published_or_final_version / Sociology / Master / Master of Social Sciences

I examine the role of human capital in shaping entrepreneurial activity by studying the entrepreneurial careers of scientists and engineers from entry to exit. I analyze how the development of human capital through education and employment affects entrepreneurial entry, performance, and exit. Empirically I utilize the restricted-use National Science Foundation (NSF) Scientists and Engineers Statistical Data System (SESTAT) which is a large data set focused on scientists and engineers to identify possible drivers of transitions to entrepreneurship amongst knowledge workers. Additionally, I am able to analyze the extent to which transitioning to entrepreneurship allows scientists and engineers to increase their financial and non-financial work outcomes and finally, what mechanisms are associated with their exit from entrepreneurship and subsequent return to wage work.

The decision process of venture capitalists has received much attention from researchers and it is a complex and unclear process. There are plenty of factors that affect venture capitalists´ investment decisions. The purpose of this study is to find out the important factors in the due diligence process for the venture capital firms and venture capitalists during their investments. The authors find it interesting to find out factors that influence venture capitalists during their investment decisions according to due diligence process. Qualitative method was seen suitable for this study. Three phone interviews were conducted with three venture capital firms in Turkey named Is Private Equity, Ilab Ventures and Bosphorous Group. The authors find out management, market, location, product, industry and financial factors are important factors for venture capitalists to decide whether to invest or not.

Sammanfattning Under 2000-talet har hotet mot miljön och klimatet nått oss med stormsteg och en omställning till ett hållbart samhälle är ett krav och omställningen förväntas bli lång. I ljuset av miljöhotet utvecklas nya investeringssektorer med fokus på att stilla den ökande efterfrågan på produkter och tjänster som minskar miljöpåverkan, en av de mest populära investeringssektorerna med fokus på miljön är Cleantech. Cleantech är en ny investeringskategori som kom till Sverige runt år 2005. Begreppet och investeringskategorin, Cleantech härstammar från USA och organisationen Cleantech Group LLC. Cleantech Group delar upp Cleantech i elva olika segment på följande sätt; (1) Energiproduktion, (2) Energiförvaring, (3) Energiinfrastruktur, (4) Energieffektivitet, (5) Transport, (6) Vatten & Avfallsvatten, (7) Luft & Miljö, (8) Material, (9) Tillverkning/Industriell, (10) Jordbruk, (11) Återvinning & Avfallshantering. För att Cleantech-bolag skall utvecklas så används ofta riskkapital från VC-bolag. En enkätstudie visar att 56 % av svenska riskkapitalister anser att Cleantech är det mest intressanta investeringsområdet. En klartläggning över vilka svenska Cleantech-bolag som har erhållit VC-investeringar kompletterar den tidigare forskningen, vilket gör ämnet intressant att studera. Genom en abduktiv forskningsansats och en kvantitativ inriktning har jag valt att studera samtliga VC-bolag som är registrerade i SVCA’s medlemsregister och identifierade VC- bolagens tillhörande portföljbolag. Syftet är att kategorisera och klassificera vilka svenska Cleantech-bolag som har erhållit VC-investeringar. Studien har ett delsyfte genom att undersöka om det finns tillväxtskillnader mellan Cleantech-bolag och Övriga bolag som inkluderas i studien. De teoretiska utgångspunkterna är Cleantech-segmenteringen som hänförs till Cleantech Group och väsentligheten kring ett väldefinierat begrepp kring Cleantech. Jämförelser och paralleller dras till Cleantech Group’s årliga Cleantech- undersökning, Global Cleantech 100. Tillväxtskillnader mellan Cleantech-bolag och Övriga bolag analyseras med kopplingar till agentteori och teorin kring informationsasymmetri. Studien genomförs med en systematisk datainsamling med indelning i två faser; första fasen innehåller kategorisering och klassificeringen av funna portföljbolag som antingen Cleantech- bolag eller Övriga bolag och andra fasen innehåller inhämtade av bokslutsdata för respektive portföljbolag, vilket möjliggör en analys av tillväxtskillnaderna mellan Cleantech-bolag och Övriga bolag, mätt i antal anställda och omsättningsförändring. Datainsamlingen omfattar 703 stycken unika portföljbolag som i sin tur finansieras av 71 olika VC-bolag. Studien visar att Energiproduktion med 23 stycken portföljbolag attraherar VC-kapital i störst utsträckning följt av Energieffektivitet med 18 stycken portföljbolag och Tillverkning/Industriell med 13 stycken portföljbolag. Studien påvisar också att Cleantech- bolag har signifikant högre tillväxt både mätt i antal anställda och omsättningsförändring i förhållande till bolag kategoriserade som Övriga bolag. I rådande studie har en totalundersökning av VC-bolag registrerade i SVCA’s medlemsregister genomförts, vilket innebär att applicerbarheten och jämförbarheten är gentemot populationer som överensstämmer med kriterierna för VC-bolagen och har en likvärdig Cleantech-segmentering. Generaliserbarheten minskar vid överföring till VC-bolag som investerar i utländska bolag på grund av att dessa bolag inte styrs av svenska marknadsmöjligheter och svenska regleringar.

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We develop and test asset pricing model formulations that are simultaneously conditional and nonlinear. Formulations based upon five popular asset pricing models are tested against the widely studied Fama and French (1993) twenty-five size and book-to-market sorted portfolios. Test results indicate that the conditional nonlinear specification of the Fama and French (1993) three state variable model (FF3) is the only specification not rejected by the data and thus capable of pricing the "size" and "book-to-market" effects simultaneously. The pricing performance of the FF3 conditional nonlinear pricing kernel is corifirmed by robustness tests on out-of-sample data as well as tests with alternative instrumental and conditioning variables. While Bansal and Viswanathan (1993) and Chapman (1997) find unconditional nonlinear pricing kernels sufficient to capture the size effect alone, our results indicate that similar unconditional nonlinear pricing kernels considered here do not price the size and book-to-market effects simultaneously. However, nested model tests indicate that, in isolation, both conditioning information and nonlinearity significantly improve the pricing kernel performance for all five asset pricing models. The success of the conditional nonlinear FF3 model also suggests that the combination of conditioning and nonlinearity is critical to pricing kernel design. Implications for both academic researchers and practitioners are considered.

This thesis consists of two essays which contribute to different but related aspects of the empirical asset pricing literature. The common theme is that incorrect restrictions can lead to inaccurate decisions. The first essay demonstrates that failure to account for the Federal Reserve experiment can lead to incorrect assumptions about the explosiveness of short-term interest rate volatility, while the second essay demonstrates that we need to incorporate skewness to develop models that adequately account for the cross-section of equity returns. Essay 1 empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the elasticity of volatility parameter for single-regime models unanimously indicate an explosive volatility process, whereas the Markov-switching models estimates are reasonable. We find that either Markov-switching or stochastic volatility, but not both, is needed to adequately fit the data. A robust conclusion is that volatility depends on the level of the short rate. Finally, the Markov-switching model is the best for forecasting. A technical contribution of this paper is a presentation of quasi-maximum likelihood estimation techniques for the Markov-switching stochastic-volatility model. Essay 2 proposes a new approach to estimating and testing nonlinear pricing models using GMM. The methodology extends the GMM based conditional mean-variance asset pricing tests of Harvey (1989) and He et al (1996) to include preferences over moments higher than variance. In particular we explore the empirical usefulness of the conditional coskewness of an assets return with the market return in explaining the cross-section of equity returns. The methodology is both flexible and parsimonious. We avoid modelling any asset specific parameters and avoid making restrictive assumptions on the dynamics of co-moments. By using GMM to estimate the models' parameters we also avoid making any assumptions about the distribution of the data. The empirical results indicate that coskewness is useful in explaining the cross-section of equity returns, and that both covariance and coskewness are time varying. We also find that the usefulness of coskewness is robust to the inclusion of Fama and French's (1993) SMB and HML factor returns. There is an interesting debate raging in the empirical asset pricing literature comparing the SDF versus beta methodologies. This paper's technique is a conditional version of the beta methodology, which turns out to be directly comparable with the SDF methodology with only minor modifications. Our SDF version imposes the CAPM's restrictions that the coefficients in the pricing kernel are known functions of the moments of market returns, which are modelled using macro-variables. We find that the SDF implied by the three-moment CAPM provides a better fit in this data set than current practice of parameterizing the coefficients on market returns in the SDF. This has an interesting application to the current SDF versus beta methodology debate.

Social capital refers to the relationships between people that are productive: it can provide people with access to resources, ease transactions, and facilitate social and economic development at the community level. It has been conceptualized as both associations between people and attitudes of trust and cooperation that enable ties to be productive. Within communities, these attitudes underlie social cohesion, which can be defined as social integration and a propensity to cooperate and contribute to the community. Moreover, it is interaction and social engagement that develops social capital by creating and maintaining relationships and fostering social cohesion. This thesis presents an analysis of the development of social capital in coastal British Columbia by considering how the social participation of community members generates socially cohesive attitudes. Moreover, I empirically consider how this relationship varies for different people in different places and across two different types of participation. Formal participation refers to engagement in structured and organized group activities, such as rotary clubs or sports teams, while informal activities are casual irregular and often spontaneous, such as visiting with friends. Using a series of multiple linear regressions on survey data from rural coastal communities in British Columbia, I test how the relationship between these two types of participation and social cohesion varies according to people’s socio-demographic characteristics or the communities in which they live. Not only does this research consider who develops social capital in this way, but also whether the relationship between participation and social cohesion differ along these social lines. The results show that processes of social capital development reflect the characteristics and social environments of community members in coastal British Columbia. The variability shows that social capital development is embedded within particular contexts in ways can lead to inequalities in social capital.

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