Entrepreneurial Success – a Comparative Approach on German and Swedish Entrepreneurs During the Nineteenth and Twentieth Century

Linköping University | Department of Management and Engineering Master thesis, 30 credits | Economics Spring 2018 | LIU-IEI-FIL-A--18/02909--SE

Entrepreneurial success – A comparative approach on German and Swedish entrepreneurs during the nineteenth and twentieth century

Nada Faraj Farhijo Hashi

Supervisor: Hans Sjögren and Joakim Persson

Linköpings universitet SE-581 83 Linköping, Sweden 013-28 10 00, www.liu.se

Title: Entrepreneurial success - A comparative study on German and Swedish entrepreneurs during the nineteenth and twentieth century

Authors: Nada Faraj Farhijo Hashi

Supervisor: Hans Sjögren Joakim Persson

Publication typ: Master’s thesis in Economics Master’s program in Economics Advanced level, 30 credits Spring 2018

Linköping University Department of Management and Engineering (IEI) www.liu.se

Acknowledgements

To our supervisor Hans Sjögren and assisting supervisor Joakim Persson for valuable comments and helpful suggestions. The participants in our group seminars for providing interesting discussions regarding entrepreneurship. During this process, we had the possibility to visit Bocconi University in Milan, we would like to thank Andrea Colli for new and interesting viewpoints.

Linköping, 2018-05-30

Abstract

This paper examines the similarities and differences between German and Swedish entrepreneurs born in the nineteenth and twentieth century. It is a replicated study by Vasta et al. (2015). The study is a part of an international project, where the characteristics of entrepreneurs’ is reviewed in multiple countries. The information of entrepreneurs is collected from biographical dictionaries and the qualitative data is later on converted into quantitative data, where several different variables are measured. The applied method, Principal Component Analysis (PCA) generated three different dimensions of entrepreneurial success. The components are the following: celebrity (captures entrepreneurs’ visibility in the various sources), economic success (describes the economic performance of an entrepreneur) and social mobility (measures entrepreneurs’ social class improvement). By using a prosopographical approach, we found among other things that a level of innovation intensity is required in order to reach a higher level of economic success for both German and Swedish entrepreneurs.

Keywords: Germany, Sweden, entrepreneurs, entrepreneurship, economic business history, founder, inventor

Table of content

1. Introduction 4 1.1 Problem discussion 5 1.2 Purpose 7 1.3 Research questions 7 1.4 Limitations 7 1.5 Implementation 8 1.6 Research contributions 9 2. Theory 10 2.1 The Schumpeterian entrepreneur 10 2.2 Risk and uncertainty 11 2.3 Previous studies 13 3. Sources and data 17 3.1 Biographical studies of German entrepreneurs 17 3.2 Biographical studies of Swedish entrepreneurs 18 3.3 Description and selection of variables 19 4. Method 26 4.1 Definition of Principal Component Analysis (PCA) 26 4.1.1 Example: Data reduction using Principal Components 27 4.2 Properties of Principal Components 29 4.3 Detection of Outliers and Robust Estimation 30 4.4 Rotation method and interpretation of principal components 31 4.5 Regression model 31 4.6 Criticism of the method 32 5. Results 34 5.1 Dimensions of entrepreneurial success for the German sample 34 5.2 Dimensions of entrepreneurial success for the Swedish sample 41 5.3 Multiple linear regressions 47 5.3.1 Regressions for the German sample 47 5.3.2 Regressions for the Swedish sample 48 5.4 Detecting heteroscedasticity and multicollinearity for both samples 50 6. Discussion 51 6.1 A comparative analysis of the components for Germany, Italy and Sweden 51 6.2 A comparative analysis of the results from regression models 54 6.3 A comparative analysis of American, British, French, German Italian, Spanish and Swedish entrepreneurs 61

7. Conclusion 65 References 67 Appendix 70

1. Introduction

Entrepreneurship in Europe is evident and several entrepreneurs throughout history have led by example and been role models for many. A few successful entrepreneurs that were active in Germany respectively Sweden during the industrialization were Hugo Stoltzenberg, Otto Meyer, Axel Wenner-Gren and Alfred Nobel (Deutsche Biographie and Swedish Biographic Lexikon, 2018). In connection to the industrialization in the late nineteenth century, institutions gave individuals the possibilities of making success based on their individual drive and knowledge. The establishment of firms across Europe contributed to economic growth which would not have been possible without the help of financial institutions (Sylla and Toniolo, 1991). According to German economic history, Germany had by 1890 one of the largest economies in the world and was among the leading countries in the industrial sector (Plumpe, 2016). In the beginning of the twentieth century, German industrial sector was very successful internationally due to high production standards and low costs. Germany was also able to use skilled labors more efficiently than any other country in Europe (ibid).

From 1850 to 1913 Sweden underwent industrialization and experienced economic growth at an accelerating rate (Myhrman, 2003). Between 1870 and 1970, Sweden’s economy was amongst the fastest growing economies in the world second to Japan (Johnson, 2006). The success of Sweden was mainly due to political reforms that occurred throughout the nineteenth century, for example the formation of the joint stock corporation law (Aktiebolagslagen) which took in effect 1848. The implementation of a renewed and more modern bankruptcy law in 1862 which simplified the reconstruction of firms for business owners. As well as the introduction of an improved patent and trademark law to secure innovations from possible imitation in 1884 (ibid).

The impact entrepreneurship has on economic growth has been examined from different perspectives such as the relationship between entrepreneurship and job creation (Malchow- Møller et al., 2015). In high income countries entrepreneurship have shown to have a positive impact on macroeconomic growth, due to entrepreneurs’ efforts to maintain and develop their firm (Stam et al., 2011). Also, the entrepreneur attracted the attention of management which in turn forced owners to find the appearances that distinguish entrepreneurs from managers (Hébert and Link, 2009).

Previous literature has examined the traits and personality characteristics of entrepreneurs to find specific traits that defines an entrepreneur. The entrepreneur has been defined as a risk taker, innovator, manager of a business venture, decision maker, coordinator and asset owner. (Gartner, 1988). According to Schumpeter (1911) the entrepreneur is an innovator who identifies and introduces new products, production methods, resources and organizational structure (Henrekson and Stenkula, 2015). For many other researchers, entrepreneurs differ from each other and each of them are considered as a unique person whose qualities need to be examined (Gartner, 1988).

In this study, we have examined 100 German and 100 Swedish entrepreneurs born between 1800 and 1970. Germany and Sweden stand out mainly due to their similarities in recent technological innovation (Government of Sweden, 2017). Taking this into account we find it suitable to examine entrepreneurs that are founders which have contributed to the establishment of enterprises and inventors. This study is a replicate of a previous study conducted by Nuvolari, Toninelli and Vasta (2015), “What makes a successful (and famous) entrepreneur? Historical evidence from Italy (XIX-XX centuries)”.

1.1 Problem discussion

According to the entrepreneurship barometer1 from 2016, nearly half of Sweden’s population in working age have a positive attitude towards becoming entrepreneurs, in terms of starting or managing a business. The results have also showed that the distance between having a positive attitude towards entrepreneurship and starting a business is vast. This is partly because many individuals lack a business idea or believe the risks are too big (Swedish Agency for Economic and Regional Growth, 2017)2. It is therefore important that the public sector can lead the next generation of entrepreneurs to their goals, by influencing laws and regulations.

Recently, studies have chosen to examine entrepreneurs’ biographies to find the factors and characteristics of individuals that led them to their entrepreneurial success. The first study

1 The entrepreneurship barometer is an attitude survey about Swedish people´s views on starting and managing a business. In the survey for 2016, 10 300 people in working age have participated. 2 The Swedish Agency for Economic and Regional Growth (Tillväxtverket) is a government agency under the Ministry of Enterprise and Innovation.

was conducted by Nicholas (1999) who examined British entrepreneurs with a lifetime wealth accumulation as a measure of entrepreneurial success. In another study by Vasta et al. (2015), researchers have examined the determinants of entrepreneurial success for Italian entrepreneurs in nineteenth and twentieth century. The idea of studying the entrepreneurs’ biographies and transforming the qualitative data into quantitative but on different samples and frameworks has spread amongst other authors. There are four replicated studies of Vasta et al. (2015) that has used a similar sample of American entrepreneur (Magenes, 2015), British entrepreneurs (Piantanida, 2017), French entrepreneurs (Bonsignore, 2018) and Spanish entrepreneurs (Zollet, 2018) for the same time period. There is not a similar comparative study conducted for German and Swedish entrepreneurs trying to explain the main factors and differences between their success.

In contemporary times both Germany and Sweden are categorized as innovation-driven economies based on reports from The Global Entrepreneurship Monitor (2016). According to the German Federal Ministry for Economic Affairs, start-up companies and entrepreneurs are necessary to create a lasting competitive market and to develop job opportunities. Also, both countries offer government state-support for new start-ups and sizable information on how to start a successful business (Federal Ministry of Finance3 (2018) and Swedish National Audit Office (2016)).

The Government of Sweden and the Federal Government of Germany have agreed on a unique partnership for innovation in January 31, 2017. The agreement on cooperation is to promote innovative community solutions, launch new products to export and to strengthening the competitiveness of the countries. One of the cooperation is about strengthen the digitalization of small and medium sized (SME) companies and the purpose is to match Swedish and German SMEs to exchange experiences through the countries’ agencies (Government of Sweden, 2017). We found it relevant to implement a comparative approach of German and Swedish entrepreneurs due to the partnership for innovation that will bring governments, businesses and institutions together, hopefully. When examining the underlying determinants of entrepreneurial success, we have studied the following factors for both German and Swedish entrepreneurs; education level, experience abroad, innovation intensity, involvement in politics, level of risk taking and scientist.

3 The German Federal Ministry of Finance, Olaf Scholz is responsible for the German fiscal and tax policy.

1.2 Purpose

The aim of the study is to examine the underlying determinants and differences between German and Swedish entrepreneurial success from 1800 to 1970.

1.3 Research questions

● Which factors have a significant impact upon the different dimensions of entrepreneurial success, regarding German and Swedish entrepreneurs?

● What does German and Swedish entrepreneurs have in common with the international studies based on American, British, French, Italian and Spanish entrepreneurs?

1.4 Limitations

Our sample consists of founders and inventors born no earlier than 1800 and who have been and are operating in the following sectors: agriculture or mining, industry, services and financial services. The chosen set of variables in this study differ slightly from the study by Vasta et al. (2015) because we found them to be relevant for the study’s aim (see more in Section 3.3). Furthermore, we chose to search with following keywords: gründer (grundare) and erfinder (uppfinnare) in the Deutsche Biographie (DB) and the Swedish National Biography (Svenskt Biografiskt lexikon, SBL). Due to the lack of time, we selected 100 German and 100 Swedish entrepreneurs. Regarding German entrepreneurs, we were able to sort by relevance in the DB, meaning that individuals were sorted according to their contribution for German history. The DB provides information of individuals in the German speaking areas from the Middle Ages to the present (DB, 2018). We chose to proceed with only German individuals, which means individuals from Austria and Switzerland are excluded from our sample.

The main source regarding Swedish entrepreneurs is SBL. For the Swedish entrepreneurs’ further limitations was made due to keywords being found in individual’s biographical information based on father’s or relative’s occupation. Those individuals have been excluded from the sample. Since we want a sample of 100 entrepreneurs and the search in SBL resulted in 75 individuals, we have completed with some additional individuals from Swedish entrepreneurial literature. The literatures used are Johnson (2006), Peterson (2012) and a

series of news articles regarding entrepreneurs from the Swedish daily newspaper (Svenska Dagbladet, SvD).

1.5 Implementation

We have replicated the method used by Vasta et al. (2015), by using a prosopographical approach. Firstly, we have transformed qualitative data into quantitative in order to analyze the data. Biographical information of German entrepreneurs has been translated from German to Swedish in order to encode the information. Google Translate has been used to translate the German text. We have checked the translated texts with the content provided by Encyclopedia Britannica which showed to be similar. Biographical information of Swedish entrepreneurs has been obtained from different relevant sources as described above, and the qualitative information has been transformed into quantitative data in the same way as for German entrepreneurs.

Secondly, we performed Kaiser-Meyer-Olkin (KMO) test that indicate the suitability of our samples for structure detection and it examines the proportion of variance in the variables that possibly can be caused by the underlying factors. KMO generates a value between 0 and 1, values less than 0.50 imply that the sample is not suitable for structure detection and higher values imply the sampling adequate. Additionally, Bartlett’s test of sphericity has been used to test if the selected variables are related, which is required for structure detection. It tests the hypothesis that the correlation matrix is an identity matrix (variables are uncorrelated) and in order to proceed it has to generate p-values less than 0.05 (IBM, 2018).

Thereafter, the Principal Component Analysis (PCA) has been employed to reduce the dimensionality of the data set and with help of the method, different dimensions of entrepreneurial success were derived. Before we continued to estimate the regressions with the components as dependent variables, we studied the retained components to detect any outliers. The regression method used is multiple linear regression because we found it to be the most appropriate method. The most important assumptions for a multiple regression model is homoscedasticity, no multicollinearity and that the model is correctly specified. The Ordinary Least Square (OLS) method minimizes the sum of squares of the residuals and the estimator is efficient compared to the others, see more in Section 4.5 (Gujarati, 2004 p. 202 - 208). The regression models have been estimated with different dependent variables similar

as in the study by Vasta et al. (2015). The variables used as proxies differ due to the different sample applied. The approach is explained in section 5.1 and 5.2.

In order to maintain robust regressions, we have performed tests for heteroscedasticity and multicollinearity. Regarding heteroscedasticity, Breusch-Pagan-Godfrey test has been conducted, which is appropriate when dummy variables are included in the model. It tests the hypothesis of no heteroscedasticity (Gujarati, 2004 p. 400). The second test involves identifying multicollinearity, by examining the variables Variance Inflation Factor (VIF). According to the rule of thumb, a VIF-value that exceeds 10 indicates multicollinearity problems (Gujarati, 2004 p. 359). Also, the correlation matrix of the variables has been examined to identify highly correlated variables. After we conducted the test, we re-estimated all regressions with robust standard errors due to the uncertainty of having heteroscedasticity. In this study, we have used the statistical software programs Eviews and SPSS.

1.6 Research contributions

Our contribution to this field is mainly the comparison of Germany and Sweden, which have not been previously studied or compared. We believe that our aim of focusing on entrepreneurs who are founders and inventors are relevant for the studied time period. The study is based on a new dataset that we have created with the help of biographical information from the mentioned sources in section 1.4.

2. Theory

The definition of the concept entrepreneurship is a highly discussed topic, it has been frequently discussed in the fields of business, sociology and economic psychology. There is a theoretical difference between social, political and institutional entrepreneurship (Henrekson and Stenkula, 2016). Even if the topic has been discussed over a long period in various fields, there is yet not a common definition of an entrepreneur. The factors that form an entrepreneur is a question of interpretation. Hébert and Link (2009) estimated that there are approximately 12 different interpretations of the term, given that all theoretical understandings are considered. Entrepreneurship can generally be explained by four classic entrepreneurial factors; the innovator, the arbitrage, the uncertainty carrier and the coordinator (Henrekson and Stenkula, 2016). In section 2.1 and 2.2, we define the differences and what distinguishes the four classical functions.

2.1 The Schumpeterian entrepreneur

Amongst the many interpretations in economic literature, the Schumpeterian entrepreneur is the most frequently mentioned (Henrekson and Stenkula, 2016). Joseph Schumpeter, an economist active in Austria and later on in the United States during the twentieth century drew the conclusion that an entrepreneur is someone who is an innovator. His economic theory on entrepreneurship focused on the development of ideas, the characteristics and timeframe used by an individual (Hébert and Link, 2008). Schumpeter referred to entrepreneurs as individuals that during a certain period are perceived as inventors during the process of the innovation (Schumpeter, 1911). Hence, entrepreneurship is a creating process which has a time frame and because of that, entrepreneurship is a scarce resource. Schumpeter viewed entrepreneurs as “primus motor”, a way to describe the entrepreneurs as head of the economic development (Henrekson and Stenkula, 2016). According to American economist William Baumol, the impact entrepreneurship has on economic growth is unquestionable, which is in line with Schumpeter’s definition of the entrepreneur being the “primus motor” of economic growth (Baumol, 2010).

In neoclassical growth theory, economic growth tends to come from knowledge as an exogenous factor. The impact entrepreneurs have contributed is barely recognized. Entrepreneurs connect knowledge with growth, by using knowledge and turning it to services

and products (Braunerhjelm, 2008). The Schumpeterian entrepreneur influenced many including Swedish economist Erik Dahmén, who studied the impact of entrepreneurship on the Swedish industrial development. Dahmén believed entrepreneurs to be the driving force of the economy. This because of the initiatives that entrepreneurs took, which in turn helped the development and creation of commercial business possibilities (Johansson and Karlson, 2002).

Along with other economists such as Joseph Kirzner, Schumpeter did not acknowledge the risks associated with entrepreneurship (Braunerhjelm, 2008). Kirzner described an entrepreneur as someone with the ability to identify and discover new opportunities. He believed in order to detect the many possibilities available which are to be utilized, an entrepreneur must have characteristics like alertness. Kirzner often used the term alertness in context with entrepreneurship, more specifically he described an entrepreneur as an arbitrage. An individual that utilizes the imbalances between markets and the market price differences (Henrekson and Stenkula, 2016). In conjunction with alertness which raises competition, the entrepreneur is responsible for moving the economy towards the equilibrium (Braunerhjelm, 2008).

2.2 Risk and uncertainty

In many cases entrepreneurship is strongly associated with profit making and enterprises. Richard Cantillon was the first economist to study the entrepreneurs’ central role in economic development and interpreted the establishment of companies as a vast part of the idea behind entrepreneurship (Henrekson and Stenkula, 2016). According to Cantillon the market economy consisted of three classes. The entrepreneur was one of the three classes which was explained as an economic agent together with landowners and laborers. In the same direction, French economist Jean-Baptiste Say used the term coordinator to describe the responsibilities bestowed upon entrepreneurs. The responsibilities involved overseeing production as well as making decisions regarding capital and division of labor.

An entrepreneurs’ willingness to take risks by investing in new markets as well as being a decision maker are the most important aspects of Cantillon’s theoretical point of view (ibid). An individual will in search for profit encounter uncertainty, Cantillon focused on the actions made by the entrepreneur and not their personalities (Hébert and Link, 1989). To determine

who is an entrepreneur, he distinguished between individuals and categorized them into two separate groups, those who worked for a certain income and those which had an uncertain income. Based on that narrative Cantillon concluded that individuals that operate in uncertain markets and make risk full choices can be perceived as entrepreneurs. Hence, entrepreneurs are risk takers (Boutillier and Uzunidis, 2016). Later, economists with inspiration from Cantillon further developed theories on risk taking, amongst them was the German economist Johann Heinrich von Thünen. He believed that in order to be accepted as an entrepreneur one should always accept a part of the risk if complications arise. This due to insurance companies who may not cover all expenses causing the entrepreneur to accept an uninsurable risk (Hébert and Link, 2008). He also agreed with the certain vs uncertain income statement, where he explains how an entrepreneur differs from individuals with managerial profession.

Managers according to von Thünen should not be mistaken for entrepreneurs, since they have a secure income source. He believed that individuals with a secured income do not have to worry while an entrepreneur is constantly concerned and it is affected by sleepless nights. Furthermore, Von Thünen explained the period of concern that an entrepreneur endures, as a period of a productive stage where ideas are further developed to innovations and the risk taker develops to becoming an inventor (Hébert and Link, 2009). In 1921, the American economist Frank Knight published the book Risk, Uncertainty and Profit, where he reviewed entrepreneurs as uncertainty carriers. The focus was on individuals that make decisions during uncertainty. Knight’s theory differs from the risk-taking entrepreneur by only referring to the decisions that solely are made due to genuine uncertainty. Knight distinguished between risk and genuine uncertainty by claiming that unlike risk during genuine uncertainty individuals are not able to calculate the likelihood for a successful or a failed decision (Henrekson and Stenkula, 2016).

Cantillon’s definition on risk takers and Say’s description of a coordinator are in line with our sample of founders, an individual that creates jobs and can estimate customer demand as well as the markets. This can be described as an economic catalyst, a form of industrial leader, hence the relevance of the word founder (Hébert and Link, 2009). Our definition of an entrepreneur in this study can be summarized as an individual who introduces new products, a market or methods to a previously underdeveloped market. Meanwhile having the expectations to grow as well as contributing to a country’s macroeconomic growth.

2.3 Previous studies

The pioneer of applying a quantitative prosopographical approach4 is Nicholas (1999) who explained entrepreneurial performance in Britain since 1850. By assuming the profit as the main motive for entrepreneurship, he used information on lifetime wealth accumulation as a measure of entrepreneurial performance. The wealth accumulation outcome is determined by initial wealth, profit (equivalent to the entrepreneurs’ income), consumption and rate of return. Moreover, entrepreneurial type was categorized as either inheritors or non-inheritors entrepreneur (such as firm founders or managers) in order to distinguish value of wealth obtained by inherited wealth from values of wealth obtained by entrepreneurial activities. The result specified two important determinants of entrepreneurial performance; entrepreneurial type and education. Both determinants indicated to have a negative impact on entrepreneurial performance. Other findings such as region of activity, religious affiliation and industry of occupation has shown not to be determinants of entrepreneurial performance.

The French economic literature in the early nineteenth century, considered entrepreneur as a vital component of a market economy who assumes risk associated with uncertainty (Hébert and Link, 2009). Foreman-Peck, Boccaletti and Nicholas (1998) examined the determinants of nineteenth century French entrepreneurship and management by a sample consisting of 244 French businessmen. They constructed two models for demand and supply of entrepreneurship and management success. The demand for entrepreneurs occurs due to their productivity, which in turn depends upon the opportunities given by the country’s economy. While, the supply for entrepreneurs is based on the assumption of free entry and exit of firms and it includes several determinants of starting a business. The findings on the demand side specified textiles to be the most promising industry rather than iron and steel, for increasing individual wealth. The findings on the supply side implied that secondary and University education have a negative effect on the probability of starting a firm.

Recently, researchers have focused on the relationship between education and development, since more knowledge and skill causes entrepreneurs to implement new methods more efficiently (Paul and Siegel, 2000). A comparative approach conducted by Tortella, Quiroga

4 The method is based on biographical studies of individuals and proceeds by analyzing the quantitative data statistically.

and Moral (2010) explored education’s effect upon entrepreneurial success by comparing Spanish with British entrepreneurs of the nineteenth and twentieth centuries. With economic versatility5 as a measure of entrepreneurial success, the positive effect of education showed to be more evident for British than for Spanish entrepreneurs. This is due to the countries different educational systems. A remarkable finding from this study is that wealth measured by entrepreneurs’ family income had negligible impact on economic versatility for both samples. Nevertheless, Nicholas (1999) found entrepreneurial type (inheritors or non- inheritors) and education as the determinants of entrepreneurial performance in case of British entrepreneurs.

Toninelli, Vasta and Zavarrone (2014) explained entrepreneurial success of Italian entrepreneurs based on a new data set provided by Toninelli and Vasta (2010). They selected firm’s growth as a proxy of entrepreneurial success. Recently, Vasta et al. (2015) examined the determinants of entrepreneurial success in Italy by adding two additional components for entrepreneurial success. The authors began with coding biographical information into a series of categorical variables and observed eight variables6 which considered to be theoretically relevant. Afterward, they performed factor analysis, to explore the relationship between the chosen variables and to create measures of entrepreneurial success. The study contributes with three dimensions of entrepreneurial success which were later used as dependent variables. The first dimension of entrepreneurial success is celebrity and it captures the visibility of entrepreneurs in various sources (number of rows in Wikipedia and Italian dictionary). The second, economic dimension is measured by the firm’s growth, geographically expansion of business and by innovation. Lastly, social mobility and entrepreneurial type was selected as proxies for the social mobility dimension of entrepreneurial success. Thereafter, the authors performed multiple linear regressions to find the determinants for the different dimensions of entrepreneurial success. The following variables are included in their model; education, experience abroad, innovation intensity, involvement in politics and scientist. The determinants of Italian entrepreneurial success have indicated to be education level and political connections when celebrity was set as a

5 The entrepreneur is considered to be adaptable if he operated in several sectors and not adaptable if he operated in only one sector. 6 Employment growth, expansion of business at geographical growth, introduction of successful brand or product, social class improvement, entrepreneurial type, number of rows in Italian dictionary, number of words in English and Italian Wikipedia.

dependent variable. Both factors have a positive impact on celebrity. Italian entrepreneurs with higher level of education gained more attention in the various sources, and it can also describe their social class in society. Similarly, being politically involved increased their visibility and it indicates that being politically involved played a role during the Italian capitalism. With economic success as dependent variable, the result specified following significant variables; innovation intensity, education level and experience abroad. The factors have also a positive impact on economic success. This finding confirm that Italian entrepreneurs were innovators, in accordance with Schumpeter (1911) definition of an entrepreneur. Regarding the last dimension of entrepreneurial success, social mobility, the authors did not present any results of the determinants (Vasta et al., 2015).

There are four replicated studies of Vasta et al. (2015) which examine the drivers of entrepreneurial success for the same time period but on different samples. The studies have replicated the method used by Vasta et al. (2015) and, also the same explanatory variables have been used. The following countries have been studied: France (Bonsignore, 2018), Great Britain (Piantanida, 2017), Spain (Zollet, 2018) and United States of America (Magenes, 2015). The determinant for the first dimension of entrepreneurial success celebrity, is innovation intensity and it have a positive impact in all four studies. Findings imply that American, British, French and Spanish entrepreneurs became more visible on the various sources by being more innovative and it is also consistent with Schumpeter’s (1911) definition of an entrepreneur as an inventor. The common determinant for American, British and Spanish entrepreneurs is experience abroad. The effect of experience abroad in terms of broader network and increased level of knowledge is shown to be positive in which increased entrepreneurs’ visibility on the various sources. Another common determinant for British, French and Spanish entrepreneurs is involvement in politics, with a positive impact on celebrity. British entrepreneurs’ visibility in the public domain has increased with higher level of education, while American entrepreneurs became less visible on sources. They also became less visible if they were educated or trained in science or engineering (captured by the variable scientist), in contrast it increased the visibility of Spanish entrepreneurs.

The common determinant for economic success in the mentioned studies is innovation intensity, with a positive impact on the economic performance of the entrepreneur. The ability to innovate and identifying opportunities occurring on a market are the fundamental driver of celebrity and economic success dimension of entrepreneurial success. The common

determinant of economic success with a negative impact is education which is found in all studies except in the Spanish sample, where it is positive. The result indicates that the more educated the American, French and British entrepreneur was, the less successful he or she became. The same reasoning applies to Spanish entrepreneurs but with the opposite effect. Moreover, other findings imply that American, French and Spanish entrepreneurs became less successful due to being politically involved. When British and French entrepreneurs were educated or trained in science or engineering, they also became less successful.

In the social mobility dimension, the common negative associated determinant for all four studies is education. As discussed earlier, pursuing higher education was less preferred by individuals that had potential successful ideas, which they rather focused on. The determinant scientist has a positive impact for American and French entrepreneurs which implies that the drivers of social mobility is same for both type of entrepreneurs. British and Spanish entrepreneurs social class did not improve when they were politically involved, but it did improve when they became more innovative. Therefore, the determinants of social mobility are the same for British and Spanish entrepreneurs.

3. Sources and data

The method of using biographical information with quantitative approach has spread progressively amongst business historians and economists. Biographical studies of entrepreneurs provide a better understanding of historical motivations, strategies and the development of entrepreneurs. Additionally, we get a better understanding of how society and institutions have influenced entrepreneurship over time and their impact on society as a whole (Mokyr et al., 2012). The entrepreneurial biography from reliable sources has been used to identify the different characteristics and the entrepreneurial activities for German and Swedish entrepreneurs. Generally, the method of collecting our dataset can be described as primary. According to Hox and Boeije (2005) primary data is described as original data which is collected for a specific research purpose by the researchers. As previously mentioned this study is limited to entrepreneurs that are founders and inventors. In below sections 3.1 and 3.2 follows a description of how the search for the observations was conducted, and in section 3.3 information regarding the selected variables are presented.

3.1 Biographical studies of German entrepreneurs

The Deutsche Biographie is funded by the German Research Foundation and it is managed by the Historical Commission and Bayerische Staatsbibliothek. Since 2010, it has been providing digital full texts of more than 130,000 individuals from the German-language areas. The service provides more than 48,000 biographical articles obtained from the Allgemeine Deutsche Biographie. It also provides 24 volumes (since 1953) of the Neue Deutsche Biographie. The DB provides well founded articles by experts and the dictionary contains deceased individuals whose achievements influenced the social development in Germany. We believe that the DB is a reliable source, since the information is provided by experts in the field. It is also considered to be the most relevant biographic source of the German-language area (DB, 2018).

As mentioned earlier, the DB consist of people from all German-language areas and we have chosen to focus on individuals only from Germany. We conducted an advanced search to find founders and inventors. In the advanced search selection of gender, beliefs and professional fields can be made. Due to time constraint, we selected the first 50 founders and the first 50 inventors from the search results in the fields of business, industry and technology. With the

keyword “gründer” we received 1534 individuals and the second keyword “erfinder” gave 441 individuals. Therefore, we have not supplemented with additional sources. The figure below shows the number of German entrepreneurs by year of birth.

Figure 1: Number of German entrepreneurs by year of birth

2 Number of of entreprenuersNumber 1

Year of birth

Source: Deutsche Biographie. Own illustrated figure.

3.2 Biographical studies of Swedish entrepreneurs Our primary source, the dictionary of Swedish National Biography (SBL) is a government funded project and is a part of the National Archives of Sweden (Riksarkivet). SBL consists only of deceased individuals who during their lifetime were active in Sweden or abroad but remained ties with their home country. It includes those who had a significant impact on Swedish history and contributed to the different stages in the development of the Swedish society. The first publication of the dictionary occurred in 1917 and the process of digitalization started in 2012. The publication is an ongoing work (SBL, 2018). SBL provides the ability to perform text search in their database and with the help of keywords we searched for founders and inventors. The search resulted in 47 individuals who are recognized as “founders” and 28 individuals as “inventors”, all born between 1800 and 1970. The information provided by SBL are from first-hand sources with high credibility and the authors of the articles are qualified experts in the fields. Since our search in SBL led to a total of 75 individuals, we have supplemented our sample with additional individuals. This by using literature of entrepreneurship by Johnson (2006), Petersson (2012) and as well as a

series of news articles published in the Swedish daily paper (Svenska Dagbladet, SvD). This completes our sample of 100 individuals. Figure 2 displays the number of Swedish entrepreneurs by year of birth.

Figure 2: Number of Swedish entrepreneurs by year of birth.

1 Number of of entreprenuersNumber

Year of birth

Source: Swedish National Biography and Nationalencyklopedin (NE). Own illustrated figure.

3.3 Description and selection of variables

In this study, we have collected a total of 18 variables as shown in table 1. The set of variables has been chosen according to their relevance for the study’s aim and they are also theoretically relevant. Out of the total, we have focused on 7 main variables for derivation of the different dimensions of entrepreneurial success. The variables are the following: entrepreneurial type, geographical growth, growth in terms of employment, number of rows in the dictionaries (DB and SBL), number of words in Wikipedia (English, German and Swedish) and social mobility. The set of variables are identical to the variables used by Vasta et al. (2015) except the exclusion of the variable introduction of successful brand or product. This depends on findings made by Vasta et al. (2015), which implies that the variable holds a greater bias when encoding than the remaining variables. As previously stated we have transformed qualitative data into quantitative, then later on constructed a number of categorical variables as well as dummy variables. Majority of the variables are set up as categorical and are measured in scales, which have been defined by Vasta et al (2015).

It is important to highlight that we have from the best of our ability encoded the biographical information for each observant equally. This by using the same criteria and interpretation for each variable. We have taken this into consideration, regardless there may still appear biasness in the samples.

The biographical sources have given us clear demographic description of each individual, as well as an overview of their lifetime and achievements. With the help of the location of birth we created the variable area of birth, which is based on the geographical location in each country. This variable was executed separately for Germany and Sweden. For Sweden, the following categories were created: north (Norrland), middle (Svealand) and south (Götaland) and for Germany: north, west, east and south. If an individual was born in another country, it was categorized as abroad. The difference between the categorization in our case depends on a countries geographical appearance, since Sweden is an elongated country it was categorized as stated and vice versa for Germany. Following variables, area of birth, gender and year of birth are relevant for our descriptive statistics and it is also relevant when studying the demographics of the samples.

An interesting variable is father’s main activity, which is defined as the father's occupation. This information simplifies the determination for the variable social mobility of the entrepreneurs. Hence, the variable social mobility was later on created with the help of father’s main activity. The variable is scaled in a range from 0 to 2, where 0 indicates that no improvement was made, 1 from middle class to upper class and 2 from lower to upper class. With the help of these variables, we take into account the social development each individual experienced over the time. In the field of social science, researchers have made repeated discoveries that a child’s level of achievements can be based on their social environment as well as their parents educational and occupational background (Davis-Kean, 2005). Taking this into consideration the outcome of the mentioned variables is of great interest.

In contemporary times, many may argue that education level is vital in order to reach high achievements. In order to chart how educational background can differentiate, we collected the highest level of education reached. The following categories was available; illiterate/primary school, middle school, high school/college or University. From the data we collected regarding education, we found that a number of individuals completed internships

or studies abroad. This information led us to our next variable experience abroad, which is set as a dummy variable. The variable reveals whether an individual during his or her lifetime travelled overseas for educational purpose or for work. The remaining variables regarding experience and social engagements are scientist (education or training in science) and politically involved, if an individual was politically involved during his career7.

To determine what type of entrepreneur an individual is, we differentiate between if an individual is a founder, co-founder, co-founder with family, inheritage or by purchasing along with which sector they operated in. There is a variety of sectors in which entrepreneurs was or still active in. With the help of the variable main sector of activity, we are able to distinguish them. An entrepreneurs’ level of risk taking, product innovation level and process innovation level are also included in the dataset and are categorized according to a scale of 0 to 1.5. The variable level of risk taking is a measurement based on individuals’ entrepreneurial decisions. If an entrepreneur formed an enterprise by investing in new markets, he or she obtains a value of 0.5. The formation of an enterprise in an uncertain market or the introduction of an invention gives a higher level of risk taking, a value of 1. The highest value of 1.5 implies that an individual has spent many resources or have introduced a new product in a highly risky market. Risk taking is heavily discussed in theory and according to Cantillon and Von Thünen entrepreneurial activity induce risk and create uncertainty. An inclusion of the aspect in our empirical study is essential.

Product and process innovation level can be described as a measurement for Schumpeter's theory and the belief that innovation is the central role of an entrepreneur. In the study by Vasta et al. (2015), they created an additional variable named innovation intensity which is the sum of product and process innovation. It measures how well an entrepreneur is in introducing both a process and a production innovation. The level of exposure an entrepreneur has experienced for their innovation or enterprise around the world is described by the variable geographical growth. The variable growth in terms of employment can explain the size of enterprises and the development achieved by an entrepreneur. A few enterprises may have participated in horizontal integration, it is the acquisition of a

7 Overall for German entrepreneurs, it was difficult to find information whether they were politically involved. When there was not any information mentioned in DB, we assumed that the entrepreneur was not politically involved.

competing company that is at the same level and the variable is set up as a dummy variable. Finally, we look at visibility and popularity of each individual based on their accomplishments and history by observing the number of words in Wikipedia (English, German and Swedish versions) and number of rows in dictionary (DB and SBL).

From the descriptive statistics in table 1, it is possible to identify the possible effect of the variables from the regression analysis section. We believe that the effect of education level will be more evident for German entrepreneurs than for Swedish entrepreneurs. Majority of German entrepreneurs (63 %) have obtained a University degree, while the largest group of Swedish entrepreneurs (32 %) accomplished only high school/college diplomas. Also, none of the German entrepreneurs attained primary school meanwhile 8 % of Swedish entrepreneurs did. This strengthens our belief that education may have a positive impact on entrepreneurial success regarding German entrepreneurs. A negative impact or a non- significant result can possibly be obtained for Swedish entrepreneurs. We also believe early introduction of a compulsory school attendance law may generate a higher educated population. The compulsory school attendance law took effect in 1763 in Germany (Spielvogel, 1999) and 1842 in Sweden (Johnsson, 2006). Reviewing experience abroad, majority of both German and Swedish entrepreneurs (78 % and 74 %, respectively) travelled overseas for educational purpose or for work. Therefore, the variable is excepted with great probability to have a positive impact on entrepreneurial success for both samples. Regarding innovation intensity, the proportion of German entrepreneurs who obtains the highest value of 3.0 is higher (39 %) than Swedish entrepreneurs (24 %). German and Swedish entrepreneurs have introduced both a process and a production innovation during their career. Thus, innovation intensity should have promoted entrepreneurs’ success as Schumpeter's theory of entrepreneurship.

Majority of German and Swedish entrepreneurs (73 % and 80 %, respectively) was not politically involved during their career. It was difficult to find information on DB and SBL whether both type of entrepreneurs were politically involved. When there was no information mentioned in the sources, we assumed that the entrepreneur was not politically involved. We believe that being political engaged does not have a positive impact on entrepreneurial success. According to the results all entrepreneurs have acquired some level of risk, although it appears that majority (82 %) of the German sample and (44 %) of Swedish sample maintained a low level of risk.

None of the entrepreneurs were free of risk therefore we have a strong believe that any form of risk taking has a positive effect on entrepreneurship. The last variable scientist, may have a positive impact for German entrepreneurs (71 % were educated or trained in science) and a negative impact for Swedish entrepreneurs (26 % were educated or trained in science).

Table 1. Descriptive statistics (in percent), 100 observations.

Germany Sweden Germany Sweden Gender Scientist (education or training in science or engineering) Female 1 9 No 29 74 Male 99 91 Yes 71 26

Area of birth Involvement in politics North 19 North 8 No 73 80 West 26 Middle 42 Yes 27 20 East 15 South 40 South 24 Abroad 10 Entreprenurial type Abroad 16 Founder 67 71 Co-founder 20 18 Fathers main activity Co-founder with family 6 6 Farmer 8 14 Inheritage 6 3 Laborer 1 9 Purchasing 1 2 Manager 37 5 Technician 1 3 Main sector of activity Agriculture, fishing and Craftsman 12 11 5 7 mining Entrepreneur 15 1 Industry 60 46 Freelance 4 2 Service (not financial) 32 41 Employee 10 14 Financial service 3 6 Merchant 10 23 Church minister 2 4 Geographical growth Military/politician 0 14 Local 0 8 National 39 60 Social mobility International 61 32 No improvement 6 7 From middle to upper class 75 64 From lower to upper class 19 29 Growth in term of employment Increasing growth 28 34 Education level Workforce doubled 1 1 Primary school/illiterate 0 8 Workforce more than doubled 71 65 Middle school 23 29 High school/College 14 32 Horizontal integration University degree 63 31 No 93 87 Yes 7 13 Experience abroad No 22 26 Yes 78 74

Germany Sweden Germany Sweden Level of risk taking Numbers of words Wikipedia (English) 0.5 (low risk) 82 44 0 45 62 1.0 (medium risk) 16 48 1-200 4 9 1.5 (high risk) 2 8 201-400 4 2 401-600 3 5 Innovation intensity (sum of product and process innovation) 601-1000 4 5 0.5 0 9 >1001 40 17 1.0 4 17 1.5 2 16 Number of dictionary rows (DB respectively SBL) 2.0 20 26 0-100 84 68 2.5 35 8 101-200 12 24 3.0 39 24 201-300 3 4 Numbers of words Wikipedia (German and Swedish) 301-400 1 2 0 6 12 401-500 0 2 1-200 5 9 >501 0 0 201-400 8 17 401-600 5 11 601-1000 9 4 >1001 67 47

Source: Own database.

4. Method

Factor Analysis and Principal Component Analysis (PCA) are two different techniques for reducing original variables into a reduced set of variables that are easier to interpret. Principal component analysis has often been described as a special case of factor analysis and statistical programs treat it as an option for factor analysis. The differences between these techniques is in the definition of their model, the estimation process and the dimensionality of the model can result in more radical effects on factor analysis than in principal component analysis8. The present chapter focuses on principal component analysis as the chosen method of this study.

4.1 Definition of Principal Component Analysis (PCA)

Pearson (1901) was first to introduce the Principal Component Analysis and it is a well- known technique of multivariate analysis. PCA is a method for reducing the dimensionality of a data set, containing a large number of correlated variables and it generates a new reduced set of uncorrelated variables, the principal components. The components are ordered in such a way so that the first components retain most of the variation from the original variables and the method focuses mainly on the total variation among the variables. The PCs can be derived from covariance and correlation matrices, in which both are based on the eigenvectors and eigenvalues of the matrices. However, the PCs does not provide same information due to using different transformations technique of the variables (Jolliffe, 2002 p. 1)9.

The principal component, denoted as PC are a function of p random variables, denoted as x (see equation 1) and it is the covariances or correlations between p variables that are of interest. Unless the number of variables are small, or the structure of the covariances or correlations between p is simple, it will be difficult to analyze the structure of the variables. Through PCA, we maintain few derived variables with as much information as possible given by the variables covariances and variances. Firstly, we select a linear function w´1 x (PC1) of a vector of variables that have maximum variance and the second linear function w´2 x chosen has to be uncorrelated with w´1 x and also have maximum variance. The functions proceed to

8 Further discussion of differences and similarities is found in p. 150 – 161 in ” Principal Component Analysis”, by Jolliffe. 9 The properties presented in Section 4.2 is also valid for covariance matrices.

be generated in the same way, so that the last linear function w´k x having maximum variance are uncorrelated with w´1 x, w´2 x, …, w´k-1 x. The derived PC can be as many as p but the goal is to retain m PCs, where m < p, that account for most of the variation in the variables.

The PCk is given by w´k x, where w´k is an eigenvector of a covariance or correlation matrix which in turn is consistent to its largest eigenvalue. Further explanation of eigenvalues is given below in section 4.2. Additionally, it is clear from the equation below that PCA assumes that the measurement is without error due to the exclusion of an error term (ibid).

푝 PC1: w´1 x = w11 x1 + w12 x2 + … + w1p xp = ∑푗=1 푤1푗 푥푗

푝 PC2: w´2 x = w21 X1 + w22 x2 + … + w2p xp = ∑푗=1 푤2푗 푥푗 (1) : 푝 PCk: w´k x = wk1 x1 + wk2 x2 + … + wkp xp = ∑푗=1 푤푘푗 푥푗 k: the number of principal components p: the number of random variables

PCj: principal component xj: a random variable x: a vector of xj wkj: the weight of xj that maximizes the ratio of the variance of PCj to the total variation, i = 1, 2, …, k, and p = 1, 2, … , j w´k: a vector of xj coefficients and ´ denotes a vector

4.1.1 Example: Data reduction using Principal Components

The reduction attained by transforming the original variables to PCs, can be illustrated by considering two variables, x(1) and x(2) containing totally 50 observations. Figure 3 shows a plot of 50 observations on x(1) and x(2) in two dimensions and due to their concentration near the value of 1 and -1, it indicates that the variables are highly correlated. There is also a great variation in both variables but the variation is greater in x(2) than in x(1). Figure 4 provides a plot of the 50 observations after transforming the variables to PCs: z(1) and z(2). The variation is greater in z(1) than the variation of the original variables, while the variation in z(2) is negligible. Generally, if a set of variables (more than two) are correlated with each other, then the first PCs will account for most of the variation so that as much information as possible got retained from the original variables. In contrast, the last PCs will account for those with

negligible variation in which it identifies near constant linear relationship among the original variables. The last PCs are not impractical, they can be useful in regressions or in case of detection of outliers (Jolliffe, 2002 p. 3 - 5), see more in section 4.4.

Figure 3: Figure 4: Plot of 50 observations on x(1) and x(2) Plot of 50 observations with transformed PCs, z(1) and z(2)

Source: Jolliffe, 2002. Own illustrated figures.

How to find Principal Components?

The PCk is given by w´k x, where w´k is an eigenvector of a covariance or correlation matrix, as mentioned earlier. If wk is equal to unity, then the variance of w´k x will be equal to its eigenvalue. The derivation can easily be presented by considering w´1 x as an example, in which the vector w1 maximizes the variance of w´1 x by using the eigenvalue as a Lagrange 10 multiplier . The approach approves that the coefficients and variances of PCk are the eigenvectors, denoted as k, of a covariance matrix. Hence,

var(w´k x) = k (2)

10 The full derivation is found in p. 5 in ”Principal Component Analysis”, by Jolliffe.

4.2 Properties of Principal Components

Consider the derivation of PCs given in Section 4.1 and by denoting the transformed vector of variables as z, we obtain

z = A´ x (3) where A consists of columns of the eigenvector of a correlation matrix and x* consists of (original) standardized variables. The purpose is to find PCs of x*, which is a standardized (transformed) version of x and the covariance matrix of x* is the correlation matrix of x. This implies that PCs are defined as a function of x* and they are invariant in orthogonal transformations (i.e. preserves lengths and angles between vectors) of x. When interpreting the functions, the standardized variables can easily be transformed back to x, by multiplying x* with the standard deviation of x. An important property of PCs is that they depend on the correlation ratio11 and not on their absolute values. Also, probability distributions for the eigenvectors and eigenvalues of a covariance matrix, are generally asymptotic and they assume the multivariate normal distribution for the original set of variables (Jolliffe, 2002 p. 30 - 47).

How many components to retain? It is important to know how many m PCs can be selected without any information loss and there are several rules for choosing an appropriate amount of m. The most used criterion is the cumulative percentage of total variation, referring to the first m PCs who accounts for the largest possible variance in terms of percentage of total variation. The total variances of PCs are equal to the total variances of the elements of x and it is influenced by the number of observations included. As the number of observations increases, the variance becomes smaller. Furthermore, it has shown that a sensible cut-off is somewhere between 70 % and 90 %, although the limit can change depending on the particular dataset.

The second rule for computing PCs is described as following; if the elements of x are uncorrelated, then the number of PCs will be as many as the original variables and all having unit variances. When m PCs are equal to p variables, it becomes meaningless to retain. The

11 It is a coefficient of a non-linear association.

rule, Kaiser´s rule (introduced by Kaiser, 1960)12 is to retain only PCs whose variances is greater than one. As mentioned earlier, the variances of PCk are the eigenvectors (see equation 2) and in the forthcoming, we refer to the eigenvalues instead of variances. Thus, components whose eigenvalues is less than one will be excluded because they contain less information than one of the original variables (Jolliffe, 2002 p. 111 - 115).

An alternative approach involves studying the scree graph, which displays a plot of eigenvalues against m components. The number of components to be retained is decided by selecting the components whose eigenvalues is greater than one. In the graph, the plotted eigenvalues are steep to the left of the component axis (lk > 1) and then the slope remains less steep to the right of the component axis (lk > 1). When the slope of the line changes, it refers to the next component and those break-points tells us how many components m to retain. This approach is a complement to the upper approach, as the scree graph mainly contributes to better understanding of how the eigenvalues of the components appears graphically (Jolliffe, 2002 p. 115 - 118).

4.3 Detection of Outliers and Robust Estimation

To make the analysis more robust, it is important to detect outliers in the dataset to determine which outlying observations have or do not have a large effect. The later, are influential observations and they can be handled by either removing the observation or by diminishing the effect. Furthermore, an observation is considered to be an outlier if its value is greater or lower than 3.5 units (Engineering Statistics Handbook, 2018).

An observation can have different effects for different types of analysis or parameter, such as the coefficients, the variances and the PCs. In PCA, influential observations that have effect on the coefficients of a PC does not necessarily mean that they also are influential for the variance of the same PC, and vice versa. It is mentioned in section 4.2 that the distribution of a variable is not important, instead it is important to classify whether an outlier is influential or not. Since, the effect of influential observations on the PCs are inconsistent and if the analysis proceeds without considering if there are any influential observations, then the result is with great probability determined by such observations. Thus, a comparison between

12 The rule is constructed for PCA based on correlation matrices, but it can in some circumstances be used for some types of covariance matrices.

estimated and actual influence of observations should be conducted and one approach is to observe changes in eigenvalues. The estimated changes in eigenvalues are calculated from theoretical influence functions, while the actual changes in eigenvalues compare the change in eigenvalue with and without an observation. By removing a particular observation, it is possible to estimate its influence (Jolliffe, 2002 p. 232 - 250, 263 - 264).

4.4 Rotation method and interpretation of principal components

The interpretation of PCs as a linear function of all the random variables is not easy and an alternative for simplifying interpretation is to rotate the PCs. There are several techniques that provide replacements for PCs which simplifies the interpretation of the components retained. The most used approach is to rotate the PCs in which the axes of the first m components that account for most of the variation is rotated so that it clarifies the interpretation of the axes. Furthermore, by rotating the components it becomes clear which variables are most important, they have the largest values for their loadings and the less important variables have loadings near zero. The most used simplicity criterion is the varimax criterion (rotation method) which takes into account the variance maximization. It has been found that the different simplicity criterion, such as quartimax have no major differences (Jackson, 1991). Hence, the choice of simplicity criterion is not very important (Jolliffe, 2002 p. 269 – 272).

4.5 Regression model

The regression method used is multiple linear regression because we found it to be the most appropriate method. The theoretically relevant explanatory variables are included in our regression model. The variables are the same as in the study by Vasta et al. (2015) but with one additional variable, level of risk taking. The variable is relevant due to explaining the three different dimensions of entrepreneurial success and, also the variable is theoretically relevant (see more in Section 3.3). In order to maintain robust regressions, we have estimated all regressions with robust standard errors which handle heteroscedasticity. In case of absence of heteroscedasticity, the robust standard errors are appropriate. The following model has been used for each dependent variable (the dimensions retained), denoted as Y, for both German and Swedish sample,

Y = c + 1*education level + 2*experience abroad + 3*innovation intensity (4)

+ 4* involvement in politics + 5*level of risk taking + 6*scientist + 

where c is the intercept term, (1-6) is the coefficients of the variables and  is the error term.

4.6 Criticism of the method

In the following section, we discuss advantages and disadvantages of the used method and also the alternative approaches. The discussion criticizes the whole study’s approach. Firstly, the use of biographical studies of entrepreneurs to further analyze different research questions is the only method used so far by other researchers. The reason for transforming qualitative information into quantitative, is due to the fact that there is no complete dataset of entrepreneurs. We believe that the use of a prosopographical approach is appreciated given that the information of entrepreneurial biography is obtained from reliable sources.

Secondly, the PCA is an appropriate method for the study's aim because we needed to reduce different measures into a composite component. The PCs is derived based on correlations matrices because our chosen variables are measured in different units. In this case, it is not possible to conduct a covariance matrix which requires that the variables are measured in same units (Jolliffe, 2002 p. 22-24). Another reason for not using covariance matrices is because the differences between the variances of the variables are large, in terms of high standard deviation (see more in Section 5.1 and 5.2). Because those variables with the largest variance are likely to dominate the first few PCs. An advantage of using correlation matrices is that the results obtained from different analyzes are comparable (Jolliffe, 2002 p. 22).

Thirdly, the purpose of PCA is to receive uncorrelated components who accounts for most of the variation from the original variables. An alternative approach is the Maximum-likelihood extraction method, which is used to extract parameters that have caused the correlation between the set of variables. The method requires that the sample has a multivariate normal distribution (ibid). Taking this into consideration, our conclusion is that the PCA is a more appropriate method for our dataset, partially due to our dataset not fulfilling the requirement for a multivariate normal distribution.

Fourthly, the OLS estimator is relatively robust compared to alternative estimators, due to its efficiency and simplicity. We have not found any alternative robust estimators that is suitable for our dataset. An issue arises when one or several relevant explanatory variables are excluded from the regression model, and such variables are captured by the model’s error term (Gujarati, 2004 p. 222). We hope that the influence of the omitted variables is small. Another issue that may arise with the OLS estimator is if heteroscedasticity or multicollinearity exists. Although, the previously mentioned problems exist in our sample, the estimator is still unbiased given that the model is correctly specified. On the other hand, the estimates provide incorrect variance. In this study, we are not in need of the variances because the study’s purpose is not to establish confidence intervals or to test statistical hypotheses (such as the F-test and t-test).

Due to the limitation in our sample which is focused on founders and inventors, the results obtained cannot be generalized for the entire population. If the opportunity is given to include more individuals, it is possible to replicate this study and maintain results which can be generalized.

5. Results

In this chapter, we present the results for our samples, the results have been generated with the help of the statistical software programs Eviews and SPSS. In section 5.1 and 5.2, we present the derived different dimensions of entrepreneurial success for Germany respective Sweden. Thereafter, in section 5.3 we present the results for the multiple linear regressions models. In section 5.4, we discuss how we solve heteroscedasticity and multicollinearity complications for our samples.

5.1 Dimensions of entrepreneurial success for the German sample

The correlation between the set of seven variables; entrepreneurial type, geographical growth, growth in terms of employment, number of DB rows, number of words in English and German Wikipedia and social mobility is presented in table 2. The variables, geographical growth and growth in terms of employment are positive correlated (0.109) but they are not highly correlated. Regarding entrepreneurs’ visibility in Wikipedia and DB, the result indicated a positive correlation between number of words in English and German Wikipedia (0.528). Both variables in turn are not highly correlated with number of words in DB (0.090 and 0.145, respectively). The remaining variables, entrepreneurial type and social class improvement are not highly correlated with the other variables. It is worth noting that social mobility is positive correlated with growth in terms of employment (0.102). The before mentioned variables are lowly correlated, the correlation size is almost the same as the correlation between geographical growth and growth in terms of employment. This means that the three variables are connected in some way, but social mobility explains entrepreneurs’ social class improvement, while the other two variables explain growth in terms of expansion of business or inventions and employment. Therefore, the relationship is not reliable giving their description and correlation sizes. Furthermore, there are large differences in standard deviation, which is mainly caused by differences in scale. It indicates that the PCA should be based on correlation rather than covariance matrix.

Table 2: Correlation matrix for German sample

Entrepreneurial Geographical Growth in No. of DB No. of words No. of Social type growth terms of rows in EN words in mobility employment Wikipedia GE Wikipedia Entrepreneurial 1.000 -0.132 -0.063 0.054 -0.167 -0.040 -0.045 type Geographical -0.132 1.000 0.109 0.007 -0.075 -0.174 -0.082 growth Growth in terms -0.630 0.109 1.000 -0.105 0.000 0.007 0.102 of employment No. of DB rows 0.054 0.007 -0.105 1.000 1.000 0.145 -0.028 No. of words in -0.167 -0.075 0.000 0.145 1.000 0.528 0.117 EN Wikipedia No. of words in -0.040 -0.174 0.007 0.090 0.528 1.000 0.054 GE Wikipedia Social mobility -0.045 -0.082 0.102 -0.028 0.117 0.054 1.000 Std. Deviation 0.926 0.490 0.902 0.537 2.355 1.571 0.485 Note: The table of correlation matrix classifies those variables who are positive and negative correlated. The correlation size between two variables are given in the cells. Source: Own database.

Kaiser-Meyer-Olkin (KMO) tests for sampling adequacy, the sample generated a value of 0.518 (view Table 3). According to Kaiser´s reference the value is miserable but acceptable (IBM, 2018). The Bartlett´s test of sphericity generated a p-value of 0.001 which means we can reject the null hypothesis that the variables are not related to each other. Hence, the sample is suited for PCA.

Table 3: Kaiser-Meyer-Olkin (KMO) test and Bartlett´s test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.518 Bartlett´s Test of Sphericity Approx. Chi-Square 47.768

df 21 Sig. 0.001 Note: KMO generates values between 0 and 1, values < 0.50 imply that the sample is not suitable for structure detection and values > 0.50 imply the sampling adequate (IBM, 2018). The Barlett’s of sphericity tests the hypothesis that the correlation matrix is an identity matrix. Source: Own database.

Table 4 displays the three components to retain according to the Kaiser criterion, the first components whose eigenvalues are greater than one. The first component has the highest eigenvalue of 1.662, the second component has an eigenvalue of 1.251 and the last, third component has the lowest eigenvalue of 1.098. The first three components account for 57.30 % of total variation and it is less than the sensible cut-off of 70 % and 90 %. As mentioned

earlier in Section 4.2, the limit can change depending on the dataset. The variance of 57.30 % is low not because of the small sample of 100 observations but because entrepreneurial success is a complex phenomenon explained by different factors (Vasta et al., 2015). Also, the components retained in the study by Vasta et al. (2015) accounts for 68.20 % of total variation and their sample consists of 608 observations. A scree plot of eigenvalues of the components is given in figure 5 in the appendix.

Table 4: Total variance explained, initial eigenvalues

Component Total % of Variance Cumulative % 1 1.662 23.737 23.737 2 1.251 17.878 41.614 3 1.098 15.691 57.305 4 0.932 13.309 70.614 5 0.891 12.727 83.341 6 0.723 10.324 93.665 7 0.443 6.335 100.000 Note: The components are the seven variables; employment growth, entrepreneurial type, geographical growth, number of DB rows, number of words in English and German Wikipedia and social mobility. Source: Own database.

The factor loadings of the three components retained are presented in table 5. The first component is described by the highest loadings of number of words in English (0.857) and German Wikipedia (0.818). It captures the visibility of entrepreneurs on Wikipedia. Hence, the component is used as a proxy for the above mentioned and it is labeled as celebrity. The second component loads high mainly on geographical growth (0.770) and less on employment growth (0.319). Both variables describe the growth achieved by the entrepreneur, in terms of expansion of business or invention at geographical level and in terms of employees or use of invention. Thus, the component explains economic performances achieved and, consequently it is labeled as economic success. The third component is defined by the high loaded variables, social mobility (0.625) and employment growth (0.603) which measurers entrepreneurs social class improvement throughout their career, along with the expansion of their business or invention. The last component is selected as a proxy for the social class improvement dimension and it is labeled as social mobility.

The descriptive statistics of celebrity, economic success and social mobility is provided in table 25 in appendix. Additionally, there is a positive correlation between the second and third component (0.654), while the correlation between the first and third component is small (see Table 27 in appendix). By using the retained components, we have in table 6 - 8 ranked the top 10 German entrepreneurs for each component with individual descriptions.

Table 5: Factor loadings of the German sample

Component

Variable 1 2 3 Entrepreneurial type -0.250 -0.666 -0.152 Geographical growth -0.241 0.770 -0.129 Growth in terms of -0.006 0.319 0.603 employment No. of DB rows 0.281 0.025 -0.607 No. of words in EN 0.857 0.110 -0.024 Wikipedia No. of words in GE 0.818 -0.102 -0.011 Wikipedia Social mobility 0.233 -0.164 0.625 Note: The values in each cell are the factor loading of the specific variable. The three components load high mainly on the variables whose value is in bold (type). Source: Own database.

Table 6: Top 10 German entrepreneurs for celebrity Ranking Name Year of birth Place of birth Education Main sector of Ways of Short bio level activity company acquisition

1 Lassalle, Ferdinand 1825 Breslau University Services Founder Founder of General degree/PhD German Workers’ Association 2 Linde, Carl Ritter 1842 Oberfranken University Industry Co-founder The Linde Group von degree/PhD 3 Keller, Friedrich 1816 Sachsen Middle school Agriculture/mining Founder Invented the wood pulp process for use in papermaking 4 Reis, Johann Philipp 1834 Hessen University Services Founder Constructed the make- degree/PhD and break telephone

5 Schubert, Johann 1804 Hannover High school Industry Founder Telegraphenbauanstalt Andreas Siemens and Halske 6 Miller, Oskar von 1855 München University Industry Founder The Deutsches degree/PhD Museum of science and technology

7 Auer von Welsbach, 1813 Wels University Services Founder Discovery of Nature Alois Ritter von degree/PhD Printing-Process

8 Hartmann, Richard 1809 Barr Middle school Industry Founder Sächsische Maschinenfabrik zu Chemnitz 9 Lippisch, Alexander 1894 München University Industry Founder A pioneer degree/PhD of aerodynamics

10 Schliemann, Johann 1822 Mecklenburg- Middle school Industry Founder Indigo trade Ludwig Heinrich Schwerin

Table 7: Top 10 German entrepreneurs for economic success Ranking Name Year of birth Place of birth Education level Main sector of Ways of Short bio activity company acquisition

1 Stoltzenberg, Hugo 1883 Hamburg University Services Founder Chemische Fabrik Gustav Adolf degree/PhD Dr. Hugo Stoltzenberg (CFS) 2 Meyer, Otto 1882 Regensburg University Industry Founder Built aircrafts and degree/PhD Different types of public vehicles 3 Körting, Ernst 1842 Hannover University Industry Founder Pioneer in jet pump degree/PhD technology

4 Benz, Carl Friedrich 1844 Karlsruhe University Industry Founder Mercedes-Benz degree/PhD 5 Diesel, Rudolf 1858 France University Industry Founder Invented the Christian Karl degree/PhD internal-combustion engine 6 Junkers, Hugo 1859 Rheydt University Industry Founder Junkers and Co., degree/PhD Fabrik für Gasapparate 7 Mergenthaler, Ottmar 1854 Württemberg Middle school Industry Founder Developed the Linotype machine 8 Oberth, Hermann Julius 1894 Siebenbürgen University Industry Founder The Rocket in degree/PhD Interplanetary Space 9 Rathenau, Emil Moritz 1838 Berlin University Industry Founder Allgemeine- degree/PhD Elektrizitäts- Gesellschaft (AEG) 10 Reuleaux, Franz 1829 Aachen University Financial Founder The father degree/PhD of kinematics

Table 8: Top 10 German entrepreneurs for social mobility Ranking Name Year of birth Place of birth Education Main sector of Ways of Short bio level activity company acquisition

1 Meydenbauer, Albrecht 1834 Hunsrück High school Services Founder Founder of Architectural photogrammetry 2 Keller, Friedrich 1816 Sachsen Middle Agriculture/mining Founder Invented the wood School pulp process for use in papermaking 3 Reis, Johann Philipp 1834 Hessen University Services Founder Invented telephone degree/PhD

4 Schubert, Johann 1808 Vogtland University Industry Founder Saxon Steamship GmbH Andreas degree/PhD and Co. Conti Elbschiffahrts KG 5 Gehe, Franz Ludwig 1810 Oschatz Middle Services Co-founder Gehe and Co school 6 Pauli, Friedrich August 1802 Rheinpfalz University Industry Co-founder The creator of the Royal von degree/PhD Bavarian State Railways 7 Auer von Welsbach, 1813 Wels University Services Co-founder Discovery of Nature Alois Ritter von degree/PhD Printing-Process 8 Hartmann, Richard 1809 Barr Middle Industry Co-founder Sächsische school Maschinenfabrik zu Chemnitz 9 Sack, Rudolph Christian 1824 Sachsen High school Agriculture/mining Founder Founded the agriculture machinery factory

10 Sauer, Hans 1923 Kreis troppau University Industry Founder SDS (Secure-By- degree/PhD System)

5.2 Dimensions of entrepreneurial success for the Swedish sample

Considering the same set of seven variables, the correlation table for the Swedish sample presents the level of correlation each variable has with the others (view Table 9). Geographical growth has the highest correlation with number of words in English Wikipedia (0.416) and growth in terms of employment (0.317). Additionally, geographical growth also shows a lower level of correlation with the remaining variables, such as with number of words in Swedish Wikipedia (0.244). Given that the information extracted from Wikipedia and SBL have no theoretical background or relevance for geographical growth, we believe that this correlation is unreliable and possibly can be caused by other factors. Therefore, we discount the relationship. As stated earlier, the positive relationship between geographical growth and growth in terms of employment (0.317) is credible, since the expansion of business or invention at geographical level often leads to an increase in hired labor. The variable, number of words in Swedish Wikipedia displays a high level of correlation with number of words in English Wikipedia (0.550) and, also with number of rows in SBL (0.380). Entrepreneurial type has a low level of correlation with all variables and social class improvement is barely correlated with any variable. Furthermore, the standard deviation for the Swedish sample is comparatively the same as for the German sample.

Table 9: Correlation matrix for Swedish sample

Entrepreneurial Geographical Growth in No. of SBL No. of No. of Social type growth terms of rows words in words in mobility employment EN SW Wikipedia Wikipedia Entrepreneurial 1.000 0.014 0.142 0.018 0.122 0.111 0.014 type Geographical 0.014 1.000 0.317 0.194 0.416 0.244 0.036 growth Growth in terms 0.142 0.317 1.000 -0.091 0.251 0.111 0.021 of employment No. of SBL rows 0.018 0.194 -0.091 1.000 0.402 0.380 -0.084 No. of words in 0.122 0.416 0.251 0.402 1.000 0.550 0.070 EN Wikipedia No. of words in 0.111 0.244 0.111 0.380 0.550 1.000 -0.161 SW Wikipedia Social mobility 0.014 0.036 0.021 -0.084 0.070 -0.161 1.000 Std. Deviation 0.893 0.588 0.928 0.926 1.985 1.869 0.588

Note: the table of correlation matrix classifies those variables who are positive and negative correlated. The correlation size between two variables are given in the cells. Source: own database.

The Kaiser-Meyer-Olkin test generated a value of 0.637, meaning that the sample is suitable for structure detection. The second test conducted is the Bartlett´s test which generated a p- value of 0, implying that the set of seven variables are connected (see Table 10).

Table 10: Kaiser-Meyer-Olkin test and Bartlett´s test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.637 Bartlett´s Test of Sphericity Approx. Chi-Square 101.628

df 21 Sig. 0.000 Note: KMO generates values between 0 and 1, values < 0.50 imply that the sample is not suitable for structure detection and values > 0.50 imply the sampling adequate (IBM, 2018). The Barlett’s of sphericity tests the hypothesis that the correlation matrix is an identity matrix. Source: Own database.

The three components whose eigenvalue is greater than one is presented in table 11. Those three components together account for 64.20 % of the total variation in the original seven variables. The eigenvalues are graphically illustrated in a scree plot given in figure 6 in appendix.

Table 11: Total variance explained, initial eigenvalues

Component Total % of Variance Cumulative % 1 2.222 31.744 31.744 2 1.254 17.908 49.651 3 1.019 14.553 64.204 4 0.959 13.704 77.908 5 0.633 9.041 86.948 6 0.544 7.774 94.722 7 0.369 5.278 100.000 Note: The components are the seven variables; employment growth, entrepreneurial type, geographical growth, number of DB rows, number of words in English and German Wikipedia and social mobility. Source: Own database.

Table 12 displays the factor loadings, the first component has the highest loading for the following variables; geographical growth (0.530), number of SBL rows (0.743), number of words in English and Swedish Wikipedia (0.803 and 0.772, respectively). The component derived out of these variables describes how noticeable an entrepreneur is. As in the study by

Vasta et al. (2015), we interpret this component as a proxy for celebrity. The following component loads high mainly on growth in terms of employment (0.771), entrepreneurial type (0.664) and geographical growth (0.329). Given the variables explanation, it describes the economic performance of an entrepreneur, hence it is labeled as economic success. The third component consists of geographical growth (0.412) and social mobility (0.789), and it is labelled as social mobility.

Moreover, the descriptive statistics of celebrity, economic success and social mobility is given in table 26 in appendix. The correlation matrix of the components indicated that the second component economic success is correlated with the third component social mobility at a level of 0.580 (see Table 28 in appendix). The acquirement of the values for the three components gives the possibility to list each Swedish individual after rank by the proxies (view Table 13 - 15).

Table 12: Factor loadings of the Swedish sample Component

Variable 1 2 3 Entrepreneurial type 0.001 0.664 -0.374 Geographical growth 0.530 0.329 0.412 Growth in terms of 0.112 0.771 0.253 employment No. of SBL rows 0.743 -0.285 -0.137 No. of words in EN 0.803 0.240 0.171 Wikipedia No. of words in SW 0.772 0.120 -0.237 Wikipedia Social mobility -0.107 -0.012 0.789 Note: The values in each cell are the factor loading of the specific variable. The three components load high mainly on the variables whose value is in bold (type). Source: Own database.

Table 13: Top 10 Swedish entrepreneurs for celebrity Ranking Name Year of birth Place of Education Main sector of Ways of company Short bio birth level activity acquisition

1 Ericsson, John 1803 Värmland University Industry Founder Inventor of the hot air engine

2 Nobel, Alfred B 1833 Stockholm Middle Industry Founder Chemist and inventor of school dynamite. Founder of the Nobel prize 3 Kreuger, Ivar 1880 Kalmar University Financial Founder Engineer and business leader, services founder of the Kreuger concern 4 Dalén, Gustaf 1869 Västra University Industry Founder Inventor of AGA cooker and Götaland Dalen light, business leader, of AGA concern 5 Göransson, Göran 1819 Gävle High school Industry Founder Industrialist and founder of Fredrik Sandvik

6 Ericsson, Lars 1846 Värmland Primary Industry Founder Inventor and entrepreneur, Magnus school founder of Ericsson LM

7 Gabrielsson, Assar 1891 Skaraborg University Industry Co-founder Co- founder of Volvo

8 Rausing, A Ruben 1895 Skåne University Industry Founder Founder of Tetra Pak

9 Hazelius, Artur I 1833 Stockholm University Services (no Founder Founder of the Nordic financial) museum and Skansen

10 Ottesen-Jensen, Elise 1886 Norway High school Services Founder Journalist and founder of RFSU (Riksförbundet för sexuell upplysning)

Table 14: Top 10 Swedish entrepreneurs for economic success Ranking Name Year of birth Place of birth Education Main sector of Ways of company Short bio level activity acquisition 1 Wenner-Gren, Axel 1881 Västra Götaland High school Industry Purchasing Founder of Electrolux

2 Thambert, Åke 1917 Småland High school Services Purchasing Founder of clothing company Indiska 3 Stenbeck, Jan 1942 Stockholm University Financial Inheritage Head of Kinnevik Group and founder of Tele2, Comviq 4 Sachs, Josef 1872 Stockholm High school Financial Inheritage Founder of Nordiska Kompaniet 5 Knutsson, Filippa 1965 United Middle Services Co-founder with Founder of clothing Kingdom school family brand Filippa K

6 Broström, Dan 1870 Värmland High school Industry Inheritage Head of the Broströmconcern

7 Hedlund, Petrus E 1882 Värmland University Industry Co-founder with Co-founder of family Bröderna Hedlund AB 8 Lachmann, Jacob 1844 Middle Agriculture Co-founder with Co-founder of Ystads Denmark school family sugar refinery

9 Åhlen, Johan Petter 1879 Dalarna Middle Services Co-founder with Founder of warehouse school family store Åhléns

10 Hult, Bertil 1941 Stockholm High school Services Co-founder Co-founder of EF education

Table 15: Top 10 Swedish entrepreneurs for social mobility Ranking Name Year of birth Place of birth Education Main sector of Ways of Short bio level activity company acquisition

1 Ljung, Efraim 1878 Småland Middle Financial Founder Founder of Dux AB school

2 Nyberg, Carl R 1858 Västmanland Middle Industry Founder Inventor of the School blowtorch 3 Johansson, Johan Petter 1853 Älvsborg Middle Industry Founder Inventor of the modern school adjustable spanner

4 Ericsson, Lars Magnus 1846 Värmland Middle Industry Founder Inventor and school entrepreneur, founder of Ericsson LM 5 Kjellberg, Oscar 1817 Värmland University Industry Co-founder Inventor and founder of ESAB 6 Larson, E Gustaf 1887 Närke University Industry Co-founder Co-founder of Volvo

7 Enhörning, Johan August 1824 Dalarna Middle Agriculture Founder Industrial and founder of school J.A Enhörning timber firm 8 Svengren, Johan 1818 Västra Götaland Middle Industry Founder Founder of Eskilstuna school Jernmanufaktur AB 9 Rausing, A Ruben 1895 Skåne University Industry Founder Founder of Tetra Pak

10 Johansson, Carl Edvard 1864 Västmanland High school Financial Founder Inventor of the gauge block set

5.3 Multiple linear regressions

The three components retained have been studied to detect any outliers because they can cause serious problems in the statistical analysis. In all the components for both the German and Swedish sample, there is not any outlier due to not having values that are greater or less than 3.5 unit. When conducting multiple linear regressions, we have used the whole sample consisting of 100 observations.

5.3.1 Regressions for the German sample

Celebrity The result of celebrity from regression 1 indicates two significant variables explaining the celebrity dimension of entrepreneurial success. They are education level and innovation intensity, with a p-value of 0.040 and 0.067, respectively. Furthermore, education level has a positive effect on celebrity. In contrast, innovation intensity has a negative effect on celebrity. The model explains the celebrity dimension of entrepreneurial success by 9 %.

Economic success The result of the second dimension of entrepreneurial success indicated one significant variable explaining economic success, which is innovation intensity with a p-value of 0. Unlike the previous regression, innovation intensity has a positive effect on economic success and, also the size of coefficient is large (0.718). The variables in the model explains 22.17 % of the variation in economic success.

Social mobility Regarding the third dimension social mobility, the results did not indicate any significant variables as stated below (view Regression 1).

Regression 1: Factors of the different dimension of entrepreneurial success for German sample

Dependent variable

Celebrity Economic success Social mobility Variable Coefficient c -0.112 -2.420*** 0.686 (0.744) (0.689) (0.747)

Education level 0.288** 0.081 -0.206 (0.133) (0.123) (0.134) Experience abroad 0.089 0.256 0.059 (0.247) (0.229) (0.272) Innovation intensity -0.434* 0.718*** 0.125 (0.215) (0.199) (0.269)

Involvement in politics -0.086 -0.450 -0.280 (0.237) (0.220) (0.235)

Level of risk taking 0.302 0.470 -0.213 (0.442) (0.410) (0.368) Scientist -0.004 -0.036 -0.201 (0.261) (0.242) (0.250) R-square 0.093 0.222 0.070 Adjusted R-square 0.034 0.171 0.010 Note: The regression model is estimated with robust standard errors. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. The values given in brackets are standard errors of the variables. Source: Own database.

5.3.2 Regressions for the Swedish sample

Celebrity With celebrity as a dependent variable, following variables is shown to be significant; experience abroad, innovation intensity and level of risk taking (view Regression 2). The mentioned variables indicate that there is a positive effect. Hence, the more innovation an entrepreneur creates or the higher level of risk taken, the more recognizable an entrepreneur will be. Entrepreneurs that have travelled abroad for education or job opportunities have a higher level of visibility in regard to entrepreneurs that have not acquired experience abroad. The variables in the model explains the celebrity dimension with 38 %.

Economic success The result of the second proxy, economic success is explained by innovation intensity and scientist. The positive effect of innovation intensity justifies that when the level of innovation intensity increases, economic success also increases. Education or training in science or engineering, captured by the variable scientist, has a negative impact upon economic success. It means that if an individual has studied or practiced science or engineering, then the individual has achieved a lower level of economic performance. This finding implies that Swedish entrepreneurs’ economic performance was mainly accomplished by being more innovative, rather than having studied or practiced in science or engineering. The low explanation rate of 7 % can depend on omitted variables that may explain the economic success dimension of entrepreneurial success.

Social mobility The third dimension, social mobility which regards the social development of an entrepreneur is only explained by education level and it has a negative impact (view Regression 2). The explanation rate is 21 % and the negative impact of education raise questions. If we review the Swedish dataset regarding education, we find that 32 % graduated from high school followed by 29 % that completed middle school. Several of the observed individuals entered the labor market at an early age (occasionally as young as 14), which was not uncommon in the early nineteenth century. Taking this into consideration, the negative impact of education can possibly be explained by education not being highly valued or prioritized by individuals during that time period. They may have found it more profitable to start working at early age and climb to higher positions by hard work, instead of pursuing higher education.

Regression 2: Factors of the different dimension of entrepreneurial success for Swedish sample

Dependent variable

Celebrity Economic success Social mobility Variable Coefficent c -1.915*** -0.529 0.772** (0.353) (0.434) (0.399) Education 0.034 0.015 -0.428*** (0.094) (0.116) (0.107) Experience abroad 0.728*** -0.011 -0.277 (0.199) (0.244) (0.225) Innovation intensity 0.436*** 0.308* 0.136 (0.132) (0.162) (0.149) Involvement in politics -0.261 0.043 -0.037 (0.210) (0.258) (0.237) Level of risk taking 0.588** 0.012 0.394 (0.321) (0.394) (0.363) Scientist 0.095 -0.413* 0.332 (0.213) (0.262) (0.311) R-square 0.383 0.070 0.211 Adjusted R-square 0.343 0.010 0.160 Note: The regression model is estimated with robust standard errors. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. The values given in brackets are standard errors of the variables. Source: Own database.

5.4 Detecting heteroscedasticity and multicollinearity for both samples

Multicollinearity problems was first examined by studying the correlation matrices of the explanatory variables. In all matrices, we only found innovation intensity and level of risk taking being highly correlated with each other (not greater than 0.65). The variance inflation factor generated values around one and according to the rule of thumb, the variables are not highly correlated with each other. That is, we have no problems with multicollinearity in our models. The Breusch-Pagan-Godfrey test generated p-values that are greater than 5 and 10 % significance level, meaning that we are unable to reject the null hypothesis of not having heteroscedasticity. At the same time, residuals do not show constant variance (view Figure 7 and 8 in appendix) which means the appearance of the graphs is not consistent with the results from the tests. That is why we chose to estimate the models with robust standard errors.

6. Discussion

In this chapter, we discuss the results obtained in chapter 5 as well as analyzing the outcome with help of the theories on entrepreneurship. Firstly, we examine the similarities and differences of the factor loadings for German, Italian and Swedish sample. Thereafter, the discussion continues with the results from the regression models and a comparative analysis of American, British, French, Italian and Spanish entrepreneurs follows. Later on, we compare German and Swedish entrepreneurs by mentioning a few successful entrepreneurs from the top 10 list.

6.1 A comparative analysis of the components for Germany, Italy and Sweden

In the study by Vasta et al. (2015), the Italian sample generated three components. The different components are referred to as measurements for the proxies’; celebrity, economic success and social mobility. In our study, the German and Swedish sample has also produced the same number of components. In comparison to the German results, the Swedish factor loadings consist of more variables in the first and second component. In the first component celebrity (view Table 16), the German outcome consist of number of words in English and German Wikipedia. For the Swedish sample two additional variables are included, geographical growth and number of rows in SBL. This may be due to the fact that the Swedish proxies are highly determined by the level of geographical expansion an enterprise or an innovation have acquired.

An interesting variable which may cause geographical growth is experience abroad. Experience from overseas may simplify the establishment of important connections as well as giving entrepreneurs a broader understanding for cultural and legal differences. Approximately 74 % of the Swedish entrepreneurs and 78 % of the Germans had experience from abroad (Table 1 in Section 3.3). In comparison to the Italian study, the result was 38 %, we can therefore conclude that the difference is great. Celebrity is a measurement for entrepreneurs visibility on dictionaries (DB, SBL and IT) and Wikipedia (EN, GE, SW and IT), the results are very similar for all countries. Due to celebrity having high loadings on number of words in English, German, Swedish and Italian Wikipedia.

Table 16: Summary of results for the component, celebrity

Celebrity Germany Sweden Italy Entrepreneurial type Geographical growth 0.530 Growth in terms of employment No. of DB, SBL and IT rows 0.743 0.515 No. of words in EN Wikipedia 0.857 0.803 0.891 No. of words in GE, SW and IT 0.818 0.772 0.937 Wikipedia Social mobility Note: The variables whose values is given in the table, are those variables that the component celebrity loads high on. Source: Own database and Vasta et al. (2015).

Regarding the second proxy economic success, there are some difference in the results for the German, Italian and Swedish sample (view Table 17). The determinants for economic success in the Italian sample are geographical growth, growth in terms of employment and dictionary rows. For both Germany and Sweden, geographical growth and employment growth are similarly determinants making this proxy as relevant as in the Italian study. For Sweden, a third determinant is included which is entrepreneurial type. The inclusion of this variable may produce problems for the Swedish results. The variable entrepreneurial type is scaled from 1 to 5 depending on if an individual is a founder, co-founder, founder with family, inheritage or owner by purchasing. The numerical differences may cause errors, were a value of 5 meaning that an individual have purchased an enterprise may for example be amongst the leading entrepreneurs regarding economic success. Amongst the top 10 ranked Swedish entrepreneurs for economic success (Table 14 in Section 5.2) are Axel Wenner-Gren and Åke Thambert, both have a way of company acquisition by purchasing. Considering this outcome, it is clear that economic success is not correctly estimated for the Swedish sample.

Table 17: Summary of results for the component, economic success

Economic success Germany Sweden Italy Entrepreneurial type 0.664 Geographical growth 0.770 0.329 0.808 Growth in terms of employment 0.319 0.771 0.828 No. of DB, SBL and IT rows 0.403 No. of words in EN Wikipedia No. of words in GE, SW and IT Wikipedia Social mobility Note: The variables whose values is given in the table, are those variables that the component economic success loads high on. Source: Own database and Vasta et al. (2015).

In the social mobility component (view Table 18), the single common variable for the samples is social mobility. Thereafter, the remaining variables differ. For Germany, growth in terms of employment have high loadings and for Sweden, geographical growth is highly loaded. The two variables can be interpreted to measure a similar type of growth, which is the growth of the enterprise geographically as well in size. For Italy, entrepreneurial type have as high factor loading as the variable social mobility. The proxy social mobility is a measurement for entrepreneurs social class development throughout their lifetime. How variables such as entrepreneurial type, growth in terms of employment and geographical growth effects the component is hard to determine.

Table 18: Summary of results for the component, social mobility

Social mobility Germany Sweden Italy Entrepreneurial type 0.804 Geographical growth 0.412 Growth in terms of employment 0.603 No. of DB, SBL and IT rows No. of words in EN Wikipedia No. of words in GE, SW and IT Wikipedia Social mobility 0.625 0.789 0.808 Note: The variables whose values is given in the table, are those variables that the component social mobility loads high on. Source: Own database and Vast et al. (2015).

6.2 A comparative analysis of the results from regression models

Celebrity The results from the regression model with celebrity as dependent variable indicated different significant variables for the German and Swedish sample. Regarding the German sample, education showed to have a positive impact, while innovation intensity has a negative impact on celebrity. For the Swedish sample, the result indicated positive effects of experience abroad, innovation intensity and level of risk taking. Both samples have one common factor explaining the celebrity dimension of entrepreneurial success, which is innovation intensity. However, the effect of innovation intensity on celebrity differs for both samples. It has a negative impact for the German sample and a positive effect for the Swedish sample (view Table 19). The size of the factor’s coefficients is approximately the same and the difference is considered negligible. Historically, the more innovative a Swedish entrepreneur was, the more noticeable he or she would become. Compared to the Swedes, the Germans visibility levels did not increase when being more innovative.

Table 19: Significant factors of celebrity for German and Swedish entrepreneurs Dependent variable: Celebrity

Germany Sweden Variable Coefficient Coefficient Education 0.288** Experience abroad 0.728*** Innovation intensity -0.434* 0.436*** Involvement in politics Level of risk taking 0.588** Scientist R-square 0.09 0,383 Note: The table consist of significant variables of celebrity dimension of entrepreneurial success. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. Source: Own database and Vasta et al. (2015).

By studying the entrepreneurs background from the top lists (Table 6 – 8 and Table 13 – 15 in Section 5.1 and 5.2), it becomes easier to understand the result of the regressions. That is, if the significant factors from the regressions explains and reflects entrepreneurial success. From the top 10 German entrepreneurs for celebrity (from Table 6 in Section 5.1), there is Ferdinand Lassalle (1825) and Carl Ritter von Linde (1842) at the top of the list.

Lassalle was the leading lecturer for German socialism and he co-founded the German labor movement in 1863. After he graduated from a trade school in Leipzig, he studied philosophy, history, philology and archaeology at University of Breslau in 1843. He later continued studying in Berlin, where he met the ideas of the well-known German philosophers Hegel and Feuerbach. While planning to take a degree in philosophy, he studied the subject several times in Paris until 1847. It was due to the knowledge of the subject that made him the pioneer of socialism (DB, 2018) and not due to being more innovative13.

The German engineer, Linde studied and was a professor in Technische Hochshule in Munich. He was the first who developed refrigeration units with exact calculations of efficiency. He invented a methyl ether refrigeration in 1874, ammonia refrigerator in 1876 and the widespread method of separating liquid oxygen from liquid air. The last mentioned is still used in steel manufacture (ibid). Linde became well-known in the industry sector due to his inventions. This may indicate that the effect of innovation does not necessarily be negative, as the result of the regressions shows. It is also difficult to believe that additional innovations would make an entrepreneur less visible on various sources. Since, it is believed that actions and decisions led entrepreneurs to success.

Regarding the top 10 Swedish entrepreneurs for celebrity, there is John Ericsson (1803) and Alfred Nobel (1833) at the top of the list. Ericsson was Swedish-American engineer who invented the first armored warship (1862) and he improved the screw propeller (1836). At the age of 23, he moved to London and introduced a steam locomotive, called the Novelty, in a competition at Lancashire. Although, he did not win the prize he continued to invent devices that improved the existing warships. Later, he immigrated to America where he proposed for a new warship, a Monitor, to the Navy Department during the American Civil War. Along with an approval, the Monitor was launched in 1862. The armored rotating turret, became the revolutionary type of warship until the twentieth century (SBL, 2018).

Nobel as the second top entrepreneur, was a chemist and an engineer. He invented the dynamite along with other explosives and he is also the founder of the Nobel Prizes. At the

13 Lassalle obtains a value of 1 on the variable innovation intensity, due to establishing the reform of the labor movement.

age of 16, he was considered a skilled chemist. Thereafter he continued to study chemistry in Paris for a year and during the following four years he worked under the direction of John Ericsson. After 1859, he began to experiment with explosives in Sweden and examined the unmanageable liquid compound, nitroglycerin. Once he found a way to handle the explosive´s detonation, he invented his first detonator in 1863. The second important invention was dynamite (1867) in which was used worldwide. It was used for blasting tunnels, construction railways and other types of roads (ibid).

Both above mentioned entrepreneurs have travelled abroad for education or job opportunities. Ericsson travelled to London and then to the United States. Nobel went to St. Petersburg and Paris. During the journey, both increased their experiences by working with competitors as well as increasing their knowledge of what is needed to be developed. Thus, experience abroad has caused new and broader contacts within the industry and increased knowledge. Furthermore, both have shown to be risk takers, Ericsson chose to compete in a foreign country and when his invention was not rewarded he continued inventing. Nobel did a lot of research on the powerful explosive which caused his factory to be blown up several times. Still, he built several factories and continued researching even if the risk remained (SBL, 2018).

The biographical information of above mentioned entrepreneurs implies that with the help of experience, innovation and risk taking, they became more noticeable in the various sources. Moreover, the factor that have the greatest effect on celebrity is experience abroad because the factor has the largest coefficient size of 0.728 (view Table 19). The next greatest effect is given by level of risk taking which has a coefficient value of 0.588. The last significant factor, innovation intensity has the lowest effect on celebrity, since the size of the coefficient is 0.436. In many entrepreneurial theories risk taking is inevitable as mentioned in section (2.2). An example is Cantillon who studied the risk aspect of entrepreneurial behavior. He believed that individuals are entrepreneurs when they are confronted with risk full choices whether it is by operating or investing in uncertain markets. For the Swedish sample the results are in line with the risk aspect of entrepreneurship.

Economic success Table 20 views the results from the second regression, where economic success is set as a dependent variable. For both German and Swedish sample, innovation intensity has a positive impact on economic success. The effect is greater in the German sample since the size of the coefficient is 0.718, while the size in the Swedish sample is 0.308. Moreover, there are one factor that have a negative impact of Swedish sample which is scientist (education or training in science or engineering). It decreases an entrepreneurs’ economic performances.

Table 20: Significant factors of economic success for German and Swedish entrepreneurs Dependent variable: Economic success

German Sweden Variable Coefficient Coefficient Education Experience abroad Innovation intensity 0.718*** 0.308* Involvement in politics Level of risk taking Scientist -0.413* R-square 0.222 0.070 Note: The table consist of significant variables of economic success dimension of entrepreneurial success. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. Source: Own database and Vasta et al. (2015).

At the top 10 German entrepreneurs for economic success (view Table 7 in Section 5.1), we have Hugo Stoltzenberg (1883) and Otto Meyer (1882). Stoltzenberg was a chemist who worked for the German government’s secret chemical warfare. After he received his PhD in chemistry, he participated in World War I. In 1920, Stoltzenberg built a research center in Hamburg, the Chemische Fabric Dr. Ing. Hugo Stoltzenberg (CFS). Which was used for the production of chemical warfare. The demand for chemical weapons was high, the produced items were sold to Sweden, Hungary and other countries. In 1969 the CFS was sold and after a few years it closed due to the lack of safety. Stoltzenberg has through his research in chemical warfare and with more than 50 secret patents, influenced the development of gas weapon, gas protection and chemical warfare (DB, 2018).

The second top entrepreneur is the industrialist Meyer. He studied mechanical engineering at the Technical University, München. After a job as an inspector of the flying troops, he worked as a technical director in Bavarian Rumpler Works in Augsburg by 1917. Later on, Meyer built three aircrafts with Mercedes engines. He started an airline, where he performed daily flights from München and to other cities in Germany. During 1921 – 1925, he worked at Fritz Neumeyer AG as a technical director, where he built water turbines, tractors and small trains. With the help of the experience as technical director, he founded six research institutes in Augsburg during the years 1935 – 1955. Meyer is also known due to his contribution to the Deutsches Museum in München and in 1963 he founded the Institute for the History of Precise Science and Technology (ibid).

The product and process innovation that both above mentioned entrepreneurs created has improved their economic performances. Regarding political involvement, Stoltzenberg was later on in his career a member of the Nazi party. His membership in the Nazi party lead to the economic instability of the CFS. It was partly due to financial and political instability in Germany (DB, 2018). There is no information available on the DB if the second entrepreneur, Meyer was politically involved during his career.

For the Swedish sample, the highest-ranking individuals of economic success is Axel Wenner-Gren (1881) and Åke Thambert (1917), view Table 14 in section 5.2. Wenner-Gren was the son of a successful farmer. After studies abroad, he pursued a career as a salesman for Alfa Laval. During a business trip to Germany he encountered a new invention, the vacuum cleaner. He later on introduced the product to the Swedish market by purchasing lightning company Lux in 1917 and thereafter founding Electrolux. Under his leadership, Electrolux developed to becoming the leading company in Sweden that developed and produced household appliances. Wenner-Gren was the main owner in Electrolux until 1956, the company was later sold to the Wallenberg family and is today one of the world’s largest appliances maker. Wenner-Gren continued investing in new business possibilities but encountered many failed projects. At the height of his career, he was one of the wealthiest men in Sweden but due to many risk filled projects he lost a large part of his wealth (SBL, 2018).

Thambert was a Swedish businessman, who frequently traveled to Asia. In 1950, he purchased a small gift shop in Stockholm the Indian exhibition which sold Indian influenced handcraft. He further developed and established the successful fashion and interior chain store, namned “Indiska”. It grew to become both a national and international success, the enterprise was family owned up until 2017 (Foretagskallan, 2018).

The results from the regression model show a positive impact of innovation intensity and it has previously been discussed for both samples. The second significant variable is scientist and it has a negative impact on economic success. The above mentioned Swedish entrepreneurs have not been educated or trained in science or engineering. Hence, it is difficult to analyze the negative impact of the variable on economic success. The economic success component is not correctly derived as we discussed earlier and it can in turn make the interpretation of the significant variables difficult.

The common explanatory factor for the Germans and Swedes is the variable innovation intensity, and it has a positive effect on entrepreneurs’ economic performances. The factor has the greatest effect on the German sample since the size of the coefficient is larger than the others (0.718). Reviewing German economic history, we find that Germany had one of the largest economies in the world in 1890 (Plumpe, 2016). Innovation played a major role for the development of Germany which is partly contributed by individual persons. We find that 39 % of German entrepreneurs obtains the maximum value of 3 (view Table 1 in Section 3.3), due to inventing a widely spread product and process. While for the Swedish sample, 24 % of entrepreneurs obtain a value of 2. The great effect of innovation intensity on German entrepreneurs was therefore expected. The common factor defines entrepreneurs as innovators which is in line with Schumpeter´s (1911) definition of an entrepreneur. The above mentioned entrepreneurs had the ability to identify the main problem where they later on succeeded to discover new solutions and improved method and the outcome. Hence, entrepreneurs from the economic success dimension are in line with Kirzner (1930) description of an entrepreneur as a solution-oriented induvial.

Social mobility The result from the regression model with social mobility as dependent variable did not indicate any significant factors for the German sample. Earlier in Section 6.1, we have discussed the difficulty of interpreting the component as it consists of different variables. We do not find it relevant to present German or Swedish entrepreneurs after rank, due to difficulties to compare background information with the results.

Concerning Swedish entrepreneurs, the result from the regression indicates only one factor significant factor (view Table 21). Education has a negative impact on social mobility (Regression 2 in Section 5.3) and this finding has also been found in the study by Nicholas (1999) who found education level being negatively associated with entrepreneurial performances. Nicholas (1999) used information on lifetime wealth accumulation as a measure of entrepreneurial performance and not social mobility.

Table 21: Significant factors of social mobility for German and Swedish entrepreneurs Dependent variable: social mobility

German Sweden Variable Coefficient Coefficient Education -0.428*** Experience abroad Innovation intensity Involvement in politics Level of risk taking Scientist R-square 0.070 0.211 Note: The table consist of significant variables of social mobility dimension of entrepreneurial success. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. Source: Own database and Vasta et al. (2015).

6.3 A comparative analysis of American, British, French, German Italian, Spanish and Swedish entrepreneurs

The results of the regressions obtained for the German and Swedish sample, can be compared with the results of the American sample (Magenes, 2015), British (Piantanida, 2017), French sample (Bonsignore, 2018), Italian sample (Vasta et al., 2015) and Spanish sample (Zollet, 2018).

Celebrity There is only one factor that all samples have in common, except the Italian sample, which is innovation intensity. The effect of innovation intensity is positive for American, Britain, France, Spain and Swedish samples. While, it has a negative impact on the German sample (view Table 22). Entrepreneurs’ visibility in the public domain increased when they became more innovative and this finding is consistent with Schumpeter’s view that entrepreneurs are innovators. The explanation for the negative effect for the German sample may be that German entrepreneurs did not become more noticeable in DB and in Wikipedia (English and Deutsche) when being more innovative. At the same time, it is hard to understand how it decreases entrepreneurs’ visibility. When comparing the first ranked German entrepreneur Lassalle (1825) with the second top entrepreneur Linde (1842), we find that they obtain a value of 1 and 3, respectively in the variable innovation intensity. Lassalle improved existing product or process as well as creation of new models, while Linde contributed with a widely spread product or process. The remaining entrepreneurs are innovators at different levels and this makes it difficult to determine the expected impact of innovation intensity. Along with innovation intensity, increased level of experience abroad did improve American, British, Spanish and Swedish entrepreneurs’ visibility in the dictionaries and Wikipedia. Our beliefs of a positive effect of innovation intensity and experience abroad is also consistent with the results obtained, except for the German sample. The findings are reasonable as new inventions as well as increased knowledge and broader network usually lead to more attention of the individual.

British, German and Italian entrepreneurs became more noticeable on the various sources when higher education level was achieved. The effect is credible because the biographical information about individuals becomes longer (in terms of number of words and rows in dictionary and Wikipedia, respectively) when they have completed for example a University

degree or PhD. Also, majority of German entrepreneurs, corresponding 63 % obtained a University degree (view Table 1 in Section 3.3), therefore the positive effect of education was expected. We can conclude that German entrepreneurs are more like British and Italian entrepreneurs than Swedish entrepreneurs. While, Swedish entrepreneurs are more like American, British and Spanish entrepreneurs because of the common factors, experience abroad and innovation intensity.

Table 22: Significant factors of celebrity for American, British, French, German, Italian, Spanish and Swedish entrepreneurs Dependent variable: Celebrity America Britain France Germany Italy Spain Sweden Variable Coefficient Education level -0.182*** 0.050** 0.288** 0.162*** Experience abroad 0.490*** 0.313*** 0.309*** 0.728***

Innovation 0.402*** 0.419*** 0.253** -0.434* 0.113** 0.436*** intensity Involvement in 0.388*** 0.302** 0.252** 0.417*** politics Level of risk 0.588** taking

Scientist -0.568*** 0.591*

R-square 0.070 0.130 0.070 0.090 - 0.100 0,383 Adjusted R-square 0.060 0.120 0.050 0.034 - 0.090 0.343 Observations 495 732 242 100 608 619 100 Note: The different samples have been estimated with OLS. The German and Swedish sample is estimated with robust standard errors. The variable level of risk takin is only included in the German and Swedish regression model. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. Source: Magenes (2015), Piantanida (2017), Bonsignore (2018), Vasta et al. (2015), Zollet (2018) and own database.

Economic success The second dimension of entrepreneurial success, economic success is heavily influenced by innovation intensity for all countries and this outcome is of high interest. A positive influence of the variable is consistent with our set of hypothesis which was that innovation intensity can influence entrepreneurs’ economic performances positively. The results are also in line with the entrepreneurial theories of Schumpeter as well as Kirzner. To be viewed as an entrepreneur one must be innovative and be able to find solutions when faced with

complications. In addition, Swedish entrepreneurs are most comparable to British and French entrepreneurs. All three samples also hold a negative impact on the variable scientist. Less than 26 % of the Swedes attained education or training in science in comparison to 71 % of Germans. In our hypothesis, we believed that due to the great difference between German and Swedish entrepreneurs, it would affect the outcome causing the Swedes to have a negative impact. It is difficult to draw any conclusion whether German entrepreneurs are comparable to other entrepreneurs, because the factor innovation intensity is the only determinant in which is found in all the samples.

Table 23: Significant factors of economic success for American, British, French, German, Italian, Spanish and Swedish entrepreneurs Dependent variable: Economic success

America Britain France Germany Italy Spain Sweden Variable Coefficient Education level -0.151*** -0.112*** -0.129** 0.077* 0.039** Experience abroad 0.314***

Innovation 0.497*** 0.402*** 0.291*** 0.718*** 0.194*** 0.437*** 0.308* intensity Involvement in -0.640*** -0.360*** -0.387*** politics Level of risk taking Scientist -0.374** -0.336** -0.413*

R-square 0.140 0.100 0.120 0.222 - 0.210 0.070 Adjusted R-square 0.130 0.090 0.100 0.171 - 0.200 0.010 Observations 495 732 242 100 608 619 100 Note: The different samples have been estimated with OLS. The German and Swedish sample is estimated with robust standard errors. The variable level of risk takin is only included in the German and Swedish regression model. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. Source: Magenes (2015), Piantanida (2017), Bonsignore (2018), Vasta et al. (2015), Zollet (2018) and own database.

Social mobility All previous studies expressed difficulties on drawing credible conclusions regarding the measurements for the social mobility dimension. From Table 24, there is only one common factor from our study with the previous studies which is education level. For the Swedish sample, together with the American, British and Spanish sample education level causes a negative impact on social mobility. A possible explanation is the unbalanced distribution in education level for the Swedish sample. The lack of highly educated individuals amongst the

Swedes may be the cause for this negative result. We predicted this outcome for the Swedish sample with the hypothesis that education level will either have a negative impact or it may be non-significant. Historically, Sweden was an agricultural community that due to industrialization attained a large working class. In our sample, several of the Swedish entrepreneurs came from working class families, many of the entrepreneurs started off as laborer and quickly advanced. Compulsory schooling in Sweden was not introduced until 1842 and higher education was limited to few. In comparison, Germany introduced a compulsory school attendance law as early as in 1763. The difference between the year when compulsory education was introduced for each country may have a valid impact on the factor education level. It also may explain why the Germans have reached higher level of education in a larger scale than the Swedes.

Table 24: Significant factors of social mobility for American, British, French, German, Italian, Spanish and Swedish entrepreneurs Dependent variable: Social mobility

America Britain France Germany Italy Spain Sweden Variable Coefficient Education level -0.186*** -0.091*** 0.477*** -0.132*** -0.428*** Experience abroad

Innovation 0.249*** 0.108** intensity Involvement in -0.259*** -0.245*** politics Level of risk taking Scientist 0.405*** -0.350**

R-square 0.060 0.070 0.260 0.070 - 0.110 0.211 Adjusted R-square 0.050 0.060 0.250 0.010 - 0.100 0.160 Observations 495 732 242 100 608 619 100 Note: The different samples have been estimated with OLS. The German and Swedish sample is estimated with robust standard errors. The variable level of risk takin is only included in the German and Swedish regression model. The notation *, ** and *** indicates significance level of the variable at 10 %, 5 % and 1 % significance level. Source: Magenes (2015), Piantanida (2017), Bonsignore (2018), Vasta et al. (2015), Zollet (2018) and own database.

7. Conclusion

For German entrepreneurs, the celebrity dimension of entrepreneurial success is explained by education and innovation intensity. The economic success dimension is also explained by innovation intensity and political involvement. The last dimension, social mobility is not explained by any factor. Regarding Swedish entrepreneurs, the determinants for celebrity is experience abroad, innovation intensity and level of risk taking. Moreover, economic success is determined by the factors, education or training in science or engineering and innovation intensity. The social mobility dimension has shown to be determined only by education for the Swedish sample.

British, German and Italian entrepreneurs’ visibility in the various sources increased when higher education level was achieved. American, British, Spanish and Swedish entrepreneurs became more noticeable when being more innovative and by increasing their experiences abroad. Hence, German entrepreneurs are more like British and Italian entrepreneurs. While, Swedish entrepreneurs are comparable to broader type of entrepreneurs which are American, British and Spanish entrepreneurs. Economic success is greatly influenced by innovation intensity, that is the only common factor for all countries. Being innovative is important for all countries. Unlike the Germans, Swedish entrepreneur are very similar to British and French entrepreneurs’ due to their economic performances being influenced negatively of studying in science or engineering. We can conclude that social mobility is indeed a complicated dimension to draw conclusions from. The German sample had no common determinant with the other studies. While, Swedish entrepreneurs social class improvement was negatively associated with education level and similar to American, British and Spanish entrepreneurs.

In accordance with the results obtained, innovation is shown to be an important factor for success for the individual person and for the economy as whole. Start-up companies and entrepreneurs creates a lasting competitive market and they also develop new job opportunities. Entrepreneurs in the markets compete with number of new products, services and business models they manage to develop and, also by how many existing ones are developed. When discussing innovation, it does not necessary mean the introduction of a new successful product or process. It can also be the matching of a right innovator with the right company in order to achieve business growth. It is important that the public sector offers

support to new and existing innovators as well as companies. Innovation contests has already been introduced in Germany, the Innovation Contest Inventory (ICI) explores various possibilities that an innovation contest can offer for different types of companies (ICI, 2018). In order for Swedish entrepreneurs to be on the same level as German entrepreneurs, innovation contests should also be implemented in Sweden.

An idea for future research is to construct German and Swedish entrepreneurial dictionaries, which would include all entrepreneurs throughout history. This would simplify future studies on entrepreneurship for both countries. During this study, we found that the number of Swedish entrepreneurs is small in comparison to the Germans. This can depend on the selection made to limit the study to only founders and inventors. Also, the difference can depend on that Sweden is a smaller country in comparison to Germany, both geographically as well as in population size. To maintain a larger sample, other Scandinavian countries such as Finland, Denmark and Norway can be included. Given that many of the Scandinavian countries are similar in terms of population size and have cultural similarities.

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Appendix

Figure 5: Scree plot of eigenvalues for German sample

Source: Own illustrated figure.

Figure 6: Scree plot of eigenvalues for Swedish sample

Source: Own illustrated figure.

Table 25: Descriptive statistics of celebrity, economic success and social mobility for German sample Component Mean Maximum Minimum Celebrity 4.00E-07 2.272 -2.262 Economic success 3.00E-07 1.214 -2.808 Social mobility 3.66E-17 1.775 -2.809 Source: Own database.

Table 26: Descriptive statistics of celebrity, economic success and social mobility for Swedish sample Component Mean Maximum Minimum Celebrity -2.00E-07 3.342 -1.550 Economic success -4.00E-07 2.837 -2.433 Social mobility 2.00E-07 2.940 -2.772 Source: Own database.

Table 27: Correlation matrix of the components for German sample

Component 1 2 3 1 0.996 -0.085 0.009 2 0.058 0.754 0.654 3 0.062 0.651 -0.756 Source: Own database.

Table 28: Correlation matrix of the components for Swedish sample

Component 1 2 3 1 0.937 0.347 0.051 2 -0.311 0.753 0.580 3 0.162 -0.559 0.813 Source: Own database.

Figure 7: Residuals for the Swedish sample 3

-1

-2

-3

CELEBRITY ECONOMIC SUCCESS SOCIAL MOBILITY

Source: Own illustrated figure.

Figure 8: Residuals for the German sample

-1

-2

-3

CELEBRITY ECONOMIC SUCCESS SOCIAL MOBILITY

Source: Own illustrated figure.