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Jeffrey Naecker

Personal Details

First Name:Jeffrey
Middle Name:Kendell
Last Name:Naecker
Suffix:
RePEc Short-ID:pna439
[This author has chosen not to make the email address public]
http://jeffnaecker.com
Twitter: @jnaecker
Terminal Degree:2015 Department of Economics; Stanford University (from RePEc Genealogy)

Affiliation

(10%) Google

https://research.google/
Mountain View, CA

Research output

as
Jump to: Working papers Articles

Working papers

  1. B. Douglas Bernheim & Daniel Björkegren & Jeffrey Naecker & Michael Pollmann, 2021. "Causal Inference from Hypothetical Evaluations," NBER Working Papers 29616, National Bureau of Economic Research, Inc.
  2. James Andreoni & Deniz Aydin & Blake Barton & B. Douglas Bernheim & Jeffrey Naecker, 2018. "When Fair Isn't Fair: Understanding Choice Reversals Involving Social Preferences," NBER Working Papers 25257, National Bureau of Economic Research, Inc.
  3. Jeffrey Naecker, 2015. "The Lives of Others: Predicting Donations with Non-Choice Responses," Discussion Papers 15-021, Stanford Institute for Economic Policy Research.
  4. Christine L. Exley & Jeffrey K. Naecker, 2015. "Observability Increases the Demand for Commitment Devices," Harvard Business School Working Papers 16-064, Harvard Business School, revised Mar 2016.
  5. B. Douglas Bernheim & Daniel Bjorkegren & Jeffrey Naecker & Antonio Rangel, 2013. "Non-Choice Evaluations Predict Behavioral Responses to Changes in Economic Conditions," NBER Working Papers 19269, National Bureau of Economic Research, Inc.

Articles

  1. James Andreoni & Deniz Aydin & Blake Barton & B. Douglas Bernheim & Jeffrey Naecker, 2020. "When Fair Isn’t Fair: Understanding Choice Reversals Involving Social Preferences," Journal of Political Economy, University of Chicago Press, vol. 128(5), pages 1673-1711.
  2. Christine L. Exley & Jeffrey K. Naecker, 2017. "Observability Increases the Demand for Commitment Devices," Management Science, INFORMS, vol. 63(10), pages 3262-3267, October.
  3. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. B. Douglas Bernheim & Daniel Björkegren & Jeffrey Naecker & Michael Pollmann, 2021. "Causal Inference from Hypothetical Evaluations," NBER Working Papers 29616, National Bureau of Economic Research, Inc.

    Cited by:

    1. Romuald Meango, 2023. "Using Probabilistic Stated Preference Analyses to Understand Actual Choices," Papers 2307.13966, arXiv.org.
    2. Ingvild Almås & Orazio Attanasio & Pamela Jervis, 2024. "Presidential Address: Economics and Measurement: New Measures to Model Decision Making," Econometrica, Econometric Society, vol. 92(4), pages 947-978, July.

  2. James Andreoni & Deniz Aydin & Blake Barton & B. Douglas Bernheim & Jeffrey Naecker, 2018. "When Fair Isn't Fair: Understanding Choice Reversals Involving Social Preferences," NBER Working Papers 25257, National Bureau of Economic Research, Inc.

    Cited by:

    1. Arthur E. Attema & Olivier L'Haridon & Gijs van de Kuilen, 2023. "An experimental investigation of social risk preferences for health," Post-Print hal-04116959, HAL.
    2. Falch, Ranveig, 2021. "How Do People Trade Off Resources Between Quick and Slow Learners?," Discussion Paper Series in Economics 5/2021, Norwegian School of Economics, Department of Economics.
    3. Andreoni, James & Serra-Garcia, Marta, 2021. "Time inconsistent charitable giving," Journal of Public Economics, Elsevier, vol. 198(C).
    4. Duell, Dominik & Valasek, Justin, 2019. "Political polarization and selection in representative democracies," Journal of Economic Behavior & Organization, Elsevier, vol. 168(C), pages 132-165.
    5. Ville Korpela & Michele Lombardi & Riccardo Saulle, 2022. "Designing Rotation Programs: Limits and Possibilities," Working Papers 202221, University of Liverpool, Department of Economics.
    6. James Berry & Rebecca Dizon-Ross & Maulik Jagnani, 2020. "Not Playing Favorites: An Experiment on Parental Fairness Preferences," Working Papers 2020-06, Becker Friedman Institute for Research In Economics.
    7. Gago, Andrés, 2021. "Reciprocity and uncertainty: When do people forgive?," Journal of Economic Psychology, Elsevier, vol. 84(C).
    8. Sandro Ambuehl & B. Douglas Bernheim, 2021. "Social preferences over ordinal outcomes," ECON - Working Papers 395, Department of Economics - University of Zurich, revised Dec 2024.
    9. Erik Schokkaert & Benoît Tarroux, 2021. "Empirical research on ethical preferences: how popular is prioritarianism?," Working Papers 2104, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    10. Nguyen, Cuong Viet, 2019. "The effect of inequality in stakes on sharing behavior: Evidence from an experimental study," Economics Letters, Elsevier, vol. 184(C).
    11. Peter Andre, 2021. "Shallow Meritocracy: An Experiment on Fairness Views," ECONtribute Discussion Papers Series 115, University of Bonn and University of Cologne, Germany.
    12. Guilherme Lichand & Juliette Thibaud, 2020. "Parent-bias," ECON - Working Papers 369, Department of Economics - University of Zurich, revised Jun 2022.
    13. Sandro Ambuehl & B. Douglas Bernheim, 2021. "Interpreting the Will of the People - A Positive Analysis of Ordinal Preference Aggregation," CESifo Working Paper Series 9317, CESifo.

  3. Jeffrey Naecker, 2015. "The Lives of Others: Predicting Donations with Non-Choice Responses," Discussion Papers 15-021, Stanford Institute for Economic Policy Research.

    Cited by:

    1. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.

  4. Christine L. Exley & Jeffrey K. Naecker, 2015. "Observability Increases the Demand for Commitment Devices," Harvard Business School Working Papers 16-064, Harvard Business School, revised Mar 2016.

    Cited by:

    1. Andreoni, James & Serra-Garcia, Marta, 2021. "Time inconsistent charitable giving," Journal of Public Economics, Elsevier, vol. 198(C).
    2. Che-Wei Liu & Guodong (Gordon) Gao & Ritu Agarwal, 2019. "Unraveling the “Social” in Social Norms: The Conditioning Effect of User Connectivity," Information Systems Research, INFORMS, vol. 30(4), pages 1272-1295, April.
    3. James Andreoni & Marta Serra-Garcia, 2019. "The Pledging Puzzle: How Can Revocable Promises Increase Charitable Giving," CESifo Working Paper Series 7965, CESifo.
    4. Toke Fosgaard & Adriaan Soetevent, 2025. "I will donate later! A field experiment on cell phone donations to charity," Natural Field Experiments 00814, The Field Experiments Website.
    5. Mohamed Abouaziza, 2022. "Farmer constraints and relational contracts: evidence from agricultural value chains in East Africa," Economics PhD Theses 0122, Department of Economics, University of Sussex Business School.
    6. Meyer, Christian Johannes & Tripodi, Egon, 2021. "Image concerns in pledges to give blood: Evidence from a field experiment," Journal of Economic Psychology, Elsevier, vol. 87(C).
    7. Oliver Himmler & Robert Jäckle & Philipp Weinschenk, 2019. "Soft Commitments, Reminders, and Academic Performance," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 114-142, April.
    8. Mariana Carrera & Heather Royer & Mark Stehr & Justin Sydnor & Dmitry Taubinsky, 2019. "Who Chooses Commitment? Evidence and Welfare Implications," NBER Working Papers 26161, National Bureau of Economic Research, Inc.
    9. Le Yaouanq, Yves, 2015. "Anticipating Preference Reversal"," TSE Working Papers 15-585, Toulouse School of Economics (TSE).
    10. Frank Schilbach, 2019. "Alcohol and Self-Control: A Field Experiment in India," American Economic Review, American Economic Association, vol. 109(4), pages 1290-1322, April.
    11. Andrej Woerner, 2021. "Overcoming Time Inconsistency with a Matched Bet: Theory and Evidence from Exercising," CESifo Working Paper Series 9503, CESifo.
    12. Segovia, Michelle S. & Palma, Marco A. & Nayga, Rodolfo M., 2020. "Can episodic future thinking affect food choices?," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 371-389.
    13. Woerner, Andrej, 2023. "Overcoming Time Inconsistency with a Matched Bet: Theory and Evidence from Exercising," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277711, Verein für Socialpolitik / German Economic Association.

  5. B. Douglas Bernheim & Daniel Bjorkegren & Jeffrey Naecker & Antonio Rangel, 2013. "Non-Choice Evaluations Predict Behavioral Responses to Changes in Economic Conditions," NBER Working Papers 19269, National Bureau of Economic Research, Inc.

    Cited by:

    1. Raj Chetty, 2015. "Behavioral Economics and Public Policy: A Pragmatic Perspective," American Economic Review, American Economic Association, vol. 105(5), pages 1-33, May.
    2. Bart Los & Marcel P. Timmer, 2018. "Measuring Bilateral Exports of Value Added: A Unified Framework," NBER Working Papers 24896, National Bureau of Economic Research, Inc.
    3. Krecik, Markus, 2024. "A needs-based framework for approximating decisions and well-being," Discussion Papers 2024/2, Free University Berlin, School of Business & Economics.
    4. Diane Coyle & Leonard Nakamura, 2019. "Towards a Framework for Time Use, Welfare and Household-centric Economic Measurement," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-01, Economic Statistics Centre of Excellence (ESCoE).
    5. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.

Articles

  1. James Andreoni & Deniz Aydin & Blake Barton & B. Douglas Bernheim & Jeffrey Naecker, 2020. "When Fair Isn’t Fair: Understanding Choice Reversals Involving Social Preferences," Journal of Political Economy, University of Chicago Press, vol. 128(5), pages 1673-1711.
    See citations under working paper version above.
  2. Christine L. Exley & Jeffrey K. Naecker, 2017. "Observability Increases the Demand for Commitment Devices," Management Science, INFORMS, vol. 63(10), pages 3262-3267, October.
    See citations under working paper version above.
  3. Peysakhovich, Alexander & Naecker, Jeffrey, 2017. "Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity," Journal of Economic Behavior & Organization, Elsevier, vol. 133(C), pages 373-384.

    Cited by:

    1. Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "BEAST-Net: Learning novel behavioral insights using a neural network adaptation of a behavioral model," OSF Preprints kaeny, Center for Open Science.
    2. Halko, Marja-Liisa & Lappalainen, Olli & Sääksvuori, Lauri, 2021. "Do non-choice data reveal economic preferences? Evidence from biometric data and compensation-scheme choice," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 87-104.
    3. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," GRU Working Paper Series GRU_2018_023, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    4. Paul Feldman & John Rehbeck, 2022. "Revealing a preference for mixtures: An experimental study of risk," Quantitative Economics, Econometric Society, vol. 13(2), pages 761-786, May.
    5. Richard J. Arend, 2020. "Strategic decision-making under ambiguity: a new problem space and a proposed optimization approach," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 1231-1251, November.
    6. Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2017. "The Theory is Predictive, but is it Complete? An Application to Human Perception of Randomness," PIER Working Paper Archive 18-010, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Aug 2017.
    7. Heller, Yuval & Tubul, Itay, 2023. "Strategies in the repeated prisoner’s dilemma: A cluster analysis," MPRA Paper 117444, University Library of Munich, Germany.
    8. Shao, Yongtong & Xiong, Tao & Li, Minghao & Hayes, Dermot & Zhang, Wendong & Xie, Wei, 2020. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach," ISU General Staff Papers 202001010800001619, Iowa State University, Department of Economics.
    9. Daoud, Adel & Kim, Rockli & Subramanian, S.V., 2019. "Predicting women's height from their socioeconomic status: A machine learning approach," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    10. Christoph Bühren & Fabian Meier & Marco Pleßner, 2023. "Ambiguity aversion: bibliometric analysis and literature review of the last 60 years," Management Review Quarterly, Springer, vol. 73(2), pages 495-525, June.
    11. Zachary Breig, 2020. "Prediction and Model Selection in Experiments," The Economic Record, The Economic Society of Australia, vol. 96(313), pages 153-176, June.
    12. Drew Fudenberg & Jon Kleinberg & Annie Liang & Sendhil Mullainathan, 2019. "Measuring the Completeness of Theories," Papers 1910.07022, arXiv.org.
    13. Daniele Guariso, 2018. "Terrorist Attacks and Immigration Rhetoric: A Natural Experiment on British MPs," Working Paper Series 1218, Department of Economics, University of Sussex Business School.
    14. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
    15. Drew Fudenberg & Wayne Gao & Annie Liang, 2020. "How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories," Papers 2007.09213, arXiv.org, revised Aug 2023.
    16. Shoshan, Vered & Hazan, Tamir & Plonsky, Ori, 2023. "AS-BEAST: Using machine learning to create an adaptable, scalable, and interpretable behavioral model," OSF Preprints kaeny_v1, Center for Open Science.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-EXP: Experimental Economics (5) 2013-08-05 2015-06-20 2015-12-01 2018-12-17 2022-02-07. Author is listed
  2. NEP-DCM: Discrete Choice Models (1) 2013-08-05
  3. NEP-ECM: Econometrics (1) 2022-02-07
  4. NEP-EVO: Evolutionary Economics (1) 2018-12-17
  5. NEP-HPE: History and Philosophy of Economics (1) 2018-12-17
  6. NEP-ORE: Operations Research (1) 2022-02-07
  7. NEP-UPT: Utility Models and Prospect Theory (1) 2013-08-05

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