SynPop-DE: Synthetic population of 40 million German households using generative neural networks

Jakob Napiontek and Peter-Paul Pichler
Additional contact information
Jakob Napiontek: Potsdam Institute for Climate Impact Research (PIK)

No zha8v_v1, SocArXiv from Center for Open Science

Abstract: Household microdata combining socio-demographic, housing, income and expenditure attributes are a core resource for many studies in quantitative social science, such as modelling the household-level impacts of the energy transition. Yet no such data are openly available for Germany's full population. SynPop-DE provides a synthetic population of 40,235,916 households and their 82,039,613 members in all 400 German districts, calibrated to the 2022 census, with 34 attributes per household. Synthetic households are generated by estimating the joint attribute distribution of the German Household Budget Survey through a two-stage machine learning architecture. While an autoencoder first compresses high-dimensional categorical data into a continuous latent space, a generative adversarial network subsequently learns to sample new records from this representation. These records are then aligned with census marginals for all German districts using iterative proportional updating to ensure spatial representativeness. Validation along three dimensions confirms that the model learns attribute relationships and generates synthetic households that reproduce the statistical properties of the survey data (fidelity), supports downstream analyses with accuracy comparable to the original survey (utility), and prevents disclosure of individual respondents (privacy). The dataset is openly available at https://synpop.de.

Date: 2026-04-12
New Economics Papers: this item is included in nep-ene
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/69da6ccd5a0c3f846f2d84a4/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:zha8v_v1

DOI: 10.31219/osf.io/zha8v_v1

Access Statistics for this paper

More papers in SocArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().