Inequality in Mortality in Spain*

We analyse the evolution of mortality rates in Spain by age and gender between 1990 and 2018. We compare municipalities, ranked by socio‐economic status (SES) and grouped into bins of similar population size, to study changes not only in levels but also in inequality in mortality across the SES spectrum. We document large decreases in mortality rates throughout the period for all age groups, including children, even after 2000, and continuing after the Great Recession. These declines are stronger for boys and men, who had higher mortality rates to begin with. We find that inequality in mortality across municipalities was low among the young by 2018, while it was higher among adult men and older women. Inequality in fact increased over the period for older men. We explore the role of different causes of death and find that this increase in inequality is driven by stronger improvements in cancer‐related mortality among men living in richer areas. These improvements are not found among women, given their increases in mortality due to lung cancer.     

Diverging Mortality Inequality Trends among Young and Old in the Netherlands*

We analyse the trends in inequality in mortality across poverty groups at different ages over the period 1996–2016 in the Netherlands. In addition, we examine whether these trends are related to unequal changes in avoidable mortality, separated by preventable and treatable causes of death. We find that while inequalities in mortality have decreased at ages up to 65, inequalities increased for the oldest age groups. The decline in inequality at the younger ages can, to a large extent, be explained by a strong decrease of mortality from preventable and cardiovascular causes among the poor. The link between inequality and avoidable mortality at the oldest ages is less straightforward. The increasing inequality at old age might be the result of the inequalities shifting from the young to the older age groups, or of the rich benefiting more from the recent health (care) improvements than the poor.     

Quantifying mortality risk in small defined-benefit pension schemes

A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is, there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating significantly from the expected liability value, as compared to a large scheme. We quantify this risk through examining the coefficient of variation of a scheme's liability value relative to its expected value. We examine how the coefficient of variation varies with the number of members and find that, even with a few hundred members in the scheme, idiosyncratic mortality risk may still be significant. Next we quantify the amount of the mortality risk concentrated in the executive section of the scheme, where the executives receive a benefit that is higher than the non-executive benefit. We use the Euler capital allocation principle to allocate the total standard deviation of the liability value between the executive and non-executive sections. The results suggest that the mortality risk of the scheme should be monitored and managed within the sections of a scheme and not only on a scheme-wide basis.     

Beyond the Gompertz law: exploring the late-life mortality deceleration phenomenon

Using a new distribution capable of exhibiting all the possible modes of accelerating and decelerating mortality, we conduct a systematic investigation of late-life mortality in humans. We check the insensitivity of the distribution to age cutoffs in the data relative to the logistic mortality model and propose a method to forecast evolution in the characteristic deceleration ages of the distribution. A number of data sets have been explored, with a particular emphasis on those originating from Scandinavia. Although those from Australia, Canada, and the USA are compatible with Gompertzian mortality, those from the other countries examined are not. We find in particular that the onset of mortality deceleration is being progressively delayed in Western societies but that there is evidence of mortality plateauing at earlier ages.     

Rethinking age-period-cohort mortality trend models

Longevity risk arising from uncertain mortality improvement is one of the major risks facing annuity providers and pension funds. In this article, we show how applying trend models from non-life claims reserving to age-period-cohort mortality trends provides new insight in estimating mortality improvement and quantifying its uncertainty. Age, period and cohort trends are modelled with distinct effects for each age, calendar year and birth year in a generalised linear models framework. The effects are distinct in the sense that they are not conjoined with age coefficients, borrowing from regression terminology, we denote them as main effects. Mortality models in this framework for age-period, age-cohort and age-period-cohort effects are assessed using national population mortality data from Norway and Australia to show the relative significance of cohort effects as compared to period effects. Results are compared with the traditional Lee–Carter model. The bilinear period effect in the Lee–Carter model is shown to resemble a main cohort effect in these trend models. However, the approach avoids the limitations of the Lee–Carter model when forecasting with the age-cohort trend model.     

Halloween or January? Yet another puzzle

Recent works suggest a potentially exploitable effect in US markets, the ‘Halloween Indicator’. This suggests that the greater part of changes in equity markets arises over the November-April period, with little change over the summer months, simultaneous with no evident changes in the risk profiles of the two six-month periods. We re-examine this and find contradictory evidence. Over the 1926-2002 period we find rather that the effect demonstrated may well be a reflection of the well-known January anomaly. Our conclusion therefore is that the jury remains out on the existence of a semi-annual seasonality.     

Unequal inequalities: Do progressive taxes reduce income inequality?

This paper analyzes the effect of changes in the structural progressivity of national income tax systems on observed and actual income inequality. Using several unique measures of progressivity over the 1981–2005 period for a large panel of countries, we find that progressivity reduces inequality in observed income, but has a significantly smaller impact on actual inequality, approximated by consumption-based Ginis. An empirical comparative analysis shows that the differential effect on observed versus actual inequality is much larger in countries with weaker legal institutions. We also find that structural progressivity has a greater equalizing effect in environments that support pro-poor redistribution. Substantial differences in inequality response to changes in top versus bottom rates are also uncovered.     

Cohort extensions of the Poisson common factor model for modelling both genders jointly

The earlier work on mortality modelling and forecasting has largely focused on the study of a single population. Recently, there is an emerging strand of literature that emphasises the interrelationship between multiple populations. In this paper, we examine some cohort extensions of the Poisson common factor model for modelling both genders jointly. The cohort effect is specified in six alternatives which are applied to data-sets from five developed regions. We find that direct parameterisation of cohort effect could improve model fitting, reduce the need for additional period factors, and produce consistent mortality forecasts for females and males. Furthermore, we find that the cohort effect appears to be gender indifferent for the populations examined and has an interaction effect with age in certain cases.     

A General Procedure for Constructing Mortality Models

Recently a large number of new mortality models have been proposed to analyze historic mortality rates and project them into the future. Many of these suffer from being over-parametrized or have terms added in an ad hoc manner that cannot be justified in terms of demographic significance. In addition, poor specification of a model can lead to period effects in the data being wrongly attributed to cohort effects, which results in the model making implausible projections. We present a general procedure for constructing mortality models using a combination of a toolkit of functions and expert judgment. By following the general procedure, it is possible to identify sequentially every significant demographic feature in the data and give it a parametric structural form. We demonstrate using U.K. mortality data that the general procedure produces a relatively parsimonious model that nevertheless has a good fit to the data.     

Understanding the term structure of credit default swap spreads

The shape of the term structure of credit default swap (CDS) spreads displays large variations over time and across firms. Consistent with the predictions of structural models of credit risk, we find that the slope of CDS spread term structure increases with firm leverage and volatility, but decreases with the level and the slope of the Treasury yield curve. However, these variables together have rather limited explanatory power for CDS slope and there is a significant common component in the regression residuals. In addition, we find that CDS slope predicts future changes in the CDS spreads, even after controlling for the contemporaneous variables that determine changes in the CDS spreads according to the structural models. Our results suggest that while structural models are qualitatively useful for understanding the shape of credit term structure, there are missing factors that importantly affect the term structure of CDS spreads.     

The Lee-Carter quantile mortality model

The Lee-Carter (LC) stochastic mortality model has been widely used for making future projections of mortality rates. In the framework of the LC model, the response function is non-linear in parameters. Here, we adapt this LC framework to compute conditional quantiles. The LC quantile model can be defined as quantile non-linear regression conditioned to age and the calendar year. Two strategies for estimating coefficients based on interior-point methods are described. We show that the LC quantile model provides additional information to that furnished by the traditional LC conditional mean. An application to Spanish mortality data is reported.     

Earnings management following chief executive officer changes: the effect of contemporaneous chairperson and chief financial officer appointments

Using a sample of listed Australian firms from 1999 to 2007, we examine the relationship between discretionary accruals and concurrent senior management appointments. Employing panel data regression models and focusing on a measure of discretionary accruals that excludes the effect of transparent write‐downs such as restructuring charges, we find that chief executive officer (CEO) appointments, as a general phenomenon, are not significantly associated with opaque earnings management in the year of appointment or the following year. However, we find that CEO changes accompanied by a concurrent change in board chairperson are associated with significant income‐decreasing earnings management in the year of appointment. We detect no significant relationship between contemporaneous CEO and chief financial officer changes and discretionary accruals. We find no evidence of earnings management in the first compete financial period following CEO appointment, regardless of whether or not concurrent Chair or chief financial officer appointments occurred.     

Multi-population mortality models: fitting,forecasting and comparisons

We review a number of multi-population mortality models: variations of the Li & Lee model, and the common-age-effect (CAE) model of Kleinow. Model parameters are estimated using maximum likelihood. Although this introduces some challenging identifiability problems and complicates the estimation process it allows a fair comparison of the different models. We propose to solve these identifiability problems by applying two-dimensional constraints over the parameters. Using data from six countries, we compare and rank, both visually and numerically, the models’ fitting qualities and develop forecasting models that produce non-diverging, joint mortality rate scenarios. It is found that the CAE model fits best. But we also find that the Li and Lee model potentially suffers from robustness problems when calibrated using maximum likelihood.     

The behavior of financial analysts during the Asian financial crisis in Indonesia,Korea, Malaysia,and Thailand

This study investigates the behavior of a potentially influential class of market participants, the stock analysts, around the period of market crashes in four Asian countries. We find that analysts not only failed to anticipate the weaknesses in the firms they covered before the crash, they also failed to sufficiently adjust their forecasts after these markets crashed. Throughout the entire period of the study, the magnitudes of forecast errors were several times to that observed in the pre-crash period. However, we also do not find evidence of panic or herding on a large scale. Other issues investigated in this paper include the changes in coverage, changes in forecasts accuracy, the extent analysts agree and the patterns of their convergence for the sample. We also compare analyst forecasts for large versus small firms, and for high- versus low-growth/quality firms. A contribution to the literature is the cataloguing of models that may provide explanations for investors' expectations during a market crash.     

Stochastic mortality models: an infinite-dimensional approach

Demographic projections of future mortality rates involve a high level of uncertainty and require stochastic mortality models. The current paper investigates forward mortality models driven by a (possibly infinite-dimensional) Wiener process and a compensated Poisson random measure. A major innovation of the paper is the introduction of a family of processes called forward mortality improvements which provide a flexible tool for a simple construction of stochastic forward mortality models. In practice, the notion of mortality improvements is a convenient device for the quantification of changes in mortality rates over time, and enables, for example, the detection of cohort effects. We show that the forward mortality rates satisfy Heath–Jarrow–Morton-type consistency conditions which translate to conditions on the forward mortality improvements. While the consistency conditions for the forward mortality rates are analogous to the classical conditions in the context of bond markets, the conditions for the forward mortality improvements possess a different structure. Forward mortality models include a cohort parameter besides the time horizon, and these two dimensions are coupled in the dynamics of consistent models of forward mortality improvements. In order to obtain a unified framework, we transform the systems of Itô processes which describe the forward mortality rates and improvements. In contrast to term structure models, the corresponding stochastic partial differential equations (SPDEs) describe the random dynamics of two-dimensional surfaces rather than curves.     

Evolving Macroeconomic Dynamics in a Small Open Economy: An Estimated Markov Switching DSGE Model for the UK

This paper investigates the possibility of shifts in the UK economy using a Markov switching open economy dynamic stochastic general equilibrium (DSGE) model. We find overwhelming evidence to reject the hypothesis that the deep structural parameters of the underlying structural model had stayed constant throughout the sample period and there is significant changes to the volatility of the structural shocks. Counterfactual experiments based on the model with the best empirical fit indicate that the change in the policy rule as well as changes to the volatility of the structural shocks over the sample period are crucial features in explaining UK’s macroeconomic performance.     

Best-performing US mutual fund families from 1993 to 2008: Evidence from a novel two-stage DEA model for efficiency decomposition

When analyzing relative performance, especially at the institutional level, the traditional data envelopment analysis (DEA) models do not recognize vastly different and important activities as separate functions and therefore cannot identify which function may be the main source of inefficiency. We propose a novel two-stage DEA model that decomposes the overall efficiency of a decision-making unit into two components and demonstrate its applicability by assessing the relative performance of 66 large mutual fund families in the US over the period 1993–2008. By decomposing the overall efficiency into operational management efficiency and portfolio management efficiency components, we reveal the best performers, the families that deteriorated in performance, and those that improved in their performance over the sample period. We also make frontier projections for poorly performing mutual fund families and highlight how the portfolio managers have managed their funds relative to the others during financial crisis periods.     

Modeling cause-of-death mortality using hierarchical Archimedean copula

Studying changes in cause-specific (or competing risks) mortality rates may provide significant insights for the insurance business as well as the pension systems, as they provide more information than the aggregate mortality data. However, the forecasting of cause-specific mortality rates requires new tools to capture the dependence among the competing causes. This paper introduces a class of hierarchical Archimedean copula (HAC) models for cause-specific mortality data. The approach extends the standard Archimedean copula models by allowing for asymmetric dependence among competing risks, while preserving closed-form expressions for mortality forecasts. Moreover, the HAC model allows for a convenient analysis of the impact of hypothetical reduction, or elimination, of mortality of one or more causes on the life expectancy. Using US cohort mortality data, we analyze the historical mortality patterns of different causes of death, provide an explanation for the ‘failure’ of the War on Cancer, and evaluate the impact on life expectancy of hypothetical scenarios where cancer mortality is reduced or eliminated. We find that accounting for longevity improvement across cohorts can alter the results found in existing studies that are focused on one single cohort.     

Survival analysis of pension scheme mortality when data are missing

Missing data is a problem that may be faced by actuaries when analysing mortality data. In this paper we deal with pension scheme data, where the future lifetime of each member is modelled by means of parametric survival models incorporating covariates, which may be missing for some individuals. Parameters are estimated by likelihood-based techniques. We analyse statistical issues, such as parameter identifiability, and propose an algorithm to handle the estimation task. Finally, we analyse the financial impact of including covariates maximally, compared with excluding parts of the mortality experience where data are missing; in particular we consider annuity factors and mis-estimation risk capital requirements.     

Investment restrictions and fund performance

We examine the effect of investment restrictions on mutual fund performance. Utilizing a unique panel of mutual fund contract changes, we explore several ways these changes affect a fund, including: performance, funding risk, and managerial contracting. We find that the general shift towards fewer restrictions over the period 1996–2011 has provided little benefit to mutual funds. Specifically, neither performance nor flow increased and we observe no changes in risk on average. We do find, however, an increased likelihood of management turnover when restrictions are removed. We conclude that contract restrictions do not explain the general underperformance of mutual funds, and that these investment restrictions are not binding.