University of Gothenburg
Reading list

Economic Research Process

Nationalekonomisk forskningsprocess

Course
GM0754
Second cycle
7.5 credits (ECTS)

About the Reading list

Valid from
Spring semester 2025 (2025-01-20)
Decision date
2024-11-25

Impact Evaluation (Annika)

  • Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press.

Available as e-book at the library

  • Roth, J., Sant'Anna, P. H., Bilinski, A., & Poe, J. (2022). What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature. arXiv preprint arXiv:2201.01194.
  • Cattaneo, M. D., & Titiunik, R. (2022). Regression discontinuity designs. Annual Review of Economics, 14, 821-851.
  • Murray, M. P. (2006). Avoiding invalid instruments and coping with weak instruments. Journal of economic Perspectives, 20(4), 111-132.
  • Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical science: a review journal of the Institute of Mathematical Statistics, 25(1), 1.

Experimental Methods (Eva)

The literature for Lecture 1-3 can be read in any order. The articles are here listed approximatively under the lecture where we discuss the article the most, but they will be relevant for all of the lectures 1-3.

Lecture 1: Introduction to Experiments

  • Falk, Armin, and James Heckman. 2009. “Lab Experiments Are a Major Source of Knowledge in the Social Sciences.” Science, Vol. 326, Issue 5952, pp. 535-538.

  • *Smith, Vernon L, 1976. "Experimental Economics: Induced Value Theory," American Economic Review, American Economic Association, vol. 66(2), pages 274-279, May.

    (You can skip the more math-based part and still understand the point.)


Lecture 2: Experiments continued

  • *de Quidt, Jonathan, Johannes Haushofer, and Christopher Roth. 2018. "Measuring and Bounding Experimenter Demand." American Economic Review, 108 (11): 3266-3302. (You don´t have to work through the math but should understand the arguments.)

  • *Cason, Timothy N., & Plott, Charles R. (2014). Misconceptions and game form recognition: challenges to theories of revealed preferences and framing. Journal of Political Economy, 122(6), 1235–1270.


Lecture 3: Field Experiments

  • Levitt, Steven, D., and John A. List. 2007.* "What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World?" Journal of Economic Perspectives, *21 (2): 153-174

  • *Abhijit V. Banerjee & Esther Duflo, 2009. "The Experimental Approach to Development Economics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 151-178, 05.

    (A discussion of field experiments. Great to read for those who think about a master thesis in development economics or using field experiments)

  • *Stefano DellaVigna & John A. List & Ulrike Malmendier & Gautam Rao, 2013. "The Importance of Being Marginal: Gender Differences in Generosity," American Economic Review, American Economic Association, vol. 103(3), pages 586-90.

    (Reading to understand the experimental set up is enough)


Lecture 4: Applications to Behavioral Economics

The listed articles are all examples of field interventions inspired by experimental economics. The paper varies in their difficulty, and you can choose to read the paper you think is the most interesting. If you find the theory difficult, focus on understanding the rational of the experiment, and not the details of the theory. I will talk about Thaler & Benarzi (2004) in class.

  • *Ashraf Nava, Dean Karlan & Wesley Yin. 2006. Tying Odysseus to the Mast: Evidence From a Commitment Savings Product in the Philippines, The Quarterly Journal of Economics, Volume 121, Issue 2, Pages 635–672,

  • *Duflo, Esther, Michael Kremer, and Jonathan Robinson. 2011.* "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya." American Economic Review, *101 (6): 2350-90.

  • Richard H. Thaler and Shlomo Benartzi. 2004. Save More Tomorrow™: Using Behavioral Economics to Increase Employee Saving. Journal of Political Economy 2004 112:S1, S164-S187.


Additional voluntary material


Choice Experiment (Elina)

  1. Lecture about choice experiment (CE) and hypothetical bias:

    • “Choice Experiments Chapter Final March 4”, By T. Holmes, W. Adamowicz, and F. Carlsson, in: A Primer on Nonmarket Valuation, (eds.) Patricia A. Champ, K.J. Boyle, Thomas C. Brown, unpublished manuscript, You should read: pp 1-18 (until 4.1). (Posted on the course web site)


Hypothetical bias:

  • Murphy, J. G. Allen, T. Stevens and D. Weatherhead (2005) A Meta-analysis of Hypothetical Bias in Stated Preference Valuation. Environmental and Resource Economics 30: 313-325. (Posted on the course website)
  • List, J. and C. Gallet (2001), What Experimental Protocol Influence Disparities between Actual and Hypothetical Stated Values? Environmental and Resource Economics 20: 241-254. (Posted on the course website).
  • Loomis, J. B. (2014). 2013 WAEA keynote address: Strategies for overcoming hypothetical bias in stated preference surveys. Journal of Agricultural and Resource Economics, 34-46. (Posted on the course website).



2. Lecture about CE econometrics

Econometrics:

  • “Choice Experiments Chapter Final March 4”, By T. Holmes, W. Adamowicz, and F. Carlsson, in: A Primer on Nonmarket Valuation, (eds.) Patricia A. Champ, K.J. Boyle, Thomas C. Brown, unpublished manuscript, You should read: pp. 33 (from Random Utility Model) -44 (until 6.2) and pp. 49-51(i.e. Chapter 7.2). (Posted on the course web site).


Reading before the first lab:

  • Carlsson F., P. Frykblom, and C. Liljenstolpe (2003), Valuing wetland attributes: An application of choice experiments. Ecological Economics, 47 (95-103). (Posted on the course website). This article gives information about the Wetland data that will be used in the lab assignment.


  • Carlsson, F., Kataria, M., Lampi, E., Nyberg, E., & Sterner, T. (2022). Red, yellow, or green? Do consumers’ choices of food products depend on the label design?. European Review of Agricultural Economics, 49(5), 1005-1026. This article gives information about the meat substitute data that will be used in the lab assignment.