Summer School 2025

The 16th BESLAB Experimental Economics Summer School in Macroeconomics

Gabriele Camera

• Monetary Experiments and Economic Growth Experiments:

The lecture is divided into two segments: Monetary Experiments and Economic Growth Experiments. At the start of each segment a brief theory part will develop a theoretical platform for monetary models (first segment of lecture) and classical growth models (second segment of lecture). The second segment will focus on models of representative agents and competitive equilibrium. The first segment will focus on models of strategic interaction and Nash equilibrium, which have been adopted to study a variety of topics including labor, IO, money and finance; this overlap opens the door to exploring related applications, such as cooperation, collusion, trust, equilibrium selection, coordination.

The main goals are: (1) To review basic techniques used in theoretical modeling of growth and of money; (2) To study how those models have been brought to the lab, (3) To understand the insights emerging from recent experimental applications. At the end of the course students will possess an expanded set of tools, which will be helpful to read critically the literature and to engage independent research.

The papers listed in the Literature below are in addition to relevant papers that are already covered elsewhere in the workshop.

 

• General literature

Bigoni, M., G. Camera, and M. Casari (2014). Money is more than memory. Economic Science Institute working paper # 14-17

Bigoni, M., G. Camera, and M. Casari (2015). Money and the scale of cooperation. Economic Science Institute working paper # 15-28

Camera, G., and M. Casari (2009). Cooperation among strangers under the shadow of the future. American Economic Review 99(3), 979–1005.

Camera, G., M. Casari, and M. Bigoni (2013). Experimental Markets with Frictions. Journal of Economic Surveys 27(3), 536–553.

Camera, G., M. Casari, and M. Bigoni (2013). Money and trust among strangers. Proceedings of the National Academy of Sciences 110(37), 14889-14893.

Camera, G., and M. Casari (2014). The coordination value of monetary exchange: experimental evidence. American Economic Journal: Microeconomics 6(1), 290-314.

Diamond P., (1982). Aggregate Demand Management in Search Equilibrium. Journal of Political Economy 90, 881‑894.

Duffy, John (forthcoming). Macroeconomics: A Survey of Laboratory Research. In: Handbook of Experimental Economics, volume 2, John Kagel and Al Roth editors.

Ellison, Glenn (1994). Cooperation in the prisoner's dilemma with anonymous random matching. Review of Economic Studies, 61, 567-88.

Kandori, Michihiro (1992). Social norms and community enforcement. Review of Economic Studies, 59, 63-80.

Kocherlakota, N. (1998). Money is memory. Journal of Economic Theory 81, 232-251

Lei V., and C. Noussair (2002). An Experimental Test of an Optimal Growth Model. American Economic Review 92(3), 549-570.

Capra, M., C. Camerer, T. Tanaka, L. Feiler, V. Sovero, and C. Noussair (2009). The Impact of Simple Institutions in Experimental Economies with Poverty Traps. Economic Journal 119(539), 977 - 1009.

Smith, Vernon L. (1994). Economics in the Laboratory. Journal of Economic Perspectives 8(1), 113-131

Townsend, R. (1980). Models of Money with Spatially Separated Agents. In Models of Monetary Economies, J. Kareken and N. Wallace editors, p. 265-303

 

John Duffy

• Introduction to Macroeconomic Experiments

This lecture provides an overview of experimental research addressing key questions in macroeconomics. It surveys experiments on expectation formation, intertemporal optimization, multiple equilibria and coordination failures, monetary policy and central bank communication. The objective is to demonstrate how experimental methods yield valuable insights into the behavioral foundations of macroeconomic models, allow for direct tests of theoretical predictions, and inform policy design in environments characterized by strategic uncertainty, incomplete information, and bounded rationality.

• Reading List

Arifovic, J., & Duffy, J. (2018). Heterogeneous agent modeling: Experimental evidence. In C. Hommes & B. LeBaron (Eds.), Handbook of Computational Economics (Vol. 4, pp. 491–540). Elsevier.

Assenza, T., Bao, T., Hommes, C., & Massaro, D. (2014). Experiments on expectations in macroeconomics and finance. In J. Duffy (Ed.), Experiments in Macroeconomics (pp. 11–70). Emerald Group Publishing.

Driscoll, J. C., & Holden, S. (2014). Behavioral economics and macroeconomic models. Journal of Macroeconomics, 41, 133–147.

Duffy, J. (2022). Why macroeconomics needs experimental evidence. The Japanese Economic Review, 73(1), 5–29.

Duffy, J. (2016). Macroeconomics: A survey of laboratory research. In J. H. Kagel & A. E. Roth (Eds.), Handbook of Experimental Economics (Vol. 2, pp. 1–90). Princeton University Press.

Duffy, J. (2010). Experimental macroeconomics. In S. N. Durlauf & L. E. Blume (Eds.), Behavioural and Experimental Economics. The New Palgrave Economics Collection (pp. 113–119). Palgrave Macmillan.

• Consumption Smoothing and Asset Pricing Experiments

This lecture reviews experimental evidence on intertemporal consumption and savings decisions. Over the lifecycle, individuals often overconsume and undersave when young, resulting in insufficient wealth and underconsumption when old. Experiments also document an aversion to borrowing (but not to saving) for the purpose of intertemporal consumption smoothing. We then examine experimental evidence on the pricing of both finitely and indefinitely lived assets. Finally, we connect these literatures through the consumption-based asset pricing model of Lucas (1978), where agents trade assets to smooth consumption over time.

• Reading List

Anderhub, V., Güth, W., Müller, W., & Strobel, M. (2000). An experimental analysis of intertemporal allocation behavior. Experimental Economics, 3(2), 137–152.

Asparouhova, E., Bossaerts, P., Roy, N., & Zame, W. (2016). “Lucas” in the laboratory. The Journal of Finance, 71(6), 2727–2780.

Ballinger, T. P., Palumbo, M. G., & Wilcox, N. T. (2003). Precautionary saving and social learning across generations: An experiment. The Economic Journal, 113(490), 920–947.

Brown, A. L., Chua, Z. E., & Camerer, C. F. (2009). Learning and visceral temptation in dynamic saving experiments. The Quarterly Journal of Economics, 124(1), 197–231.

Carbone, E., & Duffy, J. (2014). Lifecycle consumption plans, social learning and external habits: Experimental evidence. Journal of Economic Behavior & Organization, 106, 413–427.

Carbone, E., & Hey, J. D. (2004). The effect of unemployment on consumption: An experimental analysis. The Economic Journal, 114(497), 660–683.

Crockett, S., Duffy, J., & Izhakian, Y. (2019). An experimental test of the Lucas asset pricing model. The Review of Economic Studies, 86(2), 627–667.

Duffy, J., Hua Jiang, J., & Xie, H. (2024). Pricing indefinitely lived assets: Experimental evidence. Management Science, 70(12), 8772–8790.

Duffy, J., & Li, Y. (2019). Lifecycle consumption under different income profiles: Evidence and theory. Journal of Economic Dynamics and Control, 104, 74–94.

Duffy, J., & Li, Y. (2025, forthcoming). Do tax-deferred accounts improve lifecycle savings? Experimental evidence. Review of Economics and Statistics.

Levy, M. R., & Tasoff, J. (2016). Exponential growth bias and lifecycle consumption. Journal of the European Economic Association, 14(3), 545–583.

Lucas, R. E. (1978). Asset prices in an exchange economy. Econometrica, 46(6), 1429–1445.

Meissner, T. (2016). Intertemporal consumption and debt aversion: An experimental study. Experimental Economics, 19(2), 281–298.

Plott, C. R., & Sunder, S. (1988). Rational expectations and the aggregation of diverse information in laboratory security markets. Econometrica, 56(5), 1085–1118.

Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica, 56(5), 1119–1151.

 

Frank Heinemann

• Understanding Financial Crises: The Contribution of Experimental Economics

The patterns of financial crises are remarkably predictable. Minsky (1972) has described these patterns by phases, some of which contain behavioural hypotheses that can be tested by laboratory experiments. Under which conditions can bubbles arise? When do they burst? Why do people herd and does herding destabilize financial markets? What triggers a bank run and how do people coordinate in an environment with multiple equilibria? This lecture will lay out experimental evidence containing some answers to these questions. In particular, we will look at experiments on games with strategic complementarities. How predictable are choices if the game has multiple equilibria and which theory is well-suited to give advice for individual behavior? Managing information flow is one of the major challenges for central banks and bank supervisors. The lecture explains what we can learn from experiments for managing information flow in the presence of strategic complementarities.

• Literature:

Minsky, H.P. (1972), Financial Instability Revisited: the Economics of Disaster

Brunnermeier, Markus, and John Morgan (2008), Clock Games: Theory and Experiments, Games and Economic Behavior, forthcoming, http://www.princeton.edu/~markus/research/papers/clock_games.pdf

Kübler, Dorothea, and Georg von Weizsäcker (2004), Limited Depth of Reasoning and Failure of Cascade Formation in the Laboratory, Review of Economic Studies 71, 425-442.

Schotter, Andrew, and Tanju Yorulmazer (2009), On the Severity of Bank Runs,  Journal of Financial Intermediation 18, 217-241.

Heinemann, Frank, Rosemarie Nagel, and Peter Ockenfels (2009), Measuring Strategic Uncertainty in Coordination Games, Review of Economic Studies 76, 181-221.

Cornand, C., and F. Heinemann (2010), Measuring Agents' Reaction to Private and Public Information in Games with Strategic Complementarities, CESifo Working Paper 2947

Heinemann, Frank (2012), Understanding Financial Crises: The Contribution of Experimental Economics, Annals of Economics and Statistics, 107-108, 2012, pp. 7-29,

http://www.macroeconomics.tu-berlin.de/fileadmin/fg124/heinemann/publications/Understanding_Financial_Crises.pdf

• Speculative Attacks and the Theory of Global Games - Experimental Tests of Global Game Predictions

Speculative attacks can be viewed upon as coordination games: if a sufficient number of traders (and a sufficient amount of capital) is involved in an attack, the pressure on foreign exchange markets forces the central bank to devaluate its currency. Then, all attacking traders gain from the devaluation. But, if the number of attackers is too small, the central bank can defend the peg, and attacking traders lose on transaction costs. Speculative-attack games have multiple equilibria if payoff functions are common knowledge. The theory of global embeds a coordination game in an environment with private information about parameters of the payoff function. If private information is sufficiently precise, the global game has a unique equilibrium. Hence, the theory of global games can be used for a unique prediction of the outcome of a speculative-attack game. This theory provides a number of hypotheses that can be tested in laboratory experiments. This lecture first presents some of the theoretical background and derives testable hypotheses. Then, it explains experiments that have been used for these tests and shows how they have been analyzed.

• Literature:

Introduction:

Heinemann, Frank (2002), "Exchange-Rate Attack as a Coordination Game: Theory and Experimental Evidence," Oxford Review of Economic Policy 18, 462-478.

• Theory:

Obstfeld, Maurice (1997), "Destabilizing Effects of Exchange-Rate Escape Clauses," Journal of International Economics, 61-77.

Carlsson, Hans and Eric van Damme (1993), "Global Games and Equilibrium Selection," Econometrica 61, 989-1018.

Morris, S., and H.S. Shin (1998), "Unique Equilibrium in a Model of Self-Fulfilling Currency Attacks," American Economic Review, 88, 587-597.

Heinemann, Frank (2000), "Unique Equilibrium in a Model of Self-Fulfilling Currency Attacks: Comment," American Economic Review 90, 316-318.

Hellwig, Christian (2002), "Public Information, Private Information, and the Multiplicity of Equilibria in Coordination Games," Journal of Economic Theory 107, pp. 191-222.

• Experiments:

Heinemann, F., R. Nagel, and P. Ockenfels (2004), "The Theory of Global Games on Test: Experimental Analysis of Coordination Games with Public and Private Information," Econometrica 72 (5), 2004, pp. 1583-1599.

Cornand C. (2006), "Speculative Attacks and Informational Structure: An Experimental Study," Review of International Economics 14, 797-817.

 

Rosemarie Nagel

• Methodology

This lecture introduces the methods of experimental economics. We will discuss what is an economic experiment (field vs lab experiment), the different areas in experimental economics and behavioral economics, the link between experimental economics, theory and empirical work, important design issues, and the link between micro and macro experiments. All this will be discussed with the classical Keynesian Beauty Contest game (see *Mauersberger and Nagel (2018) in references as recommended reading). This introduction is designed to provide a brief overview for those who have never taken an experimental economics course.

• General literature :

Akerlof, G.A. (2002), "Behavioral Macroeconomics and Macroeconomic Behavior, "American Economic Review," 92. 411-433.

Handbook of Experimental Economics, Vol1 (1995), Vol2 (2016), editors J. Kagel &A. Roth

Camerer, C. (2003), "Behavioral Game Theory," Princeton University Pre2016ss

Friedman, D. and Sunder, S. (1994), Experimental Methods. Cambridge Univ. Press: Chapters 1-2: 1-20.

Plott, C. and Smith, V. (2003), Handbook of Experimental Economics Results, North-Holland, Amsterdam.

Smith, V.L. (2002), "Method in Experiment: Rhetoric and Reality." Experimental Economics 5(2): 91-110.

Special issue (2005), Experiment, Theory, World: A Symposium on the Role of Experiments in Economics. Journal of Economic Methodology 12(2).

Papers related to the Beauty Contest game

Mauersberger, F. and Nagel, R. (2018). Levels of Reasoning in Keynesian Beauty Contests: A Generative Framework in the Handbook of Computational Economics, Volume 4, Heterogeneous Agents. Editors: Cars Hommes and Blake LeBaron. Amsterdam: North-Holland.

Vincent P. Crawford, Miguel A. Costa-Gomes, and Nagore Iriberri (2012) "Structural Models of Nonequilibrium Strategic Thinking: Theory, Evidence, and Applications," forthcoming in Journal of Economic Literature. http://weber.ucsd.edu/~vcrawfor/CGCIJEL4April12

Antoni Bosch-Domènech , Jose García-Montalvo, Rosemarie Nagel, and Albert Satorra, "One, Two, (Three), Infinity...: Newspaper and Lab Beauty-Contest Experiments", American Economic Review December 92 (5), 2002, pp 1687-1701.

Giorgio Coricelli, Rosemarie Nagel, "Neural correlates of depth of strategic reasoning in medial prefrontal cortex" Proceedings of the National Academy of Sciences (PNAS): Economic Sciences, PNAS June 9, 2009 vol. 106.

Nagel Rosemarie (1995), "Unraveling in Guessing Games: An Experimental Study." American Economic Review 85,5 1313-1326.

 

Luba Petersen

Monetary Policy Experiments

In this lecture, we will explore how laboratory experiments can be used to inform the design and implementation of monetary policy. We will begin by reviewing the various approaches to studying the effects of monetary policy (theory, computational, empirics, experiments), and considering their strengths and weaknesses. Different experimental approaches to studying monetary policy will be considered (individual choice experiments, coordination experiments involving expectations and real decisions), followed by a survey of the literature and findings. An important question is: why does monetary policy not work better? Are there cognitive limitations that inhibit the public's ability to respond to monetary policy? Or does the weak

transmission of monetary policy stem from an inability to coordinate. We will consider empirical and experimental evidence on the transmission of monetary policy, and possible directions forward for research.

• Theory

Walsh, C. E. (2017). Monetary theory and policy. MIT Press.

Woodford, M. (2011). Interest and prices: Foundations of a theory of monetary policy. Princeton University Press.

• Experiments

Adam K. (2007), “Experimental Evidence on the Persistence of Output and Inflation,” Economic Journal 117, 603–635.

Arifovic, J. and L. Petersen (2017). “Stabilizing expectations at the zero lower bound: Experimental evidence”, Journal of Economic Dynamics and Control, 82, 21-43.

Arifovic J. and T. J. Sargent (2003), “Laboratory Experiments with an Expectational Phillips Curve,“ in D. E. Altig, and B. D. Smith (eds.), Evolution and Procedures in Central Banking,

Cambridge University Press, Cambridge. Arifovic J. and J. H. Jiang (2013), "Experimental Evidence of Sunspot Bank Runs," mimeo Bank of Canada.

Assenza, T., Heemeijer, P., Hommes, C., & Massaro, D. (2013). Individual expectations and aggregate macro behavior.

Bernasconi M. and O. Kirchkamp (2000), “Why do monetary policies matter? An experimental study of saving and inflation in an overlapping generations model,” Journal of Monetary Economics 46, 315‐ 343.

Blinder A. S. and J. Morgan (2005), “Are Two Heads Better than One? Monetary Policy by Committee,” Journal of Money, Credit, and Banking 37, 789‐812. Blinder A. S. and J. Morgan (2008), “Leadership in Groups: A Monetary Policy Experiment,” International Journal of Central Banking 4(4), 117‐150.  

Bosch‐Domenech A. and J. Silvestre (1997), “Credit Constraints in a General Equilibrium: Experimental Results,” Economic Journal 107, 1445‐1464.

Cornand, C., & M'baye, C. K. (2016). Does inflation targeting matter? an experimental investigation. Macroeconomic Dynamics, 1-40.

Engle‐Warnick J. and Turdaliev N. (2010), “An Experimental Test of Taylor‐Type Rules with Inexperienced Central Bankers,” Experimental Economics 13, 146‐166.

Fehr E. and J.‐R. Tyran (2001), “Does Money Illusion Matter?,” American Economic Review 91, 1239‐ 1262.

Fehr E. and J.R. Tyran (2005), “Individual Irrationality and Aggregate Outcomes," Journal of Economic Perspectives 19, 43‐66.

Fehr E. and J.‐R. Tyran (2008), “Limited Rationality and Strategic Interaction: The Impact of the Strategic Environment on Nominal Inertia,” Econometrica 76, 353‐394.

Fehr E. and J.‐R. Tyran (2014), “Does Money Illusion Matter?: Reply,” American Economic Review 104, 1063‐1071.

Fenig, G., Mileva, M., & Petersen, L. (2018). Deflating asset price bubbles with leverage constraints and monetary policy. Journal of Economic Behavior and Organization, 155, 1-27.

Fischbacher, U., Hens, T., & Zeisberger, S. (2013). The impact of monetary policy on stock market bubbles and trading behavior: Evidence from the lab. Journal of Economic Dynamics and Control, 37(10), 2104-2122.

Kryvtsov O and L. Petersen (2013), “Expectations and Monetary Policy: Experimental Evidence,” Bank of Canada Working Paper.

Lian P. and C. Plott (1998), “General Equilibrium, Markets, Macroeconomics and Money in a Laboratory Experimental Environment,” Economic Theory 12,  21‐75.

Noussair C. N., D. Pfajfar and J. Zsiros (2014), “Frictions, Persistence, and Central Bank Policy in an Experimental Dynamic Stochastic General Equilibrium Economy,” in J. Duffy (eds.), “Experiments in Macroeconomics”, Research in Experimental Economics 17, Emerald Press.  

Petersen L. and A. Winn (2014), “Does money illusion matter?: Comment,” American Economic Review 104, 1047‐1062.

Pfajfar, D., & Žakelj, B. (2016). Inflation expectations and monetary policy design: Evidence from the laboratory. Macroeconomic Dynamics, 1-41.

 

Shyam Sunder

• Complexity and Abstraction: Designing Macro Experiments

Relevant real world phenomena and relevant models of interest serve as two important benchmarks in designing laboratory experiments. With their fractal structure, phenomena in field are endlessly complex. Accordingly, realism (i.e., fidelity to the field environment) and theory (i.e., fidelity to the model) place important, often conflicting, demands on design of laboratory experiments.  Do the details matter? Which ones do? How do we find out? Why do the details that "do not matter" exist in the field? If they are just matters of refinement, which refinements are and are not to be ignored in laboratory? How generalizable are the laboratory findings? How does an experimenter find his way through this maze that connects limitless complexity of the field to simple tidy models of economics to gain a better understanding of economic phenomena? We shall explore the practical problems of identifying interesting questions, and developing experimental designs to address them using some examples, notes, and some macro experiments.

Sunder, Shyam. "Determinants of Economic Interaction: Behavior or Structure."Journal of Economic Interaction and Coordination 1, no. 1 (May 2006): 21-32. Text (PDF).

Sunder, Shyam. "Real Phenomena, Theory and Design of Laboratory Experiments in Economics." Notes. Text (PDF).

Lim, Suk S., Edward C. Prescott and Shyam Sunder. "Stationary Solution to the Overlapping Generations Model of Fiat Money: Experimental Evidence." Empirical Economics 19, no. 2 (1994): 255-277. Text (PDF)

Marimon, Ramon and Shyam Sunder. "Indeterminacy of Equilibria in a Hyperinflationary World: Experimental Evidence." Econometrica 61, no. 5 (1993): 1073-1108. Text (PDF).

Marimon, Ramon and Shyam Sunder. "Expectations and Learning Under Alternative Monetary Regimes: An Experimental Approach." Economic Theory 4 (1994), 131-162. Text (PDF)

Huber, Juergen, Martin Shubik, and Shyam Sunder. "Financing of Public Goods through Taxation in a General Equilibrium Economy: Theory and Experimental Evidence," Cowles Foundation Discussion Paper 1830, October 23, 2011.

Huber, Juergen, Martin Shubik and Shyam Sunder. "Sufficiency of an Outside Bank and a Default Penalty to Support the Value of Fiat Money: Experimental Evidence." Cowles Foundation Discussion Paper No. 1675, Revised June 12, 2011.

• Experiments with Minimally Intelligent Agents and Minimal Institutions

Laboratory exploration of properties of economic institutions and policies has traditionally been done using profit motivated human traders. Outcomes of such experiments, when compared with outcomes of identical economies populated with minimally intelligent algorithmic agents yield valuable insights. We can isolate which of the properties of the economies of interest arise from their structure, and which ones are attributable to the behavior of agents. Starting with three micro applications, we shall study three macro applications of this human-algorithm hybrid approach to experimentation.

Gode, Dhananjay K. and Shyam Sunder. "Allocative Efficiency of Markets with Zero Intelligence Traders: Market as a Partial Substitute for Individual Rationality." The Journal of Political Economy 101, no. 1 (February 1993): 119-137.Text (PDF).

Gode, Dhananjay and Shyam Sunder. "What Makes Markets Allocationally Efficient?" Quarterly Journal of Economics 112, no. 2 (May 1997), 603-630. GSIA Reprint No. 1473.Abstract (PDF), Text (PDF).

Gode, Dhananjay K., and Shyam Sunder. "Double Auction Dynamics: Structural Effects of Non-Binding Price Controls."Journal of Economic Dynamics and Control 28, no. 9 (July 2004): 1707-1731. Abstract(PDF), Text (PDF).

Huber, Juergen, Martin Shubik and Shyam Sunder. "Three Minimal Market Institutions: Theory and Experimental Evidence."Games and Economic Behavior 70 (2010) 403-424.

Angerer, Martin, Juergen Huber, Martin Shubik and Shyam Sunder. "An Economy with Personal Currency: Theory and Experimental Evidence." Annals of Finance, Volume 6, Number 4, October 2010, pp.475-509.

Huber, Juergen, Martin Shubik and Shyam Sunder. "Default Penalty as a Selection Mechanism Among Multiple Equilibria."Cowles Foundation Discussion Paper 1730R, Revised February 6, 2011.

 

Daniela Puzzello

• Lecture 1: Monetary Exchange in the Laboratory

Vernon Smith was awarded the Nobel Prize in Economics in 2002 “for having established laboratory experiments as a tool in empirical economic analysis, especially in the study of alternative market mechanisms.” Experimental methods have since become widely used in microeconomics and finance, yielding valuable insights into behavior, institutions, and market design. Building on this tradition, this lecture explores how similar experimental approaches, combined with micro-founded theoretical models can generate important insights in monetary economics. Indeed, laboratory experiments provide a powerful tool to investigate fundamental questions in monetary economics, such as:

• Under what conditions is money essential?
• Do monetary policies work as intended, and if not, why?
• What are the welfare implications of replacing cash with central bank digital currencies (CBDCs), and how can the differences between them be leveraged to improve outcomes?
• How do legal restrictions and government interventions shape the circulation of multiple currencies?
• Which bargaining solutions or weights best organize behavior in monetary and labor settings, particularly in two-dimensional bargaining environments?

The lecture will highlight selected examples to illustrate how the integration of experimental methods with theoretical models can shed light on key questions in monetary economics. 

Selected readings:

Theory

Aliprantis, C. D., Camera, G., and Puzzello, D. (2007). Contagion Equilibria in a Monetary Model. Econometrica, 75(1), 277–282. http://www.jstor.org/stable/4123115
 
Araujo, L., Camargo, B., Minetti, R., and Puzzello, D. (2012). The Essentiality of Money in Environments with Centralized Trade. Journal of Monetary Economics, 59(7), 612–621. https://doi.org/10.1016/j.jmoneco.2012.10.007
 
Kiyotaki, N., and Wright, R. (1989). On Money as a Medium of Exchange. Journal of Political Economy, 97(4), 927–954. http://www.jstor.org/stable/1832197
 
Kovenock, D., and De Vries, C. G. (2002). Fiat exchange in finite economies. Economic Inquiry, 40(2), 147–157. https://doi.org/10.1093/ei/40.2.147
 
Lagos, R., and Wright, R. (2005). A Unified Framework for Monetary Theory and Policy Analysis. Journal of Political Economy, 113(3), 463–484. https://doi.org/10.1086/429804
 
Experiments

Ding, S., and Puzzello, D. (2020). Legal Restrictions and International Currencies: An Experimental Approach. Journal of International Economics, 126, 103342. https://doi.org/10.1016/j.jinteco.2020.103342

Duffy, J., and Ochs, J. (1999). Emergence of Money as a Medium of Exchange: An Experimental Study. American Economic Review 89 (4): 847–877. https://www.aeaweb.org/articles?id=10.1257/aer.89.4.847

Duffy, J., and Puzzello, D. (2014). Gift Exchange versus Monetary Exchange: Theory and Evidence. American Economic Review 104 (6): 1735–76. https://www.aeaweb.org/articles?id=10.1257/aer.104.6.1735

Duffy, J., and Puzzello, D. (2021). The Friedman Rule: Experimental Evidence. International Economic Review, 63(2). https://doi.org/10.1111/iere.12549

Duffy, J., and Lebeau, L. and Puzzello, D. Bargaining Under Liquidity Constraints: Experimental Evidence. Journal of Economic Theory, forthcoming

Jiang, J., Puzzello, D., and Zhang, C. (2023). Inflation, Output, and Welfare in the Laboratory, European Economic Review, 152. https://doi.org/10.1016/j.euroecorev.2022.104351

Jiang, J. H., Norman, P., Puzzello, D., Sultanum, B., and Wright, R. (2024). Is Money Essential? An Experimental Approach. Journal of Political Economy, 132(9), 2972–2998. https://doi.org/10.1086/730199

Lecture 2: Survey and Experiments on Inflation Expectations

Understanding how individuals form and act on inflation expectations is central to both macroeconomic modeling and policy design. While traditional surveys have long served as a primary measurement tool, experimental methods- broadly intended- provide a valuable tool to better understand how expectations are shaped, how they influence decisions, and how they respond to different informational treatments and marginal incentives.
This lecture explores recent advances leveraging the strengths of both laboratory and survey experiments to study inflation expectation. It focuses on key questions:
- How does information provision affect inflation expectations?
- How do people perceive inflation?
- What are the transmission channels from inflation expectations to stated spending?
- Do incentives affect the accuracy or consistency of reported expectations?

The lecture will cover key findings from recent experimental studies, including evidence on the impact of information treatments, the role of monetary incentives, and the potential of using large language models to simulate household or professional forecasters’ expectations. By integrating experimental methods into surveys, the lecture illustrates how economists can improve the measurement of expectations, and, ultimately, their incorporation into models and monetary policy design.

Selected readings:

Armantier, O., Bruine de Bruin, W., Topa, G., van der Klaauw, W., and Zafar, B. (2015). Inflation Expectations and Behavior: Do Survey Respondents Act on Their Beliefs? International Economic Review, 56(2), 505–536. https://doi.org/10.1111/iere.12113

Coibion, O., D’Acunto, F., and Weber, M. (2022). The Subjective Inflation Expectations of Households and Firms: Measurement, Determinants, and Implications. Journal of Economic Perspectives, 36(4), 157–184. https://doi.org/10.1257/jep.36.4.157

Danz, D., Vesterlund, L., and Wilson, A. J. (2022). Belief Elicitation and Behavioral Incentive Compatibility. American Economic Review, 112(9), 2851–2883. https://doi.org/10.1257/aer.20201248

Drobot, S., Puzzello, D., Rholes, R. and Wabitsch, A. (2025). Incentivizing Inflation Expectations. https://awabit.github.io/incentives/Incentivizing%20Inflation%20Expectations.pdf

Fuster, A., & Zafar, B. (2023). Survey Experiments on Economic Expectations. In R. Bachmann, G. Topa, & W. van der Klaauw (Eds.), Handbook of Economic Expectations (pp. 107–130). Academic Press. https://doi.org/10.1016/B978-0-12-822927-9.00010-0

Haaland, I., Roth, C., & Wohlfart, J. (2023). Designing Information Provision Experiments. Journal of Economic Literature, 61(1), 3–40. https://doi.org/10.1257/jel.20211658

Hansen, A., Horton, J., Kazinnik, S., Puzzello, D. and Zarifhornarvar, A. (2024). Simulating the Survey of Professional Forecasters. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5066286

Jiang, J. H., Kamdar, R., Lu, K., and Puzzello, D. (2024). How Do Households Respond to Expected Inflation? An Investigation of Transmission Mechanisms.

Schlag, K. H., Tremewan, J., & van der Weele, J. J. (2015). A Penny for Your Thoughts: A Survey of Methods for Eliciting Beliefs. Experimental Economics, 18(3), 457- 490. https://doi.org/10.1007/s10683-014-9416-x

Stancheva, S. (2022). How to Run Surveys: A Guide to Creating Your Identifying Variation and Revealing the Invisible. Annual Review of Economics, 15(1), 205–234. https://doi.org/10.1146/annurev-economics-051520-1054