Now-casting Romanian Migration into the United Kingdom by using Google Search engine data

Background: Short-term forecasts of international migration are often based on data that are incomplete, biased, and reported with delays. There is also a scarcity of migration forecasts based on combined traditional and new forms of data. Objective: This research assessed an inclusive approach of supplementing official migration statistics, typically reported with a delay, with the so-called big data from Google searches to produce short-term forecasts ("now-casts") of immigration flows from Romania to the United Kingdom. Methods: Google Trends data were used to create composite variables depicting the general interest of Romanians in migrating into the United Kingdom. These variables were then assessed as predictors and compared with benchmark results by using univariate time series models. Results: The proposed Google Trends indices related to employment and education, which exhaust all possible keywords and eliminate language bias, match trends observed in the migration statistics. They are also capable of moderate reductions in prediction errors. Conclusions: Google Trends data have some potential to indicate up-to-date current trends of interest in mobility, which may serve as useful predictors of sudden changes in migration. However, these data do not always improve the accuracy of forecasts. The usability of Google Trends is also limited to short-term migration forecasting and requires understanding of contexts surrounding origin and destination countries. Contribution: This work provides an example on combining Google Trends and official migration data to produce short-term forecasts, illustrated with flows from Romania to the UK. It also discusses caveats and suggests future work for using these data in migration forecasting

Migration Scenario Narratives

A considerable amount of literature has been published on global and migration scenarios in recent decades. Central to the migration scenarios and narratives are the concepts of migration drivers and migration dynamics. Demographic, economic, technological, social, political, and environmental developments and complex interrelations between these domains are considered essential in creating plausible future migration developments. This report provides a set of internally consistent and evidence-based qualitative scenario narratives.The narratives are built on consistent demographic, socio-economic, environmental and political alternate futures generated for the EU and developing countries based on the qualitative and quantitative evidence gathered in the FUME project. Each narrative describes the future for the EU and developing countries in the short-term until 2030 and in the long-term between 2030 and 2050. These alternative futures are complemented by the potential changes in the future migratory demand and pressure from the expert survey conducted in the project and the characteristics of future migrants from the Delphi survey.<br/

Bayesian Multi-Dimensional Mortality Reconstruction

Even though mortality differentials by socio-economic status and educational attainment level have been widely examined, this research is often limited to developed countries and recent years. This is primarily due to the absence of consistently good-quality inherent data. Systematic studies with a broad geographical and temporal spectrum that engage with the link between educational attainment and mortality are lacking. In this paper, we propose a mortality rates reconstruction model based on multiple patchy data sources, and provide mortality rates by level of education. The proposed model is a hierarchical Bayesian model that combines the strengths of multiple sources in order to disaggregate mortality rates by time periods, age groups, sex and educational attainment. We apply the model in a case study that includes 13 countries across South-East Europe, Western Asia and North Africa, and calculate education-specific mortality rates for five-year age groups starting at age 15 for the 1980-2015 time period. Furthermore, we evaluate the model’s performance relying on standard convergence indicators and trace plots, and validate our estimates via posterior predictive checks. This study contributes to the literature by proposing a novel methodology to enhance the research on the relationship between education and adult mortality. It addresses the lack of educations-pecific mortality differentials by providing a flexible method for their estimation

Delphi Study – Future Migration Scenarios for Europe

This report describes the methodology, implementation and results of a two-round Delphi survey looking at predictions made by a panel of experts focusing on migration-related: drivers, composition and policies for the EU in the next 10 years. The survey has been carried out amongst experts with experience in policymaking or advising policymakers in the area of migration. This report documents responses collected in both rounds of the survey. The first round was carried out in March 2021, whereas the second round was collected in June-August 2021. This work constitutes Task 3.2 in Work Package 3 of the Future Migration Scenarios for Europe (FUME) project

Integrated Modelling of European Migration: Background, specification and results

The aims of this paper are to present the background and specification of the Integrated Modelling of European Migration (IMEM) model. Currently, international migration data are collected by individual countries with separate collection systems and designs. This creates problems when attempting to understand or predict population movements between countries as the reported data are inconsistent in terms of their availability, definitions and quality. Rather than wait for countries to harmonise their migration data collection and reporting systems, we propose a model to overcome the limitations of the various data sources. In particular, we propose a Bayesian model for harmonising and correcting the inadequacies in the available data and for estimating the completely missing flows. The focus is on estimating recent international migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA) from 2002 to 2008, using data collected by Eurostat and other national and international institutions. We also include additional information provided by experts on the effects of undercount, measurement and accuracy. The methodology is integrated and capable of providing a synthetic data base with measures of uncertainty for international migration flows and other model parameters.

A flexible model to reconstruct education-specific fertility rates: Sub-Saharan Africa case

The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction

Estimating International Migration Flows for the Asia-Pacific Region: Application of a Generation-Distribution Model

This is a repository for our paper in Migration Studies. The paper estimates annual flows of international migration among 53 populations in the Asia-Pacific region and four macro world regions from 2000 to 2019 using a generation-distribution framework. This release contains: Simulated input data Code to produce the estimates Final estimated flows in the paper For questions with the code or request for all estimated flows with 1000 iterations, please email [email protected] or [email protected]

Immigration, diversity and trust: the competing and intersecting role of English language ability in the community

A growing number of studies have shown adverse effects of ethnic diversity on social cohesion routed in ethnic categorical differences, competition and racial threat. We build on previous research by examining the hypothesis that cultural (language) differentiation leads to lower intra-neighbourhood trust through feelings of anomie. Our results, based on data from the 2009–2011 UK Citizenship survey and a multilevel modelling framework, do not offer support for the proposition that diversity lowers trust through linguistic diversity and poor communication. In line with other studies, we find a negative association between ethnic diversity and trust and show that for the white group, this relationship does not depend on migrants’ levels of fluency in the majority language. In contrast, in neighbourhoods where migrants cannot speak English well, increases in ethnic diversity are associated with higher levels of neighbour trust among the non-white group

A Bayesian Estimation of Child Labour in India

From Springer Nature via Jisc Publications RouterHistory: accepted 2020-04-15, registration 2020-04-15, online 2020-06-18, pub-electronic 2020-06-18, pub-print 2020-12Publication status: PublishedFunder: University of ManchesterAbstract: Child labour in India involves the largest number of children in any single country in the world. In 2011, 11.8 million children between the ages of 5 and 17 were main workers (those working more than 6 mo) according to the Indian Census. Our estimate of child labour using a combined-data approach is slightly higher than that: 13.2 million (11.4–15.2 million) for ages 5 to 17. There are various opinions on how best to measure the prevalence of child labour. In this study, we use the International Labour Organization (ILO)‘s methodology to define hazardousness and combine it with the most recent United Nations Children’s Fund (UNICEF)‘s time thresholds for economic work and household chores. The specific aims of this study are to estimate the prevalence of child labour in the age group 5 to 17 and to suggest a combined-data approach using Bayesian inference to improve the accuracy of the child labour estimation. This study combines the National Sample Survey on Employment and Unemployment 2011/12 and the India Human Development Survey 2011/12 and compares the result with the reported figures for the incidence of child labour from the Indian Census. Our unique combined-data approach provides a way to improve accuracy, smooth the variations between ages and provide reliable estimates of the scale of child labour in India