Effects of targets and aggregation on the propagation of error in mortality forecasts

Official forecasts of mortality depend on assumptions about target values for the future rates of decline in mortality rates. Smooth functions connect the jump‐off (base‐year) mortality to the level implied by the targets. Three alternative sets of targets are assumed, leading to high, middle, and low forecasts. We show that this process can be closely modeled using simple linear statistical models. These explicit models allow us to analyze the error structure of the forecasts. We show that the current assumption of perfect correlation between errors in different ages, at different forecast years, and for different causes of death, is erroneous. An alternative correlation structure is suggested, and we show how its parameters can be estimated from the past data.

The effect of the level of aggregation on the accuracy of mortality forecasts is considered. It is not clear whether or not age‐ and cause‐specific analyses have been more accurate in the past than analyses based on age‐specific mortality alone would have been. The major contribution of forecasting mortality by cause appears to have been in allowing for easier incorporation of expert opinion rather than in making the. data analysis more accurate or the statistical models less biased.     

Assessing Uncertainty in Small Area Forecasts: State of the Practice and Implementation Strategy

Forecasts are needed for everyday decisions and must be in the form of numbers. Yet forecasts invariably turn out to be different than the numbers that actually occur. Yet, most producers of forecasts only present a deterministic view of the future in the form of point predictions. However, the presence of uncertainty is inherent in management or policy decisions and there is often concern that benefits are overstated and risks are understated. Such concerns are difficult to address by providing only point forecasts with no assessment of their uncertainty. Having a better understanding of uncertainty can enhance the usefulness of forecasts and make the work of forecasting agencies an even more valuable product for planners, policy makers, and the public. The purpose of this paper is twofold. First, it presents an overview of the current state-of-the-practice is assessing forecast uncertainty. Second, it offers a guidelines and options for implementing and building uncertainty into small area forecasting processes. There are options for assessing forecasting uncertainty that can and should be implemented by most, if not all, producers of forecasts.     

Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts

Evaluating the predictive ability of mortality forecasts is important yet difficult. Death rates and mean lifespan are basic life table functions typically used to analyze to what extent the forecasts deviate from their realized values. Although these parameters are useful for specifying precisely how mortality has been forecasted, they cannot be used to assess whether the underlying mortality developments are plausible. We therefore propose that in addition to looking at average lifespan, we should examine whether the forecasted variability of the age at death is a plausible continuation of past trends. The validation of mortality forecasts for Italy, Japan, and Denmark demonstrates that their predictive performance can be evaluated more comprehensively by analyzing both the average lifespan and lifespan disparity—that is, by jointly analyzing the mean and the dispersion of mortality. Approaches that account for dynamic age shifts in survival improvements appear to perform better than others that enforce relatively invariant patterns. However, because forecasting approaches are designed to capture trends in average mortality, we argue that studying lifespan disparity may also help to improve the methodology and thus the predictive ability of mortality forecasts.     

Political alienation as a social indicator: Attitudes and action

This paper presents a conceptualization of political alienation as an evaluative orientation toward the political system at the regime level. It then reviews the conceptual status of the most widely used measures of alienation, and concludes that the distinctions among them reflect differences in the attitude object and in the format of the survey questions which make them up. Next the paper reviews trends in attitudes toward American political institutions between 1964 and 1974, demonstrating that the decline in confidence in national leaders was only rarely accompanied by a repudiation of systemic values and processes. These findings are based upon national opinion surveys. The main body of the paper reports on evidence about the relationship between political alienation and political action, drawn from surveys in the San Francisco Bay Area conducted by Berkeley's Survey Research Center in 1972 and 1973. A new Political Alienation Index is used as the attitude measure, and a model is developed to account for alienation's causal influence on participation in unconventional political protest. By the use of multiple regression analysis involving multiplicative terms, it is shown that political alienation interacts with the individual's cognitive and political skills, age, attitudes toward the protest act, and structural opportunities for action to promote protest behavior. Thus the political relevance of rising disaffection from the ongoing order can only be assessed when other characteristics of the alienated and their political context have been established. The Inter University Consortium for Political Research provided access to the University of Michigan's national election surveys. I am grateful to Rober Kahn for permission to reproduce findings from the Bureaucratic Encounter Survey.     

Short-term, population-based forecasting in the public sector

There has recently been a tremendous expansion of the range of problems to which the demographic perspective is applied. Development of a new population-based method to solve the problem of forecasting income assistance caseloads for the state of Washington represents yet another effort in which the demographic perspective helps solve two major public-policy problems: (1) providing accurate and useful forecasts of caseloads, and (2) creating a dynamic model with which to analyze alternative policy proposals. When forecasting or examining the caseload history, it is also common to look at these caseload levels as a time-series. A caseload grows and shrinks as time passes because new members enter the caseload from a population of potential clients while other members exit the caseload. Population-based forecasting, as reported here, is really quite a novel approach to forecasting public assistance caseloads. In most situations, simple extrapolations of past trends or econometric time-series models are used. Characteristics associated with entries and exits can be used to develop dynamic models of current and future caseload changes. For budgeting purposes, these models can be readily translated into average annual caseload levels and can be directly used to examine policy alternatives and programmatic options. Entry and exit rates and volumes can be related to historical, current, and anticipated changes in economic, social, and programmatic conditions to develop models of caseload behavior, and ultimately, forecasts of caseload levels that are used for budget development.     

Reconciling Forecasts of Infant Mortality Rates at National and Sub-National Levels: Grouped Time-Series Methods

Mortality rates are often disaggregated by different attributes, such as sex, state, education, religion, or ethnicity. Forecasting mortality rates at the national and sub-national levels plays an important role in making social policies associated with the national and sub-national levels. However, base forecasts at the sub-national levels may not add up to the forecasts at the national level. To address this issue, we consider the problem of reconciling mortality rate forecasts from the viewpoint of grouped time-series forecasting methods (Hyndman et al. in, Comput Stat Data Anal 55(9):2579–2589, 2011). A bottom-up method and an optimal combination method are applied to produce point forecasts of infant mortality rates that are aggregated appropriately across the different levels of a hierarchy. We extend these two methods by considering the reconciliation of interval forecasts through a bootstrap procedure. Using the regional infant mortality rates in Australia, we investigate the one-step-ahead to 20-step-ahead point and interval forecast accuracies among the independent and these two grouped time-series forecasting methods. The proposed methods are shown to be useful for reconciling point and interval forecasts of demographic rates at the national and sub-national levels, and would be beneficial for government policy decisions regarding the allocations of current and future resources at both the national and sub-national levels.     

A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes

Mortality forecasts are critically important inputs to the consideration of a range of demographically-related policy challenges facing governments in more developed countries. While methods for jointly forecasting mortality for sub-populations offer the advantage of avoiding undesirable divergence in the forecasts of related populations, little is known about whether they improve forecast accuracy. Using mortality data from ten populations, we evaluate the data fitting and forecast performance of the Poisson common factor model (PCFM) for projecting both sexes’ mortality jointly against the Poisson Lee–Carter model applied separately to each sex. We find that overall the PCFM generates the more desirable results. Firstly, the PCFM ensures that the projected male-to-female ratio of death rates at each age converges to a constant in the long run. Secondly, using out-of-sample analysis, we find that the PCFM provides more accurate projection of the sex ratios of death rates, with the advantage being greater for longer-term forecasts. Thus the PCFM offers a viable and sensible means for coherently forecasting the mortality of both sexes. There are also significant financial implications in allowing for the co-movement of mortality of females and males properly.     

基层民主自治进程中乡村计划生育政策落实的困境及出路——基于基层治理中“不出事逻辑”的分析视角

本文对基层民主自治进程中乡村计划生育政策落实的困境及出路进行了探讨。基层民主自治是我国的一项基本政治制度,实行基层民主自治是发展中国特色社会主义民主政治的重要内容。但是在乡村推行基层民主自治的过程中,乡村计划生育政策的落实陷入一些困境,这些困境集中表现为计生政策与村民生育愿望、与村委会选举、与社会保障体系、与利益导向机制及与其它相关部门和政策的冲突与矛盾。鉴于此,本文相对应地提出了加强宣传教育、完善村民自治、健全社会保障体系、健全舆论导向机制以及加强政策的同向性与协调度等对策建议,以期推动计划生育政策在乡村进一步落实。     

Labour market forecasting in Australia: The science of the art

Since 1987 nearly 50 labour market forecasts have been undertaken in Australia to assist decisions relating to government policy and budget, investment and career planning. More than 20 of these forecasts have been disaggregated by age, occupation, industry or regional labour markets. One of the chief aims of disaggregated forecasts is to help policy makers avoid future shortages or surpluses of skilled labour. A survey encompassing government departments, private research institutes and banks was undertaken to overview recent labour market forecasting exercises in Australia. This paper, which attempts to summarize these efforts, also discusses the main advantages and disadvantages of each major type of forecasting technique. Methods employed have ranged from anticipatory surveys to data-intensive input-output models. Formal evaluation of labour market forecasts requires considerable resources and no known assessments have been conducted in Australia to date. It is unclear how significant disaggregated labour market forecasts have been in guiding the allocation of funds between competing education and training courses. Nevertheless, governments eager to avoid future shortages and surpluses of skilled labour, but less enthusiastic about forecasting, could aim to make the labour market more flexible and responsive instead. Like forecasting, however, the effectiveness of this approach has yet to be scrutinized.     

Are mortality projections always more pessimistic when disaggregated by cause of death?

"It is often observed that mortality projections are more pessimistic when disaggregated by cause of death. This article explores the generality and strength of this relationship under a variety of forecasting models. First, a simple measure of the pessimism inherent in cause-based mortality forecasts is derived. Second, it is shown that the pessimism of cause-based forecasts can be approximated using only data on the distribution of deaths by cause in two pervious time periods. Third, using Japanese mortality data during 1951-1990, the analysis demonstrates that the pessimism of cause-based forecasts can be attributed mainly to observed trends in mortality due to cancer and heart disease, with smaller contribution due to trends in stroke (women only), pneumonia/bronchitis, accidents, and suicide. The last point requires the important qualification, however, that observed trends in cancer and heart disease may be severely biased due to changes in diagnostic practice." (SUMMARY IN FRE)     

Some fundamental problems in population projection]

When family planning work in China developed, during the 1970's, the work of population projection also expanded. Population projections were done for China and its regions beginning in 1974 and remains a relatively new activity. Some question its validity, while others speculate about its methods and beleive only higher mathematics can be used, but this is all due to a lack of understanding of the nature of population forecasting. It is possible to predict population because if a current population situation and its changes are known, population of a particular future period can be projected e.g. for each year that is lived, a person will be 1 year older. And, population changes are primarily based on changes in births and deaths. These changes in turn are influenced by social and economic factors. Population projection is basically a forecasting of a certain period's total population, age and sex structure, the number of births and deaths, and migration. Different methods and formulas can be used to measure different population indicators, but all methods utilize comparisons. There are basically 2 methods for projecting total population: 1) the "direct method" regards total population as a quantity that itself changes and 2) the "separate factor method" breaks down total population into births, deaths, and migration. In the past, population projection has focused on the natural development of population which can be called "uncontrolled" because it makes "passive measurements" of possible population developments. In China, however, population projection is "controlled." Although it too measures future population developments, China's projections are not based on natural developments, but on definite population policies and estimates of results of family planning efforts.     

Spatial weighting improves accuracy in small-area demographic forecasts of urban census tract populations

Existing research in small-area demographic forecasting suffers from two important limitations: (1) a paucity of studies that quantify patterns of error in either total or age/sex-specific estimates and (2) limited methodological innovation aimed specifically at improving the accuracy of such forecasts. This paper attempts to fill, in part, these gaps in existing research by presenting a comparative evaluation of the accuracy of standard and spatially-weighted Hamilton–Perry forecasts for urbanized census tracts within incorporated New Mexico municipalities. These comparative forecasts are constructed for a 10-year horizon (base 1 April 2000 and target 1 April 2010), then compared to the results of the 2010 Census in an ex post facto evaluation. Results are presented for the standard Hamilton–Perry forecasts as well as two sets that incorporate two common variants of spatial weights to improve forecast accuracy. Findings are discussed in the context of what is currently known about error in small-area demographic forecasts and with an eye toward continued innovations.     

The uses and analysis of census data in the Philippines. Demographic note

The collection of census data on a given population is largely expensive for nations. Census do, however, help program planners and administrators understand prevailing economic and social conditions, as well as cultural characteristics within the country. Explaining the political, legislative, administrative, and research and academic uses of such data, specific application in the Philippines is presented. Where 1 representative is allowed per 250,000 population in the Philippine House of Representatives, data are necessarily employed politically to determine the total number of representatives to be elected from respective legislative districts. Politicians also use this data in the formulation of political strategy, while election registrars need it to validate the number of registered voters per precinct. Legislatively, census data are used at the national level to define and create administrative areas according to the number of inhabitants. National services and national revenues, as well as permits, are also allotted on the basis of area population size. Furthermore, a host of administrative demands in both the public and private sectors are met by these data, including planning, policy making, and managerial tasks. Academicians and researchers depend upon these data in demonstrating the impact of population growth on the environment, linking population studies to other disciplines, and examining social characteristics and population behavior. Steps taken in the planning of and preparation for the 1990 population and housing censuses are discussed.     

Backlash or a Positive Response?: Public Opinion of LGB Issues After Obergefell v. Hodges

ABSTRACT

Following Obergefell v. Hodges, same-sex marriage remains controversial and anti-LGBT state legislation has been passed, which raises questions about whether the Supreme Court’s ruling may have created a backlash. We use data from two waves of a general population survey of Nebraskans conducted before and after the decision to answer three questions. First, we test three theories of how the court decision influenced public opinion. We find that support for same-sex marriage was significantly higher following the ruling, suggesting that there was not a backlash to it. Second, we assess whether people perceive that the court accurately reflects the public’s opinion. We find that people who favor same-sex marriage are more likely to think that the ruling refects public opinion very well; those who oppose same-sex marriage are more likely to think that the ruling does not at all reflect public opinion. Third, we examine the association between discussing gay rights and support for same-sex marriage, finding that those who talk about LGB issues very often are more likely to favor same-sex marriage. We discuss the implications of these findings in relation to two of the themes of this special issue: the influence of marriage equality on Americans’ understandings of marriage and the impact of marriage equality on future LGBT activism.     

American political affiliation, 2003-43: a cohort component projection

The recent rise in identification with American political parties has focused interest on the long-term dynamics of party support. Liberal commentators cite immigration and youth as forces that will produce a natural advantage for the Democrats in the future, while conservative writers highlight the importance of high fertility amongst Republicans in securing growth. These opinions are not based on demographic analysis. We addressed this omission by undertaking the first ever cohort component projection (up to 2043) of populations by American party allegiance, based on survey and census data. On current trends, we predict that American partisanship will change much less than the nation's ethnic composition because the parties are similar in age structure. Nevertheless, our projections suggest that the Democrats will gain 2-3 per cent more support than the Republicans by 2043, mainly through immigration, although the higher fertility of Republicans may eventually offset that advantage.     

A combined Brass-random walk approach to probabilistic household forecasting: Denmark,Finland, and the Netherlands, 2011–2041

Probabilistic household forecasts to 2041 are presented for Denmark, Finland, and the Netherlands. Future trends in fertility, mortality and international migration are taken from official population forecasts. Time series of shares of the population in six different household positions are modelled as random walks with drift. Brass’ relational model preserves the age patterns of the household shares. Probabilistic forecasts for households are computed by combining predictive distributions for the household shares with predictive distributions of the populations, specific for age and sex. If current trends in the three countries continue, we will witness a development towards more and smaller households, often driven by increasing numbers of persons who live alone. We can be quite certain that by 2041, there will be between two and four times as many persons aged 80 and over who live alone when compared with the situation in 2011.     

Modeling and Forecasting Health Expectancy: Theoretical Framework and Application

Life expectancy continues to grow in most Western countries; however, a major remaining question is whether longer life expectancy will be associated with more or fewer life years spent with poor health. Therefore, complementing forecasts of life expectancy with forecasts of health expectancies is useful. To forecast health expectancy, an extension of the stochastic extrapolative models developed for forecasting total life expectancy could be applied, but instead of projecting total mortality and using regular life tables, one could project transition probabilities between health states simultaneously and use multistate life table methods. In this article, we present a theoretical framework for a multistate life table model in which the transition probabilities depend on age and calendar time. The goal of our study is to describe a model that projects transition probabilities by the Lee-Carter method, and to illustrate how it can be used to forecast future health expectancy with prediction intervals around the estimates. We applied the method to data on the Dutch population aged 55 and older, and projected transition probabilities until 2030 to obtain forecasts of life expectancy, disability-free life expectancy, and probability of compression of disability.     

Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models

When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations.     

A time series approach to forecasting Australian total live-births

The relationship between classical demographic deterministic forecasting models, stochastic structural econometric models and time series models is discussed. Final equation autoregressive moving average (ARMA) models for Australian total live-births are constructed. Particular attention is given to the problem of transforming the time series to stationarity (and Gaussianity) and the properties of the forecasts are analyzed. Final form transfer function models linking births to females in the reproductive age groups are also constructed and a comparison of actual forecast performance using the various models is made. Long-run future forecasts are generated and compared with available projections based on the deterministic cohort model after which some policy implications of the analysis are considered.     

Monitoring Optimism in South Africa

The last two decades have been the most crucial and eventful ones in South Africa's history. During this time, the empirical research and knowledge provided by opinion polls yielded much-needed insight into the grass roots of a society in transition, providing an instrument with which to measure the political climate, to observe trends and developments, and to give input into the decision-making process of companies and political parties. Although the process of change and political transition still remains difficult to define properly, this article will provide some insight into the attitudes, perceptions and values of the South African public, by giving attention to trends that have transpired over the years. Special attention is devoted to perceptions on pertinent issues such as the mood in the country, optimism, economic well-being, social harmony and trust – all essential elements in an emerging democracy. Extensive use is made of data and findings of Markinor's bi-annual Socio-Political Trends surveys, the World Values Studies and the annual Gallup End-of-Year Poll.