Research chronicle
Papers, preprints, data releases, and policy documents related to the indicators in this dataset, gathered daily and summarized by AI.
Demographic Change and Private Consumption Expenditure in Germany: A Long-Term Projection
AI summaryThis paper uses a microsimulation model combining household data, official population forecasts, and a macroeconomic framework to project changes in private consumption structure in Germany through 2060. The findings indicate that demand composition will shift substantially, with income growth exerting a stronger influence than population aging, carrying significant implications for VAT revenues and the broader economic supply side.
Abstract
This paper investigates the structure of private consumption in Germany in the very long run (until 2060). Using a large household data set, the official population forecast, and a long-term macroeconomic framework, we develop a comprehensive microsimulation model to study the implications of aging and of income growth for demand composition. Our results show that the structure of private demand in Germany will change dramatically in the long run, and that the income effect will be stronger than the population aging effect. These changes will have profound implications for economic policy, affecting both tax revenues, particularly VAT revenues, and the supply side of the economy. AI disclosure: The authors used OpenAI’s ChatGPT for language editing, improving clarity and consistency, and proofreading. It was not used to generate data, conduct econometric analyses, or derive empirical results. All output was reviewed and verified by the authors.
Probabilistic population forecasts for small regions
AI summaryThis study introduces Bayesian methods for age-specific population forecasting in small subnational regions, extending the Lee–Carter model with an age-region interaction term, skewed error terms for net-migration, and Dirichlet regression for age patterns of migration and fertility. Applied to 13 Bavarian regions, the approach outperforms standard methods in out-of-sample forecast accuracy, demonstrating a new, effective method for probabilistic subnational population projections.
Abstract
BACKGROUND: Age-specific population forecasts for small areas or subnational regions are a valuable tool for local governments. However, typical population projection methods based on the cohort-component approach are difficult to apply on a smaller subnational scale. OBJECTIVE: We introduce Bayesian methods suitable for obtaining reliable age-specific population forecasts for small regions using the cohort-component method. METHODS: Our approach improves fertility forecasting by extending the Lee–Carter model with an age-region interaction term. We propose to forecast net-migration counts using skewed error terms, and introduce a Dirichlet regression to model migration age patterns as well as age proportions of fertility. RESULTS: We run our model to produce age-specific population forecasts for a set of 13 heterogeneous regions in Bavaria, Germany. We compare our method with other standard approaches and find that it produces superior out-of-sample forecasts according to both point measures and scoring rules. CONCLUSIONS: The findings suggest that the proposed Bayesian methods offer good predictive accuracy and are suitable in obtaining precise forecasts of age-specific population for smaller geo-graphical regions. CONTRIBUTION: We introduce a new method for the probabilistic projection of subnational population that works well and outperforms other current methods.