Generalized reciprocity -- the tendency to help others after receiving help oneself -- is widely theorized as a mechanism sustaining cooperation on online knowledge-sharing platforms. Yet robust empirical evidence from field settings remains surprisingly scarce. Prior studies relying on survey self-reports struggle to distinguish reciprocity from other prosocial motives, while observational designs confound reciprocity with baseline user activity, producing upward-biased estimates. We address these empirical challenges by developing a matched difference-in-differences survival analysis that leverages the temporal structure of help-seeking and help-giving on Stack Overflow. Using Cox proportional hazards models on over 21 million questions, we find that receiving an answer significantly increases a user's propensity to help others, but this effect is concentrated among newcomers and declines with platform experience. This pattern suggests that reciprocity functions primarily as a contributor-recruitment mechanism, operating before platform-specific incentives such as reputation and status displace the general moral impulse to reciprocate. Response time moderates the effect, but non-linearly: reciprocity peaks for answers arriving within a re-engagement window of roughly thirty to sixty minutes. These findings contribute to the theory of generalized reciprocity and have implications for platform design.

Decisions to pursue higher education are not fully explained by economic incentives, with social influence and peer effects playing a crucial, yet dynamically understudied, role. This paper develops a theoretical non-linear dynamics model analysing the interplay between economic returns and social pressure. We model a heterogeneous population of "Followers" who exhibit imitative behaviour, and "Positional Agents" who display counter-adaptive behaviour. Agents' preferences for education evolve endogenously, reacting to both aggregate enrolment and an endogenous wage premium that declines with the supply of educated workers. The aggregate dynamics are governed by a one-dimensional non-linear map. By assuming fixed population structure. we show that the social conflict between pro-cyclical imitative forces and counter-cyclical positional forces can destabilize the steady state, generating a period-doubling route to chaos. These complex, endogenous fluctuations in enrolment emerge only for intermediate, heterogeneous population mixes, while homogeneous populations remain stable. We argue that this instability represents a significant coordination failure, scrambling economic signals and hindering rational long-term planning for both students and institutions, making it a key policy concern. Finally, we also extend the result to the case where the population structure is endogenous.

We analyse the UK income distribution from 2000 to 2023 using HMRC annual percentile data for both pre-tax and post-tax income. We fit a prefactor-adjusted $κ$-generalised specification to the data by weighted non-linear least squares and use inverse transform sampling to generate simulated income populations. The results suggest a redistribution of income shares over the period: the bottom 40\% appears to have increased its share, the middle-upper part of the distribution (50th--90th percentiles) lost share, the top 10\% remained broadly stable, and the top 1\% increased its share of pre-tax income. Because the modified specification is defined only above a positive threshold, conclusions concerning the lower tail should be interpreted with some caution. Using simulated 2023 pre-tax incomes to examine tax reform scenarios, we find that revenue-equivalent tax increases on high-income earners must be more than four times as large as comparable increases on lower-income earners. This suggests that, despite increased concentration at the top, the UK tax base remains driven primarily by the large number of taxpayers outside the very top of the distribution.

We establish a variant of Monge--Kantorovich duality for a constrained optimal transport problem with a continuum of agents, a finite set of alternatives, and general linear constraints. As an application, we revisit the large-market model of indivisible goods in Azevedo et al. (2013), identify a flaw in the original equilibrium-existence proof stemming from an incorrect compactness claim, and recover equilibrium existence via our duality approach. We also characterize equilibrium prices as minimizers of a potential function, which yields a method for computing equilibrium prices.

Renewable power sources have low marginal pro-duction costs, but may result in high balancing costs due to the inherent production uncertainty. Current day-ahead markets elicit only point production profiles and neglect the degree of uncertainty associated with each generating asset, preventing the market operator from accounting for balancing costs in day-ahead dispatch and ancillary service procurement. This increases total system costs and undermines market efficiency, especially in renewable-heavy power systems. To address this, we propose a new market clearing paradigm based on a two-stage mechanism, where producers report their production forecast distribution in the day-ahead stage, followed by the realized production in the real-time stage. By extending the Vickery-Clarke-Groves (VCG) payments to the two-stage setting, we show appealing properties in terms of incentive compatibility and individual rationality. An electricity market case study validates the theoretical claims, and illustrates the effectiveness of the proposed mechanism to reduce system costs.

This paper establishes the theoretical and practical foundations for using Large Language Models (LLMs) as measurement instruments for latent economic variables -- specifically variables that describe the cognitive content of occupational tasks at a level of granularity not achievable with existing survey instruments. I formalize four conditions under which LLM-generated scores constitute valid instruments: semantic exogeneity, construct relevance, monotonicity, and model invariance. I then apply this framework to the Augmented Human Capital Index (AHC_o), constructed from 18,796 O*NET task statements scored by Claude Haiku 4.5, and validated against six existing AI exposure indices. The index shows strong convergent validity (r = 0.85 with Eloundou GPT-gamma, r = 0.79 with Felten AIOE) and discriminant validity. Principal component analysis confirms that AI-related occupational measures span two distinct dimensions -- augmentation and substitution. Inter-rater reliability across two LLM models (n = 3,666 paired scores) yields Pearson r = 0.76 and Krippendorff's alpha = 0.71. Prompt sensitivity analysis across four alternative framings shows that task-level rankings are robust. Obviously Related Instrumental Variables (ORIV) estimation recovers coefficients 25% larger than OLS, consistent with classical measurement error attenuation. The methodology generalizes beyond labor economics to any domain where semantic content must be quantified at scale.

This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimator and provide conditions for root-n consistency and asymptotic normality of debiased functional estimates, covering both linear and nonlinear functionals. Monte Carlo experiments on average derivative show that the PGMM-based debiased estimator performs on par with the analytical debiased estimator that uses the known closed-form Riesz representer, achieving 90-96% coverage while the plug-in estimator falls below 5%. We apply our procedure to estimate mean own-price elasticities in a semiparametric demand model for differentiated products. Simulations confirm near-nominal coverage while the plug-in severely undercovers. Applied to IRI scanner data on carbonated beverages, debiased semiparametric estimates are approximately 20% more elastic compared to the logit benchmark, and debiasing corrections are heterogeneous across products, ranging from negligible to several times the standard error.

Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, largely due to their convenient closed-form expressions for choice probabilities. However, these models entail restrictive assumptions on the stochastic utility component, constraining our ability to capture realistic and theoretically grounded choice behavior$-$most notably, substitution patterns. In this work, we propose an amortized inference approach using a neural network emulator to approximate choice probabilities for general error distributions, including those with correlated errors. Our proposal includes a specialized neural network architecture and accompanying training procedures designed to respect the invariance properties of discrete choice models. We provide group-theoretic foundations for the architecture, including a proof of universal approximation given a minimal set of invariant features. Once trained, the emulator enables rapid likelihood evaluation and gradient computation. We use Sobolev training, augmenting the likelihood loss with a gradient-matching penalty so that the emulator learns both choice probabilities and their derivatives. We show that emulator-based maximum likelihood estimators are consistent and asymptotically normal under mild approximation conditions, and we provide sandwich standard errors that remain valid even with imperfect likelihood approximation. Simulations show significant gains over the GHK simulator in accuracy and speed.

Restaurants, cafes, pubs, and takeaways are among the most visible markers of neighborhood change, yet whether their arrival is capitalised into nearby housing values remains empirically unsettled. We assemble a London-wide panel linking Land Registry prices, non-domestic EPC lodgement timings for food-and-drink establishments, and neighborhood amenity measures at the LSOA level. Our preferred annual event-study design defines treatment as the first clean-onset year in which an LSOA records at least two eligible EPC lodgements for food-and-drink establishments, after a two-year lookback with no prior entries. In this specification, pre-trend tests are not rejected in either the stacked or Sun-Abraham estimators, and log house prices rise gradually from about 0.5% in the event year to roughly 3.4--3.7% by years four and five. The results are consistent with local amenity capitalization following commercial entry, while remaining appropriately cautious about endogenous siting and concurrent redevelopment.

Distributional effects, captured by quantile frameworks, are well-received for characterizing heterogeneous impacts of economic factors across the unobserved relative ranks. Censored outcome, endogenous regressor and heteroskedastic error are prevalent in empirical work, yet challenge the consistency of existing quantile estimation methods. This paper proposes a two-nested-step(TNS) estimation method for distributional effects in censored quantile models with endogeneity and heteroskedasticity. It combines the sequential analysis with the control function approach, adapting for heterogeneous distributional effects. The estimation algorithm is a two-step procedure nested with a sequence of series quantile regressions, thereby providing applied researchers with a computationally tractable and practically feasible tool. Monte Carlo simulation results demonstrate the good performance of our estimator in a finite sample. We apply the proposed method to estimate heterogeneous income elasticities of households across relative ranks of commodity expenditure using data from the UK Family Expenditure Survey.

fixest is an R package for fast and flexible econometric estimation. It provides a unified framework for applied research, with comprehensive support for a diverse class of models: ordinary least squares, instrumental variables, generalized linear models, maximum likelihood, and difference-in-differences. The package particularly excels at fixed-effects estimation, supported by a novel fixed-point acceleration algorithm implemented in C++. This algorithm achieves rapid convergence across a variety of data contexts and enables efficient estimation of complex models, including those with varying slopes. An expressive formula interface facilitates multiple estimations, stepwise regressions, and variable interpolation in a single call. Users can adjust inference strategies on the fly, choosing from an array of built-in robust standard errors. The package also provides methods for publication-ready regression tables and coefficient plots. Benchmarks demonstrate that fixest offers best-in-class performance against leading alternatives in R, PYTHON, and JULIA.

Information about peers' performance is pervasive in workplaces, yet its effects on worker behavior are mixed. We show that a key reason is that workers differ in how they value such information. In a real-effort experiment with 793 workers, we elicit willingness-to-pay for peer information delivered either before or after the task. We document substantial heterogeneity in demand for peer information: some workers are indifferent, some prefer to avoid it before the task, and others value it more as their relative performance increases. These differences strongly predict effort responses to peer information. Notably, 15% of workers would pay to avoid information ex ante due to stress and exhibit no productivity gains from it. We further show that uniform feedback policies can impose welfare losses on such workers, while tailoring the timing of peer information increases welfare by up to 48%. Our results highlight the importance of accounting for heterogeneous information preferences when designing workplace feedback policies.

Open defecation, which is linked to poor health outcomes and lower cognitive ability has been widespread in India. Improved sanitation practice generates local health externalities, which implies that the returns to private toilet usage depend on community wide compliance. Therefore, beliefs about others toilet usage behavior may influence both sanitation choices and the perceived value of residing in areas with lower prevalence of open defecation. This paper evaluates a randomized, intervention in peri-urban Tamil Nadu designed to shift social expectations regarding sanitation practice. The intervention combines descriptive norm messaging, community engagement, and information provision to increase perceived prevalence and approval of toilet usage. The findings indicate a significant increase in toilet access in the intervention areas by 8 percentage points and consistent toilet usage by 6 to 7 percentage points relative to control areas. The effects are robust to potential bias due to disruption due to local socio-political protest and the COVID 19 pandemic during the study period. We also find significant increase in empirical expectations (beliefs about the prevalence of toilet usage) and normative expectations (beliefs about its approval). Mediation analysis indicates that the improvement in toilet access and usage is substantially driven by changes in these social expectations. We further observe significant increase in the willingness to pay among respondents for residing in open defecation free surroundings, suggestive of an increased perceived valuation of cleaner sanitation environments because of the intervention. The findings underscore the need for norm-centric interventions to propel change in beliefs and achieve long-term and sustainable sanitation behavior.

Non-Fungible Tokens (NFTs) are transforming how content creators, such as artists, price and sell their work. A key feature of NFTs is the inclusion of royalties, which grant creators a share of all future resale proceeds. Although widely used, critics argue that sophisticated speculators, who dominate NFT markets, simply price in royalties upfront, neutralizing their impact. We show this intuition holds only under perfect, frictionless markets. Under more realistic market conditions, royalties enable creators to capitalize on the presence of speculators in at least three ways: They can enable risk sharing (under risk aversion), mitigate information asymmetry (when speculators are better informed), and unlock price discrimination benefits (in multi-unit settings). Moreover, in all three cases, royalties meaningfully expand trade, implying increased transaction volume for platforms. These results offer testable predictions that can guide both empirical research and platform design.

We show that identification in a general class of dynamic panel logit models with fixed effects is related to the truncated moment problem from the mathematics literature. We use this connection to show that the identified set for structural parameters and functionals of the distribution of latent individual effects can be characterized by a finite set of conditional moment equalities subject to a certain set of shape constraints on the model parameters. In addition to providing a general approach to identification, the new characterization can deliver informative bounds in cases where competing methods deliver no identifying restrictions, and can deliver point identification in cases where competing methods deliver partial identification. We then present an estimation and inference procedure that uses semidefinite programming methods, is applicable with continuous or discrete covariates, and can be used for models that are either point- or partially-identified. Finally, we illustrate our identification result with a number of examples, and provide an empirical application to employment dynamics using data from the National Longitudinal Survey of Youth.