The rise in healthcare expenditures has raised doubts about the sustainability of health systems and instigated a discussion on their design. Policy making in this field requires a proper understanding of how healthcare expenditures evolve throughout an individual's lifetime, and of how they vary between individuals. Given the lack of data on healthcare expenditures during an individual's lifetime, we developed a new nearest neighbour resampling approach to construct realistic individual life cycles of healthcare expenditures based on cross-sectional data from the Netherlands. This approach provides insight into lifetime healthcare expenditures. Our main finding is that the inequality in lifetime healthcare expenditures is much smaller than the inequality as derived from cross-sectional healthcare expenditures.

Surveillance data collected on several hundred different infectious organisms over 20 years have revealed striking power relationships between their variance and mean in successive time periods. Such patterns are common in ecology, where they are referred to collectively as Taylor's power law. In the paper, these relationships are investigated in detail, with the aim of exploiting them for the descriptive statistical modelling of infectious disease surveillance data. We confirm the existence of variance-to-mean power relationships, with exponent typically between 1 and 2. We investigate skewness-to-mean relationships, which are found broadly to match those expected of Tweedie distributions, and thus confirm the relevance of the Tweedie convergence theorem in this context. We suggest that variance- and skewness-to-mean power laws, when present, should inform statistical modelling of infectious disease surveillance data, notably in descriptive analysis, model building, simulation and interval and threshold estimation, threshold estimation being particularly relevant to outbreak detection.

Longitudinal monitoring of biomarkers is often helpful for predicting disease or a poor clinical outcome. We consider the prediction of both large and small for gestational age births by using longitudinal ultrasound measurements, and we attempt to identify subgroups of women for whom prediction is more (or less) accurate, should they exist. We propose a tree-based approach to identifying such subgroups, and a pruning algorithm which explicitly incorporates a desired type I error rate, allowing us to control the risk of false discovery of subgroups. The methods proposed are applied to data from the Scandinavian Fetal Growth Study and are evaluated via simulations.

We apply parametric and non-parametric regression discontinuity methodology within a multinomial choice setting to examine the effect of public healthcare user fee abolition on health facility choice by using data from South Africa. The non-parametric model is found to outperform the parametric model both in and out of sample, while also delivering more plausible estimates of the effect of user fee abolition (i.e. the ‘treatment effect’). In the parametric framework, treatment effects were relatively constant—around 10%—and that increase was drawn equally from home care and private care. In contrast, in the non-parametric framework treatment effects were largest for large (and poor) families located further from health facilities—approximately 5%. More plausibly, the positive treatment effect was drawn primarily from home care, suggesting that the policy favoured children living in poorer conditions, as those children received at least some minimum level of professional healthcare after the policy was implemented.

]]>Sampling hidden populations is particularly challenging by using standard sampling methods mainly because of the lack of a sampling frame. Respondent-driven sampling is an alternative methodology that exploits the social contacts between peers to reach and weight individuals in these hard-to-reach populations. It is a snowball sampling procedure where the weight of the respondents is adjusted for the likelihood of being sampled due to differences in the number of contacts. The structure of the social contacts thus regulates the process by constraining the sampling within subregions of the network. We study the bias induced by network communities, which are groups of individuals more connected between themselves than with individuals in other groups, in the respondent-driven sampling estimator. We simulate different structures and response rates to reproduce real settings. We find that the prevalence of the estimated variable is associated with the size of the network community to which the individual belongs and observe that low degree nodes may be undersampled if the sample and the network are of similar size. We also find that respondent-driven sampling estimators perform well if response rates are relatively large and the community structure is weak, whereas low response rates typically generate strong biases irrespectively of the community structure.

The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic accidents data at ward level in England over the period 2005–2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective.

The instability of ethnicity measured in the national census is found to have doubled from the period 1991–2001 to the period 2001–2011, using the Longitudinal Study that links a sample of individuals’ census records across time. From internal evidence and comparison with results from the Census Quality Survey and the Labour Force Survey, estimates are made of instability due to changing question wording, imputation of missing answers, proxy reporting, recording errors and changes in the allocation of write-in answers. Of the remaining instability, durable changes of ethnicity by individuals are thought to be considerably less common than changes due to a person's sense of identity not closely fitting the categories offered in the census question. The instability creates a net change in size of some ethnic groups that is usually small compared with the change in population between censuses from births, deaths and migration. Consequences for analysis of census aggregate and microdata are explored.

We present new auto-regressive logit models for forecasting the probability of a time series of financial asset returns exceeding a threshold. The models can be estimated by maximizing a Bernoulli likelihood. Alternatively, to account for the extent to which an observation does or does not exceed the threshold, we propose that the likelihood is based on the asymmetric Laplace distribution, which has been found to be useful for quantile estimation. We incorporate the exceedance probability forecasts within a new time varying extreme value approach to value at risk and expected shortfall estimation. We provide an empirical illustration using daily stock index data.

The analysis of national mortality trends is critically dependent on the quality of the population, exposures and deaths data that underpin death rates. We develop a framework that allows us to assess data reliability and to identify anomalies, illustrated, by way of example, using England and Wales population data. First, we propose a set of graphical diagnostics that help to pinpoint anomalies. Second, we develop a simple Bayesian model that allows us to quantify objectively the size of any anomalies. Two-dimensional graphical diagnostics and modelling techniques are shown to improve significantly our ability to identify and quantify anomalies. An important conclusion is that significant anomalies in population data can often be linked to uneven patterns of births of people in cohorts born in the distant past. In the case of England and Wales, errors of more than 9% in the estimated size of some birth cohorts can be attributed to an uneven pattern of births. We propose methods that can use births data to improve estimates of the underlying population exposures. Finally, we consider the effect of anomalies on mortality forecasts and annuity values, and we find significant effects for some cohorts. Our methodology has general applicability to other sources of population data, such as the Human Mortality Database.

Age and sex patterns of migration are essential for understanding drivers of population change and heterogeneity of migrant groups. We develop a hierarchical Bayesian model to estimate such patterns for international migration in the European Union and European Free Trade Association from 2002 to 2008, which was a period of time when the number of members expanded from 19 to 31 countries. Our model corrects for the inadequacies and inconsistencies in the available data and estimates the missing patterns. The posterior distributions of the age and sex profiles are then combined with a matrix of origin–destination flows, resulting in a synthetic database with measures of uncertainty for migration flows and other model parameters.

Macroeconomic indicators about the labour force, published by national statistical institutes, are predominantly based on rotating panels. Sample sizes of most labour force surveys in combination with the design-based or model-assisted mode of inference obstruct the publication of such indicators on a monthly frequency. Previous research proposed a multivariate structural time series model to obtain more precise model-based estimates by taking advantage of sample information observed in previous periods. In the paper this model is extended to use sample information from other domains or strongly correlated auxiliary series. A relatively parsimonious version of these models is currently used by Statistics Netherlands to produce official monthly figures about the labour force.

We consider two econometric problems when investigating the effect of family size on labour market outcomes using the popular twin birth instrument. The first is the potential for omitted variable bias caused by the fact that fertility treatments are linked to twin births and are typically unobserved. We present estimates that are corrected for this bias and find that it is comparatively small. Second, we show that the effects of twin-birth-induced variation in family size, as well as characteristics of the compliers, varies substantially with time passed since birth, which has consequences for the interpretation of estimates across samples and time.

The paper uses a symmetric entropy statistic to study income inequality. The index quantifies the information content of a two-way message that transforms the empirical income distribution into an egalitarian reference distribution, and then back to the original. This allows the measure to be interpreted as an average of *n* income-to-mean divergences such that the inequality estimate can be broken down into contributions across population subgroups. Various properties of the index are analysed and an application comparing the USA, Germany and Britain is provided. We focus on the sensitivity of inequality to the tails of the income distribution and show that the extreme right-hand tail accounts for a large and generally increasing proportion of total inequality. This result holds even if incomes are measured at the household level, averaged over a 5-year period and taken after government taxes and transfers.

We present substantial evidence for the existence of a bias in the distribution of births of leading US politicians in favour of those who were the eldest in their cohort at school. This result adds to the research on the long-term effects of relative age among peers at school. We discuss parametric and non-parametric tests to identify this effect, and we show that it is not driven by measurement error, redshirting or a sorting effect of highly educated parents. The magnitude of the effect that we estimate is larger than what other studies on ‘relative age effects’ have found for broader populations but is in general consistent with research that looks at professional sportsmen. We also find that relative age does not seem to correlate with the quality of elected politicians.

We explore the existence of short- and long-term effects of retirement on health. Short-term effects are estimated with a regression discontinuity design which is robust to weak instruments and where the underlying assumptions of continuity of potential outcomes are uncontroversial. To identify the long-term effects we propose a parametric model which, under strong assumptions, can separate normal deterioration of health from the causal effects of retirement. We apply our framework to the British Household Panel Survey and find that retirement has little effect on health. However, our estimates suggest that retirement opens the gate to a sedentary life with an impoverished social component and this is a channel through which retirement could indirectly affect health in the long run.

Recently, various indicators have been proposed as indirect measures of non-response error in surveys. They employ auxiliary variables, external to the survey, to detect non-representative or unbalanced response. A class of designs known as adaptive survey designs maximizes these indicators by applying different treatments to different subgroups. The natural question is whether the decrease in non-response bias that is caused by adaptive survey designs could also be achieved by non-response adjustment methods. We discuss this question and provide theoretical and empirical considerations, supported by a range of household and business surveys. We find evidence that more balanced response coincides with less non-response bias, even after adjustment.

Using Bayesian Markov chain clustering analysis we investigate career paths of Austrian women after their first birth. This data-driven method allows characterizing long-term career paths of mothers over up to 19 years by transitions between parental leave, non-employment and different forms of employment. We classify women into five cluster groups with very different long-run career trajectories after childbearing. We further model group membership with a multinomial specification within the finite mixture model. This approach gives insights into the determinants of long-run outcomes. In particular, giving birth at an older age appears to be associated with very diverse outcomes: it is related to higher odds of dropping out of the labour force, on the one hand, but also to higher odds of reaching a high wage career track, on the other hand.

Research demonstrates that police reduce crime. We study this question by using a natural experiment in which a private university increased the number of police patrols within an arbitrarily defined geographic boundary. Capitalizing on the discontinuity in patrols at the boundary, we estimate that the extra police decreased crime in adjacent city blocks by 43–73%. Our results are consistent with findings from prior work that used other kinds of natural experiment. The paper demonstrates the utility of the geographic regression discontinuity design for estimating the effects of extra public or private services on a variety of outcomes.

Sequence analysis is widely used in life course research and more recently has been applied by survey methodologists to summarize complex call record data. However, summary variables derived in this way have proved ineffective for post-survey adjustments, owing to weak correlations with key survey variables. We reflect on the underlying optimal matching algorithm and test the sensitivity of the output to input parameters or ‘costs’, which must be specified by the analyst. The results illustrate the complex relationship between these costs and the output variables which summarize the call record data. Regardless of the choice of costs, there was a low correlation between the summary variables and the key survey variables, limiting the scope for bias reduction. The analysis is applied to call records from the Irish Longitudinal Study on Ageing, which is a nationally representative, face-to-face household survey.

Multiple-imputation (MI) methods for imputing missing data in observational health studies with repeated measurements were evaluated with particular focus on incomplete time varying explanatory variables. Standard and random-effects imputation by chained equations, multivariate normal imputation and Bayesian MI were compared regarding bias and efficiency of regression coefficient estimates by using simulation studies. Flexibility of the methods in handling different types of variables (binary, categorical, skewed and normally distributed) and correlations between the repeated measurements of the incomplete variables were also compared. Multivariate normal imputation produced the least bias in most situations, is theoretically well justified and allows flexible correlation for the repeated measurements. It can be recommended for imputing continuous variables. Bayesian MI is efficient and may be preferable in the presence of categorical and non-normally distributed continuous variables. Imputation by chained equations approaches were sensitive to the correlation between the repeated measurements. The moving time window approach may be used for normally distributed continuous variables with auto-regressive correlation.

We conduct a quasi-Monte-Carlo comparison of the recent developments in parametric and semiparametric regression methods for healthcare costs, both against each other and against standard practice. The population of English National Health Service hospital in-patient episodes for the financial year 2007–2008 (summed for each patient) is randomly divided into two equally sized subpopulations to form an estimation set and a validation set. Evaluating out-of-sample using the validation set, a conditional density approximation estimator shows considerable promise in forecasting conditional means, performing best for accuracy of forecasting and among the best four for bias and goodness of fit. The best performing model for bias is linear regression with square-root-transformed dependent variables, whereas a generalized linear model with square-root link function and Poisson distribution performs best in terms of goodness of fit. Commonly used models utilizing a log-link are shown to perform badly relative to other models considered in our comparison.

We show how a moment-based estimation procedure can be used to compute point estimates and standard errors for the two components of the widely used Olley–Pakes decomposition of aggregate (weighted average) productivity. When applied to business level microdata, the procedure allows for autocovariance and heteroscedasticity robust inference and hypothesis testing about, for example, the coevolution of the productivity components in different groups of firms. We provide an application to Finnish firm level data and find that formal statistical inference casts doubt on the conclusions that one might draw on the basis of a visual inspection of the components of the decomposition.

The paper argues that we need more general statistical indices for the analysis of the European labour markets. First, the paper discusses some normative aspects that are implicit in the current definition of the employment rate, which is a fundamental policy target in the new strategy Europe 2020. Second, it proposes a class of generalized indices based on work intensity, as approximated by the total annual hours of work relative to a benchmark value. Third, it derives, in a consistent framework, household level employment indices. These indices provide a more nuanced picture of the European labour markets, which better reflects the diversity in the use of part-time and fixed term jobs as well as other factors affecting the allocation of work between and within households.

]]>Some sociologists argue that non-intact family structures during childhood have a negative effect on adult children's civic engagement, since they undermine, and in some cases prevent, the processes and activities through which parents shape their children's political attitudes and orientations. In this paper, we evaluate this hypothesis on the basis of longitudinal data from the German Socio-Economic Panel. In a first step, we construct various measures of family structure during childhood and perform both cross-sectional and sibling difference analyses for various indicators of young adults' civic engagement. Both exercises reveal a significant negative relationship between growing up in a non-intact family and children's political engagement as adults. In a second step, we implement a novel technique—proposed by Oster—for evaluating robustness of results to omitted variable bias. The distinctive feature of this technique is that it accounts for both coefficient movements *and* movements in -values after the inclusion of controls. Results suggest that our estimates do not suffer from omitted variable bias.

Low front-end cost and rapid accrual make Web-based surveys and enrolment in studies attractive, but participants are often self-selected with little reference to a well-defined study base. Of course, high quality studies must be internally valid (validity of inferences for the sample at hand), but Web-based enrolment reactivates discussion of external validity (generalization of within-study inferences to a target population or context) in epidemiology and clinical trials. Survey research relies on a representative sample produced by a sampling frame, prespecified sampling process and weighting that maps results to an intended population. In contrast, recent analytical epidemiology has shifted the focus away from survey-type representativity to internal validity in the sample. Against this background, it is a good time for statisticians to take stock of our role and position regarding surveys, observational research in epidemiology and clinical studies. The central issue is whether conditional effects in the sample (the study population) may be transported to desired target populations. Success depends on compatibility of causal structures in study and target populations, and will require subject matter considerations in each concrete case. Statisticians, epidemiologists and survey researchers should work together to increase understanding of these challenges and to develop improved tools to handle them.

Statistics Netherlands applies a design-based estimation procedure to produce road transportation figures. Frequent survey redesigns caused discontinuities in these series which obstruct the comparability of figures over time. Reductions in the sample size and changes in the sample design resulted in variance breaks and unacceptably large sampling errors in the recent part of the series. Both problems are addressed and solved simultaneously. Discontinuities and small sample sizes are accounted for by using a multivariate structural time series model that borrows strength over time and space. The paper illustrates an increased precision when we move from univariate models to a multivariate model where the domains are jointly modelled. This increase is especially significant in the most recent period when sample sizes become smaller, with standard errors of the design-based estimator of the target variables being reduced by 40 & #x2013;70 & #x0025; with the model-based approach.

A mismatch between the timescale of a structural vector auto-regressive model and that of the time series data used for its estimation can have serious consequences for identification, estimation and interpretation of the impulse response functions. However, the use of mixed frequency data, combined with a proper estimation approach, can alleviate the temporal aggregation bias, mitigate the identification issues and yield more reliable responses to shocks. The problems and possible remedy are illustrated analytically and with both simulated and actual data.

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, *M*-quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

A new semiparametric and robust approach to small area estimation for discrete outcomes is proposed. The methodology represents an efficient and easily computed alternative to prediction by using a generalized linear mixed model and is based on an extension of *M*-quantile regression. In addition, two estimators of the prediction mean-squared error are described: one based on Taylor linearization and another based on the block bootstrap. The methodology proposed is applied to UK annual Labour Force Survey data for estimating the proportion of the unemployed in local authorities in the UK. The properties of estimators are further empirically assessed in model-based simulations.

This is a comparative study of the multiple ways of measuring dissimilarities between state sequences. The originality of the study is the focus put on the differences between sequences that are sociologically important when studying life courses such as family life trajectories or professional careers. These differences essentially concern the sequencing (the order in which successive states appear), the timing and the duration of the spells in successive states. The study examines the sensitivity of the measures to these three aspects analytically and empirically by means of simulations. Even if some distance measures underperform, the study shows that there is no universally optimal distance index, and that the choice of a measure depends on which aspect we want to focus on. From the review and simulation results, the paper derives guidelines to help the end user to choose the right dissimilarity measure for her or his research objectives. This study also introduces novel ways of measuring dissimilarities that overcome some flaws in existing measures.

Passing the ball is one of the key skills of a football player yet the metrics commonly used to evaluate passing ability are crude and largely limited to various forms of a pass completion rate. These metrics can be misleading for two general reasons: they do not account for the difficulty of the attempted pass nor the various levels of uncertainty involved in empirical observations based on different numbers of passes per player. We address both these deficiencies by building a statistical model in which the success of a pass depends on the skill of the executing player as well as other factors including the origin and destination of the pass, the skill of his teammates and the opponents, and proxies for the defensive pressure put on the executing player as well as random chance. We fit the model by using data from the 2006–2007 season of the English Premier League provided by Opta, estimate each player's passing skill and make predictions for the next season. The model predictions considerably outperform a naive method of simply using the previous season's completion rate as a predictor of the following season's completion rate. In particular, we show how a change in the difficulty of passes attempted in both seasons explains a significant proportion of the shift in the observed performance of some players—a fact that is ignored if the raw completion rate is used to evaluate player skill.

We use data from the four sweeps of the UK Millennium Cohort Study of children born at the turn of the 21st century to document the effect that poverty, and in particular persistent poverty, has on their cognitive development in their early years. Using structural equation modelling, we show that children born into poverty have significantly lower test scores at age 3, age 5 and age 7 years, and that continually living in poverty in their early years has a cumulative negative effect on their cognitive development. For children who are persistently in poverty throughout their early years, their cognitive development test scores at age 7 years are almost 20 percentile ranks lower than children who have never experienced poverty, even after controlling for a wide range of background characteristics and parental investment.

The paper uses data from the consumer expenditure surveys to demonstrate that the mode of collection is important for the analysis of consumption data. We first show that population figures obtained with diaries markedly differ from figures obtained by using recall questions. We then exploit multiple measurements of food expenditure to identify the effects of the mode of collection on the distribution of reported consumption. Finally, we show how to combine information from multiple reports to obtain a single measure of total expenditure in consumer expenditure surveys. The paper concludes by offering guidelines for empirical analyses based on these data, and by providing an application of the methods proposed to the measurement of inequality and wellbeing.

Speculations about whether strategic voting made a difference to the outcome of an election regularly whip up the passions of pundits, party strategists, electoral reformers and scholars alike. Yet, research on strategic voting's political effect has been hampered by the scarcity of data on district level party preferences. We propose the use of Bayesian small area estimation to predict district level preferences from just a handful of survey responses per district and comparing these predictions against election results to estimate how many voters switched sides in each district. We apply the approach to estimate how many seats changed hands as a result of strategic voting at the 1997 and 2001 UK general elections. Despite similar rates of strategic voting in both elections, the number of seats that were affected was markedly greater in 1997. Interestingly, the Liberal Democrats turn out to win the most seats because of strategic voting. We also estimate how many votes went in the ‘wrong’ direction—away from otherwise viable candidates. We validate our results by using journalistic sources and compare them with previous published estimates.

We discuss the problem of random measurement error in two variables when using a cross-lagged panel design. We apply the problem to the question of the causal direction between socio-economic status and subjective health, known also as health selection *versus* social causation. We plot the bias of the ratio between the social causation and the health selection coefficient as a function of the degree of measurement error in subjective health and socio-economic status for different scenarios which might occur in practice. Using simulated data we give an example of a Bayesian model for the treatment of measurement error that relies on external information about the degree of measurement error.