You’ve Got Mail: A Randomized Field Experiment on Tax Evasion

Cheating in the Lab Predicts Fraud in the Field: An Experiment in Public Transportation

We conduct an artefactual field experiment using a diversified sample of passengers of public transportation to study attitudes toward dishonesty. We find that the diversity of behavior in terms of (dis)honesty in laboratory tasks and in the field correlate. Moreover, individuals who have just been fined in the field behave more honestly in the lab than the other fare dodgers, except when context is introduced. Overall, we show that simple tests of dishonesty in the lab can predict moral firmness in life, although fraudsters who care about social image cheat less when behavior can be verified ex post by the experimenter. Data and the online appendix are available at https://doi.org/10.1287/mnsc.2016.2616 . This paper was accepted by Uri Gneezy, behavioral economics.

How Field Experiments in Economics Can Complement Psychological Research on Judgment Biases

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.

Integrative experiments identify how punishment affects welfare in public goods games

Despite decades of research, the conditions under which punishment promotes cooperation remain unclear. Through an integrative experiment varying 14 design parameters of public goods games across 360 experimental conditions (147,618 decisions from 7100 participants), we reveal substantial heterogeneity in punishment effectiveness: Its impact on welfare ranges from 43% improvement to 44% reduction depending on the game parameters. To characterize these patterns, we developed models that outperformed human forecasters in predicting punishment effectiveness in new experiments. Communication emerges as the most important factor, followed by contribution framing (opt out versus opt in), contribution type (variable versus all-or-nothing), game length, and outcome visibility, though these factors often interact. The results reframe the debate from whether punishment works to when it does, demonstrating how integrative experiments enable discovery of generalizable patterns in social phenomena. , Editor’s summary People face conflicts between maximizing personal gain versus supporting collective interests. If we cooperatively recycle or donate to charities, it benefits society, but it also costs us time and resources that could be selfishly preserved for ourselves. We impose penalties to deter those undesirable or selfish behaviors, but under what conditions do punishments or penalties effectively modify behavior to benefit group welfare? Alsobay et al . systematically and simultaneously varied 14 factors together instead of in isolation. Punishment was unequivocally most effective when paired with consistent communication, particularly over time. Another effective factor was “opting out” or withdrawing some, but not all, endowments already in the public fund. These methodological advances revealed when, rather than whether, punishment works. —Ekeoma Uzogara , INTRODUCTION Human societies face many situations where individual and collective interests conflict, often referred to as social dilemmas. Costly peer punishment has been studied for more than 25 years in public goods games (stylized behavioral experiments in which individuals decide how much to contribute to a shared pool that benefits everyone) as a mechanism to promote cooperation. Prior research has identified many contextual factors that moderate punishment’s effectiveness, including game length, communication, group size, punishment cost, and so on. However, the specific conditions under which punishment improves group welfare remain unclear. RATIONALE We argue that this lack of clarity derives from the dominant experimental paradigm, in which any given study manipulates only one or a few theoretically informed factors. Because such studies differ in many ways (different experimental procedures, populations), their results are often difficult to compare or integrate. Consequently, one can list many factors that have some effect, but cannot say how much each matters relative to the others, or how they work together, and as a result, cannot predict when punishment will help or harm welfare in new settings. To address this fundamental knowledge gap, we use an integrative experimental design and systematically vary 14 parameters across 360 conditions (147,618 decisions from 7100 participants) to elucidate when punishment improves versus undermines welfare in public goods games, which factors matter most, and how they interact. RESULTS The effect of punishment on welfare ranged from 43% improvement to 44% reduction depending on the specific combination of game parameters. To characterize this heterogeneity, we trained a model that outperformed all 553 human forecasters (laypeople and experts) in predicting whether punishment would help or harm welfare in new experiments. Communication emerged as roughly three times more important than any other factor, followed by contribution framing (opt in versus opt out), contribution type (variable versus all-or-nothing), game length, and peer outcome visibility (whether participants can see others’ earnings). These factors often interact. For example, longer games enhance punishment’s effectiveness only when communication is available, and contribution framing effects depend on both contribution type and outcome visibility. CONCLUSION Many phenomena in social science are shaped by many factors whose interactions are consequential, yet the dominant experimental paradigm often limits its inquiry to “does a given effect exist?” and examines hypothesized factors in isolation. As a result, research programs can accumulate many partial explanations without a clear picture of how they combine to determine outcomes across settings. Knowing that factors matter individually is fundamentally different from knowing how much each matters and how they interact. The integrative approach implemented here offers one way forward. It varies many factors simultaneously within a shared design space, evaluates models by their predictive accuracy on new experiments, and probes those models to constrain and develop theory. Our hope is that integrative experiment designs, combined with models that integrate prediction and explanation, represent a path toward more cumulative social science. Integrative experiment reveals when punishment helps versus harms. We systematically varied 14 design parameters across 360 experimental conditions. The effect of punishment on cooperation efficiency ranged from −44% to +43% depending on the specific game parameters. Communication emerged as three times more important than any other factor, followed by contribution framing, contribution type, and game length.

Mechanism Experiments and Policy Evaluations

Randomized controlled trials are increasingly used to evaluate policies. How can we make these experiments as useful as possible for policy purposes? We argue greater use should be made of experiments that identify the behavioral mechanisms that are central to clearly specified policy questions, what we call "mechanism experiments." These types of experiments can be of great policy value even if the intervention that is tested (or its setting) does not correspond exactly to any realistic policy option.

Putting nudges in perspective

Conventional economic policy focuses on ‘economic’ solutions (e.g. taxes, incentives, regulation) to problems caused by market-level factors such as externalities, misaligned incentives and information asymmetries. By contrast, ‘nudges’ provide behavioural solutions to problems that have generally been assumed to originate from limitations in human decision making, such as present bias. While policy-makers have good reason for exploiting the power of nudges, we argue that these extremes leave open a large space of policy options that have received less attention in the academic literature. First, there is no reason that solution and problem need have the same theoretical basis: there are promising behavioural solutions to problems that have causes that are well explained by traditional economics, and conventional economic solutions often offer the best line of attack on problems of behavioural origin. Second, there is a wide range of hybrid policy actions with both economic and behavioural components (e.g. framing a tax or incentive in a specific way), and there exist many societal problems – perhaps the majority – that arise from both economic and behavioural factors (e.g. firms’ exploitation of consumers’ behavioural biases). This paper aims to remind policy-makers that behavioural economics can influence policy in a variety of ways, of which nudges are the most prominent but not necessarily the most powerful.

The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework

We review 74 experiments with no, low, or high performance-based financial incentives. The modal result has no effect on mean performance (though variance is usually reduced by higher payment). Higher incentive does improve performance often, typically judgment tasks that are responsive to better effort. Incentives also reduce “presentation” effects (e.g., generosity and risk-seeking). Incentive effects are comparable to effects of other variables, particularly “cognitive capital” and task “production” demands, and interact with those variables, so a narrow-minded focus on incentives alone is misguided. We also note that no replicated study has made rationality violations disappear purely by raising incentives.

Monetary incentives, what are they good for?

This paper is a critical reflection on the use of monetary incentives in economic experiments. The argument is that incentives have their effect through their influence on one or more of three fact...

Moral Incentives in Credit Card Debt Repayment: Evidence from a Field Experiment

We study the role of morality in debt repayment, using an experiment with the credit card customers of a large Islamic bank in Indonesia. In our main treatment, clients receive a text message stating that “non-repayment of debts by someone who is able to repay is an injustice.” This moral appeal decreases delinquency by 4.4 percentage points from a baseline of 66 percent and reduces default among customers with the highest ex ante credit risk. Additional treatments help benchmark the effects against direct financial incentives and rule out competing explanations, such as reminder effects, priming religion, and provision of new information.

Behaviorally Informed Policies for Household Financial Decisionmaking

Low incomes, limited financial literacy, fraud, and deception are just a few of the many intractable economic and social factors that contribute to the financial difficulties that households face today. Addressing these issues directly is difficult and costly. But poor financial outcomes also result from systematic psychological tendencies, including imperfect optimization, biased judgments and preferences, and susceptibility to influence by the actions and opinions of others. Some of these psychological tendencies and the problems they cause may be countered by policies and interventions that are both low cost and scalable. We detail the ways that these behavioral factors contribute to consumers’ fiancial mistakes and suggest a set of interventions that the federal government, in its dual roles as regulator and employer, could feasibly test or implement to improve household financial outcomes in a variety of domains: retirement, short-term savings, debt management, the take-up of government benefits, and tax optimization.

Are Information Disclosures Effective? Evidence from the Credit Card Market

Consumer protection in financial markets in the form of information disclosure is high on government agendas, even though there is little evidence of its effectiveness. We implement a randomized control trial in the credit card market for a large population of indebted cardholders and measure the impact of Truth-in-Lending-Act-type disclosures, de-biasing warning messages and social comparison information on default, indebtedness, account closings, and credit scores. We conduct extensive external validity exercises in several banks, with different disclosures, and with actual policy mandates. We find that providing salient interest rate disclosures had no effects, while comparisons and de-biasing messages had only modest effects at best.

Sharing the Algorithm: The Tax Solution to Generative AI

This article argues that tax policy offers a core tool for mitigating the sweeping public policy challenges of generative Artificial Intelligence ("AI"

Mis-Nudging Morality

Morals constrain self-serving behavior. Yet, self-regulation failures in the face of monetary temptation are common at the workplace. To limit such failures, organizations can design environments that limit the temptation to behave self-servingly, nudging workers to uphold their morals. In a series of experiments where participants may be tempted to take excessive pay after exerting effort, we study whether a simple intervention—asking individuals to state the wage they believe should be paid ex ante, before facing the temptation to take excessive compensation—prevents self-serving behavior. In contrast to lay beliefs and the predictions from prior work, we find that such an intervention is not effective, leading to self-serving behavior. However, a more realistic elicitation procedure of the appropriate wage mitigates this effect. These findings contribute to work on the malleability of moral behavior showing that simple interventions thought to effectively mitigate self-serving behavior can prompt individuals to stretch their moral boundaries. They also stress the importance of properly testing interventions that might seem intuitive. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Financial support from the Israel Science Foundation [Grant 766/19] is gratefully acknowledged. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2022.4344 .

Do people like financial nudges?

Do people like financial nudges? To answer that question we conducted a pre-registered survey presenting people with 36 hypothetical scenarios describing financial interventions. We varied levels of transparency (i.e., explaining how the interventions worked), framing (interventions framed in terms of spending, or saving), and ‘System’ (interventions could target either System 1 or System 2). Participants were a random sample of 2,100 people drawn from a representative Australian population. All financial interventions were tested across six dependent variables: approval, benefit, ethics, manipulation, the likelihood of use, as well as the likelihood of use if the intervention were to be proposed by a bank. Results indicate that people generally approve of financial interventions, rating them as neutral to positive across all dependent variables (except for manipulation, which was reverse coded). We find effects of framing and System. People have strong and significant preferences for System 2 interventions, and interventions framed in terms of savings. Transparency was not found to have a significant impact on how people rate financial interventions. Financial interventions continue to be rated positive, regardless of the messenger. Looking at demographics, we find that participants who were female, younger, living in metro areas and earning higher incomes were most likely to favor financial interventions, and this effect is especially strong for those aged under 45. We discuss the implications for these results as applied to the financial sector.

Political Bias Audits of LLMs Capture Sycophancy to the Inferred Auditor

Large language models (LLMs) are commonly evaluated for political bias based on their responses to fixed questionnaires, which typically place frontier models on the political left. A parallel literature shows that LLMs are sycophantic: they adapt their answers to the views, identities, and expectations of the user. We show that these findings are linked: standard political-bias audits partly capture sycophantic accommodation to the inferred auditor. We employ a factorial experiment across three major audit instruments--the Political Compass Test, the Pew Political Typology, and 1,540 partisan-benchmarked Pew American Trends Panel items--administered to six frontier LLMs while varying only the asker's stated identity (N = 30,990 responses). At baseline, all six models lean left. When the asker identifies as a conservative Republican, responses shift sharply: the share of items closer to Democrats falls by 28-62 percentage points, and all six models move right of center. A mirror-image progressive-Democrat cue produces little change; rightward accommodation is 8.0$\times$ larger than leftward. When asked who the default asker is, models identify an auditor, researcher, or academic; when asked what answer that asker expects, they select the Democrat-coded option 75% of the time, nearly the rate under an explicit progressive cue. These patterns are inconsistent with a purely fixed model ideology and indicate that single-prompt audits capture an interaction between model and inferred interlocutor. Political bias in LLMs is therefore not a fixed point on an ideological scale but a response profile that must be mapped across realistic interlocutors.

Unwillingness to pay for privacy: A field experiment

We measure willingness to pay for privacy in a field experiment. Participants bought at most one DVD from one of two competing online stores. One store consistently required more sensitive personal data than the other, but otherwise the stores were identical. In one treatment, DVDs were one Euro cheaper at the store requesting more personal information, and almost all buyers chose the cheaper store. Surprisingly, in the second treatment when prices were identical, participants bought from both shops equally often.

Using AI and Behavioral Finance to Cope with Limited Attention and Reduce Overdraft Fees

<p><span>We test how effective a human-algorithm interaction is at stopping users from overdrawing their bank accounts. We use a randomized field experiment and