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Targeting with machine learning: An application to a tax rebate program in Italy

Citations

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Cited by:

  1. Luca Villamaina & Paolo Acciari, 2023. "Taxation and labour supply decisions: an evaluation of the earned income tax credit in Italy," Working Papers wp2023-20, Ministry of Economy and Finance, Department of Finance.
  2. Michael J. Weir & Thomas W. Sproul, 2019. "Identifying Drivers of Genetically Modified Seafood Demand: Evidence from a Choice Experiment," Sustainability, MDPI, vol. 11(14), pages 1-21, July.
  3. Matilde Cappelletti & Leonardo M. Giuffrida, 2024. "Targeted Bidders in Government Tenders," CESifo Working Paper Series 11142, CESifo.
  4. Fontana, S.; & Guccio, C.; & Pignataro, G.; & Romeo, D.;, 2024. "Cash Transfers and Health Outcomes: Evidence from Italian Municipalities," Health, Econometrics and Data Group (HEDG) Working Papers 24/04, HEDG, c/o Department of Economics, University of York.
  5. Michael Allan Ribers & Hannes Ullrich, 2020. "Machine Predictions and Human Decisions with Variation in Payoffs and Skill," CESifo Working Paper Series 8702, CESifo.
  6. Michael Allan Ribers & Hannes Ullrich, 2019. "Battling Antibiotic Resistance: Can Machine Learning Improve Prescribing?," Papers 1906.03044, arXiv.org.
  7. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
  8. Ignacio González García & Alfonso Mateos, 2021. "Use of Social Network Analysis for Tax Control in Spain," Hacienda Pública Española / Review of Public Economics, IEF, vol. 239(4), pages 159-197, November.
  9. Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2021. "Local mortality estimates during the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(4), pages 1189-1217, October.
  10. Garbero, Alessandra & Sakos, Grayson & Cerulli, Giovanni, 2023. "Towards data-driven project design: Providing optimal treatment rules for development projects," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
  11. Cerqua, Augusto & Letta, Marco, 2022. "Local inequalities of the COVID-19 crisis," Regional Science and Urban Economics, Elsevier, vol. 92(C).
  12. Coco, Giuseppe & Monturano, Gianluca & Resce, Giuliano, 2025. "Predicting Delays in Cohesion Infrastructure Projects," Economics & Statistics Discussion Papers esdp25099, University of Molise, Department of Economics.
  13. Radermacher, Jan W., 2023. "Mamma Mia! Revealing hidden heterogeneity by PCA-biplot: MPC puzzle for Italy's elderly poor," SAFE Working Paper Series 382, Leibniz Institute for Financial Research SAFE.
  14. Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
  15. Guido de Blasio & Alessio D'Ignazio & Marco Letta, 2020. "Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities," Working Papers 16/20, Sapienza University of Rome, DISS.
  16. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Machine-Learning Approach," Economics working papers 2021-08, Department of Economics, Johannes Kepler University Linz, Austria.
  17. Kadriye Hilal Topal & Ebru Çağlayan Akay, 2020. "Hanehalkı Tüketim Harcamalarının Mikroekonometrik Analizi: LAD-LASSO Yöntemi," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(33), pages 13-31, December.
  18. Di Stefano, Roberta & Resce, Giuliano, "undated". "The Determinants of Missed Funding: Predicting the Paradox of Increased Need and Reduced Allocation," Economics & Statistics Discussion Papers esdp23092, University of Molise, Department of Economics.
  19. Caravaggio, Nicola & Resce, Giuliano & Idola Francesca, Spanò, 2024. "Is Local Taxation Predictable? A Machine Learning Approach," Economics & Statistics Discussion Papers esdp24098, University of Molise, Department of Economics.
  20. Antulov-Fantulin, Nino & Lagravinese, Raffaele & Resce, Giuliano, 2021. "Predicting bankruptcy of local government: A machine learning approach," Journal of Economic Behavior & Organization, Elsevier, vol. 183(C), pages 681-699.
  21. Altındağ, Onur & O'Connell, Stephen D. & Şaşmaz, Aytuğ & Balcıoğlu, Zeynep & Cadoni, Paola & Jerneck, Matilda & Foong, Aimee Kunze, 2021. "Targeting humanitarian aid using administrative data: Model design and validation," Journal of Development Economics, Elsevier, vol. 148(C).
  22. Ginevra Buratti & Alessio D'Ignazio, 2023. "Improving the effectiveness of financial education programs. A targeting approach," Questioni di Economia e Finanza (Occasional Papers) 765, Bank of Italy, Economic Research and International Relations Area.
  23. Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
  24. Michael Allan Ribers & Hannes Ullrich, 2024. "Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing," Quantitative Marketing and Economics (QME), Springer, vol. 22(4), pages 445-483, December.
  25. Tobias Cagala & Ulrich Glogowsky & Johannes Rincke & Anthony Strittmatter, 2021. "Optimal Targeting in Fundraising: A Causal Machine-Learning Approach," Papers 2103.10251, arXiv.org, revised Sep 2021.
  26. Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
  27. de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  28. Elliott Ash & Sergio Galletta & Tommaso Giommoni, 2021. "A Machine Learning Approach to Analyze and Support Anti-Corruption Policy," CESifo Working Paper Series 9015, CESifo.
  29. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
  30. Hannes Ullrich & Michael Allan Ribers, 2023. "Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing," Berlin School of Economics Discussion Papers 0027, Berlin School of Economics.
  31. Lucchetti, Riccardo & Pedini, Luca & Pigini, Claudia, 2022. "No such thing as the perfect match: Bayesian Model Averaging for treatment evaluation," Economic Modelling, Elsevier, vol. 107(C).
  32. Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022. "Machine learning in the service of policy targeting: The case of public credit guarantees," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
  33. Dutt, Satyajit & Radermacher, Jan W., 2023. "Age, wealth, and the MPC in Europe: A supervised machine learning approach," SAFE Working Paper Series 383, Leibniz Institute for Financial Research SAFE.
  34. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
  35. Cerqua, Augusto & Letta, Marco, 2020. "Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories," MPRA Paper 104404, University Library of Munich, Germany.
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