The Effects of Changing Marital Status Patterns on Social Security Expenditures in the Netherlands, 1985-2050

Projections of expenditures for old-age pensions, survivor pensions, and disability pensions were made for the period 1985-2050 on the basis of future developments in the population structure by age, sex, and marital status. Five demographic scenarios were formulated: (i) a Benchmark scenario, with demographic rates kept constant at their 1980-84 level; (ii) a Fertility scenario, with a rise of the Total Fertility Rate (TFR) towards replacement level; (iii) a Mortality scenario, with reductions in mortality rates of 30 percent for females, and 45 percent for males; (iv) a Western scenario, which combines extreme demographic conditions of several West European countries: a TFR of 1.28, proportions never-marrying of one-third, one-third of all marriages ending in divorce, and male and female life expectancies of 74 and 81 years, respectively; and a Realistic scenario, which is the only one to include international migration, and which corresponds closely to the official population forecasts for the Netherlands. Two pension scenarios and two labor market scenarios were combined with the demographic scenarios. The current pension system, with its flat benefit rate, was combined with all five demographic scenarios. Also, the consequences of the system which was in use in the Federal Republic of Germany in 1985 were traced. Finally, the impact of high female labor force participation, and a rise in the average age at retirement were investigated. The results indicate that changes in demographic conditions (e.g. a fertility rise, or a persistent influx of immigrants) cannot prevent increases in and funding problems for pension expenditures in the Netherlands. Linking pension benefits to the labor market history of the individuals concerned brings no relief either. However, raising the average age at retirement would, to a large extent, avoid funding problems for old-age pensions for a great deal, with a large amount of overfunding in the short run

All Doors Lead to the Kitchen – Sustainability and Wellbeing Challenges in a Shared Centrepiece of Living

The kitchen figures a central place in the home where a significant share of a household’s resource consumption takes place. Sharing the kitchen between multiple households has potential to bring positive sustainability effects due to more efficient use of both material resources and energy. The concept of shared kitchens has, however, thus far had a limited diffusion. This paper explores the potential of shared kitchens as a future sustainable living environment by studying user experiences from a Living Lab setting. It builds the base for an overarching larger European collaboration on how future shared kitchens should be designed in order to support everyday practices while optimising the conditions for achieving positive impact on both sustainability and wellbeing. Findings are presented from five focus areas concerning different use contexts: (1) accessing, (2) cooking, (3) living and socialising, (4) storing, and (5) cleaning

Assessing time series models for forecasting international migration : lessons from the United Kingdom

Funding: This work was funded by the Migration Advisory Committee (MAC), UK Home Office, under the Home Office Science contract HOS/14/040, and also supported by the ESRC Centre for Population Change grant ES/K007394/1.Migration is one of the most unpredictable demographic processes. The aim of this article is to provide a blueprint for assessing various possible forecasting approaches in order to help safeguard producers and users of official migration statistics against misguided forecasts. To achieve that, we first evaluate the various existing approaches to modelling and forecasting of international migration flows. Subsequently, we present an empirical comparison of ex post performance of various forecasting methods, applied to international migration to and from the United Kingdom. The overarching goal is to assess the uncertainty of forecasts produced by using different forecasting methods, both in terms of their errors (biases) and calibration of uncertainty. The empirical assessment, comparing the results of various forecasting models against past migration estimates, confirms the intuition about weak predictability of migration, but also highlights varying levels of forecast errors for different migration streams. There is no single forecasting approach that would be well suited for different flows. We therefore recommend adopting a tailored approach to forecasts, and applying a risk management framework to their results, taking into account the levels of uncertainty of the individual flows, as well as the differences in their potential societal impact.Publisher PDFPeer reviewe

Evaluation of Sub-National Population Projections: a Case Study for London and the Thames Valley

Sub-national population projections help allocate national funding to local areas for planning local services. For example, water utilities prepare plans to meet future water demand over long-term horizons. Future demand depends on projected populations and households and forecasts of per household and per capita domestic water consumption in supply zones. This paper reports on population projections prepared for a water utility, Thames Water, which supplies water to over nine million people in London and the Thames Valley. Thames Water required an evaluation of the accuracy of the delivered projections against alternatives and estimates of uncertainty. The paper reviews how such evaluations have been made by researchers. The factors leading to variation in sub-national projections are identified. The methods, assumptions and results for English sub-national areas, used in five sets of projections, are compared. There is a consensus across projections about the future fertility and mortality but varying views about the future impact of internal and international migration flows. However, the greatest differences were between projections using ethnic populations. and those using homogeneous populations. Areas with high populations of ethnic minorities were projected to grow faster when an ethnic-specific model was used. This result is important for assessing projections for countries housing diverse populations with different demographic profiles. Historic empirical prediction intervals are used to assess the uncertainty of the London and the Thames Valley projections. By 2101 the preferred projection suggests that the population of the Thames Water region will have grown by 85% within an 80% empirical prediction interval between 45 and 125%

Golden Rule of Forecasting: Be Conservative

This article proposes a unifying theory, or the Golden Rule, or forecasting. The Golden Rule of Forecasting is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek out and use all knowledge relevant to the problem, including knowledge of methods validated for the situation. Twenty-eight guidelines are logically deduced from the Golden Rule. A review of evidence identified 105 papers with experimental comparisons; 102 support the guidelines. Ignoring a single guideline increased forecast error by more than two-fifths on average. Ignoring the Golden Rule is likely to harm accuracy most when the situation is uncertain and complex, and when bias is likely. Non-experts who use the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data, have led forecasters to violate the Golden Rule. As a result, despite major advances in evidence-based forecasting methods, forecasting practice in many fields has failed to improve over the past half-century

New Approaches to the Conceptualization and Measurement of Age and Ageing

People’s views on population ageing are influenced by the statistics that they read about it. The statistical measures in common use today were first developed around a century ago, in a very different demographic environment. For around two decades, we have been studying population ageing and have been arguing that its conventional portrayal is misleading. In this chapter, we summarize some of that research, which provides an alternative picture of population ageing, one that is more appropriate for twenty-first century. More details about our new view of population ageing can be found in. (Sanderson and Scherbov 2019). Population ageing can be measured in different ways. An example of this can found in the UN’s Profiles in Ageing, 2017. One way is to report on the forecasted increase in the number of people 60+ years old in the world

How Does the Household Structure Shape the Urban Economy?

Households in real cities are heterogeneous regarding their size and composition. This implies that the household structure -i.e. the (average) household size, the composition, the relative share of different household types, and the number of households - differs across cities. This aspect is usually neglected in urban models used to study economic and policy issues that arise in today's cities. Furthermore, the household structure might change over time. For instance, over the last decades average household size has decreased in many countries. Several implications of this change have been discussed, but usually not in regard to an urban economy with its interdependencies. We develop an applied urban general equilibrium model which explicitly takes the household structure into account and thus allows studying the impacts of changes in the household structure on an urban economy and its spatial pattern. The paper shows that changes in the household structure affect an urban economy in various ways and may contribute to explain economic and spatial effects on cities. Compared to a 'Base City' which reflects the actual household structure in the United States, urban labor force participation, housing demand, rents, wages as well as urban commuting and shopping patterns are considerably affected by, e.g., changes in the average household size in a city. For instance, wage inequality between differently skilled workers rises and extreme cross commuting drops to almost zero when the city turns into a pure 'Singles City'

Tempo and the TFR

Tempo effects in period fertility indicators are widely regarded as a source of bias or distortion. But is this always the case? Whether tempo change results in bias depends, in the view advanced here, on the measure used, the meaning of bias/distortion, and the objective of analysis. Two ways of construing bias in period measures are suggested, and their relevance is discussed in the context of five broad purposes for measuring period fertility: describing and explaining fertility time trends, anticipating future prospects, providing input parameters for formal models, and communicating with nonspecialist audiences. Genuine timing effects are not biasing when period fertility is the explanandum but are distorting when the aim is to estimate cohort fertility. Alternatives to tempo adjustment are available that are a more defensible solution to the issue of timing change. Tempo adjustment could be more fruitfully considered a form of modeling rather than empirical measurement. The measurement of period fertility could be improved by relying more on a statistical approach and less on indicators based on stable assumptions. Future progress will depend on integrating research on measurement with substantive investigation

Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm

The paper suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in a previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution, i.e. fertility, mortality and migration. Here two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but it is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution and a Markov Chain Monte Carlo algorithm is designed to approximate this posterior. The paper provides the questionnaire which was designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian Population from 2010 up to 2065 is proposed