Sweden has seen a reversal in marriage trends, from mainly declining marriage rates since the 1960s to increasing rates from 1998 and onwards. By applying event-history techniques to Swedish register data, this study examines whether the trend reversal is related to compositional changes in various socio-economic and demographic characteristics of the population, with special focus on childbearing. Only first marriages of women are studied as these largely represent the general marriage trends. The results show that the trend reversal only partly can be ascribed to compositional changes, more specifically to changes in labor-market attachment and childbearing. Thus, there is evidence of a new marriage trend in Sweden that does not conform very well to that of generally declining marriage rates as is often depicted in demographic literature. Furthermore, there is evidence of pro-cyclical marriage and childbearing trends. These patterns are especially interesting because Sweden is a country that in many aspects has been a forerunner in the development of new trends in family-demographic behavior and a country where childbearing and marriage are not necessarily seen as very closely interrelated.

abstract: Time-to-event analysis or equivalently, survival analysis deals with two variables simultaneously: when (time information) an event occurs and whether an event occurrence is observed or not during the observation period (censoring information). In behavioral and social sciences, the event of interest usually does not lead to a terminal state such as death. Other outcomes after the event can be collected and thus, the survival variable can be considered as a predictor as well as an outcome in a study. One example of a case where the survival variable serves as a predictor as well as an outcome is a survival-mediator model. In a single survival-mediator model an independent variable, X predicts a survival variable, M which in turn, predicts a continuous outcome, Y. The survival-mediator model consists of two regression equations: X predicting M (M-regression), and M and X simultaneously predicting Y (Y-regression). To estimate the regression coefficients of the survival-mediator model, Cox regression is used for the M-regression. Ordinary least squares regression is used for the Y-regression using complete case analysis assuming censored data in M are missing completely at random so that the Y-regression is unbiased. In this dissertation research, different measures for the indirect effect were proposed and a simulation study was conducted to compare performance of different indirect effect test methods. Bias-corrected bootstrapping produced high Type I error rates as well as low parameter coverage rates in some conditions. In contrast, the Sobel test produced low Type I error rates as well as high parameter coverage rates in some conditions. The bootstrap of the natural indirect effect produced low Type I error and low statistical power when the censoring proportion was non-zero. Percentile bootstrapping, distribution of the product and the joint-significance test showed best performance. Statistical analysis of the survival-mediator model is discussed. Two indirect effect measures, the ab-product and the natural indirect effect are compared and discussed. Limitations and future directions of the simulation study are discussed. Last, interpretation of the survival-mediator model for a made-up empirical data set is provided to clarify the meaning of the quantities in the survival-mediator model. / Dissertation/Thesis / Doctoral Dissertation Psychology 2017

Financial aid contributes significantly to higher education participation in South Africa. However, while research focused on how financial aid improved access to University, little is known about its effect on ‘on-time' degree completion. Therefore, this research aimed to investigate to what extent financial aid affected the likelihood of on-time graduation as a function of selected student input and higher educational factors for students' success in studying towards a Bachelor of Commerce (BCom) at the University of Cape Town (UCT). To better understand the above factors, the researcher conducted a quantitative study applying Event History Analysis (EHA) and the Input-Environment-Output (I-E-O) model (Astin, 1993). A logistic regression within a discrete-time model with a personperiod dataset was performed. Data from the 2013 to 2018 UCT student records formed the basis of this research with a sample size of N=842. Based on the literature review, the input variables were gender, race, age, school type, final grade 12 marks and students' performance in the National Benchmark Tests (NBT). The environmental variables were enrollment in the academic development and support programme ‘Step-Up' and financial aid. Contrary to expectation, the results for the environmental variables surprisingly revealed that receiving financial aid decreased the likelihood of on-time graduation. Receiving GAP Aid indicated no statistically significant association with on-time graduation. Furthermore, enrolment in Step Up significantly increased the likelihood of on-time graduation, all else being equal. The findings for the input variables indicated that females and younger students were more likely to graduate on time than males and older students. White students were more likely to graduate on time than Black, Coloured and Indian/Asian students, which shows that this issue remains a transformation issue for Higher Education Institutions (HEIs). Another unexpected result was that the type of school attended and participation in NSC English indicated no association with on-time graduation. Most notably, students with NSC Accounting had a more significantly positive likelihood of on-time graduation than students with NSC Mathematics only. The research findings contribute value to the discussions on accounting education research and how to improve on-time degree completion. Secondly, the results could benefit policy decisions for student admission to the BCom programme. While the research results will reflect the nature of this specific university, the findings may also be valuable to other public HEIs with similar admission standards and student bodies.

This dissertation contributes to the disaster resilience policy literature by examining the diffusion of hazard mitigation policy in the U.S. Using the three-paper model, it investigates the adoption of local hazard mitigation plans (LHMPs) from an intergovernmental perspective. The first paper focuses on horizontal diffusion in hazard mitigation planning among local communities. Special attention is paid to the potential factors affecting the adoption of FEMA-approved LHMPs, Hazard Mitigation Grant Program (HMGP) projects and Pre-Disaster Program (PDM) projects at the county level. The Event History Analysis (EHA) Logit Model and Spatial Autocorrelation Models test the hypotheses corresponding to external factors such as the neighboring effects and internal factors, including disaster risks, neighborhood disadvantage and affluence, government capacity, local disaster resilience advocacy groups, and political support. The empirical results confirmed the significant influence of neighboring effects, indicating that counties are more likely to implement the same mitigation strategies if neighboring counties have done so. The results also revealed that disaster experience, government capacity, and strong democratic support significantly impact the likelihood of adopting LHMP and HMGP. Additionally, the results suggested that disadvantaged communities were more likely to adopt mitigation policies, while affluent communities were less likely to adopt such policies. The second paper evaluates the effectiveness of the FEMA's Program Administration by State Pilot (PAS). By integrating the Propensity Score Matching (PSM) technique with the Difference-in-Differences (DID) analysis, the empirical evidence demonstrated a significant reduction in the approval times for both LHMP and HMGP in pilot states compared to non-pilot states, with an average reduction nearing 30%. This suggests that the PAS program has effectively streamlined administrative processes, thereby enhancing efficiency in disaster management within pilot states. The analysis also indicated that the impact of PAS on the actual funding received through HMGP was insignificant, suggesting that while administrative processes were expedited, the allocation of financial resources remained unaffected. The third paper attempts to understand how local governments respond to top-down policy pressures in vertical diffusion by analyzing the text similarities of hazard mitigation strategies between state hazard mitigation plans and county LHMPs in Ohio using the word embedding technologies. The study employs the Word2Vec algorithm to assess the policy similarity between the hazard mitigation goals outlined in LHMPs and SHMPs. Building on this initial analysis, this research further uses the Beta Regression model to examine the textual similarities within LHMPs in Ohio, focusing on how the type of author - government versus private consultants, and the nature of the goals, whether action-based or hazard-based, affect these alignments. The regression analysis shows that LHMPs authored by government entities tend to exhibit higher textual similarity, reflecting the influence of standardized approaches driven by state and federal guidelines. This suggests a compliance-driven alignment in government-written plans. Conversely, LHMPs authored by private consultants display greater variability, suggesting that these plans are customized to the specific needs and risk assessments of local communities. Additionally, the regression results indicate that action-based and mixed-goal LHMPs are associated with higher textual similarity across counties. To carry out the empirical analysis mentioned above, this dissertation builds a panel dataset for all counties from 2000 to 2020, which contains data on LHMPs, HMA projects, disaster risks, socioeconomic characteristics, regional economic and political indicators, etc. / Doctor of Philosophy / Hazard mitigation in the United States is a critical issue, especially as the frequency and cost of disasters continue to rise. This dissertation investigates the dynamics of hazard mitigation planning within a multi-level governmental framework, focusing on the adoption of Federal Emergency Management Agency (FEMA) approved Local Hazard Mitigation Plans (LHMPs), Hazard Mitigation Grant Program (HMGP) projects, and Pre-Disaster Program (PDM) projects. across U.S. counties and the influence of federal and state policies on these local initiatives. The first paper examines the horizontal diffusion of LHMPs among local communities, revealing the significant influence of neighboring counties. This "neighboring effect" shows that counties are more likely to adopt similar mitigation strategies if their neighbors have done so, emphasizing the role of regional collaboration in spreading effective disaster resilience practices. Additionally, the study found that counties with more disaster experience and greater governmental capacity are more likely to implement LHMPs, highlighting the importance of preparedness and resources in driving policy adoption. Furthermore, this research finds counties with higher socioeconomic disadvantages are more proactive in adopting mitigation policies, which could be attributed to the higher perceived risks and available federal funding targeted at these communities. The second paper evaluates the impact of FEMA's Program Administration by State (PAS) pilot program on the administrative efficiency of LHMP and HMGP approvals. The findings indicate a significant reduction in approval times in pilot states, suggesting that the PAS program has successfully streamlined administrative processes. However, this expedited process did not lead to increased funding or broader adoption, pointing to the need for further policy enhancements to ensure that administrative improvements translate into real-world benefits for disaster preparedness. The third paper explores the vertical diffusion of policy from state to local governments, using Ohio as a case study. It employs advanced text analysis to measure the similarity between state and local hazard mitigation plans. The results show that government-authored LHMPs tend to closely follow state guidelines, indicating a top-down influence that ensures compliance with federal and state objectives. In contrast, LHMPs authored by private consultants were more varied and aligned to the specific needs and risks of local communities. This suggests that a balance is needed between standardized policies and local customization to effectively address the unique challenges of different regions. By integrating these findings, this dissertation provides a comprehensive overview of how hazard mitigation policies are adopted and implemented across various governmental levels. The research concludes with policy recommendations that advocate for sustained reforms in hazard mitigation funding, emphasizing the need for equitable resource distribution among disadvantaged communities. It also offers critical insights into improving intergovernmental cooperation and policy effectiveness, ensuring that all communities, regardless of their socio-economic status, can enhance their resilience and better prepare for future disasters. This research ultimately serves as a guide for policymakers to refine strategies that foster robust, community-centered resilience practices, enhancing the nation's overall disaster preparedness and response capabilities.

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models. Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the observed data likelihood function is based on the model of a joint distribution of the response longitudinal event history data and the discrete covariate with missing values. Secondly, we proposed a maximum simulated likelihood method to deal with the missing continuous covariate when applying multi-state models. The observed data likelihood function was approximated by using the Monte Carlo simulation method. At last, an EM algorithm was used to deal with multiple missing covariates when estimating the parameters of multi-state model. The EM algorithm would be able to handle multiple missing discrete covariates in general missing pattern efficiently. All the proposed methods are justified by simulation studies and applications to the datasets from the SMART project, a consortium of 11 different high-quality longitudinal studies of aging and cognition.

新竹科學園區對我國的社經發展有著卓越的貢獻，就2006年的統計數據來看，年營業額佔全國工業生產毛額的比率約35%，佔台灣GDP的比率則將近10%，佔全國出口貿易總值的8%，就業人數則約佔全國就業者的1.2%，其實質重要性可見一般，所以竹科廠商的存活是與全國、地方的社經發展緊密交織無可劃分，竹科廠商的退出可能會造成經濟成長停滯、工作機會短少、政策資源錯置等問題。 既有研究多半強調竹科成功的要素，但往往會使我們將目光只放在成功廠商，而忽視廠商退出竹科的現象，又或者未正視高科技產業的特質之一：高風險，因此本研究將試圖運用事件史分析來捕捉在竹科特殊的產業生態中，高科技產業其風險的真正面貌，並運用資源基礎、資源依賴以及組織生態學的觀點來探討竹科的不同廠商所涉風險之影響因素。 / The Hsinchu science park has made remarkable contributions to Taiwan’s development, so far as statistics in 2006, the annual sales volume takes up 35% of the national industrial production volume, nearly 10% of Taiwan’s GDP, it also accounts for 8% of the national export trade. The employment of the Hsinchu science park takes up 1.2% of the total employment of Taiwan. Its significant importance is easy to be understood fully at a glance, so the survival of firms in the Hsinchu science park interweaved with Taiwan’s society and development. Firm exit not only makes the working opportunity deficient, but also affect the usage of policy resources and the socio-economic development of Taiwan. Academic researches about the Hsinchu science park only emphasized the successful element, placed sight on the successful manufacturer, and ignored the phenomenon that some firms have exited from the Hsinchu science park. Actually, one characteristic of the Hi-Tech industry was high risk. So this research will attempt to use the event history analysis to realize firm survival in the Hsinchu science park, catch the real risk of Hi-Tech industry and understand the important factor that effect firm exit from the Hsinchu science park through resource-based theory, resource dependency theory, and organizational ecology perspective.

This doctoral thesis provides a set of studies of social influences on fertility timing. Swedish register data are used to link individuals to their parents and siblings, thereby allowing the study of impacts of family of origin, social background, and parental death on fertility. The Swedish Medical Birth Register is used to investigate the effect of mode of delivery on higher order births. The thesis consists of an introductory chapter with an overview of the consequences and predictors of the timing of childbearing, and a theoretical framework to explain these relationships. This chapter also includes a section where the contribution to existing knowledge, the relation of the findings to life course theory, and suggestion for further research are discussed. This chapter is followed by four original empirical studies. The first study applies sister and brother correlations to investigate and estimate the impact of family of origin on fertility. It shows that family of origin matters for fertility timing and final family size. The study also shows that the overall importance of family of origin has not changed over the approximately twenty birth cohorts that were studied. The second study introduces three dimensions of social background - occupational class, status, and education - into fertility research. It suggests that social background, independent of individuals’ own characteristics, matters for the timing of first birth and the risk of childlessness. The study also shows that different dimensions of social background should not be used interchangeably. The third study uses the Swedish Medical Birth Register to investigate the effect of mode of delivery on the propensity and birth interval of subsequent childbearing. It demonstrates that mode of delivery has an impact on the progression to the second and third births but that a first delivery by vacuum extraction does not reduce the propensity of subsequent childbearing to the same extent as a first delivery by emergency or elective caesarean section. The fourth study explores the effects of parental death on adult children's fertility. The findings reveal that parental death during reproductive ages can affect children’s fertility. The effects are moderated by the gender of the child and when in the life course bereavement occurs. The combined output of these four studies provides evidence that human fertility behavior is embedded in social relationships with kin and friends throughout life. Family of origin, social background, an older sibling's birth, and bereavement following parental death influence the adult child's fertility. These findings add knowledge to previous research on intergenerational and social network influences in fertility.

A Second Child in the Family - the Consequences of Changing Family and Fertility Patterns in the Czech Republic Anna Šťastná Abstract The theme of this study is the two-child family model and second order births in the context of a changing Czech society. The study investigates the conditions and context surrounding the birth of a second child and the general values associated with children and focuses on selected factors associated with the decision-making process surrounding having a second child including the factors that a woman takes into account when deciding whether to have another child. Furthermore, the author studies the determinants of having a second child in Czech society and the relationship between the likelihood of the birth of a second child and different micro-level covariates and attempts to provide an explanation of the processes acting upon second childbearing through interaction with education and partnerships/union dynamics. The thesis is divided into three thematic sections. Following the introductory chapters, the first part of the study addresses fertility patterns in the Czech Republic, societal conditions and theoretical concepts which deal with possible explanations for changing fertility behaviour. The second part of the study considers decision-making processes and value...

L'analyse démographique se déroule principalement dans un cadre de référence défini par des variables temporelles ; ce faisant, elle débouche sur des résultats mettant en évidence des régularités remarquables. Cette constatation pourrait laisser croire automatiquement à une valeur explicative substantielle des variables en cause. En fait, généralement, l'âge, la date ou le moment de naissance, par exemple, n'ont en eux-mêmes aucune vertu explicative, si ce n'est à travers des phénomènes physiologiques ou sociologiques, par exemple. En prenant le cas de la mortalité des personnes âgées, l'âge peut être, au moins en partie, considéré comme un proxy du processus physiologique du vieillissement. L'objectif principal de cette thèse est de voir si les méthodes des démographes pourraient se transposer en optant pour un cadre de référence non temporel qui serait régi par une variable éventuellement plus proche de causes en rapport avec un phénomène étudié. La réponse à cette question est affirmative. Sur un plan purement mathématique, tout cadre de référence composé de trois variables formant une combinaison linéaire et dont une est invariable individu par individu peut remplacer le cadre de référence temporel le plus classique où l'âge correspond au temps moins le moment de naissance et où le moment de naissance est invariant tout au long de la vie d'un individu. Ainsi, en va-t-il, par exemple, de la taille (ou du poids) des nourrissons, de leur taille (ou poids) à la naissance et de leur gain de taille (ou de poids) depuis la naissance. Des diagrammes de Lexis ont été construits en remplaçant le temps par la taille (ou le poids) des nourrissons ; le moment de naissance, par leur taille (ou leur poids) à la naissance et l'âge, par la variation de leur taille (ou de leur poids) depuis la naissance. Les cohortes se définissent non plus par rapport au moment de naissance, mais par rapport à la taille (ou au poids) de naissance. Ces diagrammes ont montré qu'il existe une relation négative entre le gain de taille durant la première année de vie et la taille à la naissance, mais qu'aucune relation de ce type n'était pas présente dans le cas du poids. L'équivalent d'une table d'extinction classique a été élaboré en remplaçant l'âge par le gain de taille (ou de poids). Par ailleurs, toujours en remplaçant l'âge par le gain de taille (ou de poids), des régressions logistiques ont permis d'identifier des variables ayant un effet significatif sur la croissance en taille ou en poids durant la première année de vie. Ces régressions ont notamment confirmé l'influence de la taille à la naissance sur le gain de taille et la non-influence du poids de naissance sur le gain de poids. Il est à noter que pour employer ces méthodes de la table ou de la régression, il faut en plus remplir certaines conditions ; si ce n'est pas le cas, il faut soit procéder à des adaptations, soit adopter des hypothèses supplémentaires, soit carrément renoncer à l'emploi de ces méthodes. La réflexion menée dans ce travail a suggéré d'enrichir la panoplie des méthodes de l'analyse démographique : à l'analyse transversale classique se concentrant sur une période et donc sur un couloir vertical du diagramme de Lexis, on peut adjoindre un autre type d'analyse transversale portant cette fois sur un couloir horizontal. Si cette nouvelle perspective d'analyse est sans intérêt pratique avec le cadre de référence classique en temps, elle pourrait donner lieu à l'établissement de résultats dignes d'intérêt en cas de cadre de référence autre. Par ailleurs, ce travail aura aussi montré en quoi, dans certaines circonstances, la corrélation et la régression pourraient avoir intérêt à intégrer la notion de cohorte. Si ce travail montre que le cadre de référence temporel n'est pas le seul envisageable, il ne débouche nullement sur la conclusion que ce cadre classique est à abandonner définitivement au profit d'autres. En effet, les variables temporelles présentent des spécificités qui les rendent particulièrement efficaces pour organiser une analyse.

The proportional hazards model developed by Cox (1972) is by far the most widely used method for regression analysis of censored survival data. Application of the Cox model to more general event history data has become possible through extensions using counting process theory (e.g., Andersen and Borgan (1985), Therneau and Grambsch (2000)). With its development based entirely on counting processes, Aalen’s additive risk model offers a flexible, nonparametric alternative. Ordinary least squares, weighted least squares and ridge regression have been proposed in the literature as estimation schemes for Aalen’s model (Aalen (1989), Huffer and McKeague (1991), Aalen et al. (2004)). This thesis develops data-driven parameter selection criteria for the weighted least squares and ridge estimators. Using simulated survival data, these new methods are evaluated against existing approaches. A survey of the literature on the additive risk model and a demonstration of its application to real data sets are also provided. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2007-07-18 22:13:13.243