A landmark practical guide from the twenty-first-century pioneer in economics.

Experimental economics—generating and interpreting data to understand human decisions, motivations, and outcomes—is today all but synonymous with economics as a discipline. The advantages of the experimental method for understanding causal effects make it the gold standard for an increasingly empirical field. But until now the discipline has lacked comprehensive and definitive guidance for how to optimally design and conduct economic experiments.

For more than 30 years, John A. List has been at the forefront of using experiments to advance economic knowledge, expanding the domain of economic experiments from the lab to the real-world. Experimental Economics is his A-to-Z compendium for students and researchers on the ground floor of designing, conducting, analyzing, and interpreting data that they generate. List seeks not only to guide readers on how to develop and implement their experimental projects—everything from design to administrative and ethical considerations—but to help them avoid all the mistakes he’s made in his career, too. Experimental Economics codifies its author’s refined approach to the design, execution, and analysis of laboratory and field experiments. It is a milestone work poised to become the definitive reference for the next century of economics (and economists).

Preface

Part I. Experimental Methods in Economics
1. Introduction
Key Ideas
1.1 Causal Inference
Experimental Problem 1: Quantifying Economic Fundamentals, Measuring Treatment Effects, and Identifying Key Mediators and Moderators in an Ethically Responsible Manner
Experimental Problem 2: Predicting If the Causal Impacts of Treatments Implemented in One Environment Transfer to Other Environments, Whether Spatially, Temporally, or Scale Differentiated
1.2 The Book’s Game Plan
Notes
References
2. A Primer on Economic Experiments
Key Ideas
2.1 Four Running Examples
2.2 The Empirical Approach in Economics
2.3 Experiments in Economics
2.3.1 Laboratory Experiments
2.3.2 Field Experiments
2.3.2.1 Seven Criteria That Define Field Experiments
Experimental Subjects: Population and Selection
Experimental Environment
2.3.3 What Parameters Do the Various Experimental Types Recover?
2.4 What Experimental Type to Choose
2.4.1 Control across the Experimental Spectrum for Identification Purposes
2.4.2 Control across the Experimental Spectrum for Measurement Purposes
2.4.3 The Ability to Replicate across the Experimental Spectrum
2.4.4 Control across the Experimental Spectrum for Inferential Purposes
2.4.5 Control across the Experimental Spectrum to Ensure External Validity
2.5 Conclusions: Key Complementarities Exist across the Lab and Field
Appendix 2.1 Introducing General Potential Outcomes Notation
Notes
References
3. Internal Validity: Identification in Economic Experiments
Key Ideas
3.1 Four Running Examples
3.2 The Assignment Mechanism
3.3 Potential Outcomes Framework
3.4 From Individual Treatment Effects to Average Treatment Effects
3.5 How Selection Leads to Bias
3.5.1 Using Randomization to Solve the Selection Problem
3.6 Introducing EPATE: The Case When τPi = 1 ≠ τ
3.7 Recovering and Interpreting Heterogeneity of Treatment Effects
3.8 Violations of the Exclusion Restrictions
3.8.1 SUTVA
3.8.2 Observability
3.8.3 Compliance
3.8.4 Statistical Independence
3.9 Conclusions
Appendix 3.1 Recovering the Wedge between τ and τ̃​τ (Derivation of Equation 3.5)
Appendix 3.2 The Brass Tacks of Estimating the Effects of Training Programs
Notes
References
4. Statistical Conclusion Validity: Measurement in Economic Experiments
Key Ideas
4.1 Two Running Examples
4.2 Perspectives on Sampling Frameworks
4.2.1 Subpopulations in the Superpopulation Framework
4.3 Estimating Treatment Effects and Making Inference
4.3.1 Motivating the Difference-in-Means Estimator for ATE Parameters
4.3.2 Single Hypothesis Testing and Statistical Power
4.3.3 Multiple Hypothesis Testing
4.3.3.1 Family-Wise Error Rate
4.3.3.2 Approaches to Controlling the FWER
Bonferroni Correction
Holm Stepdown Correction
List et al. FWER Correction
4.3.4 Introducing the Difference-in-Differences Estimator for ATE Parameters
4.3.5 Introducing an Alternative to ATE Parameters: Fisher’s Randomization Inference
4.4 Conclusions
Appendix 4.1 Code for List et al. (2019) and List et al. (2023)
Installation (2019)
Command Procedure (2019)
Installation (2023)
Command Procedure (2023)
Notes
References

Part II. Designing Economic Experiments
5. Optimal Experimental Design
Key Ideas
5.1 Three Running Examples
5.2 Basic Principles of Statistical Power
5.3 The Case of a Binary Treatment with Continuous Outcomes
5.3.1 Putting It All Together to Create an Optimal Design
5.4 The Case of a Binary Treatment with Binary Outcomes
5.5 Varying Treatment Levels with Continuous Outcomes
5.6 Expanding the Tool Kit
5.6.1 Heterogeneity in Participant Costs
5.6.2 Clustered Experimental Designs
5.6.3 Optimal Design with Multiple Hypothesis Adjustment
5.7 Less Considered Design Choices to Enhance Statistical Power
5.7.1 Including Covariates in the Estimation Model
5.7.2 Designs to Maximize Compliance
5.7.3 The Nature of the Sample
5.7.4 Measurement Choices
5.7.5 Factorial Designs
5.8 Conclusions
Appendix 5.1 An Example of the Power of Simulation Methods: The Case of Varying Treatment Levels with Binary Outcomes
Appendix 5.2 Step-by-Step Flexible Regression Adjustment
Appendix 5.3 Introducing Full and Fractional Factorial Designs
Three Factors
Appendix 5.4 A Walk-Through Example
Notes
References
6. Randomization Techniques
Key Ideas
6.1 Three Running Examples
6.2 Classical Assignment Mechanisms
6.3 Classical Randomization Approaches
6.3.1 Bernoulli Trials
6.3.2 Completely Randomized Experiments (CRE)
6.3.3 Randomized Block (Stratified) Experiments
6.3.4 Rerandomization Approaches
6.3.5 Optimal Stratification with Matched-Pairs Designs
6.3.5.1 Efficient Matching Minimizing Mean-Squared Error
6.4 Design-Conscious Inference
6.4.1 Statistical Inference in CREs
6.4.2 Adjusting Inference under Alternative Randomization Schemes
6.5 What to Do with Unanticipated Covariates
6.6 Conclusions
Appendix 6.1 A Review of Rerandomization Approaches
Notes
References
7. Heterogeneity and Causal Moderation
Key Ideas
7.1 Four Running Examples
7.2 Estimating Heterogeneities in Simple Cases
7.2.1 Using Causal Forests to Estimate Heterogeneities
Eight-Step Causal Forest Procedure
7.3 Basic Mechanics of Causal Moderation
7.3.1 Causal Moderation in Economic Experiments
7.4 Two Crucial Margins of Heterogeneity: Intensive and Extensive
7.4.1 Bounding the Intensive and Extensive Margin Effects
7.4.2 Using Baseline Outcome Data to Identify Intensive Margin Effects
7.4.3 A Tobit Approach to Estimating Margins
7.5 Conclusions
Notes
References
8. Mediation: Exploring Relevant Mechanisms
Key Ideas
8.1 Three Running Examples
8.2 Mediation: The Basics of Causal Pathways
8.2.1 Decomposing Total Effects in the Presence of Mediators
8.2.2 Moving the Goalposts: Controlled and Principal-Strata Effects
8.3 Applied Mediation Analysis for Economic Experiments
8.3.1 A Parametric Workhorse and Its Pitfalls
8.3.2 Basic Case: Binary Randomized Treatment
8.3.3 Separate Randomization of Treatment and Mediator
8.3.4 Paired Design
8.3.5 Crossover Design
8.4 Conclusions
Appendix 8.1 Putting It All Together: Traditional Mediation Analysis and Alternative Approaches Using an In-Home Parent Visitation Program
Notes
References
9. Experiments with Longitudinal Elements
Key Ideas
9.1 Three Running Examples
9.2 Potential Outcomes in Repeated Exposure Designs
9.2.1 Treatment Effects in the Presence of Repeated Exposures
9.3 Staggered Experimental Design
9.4 Leveraging Pre- and Post-treatment Outcomes to Increase Power
9.4.1 Including Covariates and Pre-treatment Outcomes in the Estimation Model
9.4.2 Leveraging Pre-treatment Outcomes in a Panel Data Estimation Model
9.4.2.1 Gains from Pre-treatment Outcome Measures
9.4.2.2 Autocorrelations That Vary with Treatment
9.4.3 Choosing the Optimal Number of Pre-treatment and Post-treatment Periods
9.4.4 Threats to Internal Validity
9.5 Experimental Designs with Outcomes Measured Long after Treatment
9.5.1 Identification Assumptions When Outcomes Are Far Removed from Treatment
9.5.2 Statistical Surrogates
9.5.2.1 Internal Validity of Statistical Surrogates
9.5.2.2 Putting the Comparability and Surrogacy Assumptions into Perspective
9.5.2.3 Interpreting Surrogates
9.5.2.4 Multiple Surrogates
9.6 Conclusions
Appendix 9.1 Optimal Staggered Designs
Appendix 9.2 Clustered Design in Panel Data Settings
Appendix 9.3 Cluster-Randomized Experiments in Settings That Generate Short Panel Data
Notes
References
10. Within-Subject Experimental Designs
Key Ideas
10.1 Three Running Examples
10.2 Potential Outcomes in a Within-Subject Design
10.3 Identification Assumptions in a Within-Subject Design
10.4 Threats to the Internal Validity of Within-Subject Designs
10.4.1 Threats to Balanced Panel
10.4.2 Threats to Temporal Stability
10.4.2.1 Crossover Designs and Latin Squares
10.4.3 Threats to Causal Transience
10.4.3.1 Washout Periods
10.5 Key Advantages of Within-Subject Designs
10.5.1 Heterogeneity and the Full Distribution of Treatment Effects
10.5.2 Experimental Power
10.5.2.1 Minimum Detectable Effects for Within-Subject Designs
10.6 Conclusions
Notes
References

Part III. Violations of Exclusion Restrictions
11. SUTVA: Interference and Hidden Treatments
Key Ideas
11.1 Three Running Examples
11.2 SUTVA Violation: Interference
11.2.1 Treatment Effect Parameters
11.2.2 Difference-in-Means
11.3 Approaches to Dealing with Interference Violations
11.3.1 Linear-in-Means Model
11.3.2 Clustered Randomized Trials to Attenuate Spillovers
11.3.3 Randomization Inference under Interference
11.4 Embracing Spillovers: Randomized Saturation Designs
11.4.1 Designs to Explore Spillovers
11.5 Hidden Versions of Treatment
11.5.1 Potential Outcomes with Hidden Versions of Treatment
11.5.2 Implications of Hidden Versions of Treatment
11.6 Conclusions
Appendix 11.1 Optimal Saturation Designs
Notes
References
12. Observability: Nonrandom Attrition
Key Ideas
12.1 Two Running Examples
12.2 Attrition in the Potential Outcomes Framework
12.2.1 Internal Validity for Respondents
12.2.2 Internal Validity for Study Participants
12.3 Tests for Internal Validity
12.3.1 Tests Using Baseline Outcome Data
12.3.2 Selective Attrition Test
12.3.3 Determinants of Attrition Test
12.3.4 Attrition Rates That Vary by Treatment
12.4 Analyzing Data with Attrition
12.4.1 Available Case Analysis
12.4.2 Horowitz and Manski Bounds
12.4.3 Inverse Probability Weighting
12.4.4 Selection Models
12.4.5 Lee Bounds
12.5 Missing Covariates
12.5.1 Complete and Available Case Analysis
12.5.2 Dummy Variable Adjustment
12.5.3 Imputation
12.6 Six Design Tips to Attenuate Attrition
12.7 Conclusions
Appendix 12.1 Putting It All Together with CHECC
Notes
References
13. Complete Compliance: One-Sided and Two-Sided Violations
Key Ideas
13.1 Two Running Examples
13.2 A Framework for Imperfect Compliance
13.2.1 As-Treated Analysis Reintroduces the Selection Problem
13.2.2 Intention-to-Treat (ITT) Analysis
13.3 Randomization as an Instrumental Variable and New Assumptions
13.4 Calculating ATEs for Compliers
13.4.1 Characterizing Compliers
13.4.2 Widening the Goalposts: Bounding the ATE
13.5 Six Design Tips to Attenuate Noncompliance
13.6 Conclusions
Appendix 13.1 Encouragement Designs
Notes
References
14. Statistical Independence and Compromised Randomization
Key Ideas
14.1 Three Running Examples
14.2 Statistical Independence: The Basics
14.3 Tests for Compromised Randomization
14.3.1 Comparing Planned versus Actual Assignment
14.3.2 Computing P-Values to Test for Compromised Randomization
14.3.3 Informal Checks of Compromised Randomization
14.4 Case 1: A Rerandomization Approach
14.5 Case 2a: Inference with Compromised Randomization and Full Documentation
14.5.1 Inference When the Randomization Procedure Is Correlated with Potential Outcomes
14.6 Case 2b: Inference with Compromised Randomization and Only Partial Documentation
14.6.1 An Example of Compromised Randomization Being Partly Understood at the Aggregate Level
14.6.2 Breaking Down the Randomization Procedure
14.6.3 A Basic Model
14.6.4 Testing a Single Joint Null Hypothesis
14.7 A Decision-Theoretic Framework with Incomplete Documentation
14.7.1 Modeling the Randomization Protocol
14.7.2 Partially Identifying Model Parameters
14.7.3 Worst-Case Randomization Test
14.8 Seven Design Tips to Prevent Compromised Randomization
14.8.1 Three Tips When the Researcher Is Responsible for Randomization
14.8.2 Four Tips When the Experimenter Relies on Partners for Randomization
14.9 Conclusions
Appendix 14.1 Using Fisher’s Sharp Inference with Compromised Randomization
Appendix 14.2 Putting the Ideas of Section 14.6 in Motion
Appendix 14.3 Extending Section 14.6 to Test Multiple Hypotheses
Notes
References

Part IV. Building Scientific Knowledge
15. Building Confidence in (and Knowledge from) Experimental Results
Key Ideas
15.1 Three Running Examples
15.2 The Philosophy of Building Knowledge from Experimental Results
15.3 A Framework for Building Confidence in Experimental Results
15.3.1 Effects of α and β on the PSP
15.3.2 Null Results Are Informative Too
15.4 From the Researcher to the Research Community
15.4.1 Replication Types
15.4.1.1 Interpreting Replication Results
15.4.1.2 Building Knowledge and Confidence with Replications
15.4.1.3 Why Are Replications an Endangered Species in Economics?
15.5 The Beauty of Selective Data Generation: From the Lab to the Field
15.6 Conclusions
Appendix 15.1 Gaining Insights into Equation 15.5 and Beyond
Unbiased, Sympathetic, and Adversarial Replications
Heterogeneity across Replicating Teams
Should We Have Confidence in Our Updating from Experimental Results?
Notes
References
16. Generalizability and Scaling
Key Ideas
16.1 Two Running Examples
16.2 External Validity Primers
16.2.1 From Treatment Effects to the Parameter of Interest
16.2.2 Three Types of Horizontal Generalizability
16.2.3 Assumptions Yielding τ = τ*
16.3 Digging Deeper into Assumptions 16.1–16.4
16.3.1 Assumption 16.1: Selection into the Experiment
16.3.1.1 A Model of Selection into Experiments
16.3.1.2 How Experimental Design Affects Selection
16.3.2 Assumption 16.2: Representativeness of the Population
16.3.3 Assumptions 16.3 and 16.4: Investigation Neutrality and Parallelism
16.3.3.1 Experimenter Scrutiny: Effects of A
16.3.3.2 Experimental Environment: Effects of E
16.3.3.3 Stakes: Effects of Ii
16.4 Scaling
16.4.1 A Behavioral Model of Scaling
16.4.2 Constructive Steps Forward: The SANS Conditions
Author Onus Probandi
16.4.3 Three Waves of Scientific Research
16.5 Conclusions
Appendix 16.1 Mechanics of Scaling Up
Notes
References

Part V. The Ethical and Practical Sides of Economic Experiments
17. The Ethics of Economic Experiments
Key Ideas
17.1 Four Running Examples
17.2 Ethics Primer
17.2.1 A Simple Economic Model
17.2.2 A Simple Philosophical Framework
17.3 Three Theories of (Research) Ethics
17.3.1 Consequentialism
17.3.2 Deontological Ethics
17.3.3 Rule Consequentialism
17.4 Putting It All Together
17.4.1 Truthful, Unbiased, and Transparent Reporting of Results and Conflicts of Interest
17.4.2 Appropriate Data Governance and Management
17.4.3 Conflicts between Individual Protections and Scientific Discovery
17.4.3.1 Should You Even Do an Experiment?
17.4.3.2 With Whom Should You Experiment?
17.4.3.3 How Should You Experiment?
17.4.3.3.1 Informed Consent: Respecting Autonomy
17.4.3.3.2 Defining Benefits and Harm: From the Subject to Innocent Bystanders
17.4.3.3.3 Outright Deception and Incomplete Disclosure
17.5 Benchmarking Research Ethics: Gold to Plutonium-239
17.6 Conclusions
Appendix 17.1 Data Governance and Management Playbook
Being Trustworthy for Knowledge Creation
Being Trustworthy regarding Subjects
Differential Privacy
Being Trustworthy for Third Parties
Accessibility and Accountability
Security
Notes
References
18. Pre-treatment Administrative Responsibilities
Key Ideas
18.1 One Running Example
18.2 Overarching Goals of Pre-treatment Tasks
18.3 Institutional Review
18.3.1 IRBs and Research Ethics
18.3.2 IRB Application Materials
18.3.2.1 IRB Requirements: Who, What, How, and to Whom?
18.3.3 IRB Review Process and Determinations
18.3.3.1 IRBs and Informed Consent
18.3.3.2 IRBs and Outright Deception
18.3.3.3 IRBs and Pilots
18.3.3.4 IRBs and Multi-institutional Research
18.3.3.5 Communication with IRBs
18.4 Registries and Pre-analysis Plans
18.4.1 Trial Registries
18.4.1.1 Existing Registries
18.4.1.2 The AEA Registry
18.4.1.3 Registry Limitations
18.4.2 Pre-analysis Plans
18.5 Data Use Agreements and Outside Partners
18.5.1 Components of a DUA
18.6 Due Diligence Administrative Checklist
18.7 Conclusions
Appendix 18.1 A Plea to the IRB
What Should IRBs Do?
A. Gather Information Typically Contained in Pre-registrations and PAPs
B. Focus on the Relevant
C. Be Honest with Themselves
D. Be Clear and Consistent
E. Guide How Researchers Should Work with Third Parties
Notes
References
19. Optimal Use of Incentives in Economic Experiments
Key Ideas
19.1 Four Running Examples
19.2 A Simple Economic Model
19.2.1 Extending the Model to Explore Knowledge Creation: Internal Validity
19.2.1.1 Within-Subject versus Between-Subject Design
19.2.1.2 Statistical Surrogates
19.2.2 Extending the Model to Explore Knowledge Creation: Improving Inference
19.2.2.1 Nuts and Bolts of Design
19.2.2.2 Pilot Experiments
19.2.2.3 Mediators and Moderators
19.2.2.4 EP2: From τPi = 1 to τ and Beyond
19.2.2.5 EP2: From One Environment to Another
19.2.2.6 EP2: Fostering Scaling by Adding Option C Thinking to Designs
19.3 Creating the Microeconomic Environment
19.3.1 Using Induced Values for Control
19.3.2 Potentially Losing Control
19.3.2.1 An Inferential Challenge: Flat Payoffs
19.3.2.2 An Inferential Challenge: Construct Validity
19.3.2.3 Experimental Instructions across the Empirical Spectrum
19.4 Conclusions
Appendix 19.1 Inducing Risk Posture
Appendix 19.2 Tips for Writing Experimental Instructions across the Empirical Spectrum
10 Tips for Writing Laboratory Experimental Instructions
From the Lab to the Field
8 Tips for Artefactual Field Experiments (AFEs)
6 Tips for Framed Field Experiments (FFEs)
5 Tips for Natural Field Experiments (NFEs)
Practical Implementation
Conclusion
Notes
References
20. Epilogue: The (Written) Road to Scientific Knowledge Diffusion
Key Ideas
20.1 Give the People What They Want! But . . . What Do They Want?
20.2 Creating a Logical Framework
20.2.1 Applying BEC Holistically
PREP
20.3 Your Writing Style
20.3.1 Getting Started: An Eight-Step “Inside-Out Approach” to Writing Scientific Studies
20.4 Introducing Your Pen to the World
20.5 Epilogue
Appendix 20.1 PREP Checklist: Proper Reporting in an Experimental Paper
Notes
References

Part VI. “How To” Supplements
S1: How to Conduct Experiments in Markets: From the Lab to the Field
S2: How to Conduct Experiments with Organizational Partnerships
S3: How to Conduct Experiments with Children
S4: How to Conduct Experiments to Measure Preferences, Beliefs, and Constraints
S5: How to Conduct Experiments to Generate Unconventional Data

Glossary
Notation Crib Sheet
Further Readings
Index