International entrepreneurship (IE) is “a combination of innovative, proactive and risk-seeking behaviour that crosses national borders and is intended to create value in organizations”. The IE literature has been concerned with entrepreneurial behaviour in multiple countries and cross-border studies of entrepreneurship and international activities of small and medium-sized enterprises (SME). Due to the potential for SMEs to serve as significant sources of export, considerable research has been conducted regarding their internationalization. However, despite attempts to integrate concrete frameworks of international entrepreneurship, some primary issues have not been adequately addressed and IE researchers are faced with challenges including insufficient micro-data for advancing quality research. The main objective of this thesis is to study and explore the limitations on researchers to access governmental data regarding small firms operating internationally and use it for scientific purposes. Despite company data being compiled and publicly available in some countries, such as Germany, other countries, including Canada, have not made any such efforts in a coherent way. There is a significant disconnect in the Canadian context between internationalization and firms’ data. This shortcoming may stem from various sources, including the legal framework in Canada for accessing data and a lack of sufficient financial support and expertise to gather and integrate such data. Furthermore, the type of data available to the research community through statistical institutions were identified and analyzed, as were access methods. With the increasing interest of researchers in accessing data gathered by the government, the formation of anonymized records or anonymized micro-datasets has acquired great importance. Therefore, the primary approach is to explore the extent to which data regarding firms’ characteristics and internationalization activities are currently available to the research community, as well as to ensure the confidentiality of official statistics, most notably in the Canadian context. The research resulted in the confirmation of data availability in Canada through government and statistical organizations. The latter bodies can provide researchers and research organizations access to some data but limitations arise in providing micro-datasets to researchers due to confidentiality issues; these constraints were identified and further analyzed. Moreover, this research has studied methods to overcome these limitations and assess the shortcomings in micro-data in order to advance quality research. Methods and recommendations were introduced and studied to allow researchers access to essential data and information while maintaining confidentiality.

International entrepreneurship (IE) is “a combination of innovative, proactive and risk-seeking behaviour that crosses national borders and is intended to create value in organizations”. The IE literature has been concerned with entrepreneurial behaviour in multiple countries and cross-border studies of entrepreneurship and international activities of small and medium-sized enterprises (SME). Due to the potential for SMEs to serve as significant sources of export, considerable research has been conducted regarding their internationalization. However, despite attempts to integrate concrete frameworks of international entrepreneurship, some primary issues have not been adequately addressed and IE researchers are faced with challenges including insufficient micro-data for advancing quality research. The main objective of this thesis is to study and explore the limitations on researchers to access governmental data regarding small firms operating internationally and use it for scientific purposes. Despite company data being compiled and publicly available in some countries, such as Germany, other countries, including Canada, have not made any such efforts in a coherent way. There is a significant disconnect in the Canadian context between internationalization and firms’ data. This shortcoming may stem from various sources, including the legal framework in Canada for accessing data and a lack of sufficient financial support and expertise to gather and integrate such data. Furthermore, the type of data available to the research community through statistical institutions were identified and analyzed, as were access methods. With the increasing interest of researchers in accessing data gathered by the government, the formation of anonymized records or anonymized micro-datasets has acquired great importance. Therefore, the primary approach is to explore the extent to which data regarding firms’ characteristics and internationalization activities are currently available to the research community, as well as to ensure the confidentiality of official statistics, most notably in the Canadian context. The research resulted in the confirmation of data availability in Canada through government and statistical organizations. The latter bodies can provide researchers and research organizations access to some data but limitations arise in providing micro-datasets to researchers due to confidentiality issues; these constraints were identified and further analyzed. Moreover, this research has studied methods to overcome these limitations and assess the shortcomings in micro-data in order to advance quality research. Methods and recommendations were introduced and studied to allow researchers access to essential data and information while maintaining confidentiality.

This thesis consists of three main parts. The first and most important part, in terms of effort and time spent, is devoted to the estimation of the importance of accessibility for production at the firm or plant level using three different econometric estimation approaches. The results could have implications for the calculation of "wider" economic benefits of transport infrastructure, stemming from agglomeration externalities (e.g., scale economies). There are both methodological and result-wise conclusions that can be drawn from this research: methodologically, first, using unbalanced firm-level data requires the use of proxy variables to account for (initial) firm-specific unobserved productivity effects, and non-random exit from the dataset. Second, there are unsolved theoretical problems when applying an essentially aggregate approach to productivity analysis on disaggregate data, viz., relating to the existence of aggregate production functions, and to the aggregation of productivity from a disaggregate level to a more aggregate level in a spatial framework. Result-wise, clear productivity differences are presented, when comparing firms in the same time period but in different locations with different accessibility. However, it is not possible in this dataset to detect increased productivity for representative firms stemming from the opening of the Öresund link. It is therefore discussed whether the reason for this result could be the inappropriateness of output measures in a competitive business environment, where a large portion of the benefits are gradually transferred to consumers and thus remain unmeasured. Other, more comprehensive structural approaches to econometrics, including the demand side of the economy, are also recommended. The second part of the thesis treats an unjustly neglected area of transport research: the validation of transport demand models. These transport models are for example used to calculate the new traffic patterns and changes in accessibility from a transport infrastructure investment like the Öresund fixed link, around which most of this thesis orbits. The third and last part, written with two co-authors, deals with the "vulnerability" of the road network, in terms of effects on the travel time delays of the users when a link is disrupted. The calculated indices of importance and exposure could also be seen as extreme forms of accessibility, especially when there is no alternative route besides the one that is cut-off. / QC 20110513

This study analyzes the determinants of return migration from Sweden to Bosnia and Herzegovina, and outmigration to third country during the time period 1994-2003. The study is limited to the refugees who arrived to Sweden 1993-1994. One important aim is also to find out to what extent the propensity of return migration is affected by integration and participation in the Swedish labor market. There is a larger fraction of the refugees from Bosnia and Herzegovina who return than migrate to a third country. The results show that a higher education is affecting the return migration decision positively, but not the migration to another country. Since the social protection system in Bosnia and Herzegovina is partially undeveloped, only those with a well paid job or wealthy relatives can afford any mishaps. Highly educated individuals are expected to have these economical prerequisites. Being employed in Sweden or receiving social benefits there, give negative marginal effects on the probability of emigration. Therefore, the position on the Swedish labor market has importance for an emigration decision. Being married or having children decreases the probability of emigration. However, the family status effects are stronger for outmigration to a third country. Further, it is more likely for a family to return than emigrate to a third country. It is also more likely for women to return, while there is a larger fraction of men that migrate to a third country. Summarizing the most important findings, the probability of outmigration is strongly reduced by the level of integration. This is not only an analysis of individual micro data. The political and economic differences between home country and source country are also compared. Pull-factors seem to dominate return migration since Sweden has a more stabilized economic and political situation. However, the refugees must have strong economic prerequisites or wealthy relatives to support them, in order to realize a return migration decision. A large fraction of the refugees who wish to return do not have the possibilities to realize their return intentions. They consider themselves as temporary migrants, but have involuntary become permanent migrants in Sweden.

<p>This study analyzes the determinants of return migration from Sweden to Bosnia and Herzegovina, and outmigration to third country during the time period 1994-2003. The study is limited to the refugees who arrived to Sweden 1993-1994. One important aim is also to find out to what extent the propensity of return migration is affected by integration and participation in the Swedish labor market.</p><p>There is a larger fraction of the refugees from Bosnia and Herzegovina who return than migrate to a third country. The results show that a higher education is affecting the return migration decision positively, but not the migration to another country. Since the social protection system in Bosnia and Herzegovina is partially undeveloped, only those with a well paid job or wealthy relatives can afford any mishaps. Highly educated individuals are expected to have these economical prerequisites. Being employed in Sweden or receiving social benefits there, give negative marginal effects on the probability of emigration. Therefore, the position on the Swedish labor market has importance for an emigration decision. Being married or having children decreases the probability of emigration. However, the family status effects are stronger for outmigration to a third country. Further, it is more likely for a family to return than emigrate to a third country. It is also more likely for women to return, while there is a larger fraction of men that migrate to a third country. Summarizing the most important findings, the probability of outmigration is strongly reduced by the level of integration.</p><p>This is not only an analysis of individual micro data. The political and economic differences between home country and source country are also compared. Pull-factors seem to dominate return migration since Sweden has a more stabilized economic and political situation. However, the refugees must have strong economic prerequisites or wealthy relatives to support them, in order to realize a return migration decision. A large fraction of the refugees who wish to return do not have the possibilities to realize their return intentions. They consider themselves as temporary migrants, but have involuntary become permanent migrants in Sweden.</p>

The purpose of this study is to fill the gap in research about women in Venezuela by investigating the determinants of their labor force participation between 1995 and 1998. The Central Office of Statistics and Information in Venezuela provides cross-sectional data collected semiannually about individual, demographic, socio-economic and geographical characteristics of individuals living in Venezuela during this period. This study uses binomial and multinomial logit models to test a number of hypotheses. First, the full sample of women between 15 and 60 years old is used to investigate the importance of individual, demographic, socioeconomic, and geographical characteristics in the labor force participation decision, also controlling for a time trend. The same decision is also analyzed for three subsamples: married women, single women, and women heads of household. Comparisons are made between each subsample and the full sample, and also among the different subsamples. Next, multinomial regressions using the same explanatory variables are performed to examine labor market behavior when there is a three-way choice: whether to participate in the formal sector, the informal sector or not to participate in the labor market at all. The multinomial regressions are also performed on the three subsamples as well as on the full sample. Again comparisons are made between each subsample and the full sample and also among the three subsamples. The results of these analyses show considerable differences in motivating factors among the three groups. The conclusion that must be drawn from this research is that one cannot generalize about the women's labor force participation just by studying the behavior of women in the aggregate. The relative importance of motivating factors depends strongly on the specific subsample to which a woman belongs, a fact unrevealed by previous empirical work. The more detailed analyses produced by this dissertation provide deeper understanding of the labor force participation of Venezuelan women. This information will make a valuable contribution to policy-makers who seek to encourage the important economic contribution of women to this previously under-studied labor market.

Essay 1: Estimation of DSGE Models with Uninformative Priors DSGE models are typically estimated using Bayesian methods, but because prior information may be lacking, a number of papers have developed methods for estimation with less informative priors (diffuse priors). This paper takes this development one step further and suggests a method that allows full information maximum likelihood (FIML) estimation of a medium-sized DSGE model. FIML estimation is equivalent to placing uninformative priors on all parameters. Inference is performed using stochastic simulation techniques. The results reveal that all parameters are identifiable and several parameter estimates differ from previous estimates that were based on more informative priors. These differences are analyzed. Essay 2: A DSGE Model with Labor Hoarding Applied to the US Labor Market In the US, some relatively stable patterns can be observed with respect to employment, production and productivity. An increase in production is followed by an increase in employment with lags of one or two quarters. Productivity leads both production and employment, especially employment. I show that it is possible to replicate this empirical pattern in a model with only one demand-side shock and labor hoarding. I assume that firms have organizational capital that depreciates if workers are utilized to a high degree in current production. When demand increases, firms can increase utilization, but over time, they have to hire more workers and reduce utilization to restore organizational capital. The risk shock turns out to be very dominant and explains virtually all of the dynamics. Essay 3: Demand Shocks and Labor Hoarding: Matching Micro Data In Swedish firm-level data, output is more volatile than employment, and in response to demand shocks, employment follows output with a one- to two-year lag. To explain these observations, we use a model with labor hoarding in which firms can change production by changing the utilization rate of their employees. Matching the impulse response functions, we find that labor hoarding in combination with increasing returns to scale in production and a very high price stickiness can explain the empirical pattern very well. Increasing returns to scale implies a larger percentage change in output than in employment. Price stickiness amplifies volatility in output because the price has a dampening effect on demand changes. Both of these explain the delayed reaction in employment in response to output changes.

In recent times, there has been an explosion in the need for high-density computing and data processing. As a result the Internet and Communication Technology (ICT) demand on global energy resources has tripled in the last five years. Edge computing - bringing computing power close to the user, is set to be the cornerstone of future communication and information transport, satisfying the demand for instant response times and zero latency needed for applications such as 5G, self-driving vehicles, face recognition, and much more. The Micro Data Centre (micro DC) is key hardware in the shift to edge computing. Being self-contained, with in-rack liquid cooling systems, these micro data centres can be placed anywhere they are needed the most - often in areas not thought of as locations for datacentres, such as offices and housing blocks. This presents an opportunity to make the ICT industry greener and contribute to lowering total global energy demand, while fulfilling both the need for data processing and heating requirements. If a solution can be found to capture and utilise waste heat from the growing number of micro data centres, it would have a massive impact on overall energy consumption. This project will explore this potential synergy through investigating two different ways of utilising waste heat. The first being supplying waste heat to the District Heating network (Case 1), and the second using the micro DC as a ’data furnace’ supplying heat to the near vicinity (Case 2 and 3). Two scenarios of differing costs and incomes will be exploredin each case, and a sensitivity analysis will be performed to determine how sensitive each scenario is to changing internal and external factors. Results achieved were extremely promising. Capturing waste heat from micro data centres, and both supplying the local district heating network as well as providing the central heating of the near vicinity, is proving to be both economically and physically viable. The three different business models (’Cases’) created not only show good financial promise, but they demonstrate a way of creating value in a greener way of computing and heat supply. The amount of waste heat able to be captured is sufficient to heat many apartments in residential blocks and office buildings, and the temperatures achieved have proven to be sufficient to meet the heating requirements of these facilities, meaning no extra energy is required for the priming of waste heat. It is the hope that the investigations and analyses performed in this thesis will further the discussion around the utilisation of waste heat from lower energy sources, such as micro DCs, so that one day, potential can become reality. / På senare har tid har det skett en explosion i behovet av databehandling och databehandling med hög densitet. Som ett resultat har Internet- och kommunikationstekniksektorns (ICT) efterfråga på globala energiresurser tredubblats under de senaste fem åren. Edgecomputing för datorkraften närmre användaren och är hörnstenen i framtida kommunikation och informationsflöde. Omedelbar svarstid och noll latens som behövs för applikationersom 5G, självkörande fordon, ansiktsigenkänning och mycket mer tillfredställs av att datorkraften förs närme användaren. Micro Data Center är nycklen i övergången till edge computing. Eftersom att MicroData Center är fristående med inbyggda kylsystem kan de placeras där de behövs mest -ofta i områden som inte betraktas som platser för datacenter som exemeplvis kontor och bostadshus. Detta möjliggör för ICT-branschen att bli grönare och bidra till att sänka det totala globala energibehovet, samtidigt som behovet av databehandling kan tillgodoses. Om enlösning kan hittas för att fånga upp och använda spillvärme som genereras från växande antalet Micro Data Center, skulle det ha en enorm inverkan på den totala energiförbrukningen. Detta projekt kommer att undersöka potentiella synergier genom att undersöka två olikasätt att utnyttja spillvärme. Den första är att leverera spillvärme till fjärrvärmenätet (Case 1), och det andra att använda Micro Data Center som en "Data Furnace" som levererar värme till närområdet (Case 2 och 3). Två scenarier med olika kostnader och intäkter kommer att undersökas i varje Case och en känslighetsanalys kommer att utföras för att avgöra hur känsligt varje scenario är för ändrade interna och externa faktorer. Resultaten som uppnåtts är extremt lovande. Att fånga upp spillvärme från Micro Data Center och leverera till antingen det lokala fjärrvärmenätet eller nyttja spillvärmen lokalt har visat sig vara både ekonomiskt och fysiskt genomförbart. De tre olika affärsmodellerna (’Cases’) som skapats visar inte bara positivt ekonomiskt utfall, utan också ett sätt att skapa värde genom att på ett grönare sätt processa och lagra data och samtidigt värma städer. Mängden spillvärme som kan fångas upp är tillräcklig för att värma upp många lägenheter i bostadshus och kontorsbyggnader. Temperaturen på spillvärmen har visat sig vara tillräcklig för att uppfylla uppvärmningskraven i dessa anläggningar, vilket innebär att ingen extra energi krävs för att höja temperturen av spillvärme. Förhoppningen är att de undersökningar och analyser som utförs i denna rapport kommer att främja diskussionen kring utnyttjande av spillvärme från lägre energikällor, såsom Micro Data Center.

Les robots opèrent dans le monde réel, dans lequel essayer quelque chose prend beaucoup de temps. Pourtant, les methodes d’apprentissage par renforcement actuels (par exemple, deep reinforcement learning) nécessitent de longues périodes d’interaction pour trouver des politiques efficaces. Dans cette thèse, nous avons exploré des algorithmes qui abordent le défi de l’apprentissage par essai-erreur en quelques minutes sur des robots physiques. Nous appelons ce défi “Apprentissage par renforcement micro-data”. Dans la première contribution, nous avons proposé un nouvel algorithme d’apprentissage appelé “Reset-free Trial-and-Error” qui permet aux robots complexes de s’adapter rapidement dans des circonstances inconnues (par exemple, des dommages) tout en accomplissant leurs tâches; en particulier, un robot hexapode endommagé a retrouvé la plupart de ses capacités de marche dans un environnement avec des obstacles, et sans aucune intervention humaine. Dans la deuxième contribution, nous avons proposé un nouvel algorithme de recherche de politique “basé modèle”, appelé Black-DROPS, qui: (1) n’impose aucune contrainte à la fonction de récompense ou à la politique, (2) est aussi efficace que les algorithmes de l’état de l’art, et (3) est aussi rapide que les approches analytiques lorsque plusieurs processeurs sont disponibles. Nous avons aussi proposé Multi-DEX, une extension qui s’inspire de l’algorithme “Novelty Search” et permet de résoudre plusieurs scénarios où les récompenses sont rares. Dans la troisième contribution, nous avons introduit une nouvelle procédure d’apprentissage du modèle dans Black-DROPS qui exploite un simulateur paramétré pour permettre d’apprendre des politiques sur des systèmes avec des espaces d’état de grande taille; par exemple, cette extension a trouvé des politiques performantes pour un robot hexapode (espace d’état 48D et d’action 18D) en moins d’une minute d’interaction. Enfin, nous avons exploré comment intégrer les contraintes de sécurité, améliorer la robustesse et tirer parti des multiple a priori en optimisation bayésienne. L'objectif de la thèse était de concevoir des méthodes qui fonctionnent sur des robots physiques (pas seulement en simulation). Par conséquent, tous nos approches ont été évaluées sur au moins un robot physique. Dans l’ensemble, nous proposons des méthodes qui permettre aux robots d’être plus autonomes et de pouvoir apprendre en poignée d’essais / Robots have to face the real world, in which trying something might take seconds, hours, or even days. Unfortunately, the current state-of-the-art reinforcement learning algorithms (e.g., deep reinforcement learning) require big interaction times to find effective policies. In this thesis, we explored approaches that tackle the challenge of learning by trial-and-error in a few minutes on physical robots. We call this challenge “micro-data reinforcement learning”. In our first contribution, we introduced a novel learning algorithm called “Reset-free Trial-and-Error” that allows complex robots to quickly recover from unknown circumstances (e.g., damages or different terrain) while completing their tasks and taking the environment into account; in particular, a physical damaged hexapod robot recovered most of its locomotion abilities in an environment with obstacles, and without any human intervention. In our second contribution, we introduced a novel model-based reinforcement learning algorithm, called Black-DROPS that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. We additionally proposed Multi-DEX, a model-based policy search approach, that takes inspiration from novelty-based ideas and effectively solved several sparse reward scenarios. In our third contribution, we introduced a new model learning procedure in Black-DROPS (we call it GP-MI) that leverages parameterized black-box priors to scale up to high-dimensional systems; for instance, it found high-performing walking policies for a physical damaged hexapod robot (48D state and 18D action space) in less than 1 minute of interaction time. Finally, in the last part of the thesis, we explored a few ideas on how to incorporate safety constraints, robustness and leverage multiple priors in Bayesian optimization in order to tackle the micro-data reinforcement learning challenge. Throughout this thesis, our goal was to design algorithms that work on physical robots, and not only in simulation. Consequently, all the proposed approaches have been evaluated on at least one physical robot. Overall, this thesis aimed at providing methods and algorithms that will allow physical robots to be more autonomous and be able to learn in a handful of trials

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