Monitoring international migration flows in Europe. Towards a statistical data base combining data from different sources

The paper reviews techniques developed in demography, geography and statistics that are useful for bridging the gap between available data on international migration flows and the information required for policy making and research. The basic idea of the paper is as follows: to establish a coherent and consistent data base that contains sufficiently detailed, up-to-date and accurate information, data from several sources should be combined. That raises issues of definition and measurement, and of how to combine data from different origins properly. The issues may be tackled more easily if the statistics that are being compiled are viewed as different outcomes or manifestations of underlying stochastic processes governing migration. The link between the processes and their outcomes is described by models, the parameters of which must be estimated from the available data. That may be done within the context of socio-demographic accounting. The paper discusses the experience of the U.S. Bureau of the Census in combining migration data from several sources. It also summarizes the many efforts in Europe to establish a coherent and consistent data base on international migration. The paper was written at IIASA. It is part of the Migration Estimation Study, which is a collaborative IIASA-University of Groningen project, funded by the Netherlands Organization for Scientific Research (NWO). The project aims at developing techniques to obtain improved estimates of international migration flows by country of origin and country of destination

The Estimation of Place-to-Place Migration Flows Using an Alternative Log-Linear Parameter Coding Scheme

The log-linear model, with an alternative parameter coding scheme, is used in this paper to obtain estimates of place-to-place migration flows in situations where the data are inadequate or missing. The alternative parameter coding scheme is particularly useful in constructing the origin-destination interaction structure. To illustrate the method, two empirical examples are presented. The first demonstrates the effectiveness of the methodology by estimating known migration flows between states in the Western region of the United States during the 1985-1990 period. The second example focuses on estimating international migration flows in the Northern region of Europe during the 1999-2000 period where the data are incomplete. Both examples demonstrate the usefulness and generality of this particular method for estimating migration flows

Mortality and Longevity Projections for the Oldest-Old in Portugal

The mortality decline observed in developed countries over the last decades significantly increased the number of those surviving up to older ages. Mortality improvements are naturally viewed as a positive change for individuals and as a substantial social achievement for societies, but create new challenges in a number of different areas, ranging from the planning of all components of social security systems to labour markets. Understanding mortality and survival patterns at older ages is crucial. In this paper, we compare the results provided by a number of different methods designed to project mortality for the oldest-old in the Portuguese population. We identify the merits and limitations of each method and the consequences of their use in constructing complete life tables

Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions

Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning from Label Proportions (LLP) models for demographic and opinion inference using U.S. Census, national and state political polls, and Cook partisan voting index as population level data. In LLP classification settings, the training data is divided into a set of unlabeled bags, where only the label distribution in of each bag is known, removing the requirement of instance-level annotations. Our proposed LLP model, Weighted Label Regularization (WLR), provides a scalable generalization of prior work on label regularization to support weights for samples inside bags, which is applicable in this setting where bags are arranged hierarchically (e.g., county-level bags are nested inside of state-level bags). We apply our model to Twitter data collected in the year leading up to the 2016 U.S. presidential election, producing estimates of the relationships among political sentiment and demographics over time and place. We find that our approach closely tracks traditional polling data stratified by demographic category, resulting in error reductions of 28-44% over baseline approaches. We also provide descriptive evaluations showing how the model may be used to estimate interactions among many variables and to identify linguistic temporal variation, capabilities which are typically not feasible using traditional polling methods

Estimating multistate transition rates from population distributions

The ability to estimate transition rates (or probabilities) from population distributions has many potential applications in demography. Iterative Proportional Fitting (IPF) has been used for such estimation, but lacks a meaningful behavioral, or demographic, foundation. Here a new approach, Relative State Attractiveness (RSA), is advanced. It assumes that states become more (or less) attractive, and that rates respond accordingly. The RSA estimation procedure is developed and applied to model and actual data where the underlying rates are known. Results show that RSA provides accurate estimates under a wide range of conditions, usually yielding values similar to those produced by IPF. Both methods are then applied to U.S. data to provide new estimates of interregional migration between the years 1980 and 1990.entropy, estimation techniques, iterative proportional fitting, multistate models

In sickness and in Health? Dynamics of health and cohabitation in the United Kingdom

The purpose of this paper is to analyse the dynamics of cohabitation and functional impairments among older people. Our research has three main aims. Firstly, we want to analyse the effects of cohabitation on disability. Secondly, we want to study time trends in disability and cohabitation jointly to explore relationships between the two. Thirdly, we examine socioeconomic differences -- as captured by educational attainment -- in disability. These issues are of great interest from several points of view. Firstly, they address an emerging theoretical debate concerning the effects of cohabitation on health and contribute to a sparse empirical literature on the topic. Secondly, our findings are highly policy relevant. Concerning long-term care for older people, for example, cohabitation is of double importance: firstly, since people who cohabit tend to be healthier, and secondly, since a partner is the typical provider of informal care. In a time where family structures among the old are likely to change (due to changes in life expectancy and divorce rates), our research will be useful for planning purposes. Finally, the model can be used to simulate populations of certain characteristics. Hence, it can be used to derive insurance premiums in order to reduce the problem of selection effects in the market for long-term care insurance. Using the British Household Panel Survey dataset, we apply panel data and simulation techniques to exploit the longitudinal characteristic of the panel. We estimate the two dependent variables -- cohabitation status and disability -- jointly, and allow for time trends, age effects and unobserved heterogeneity. We find that there are systematic differences between single and cohabiting people so that a cross sectional analysis would overestimate the causal relationship; nevertheless, cohabitation has a strong and positive effect on health. Furthermore, we find that bereavement of a partner has a significant negative impact on health

Economic growth and income distribution: linking macro-economic models with household survey data at the global level

This paper describes in detail the analytical structure of the Global Income Distribution Dynamics (GIDD) model, a global macro-micro modelling framework, and provides some examples of its recent applications. GIDD is the first macro-micro global simulation model focused on long-term, global growth and distribution dynamics. GIDD has been applied in analyzing the effects of multilateral trade liberalization or mitigation of climate change damages, among others. It also explicitly considers long term time horizons during which changes in the demographic structure are crucial components of both growth and distribution dynamics. The challenges of assessing plausible worldwide distributional implications of growth, large shocks, and policy changes are daunting. Although addressing these issues in a macro-micro framework is subject to great uncertainty, a clearly superior alternative is not yet available.global income distribution; macro-micro model