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LPG Subsidy Reform, Energy Compensation, and Social Risk in Bolivia: A Machine-Learning Agent-Based Microsimulation — arXiv:2609.00414

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.00414
License (as archived): CC0-1.0
Archived: 2026-10-17T07:33:14.086Z
SHA-256 of the archived text: 8fd15e00addcfd2bd6af90cdde46eb0faafec3a6e1c1969166afa9e30d64171e
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How to cite

LPG Subsidy Reform, Energy Compensation, and Social Risk in Bolivia: A Machine-Learning Agent-Based Microsimulation — arXiv:2609.00414 — arXiv (en), https://arxiv.org/abs/2609.00414 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_562e50ee69df (Metraipolis archive dc_562e50ee69df, sha256 8fd15e00addcfd2bd6af90cdde46eb0faafec3a6e1c1969166afa9e30d64171e)

BibTeX

@misc{dc562e50ee69df,
  title = {LPG Subsidy Reform, Energy Compensation, and Social Risk in Bolivia: A Machine-Learning Agent-Based Microsimulation — arXiv:2609.00414},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.00414},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_562e50ee69df; sha256 8fd15e00addcfd2bd6af90cdde46eb0faafec3a6e1c1969166afa9e30d64171e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.00414. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This study evaluates alternative designs for reforming Bolivia's liquefied petroleum gas subsidy using a machine-learning agent-based microsimulation framework. The analysis harmonizes household survey data, expenditure data, demographic and health information, and monthly hydrocarbon production and commercialization series to simulate fiscal savings, poverty effects, energy substitution, administrative costs, and social risks under multiple reform scenarios. The model compares uncompensated subsidy removal, fixed energy transfers, full compensation for vulnerable LPG users, voucher-based compensation, maternal-child transfers, clean-energy transition kits, and hybrid policy packages. Machine-learning models are used to learn household vulnerability, fuel-use patterns, food insecurity risk, and behavioral propensities that feed into a monthly agent-based simulation. The results show that eliminating the subsidy without compensation generates the largest fiscal savings but increases poverty, extreme poverty, and pressure toward solid-fuel substitution. Full monetary compensation for Q1-Q2 LPG users substantially reduces social harm while preserving significant fiscal savings and dominates an equivalent voucher design under normal market conditions because of lower administrative friction. The most socially robust design combines targeted monetary energy compensation, maternal-child reinforcement, and clean-energy kits for households using solid fuels. The findings support a gradual replacement of the universal LPG subsidy with targeted, administratively lean, and behaviorally informed compensation mechanisms.

Tema degli esploratori: economia dei servizi IA (retro-catalogo arXiv: annunci del 2026-09-02 in econ.EM).
Categorie arXiv: Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2609.00414 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T07:22:38.388Z dalla pagina https://arxiv.org/catchup/econ.EM/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).