Life Expectancy as an Objective Factor of a Subjective Well-Being

The paper has two parts. In the first part we offer a definition of well-being which makes life expectancy an explicit variable. We recognize the importance of happiness as a significant aspect of any definition of well-being, but we side-step the issue of what determines its level or how to measure it, and concentrate instead on the consequences of our new variable, life expectancy. We argue that life is valued for its quality, and, if positive, its extension is an improvement of well-being. From this we show how, given certain assumptions, disparate problems that have moral and/or social significance can be approached from the perspective of improving well-being. We close the first part by showing that our definition has enough flexibility to be used for that class of decisions which require tradeoffs between quality of life (happiness) and life expectancy. As a corollary we show that attitudes toward risk depend on expectations, and on some occasions, age itself. In the second part we argue, first, that real economic factors, not reducible to mere psychological ones, may still offer an adequate explanation for the fact that absolute income and happiness do not always correlate well. However, we take no position on the many controversies, such as whether it is relative or absolute increases in wealth that bears most directly on changes in happiness. We confirm through statistical analysis (simple regressions) the well established influence that absolute income has on life expectancy, and, hence, by inference and definition, we argue that this must also be the case with well-being. Secondly, we find through statistical analysis that healthcare has as much impact on life expectancy as does absolute income, leading us to theoretically examine the appropriate income cost for access to healthcare if life expectancy is to improve. And thirdly, by assuming a homogeneous function of life expectancy, we theoretically show how a market oriented healthcare system can exacerbate inequities in life expectancy, and so on well-being. Lastly, we consider some policy implications of those inequities.     

A future agenda for research on climate change and human mobility

In the past 15 years, research activities focusing on the interlinkages between climate change and human mobility have intensified. At the same time, an increasing number of actors and processes have sought to address human mobility in the context of climate change from a policy perspective. Hitherto, research has been limited in terms of geographical preferences as well as conceptual and methodological focus areas. This paper argues that to address the evolving policy space, future research on climate change in the context of human mobility needs to become more differentiated, integrated and generalized. This includes concerted efforts to better integrate researchers from the global South, improved cross-linkages between different datasets, approaches and disciplines, more longitudinal and comparative studies and development of innovative qualitative and quantitative methods.     

The origin of semilingualism: Nils-Erik Hansegård and the cult of the mother tongue

‘Semilingualism’ is one of the most questionable theories produced in the language sciences. Yet, little is known about its origins. We present a critical account of the history of semilingualism, tracing its roots in the work of Nils Erik Hansegård, (1918–2002), inaugural chair of Sámi at Umeå University (1975–1979), who developed a theory of semilingualism (halvspråkighet) in the 1960s. We show how Hansegård theorized semilingualism using ideas from Nazi German linguistics, producing an unforgiving theory of linguistic pathology directed at minoritized bilinguals in Sweden's far north.     

Statistical evidence and surprise unified under possibility theory

Sander Greenland argues that reported results of hypothesis tests should include the surprisal, the base-2 logarithm of the reciprocal of a p-value. The surprisal measures how many bits of evidence in the data warrant rejecting the null hypothesis. A generalization of surprisal also can measure how much the evidence justifies rejecting a composite hypothesis such as the complement of a confidence interval. That extended surprisal, called surprise, quantifies how many bits of astonishment an agent believing a hypothesis would experience upon observing the data. While surprisal is a function of a point in hypothesis space, surprise is a function of a subset of hypothesis space. Satisfying the conditions of conditional min-plus probability, surprise inherits a wealth of tools from possibility theory. The equivalent compatibility function has been recently applied to the replication crisis, to adjusting p-values for prior information, and to comparing scientific theories.     

The stratified win statistics (win ratio,win odds,and net benefit)

The win odds and the net benefit are related directly to each other and indirectly, through ties, to the win ratio. These three win statistics test the same null hypothesis of equal win probabilities between two groups. They provide similar p-values and powers, because the Z-values of their statistical tests are approximately equal. Thus, they can complement one another to show the strength of a treatment effect. In this article, we show that the estimated variances of the win statistics are also directly related regardless of ties or indirectly related through ties. Since its introduction in 2018, the stratified win ratio has been applied in designs and analyses of clinical trials, including Phase III and Phase IV studies. This article generalizes the stratified method to the win odds and the net benefit. As a result, the relations of the three win statistics and the approximate equivalence of their statistical tests also hold for the stratified win statistics.     

Stability analysis using mixed models: A critique of tolerance interval methods and a probabilistic solution

Recently, tolerance interval approaches to the calculation of a shelf life of a drug product have been proposed in the literature. These address the belief that shelf life should be related to control of a certain proportion of batches being out of specification. We question the appropriateness of the tolerance interval approach. Our concerns relate to the computational challenges and practical interpretations of the method. We provide an alternative Bayesian approach, which directly controls the desired proportion of batches falling out of specification assuming a controlled manufacturing process. The approach has an intuitive interpretation and posterior distributions are straightforward to compute. If prior information on the fixed and random parameters is available, a Bayesian approach can provide additional benefits both to the company and the consumer. It also avoids many of the computational challenges with the tolerance interval methodology.