Analyzing the Effect of Time in Migration Measurement Using Georeferenced Digital Trace Data

Probabilistic forecasts of international bilateral migration flows

Accurate forecasts of migration trends are essential for effective migration policies. We develop a method for probabilistic forecasting of international migration flows between pairs of countries. Our model encodes the observation that the spatial distribution patterns of migration are stable over time. This model produces one-period-ahead probabilistic forecasts that are more accurate than a leading alternative and are well calibrated for international bilateral migration flows, as well as country-level migrant inflows, outflows, and net migration flows. The flow forecasts are sex and age specific and properly account for population change not attributable to migration.

We propose a method for forecasting global human migration flows. A Bayesian hierarchical model is used to make probabilistic projections of the 39,800 bilateral migration flows among the 200 most populous countries. We generate out-of-sample forecasts for all bilateral flows for the 2015 to 2020 period, using models fitted to bilateral migration flows for five 5-y periods from 1990 to 1995 through 2010 to 2015. We find that the model produces well-calibrated out-of-sample forecasts of bilateral flows, as well as total country-level inflows, outflows, and net flows. The mean absolute error decreased by 61% using our method, compared to a leading model of international migration. Out-of-sample analysis indicated that simple methods for forecasting migration flows offered accurate projections of bilateral migration flows in the near term. Our method matched or improved on the out-of-sample performance using these simple deterministic alternatives, while also accurately assessing uncertainty. We integrate the migration flow forecasting model into a fully probabilistic population projection model to generate bilateral migration flow forecasts by age and sex for all flows from 2020 to 2025 through 2040 to 2045.

Estimating international migration flows for the Asia-Pacific region: Application of a generation–distribution model

Flows of international migration are needed in the Asia-Pacific region to understand the patterns and corresponding effects on demographic, social, and economic change across sending and receiving countries. A major challenge to this understanding is that nearly all of the countries in this region do not gather or produce statistics on flows of international migration. The only information that are widely available represent immigrant population stocks measured at specific points in time—but these represent poor proxies for annual movements. In this paper, we present a methodology for indirectly estimating annual flows of international migration amongst 53 populations in the Asia-Pacific region and four macro world regions from 2000 to 2019 using a generation–distribution framework. The estimates suggest that 27–31 million persons from the Asia-Pacific region have changed their countries of usual residence during each year in the study. Southern Asia is estimated to have had the largest inflows and outflows, whilst intra-regional migration and return migration were highest in Eastern, Southern, and South-Eastern Asia. India, China, and Indonesia were estimated to have had the largest emigration flows and net migration losses. As a first attempt to estimate international migration flows in the Asia-Pacific region, this paper provides a basis for understanding the dynamics and complexity of the large-scale migration occurring in the region.

Demography: Fast and Slow

Scientific ideas on the human population tend to be rooted in a "slow demography" paradigm, which emphasizes an inertial, predictable, self-contained view of population dynamics, mostly dependent on fertility and mortality. Yet, demography can also move fast. At the country level, it is crucial to empirically assess how fast demography moves by taking migratory movements into account, in addition to fertility and mortality. We discuss these ideas and present new estimates of the speed of population change, that is, country-level population turnover rates, as well as the share of turnover due to migration, for all countries in the world with available data between 1990 and 2020. Population turnover is inversely related to population size and development, and migratory movements tend to become important factors in shaping demography for both small and highly developed countries. Longitudinally, we analyze annual turnover data for Italy and Germany, documenting the changing speed of population change over time and its determinants. Accepting the "fast and slow" demography perspective has several implications for science and policy, which we discuss.

Has demography witnessed a data revolution? Promises and pitfalls of a changing data ecosystem

Over the past 25 years, technological improvements that have made the collection, transmission, storage, and analysis of data significantly easier and more cost efficient have ushered in what has been described as the 'big data' era or the 'data revolution'. In the social sciences context, the data revolution has often been characterized in terms of increased volume and variety of data, and much excitement has focused on the growing opportunity to repurpose data that are the by-products of the digitalization of social life for research. However, many features of the data revolution are not new for demographers, who have long used large-scale population data and been accustomed to repurposing imperfect data not originally collected for research. Nevertheless, I argue that demography, too, has been affected by the data revolution, and the data ecosystem for demographic research has been significantly enriched. These developments have occurred across two dimensions. The first involves the augmented granularity, variety, and opportunities for linkage that have bolstered the capabilities of 'old' big population data sources, such as censuses, administrative data, and surveys. The second involves the growing interest in and use of 'new' big data sources, such as 'digital traces' generated through internet and mobile phone use, and related to this, the emergence of 'digital demography'. These developments have enabled new opportunities and offer much promise moving forward, but they also raise important ethical, technical, and conceptual challenges for the field.

A Framework for Estimating Migrant Stocks Using Digital Traces and Survey Data: An Application in the United Kingdom

An accurate estimation of international migration is hampered by a lack of timely and comprehensive data, and by the use of different definitions and measures of migration in different countries. In an effort to address this situation, we complement traditional data sources for the United Kingdom with social media data: our aim is to understand whether information from digital traces can help measure international migration. The Bayesian framework proposed is used to combine data from the Labour Force Survey (LFS) and the Facebook Advertising Platform to study the number of European migrants in the United Kingdom, with the aim of producing more accurate estimates of the numbers of European migrants. The overarching model is divided into a Theory-Based Model of migration and a Measurement Error Model. We review the quality of the LFS and Facebook data, paying particular attention to the biases of these sources. The results indicate visible yet uncertain differences between model estimates using the Bayesian framework and individual sources. Sensitivity analysis techniques are used to evaluate the quality of the model. The advantages and limitations of this approach, which can be applied in other contexts, are discussed. We cannot necessarily trust any individual source, but combining them through modeling offers valuable insights.