and firm size may be related. One reason for this is that older firms tend to be larger and therefore maybe less risky. We are aware of this problem, and if our regression shows a strong multicollinarity we may drop one of them. Descriptive statistics Independent variable N ROA Size effect Firm age Family ownership Industry risk stake Family firm Not family firm In Cluster Not in cluster Family firm in cluster Data 6.1 Database We plan to use data from Norwegian and public firms over the period from All this data will be collected from the Center for Corporate Governance Research (CCGR) Page 7

53 Preliminary Thesis Report, GRA In order to choose the most suited firms we will first manually pick all the firms we find relevant inside the geographical area. We will then run those firms trough Berzins, Bøhren and Rydland (2008) filter: Filter 1: Remove all companies without limited liability. Filter 2: Positive sales. Filter 3: Positive assets. Filter 4: Companies must have employees in the sample period. Filter 5: Current assets must exceed cash equivalents. Filter 6: Assets must exceed working capital. Filter 7: Remove financial firms. Filter 8: Remove subsidiaries. Filter 9: Companies must have four years or more of data. This will hopefully give us around companies from each cluster. 6.2 The different clusters Sunnmøre Maritime cluster at Sunnmøre. We choose this cluster since there are many family owned firms here. The cluster is a complete offshore oriented cluster with design and building of offshore vessels, specialized equipment producers and a lot of offshore vessels (Jakobsen, 2011) Sørlandet There are a lot of small family own, oil related businesses. The Agder counties have developed to become the global center for drilling equipment. In addition the region has several shipowners, suppliers and service providers directed towards the offshore industry (Jakobsen, 2011). Stavanger Here the ownership structure is more dominated by foreign ownership, and indirect ownership. There are also a lot of start-ups, which owners build up and Page 8

54 Preliminary Thesis Report, GRA then sell. The area is one of the world centers for oil business, with oil companies and oil service companies (Jakobsen, 2011). Kongsberg Technology based cluster at Kongsberg. The region is in the counties of Buskerud, Telemark and Vestfold and dominated by marine electronics, technological equipment and subsea equipment (Jakobsen, 2011). One big government dominated company dominates the cluster. Comparable firms We need to pick comparable firms that lie outside a cluster in order to have a comparison with the firms inside the cluster. We plan on using NACE codes for the relevant industry, then excluding companies that lie inside the geographical area of the different cluster. The sample will be a randomized pick2 from the remaining companies in our dataset. All of our chosen clusters are in a broad way all in the same industry, the oil and gas industry. Which is not strange since the Norwegian economy is dominated by the oil sector. But the clusters are not involved in exactly the same parts of the industry, and are geographically separated. Norway is after all a country that is not easy to travel in. By looking at different cluster with different ownership structures we hope to see how the different ownership structures influences the performance of the firms in the clusters. In our opinion, the fact that we are focusing on clusters in the same industry strengthens our study, since we then are using comparable companies that will be influenced a lot by the same macro factors as measurement. We think that family firms will have more firm specific risk, and may diversify more since the owners are not that diversified Time period Since we only have ownership data from 2000, we will base the thesis on data from 2000 to is not included due to lack of data. Page 9

55 Preliminary Thesis Report, GRA Data access All firm specific data at: The CCGR data consists of six tables: 1. Accounting data from 1994 to Consolidated accounting data for 1994 to NACE industry codes for the companies from 1998 to A company can be a member of more than one industry. 4. Governance data from 2000 to Except Misc data from 1994 to Misc data from 2000 to Firms in the different clusters: (Møre) (Sørlandet) (Stavanger) (Kongsberg) The consulting firm Menon have done a lot of research on clusters in Norway and possess information on the firms in the different clusters. When we have talked to different people that have done research on clusters they have all mentioned Menon and Erik Jakobsen. Therefore we are now trying to make an arrangement with them, were we can use the information that they have gathered in our thesis. 7. Implementation plan February 1 st Feedback from advisor Work on our presentation February-March Present out thesis for the faculty February 10 th Start on the statistical analysis April 1 st Statistical analysis done and the write up can start June 1 st First version of thesis ready Comment from advisor July 1 st Planned finish of thesis September 1 st Deadline final thesis Page 10

56 Preliminary Thesis Report, GRA Litterature Benito, Berger, de la Forest, Shum. A cluster analysis of the maritime sector in Norway. International journal of transport managment, 2003, Berzina, Janis, Bøhren, Øyvind, and Rydland, Pål. Corporate finance and governance in firms with limited liability: Basic characteristics, CCGR Research Report 1/2008, September, Retrieved Jan 7 th pdf Bodie, Zvi, Kane, Alex and Marcus, Alan J. 2009, Investments. 8 th edition. New York: McGraw Hill/Irwin Bøhren, Øyvind. Eierne, styret og ledelsen, Corporate governance i Norge. Fagbokforlaget, Fama, Eugene F. French, Kenneth R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance 47 (2): Hervik A. og Erik. W Jakobsen, (2001): Det regionale maritime Norge: En nasjonal næring med regional særpreg. BI rapport. Research/CCGR-data-extraction/ Retrieved January 11, Isaksen, Innovation Dynamics of Global Competitive Regional Clusters: The Case of the Norwegian Centres of Expertise, Regional Studies, vol 43.9, , Jakobsen, Erik.W, (2011). En kunnskapsbasert maritim næring. Forskningsrapport 5/2011. Handelshøyskolen BI, institute for strategy and logistics. Retrieved 7 th January. Page 11

57 Preliminary Thesis Report, GRA Loderer, Claudio. Waelchli Urs (2011): Firm age and governance. University of Bern, working paper. Maury, B, (2006). Family ownership and firm performance Empirical evidence from Western European countries, Journal of Corporate Finance 12, Navickas, V.a. (2009). The impact of clusterization on the development of small and medium sized enterprise sector. Journal of business and Economics Management, Porter, M. E. 1998, Clusters and the new economics of competition, Harvard Business Review, Nov/Dec98, Vol. 76 Issue 6, p77 Porter, M.E. 1990, The Competitive Advantage of Nations, Free Press, New York, Porter, Michael Location, Competition, and Economic Development: Local Clusters in a Global Economy journal=economic Development Quarterly, vol 14, no.1, pp 15-34, 2000 Poza, E. J Family business. New York: Thomson South-Western. Reve, T. Lensberg, T, & Grønhaug, K Hva bestemmer konkurranseevnen?: Reve, T. Sasson, A. Et kunnskapsbasert Norge. Universitetsforlaget Villalonga, B. and R. Amit, How do family ownership, control, and management affect firm value, Journal of Financial Economics 80, Page 12

58 Preliminary Thesis Report, GRA Exhibits Exhibit 1 Page 13

59 Preliminary Thesis Report, GRA Exhibit 2 Page 14