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Men are often considered by the health care system to be a disengaged accessory when it comes to family planning. In reality they act as an equal part in the reproductive equation. Despite qualitative research suggesting some men currently do take primary responsibility for family planning, men are further marginalised being classed as an irrational variable in large national datasets. Reports ignore men in general by failing to record basic demographics, for example, age is not captured and ethnicity has two options: white and non-white. This leaves little ability to analyse men's family planning knowledge, attitudes and beliefs. Technological advancements have resulted in new forms of male contraceptive methods reaching phase III testing (from pills to gels), and the market is moving towards diversified options that will allow even more men to take primary contraceptive responsibility. Other advancements include the sexual enhancement product Viagra becoming available over the counter, and reproductive wellbeing apps have been created to allow men to test their fertility at home. Without research to understand the ever-changing landscape for men we are ill-prepared to understand what these new products and advancements mean for men's role. Using various forms of publicly available online data and previous empirical research, this chapter will review men's response to new contraceptives, sexual enhancement products, and reproductive wellbeing apps. The results will be discussed in relation to updating the Subjective Expected Utility (SEU) Theory, the Theory of Planned Behaviour and the integrated developmental and decision-making contraceptive models used by health psychologists.

We analyze stochastic volatility effects in the context of the bond market. The short rate model is of Vasicek type and the focus of our analysis is the effect of multiple scale variations in the volatility of this model. Using a combined singular-regular perturbation approach we can identify a parsimonious representation of multiscale stochastic volatility effects. The results are illustrated with numerical simulations. We also present a framework for model calibration and look at the connection to defaultable bonds.

Microstructure noise contaminates high-frequency estimates of asset price volatility. Recent work has determined a preferred sampling frequency under the assumption that the properties of noise are constant. Given the sampling frequency, the high-frequency observations are given equal weight. While convenient, constant weights are not necessarily efficient. We use the Kalman filter to derive more efficient weights, for any given sampling frequency. We demonstrate the efficacy of the procedure through an extensive simulation exercise, showing that our filter compares favorably to more traditional methods.

This chapter provides a comprehensive presentation and discussion of special education in Sweden. The presentation and discussion are tied deeply to the country’s general education system which incorporates social and political aspects as well as beliefs in equity for all.

The municipalities in Sweden have a large degree of independence as such special education can be organized in different ways. Yet, within each municipality’s educational structure is the common theme that students are different therefore teaching cannot be the same for everyone. The following chapter sections provide the reader with a better understanding of Sweden’s general special education system today: legislative acts that ensure equal access to education; the special education context; the history of special education and service in Sweden; the expansion of special education starting in the 1960s and early 1970s; current prevalence data; a clarification of differentiation, inclusion and categorization; teacher preparation advances; problems in schools and student’s difficulties; a description of inclusive education; and current challenges to inclusive education.

In this paper, we consider the analysis of models for univariate and multivariate ordinal outcomes in the context of the latent variable inferential framework of Albert and Chib (1993). We review several alternative modeling and identification schemes and evaluate how each aids or hampers estimation by Markov chain Monte Carlo simulation methods. For each identification scheme we also discuss the question of model comparison by marginal likelihoods and Bayes factors. In addition, we develop a simulation-based framework for analyzing covariate effects that can provide interpretability of the results despite the nonlinearities in the model and the different identification restrictions that can be implemented. The methods are employed to analyze problems in labor economics (educational attainment), political economy (voter opinions), and health economics (consumers’ reliance on alternative sources of medical information).

Swedish society has systematically worked to improve children's health and well-being since the early twentieth century and is considered a leading figure globally in battling violence against children. Awareness of violence against children and its detrimental effects on development and health is generally high in Sweden. Violence is also broadly recognised as a violation of human rights. A ban on corporal punishment was enacted in 1948 in social childcare institutions, in 1958 in schools, and in 1979 at home. The more recent landmark was the United Nations Convention on the Rights of the Child, established as law on 1 January 2020. In line with convention's Article 12, stating that a child has the right to express their views in all matters affecting them, more attention to child participation has been paid since 2020. This chapter provides several recent examples of strategic measures that have been used to enhance child participation in governmental assignments and enquiries and state-funded research. We discuss the examples considering the United Nations convention and child participation methods and pinpoint opportunities and obstacles to further develop and consolidate child participation as a norm in publicly funded societal activities.

We consider the problem of estimating a varying coefficient panel data model with fixed-effects (FE) using a local linear regression approach. Unlike first-differenced estimator, our proposed estimator removes FE using kernel-based weights. This results a one-step estimator without using the backfitting technique. The computed estimator is shown to be asymptotically normally distributed. A modified least-squared cross-validatory method is used to select the optimal bandwidth automatically. Moreover, we propose a test statistic for testing the null hypothesis of a random-effects varying coefficient panel data model against an FE one. Monte Carlo simulations show that our proposed estimator and test statistic have satisfactory finite sample performance.