Abel Brodeur, Derek Mikola, Nikolai Cook, Thomas Brailey, Ryan Briggs, Alexandra de Gendre, Yannick Dupraz, Lenka Fiala, Jacopo Gabani, Romain Gauriot, Joanne Haddad, Goncalo Lima, Jörg Ankel-Peters, Anna Dreber, Douglas Campbell, Lamis Kattan, Diego Marino Fages, Fabian Mierisch, Pu Sun, Taylor Wright, Marie Connolly, Fernando Hoces de la Guardia, Magnus Johannesson, Edward Miguel, Lars Vilhuber, Alejandro Abarca, Mahesh Acharya, Sossou Simplice Adjisse, Ahwaz Akhtar, Eduardo Alberto Ramirez Lizardi, Sabina Albrecht, Synve Nygaard Andersen, Zubaria Andlib, Falak Arrora, Thomas Ash, Etienne Bacher, Sebastian Bachler, Félix Bacon, Manuel Bagues, Timea Balogh, Alisher Batmanov, Mara Barschkett, B. Kaan Basdil, Jaromneda Dower, Ondrej Castek, Jill Caviglia-Harris, Gabriella Chauca Strand, Shi Chen, Asya Chzhen, Jong Chung, Jason Collins, Alexander Coppock, Hugo Cordeau, Ben Couillard, Jonathan Crechet, Lorenzo Crippa, Jeanne Cui, Christian Czymara, Haley Daarstad, Danh Chi Dao, Dong Dao, Marco David Schmandt, Astrid de Linde, Lucas De Melo, Lachlan Deer, Micole De Vera, Velichka Dimitrova, Jan Fabian Dollbaum, Jan Matti Dollbaum, Michael Donnelly, Luu Duc Toan Huynh, Tsvetomira Dumbalska, Jamie Duncan, Kiet Tuan Duong, Thibaut Duprey, Christoph Dworschak, Sigmund Ellingsrud, Ali Elminejad, Yasmine Eissa, Andrea Erhart, Giulian Etingin-Frati, Elaheh Fatemi-Pour, Alexa Federice, Jan Feld, Guidon Fenig, Mojtaba Firouzjaeiangalougah, Erlend Fleisje, Alexandre Fortier-Chouinard, Julia Francesca Engel, Tilman Fries, Reid Fortier, Nadjim Fréchet, Thomas Galipeau, Sebastián Gallegos, Areez Gangji, Xiaoying Gao, Cloé Garnache, Attila Gáspár, Evelina Gavrilova, Arijit Ghosh, Garreth Gibney, Grant Gibson, Geir Godager, Leonard Goff, Da Gong, Javier González, Jeremy Gretton, Cristina Griffa, Idaliya Grigoryeva, Maja Grtting, Eric Guntermann, Jiaqi Guo, Alexi Gugushvili, Hooman Habibnia, Sonja Häffner, Jonathan D. Hall, Olle Hammar, Amund Hanson Kordt, Barry Hashimoto, Jonathan S. Hartley, Carina I. Hausladen, Tomáš Havránek, Jacob Hazen, Harry He, Matthew Hepplewhite, Mario Herrera-Rodriguez, Felix Heuer, Anthony Heyes, Anson T. Y. Ho, Jonathan Holmes, Armando Holzknecht, Yu-Hsiang Dexter Hsu, Shiang-Hung Hu, Yu-Shiuan Huang, Mathias Huebener, Christoph Huber, Kim P. Huynh, Zuzana Irsova, Ozan Isler, Niklas Jakobsson, Michael James Frith, Raphaël Jananji, Tharaka A. Jayalath, Michael Jetter, Jenny John, Rachel Joy Forshaw, Felipe Juan, Valon Kadriu, Sunny Karim, Edmund Kelly, Duy Khanh Hoang Dang, Tazia Khushboo, Jin Kim, Gustav Kjellsson, Anders Kjelsrud, Andreas Kotsadam, Jori Korpershoek, Lewis Krashinsky, Suranjana Kundu, Alexander Kustov, Nurlan Lalayev, Audrée Langlois, Jill Laufer, Blake Lee-Whiting, Andreas Leibing, Gabriel Lenz, Joel Levin, Peng Li, Tongzhe Li, Yuchen Lin, Ariel Listo, Dan Liu, Xuewen Lu, Elvina Lukmanova, Alex Luscombe, Lester R. 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• This study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on sixThis study pushes our understanding of research reliability by reproducing and replicating claims from 110 papers in leading economic and political science journals. The analysis involves computational reproducibility checks and robustness assessments. It reveals several patterns. First, we uncover a high rate of fully computationally reproducible results (over 85%). Second, excluding minor issues like missing packages or broken pathways, we uncover coding errors for about 25% of studies, with some studies containing multiple errors. Third, we test the robustness of the results to 5,511 re-analyses. We find a robustness reproducibility of about 70%. Robustness reproducibility rates are relatively higher for re-analyses that introduce new data and lower for re-analyses that change the sample or the definition of the dependent variable. Fourth, 52% of re-analysis effect size estimates are smaller than the original published estimates and the average statistical significance of a re-analysis is 77% of the original. Lastly, we rely on six teams of researchers working independently to answer eight additional research questions on the determinants of robustness reproducibility. Most teams find a negative relationship between replicators' experience and reproducibility, while finding no relationship between reproducibility and the provision of intermediate or even raw data combined with the necessary cleaning codes.…