Close ties between politicians and businesses affect firms’ performance and political outcomes, and while direct political control over firms has been curtailed by tightened regulation, political connections remain ubiquitous in many countries. Yet, it is unclear through which channels these linkages are maintained in strictly regulated environments. I speculate that one such channel of political control over firms is politicians’ ability to influence corporate appointment decisions. To test the claim, I employ survival models that analyze chairpersons’ turnovers in 90 Spanish savings banks between 1985 and 2010 and find strong evidence for electoral appointment cycles: bank chairpersons are more likely to lose office shortly after regional elections and when new governments enter office.

Competing risks survival analysis is used to investigate tax and zoning policy impacts on residential, commercial, and industrial development timing in a rapidly growing Midwestern county. Industrial development appears both to precede and occur concurrently with residential development, while commercial development follows other types. Although residences appear to locate away from industrial land, zoning decisions favoring industry may attract rather than deter residential development within a jurisdiction. Regions with higher infrastructure taxes experience development later. Because school taxes fund local public goods important to homeowners, they have little influence on residential timing, but strong influences on industrial and commercial timing.

Probit-based models relating a proportional response variable to a temporal explanatory variable, assuming that the times to response are normally distributed within the population, have been used in seed biology for describing the rate of loss of viability during seed ageing and the progress of germination over time in response to environmental signals (e.g. water, temperature). These models may be expressed as generalized linear models (GLMs) with a probit (cumulative normal distribution) link function, and, using GLM fitting procedures in current statistical software, parameters of these models are efficiently estimated while taking into account the binomial error distribution of the dependent variable. The fitted parameters can then be used to calculate the ‘traditional’ model parameters, such as the hydro- or hydrothermal time constant, the mean or median response of the seeds (e.g. mean time to death, median base water potential), and the standard deviation of the normal distribution of that response. Furthermore, through consideration of the deviance and residuals, performing model evaluation and modification can lead to improved understanding of the underlying physiological/ecological processes. However, fitting a binomial GLM is not appropriate for the cumulative count data often collected from germination studies, as successive observations are not independent, and time-to-event/survival analysis should be considered instead. This review discusses well-known probit-based models, providing advice on how to collect appropriate data and fit the models to those data, and gives an overview of alternative analysis approaches to improve understanding of the underlying mechanisms of seed dormancy and germination behaviour.

Actuarial practice as regards mortality analysis and projection is changing rapidly. This paper provides a short introduction to some of the limitations and risks in using trends in cause of death as a means for projecting future mortality rates. It also covers recent developments in analysing the mortality of smaller populations, including survival models and “piggyback” models.

The Nested Binomial Model presented in this paper is a new approach to modelling mortality and survival in humans and other species that seeks to reconcile individual life course risk trajectories and those population mortality patterns that arise from inter-individual heterogeneity. In describing individual trajectories it partitions mortality risk into two main elements: ‘redundancy’ and ‘interactive risk'. Interactive risk is volatile, increasing or decreasing with time and circumstance, while redundancy is a quantity which declines in a linear and largely invariable fashion throughout life. Although a biological correlate for redundancy is not identified, this assumption allows strikingly realistic modelling of mortality and survival curves, late-life mortality deceleration, Strehler-Mildvan correlation, mortality plateaux and slowing of mortality. Simple assumptions with regard to heterogeneity of parameters within the model allow close approximation to the entire human mortality curve, and provide a rationale for observed and otherwise paradoxical population mortality phenomena. As such, it fulfils biodemographic criteria for a comprehensive theory of ageing. Future challenges are to reconcile its theoretical structure with empirical findings in the biology of ageing and to render it in a form that can become a usable actuarial tool.

Data from insurance portfolios and pension schemes lend themselves particularly well to the application of survival models. In addition to the traditional actuarial risk-rating factors of age, gender and policy size, we find that using geodemographic models based on postcode provides a major boost in explaining risk variation. Geodemographic models can be better than models based on pension size in explaining socio-economic variation, but a model using both is usually better still. Models acknowledging heterogeneity tend to fit better than models which do not. Finally, bootstrapping techniques can be used to test the financial applicability of a model, while weighting the model fit can be used to address concentration risk.

Patterns and trends in late-life mortality are of growing financial importance. The growth in pension liabilities, both public and private, are of crucial interest to governments, insurers and companies with defined benefit pension schemes. This paper explores the patterns in international mortality data, and draws important lessons for actuaries in the United Kingdom.