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PreprintarXiv licencearXiv

Intermittency in firm bankruptcies from register data

  • F. P. Pijpers
  • D. Diemer
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Abstract

By analysing the clustering of bankruptcy activity from administrative register data in the Netherlands over time, the intermittency of this process is investigated. Using methodology from survival analysis a statistical characterization of bursts is obtained: short active periods alternating with quieter periods. Using Statistics Netherlands microdata on Dutch business bankruptcies declared on court hearing-days from 2009 to 2024, combined with input-output tables from the Dutch national accounts, it is studied whether this clustering can be explained by differences between sectors, years, and inter-sectoral exposure. Sector differences explain most of the heterogeneity in the data, while year effects matter to a lesser extent and the inter-sectoral exposure variables constructed from the input-output tables further improve model fit.

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Cite

BibTeXRIS
BibTeX
@misc{pijpers2026intermittency,
  title = {{Intermittency in firm bankruptcies from register data}},
  author = {F. P. Pijpers and D. Diemer},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.32376},
  eprint = {2610.32376},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.32376},
}
RIS
TY  - UNPB
TI  - Intermittency in firm bankruptcies from register data
AU  - F. P. Pijpers
AU  - D. Diemer
PY  - 2026
DA  - 2026/10/23
PB  - arXiv
DO  - 10.48550/arxiv.2610.32376
UR  - https://arxiv.org/abs/2610.32376
AB  - By analysing the clustering of bankruptcy activity from administrative register data in the Netherlands over time, the intermittency of this process is investigated. Using methodology from survival analysis a statistical characterization of bursts is obtained: short active periods alternating with quieter periods. Using Statistics Netherlands microdata on Dutch business bankruptcies declared on court hearing-days from 2009 to 2024, combined with input-output tables from the Dutch national accounts, it is studied whether this clustering can be explained by differences between sectors, years, and inter-sectoral exposure. Sector differences explain most of the heterogeneity in the data, while year effects matter to a lesser extent and the inter-sectoral exposure variables constructed from the input-output tables further improve model fit.
ER  -