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Jeffrey Kendell 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

Google

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

Research output

as
Jump to: Working papers Articles

Working papers

  1. 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.
  2. Jeffrey Naecker, 2015. "The Lives of Others: Predicting Donations with Non-Choice Responses," Discussion Papers 15-021, Stanford Institute for Economic Policy Research.
  3. 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.
  4. 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. 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. 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. James Berry & Rebecca Dizon-Ross & Maulik Jagnani, 2020. "Not Playing Favorites: An Experiment on Parental Fairness Preferences," NBER Working Papers 26732, National Bureau of Economic Research, Inc.

  2. 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 Jan 2020.

  3. 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. Oliver Himmler & Robert J├Ąckle & Philipp Weinschenk, 2019. "Soft Commitments, Reminders, and Academic Performance," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 114-142, April.
    2. Mariana Carrera & Heather Royer & Mark Stehr & Justin Sydnor & Dmitry Taubinsky, 2019. "How are Preferences For Commitment Revealed?," NBER Working Papers 26161, National Bureau of Economic Research, Inc.
    3. Le Yaouanq, Yves, 2015. "Anticipating Preference Reversal"," TSE Working Papers 15-585, Toulouse School of Economics (TSE).
    4. 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.

  4. 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. 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).
    3. 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.
    4. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Jan 2020.

Articles

  1. 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. 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.
    2. 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 17-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 09 Aug 2017.
    3. 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.
    4. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," NBER Working Papers 25200, National Bureau of Economic Research, Inc.
    5. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Jan 2020.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

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 4 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 (4) 2013-08-05 2015-06-20 2015-12-01 2018-12-17. Author is listed
  2. NEP-DCM: Discrete Choice Models (1) 2013-08-05. Author is listed
  3. NEP-EVO: Evolutionary Economics (1) 2018-12-17. Author is listed
  4. NEP-HPE: History & Philosophy of Economics (1) 2018-12-17. Author is listed
  5. NEP-UPT: Utility Models & Prospect Theory (1) 2013-08-05. Author is listed

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