How Well Can the Migration Component of Regional Population Change be Predicted? A Machine Learning Approach Applied to German Municipalities

Abstract

For several decades, demographic forecasts had predicted that the majority of Germany’s cities and rural areas would experience population decline in the early 21st century. Instead, recent trends show a growing population size in three out of every four German districts. As a result, there are currently severe shortages of housing and childcare in regions that were projected to decline but have instead grown in recent years. Other regions, by contrast, continue to lose young people in particular. Most of these differences between regions stem from within-country as well as international migration. An important question for both regional demographic research as well as local policy-makers is thus how well net migration rates in cities and rural districts can be predicted several years into the future. In this study, we develop models that predict migration (both within-country as well as international migration) at the level of municipalities for two demographic groups, namely young people aged 18 to 24 years, and families (people aged 30 to 49 years and underage children). We collect data on economic, demographic and other characteristics such as distances to large cities or universities for around 3,000 German municipalities (Gemeinden). The model is trained on a subset of these data from the period 2005-2009 and predicts net migration rates among young people on an unseen test dataset in the future (i.e. for the period 2011-2015). The results show that the model can predict future net migration by young people aged 18 to 24 years reasonably well (R² > 0.5), although there were quite significant changes during the period under study, for example refugee immigration to Germany. Family migration, on the other hand, cannot be predicted equally well (R² = 0.25). Some important lessons emerge concerning the predictability of regional and international migration and the usefulness of demographic forecasts for local policy-makers.

Transcending Time and Space: Survey Methods, Uncertainty, and Development in Human Migration Prediction

“…With the development of information technology and statistics, many forecasting methods have emerged, using approaches based on econometrics, time series analyses, and Bayesian statistics, among others. In recent years, with the rapid development of artificial intelligence (AI) technology, with big data and machine learning (ML) as its core, some scholars have attempted to use ML technology for HMP [11][12][13][14][15][16][17][18]; however, this approach is hampered by limited HM data, varying standards, and access difficulties. In addition, the uncertainty of HM drivers, and the difficulty in quantifying them, have led to the slow development of HMP research [4].…”

Transcending Time and Space: A Survey Methods, Uncertainty, and Development in Human Migration Prediction

“…internal migration, long-term, [68]; international migration, short-term, [69]; international migration, long-term, [70]; internal migration, --, [71]; international migration, short-term, [72]. internal migration, short-term, [73]; internal migration, short-term, [74]; internal migration, short-term, [75]; international migration, long-term, [76].…”

“…According to different learning methods, machine learning can be divided into classical machine learning and deep learning. As shown in Figure 3, a range of machine learning methods have been applied in population migration prediction research, including illegal migration prediction, conventional migration prediction, labour migration prediction, migration flow data generation, migration trend prediction, international migration drivers, and asylum seeker prediction [68][69][70][71][72][73][74][75][76]. Robinson and Dilkina were probably the first to use machine learning models to predict population migration; addressing the inability of traditional linear models to model the non-linear relationship between population migration and its characteristics, while proposing a comprehensive solution to the problems of data imbalance, hyperparameter tuning and performance evaluation in model training, providing a new tool and instrument for population migration prediction [98].…”

“…With the development of information technology and statistics, many forecasting methods have emerged, and these mainly focus on econometrics, time series, Bayesian statistics, etc. In recent years, with the rapid development of artificial intelligence (AI) technology with big data and machine learning (ML) as the core, some scholars have tried to use ML technology for HMP [67][68][69][70][71][72][73]; however, the limitations include the limited amount of HM data, different standards, and difficulties in access. In addition, the uncertainty of HM drivers and the difficulty of their quantification have led to the slow development of HMP research [4].…”

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

“…Several papers have also applied machine learning methods to the demographic component inputs of small area population forecasts. Weber ( 2020 ) used several machine learning methods and data from 2005 to 2009 to predict net migration rates at the municipal level in Germany for ‘education migration’ (ages 18–24) and ‘family migration’ (ages 0–17 and 30–49) for the period 2011–2015. The author reported forecasting performance by correlating observed and predicted values.…”