Das Verkehrsmittelwahlverhalten von Menschen ist komplex. So spielen soziodemografische, sozioökonomische sowie raum- und siedlungsstrukturelle Merkmale eine Rolle. In dieser Arbeit wird dieses Verhalten untersucht. Dabei wird eine homogene Grundgesamtheit geschaffen, welche alle Personen beinhaltet, die sowohl über eine Dauerkarte des öffentlichen Personenverkehrs als auch einen Personenkraftwagen verfügen. Anhand derer soll eine deskriptive Analyse und eine multinomiale logistische Regression Aufschluss geben, ob es Unterschiede zwischen den jeweiligen Nutzergruppen gibt. So lässt sich die Gruppe der ÖV-Nutzer durch folgende Charakteristiken beschreiben: der Großteil sind Frauen, sowie Personen, die eine hohe schulische und berufliche Bildung besitzen. Des Weiteren werden eher weniger Wege mit dem ÖV als mit dem PKW zurückgelegt. Erwerbstätige hingegen entscheiden sich eher für den PKW. / Human behavior towards the choice of transportation varies in very complex ways such as sociodemographics, socioeconomics as well as settlement structures. For this paper a homogenous population is created from season ticket holders for public transportation and car owners. Based on this population a descriptive analysis followed by a multinomial logistic regression is supposed to generate the differences between the user groups. The group of users of the public transportation system can be characterized as followed: the majority of users are women as well as highly educated people. Within this specific group distances are more likely to be covered by public transportation rather than by car. However the working population prefers to go by passenger car.

No description available.

<p>This research explores the impact of road infrastructure on deforestation, the role of forest governance and a carbon tax/credit mechanism in mitigating the effect on land use change and subsequent GHG emissions, with application to the states of Pará and Mato Grosso in Brazil. Few studies have addressed how policies to protect forested land affect the rate of deforestation associated with road and infrastructure improvement. This research makes three main contributions to the literature of roads and deforestation: 1) the concept of cost of access to the “closest” market in terms of time (expressed in person hours per ten ton load) is introduced to reflect variations in the road network infrastructure; 2) development of empirical evidence of the role of forest governance in diminishing the rate of deforestation linked to roads, using data from Brazil; and 3) and assessment of the efficacy of a carbon tax/credit scheme for mitigating the impact of infrastructure investment on land use and resultant changes in GHG emissions. Access cost ranged between 0.01 and 3084 person hours per load, however 80 percent of the pixels measured less than 784 person hours across the three years analyzed (2003, 2013, and 2018). This measure facilitated a contrast in spatial accessibility due to road infrastructure across pixels within the same year and across years on a same pixel. The use of a fractional logit model allowed the incorporation of proportions of different land uses within a same pixel at the same resolution of other <a></a>variables not available at the same fine scale. Strong forest governance reduced up to 25% the elasticities on forest lands with respect to access cost; in other words, the impact of roads on deforestation is reduced by one fourth when forest governance is strengthened. These larger impacts occur at the frontier where most of the efforts need to be addressed. Finally, provided a shock in road infrastructure, a carbon tax/credit level of $82/tCO2e permitted to abate an additional amount of GHG emissions estimated in 244 million tons of CO2e released due to changes in carbon stocks and flow emissions from agricultural activities induced from changes in road infrastructure. More importantly, this research provided insights of a proportion of GHG emissions that could be abated at different levels of a carbon tax/credit.</p>

The Brigham Young University Women's Volleyball Team recorded and rated all skills (pass, set, attack, etc.) and recorded rally outcomes (point for BYU, rally continues, point for opponent) for the entire 2006 home volleyball season. Only sequences of events occurring on BYU's side of the net were considered. Events followed one of these general patterns: serve-outcome, pass-set-attack-outcome, or block-dig-set-attack-outcome. These sequences of events were assumed to be first-order Markov chains where the quality of each contact depended only explicitly on the quality of the previous contact but not on contacts further removed in the sequence. We represented these sequences in an extensive matrix of transition probabilities where the elements of the matrix were the probabilities of moving from one state to another. The count matrix consisted of the number of times play moved from one transition state to another during the season. Data in the count matrix were assumed to have a multinomial distribution. A Dirichlet prior was formulated for each row of the count matrix, so posterior estimates of the transition probabilities were then available using Gibbs sampling. The different paths in the transition probability matrix were followed through the possible sequences of events at each step of the MCMC process to compute the posterior probability density that a perfect pass results in a point, a perfect set results in a point, and so forth. These posterior probability densities are used to address questions about skill performance in BYU women's volleyball.

My dissertation consists of three essays on labor supply responses, along the extensive margin (participation into the labor force) and along the intensive margin (intensity of work on the job). The first two essays focus on the labor supply responsiveness of single women with children to taxation and welfare programs. The third essay investigates the effects of marriage, the wage rate, and the associated tax rate on men's labor supply. In the first essay, to avoid bias from the fact that labor supply outcomes are being driven by self-selection, I build a dynamic stochastic discrete choice model to investigate the long run effects of the earned income tax credit and welfare policies on single mothers' labor supply. Simulated method of moments is used to estimate parameters of this dynamic model, based on March CPS data files from 1964 to 2013. I compare the performance of the dynamic stochastic discrete choice model, a static model, and a reduced-form model. My analysis concludes that the dynamic stochastic discrete choice model captures the simultaneous impact of the state variables on the predicted employment decision. My study provides evidence of the long-run positive effect of public policy on low income families in a life-cycle setting. This essay also emphasizes the importance of education in increasing single mothers' labor supply. The second essay is designed to identify factors that help single mothers leave TANF within a short span of time. I find strong evidence for the importance of child support assistance to single mothers' success in exiting TANF with a job. I uncover evidence that work-related activities do not induce TANF participants to leave within a short span of time. My analysis also suggests that health issues significantly limit the ability of single mothers to exit TANF. In the third essay, the main research question is how marital status affects the elasticity of the labor supply of males with respect to wages and taxes, in a life-cycle setting. A dynamic panel data model, which extends the literature on dynamic labor supply, indicates that the elasticity of men's labor supply with respect to wages and taxes is affected by marital status. The empirical results using the Panel Study of Income Dynamics (PSID) data show that men who are continuously married to the same wife have a lower average Frisch elasticity than others. / Ph. D.

No description available.

The present dissertation research has three purposes: the first one is to predict anthropogenic deforestation caused by small farmers firstly using only pathways of past land cover change and secondly using demographic, socioeconomic and land cover data at the farm level. The second purpose is to compare the explanatory and predictive capacity of both approaches at identifying areas at high risk of deforestation among small farms in Rondõnia, Brazil. The third purpose is to test the assumptions of stationary probabilities and homogeneous subjects, both commonly used assumptions in predictive stochastic models applied to small farmers' deforestation decisions. This study uses the following data: household surveys, maps, satellite images and their land cover classification at the pixel level, and pathways of past land cover change for each farm. These data are available for a panel sample of farms in three municipios in Rondõnia, Brazil (Alto Paraiso, Nova União, and Rolim de Moura) and cover a ten-year period of study (1992-2002). Pathways of past land cover change are graphic representations in the form of flow charts that depict Land Cover Change (LCC) in each farm during the ten-year period of study. Pathways were constructed using satellite images, survey data and maps, and a set of interviews performed on a sub-sample of 70 farms. A panel data analysis of the estimated empirical probabilities was conducted to test for subject and time effects using a Fixed Group Effects Model (FGEM), specifically the Least Square Dummy Variable (LSDV1) fixed effects technique. Finally, the two predictive modeling approaches are compared. The first modeling approach predicts future LCC using only past land cover change data in the form of empirical transitional probabilities of LCC obtained from pathways of past LCC. These empirical probabilities are used in a LSDV1 for fixed–group effects, a LSDV1 for fixed-time effects, and an Ordinary Least Square model (OLS) for the pooled sample. Results from these models are entered in a modified Markov chain model's matrix multiplication. The second modeling approach predicts future LCC using socio-demographic and economic survey variables at the household level. The survey data is used to perform a multinomial logit regression model to predict the LC class of each pixel. In order to compare the explanatory and predictive capacity of both modeling approaches, LCC predictions at the pixel level are summarized in terms of percentage of cells in which future LC was predicted correctly. Percentage of correct predicted land cover class is compared against actual pixel classification from satellite images. The presence of differences among farmers in the LSDV1-fixed group effect by farmer suggests that small farmers are not a homogeneous group in term of their probabilities of LCC and that further classification of farmers into homogeneous subgroups will depict better their LCC decisions. Changes in the total area of landholdings proved a stronger influence in farmer's LCC decisions in their main property (primary lot) when compared to changes in the area of the primary lot. Panel data analysis of the LCC empirical transition probabilities (LSDV1 fixed time effects model) does not find enough evidence to prefer the fixed time effects model when compared to a Ordinary Least Square (OLS) pooled version of the probabilities. When applying the results of the panel data analysis to a modified markov chain model the LSDV1-farmer model provided a slightly better accuracy (59.25% accuracy) than the LSDV1-time and the OLS-pooled models (57.54% and 57.18%, respectively). The main finding for policy and planning purposes is that owners type 1—with stable total landholdings over time—tend to preserve forest with a much higher probability (0.9033) than owner with subdividing or expanding properties (probs. of 0.0013 and 0.0030). The main implication for policy making and planning is to encourage primary forest preservation, given that the Markov chain analysis shows that primary forest changes into another land cover, it will never go back to this original land cover class. Policy and planning recommendations are provided to encourage owner type 1 to continue their pattern of high forest conservation rates. Some recommendations include: securing land titling, providing health care and alternative sources of income for the OT1's family members and elderly owners to remain in the lot. Future research is encouraged to explore spatial autocorrelation in the pixel's probabilities of land cover change, effects of local policies and macro-economic variables in the farmer's LCC decisions. / Ph. D.

The general focus of this thesis is on how the family, work and the welfare system are intertwined. A major determinant is the way responsibilities are shared by the state, the market and civil society in different welfare state regimes. An introductory chapter will therefore be dedicated to the development of the social dimension in the process of European integration. A first chapter will then go deeper into the comparative analysis of welfare state regimes, to comment on the provision of welfare in societies with a different mix of state, market and societal welfare roles and to assess the adequacy of existing typologies as reflections of today’s changed socio-economic, political and gender reality. Although they stand strong on their own, these first two chapters also contribute to contextualising the research subject of the remainder of the thesis: the study and comparison of the differential situation of women and men and of mothers and non-mothers on the labour markets of the EU-15 countries as well as of the role of public policies with respect to the employment penalties faced by women, particularly in the presence of young children. In our analysis, employment penalties are understood in three ways: (i) the difference in full-time equivalent employment rates between mothers and non-mothers, (ii) the wage penalty associated with motherhood, and (iii) the wage gap between part-time and full-time workers, considering men and women separately. Besides from a gender point of view, employment outcomes and public policies are thus assessed comparatively for mothers and non-mothers. Because women choose to take part in paid employment, fertility rates will depend on their possibilities to combine employment and motherhood. As a result, motherhood-induced employment penalties and the role of public policies to tackle them should be given priority attention, not just by scholars, but also by politicians and policy-makers.

綜觀台灣過去數十年來的產業經濟發展歷程，在國際間有許多正面的評價，稱之為「台灣經濟奇蹟」。在政府政策致力於塑造有利於科技產業發展的環境下，製造業為台灣產業帶來重大的貢獻，當我國不同的產業類別之製造業發揮其優勢競爭力的同時，亦使得台灣地區北、中、南三大區域發展出各自獨特且具有潛力的製造業特性。 事實上，區域產業特性與賦能之優劣將可透過區域中廠商與行動者的活動來表現，尤其是區域中的模範角色，是檢視區域中有無可學習的模範者和模仿其整體投資階段過程的重要指標，對於區域內外企業進駐和設立工廠的組織決策相當重要（Bandura, 1986; van Praag, 1996; Kriegesmann, 1999; Fornahl, 2003）。這樣的仿效行為將指向區域中旗艦廠商投資設廠區位選擇與組織決策行動，換句話說，旗艦廠商（flagship firm）是區域發展中重要的模範角色（role model）（Fornahl, 2003），而其區位選擇與組織決策行為對於區域發展將佔有舉足輕重的地位。另一方面，過去傳統投資區位選擇的討論，多論及單一廠商設立工廠在地理空間的決策行為。然而本研究認為旗艦廠商多工型工廠（multiplant）的特性在組織決策上的區位選擇行為，面對各區域經濟與產業發展特性的差異，應有不同於過去文獻與理論的考量。 是故，本研究透過天下雜誌公佈台灣地區2007年製造業1000大廠商，聚焦台灣地區製造業旗艦廠商面對投資母國區位選擇的考量，透過旗艦廠商在台投資多工型工廠歷程決策之整體分析，藉由投資時間與空間區位，以及廠商組織型態面向的討論，了解台灣地區旗艦廠商投資多工型工廠於母國區位選擇之因素。透過多項羅吉特模型的實證研究，本研究歸納研究成果，提出下列政策建議： 一、推動企業總公司在地化發展，有助區域內旗艦廠商再投資母國之活動 二、透過旗艦廠商在區域內之投資經驗，引導再投資活動，以利區域發展 三、區域內產業特性強化與升級，以加強區域高科技產業支撐力 四、科學工業園區與區域內科技基礎產業優勢分進合擊，提升產業關聯性 五、提升區域總體經濟發展，有助於吸引區域內高科技產業再投資之活動

Dans ce mémoire, nous proposons une méthodologie statistique permettant d’obtenir un estimateur de l’espérance de vie des clients en assurance. Les prédictions effectuées tiennent compte des caractéristiques individuelles des clients, notamment du fait qu’ils peuvent détenir différents types de produits d’assurance (automobile, résidentielle ou les deux). Trois approches sont comparées. La première approche est le modèle de Markov simple, qui suppose à la fois l’homogénéité et la stationnarité des probabilités de transition. L’autre modèle – qui a été implémenté par deux approches, soit une approche directe et une approche par simulations – tient compte de l’hétérogénéité des probabilités de transition, ce qui permet d’effectuer des prédictions qui évoluent avec les caractéristiques des individus dans le temps. Les probabilités de transition de ce modèle sont estimées par des régressions logistiques multinomiales. / In this master’s thesis, we develop a statistical method to estimate the lifetime expectancy of clients in the insurance domain. The forecasts are personnalized according to the clients’ own features, the most notable being the fact that they can have any combination of automobile and residential insurance products. Three approaches are compared. The first approach is the simple Markov model which assume homogeneity and stationnarity of the transition probabilities. The other model suggested – which is implemented both by direct computation and by simulation – allows for heterogeneity of the transition probabilities, thus providing forecasts which evolve in time along with the characteristics of the clients. The transitions probabilities are estimated using multinomial logistic regressions.