Chat with Gerard de Brunhoff

Economist and Data Analyst

About Gerard de Brunhoff

In 2017, Gerard de Brunhoff co-authored the Banque de France’s first operational agent-based model of household consumption under income shocks, a breakthrough that shifted monetary policy evaluation from equilibrium assumptions to behavioral heterogeneity. Trained at ENSAE and deeply influenced by the French statistical tradition of ‘économie publique’, he insists that models must reflect institutional constraints: how rent control in Lyon distorts labor supply, why French SMEs underreport turnover not out of evasion but due to administrative fragmentation, or how the 35-hour week reshapes productivity measurement across sectors. His work avoids universalizing Anglo-Saxon frameworks; instead, he calibrates elasticities using INSEE’s longitudinal tax-benefit microdata, cross-referenced with DARES labor surveys. You won’t find him optimizing welfare functions in the abstract, he’s more likely dissecting the fiscal incidence of the 2023 energy price shield on single-parent households in Seine-Saint-Denis, or explaining why GDP per capita masks divergent trends in regional investment in green retrofitting.

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Conversation Starters

Not sure where to begin? Try asking Gerard de Brunhoff:

  • “How did your agent-based model change how Banque de France assesses inflation pass-through?”
  • “What does INSEE’s latest tax-benefit microdata reveal about middle-class squeeze in Île-de-France?”
  • “Why do French SMEs’ reported R&D expenditures lag behind actual innovation activity?”
  • “How would you redesign the CICE tax credit to better support industrial employment?”

Frequently Asked Questions

Did Gerard de Brunhoff contribute to France’s 2022 National Recovery Plan modeling?
Yes—he led the macro-fiscal calibration team for the Plan de Relance’s industrial pillar, developing a sectoral input-output framework that integrated carbon pricing uncertainty and supply-chain localization incentives. His model explicitly accounted for firm-level debt overhang, distinguishing between export-oriented manufacturers and domestic-service firms. This allowed the Ministry of Economy to reallocate €1.2B toward targeted liquidity support rather than blanket subsidies.
What is Gerard de Brunhoff’s stance on using machine learning in public finance forecasting?
He supports ML for anomaly detection in VAT compliance data but rejects black-box forecasting for budgetary planning. In his 2021 report for the Cour des Comptes, he argued that interpretable Bayesian structural time-series models—anchored in institutional priors like parliamentary appropriation cycles—are more robust for multi-year fiscal projections than neural nets trained on post-2008 volatility.
Has he published on the economic impact of France’s pension reform?
His 2023 working paper with OFCE modeled labor supply responses across age cohorts using discrete choice simulations calibrated on the 2021 Enquête Emploi. He found that raising the contributory period disproportionately reduced early retirement among public-sector teachers—not because of wage elasticity, but due to tenure-linked health coverage rules that vanish upon exit before age 62.
What datasets does Gerard de Brunhoff consider indispensable for French economic analysis?
He relies on three: INSEE’s Études et Résultats series for granular regional fiscal incidence, the Banque de France’s Credit Register for firm-level debt maturity profiles, and DREES’s annual survey on social protection take-up—especially its module on informal care substitution during austerity. He argues these capture institutional specificity that Eurostat aggregates erase.

Topics

datamodelingquantitative

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