We checked 17 economics journals on Friday, February 20, 2026 using the Crossref API. For the period February 13 to February 19, we retrieved 16 new paper(s) in 7 journal(s).

Economic Journal

Motivated Risk Assessments
Marco Islam, Christoph Drobner
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Do people form risk assessments to justify their actions? We investigate this question in a field experiment studying the dynamics of risk assessments for visiting a café during the Covid-19 pandemic. By randomly varying the incentive for a visit, we find that participants with a high incentive visit cafés more often and downplay the risk compared to those participants with a low incentive. Importantly, the downplaying happens in anticipation of the visit and without new information, suggesting that the assessment update justifies engagement in risky behaviour. This finding is inconsistent with Bayesian updating but consistent with the notion of motivated reasoning.
Populism and the Skill-Content of Globalization
Frédéric Docquier, Stefano Iandolo, Hillel Rapoport, Riccardo Turati, Gonzague Vannoorenberghe
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We propose new ways to measure populism, using the Manifesto Project Database (1960-2019) as main source of data. We characterize the evolution of populism over 60 years and show empirically that it is significantly impacted by the skill-content of globalization. Specifically, imports of goods which are intensive in low-skill labor generate more right-wing populism, and low-skill immigration shifts the distribution of votes to the right, with more votes for right-wing populist parties and less for left-wing populist parties. In contrast, imports of high-skill labor intensive goods, as well as high-skill immigration flows, tend to reduce the volume of populism.
Symptom or Culprit? Social Media, Air Pollution, and Violence
Xinming Du
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This paper provides the first causal evidence that hostile activities online trigger physical violence. Given the recently documented relationship between pollution and social media, I exploit exogenous variation in local air quality as an instrument for online aggression. An event study analysis shows that air pollution increases by 5.8 standard deviations (SD) when refineries experience unexpected malfunctions. On pollution spike days, surrounding areas see 0.11 SD more aggressive tweets and 0.24 SD more crimes; geographically distant but socially networked regions see aggressive tweets increase by 0.008 SD and crimes by 0.015 SD. My findings highlight the impacts of online hostility and contribute to the debate on cyberspace regulation.
Vote or Fight?
David K Levine, Cesar Martinelli, Nicole Stoelinga
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Why do some nations allocate power through voting while others do it through fighting? When political power is indivisible, voting is a substitute for fighting—provided the losing side accepts the outcome. We study a theoretical model of this substitution, and use a country-level panel dataset to assess empirically whether the economic factors influencing fighting also shape voting. They do. We contribute several theoretical and empirical innovations. First, we apply a recently developed method for analysing conflict resolution functions to derive robust theoretical results. Second, we introduce a new explanatory variable—productive efficiency as measured by income relative to the global frontier—and we explain the theoretical and empirical relevance of this variable. Finally, we show that absolute income levels do not matter, whereas oil wealth and ethnic divisions do—though their influence is less important than that of productive efficiency in explaining patterns of fighting and voting. A key implication of our analysis is that reducing global inequality is crucial for decreasing conflict and fostering democracy.

European Economic Review

Trade and protectionist backlash: The redistributive role of democracy
Ernesto Ugolini
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Monetary policy and credit card spending
Francesco Grigoli, Damiano Sandri
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Journal of Econometrics

Generic title: Not a research article
Editorial Board
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The factor structure of jump risk
Torben G. Andersen, Yi Ding, Viktor Todorov, Seunghyeon Yu
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Sign-based tests for structural changes in multivariate volatility
Jilin Wu, Zhijie Xiao, Mengxi Zhang, Zhenhuan Zhang
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Journal of Public Economics

Sick of your poor neighborhood? Quasi-experimental evidence on neighborhood effects on health
Linea Hasager, Mia Jørgensen
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Ranking for engagement: How social media algorithms fuel misinformation and polarization
Fabrizio Germano, Vicenç Gómez, Francesco Sobbrio
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Journal of the European Economic Association

Wealth and its Distribution in Germany, 1895–2021
Thilo N H Albers, Charlotte Bartels, Moritz Schularick
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German history over the past 125 years has been turbulent. Marked by two world wars, revolutions and major regime changes, as well as a hyperinflation and three currency reforms, expropriations and territorial divisions, it comprises extreme shocks to study the role of historical events, taxation, asset price changes, portfolio heterogeneity in affecting the wealth distribution in the long run. Combining tax and archival data, household surveys, historical national accounts, and rich lists, we document that the top 1% wealth share has fallen by half, from close to 50% in 1895 to 26% today. Nearly all of this decline was the result of changes that occurred between 1914 and 1952. Using a novel decomposition framework, we show that collapsing equity prices after World War I and in the Great Depression as well as taxation in the aftermath of World War II stand out as great equalizers in 20th century German history. After unification in 1990, two trends have left their mark on the German wealth distribution. Households at the top made substantial capital gains from rising business wealth while the middle-class had large capital gains in the housing market. The wealth share of the bottom 50% has halved since 1990. Our findings speak to the importance of historical shocks to the valuation of existing wealth and taxation in driving the evolution of the wealth distribution over the long run. In addition, our data revisions reveal that Germany’s current wealth-income ratio is about 120 percentage points higher than previously thought.

The Quarterly Journal of Economics

The Macroeconomic Impact of Climate Change: Global Versus Local Temperature
Adrien Bilal, Diego R Känzig
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This paper estimates that the macroeconomic damages from climate change are an order of magnitude larger than previously thought. Exploiting natural global temperature variability, we find that 1â—‹C warming reduces world GDP by over 20% in the long run. Global temperature correlates strongly with extreme climatic events, unlike country-level temperature used in previous work, explaining our larger estimate. We use this evidence to estimate damage functions in a neoclassical growth model. Business-as-usual warming implies a present welfare loss of more than 30%, and a Social Cost of Carbon in excess of $1,200 per ton. These impacts suggest that unilateral decarbonization policy is cost-effective for large countries such as the United States.

The Review of Economic Studies

Narratives about the Macroeconomy
Peter Andre, Ingar Haaland, Christopher Roth, Mirko Wiederholt, Johannes Wohlfart
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We study narratives about the macroeconomy—the stories people tell to explain macroeconomic phenomena—in the context of a historic surge in inflation. In our empirical analysis, we field surveys with more than 10,000 US households and 100 academic experts, measure economic narratives in open-ended questions, and represent them as Directed Acyclic Graphs. Households’ narratives are strongly heterogeneous, coarser than experts’ narratives, focus more on the supply than the demand side, and often feature politically charged explanations. Moreover, narratives shape how households form inflation expectations and interpret new information, which we demonstrate in a series of experiments. Informed by these findings, our theoretical analysis incorporates narratives into an otherwise conventional New Keynesian model and demonstrates their importance for aggregate outcomes through their effect on agents’ expectations.
Coarse Bayesian Updating
Alexander M Jakobsen
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Studies have shown that the standard law of belief updating—Bayes' rule—is descriptively invalid in various settings. In this paper, I introduce and analyze a generalization of Bayes' rule—Coarse Bayesian updating—accommodating much of the empirical evidence. I characterize the model axiomatically, show how it generates several well-known biases, and derive its main implications in static and dynamic settings. Each axiom expresses a property of Bayes' rule but, conditional on the others, stops just short of making the agent fully Bayesian. The model employs standard primitives, making it suitable for applications; I demonstrate this by applying it to a standard setting of decision under risk, leading to a close relationship with the Blackwell information ordering and comparative measures of cognitive sophistication and bias.
Latent Heterogeneity in the Marginal Propensity to Consume
Daniel Lewis, Davide Melcangi, Laura Pilossoph
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We estimate the unconditional distribution of the marginal propensity to consume (MPC) using clustering regression applied to the 2008 economic stimulus payments. By deviating from the standard approach of estimating MPC heterogeneity using interactions with observables, we can recover the full distribution of MPCs. We find households spent between 4 and 133% of the rebate within a quarter, and individual households used rebates for different goods. While many observable characteristics correlate individually with our estimated MPCs, most of these relationships disappear when tested jointly. Notable exceptions are income and the average propensity to consume, which correlate positively with the MPC. Household observable characteristics explain only 8% of MPC variation, highlighting the role of latent heterogeneity.
Decision Theory for Treatment Choice Problems with Partial Identification
José Luis Montiel Olea, Chen Qiu, Jörg Stoye
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We apply classical statistical decision theory to a large class of treatment choice problems with partial identification. We show that, in a general class of problems with Gaussian likelihood, all decision rules are admissible; it is maximin-welfare optimal to ignore all data; and, for severe enough partial identification, there are infinitely many minimax-regret optimal decision rules, all of which sometimes randomize the policy recommendation. We uniquely characterize the minimax-regret optimal rule that least frequently randomizes, and show that, in some cases, it can outperform other minimax-regret optimal rules in terms of what we term profiled regret. We analyze the implications of our results in the aggregation of experimental estimates for policy adoption, extrapolation of Local Average Treatment Effects, and policy making in the presence of omitted variable bias.