Political discourse attributes the pressure on European welfare systems to foreign nationals. Yet projections of service demand rarely disaggregate service demand by citizenship status. We develop a structural demographic model and project healthcare, education, and housing demand in Austria through 2050, disaggregated by citizenship status and regions across migration scenarios. We find that migration, ageing, and fertility shape each sector differently. In healthcare, the ageing of Austrian nationals contributes 4.7 times more to demand growth than immigration, with the most acute pressures in rural, low-migration regions. In housing, migration accounts for the entire net growth in demand, concentrated in metropolitan hubs. In education, aggregate demand contracts regardless of migration assumptions, whereas future needs are driven more by the births of foreigners in Austria than by new arrivals. Foreign nationals consume services in proportion to their demographic weight, with deviations explained by age structure rather than over-utilisation. These results show that the drivers of service demand are sector-specific: migration restrictions could ease housing pressure, but would not address ageing-driven healthcare demand and may accelerate contraction in the education system.

Blockchain technology introduces asset types and custody mechanisms that fundamentally break traditional financial auditing paradigms. This paper presents an autoethnographic analysis of cryptoasset auditing challenges, build on top of prior research on a comprehensive framework addressing existence, ownership, valuation, and internal control verification. Drawing from lived experience implementing blockchain systems as an engineer, smart contract auditor, and CTO of a publicly traded cryptoasset firm, we demonstrate how autoethnographic methodology becomes necessary for understanding technical complexities that external analysis cannot capture. Through detailed examination of token airdrops, multi-signature smart contracts, and real-time on-chain reporting, we provide experimental approaches and common scenarios that auditing firms can analyze to address blockchain innovations currently considered technically insurmountable.

This article studies randomization inference for treatment effects in randomized controlled trials with attrition, where outcomes are observed for only a subset of units. We assume monotonicity in reporting behavior as in \cite{lee2009training} and focus on the average treatment effect for always-reporters (AR-ATE), defined as units whose outcomes are observed under both treatment and control. Because always-reporter status is only partially revealed by observed assignment and response patterns, we propose a worst-case randomization test that maximizes the randomization p-value over all always-reporter configurations consistent with the data, with an optional pretest to prune implausible configurations. Using studentized Hajek- and chi-square-type statistics, we show the resulting procedure is finite-sample valid for the sharp null and asymptotically valid for the weak null. We also discuss computational implementations for discrete outcomes and integer-programming-based bounds for continuous outcomes.

Randomized experiments are increasingly employed in two-sided markets, such as buyer--seller platforms, to evaluate the effects of marketplace interventions. These experiments must reflect the underlying two-sided market structure in their design and can therefore be challenging to analyze. In this paper, we develop a randomization inference framework for outcomes from two-sided experiments, with a focus on testing and inference for two-sided spillover effects. Our approach is finite-sample valid under sharp null hypotheses. Regarding weak null hypotheses, we find that the commonly used Neyman-style studentization does not universally ensure asymptotic validity, and we document how it depends on the specific formulation of the null. We then propose a two-way variance estimator for studentization that restores asymptotic validity. We further propose methods to improve testing power by exploiting the two-sided structure of the problem, which we validate empirically. We demonstrate our methods through a series of simulation studies and an applied example from a network experiment in micro-lending.

Participants in online experiments often enroll over time, which can compromise sample representativeness due to temporal shifts in covariates. This issue is particularly critical in A/B tests, online controlled experiments extensively used to evaluate product updates, since these tests are cost-sensitive and typically short in duration. We propose a novel framework that dynamically assesses sample representativeness by dividing the ongoing sampling process into three stages. We then develop stage-specific estimators for Population Average Treatment Effects (PATE), ensuring that experimental results remain generalizable across varying experiment durations. Leveraging survival analysis, we develop a heuristic function that identifies these stages without requiring prior knowledge of population or sample characteristics, thereby keeping implementation costs low. Our approach bridges the gap between experimental findings and real-world applicability, enabling product decisions to be based on evidence that accurately represents the broader target population. We validate the effectiveness of our framework on three levels: (1) through a real-world online experiment conducted on WeChat; (2) via a synthetic experiment; and (3) by applying it to 600 A/B tests on WeChat in a platform-wide application. Additionally, we provide practical guidelines for practitioners to implement our method in real-world settings.

I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether the identified set is nonempty and to sample from the identified set without rejection sampling. I compare the new algorithms to alternatives by applying them to variations of the model considered by Arias et al. (2019), who estimate the effects of US monetary policy using sign and zero restrictions on the monetary policy reaction function. The new algorithms are particularly useful when a rich set of sign restrictions substantially truncates the identified set given the zero restrictions.

In European day-ahead electricity markets, carbon allowance costs passed through by marginal fossil plants raise consumer expenditure and generate inframarginal rents for non-emitting generators. We propose a settlement modification: when the zonal day-ahead price exceeds a threshold, non-emitting generation is remunerated at the clearing price minus a fixed CO2 proxy deduction, while all other units continue to receive the uniform price. The mechanism thus reallocates a part of the inframarginal rents to consumers. Using hourly data we estimate static average expenditure reductions of about 8.5% in Austria and 4.7% in Germany in 2025. We discuss bidding incentives around the threshold, interactions with Contracts for Difference, implementation in coupled bidding zones, and a gas-cost variant for the 2022 energy crisis.

A growing literature documents how religious institutions shape behavior through social influence, but less is known about what happens when religious movements gain political power and use the tools of government to advance their agenda. We use a regression discontinuity design on close mayoral elections in Brazil to show that mayors from parties institutionally tied to Pentecostal denominations increase teenage fertility 3 per 1,000 higher (a 40% increase). This effect appears for cohorts exposed to middle school during the administration. Consistent with a school-based mechanism, we find that the likelihood that municipal schools offer sexual education programs falls by 12.5 percentage points, with no changes in state schools outside mayoral control. We also find elevated STD rates, and higher middle school dropout rates, while slightly older cohorts show no effects. Results are not explained by changes in contraceptive availability in public clinics, pointing to sexual education as the primary mechanism. We also find no effects from other right-wing parties, indicating the importance of institutional links to Pentecostal parties.

In many search markets, advance interim contracts include an explicit right to renege, granting one party the option to switch to more attractive matches that emerge later in the search process. This paper studies the design and welfare implications of such interim contracts, leveraging novel data from a brokerage firm in the trucking industry. The broker allocates advance shipment contracts to carriers through a dynamic auction mechanism and penalizes cancellations through a reputational mechanism. I develop a theoretical model linking the carrier's bidding problem to the firm's cancellation penalties through a dynamic job-search problem and structurally estimate the model from rich data on bids and cancellations. In counterfactual simulations, I show that the firm is incentivized to lower cancellation penalties as the option value of the right to renege is priced into carrier bids. The results rationalize the large degree of contractual flexibility observed in the trucking industry as an efficient market outcome rather than one constrained by limited enforcement.

This paper studies the use of highly stratified designs for the efficient estimation of a large class of treatment effect parameters that arise in the analysis of experiments. By a "highly stratified" design, we mean experiments in which units are divided into blocks of a fixed size and a proportion within each block is assigned to a binary treatment uniformly at random. The class of parameters considered are those that can be expressed as the solution to a set of moment conditions constructed using a known function of the observed data. They include, among other things, average treatment effects, quantile treatment effects, and local average treatment effects as well as the counterparts to these quantities in experiments in which the unit is itself a cluster. In this setting, we establish three results. First, we show that under a highly stratified design, the naïve method of moments estimator achieves the same asymptotic variance as what could typically be attained under alternative treatment assignment mechanisms only through ex post covariate adjustment. Second, we argue that the naïve method of moments estimator under a highly stratified design is asymptotically efficient by deriving a lower bound on the asymptotic variance of regular estimators of the parameter of interest in the form of a convolution theorem. In this sense, highly stratified experiments are attractive because they lead to efficient estimators of treatment effect parameters "by design." Finally, we strengthen this conclusion by establishing conditions under which a "fast-balancing" property of highly stratified designs is in fact necessary for the naïve method of moments estimator to attain the efficiency bound.

How do incentive levels affect strategic behaviour? We address this with an experiment that separately identifies own- and opponent-incentive effects in two dominance-solvable games that differ in strategic complexity. Higher own incentives favour more strategically sophisticated actions and increase best responding to stated beliefs. Beliefs shift in a parallel direction and participants expect more sophisticated opponent actions. Furthermore, while higher own incentives increase belief accuracy, opponents with higher incentives are harder to predict. Higher incentives also increase response times, and longer response times are associated with better performance and more sophisticated actions and beliefs. Taken together, the evidence suggests that incentives affect strategic behaviour through two main channels: by reducing payoff-dependent mistakes and by increasing the effort devoted to reasoning, with the returns to that effort shaped by strategic complexity of the environment.