Papers

We solve a natural bimatrix game related to graphs. We consider a finite directed graph $G=(V,E),$ where the strategy set of Player I is the set of vertices $V$ and that of Player II is the set of edges $E.$ There are two sets of positive weights ${\{\alpha_e\}}_{e\in E}$ and ${\{\beta_e\}}_{e\in E}.$ If Player I chooses a vertex $v$ and Player II chooses an edge $e,$ then the payoff to both players is zero if $v$ and $e$ are not incident. If $e$ originates from $v,$ then Player I obtains $\alpha_e$ and Player II obtains $-\beta_e.$ If $e$ terminates at $v,$ then Player I obtains $-\alpha_e$ and Player II obtains $\beta_e.$ For this game the payoff matrices are weighted incidence matrices of the graph $G.$ We show that when the graph is acyclic, Player I has a unique strategy in any equilibrium. At this strategy, every vertex is chosen with a probability that is proportional to the maximum length over all directed paths originating from that vertex. Defining the path matrix of the graph, it is shown that the set of all equilibrium strategies of Player II is the convex hull of the column vectors of the path matrix. This work extends earlier results of Bapat and Tijs (1997) for zero-sum games.

This paper studies general equilibrium when households and firms choose price-contingent schedules and market clearing determines prices. A unilateral schedule change therefore changes both an agent's realized allocation and the price at which it is evaluated. We call the resulting outcome a schedule equilibrium. The central object is the response of the clearing price to an agent's own schedule. This residual price response enters the household and firm first-order conditions, provides the strategic component of markups, and links conduct to observable equilibrium variation. We give conditions for existence, derive a trembling-hand refinement, and show how competitive, Cournot, and supply-function models arise as special cases. Applications to monopoly and vertical market power show how general-equilibrium feedback can alter familiar partial-equilibrium conclusions. We then allow firm mass, ownership, and technology to be endogenous. Free entry need not be efficient because entrants do not internalize their effect on the rest of the economy. Households choose capitalization and thereby ownership and firm creation, while investment moves active firms through production-function space. The same price-response mechanism therefore links market power, entry, ownership, and technological change within a common general-equilibrium framework.

Clinical algorithms can perform differently across patient groups, a concern that grows as these algorithms increasingly take the form of artificial intelligence (AI) models. In the United States, a 2024 federal rule bars healthcare providers that receive federal funds from discriminating through their use of clinical algorithms, which exposes them to liability when such use disadvantages a protected patient group. We examine how such liability considerations reshape (i) an AI firm's model design decisions that drive group-specific accuracy and (ii) a physician's decisions to use AI in healthcare delivery. The firm designs an AI model for two patient groups, and improving accuracy for the disadvantaged group is more costly. The physician (who remains the accountable decision maker) then decides whether to consult AI, weighing the reduction in clinical uncertainty against expected liability exposure when AI errors disproportionately affect the disadvantaged group. We find that this liability can induce disparate use of AI: the physician may reduce AI use overall and, over an intermediate range of liability, rely on AI less for disadvantaged patients. The effect is non-monotone. As liability increases, the physician's use of AI for disadvantaged patients first declines, then rises as the firm reallocates investment toward reducing disparity or switches to an equal-accuracy design. Mandating equal algorithmic accuracy across patient groups can then inadvertently harm both groups, because a uniform accuracy requirement distorts the firm's investment incentives and the physician's equilibrium AI-use decisions.

The dispersion of self-reported life satisfaction has been proposed and used as a comprehensive measure of societal inequality. A negative cross-country association between mean life satisfaction and its standard deviation has been read as evidence that this inequality is itself welfare-relevant, but critics have pointed to the nonlinearity and boundedness of response scales. I describe a further problem: a substantial and predictable share of respondents simplify the 0–10 scale to the subset {0, 5, 10} — "focal-value rounding" (FVR) — a behaviour that breaks not only linearity of the scale but even its order. I derive a sharp bound on the bias that FVR can induce in the standard-deviation; at empirically-typical FVR fractions the possible bias is roughly half the cross-country range of dispersion. I estimate and correct for the bias by fitting a model of FVR behavior to the Gallup World Poll Cantril ladder and three other multi-country life evaluations. FVR inflates measured standard deviation in 133 of 135 countries, by a median of 0.090 points. However, because the corrections are nearly uniform in sign across countries, rankings of the standard deviation survive essentially intact. The ordinal inequality indices advocated as the theoretically appropriate alternative suffer even worse from FVR. The correlation between mean and standard deviation survives FVR correction with modest attenuation and some caveats. Thus FVR does not, by itself, overturn previously reported correlations; it does, however, add a further reason for caution in interpreting scalar measures of subjective wellbeing inequality: candidate statistics are differently contaminated, the ordinal repairs no less than the cardinal originals, and they disagree with one another. Lastly, I show that FVR-correction can improve the pairwise dominance-comparability of full distributions.

Factor-MIDAS regressions forecast a low-frequency target by extracting common factors from a large panel of high-frequency predictors via principal component analysis (PCA). While PCA mitigates the curse of dimensionality, it relies on factor pervasiveness, an assumption often violated when factors are weak, as is common in macro-financial forecasting. We propose SsPCA-MIDAS, which integrates supervised scaled PCA (SsPCA) into the mixed-data sampling framework. We establish consistency and asymptotic normality under weak factors, permitting inference on the prediction target. Simulations show that SsPCA-MIDAS outperforms competing PCA-based and supervised methods, especially when weak factors are prevalent. Applying machine-learning techniques such as boosting to the cleaner factors it extracts yields further gains. An extensive application to U.S. macro-financial forecasting shows that SsPCA-MIDAS selects economically meaningful predictors and improves forecasts of GDP, inflation, unemployment, asset prices, and volatility.

We study a platform that prefers to sell the more profitable of two products. It designs an algorithm that determines the product the consumer encounters first, conditional on her best match. The algorithm simultaneously manipulates consumer attention (steers) and communicates information about match quality (informs). When the algorithm is opaque, it is difficult for a consumer to understand how product order is generated and what it reveals. In the platform's preferred equilibrium, it places the profitable product first. When the algorithm is transparent, the consumer understands the algorithm and what it conveys. Thus, the algorithm can persuade as well as steer. In some cases, the equilibrium algorithm deters search, in others, encourages it. In the former case, transparency helps consumers; in the latter, it harms some or all of them. Extending or shifting transparency requirements uncouples steering from information provision, reverting the consumer's welfare to opacity.

AI predicts; humans use its predictions to make decisions. These predictions are combined with human verification and analysis, queries to other statistical models, and so on. The economic value of an AI, therefore, depends on how it interacts with the surrounding decision environment. We describe the value of AI as part of this “composite experiment” where AI makes a coarse prediction of the state of the world, show what this means for optimal model training via a geometric argument, explain why optimal training can be discontinuous in economic variables, and study how heterogeneous users or monopoly model trainers affect these results. In particular, maximizing the unconditional accuracy of AI predictions is generally suboptimal.

What a city makes reachable depends less on what it contains than on who can still afford to move when travel costs rise. We leverage the 2026 US-Iran oil shock as a natural experiment, applying a hierarchical panel regression discontinuity design to 1.7 trillion point-of-interest visits across 122,000 neighbourhoods in China and the United States. Mobility range declined in nearly three-quarters of neighbourhoods, but responses varied systematically with pre-shock urban conditions. Exposure to energy-intensive travel explained the largest share of modelled heterogeneity in both countries, while adaptive capacity and activity composition further shaped how travel was reorganized. Longer baseline travel intensified contraction, whereas greater car dependence constrained adjustment. Crucially, similar mobility outcomes arose from different processes: some neighbourhoods maintained travel by absorbing higher costs, whereas others appeared structurally locked into travel they could not reorganize. Fuel-price shocks, therefore, act as urban stress tests, revealing which neighbourhoods a city keeps connected.

We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.

Designs for international climate cooperation face a trade-off between efficiency and robustness to institutional erosion by defection, renegotiation, and political turnover. We formalize this trade-off in a stylized coalition-formation game with two market-based enrolment channels, a membership premium and an outsider drain, stabilized against bounded perturbations with robust control. The free-rider gap is exact within the game, expressed in measurable primitives, and separated from the architecture-specific channels. The drain is decomposed into a fiscal border channel, capped by trade law at the rent it mirrors, and a compensated terms-of-trade channel, making every channel strength measurable. With sufficiently strong channels the model is bistable: a remnant club and a near-universal coalition are separated by a critical mass. For a newly proposed carbon currency, whose emission rights are reissued each period, extinguished upon use, and enforced at the border, the minimal nucleus is thirty per cent of global emissions, ignition from an EU-China nucleus requires compensating one fifth to three fifths of the outsiders' terms-of-trade loss, the established coalition withstands two to four times the perturbation admissible at ignition, and the tipping survives heterogeneity to about three times the membership premium. Two robustness coordinates place each architecture in a map with three regimes, opening a comparative dynamics of climate clubs: the carbon currency is self-igniting, the border adjustment founding-dependent, the export rebate permanent-support-dependent. Architectures without a drain improve efficiency within a coalition but cannot drive its formation. Robustness governs whether cooperation forms and endures; efficiency decides how much an established coalition delivers.

Outcomes are regressed on a calibrated probability vector for unobserved class membership. Under a structural conditional mean excluding the score and conditional calibration, the observed-data model reduces to a partially linear regression. The probability vector is a Berkson-type surrogate for membership, so the effect vector $\tau$ is identified without attenuation. In practice the vector is often coarsened to a hard label - an argmax, a confidence threshold - and that label need not retain the Berkson property. For any coarsening the plug-in estimator converges to $\mathcal{A}\tau$, where $\mathcal{A}-I$ is determined by the regression of the discarded part of the score on the retained part. Coarsening therefore leaves $\tau$ undistorted exactly when that regression vanishes, and otherwise distorts some contrasts far more than others. The same operator determines the bias that drives coverage loss. Where that bias is of the order of the standard error, the Wald interval has limiting coverage $\Phi(z-\nu)-\Phi(-z-\nu)$, with $\nu$ their ratio. A fixed bias sends coverage to zero. Operator, standard error, and - through the uncoarsened estimator - the bias are estimable from observed data, so the implied coverage can be approximated before the interval is reported. Simulations show severe coverage loss after argmax coarsening. Three real-data audits exhibit the direction-specific distortion.

We study two observable queues with identical service rates, serving agents who arrive stochastically over time. Agents join the queue that minimizes their expected waiting time. Assuming one queue uses the ubiquitous First-Come-First-Served (FCFS) service rule, we show that by simply modifying its service order, the other queue can capture a strict majority of the demand. We establish an upper bound on the arrival share any rule can capture against FCFS. When both queues can design their service order, we show a novel variant of Last-Come-First-Served (LCFS) is an equilibrium in a low congestion regime. Without commitment, the picture changes, and there is an equilibrium where both queues use FCFS, and agents route to the queue with the shorter waitlist.

We extend the concept of priority-neutral matching, introduced by Reny (2022) in the school choice context, to the roommate problem. We prove three main results. First, a blocking-neutral matching always exists in constrained roommate problems under arbitrary feasibility constraints (Theorem 1). Second, the set of stable matchings is contained in the set of blocking-neutral matchings, which in turn is contained in the set of Pareto-optimal matchings (Theorem 2). Third, whenever stable matchings exist, the set of blocking-neutral matchings coincides with the set of stable matchings (Theorem 3). We also show that existence fails under weak preferences and extend the concept to two-sided school choice.

This study proposes that firms move along an "organizational technology ladder": adopting one technology transforms hiring and work processes and builds skills and organizational capital that change the cost of adopting subsequent technologies. I study how firms' adoption of remote work technology during the COVID-19 period shaped later uptake of generative AI. Using U.S. job-posting data and an instrumental-variables strategy based on predicted differences in labor-market pressure to offer remote work, I estimate that a 10 percentage point increase in remote hiring in 2021-2022 increases the share of job postings mentioning generative AI in 2023-2024 by 0.4 percentage points across firms and 0.7 percentage points across occupations within firms. I provide evidence on mechanisms consistent with a technology-ladder channel: remote work adoption shifts hiring toward technical and managerial capabilities that predict faster conversion of generative AI exposure into adoption. Firms with return-to-office mandates—interpreted as revealing low remote productivity—exhibit a substantially larger response of generative AI adoption to remote work, consistent with an organizational frictions channel.

Reported estimates of the size premium, the tendency of smaller firms to earn higher average returns than larger firms, vary widely across studies, countries, periods, and designs. We examine whether generalized trust and rule of law help account for that heterogeneity. Small firms are more opaque and more dependent on outside finance, so the enforcement and information environment should matter more for them than for large firms. We study 1,613 reported size-slope estimates from 105 studies and 31 countries. The meta-regressions control for study design, specification, precision, publication context, and market and macro-financial conditions; Bayesian model averaging assesses uncertainty over the control set. The more stable association is with rule of law, and it runs against the intuitive expectation that better legal institutions shrink the premium: stronger rule of law is associated with more negative reported size slopes, hence larger conventional size premia. The trust association is conditional and less precisely estimated: where rule of law is weak, higher generalized trust is linked to less negative reported slopes (and thus a weaker premium), and this link fades as rule of law strengthens. Formal and informal institutions thus help organize part of the disagreement in this literature, although the analysis concerns variation in reported estimates and does not identify causal effects.

When generative AI drives the marginal cost of a persuasive expert artefact toward zero, production-cost signals of competence collapse and outcome-contingent liability commitments take their place. Such commitments look robust to better AI: a positive failure rate always leaves a residual to price. We show that this robustness rests on an unstated homogeneity assumption, and that a liability commitment faces three ceilings, not one. AI error decomposes into idiosyncratic and common components; capability growth eliminates the idiosyncratic part faster (Kim et al., 2025), so the residual becomes progressively common - and common error is precisely what a verifier drawn from the same foundation model cannot observe: judge scores correlate with judge-generator model similarity at an average r=0.84 (Goel et al., 2025). Provability is therefore state-dependent, theta_eff = theta_0(1 - xi*kappa). Separation requires the commitment that must be posted to fall below what can be posted, v/theta_eff <= min{Lbar, mv}, yielding the survival condition xi*kappabar <= 1 - 1/(M*theta_0) with M = min{Lbar/v, m} and a regime switch at the ticket size v* = Lbar/m: small engagements are constrained by the penalty doctrine, large ones by capital. Calibrated to six engagement types across four jurisdictions, the generative-AI insurance exclusions effective January 2026 shift eight of twenty-four cells from doctrine-bound to solvency-bound and cut the cells surviving xi=0.4 from twelve to four. The insurance withdrawal is a civil-law event; the common-law cells were already at the boundary.

A number of recent empirical papers rely on a collective model to analyze the portfolio choice of spouses, their heterogeneous risk preferences, and intra-household bargaining. I study applications of this model and highlight some important shortcomings. In its classic form, the model generates a counterintuitive result: an increase in the risk aversion of a household member can lead to an increase in household risk-taking. I offer a formal characterization of this pattern and link it to previous theoretical findings. I highlight further issues with applications of the collective approach to the portfolio choice problem in the contexts of bargaining and wealth inequality. I reconcile recent household finance papers with these findings and point to potential confusion in the literature. I emphasize existing alternatives that do not exhibit most of these issues, yet argue that there is a lack of a consistent and rigorous modeling approach.

Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approval-based committee elections. Recent work has shown that an FJR committee can be found in polynomial time, but verifying whether a given committee satisfies FJR remains coNP-complete. We introduce FJR+, a strict strengthening of FJR and EJR+ that can be verified and satisfied in polynomial time. We then analyze the Residual-Budget Greedy (RBG) algorithm and prove that it selects a partial committee such that every size-$k$ completion satisfies FJR+. This freedom allows us to use sequential Phragmén to obtain a priceable completion. The resulting rule always satisfies FJR+ and the sub-core, and it is priceable whenever at least $k$ candidates receive an approval. We also obtain a Droop-quota version of FJR+. Finally, we extend FJR+ to approval-based participatory budgeting with arbitrary project costs. A project-specific version of RBG computes this property in polynomial time and can be continued to a priceable outcome satisfying a cost-based version of the sub-core.

How should institutions compare group diversity, and which group should they select when they value diversity and merit? We take a target-based approach that evaluates the entire group composition without treating any type as intrinsically diversity-enhancing. Because different diversity indices may rank groups differently, we instead adapt majorization to construct an ordinal diversity preorder. We show that its maximally diverse selections are exactly those maximizing every index in a broad class. This characterization yields a reserve-and-quota policy that selects a maximally diverse group and, among such groups, the highest-merit agents. Any alternative is less diverse, less meritorious, or both.

We provide an estimator for the perturbed utility route choice (PURC) model that works with data at the level of individual trips. The estimator is a nested fixed-point algorithm that combines an upper bias-corrected linear regression problem with a lower individual-level perturbed utility maximization problem. We establish the statistical properties of the microPURC estimator and confirm these results with an experiment using simulated data. Finally, we demonstrate the estimator in practice using a large real-world dataset.

Decarbonizing existing coal-fired power plants can contribute to near-term climate mitigation, but identifying cost-effective retrofit strategies is complicated by interactions among mitigation technologies. Here we develop an interaction-aware optimization framework that jointly evaluates energy conservation, biomass co-firing, and carbon capture across 1,885 coal-fired power plants in China while accounting for plant heterogeneity and shared biomass and CO2 storage resources. We find that technology interactions alter both mitigation costs and the emission reductions attributable to individual measures, thereby changing cost-optimal technology portfolios and marginal abatement cost curve at the fleet level. Approximately 1.2 Gt CO2 yr-1 can be mitigated at negative marginal cost, while reaching carbon neutrality requires a marginal abatement cost of US$56 t CO2-1. Progressively deeper mitigation shifts the cost-optimal portfolio from energy conservation toward biomass co-firing and ultimately carbon capture, with biomass combined with carbon capture enabling net-negative emissions. Explicitly accounting for interactions among mitigation technologies therefore provides a more consistent basis for evaluating coal-power decarbonization and coordinating retrofit investment, infrastructure development, and climate policy.

We study how differences in AI-generated financial recommendations are transmitted into individual portfolio choices. In an experiment with 400 employed adults enrolled in workplace defined contribution pension plans in South Korea, participants allocate a hypothetical pension balance across eleven products and may revise it after receiving one of two fixed AI-generated recommendations. A $2 \times 2$ design randomizes recommendation content and whether the recommendation includes a short rationale. Approximately 37$\%$ of the experimentally induced difference between the aggressive and conservative recommendations passes through to final portfolios. This causal contrast changes expected portfolio return, volatility, allocations across risk grades, and the number of products held, but produces no detectable difference in computed Sharpe ratios. 81$\%$ of participants revise. Among revisers, 95$\%$ move toward the assigned recommendation and implement about half of the suggested adjustment. Rationales do not detectably alter pass-through. These results show that users partially and selectively transmit recommendation content into economically meaningful differences in risk exposure while retaining substantial weight on their initial choices.

We propose a comprehensive framework for solving overlapping-generations (OLG) models in continuous time with both idiosyncratic and aggregate risk. Our general characterization of equilibrium through the master equation operates on the joint distribution over the continuous idiosyncratic states, age and wealth. Our computational strategy is to take a finite-dimensional representation of this distribution as an input of a neural net which in turn outputs a finite-difference representation of the (conditional) value function. This idea can be applied generally to heterogeneous agent models with aggregate risk, and we call it finite-difference neural operator. Our method combines advantages from modern neural nets and traditional finite-difference methods: It is grid-free in the high-dimensional distribution, and retains control on boundary conditions in low-dimensional state variables. Moreover, our method is able to enforce shape constraints. We showcase its flexibility by solving a continuous-time OLG model with aggregate risk alone where we characterize the distribution by its supporting function; and to an OLG model with both types of risk.

What determines the speed of AI-driven labor displacement? I study this question in a microeconomic model in which users choose between labor and AI to complete tasks of different complexity. Profit-maximizing AI providers set the price of AI, and AI systems improve through task-specific learning from deployment. In an initial technological state where labor has the comparative advantage at complex tasks, the providers' profit maximization incentives suppress AI deployment on complex tasks, even though these are most valuable for frontier learning. I characterize when this tension gives rise to an accelerating path of AI adoption or a learning trap. Competition can accelerate AI adoption relative to monopoly by shifting AI deployment toward more complex tasks, which boosts frontier learning. Asymmetric competition can further accelerate AI adoption by inducing providers to specialize, which concentrates complex-task data.

Every statistic on laws with finite pth moment that is additive across independent risks and monotone in mean-preserving spreads depends only on an additive function of the mean when p is strictly less than 2, and only on such a function and a nonnegative multiple of the variance when p is no less than 2.

The New Keynesian example of Beraja (2023) is not identified at its printed calibration: more than one structure satisfies every condition of its identification step. Two of the paper's six identifying restrictions coincide on equilibrium equations consistent with the printed reduced form, leaving eleven conditions to determine an equation's twelve coefficients. The note derives the rank condition the identification step requires, computable from the reduced form and the restrictions alone. The note also documents and corrects a misprint in the paper's counterfactual display. A one-clause amendment to the paper's Theorem 1 restores its conclusion.

A principal seeks to allocate $k$ identical objects among n sequentially arriving, impatient agents. Each agent privately observes her valuation, and the principal's payoff from allocating an object depends on the recipient's valuation. The principal can perfectly verify the valuation of at most $m$ agents, where $m < k$, and commits to a mechanism before any agent arrives. We characterize the class of optimal mechanisms. Our main result establishes that, in states where the number of remaining objects exceeds the number of available verification checks, the optimal mechanism may involve probabilistic verification.

Standard multinomial probit (MNP) models specify symmetric latent utility distributions, implying that choice probabilities respond symmetrically to positive and negative covariate shifts of the same magnitude. This restriction is often implausible in empirical choice settings and can lead to misleading elasticity and substitution predictions. We propose a skewed multinomial probit (SMNP) model that captures asymmetric choice responses by specifying a multivariate skew-normal distribution for the latent utilities. The model preserves the flexible substitution patterns of the MNP framework, introduces alternative-specific skewness parameters, and nests the standard MNP model when skewness is zero. Introducing skewness creates identification and computational challenges because the skewness parameters interact with the MNP scale normalization and disrupt the conditional Gaussian updating structure used in Bayesian MNP estimation. We address these challenges through a covariance reparameterization that enforces identification and positive definiteness by construction, interpretable priors on the identified parameter space, and a double data-augmentation scheme that yields a Metropolis-Hastings within Gibbs sampler. Numerical experiments and applications to consumer choice data show that SMNP recovers asymmetric choice responses, improves probabilistic prediction, and produces economically meaningful differences in price elasticities and substitution patterns.

This manuscript is an English translation and extended version of a paper originally published in Japanese (Otomo, 2024). With the advancement of information and communication technology (ICT) and data analysis techniques, handling massive datasets (big data) has become feasible in spatial information science. Consequently, infrastructure is being established to generate new utility value from spatial data. Furthermore, nationwide initiatives are actively promoting the use of open data - public information provided under terms allowing secondary usage. However, in developing regions and specific municipalities, local statistical data remain scarce, unreliable, or difficult to acquire. Additionally, long-standing concerns exist regarding the inherent limitations of standard macroeconomic indicators when performing cross-regional or international comparisons. Meanwhile, high-frequency satellite data has become widely accessible. Among various observational products, nightlight data has attracted significant attention due to its versatile applications. Generally, nightlight radiance correlates with urbanization. Prior work indicates that areas with larger populations and higher commercial development display higher radiance, whereas public infrastructure - such as sanitation systems and healthcare facilities - shows little correlation with nightlight intensity (Otomo, 2021). Building on these findings, this paper analyzes the relationship between nightlight data and the spatial distribution of sports facilities, the evolution of the fitness industry, and regional environmental disparities in sports access. The analysis confirms that the spatial distribution patterns of sports facilities differ significantly between the public and private sectors.

Recent studies have demonstrated that conventional meta-analyses of partial correlation coefficients (PCC) are biased. Several adjustments have been shown in simulations to reduce these small-sample biases to negligibility. While many meta-analyses of partial correlation coefficients are conducted each year across several disciplines, the practical importance of these issues remains unknown. To address this question and to offer advice for applications, we survey 172 economic meta-analyses of PCCs. We find that small-sample biases are negligible in practice. However, some publication selection biases remain. Although Fisher's z transformations have often been recommended, they reduce neither small-sample nor publication selection biases relative to conventional random effects. Both the unrestricted weighted least squares (UWLS) and the Hunter-Schmidt (HS) estimators produce smaller, arguably less biased, estimates of the mean PCC in these applications than either random effects with or without Fisher's z transformations. These findings offer practical guidance for any discipline that meta-analyzes partial correlations.