Papers

We study a war of attrition in which each party chooses how far to escalate before conceding. If neither concedes before escalation reaches a random disaster threshold, both receive nothing; otherwise the confrontation ends peacefully. When the parties are ex-ante symmetric and their benefits are independent, improving both parties' private information about their own stakes raises both the probability of peace and each party's ex-ante equilibrium utility. The result holds for every nontrivial mean-preserving spread of posterior values—each party's expected benefit from prevailing given its private information. Higher stakes work in the opposite direction: a first-order stochastic increase in posterior values induces greater persistence, lowers the probability of peace, and can reduce both parties' ex-ante utilities. Starting from symmetry, a sufficiently small improvement in only one party's information also promotes peace; its first-order effect is exactly half that of improving both parties' information. At an asymmetric starting point, however, even a small one-sided improvement can instead lower the probability of peace.

This paper studies how willingness to pay for information depends on risk aversion. We model a decision maker who faces background risk and can acquire information before choosing from a menu of assets. We show that the comparative statics depend on two factors. The first is whether available assets constitute an investment menu, whose payoffs are procyclical with background wealth, or an insurance menu, whose payoffs are countercyclical. The second factor is the tail geometry of background risk. We show that willingness to pay for information decreases with risk aversion for investment menus when the density of background risk is log-concave, and that it increases with risk aversion for insurance menus when background risk is downward-log-convex. The proofs compare the distributions of terminal wealth with and without information. They develop new aggregation arguments for state-dependent single-crossing comparisons. We also construct reversals under strictly log-convex tails for investment menus and super-exponential left tails for insurance menus.

Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these datasets are referenced remains difficult. Such references are dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination. We present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus. A lightweight model trained on general research literature generates candidate dataset mentions from unlabeled domain documents, which a frontier large language model (LLM) reviews in context, validating or rejecting candidates and correcting their extraction boundaries. The resulting annotations are supplemented with targeted synthetic and contrastive examples and used to fine-tune the lightweight model for large-scale extraction. We evaluate the resulting model on an independent gold-standard benchmark of 1,706 text passages spanning research, humanitarian, and operational documents. Across the full benchmark, the model achieves 74.1% precision and 70.5% recall at the mention level; among passages containing dataset references, precision reaches 89.5%. At the passage level, the model achieves 88.2% accuracy and 88.6% specificity in distinguishing passages with dataset references from those without them. These results demonstrate a practical approach for constructing domain-specific supervision when labeled data are limited, and provide a technical foundation for larger-scale analysis of data use and potential gaps in the displacement data landscape.

Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities. For GEM, it combines records of sponsored placements with notice probabilities. GMMM compares expected business responses under alternative treatment sequences and establishes sufficient conditions for identifying the resulting effects. We investigate the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.

Experimental evidence suggests that ambiguity-sensitive choice can vary systematically with the circumstances of a decision. This paper isolates one channel within a stable preference relation: ambiguity weighting may depend on the act's certainty-equivalent level. After the standard Anscombe-Aumann calibration of consequence utility, a set of behavioral axioms yields a unique continuous family of normalized monotone capacities $\{\nu_v\}_{v\in(0,1)}$. Each nonendpoint act is evaluated by the Choquet integral associated with the capacity at its own interior certainty-equivalent level, while nonendpoint acts on the same indifference surface share the same capacity. Binary event comparisons identify local event weights at each elicited level and trace their cross-level variation, providing tests of the fixed-capacity restriction. Local uncertainty aversion is equivalent to convexity of $\nu_v$ and yields an implicit multiple-priors representation with certainty-equivalent-indexed local cores. Certainty translation invariance holds if and only if the capacity is fixed across levels, recovering the maintained nondegenerate fixed-capacity Choquet expected utility benchmark; global mixture-betweenness yields implicit additive utility, and imposing both restrictions recovers full-support subjective expected utility. The capacity schedule is a reduced-form ambiguity weighting whose variation may reflect changes in ambiguity perception, ambiguity attitude, or both.

Modeling the joint distribution of extreme values in high-dimensional financial time series is challenging because extremes are sparse and locally extreme observations are not necessarily extreme relative to their full marginal distribution. To address this, we introduce a time-dependent network Hüsler-Reiss model in which market-informed adjacency matrices determine how strongly observations contribute to the estimation. We propose binary and weighted specifications, including the Joint Extremes Adjacency Matrix (JEAM) which combines information about individual extremeness with historical patterns of joint extreme movements. In the forecasting evaluation part, covering one-minute stock returns from three sectors of the S&P 100, JEAM achieves the best out-of-sample log scores for both tail directions; improving scores by 12.5-13.6% in the lower tail and 11.4-14.9% in the upper tail. The results show that incorporating market-informed network structures in the estimation, improves forecast evaluation of extremes across time series.

We study a mechanism of cooperation in the Prisoner's Dilemma (PD). Incorporating social preferences as efficiency concerns into the PD game, we study how altruism translates into cooperation. Under complete information, cooperation requires the opponent's altruism to clear a threshold. We then introduce a subjective extension of Bayesian Nash equilibrium that relaxes the Common Prior Assumption, letting players hold heterogeneous, potentially misspecified beliefs about each other's altruistic type. Cooperation then depends on beliefs about altruism rather than altruism itself, and can be sustained even when opponents are, on average, only weakly altruistic. When fear of exploitation dominates the temptation to defect, beliefs about the opponent's cooperation become strategic complements, so a cooperative and an uncooperative equilibrium can coexist under identical payoffs and an identical, correctly specified prior. Using multiplier preferences, we then study how robust this belief-driven cooperation is to model misspecification. Cooperation is fragile: it survives only above a threshold level of confidence in one's own belief, and can unravel even when the belief itself correctly supports cooperation. As a formal extension, the same robust-control apparatus, applied to a player's action choice, nests Nash equilibrium, Bayesian Nash equilibrium, and logit Quantal Response Equilibrium as limiting cases. Cooperation depends less on how altruistic agents are than on what they believe about each other, and how confident they are that this belief is right.

Graphical causal inference supplies a complete theory of efficient covariate adjustment for the average treatment effect: one adjustment set, computable from the graph, is optimal under every compatible distribution. We show that this is a property of the average treatment effect's inverse-prevalence weights, not of causal estimands in general. For the average treatment effect on the treated we index the efficiency bound by the adjustment set and derive exact identities for its change under treatment-side and outcome-side extensions of a valid set. Covariates that predict only the treated-arm outcome are exactly efficiency-neutral, and covariates that predict the control-arm outcome can strictly increase the bound when the propensity is below one half – a reversal of the supplementation lemma whose source is an arithmetic-geometric-mean inequality that holds for the average treatment effect and fails for the treated-population estimand. A construction with two faithful distributions on one graph proves that no graphical optimality criterion exists for the treated-population estimand; under no effect modification the ATE-optimal set is nonetheless optimal among the graphically valid sets, with an exact expression for its advantage. The results extend to weighted average treatment effects with propensity-dependent weights, yielding symmetric thresholds for overlap weights, an estimand-drift phenomenon under instrument adjustment, and a characterization of constant weights as the only smooth positive weights for which outcome-side supplementation never increases the bound. Simulations and the LaLonde data provide illustrations.

We show how to optimally design experiments when the resulting data will be used to choose a welfare-maximizing policy subject to constraints. A decision maker seeks to maximize Bayes expected welfare by choosing a policy whose effects depend on an unknown finite-dimensional parameter. The decision maker has access to a first wave of experimental data with a fixed design but may choose the design of a second wave that will be collected before choosing the policy. The resulting experimental design–policy choice problem is a very high-dimensional dynamic program that is generally intractable in finite samples. We propose a tractable approximation based on the limit experiment and show it is asymptotically optimal using a new asymptotic representation theorem for adaptive experiments with continuous treatments. We apply the method to a conditional cash transfer experiment and demonstrate the potential for large gains from tailoring the experiment to the policy choice.

This paper studies identification in linear quantile panel models with unrestricted individual heterogeneity when the number of time periods is fixed and small. We impose strict exogeneity, whereby the conditional quantile restriction holds given the individual's complete regressor history and latent individual effect, but otherwise allow the disturbances to be arbitrarily dependent over time.

Society constantly has to determine the boundaries of what it deems acceptable, from legislative decisions to the guardrails governing autonomous systems. We initiate the axiomatic study of collective consent: given individuals' attitudes toward options, which options should receive societal consent? We organize our analysis around three principles: sufficient support, minority protection, and dominance by decisively better options. Each captures a distinct reason for withholding societal consent from an option. For each principle, we develop a corresponding solution concept that transparently implements the principle and is canonical in a mathematically precise sense. For example, for minority protection, the resulting concept is a consent-adapted version of Moulin's Proportional Veto Core. Balancing multiple principles simultaneously is more challenging. To address this, we develop a game-theoretic characterization of our veto core that naturally gives rise to a family of related concepts. From this family, we identify the Approval-Weighted Veto Core as particularly desirable. By making minorities' blocking power depend on the approval support of the options being challenged, it smoothly interpolates between proportional minority protection and majority support. Experiments on five datasets spanning high-stakes decision-making (such as political elections, ethical AI evaluations, and moral decision-making) show that solution concepts violating a principle in theory also violate it empirically.

We study peer monitoring design in knowledge production. A principal leads agents who work on different but related tasks. By working on their own tasks, agents acquire information that is useful for evaluating their peers' performance. We consider robust contracts under which effort by all agents is the unique rationalizable outcome. The optimal contract induces a hierarchy of monitoring authority: agents easier for the principal to monitor directly are assigned greater authority to evaluate other agents. Task diversity improves information value but weakens peer monitoring. This tension distorts optimal public task assignment toward the principal's core expertise. Confidential random assignment relaxes this distortion by decoupling information production from the perceived monitoring structure.

Whether organisational separation of elective and emergency care improves physician productivity remains an open question. Most existing evidence relies on cross-sectional comparisons or volume-based outcomes that cannot isolate efficiency gains from input expansion or provider selection. In contrast, this paper exploits the staggered rollout of NHS England's surgical hub programme, a set of dedicated ring-fenced elective facilities introduced as part of its Elective Recovery Plan, to provide causal estimates of the effect of elective-emergency separation on physician productivity. Using this rollout to implement a heterogeneity-robust difference-in-differences design, we estimate the impact of hub adoption on physician productivity measured as cost-weighted elective output per unit of salary-weighted physician input. We find that hub adoption increases physician productivity by 14.5% relative to the no-hub counterfactual, accompanied by a 10-day (7.6%) reduction in average patient waiting times. The size of these gains depends on how completely elective care is insulated from emergency pathways. Standalone hubs situated on dedicated elective-only sites achieve relatively larger productivity gains than integrated hubs located within main acute hospital sites. Moreover, operating multiple hubs yields gains more than double the overall average, whereas a single hub shows no statistically significant effect. For health systems seeking to raise physician productivity and address elective backlogs, these findings suggest that how separation is implemented, not simply whether it is adopted, shapes the productivity gains it delivers.

We present a comprehensive description of the network of Italian Business-to-Business commercial relationships, based on the universe of electronic invoices collected by the Italian Tax Office. The firm-to-firm detail of the data is exploited to describe the distribution of the numbers of buyers (customers) and sellers (suppliers) per firm, the centrality of each firm in the production network, and the average distance between firms. We characterize for the first time the firm-to-firm network describing the Italian production system, and present its geographic and sectoral breakdown. The analysis reveals heavy tailed distributions for the numbers of buyers and sellers per firm, implying a scale-free structure of the network. The distributions of centrality values display heavy tails as well, indicating a strong concentration of importance in a relatively small number of firms. We estimate the tail exponents for all these distributions, finding in all cases lower exponents for downstream (buyer-side) distributions than for the upstream (seller-side) ones.

We investigate the strategic communication of narratives under model uncertainty. The sender has private information about the true data-generating process of publicly observable data. The receiver is uncertain about how to interpret the data, but aware of the sender's incentives to strategically provide interpretations (“narratives”). We theoretically show that the size of the conflict of interest between the sender and the receiver is a crucial determinant of equilibrium communication. In particular, the stronger the sender's bias, (i) the more senders exaggerate their information and (ii) the more receivers correct the sender's action recommendations. In a laboratory experiment, we find evidence in line with both predictions, suggesting that people in complex and uncertain environments take a narrator's strategic incentives into account. Additional analyses reveal that narrative likelihood does not drive receiver behavior and that narratives are, on average, slightly persuasive only when bias is low.

In many power systems, wind and solar generation increasingly often exceeds electricity demand. Curtailing renewable generation in those hours matters both for prices and for the physical stability of the grid. Turning off wind turbines and solar panels is technically easier than ramping down a large power station, yet support schemes often give renewables an economic incentive to keep producing at negative prices. This paper studies wind and solar energy in Germany. For each cohort of generators it estimates, hour by hour, the incentive implied by two decades of support policy. It then sets those incentives against observed behavior, using a new estimate of market-based curtailment built from reanalysis weather data. I find that in 2025, at prices below -50 EUR/MWh, almost all wind generators had an incentive to stop producing, but only half of them did. Solar is the opposite case: nearly two thirds of the potential had no incentive to curtail at all, mostly because it receives a feed-in tariff that shields it from wholesale prices. Of the exposed remainder, just over a fifth cut production. Low exposure and response rates inflate subsidy payments and make the power system harder to operate safely. I conclude that a further expansion of wind and solar requires them to respond to price signals.

In this paper we consider situations of (multidimensional) spatial majority voting. We analyze such situations having an even number (greater than or equal to 4) of voters and assume that each voter's preference over the set of policies is “Euclidean": i.e., each voter has a most preferred “ideal" policy and the voter's pay-offs from policies decrease as the (Euclidean) distance of the policies from the ideal policy goes up. We confine attention to voting situations for which the core has a single element belonging to the interior of the policy set. It is known that under conditions usual in this literature, this element of the core, generally, is not a Condorcet winner: i.e., there is at least one policy outside the core such that the core-policy cannot be reached from it by just one round of majority domination. We demonstrate that, however, under such conditions, starting from any policy in place, by a finite sequence of majority-dominations (we assume, implicitly, sincere voting) the policy in the core is reached eventually.

I derive an explicit mapping from initial assets, income and the real interest rate to consumption for an income fluctuation problem with a borrowing constraint and CARA utility. I show that there exists a threshold of initial wealth over which the partial equilibrium consumption response to a permanent increase in the real interest rate is positive, consistent with recent empirical evidence. I further show that precautionary savings reinforce the possibility of crowding in and that the possibility of a positive response extends to a more standard CRRA utility whenever the elasticity of intertemporal substitution is strictly less than 1.

When a country's population declines, the aggregate economy appears to contract on the intensive margin: industrial diversity intact, every industry a little smaller. At the regional level, contraction is uneven and takes the extensive form: entire industries disappear, one after another. The relevant unit is the city: industries are nested by size - the hierarchy property of industrial location - each viable only above a minimum population. Necessity industries' thresholds bunch at the low end, so a city's industry count - and its jobs - is sharply concave in size (concavity on jobs). A modest loss pushes a small city below many thresholds at once; a large core sheds a few specialized industries, one at a time. Lost industries consolidate upward to the next city large enough to host them; for the worker it means a step down to a lower-paid local job. To recover that income, workers move up to the apex - the only city hosting the full industry range. Studying Japan - two decades ahead of the OECD, Tokyo at its apex - with worker-level panel data on the young workers who carry the migration, a wage regression in real, housing-inclusive wages identifies a Tokyo-bound migration incentive that varies by origin, following concavity on jobs.

In large markets, scarce attention limits partner evaluation and creates allocation loss, which stability magnifies. In an independent random market with average executable degree $d$, unmatched shares fall at rates $e^{-\sqrt d}$ under stability and $e^{-d}$ under maximum matching on the same graph. Changing consideration can make applications rejected in a provisional active-screen computation relevant again. Exact query-neutral implementation must retain allocation-relevant off-screen authorizations; otherwise, missing authorization must be reacquired. Limited-attention deferred acceptance (LA-DA) preserves valid authorizations and reengages eligible pairs. Conditional on exact next-best information and persistent execution rights, adaptive discovery saves a logarithmic factor in reached proposals relative to independent exposure. In an application to speed dating, bilateral reports let us compare stable and maximum matching on restricted graphs, separating missed opportunities from same-graph stability loss. In Chilean school choice, we document 9,502 applicants accepting higher-ranked or new placements through retained rankings.

Drug-drug interaction (DDI) errors pose serious risks to patient safety. Existing decision-support systems often require physicians to respond to alerts, disrupting workflows and contributing to high override rates. We examine whether a non-mandatory information intervention can reduce DDI errors and foster learning. Using a randomized field experiment with India's largest electronic medical record platform, we analyze 2.81 million prescriptions from 1,700 physicians using a difference-in-differences design. Treatment physicians received real-time information highlighting DDI errors without being required to respond, while control physicians received no such information. The intervention reduced DDI errors by 8.6%, corresponding to an estimated US$4.8 million in annual hospitalization cost savings and approximately 134 lives potentially saved. We identify two mechanisms: reactive correction, whereby physicians remove errors after they are flagged, and proactive learning, whereby they avoid errors before alerts occur. While early reductions are driven primarily by correction, physicians increasingly avoid errors over time. They also become less likely to repeat previously flagged errors and reduce new errors, suggesting that learning generalizes beyond specific drug pairs. The effects are consistent across physician types and do not compromise productivity or care quality. Our findings show that non-mandatory information interventions can improve patient safety through both immediate error correction and persistent, generalizable learning.

Many real-world policies and business interventions require assessing short-term effects to inform timely decisions, even though most causal inference methods focus on long-term average treatment effects. In this paper, we introduce average treatment effect localization (ATEL), which captures localized, short-term policy impacts in panel data settings with a single treated unit and provides early indicators of policy impact. To accommodate both time-varying and nonlinear effects of observed and unobserved covariates, we propose a nonparametric model for untreated outcome, interpreted as a time-varying factor model via sieve approximation. Estimating the time-varying factor model is challenging due to the boundary bias and identification. Our estimation method based on diversified projection can effectively address these issues. We develop an asymptotic distribution theory to facilitate inference for the ATEL estimator. In an empirical application, we apply our proposed methodology to assess the impact of right-to-carry laws on violent crime rate.

This paper presents a causal decision framework for list-price elasticities in intermediated retail channels, where estimates are confounded by promotions, competitor moves and shopkeeper pass-through. Rather than forcing a number when the evidence is ambiguous, the system treats abstention (Wait) as an active diagnostic output. Built on Double Machine Learning and conformal prediction, it evaluates whether conditions exist to act on a recommended price or whether withholding is preferable, and it states when: acting beats withholding exactly when the calibration slope of the true headroom on the recommended move exceeds one half, a condition that predicts the sign of the gain in 37 of 38 synthetic groups. When the system abstains, it classifies the cause and flags which products are candidates for a designed pricing experiment. Aggregating estimates to the brand or category level, where independent price designs average out, cuts the root mean squared error from 0.488 to 0.159, although aggregation alone does not make the intervals honest. Tested on synthetic data with known truth and on public scanner data, the study reports its costs: the system abstains on 52% to 87% of presentations that were identifiable, and a single-window estimate forecasts real price changes poorly (calibration slope 0.22). What it offers is a way to audit any abstention rule and to publish at the unit the evidence supports; no commercial panel or pilot was used, so it does not measure commercial uplift.

Policy decisions often hinge on conventional p-value thresholds, which ignore economic costs and benefits of further data collection. This paper recasts statistical significance as a choice between making an immediate policy recommendation and deferring it until further evidence is collected. The welfare-optimal decision corresponds, under minimax regret, to a statistical test whose level depends on the cost and precision of additional evidence. Inverting this rule, we introduce and recommend reporting the abstention-value (A-value) to determine where additional data collection is most needed. The A-value defines the break-even welfare cost of abstaining and recommending further experimentation given the initial evidence. Computing the A-value only requires a point estimate and its standard error, imposes no prior assumptions on policy effects, and can be converted to a monetary research budget using inputs already frequently used by regulators and funding agencies. When experimentation capacity is limited, we show that prioritizing additional data collection where A-values are the largest yields strong finite-sample guarantees. Applications to anti-poverty programs and to a medical study illustrate the practical benefits of using A-values.

This paper studies difference-in-differences with staggered adoption and a continuous, time-invariant dose. Each cohort-time comparison contains two margins. The level margin is the average treatment effect at realized doses. Under level parallel trends it equals the level contrast between the treated cohort and not-yet-treated controls. The response margin is the within-cohort slope of the outcome change on dose. It uses no controls, and its causal interpretation requires a response parallel trends assumption and a restriction on selection on gains. We show that the continuous-dose OLS coefficient in each cohort-time comparison is a convex combination of the response index and the level contrast per unit of mean dose, with a mixing weight that depends on the not-yet-treated share. We provide estimators of both margins, joint inference across cohort-time comparisons and event-time aggregates, and a covariate-adjusted extension. In an application to hydraulic fracturing, the level leads reject a joint zero restriction, whereas the response-index leads do not. The continuous-dose OLS coefficient draws primarily on the level margin. We report the level and response margin separately.

Whatever the policy question under consideration, reasoned and realistic evaluation requires credible policy analysis under uncertainty. This holds particularly to the study of climate policy in the United States, where science and ideology have become increasingly entangled. Welfare economics provides a transparent formal framework to describe and evaluate the subtle tradeoffs that must be addressed when comparing alternative climate policies. The largely qualitative discussions of climate policy in public-facing reports suffer from failure to use welfare economics to assess tradeoffs. I use 2025 reports by the National Academies and by the US Department of Energy as case studies.

Buy Now, Pay Later (BNPL) has moved from a novelty product to mainstream consumer finance in the space of a few years. Before 2020, BNPL was a niche offering at the checkouts of online fashion merchants. BNPL then experienced substantial growth (Consumer Financial Protection Bureau, 2022; Salem & Udis, 2025), coinciding with increased online shopping since the onset of the COVID-19 pandemic in 2020. BNPL is now a pervasive payment option. In 2026, you can use BNPL to delay and split up payments for anything ranging from a pizza to your rent, and many items in between. As the market has grown, so has the academic literature. There is now sufficient research into BNPL to take stock and review what we have learned. In this article, we summarize insights from the economics, finance, and marketing academic literature. While we have learned a great deal about BNPL in the last few years, we also discuss many remaining open questions and current concerns impacting policy decisions. We start by explaining what BNPL products are and the business models of BNPL lenders. Then, we summarize the economics and psychology behind why some consumers use these products. Next, we describe the customer base that uses BNPL. We show the effects of BNPL on consumers, an area of research with substantial policy interest. Another area of policy interest is whether BNPL loans should be reported in a consumer's credit report. We explain why BNPL is typically not on credit reports and its implications. Finally, we offer some concluding thoughts.

In-group favouritism and out-group hostility are well-documented, but real-world group settings rarely fit a simple dichotomy. Often, a "middle-group" shares some identity markers with the in-group without fully belonging to it. How do people treat such intermediate groups? We address this question using a formal identity marker framework and a multi-lab online experiment (N = 376) with a private allocation task immune to reputation effects and demand characteristics. We find that a middle-group can be treated neutrally, i.e., distinct from both in-group favouritism and out-group hostility, but only under specific conditions. When the middle-group shares two identity markers with the in-group, including university affiliation, a complete hierarchy emerges: the in-group is favoured, the middle-group is treated according to the allocation rule, and the out-group bears the loss. However, when the middle-group shares only one marker, it is treated indistinguishably from the out-group, regardless of which marker is shared. Furthermore, differentiated behaviour evolves only after participants have re-encountered the experimental instructions and group constellation in a second phase, not gradually across rounds. These findings demonstrate that participants can perceive beyond binary in-/out-group categorisation, but such perception requires both sufficient identity overlap and repeated exposure to the group setting. Our identity marker framework provides a tool for systematically studying group relations in more complex settings.

Should researchers adjust for covariates in randomized experiments, and if so, how? The literature offers three distinct prescriptions: do not adjust because randomization guarantees unbiasedness; adjust for outcome-prognostic covariates to improve precision; or adjust for covariates imbalanced between treatment arms. These competing prescriptions create confusion and uncertainty. We develop a unified framework for decision and practice. Given available information, we show that the optimal correction is what we call ex-post bias. The only relevant criterion for adjustment is prognosticity for ex-post bias; neither raw covariate imbalance nor outcome prognosticity is sufficient by itself. We also show that correcting imbalance and improving precision are two sides of the same decision problem. We develop two estimation approaches, one of which recovers familiar adjustment estimators and provides a new theoretical justification for them. Simulations compare alternative covariate-selection and adjustment strategies. Overall, our framework provides a unified foundation for covariate adjustment in randomized experiments.

As US power markets contend with growing demand for firm generation, the nuclear industry has offered Small Modular Reactors (SMRs). However, how these concepts would fare in a rapidly evolving power grid is unclear, given the paucity of operational examples. Current literature, informed by substantial cost escalations for traditional nuclear plants, focuses on the investment costs SMRs need to achieve for private investment feasibility. However, this work finds that the operating and marginal costs of SMRs are more critical to economic feasibility in market environments. This work dispatches SMRs using a flexible operations model, considering revenue from two main electric markets, capacity and wholesale energy markets, with and without policy support. Manufacturer advertised costs for investment and operating costs are used, with fuel costs calculated from manufacturer provided design parameters. Results indicate that SMRs are uneconomical primarily because investment cost reductions are offset by increased marginal costs. As such, an environment of prices and subsidies beyond historic norms are necessary to attract private investment at manufacturer advertised cost benchmarks. Current SMRs are as profitable as advanced estimates of the AP1000 traditional nuclear reactor, and if investment costs escalate at the average rate for nuclear projects, they are similar to Vogtle 3 & 4. In projected future power markets, reductions in marginal cost may be more beneficial than those in investment costs.