|
on Experimental Economics |
| By: | Harsha Thirumurthy; Elizabeth F. Bair; Dean Yang; Teniola I. Egbe; Benard Ayieko; Julius Wesonga; Sue Napierala; Connie Celum; Mary E. Putt; Carol S. Camlin; Mark Ayallo |
| Abstract: | Alcohol use, transactional sex, and gambling carry significant health and economic consequences. We conducted a randomized controlled trial in Kenya to assess whether these behaviors could be altered with a savings intervention that provided lottery-based interest, goal-setting support, and savings reminders. Over 24 months, the intervention increased formal savings and assets, financed primarily by lower spending on alcohol, transactional sex, and gambling. It also reduced hazardous drinking by 26 percent and intimate partner violence by 15 percent. HIV/STI incidence declined by 8 percent, though this was not statistically significant. Savings interventions can simultaneously boost financial security and change health behavior. |
| JEL: | I12 O15 O16 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35833 |
| By: | Hélène Benghalem; Mirjam Bächli; Doriana Tinello; Sascha Zuber; Mauro Cherubini; Damaris Aschwanden; Matthias Kliegel; Rafael Lalive; Michele Pellizzari |
| Abstract: | Cognition is likely to be important for job search, but its role is not yet well understood. We study an online field experiment with jobseekers registered at the Swiss public employment services. Participants were randomly assigned to an online cognitive training program designed to strengthen core cognitive abilities and transversal cognitive abilities relevant for job search. Combining administrative and survey data on employment and earnings, we find that access to cognitive training substantially increases employment rates and earnings while reducing reliance on transfer income. In a simple search model, these findings could arise when cognition improves search effectiveness leading to lower search effort but higher chances of employment. |
| Keywords: | Unemployment, Online job search, Cognitive Training |
| JEL: | J64 J62 C93 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26226 |
| By: | Francis Annan; Collin B. Raymond |
| Abstract: | We study how firms choose among incentive contracts and how accurately managers predict their effects. Using a survey of managerial beliefs and a large-scale field experiment, we randomly assign agents at the market level to several widely used, expenditure-equivalent incentive schemes. In the field, the best-performing contract increases agent output and firm revenue by over 20% relative to the status quo, despite being ranked lower by managers, whereas the worst-performing contract performs as predicted. Managers correctly identify underperforming contracts but systematically underestimate top- performing ones. We document the sources of performance di!erences—labor supply responses rather than selection or pricing and the determinants of managerial predictability: contract complexity and managerial hierarchy. Our results highlight the importance of contract design for firm performance and reveal systematic limits to managerial cognition in shaping incentives. |
| JEL: | D86 D91 G23 L21 O12 O16 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35788 |
| By: | Claudia Allende; Francisco Gallego; Christopher Neilson |
| Abstract: | We study how information about school quality affects families’ choices and how scaling up the intervention changes its impact. In a randomized trial in Chile, we offer families a short video and a personalized school report card before they enroll their children. Treated families choose schools with higher test scores and value added. Among families reached before enrollment, treated children score about 0.2 standard deviations higher in fourth grade, with positive but less precise estimates on later assessments. We embed the experimentally identified demand shift in a structural model of school choice and competition to study equilibrium effects at scale. Capacity constraints and centralized assignment attenuate the demand-side gains, but the same shift in demand strengthens schools’ incentives to improve quality, and the supply response roughly offsets the congestion effect. More generally, equilibrium scaling can alter experimental effects in either direction because congestion and endogenous quality responses work against each other. We use the model to guide the design of a follow-up cluster-randomized experiment, focusing on the tradeoff between precision and contamination from equilibrium spillovers. |
| JEL: | L10 L15 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35837 |
| By: | Orazio Attanasio; Lina Cardona-Sosa; Ziqian Liu |
| Abstract: | This paper considers the estimation of heterogeneous impacts within a Randomized Controlled Trial (RCT) in a situation where heterogeneous status is endogenous and is not fully observed in the control sample. We show how to use information in the treatment sample and a noisy signal on the heterogeneous status to estimate meaningful impacts under a minimal set of assumptions. We provide an application of this approach to a data set from Ghana, collected around a clustered RCT to evaluate the impacts of an early-years intervention, differentiating the impact between households that participate actively in the program from those who participate only passively, as recipients. We show that, for some outcomes, the properly estimated heterogeneous impacts differ substantially from those computed with a naive estimator. |
| JEL: | C10 C9 O15 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35872 |
| By: | Ujjwal Kumar |
| Abstract: | A planner wants to elicit partial information about a subject's preference relation. Preferences are grouped into ``types" relevant to the planner's purpose. Can the planner identify the subject's type through menu-choice data or experiment, without distinguishing preferences within the same type? We provide a complete answer by characterizing identifiable type spaces. The characterization uses a novel geometric condition, called face consistency, which governs distinctions within and across square and hexagonal faces of the permutohedron. For every identifiable type space, we explicitly construct the unique irreducible identifying experiment from its boundary. This experiment is optimal among all identifying experiments under any cost criterion for which adding menus cannot reduce cost. Using the equivalence between identifiability and elicitation with acts, as shown in Azrieli et al. (2021), we study symmetric policies requesting unranked shortlists that contain only acceptable products, include every product preferred to a selected one, and are nonempty whenever an acceptable product exists. Only two such policies are elicitable with acts: requesting the favorite acceptable product or the entire acceptable set. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.10207 |
| By: | Daniel Herbst |
| Abstract: | Compared to self-employment or performance-based pay, fixed wages reduce earnings uncertainty but are prone to market distortions through moral hazard and adverse selection. Using a model of wage contracts under asymmetric information, I show that these distortions map to marginal treatment response functions in a marginal treatment effects (MTE) framework. I apply this framework to a field experiment in which data-entry workers choose between a randomized fixed wage and a piece rate. I find evidence of both moral hazard and adverse selection: Fixed wages reduce worker productivity by an estimated 8.74 percent relative to the mean. Meanwhile, a 10 percent increase in the wage offer attracts a marginal worker whose productivity is higher by 1.90 percent of the mean. Using semi-parametric MTE estimation, I calculate the welfare loss associated with asymmetric information and the marginal values of public funds (MVPFs) for policies aimed at recovering this loss. I find that a tax on piece rates can mitigate adverse selection into fixed wages, generating tax revenue at social costs as low as $0.88 per dollar. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.06827 |
| By: | Ajzenman, Nicolás; Del Toro Mijares, Ana Teresa; Elacqua, Gregory; Hermosilla, Catalina; Veleda, Santiago Nicolas |
| Abstract: | A central obstacle to scaling many interventions is implementer quality: as a program expands and the pool of implementers broadens, average quality falls. Conversation-based interventions (counseling, mentoring) are particularly sensitive to this constraint. LLMs offer a natural way out: personalized exchanges at zero marginal cost and uniform quality. We run a nationwide experiment in Chile (N42, 000) designed to reduce teacher shortages by increasing applications to education majors. We compare an AI chatbot, Kai, with trained human counselors, both reaching high school seniors via WhatsApp. Among students with baseline interest in education (the interventions target), Kai significantly increases first-ranked education majors and the share of education majors in students choice sets; estimates in the human arm are smaller and not significant. Although the best human counselors are highly effective, Kai matches or outperforms roughly two- thirds of them. Its advantage lies in what we call “controlled variance”: it compresses the dispersion in quality observed across human implementers. Finally, text analysis reveals different interaction patterns across arms. Kai concentrates on factual content and scores higher on semantic coherence, while counselors score substantially higher on motivational and empathetic language. |
| Keywords: | Teachers;teacher shortages;Sclae-up;behavioral interventions;AI chatbots;Generative AI |
| JEL: | D91 I23 I25 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:idb:brikps:14771 |
| By: | Shruti Jha; John A. List; Rebecca C. Royer; Anya Samek |
| Abstract: | Do early investments in parents pay off even when treatment effects for children appear to fade out quickly? We answer this question by leveraging a field experiment in which households with 3-5 year-old children were randomized into a parenting program, a preschool, or a control group, combined with a natural experiment (Covid-19 school closures) that occurred ten years later. Prior to the closures, treatment effects on academic achievement had faded to zero across all groups. Yet, with Covid-19 school closures, effects of the parenting program re-emerge: test scores in control and preschool groups drop by roughly 0.25-0.30 SD, whereas scores in the parenting group drop by only 0.11 SD (difference: p = 0.01). Our findings suggest that parenting programs can generate latent human capital that only becomes visible when complementary school inputs are removed. This result is consonant with parental and school investments acting as substitutes in skill production. Because this latent parental capacity pays off precisely when school inputs fail, standard measurement tools for early childhood programs can miss crucial state-contingent value. |
| JEL: | C93 I26 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35851 |
| By: | Irlenbusch, Bernd; Laske, Katharina; Schröder, Marina; Stoczek, Rika |
| Abstract: | How firms generate and select ideas influences their strategic path and the development of entire industries. To better understand this process, we experimentally analyze the impact of knowledge on idea generation and selection in an incentivized ideation task. Between treatments, we vary whether or not ideators are provided with examples of previous successful ideas before working on the task. We provide causal evidence that knowledge about such example ideas leads to path dependencies, i.e., knowledge increases the similarity of new ideas to those provided. Consistent with the hypothesis that knowledge affects the search for new ideas, we find evidence of path dependencies during idea generation, but do not find statistically significant evidence of additional path dependencies during idea selection. Overall, when knowledge is provided, we observe an increase in selected innovative ideas that are similar to previous ideas and a decrease in selected innovative ideas that are different from previous ideas. |
| Keywords: | idea generation;idea selection;innovation;knowledge;path dependency;real-effort experiment |
| JEL: | R14 J01 J50 |
| Date: | 2026–09–28 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:141263 |
| By: | Artem Kuriksha; Kenneth Hung |
| Abstract: | We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment effect (GATE), even when the edge weights used to construct the exposure are misspecified. We formalize the assumptions needed both in terms of the linearity of potential outcomes and the sparsity of the graph, and prove the asymptotic normality of the Wald estimator. The estimator is straightforward to implement, requiring no assignment simulations over the graph. Our Monte Carlo study demonstrates the strong performance of our approach in the context of a social platform. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.07363 |
| By: | Jason Shachat (Center for Intelligence Economic Science, Southwestern University of Finance and Economics and Chapman University); J. Todd Swarthout (Department of Economics, Georgia State University); Yun Wang (School of Economics, Renmin University of China) |
| Abstract: | We examine how competitive allocation of scarce participation rights shapes behavior in a Berg-style trust game. Trustor and trustee roles are allocated through multi-unit uniform-price English auctions with varying scarcity, alongside a matched-fee random-assignment control treatment. Positive transfers and returns persist, but behavior lies near the low-trust, low-reciprocity end of comparable benchmarks. Tighter scarcity accelerates the decline in trustor transfers, while trustee returns show no monotonic pattern. Scarcity strongly raises auction prices without comparably raising participants’ gross trust-game earnings, thereby lowering net earnings. Exit bids adjust to past game profitability, indicating adaptive learning about the value of participation. |
| Keywords: | Trust Game; trust game; Scarcity; Market Allocation; Participation Rights |
| JEL: | C91 D82 D83 D84 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:chu:wpaper:26-12 |
| By: | Danilo Cavapozzi; Marco Francesconi; Cheti Nicoletti |
| Abstract: | Women perform most of the housework despite substantial convergence in labor market outcomes. We develop and estimate a semi-structural model of household housework decisions that combines randomized vignette experiments with an epidemiological identification strategy to estimate the contributions of economic utility and social norms. Equal sharing of housework yields higher economic utility for both men and women, while households use domestic work to offset differences in labor market involvement. Yet women continue to perform a disproportionate share of housework, largely because of social norms. Exposure to more egalitarian gender norms significantly increases the likelihood of more balanced housework allocations. |
| Keywords: | Intrahousehold allocation of chores, Labor supply, Vignettes, Gender role norms, Gender gaps |
| JEL: | C25 C26 D13 J16 J22 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26227 |
| By: | Canh Thien Dang; An Nguyen |
| Abstract: | Experimental studies of generative AI in education mostly report average effects, yet field evidence shows that AI can narrow attainment gaps or widen them. We argue that the direction depends on the judgement burden, the expertise a learner must supply to screen AI output before learning from it, and that expert verification before release moves this burden from students to an accountable tutor. We test the argument in a two-cohort difference-in-differences design in which one half of a compulsory firstyear university economics course received AI-generated podcasts, FAQs and quiz-based study guides, produced with a source-grounded model and checked by a named graduate teaching assistant (170 students; 340 examination marks). Access was associated with a 2.34-mark advantage on a 50-mark component. The share of marks below the upper-second classification boundary fell by 24.7 percentage points relative to the counterfactual, effects were significant at every threshold from 23 to 31 marks and at none above, and roughly three-quarters of the average originated in the bottom quintile. The threshold estimate is robust to removing the lowest-scoring students from the pre-intervention cohort; the average effect is not. Interviews and feedback from 36 students indicate that the verification label gave students a reason to engage with AI-generated material without ending their scrutiny of it. Evaluations of AI learning resources that report only mean effects cannot detect whether the students the resources are meant to help are the ones who gain. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.07097 |
| By: | Haonan Huang; Joey Xiao |
| Abstract: | Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no assistant, depends on how sellers respond -- a question single-user evidence cannot answer. We build an agent-based rental market in which language models play consumers, assistants, and six adaptive sellers guided by an algorithmic pricing tool. Half the population receives an assistant under an advisory or an executing mandate; the contract pairs a fee only the renter's physical action avoids with a pre-selected add-on an authorised assistant can cancel online. An analytical benchmark and a behaviourally calibrated rule market supply ex-ante predictions, and paired branches with frozen versus adaptive sellers separate adoption effects from market feedback. Across thirty simulated markets, executing assistants cut adopters' spending by 13.7 USD per renter-day when sellers are frozen; adaptation claws back about a third, leaving 8.7, with the gains arriving both as lower bills and as rentals completed at all. Sellers raise headline rates while cutting fees, and the calibrated forecast of the burden on unassisted consumers (+3.6) does not transfer: their mean spending change is +0.4, confidence interval -0.6 to +1.3. Seller-model swaps and a within-market transfer of fee-setting to the pricing tool show that fee conduct, and with it the division of the gains, is decided on the seller side. Assistants, we conclude, should be evaluated at market level -- completion, total spending, and non-users included -- and the comparison layers locate exactly where a calibrated behavioural forecast fails in a language-model market. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.05823 |
| By: | Minmin Zeng |
| Abstract: | Financial institutions routinely downsample cross-sectional options panels to meet bandwidth and cost constraints. The industry default---deterministic Top-$k$ selection by open interest---provides no estimate of the aggregation error it introduces, rendering downstream risk metrics unauditable. We make the identification problem precise. For deterministic rules whose selected set depends only on auxiliary variables and not on the outcome (target-blind rules, of which Top-$k$-by-open-interest is one), the target is not identified from the retained outcomes alone unless bounds or modelling assumptions are imposed; under outcome bounds it is partially identified, with a sharp worst-case interval we characterise in closed form. We propose replacing Top-$k$ with probability sampling whose inclusion probabilities are proportional to open interest, paired with the Hajek ratio estimator and a linearised design-based variance estimator, yielding approximate design-based intervals around every aggregate. Experiments on 80 US trading sessions (211 tickers/day) and 83 sessions of Hong Kong options (131 tickers/day) show that for OI-weighted implied volatility the randomized design reduces mean absolute error by 35% (MAE 5.31 vs. 8.15 at 10% retention; paired $t=6.68$, Cohen's $d=0.75$, moving-block-bootstrap 95% CI $[1.82, 3.96]$), and the sharp worst-case interval available to Top-$k$ is 2.7--6.4$\times$ wider than the sampling interval under oracle bounds, and 9--26$\times$ wider under feasible bounds. Empirical coverage is 91.9% against a nominal 95%. We further document that inclusion probabilities must match the target's influence function, motivating a design-aware framework for production pipelines. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.08801 |