This paper introduces a new approach for estimating core inflation indicators based on common factors across a broad range of price indices. Specifically, by utilizing procedures for detecting multiple regimes in high-dimensional factor models, we propose two types of core inflation indicators: one incorporating multiple structural breaks and another based on Markov switching. The structural breaks approach can eliminate revisions for past regimes, though it functions as an offline indicator, as real-time detection of breaks is not feasible with this method. On the other hand, the Markov switching approach can reduce revisions while being useful in real time, making it a simple and robust core inflation indicator suitable for real-time monitoring and as a short-term guide for monetary policy. Additionally, this approach allows us to estimate the probability of being in different inflationary regimes. To demonstrate the effectiveness of these indicators, we apply them to Canadian price data. To compare the real-time performance of the Markov switching approach to the benchmark model without regime-switching, we assess their abilities to forecast headline inflation and minimize revisions. We find that the Markov switching model delivers superior predictive accuracy and significantly reduces revisions during periods of substantial inflation changes. Hence, our findings suggest that accounting for time-varying factors and parameters enhances inflation signal accuracy and reduces data requirements, especially following sudden economic shifts.

In many local foster care systems across the United States, child welfare practitioners struggle to effectively match children in need of a home to foster families. We tackle this problem while navigating a key sensitivity in this domain: in foster care systems, individual caseworkers must assent to any proposed matching. We codify this constraint in one-sided matching markets as the problem of matching design with sufficiency. We design a mechanism that guarantees outcome sufficiency, a form of welfare-maximizing Pareto efficiency ensuring that no caseworker can ex-post gain from any child-family placement reassignment and that the foster care authority's objective preferences for child-family placements are maximally satisfied. Our work subsequently evaluates this mechanism's strategic properties. Finally, we plan to conduct a lab-in-the-field experiment to elicit real-world caseworkers' preferences and estimate the child welfare gains our algorithm produces. Current simulation-based results show dramatic improvements to welfare. Designing sufficient matching systems is an example of mechanism-reform because replacing existing systems without regard for existing agents' preferences and wishes has previously resulted in failure.

An analyst observes the frequency with which a decision maker (DM) takes actions, but does not observe the frequency of actions conditional on the payoff-relevant state. We ask when can the analyst rationalize the DM's choices as if the DM first learns something about the state before taking action. We provide a support function characterization of the triples of utility functions, prior beliefs, and (marginal) distributions over actions such that the DM's action distribution is consistent with information given the agent's prior and utility function. Assumptions on the cardinality of the state space and the utility function allow us to refine this characterization, obtaining a sharp system of finitely many inequalities the utility function, prior, and action distribution must satisfy. We apply our characterization to study comparative statics and ring-network games, and to identify conditions under which a data set is consistent with a public information structure in first-order Bayesian persuasion games. We characterize the set of distributions over posterior beliefs that are consistent with the DM's choices. Assuming the first-order approach applies, we extend our results to settings with a continuum of actions and/or states.%

We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their estimands and inference procedures.

We study the price rounding regulation in Israel, which outlawed non-0-ending prices, forcing retailers to round 9 ending prices, which in many stores, comprised more than 60 percent of all prices. The goal of the regulation was to eliminate the rounding tax, the extra amount consumers paid because of price rounding, which was necessitated by the abolition of low denomination coins, and the inattention tax, the extra amount consumers paid the retailers because of their inattention to the prices rightmost digits. Using 4 different datasets, we assess the success of the government in achieving these goals, focusing on fast moving consumer goods, a category of products strongly affected by the price rounding regulation. We focus on the response of the retailers to the price rounding regulation and find that although the government succeeded in eliminating the rounding tax, the bottom line is that shoppers end up paying more, not less, because of the regulation, underscoring, once again, the warning of Milton Friedman that policies should be judged by their results, not by their intentions.

This paper shows a cascading mechanism through which international trade-induced deforestation results in a decline of health outcomes in cities distant from where trade activities occur. We examine Brazil, which has ramped up agricultural export over the last two decades to meet rising global demand. Using a shift-share research design, we first show that export shocks cause substantial local agricultural expansion and a virtual one-for-one decline in forest cover. We then construct a dynamic area-of-effect model that predicts where atmospheric changes should be felt - due to loss of forests that would otherwise serve to filter out and absorb air pollutants as they travel - downwind of the deforestation areas. Leveraging quasi-random variation in these atmospheric connections, we establish a causal link between deforestation upstream and subsequent rises in air pollution and premature deaths downstream, with the mortality effects predominantly driven by cardiovascular and respiratory causes. Our estimates reveal a large telecoupled health externality of trade deforestation: over 700,000 premature deaths in Brazil over the past two decades. This equates to $0.18 loss in statistical life value per $1 agricultural exports over the study period.

This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, providing a foundation for generating insights into how different market structures influence liquidity. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for planned extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating the current functioning of the investment platform using an ABM specified with empirical data.