Age structure effects and growth in the OECD, 1950–1990

Economic growth depends on human resources and human needs. The demographic age structure shapes both of these factors. We study five-year data from the OECD countries 1950–1990 in the framework of an age structure augmented neoclassical growth model with gradual technical adjustment. The model performs well in both pooled and panel estimations. The growth patterns of GDP per worker (labor productivity) in the OECD countries are to a large extent explained by age structure changes. The 50–64 age group has a positive influence, and the group above 65 contributes negatively, while younger age groups have ambiguous effects. However, the mechanism behind these age effects is not yet resolved. Received: 16 January 1997/Accepted: 2 July 1998     

The next forty years in public health

In this article an attempt is made to assess the major health problems of the second half of the twentieth century in developed countries, the methods appropriate to these problems, and the pattern of medical services necessary to deal with them. From an examination of mortality and morbidity trends it is suggested that the most important problems are likely to be the mortality, malformation and disability established before birth, mental illness, and the disease and disability associated with ageing. Reasons are given for doubting whether control of breeding, the method which has contributed greatly to the improvement of plants and other animals, can be expected to be effective in the case of man, and hence reliance must continue to be placed on modifications of the environment to which achievement hitherto has been entirely due. The methods likely to be significant are: an extension of measures already in use for control ofthe physical environment; discovery and application of knowledge concerning the social environment; and elaboration of more effective methods of preventing and treating disease in the individual. Profound changes will be needed in the pattern of services through which medical knowledge is applied, the most important being unification ofthe major classes ofhospitals - acute, mental and chronic; association ofthe preventive personal health services with curative services (rather than with environmental services); and the strengthening of domiciliary medical care, particularly through a new and more intimate relationship between general practitioner and hospital services.     

Dublin’s Seventeenth Century Parishes and the Quality of Life

Social Indicators Research - The registers of Dublin’s parishes in the seventeenth century provide access to aspects of civic and religious life. In the registers are records of burials,...     

Demographic change and shifting views about marine resources and the coastal environment in Downeast Maine

Connections to the sea often define the character of coastal towns. However, as migrants arrive and economic diversification occurs, views about the use of marine resources and the ocean environment can change. Using survey data from Maine, we examined whether shifting demographics affect public perceptions of marine resource uses and coastal environmental concerns. We tested resource use and environmental items against a common set of demographic, background, and place-related variables. Results indicate that the level of education and the county of residence predict Mainers?? views about different marine resource uses and ocean-related environmental issues. Political party affiliation strongly influences environmental concern but not views about the use of marine resources. Migration history, on the other hand, has little effect. Understanding community contexts as well as individual background and ideological orientations will be critical as managers attempt to balance alternative uses of marine resources and resolve coastal environmental problems.     

Estimating heritability for cause specific mortality based on twin studies

There has been considerable interest in studying the magnitude and type of inheritance of specific diseases. This is typically derived from family or twin studies, where the basic idea is to compare the correlation for different pairs that share different amount of genes. We here consider data from the Danish twin registry and discuss how to define heritability for cancer occurrence. The key point is that this should be done taking censoring as well as competing risks due to e.g.  death into account. We describe the dependence between twins on the probability scale and show that various models can be used to achieve sensible estimates of the dependence within monozygotic and dizygotic twin pairs that may vary over time. These dependence measures can subsequently be decomposed into a genetic and environmental component using random effects models. We here present several novel models that in essence describe the association in terms of the concordance probability, i.e., the probability that both twins experience the event, in the competing risks setting. We also discuss how to deal with the left truncation present in the Nordic twin registries, due to sampling only of twin pairs where both twins are alive at the initiation of the registries.     

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone.     

Computation of the percentage points and the power for the two-sided Kolmogorov-Smirnov one sample test

Two recursive schemes are presented for the calculation of the probabilityP(g(x)≤S _n (x)≤h(x) for allx∈®), whereS _n is the empirical distribution function of a sample from a continuous distribution andh, g are continuous and isotone functions. The results are specialized for the calculation of the distribution and the corresponding percentage points of the test statistic of the two-sided Kolmogorov-Smirnov one sample test. The schemes allow the calculation of the power of the test too. Finally an extensive tabulation of percentage points for the Kolmogorov-Smirnov test is given.     

Using adaptive weighted least squares to reduce the lengths of confidence intervals

The author proposes an adaptive method which produces confidence intervals that are often narrower than those obtained by the traditional procedures. The proposed methods use both a weighted least squares approach to reduce the length of the confidence interval and a permutation technique to insure that its coverage probability is near the nominal level. The author reports simulations comparing the adaptive intervals to the traditional ones for the difference between two population means, for the slope in a simple linear regression, and for the slope in a multiple linear regression having two correlated exogenous variables. He is led to recommend adaptive intervals for sample sizes superior to 40 when the error distribution is not known to be Gaussian.     

A Bayesian approach to non-parametric monotone function estimation

Summary.  The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone.