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John A List
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Laboratory experiments have been used extensively in economics in the past several decades to lend both positive and normative insights into a myriad of important economic issues. This study discusses a related approach that has increasingly grown in prominence of late--field experiments. I argue that field experiments serve as a useful bridge between data generated in the lab and empirical studies using naturally-occurring data. In discussing this relationship, I highlight that field experiments can yield important insights into economic theory and provide useful guidance to policymakers. I also draw attention to an important methodological contribution of field experiments: they provide an empirical account of behavioral principles that are shared across different domains. In this regard, at odds with conventional wisdom, I argue that representativeness of the environment, rather than representative of the sampled population, is the most crucial variable in determining generalizability of results for a large class of experimental laboratory games.
John A List
Cited by*: 4 Downloads*: 46
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John A List
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Several experimental studies have recently provided strong evidence that the basic independence assumption, which is used in most theoretical and applied economic models to assess the operation of markets, is rarely appropriate. These results, which clearly contradict closely held economic doctrines, have led some influential commentators to call for an entirely new economic paradigm to displace conventional neoclassical theory. This paper refutes the generality of these experimental findings by going to a well-functioning marketplace and examining more than 350 individual decisions across various incentive compatible elicitation mechanisms. The data suggest that individuals with significant marketlike experience behave largely in accordance with neoclassical predictions: any observed WTA/WTP disparity amongst this group can be explained by neoclassical arguments. In light of these findings, I believe that we have discarded neoclassical explanations of the value disparity too quickly. More narrowly, these empirical results have important implications for stated valuation methods, such as contingent valuation.
John A List
Cited by*: 0 Downloads*: 19
No abstract available
John A List
Cited by*: 18 Downloads*: 3
We employ a two-step modified count data model to determine the county-level attributes that are conducive to attracting new foreign plants. Our estimation results indicate that previous counts of foreign direct investment, market size and accessibility, and land area are positively related to Foreign Direct Investment (FDI) occurrences; while higher input costs deter new foreign firm entry. Contrary to anecdotal evidence, our results suggest that stringent environmental regulations do not have a negative impact on FDI inflows. These findings have significant implications for policymakers, as flows of FDI are expected to increase dramatically given the economic integration of our global economy.
John A List
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While empirical economics has made important strides over the past half century, there is a recent attack that threatens the foundations of the empirical approach in economics: external validity. Certain dogmatic arguments are not new, yet in some circles the generalizability question is beyond dispute, rendering empirical work as a passive enterprise based on frivolity. Such arguments serve to caution even the staunchest empirical advocates from even starting an empirical inquiry in a novel setting. In its simplest form, questions of external validity revolve around whether the results of the received study can be generalized to different people, situations, stimuli, and time periods. This study clarifies and places the external validity crisis into perspective by taking a unique glimpse into the grandest of trials: The External Validity Trial. A key outcome of the proceedings is an Author Onus Probandi, which outlines four key areas that every study should report to address external validity. Such an evaluative approach properly rewards empirical advances and justly recognizes inherent empirical limitations.
John A List
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This review summarizes results of field experiments examining individual behaviors across several market settings from - open-air markets to rideshare markets to tax-compliance markets - where people sort themselves into market roles wherein they make consequential decisions. Using three distinct examples from my own research on the endowment effect, left-digit bias, and omission bias, I showcase how field experiments can help researchers understand mediators, heterogeneity, and causal moderation involved in judgment biases in the field. In this manner, the review highlights that economic field experiments can serve an invaluable intellectual role alongside traditional laboratory research.
John A List
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In 2019, I put together a summary of data from my field experiments website that
pertained to artefactual field experiments. Several people have asked me if I have an
update. In this document I update all figures and numbers to show the details for 2021. I also include the description from the 2019 paper below.
John A List
Cited by*: None Downloads*: None
In 2019, I put together a summary of data from my field experiments website that pertained to natural field experiments. Several people have asked me if I have an update. In this document I update all figures and numbers to show the details for 2022. I also include the description from the 2019 paper below.
John A List
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John A List
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In 2019, I put together a summary of data from my field experiments website that pertained to artefactual field experiments. Several people have asked me if I have an update. In this document I update all figures and numbers to show the details for the year 2023. I also include the description from the 2019 paper below. The definition of artefactual field experiments comes originally from Harrison and List (2004) and is advanced in List (2024).
John A List
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Social scientists have increasingly turned to the experimental method to understand human behavior. One critical issue that makes solving social problems difficult is "scaling" the idea from a small group to a larger group in more diverse situations. The urgency of scaling policies impacts us every day, whether it is protecting the health and safety of a community or enhancing the opportunities of future generations. Yet, a common result is that when we scale ideas most experience a "voltage drop": upon scaling, the benefit-cost profile depreciates considerably. To combat voltage drops, we must optimally generate policy-based evidence. Optimality requires answering two crucial questions: what information to generate and in what sequence. The economics underlying the science of scaling provides insights into these questions, which are in some cases at odds with conventional approaches. For example, there are important situations wherein I advocate flipping the traditional social science research model to an approach that, from the beginning, produces the type of policy-based evidence that the science of scaling demands. To do so, I propose augmenting efficacy trials by including relevant tests of scale in the original discovery process, which forces the scientist to naturally start with a recognition of the big picture: what information do I need to haves caling confidence?
John A List
Cited by*: None Downloads*: None
In 2019, I put together a summary of data from my field experiments website that pertained to natural field experiments (Harrison and List, 2024). Several people have asked me for updates. In this document I update all figures and numbers to show the details for 2023. I also include the description from the original paper below.
John A List
Cited by*: None Downloads*: None
Once believed to be an impossibility, field experiments in economics now occupy a central place in the empiricist's quiver. In the past few decades alone field experiments have taken on much greater import in academe, across organizations, as well as for policymakers. But is this emergence simply a fad that will soon return field experiments to obscurity? I argue in this article that there is something fundamental about the emergence of field experiments, as controlling the assignment mechanism in the field provides unparalleled power to both understand the "effects of causes" and the "causes of effects." This knowledge generation then begins to uncover the generalizability and scalability of knowledge. Quite the opposite of a withering tool that will be gone tomorrow, I urge economists to "double down" on this comparative advantage and in doing so I provide four methodological paths which I hope will cement the promise and growth of field experiments in the social sciences.
John A. List
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This document is meant to introduce my forthcoming book, titled "Experimental Economics: Theory and Practice," which is to be published in 2025 by The University of Chicago Press. The document first contains the book's outline followed by a Preface, which summarizes my inspiration for writing the book and my goals and aspirations for choosing the content contained in the book.
John A. List
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To identify effective policies that can scale, a third option should be added to traditional A/B tests, that accounts for the realities of a programme implemented at scale. By flipping the traditional research and policy-development model, researchers can generate policy-based evidence to help policymakers scale the best policies.
John A. List
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In 2019, I put together a summary of data from my field experiments website that pertained to artefactual field experiments. Several people have asked me if I have an update. In this document I update all figures and numbers to show the details for the year 2024. I also include the description from the 2019 paper below. The definition of artefactual field experiments comes originally from Harrison and List (2004) and is advanced in List (2006; 2024, 2025).
John A. List
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The traditional approach in experimental economics is to use a between-subject design: the analyst places each unit in treatment or control simultaneously and recovers outcome differences via differencing conditional expectations. Within-subject designs represent a significant departure from this method, as the same unit is observed in both treatment and control conditions sequentially. While some might consider the design choice straightforward (always opt for a between-subject design), I contend that researchers should meticulously weigh the advantages and disadvantages of each design. In doing so, I propose a categorization for within-subject designs based on the plausibility of recovering an internally valid estimate. In one instance, which I denote as stealth designs, the analyst should unequivocally choose a within-subject design rather than a between-subject design.
John A. List
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Contingent valuation is a widely used method for estimating the value of nonmarket commodities. Yet, a persistent issue is whether responses to Contingent Valuation Method (CVM) questions accurately reflect true values. Recent studies indicate that hypothetical bias is a significant factor that creates a gap between intentions and actions. I use a novel approach within non-market valuation - a List Experiment in the field - to test whether it can attenuate the hypothetical bias observed within CVM surveys. Using data from 400 subjects in a field experiment, I find initial promising results.
John A. List
Cited by*: Downloads*:
In 2019, I put together a summary of data from my field experiments website that pertained to natural field experiments (Harrison and List, 2004). Several people have asked me for updates. In this document I update all figures and numbers to show the details for 2024. I also include the description from the original paper below.