ABSTRACT: This paper estimates the causal effect of longevity gains on individuals’ political ideology. To address endogeneity between health, income, and preferences, we instrument country-level life expectancy with a shift–share measure based on the staggered international introduction of 1,304 New Molecular Entities (NMEs) from 1982 to 2019, interacted with pre-existing disease burdens from WHO mortality data. We construct disease exposure by matching WHO deaths to 709 ICD-10 causes mapped from the CAS Registry drug classifications. Using data from 68 countries, we show that larger gains in life expectancy make individuals significantly more right-leaning. Conditional on longevity gains, population ageing per se has no independent effect.

ABSTRACT: To address the common problem of high dimensionality in tensor regressions, we introduce a generalized tensor random projection method that embeds high-dimensional tensor-valued covariates into low-dimensional subspaces with little loss of information about the response. The method is flexible, allowing for tensor-wise, mode-wise, or combined random projections as special cases. A Bayesian inference framework is presented, featuring a hierarchical prior distribution and a low-rank parameter representation. Strong theoretical support is provided for the concentration properties of random projections and for the posterior consistency of Bayesian inference. An efficient Gibbs sampler is developed to perform inference on the compressed data. To mitigate the sensitivity introduced by random projections, Bayesian model averaging is employed, with normalizing constants estimated using reverse logistic regression. An extensive simulation study is conducted to examine the effects of different tuning parameters. Simulations indicate, and real-data applications confirm, that compressed Bayesian tensor regressions can achieve better out-of-sample predictions while significantly reducing computational costs compared to standard Bayesian tensor regressions.

ABSTRACT: We propose a test for Granger causality in copulas that is robust to temporal instabilities. Our semiparametric procedure accommodates flexible AR-GARCH type dynamics in the marginal time series and remains agnostic with respect to the copula family by leveraging distributional regression techniques together with a local Gaussian representation of the copula link function. We derive the limiting distribution of our test statistic and propose a resampling scheme based on recent results for the moving-block bootstrap of multi-stage estimators.  Monte Carlo simulations and an empirical application illustrate the finite-sample performance of our methods.

ABSTRACT: In many matching problems, it is natural to assume that agents may have preferences not only over their own potential partners but also over the matches formed by others. Once such externalities are taken into account, the set of stable matchings depends on what agents believe would occur if they were to deviate. We introduce a notion of preference-dependent beliefs, called rationalizable conjectures which parallels the notion of rationalizability in non-cooperative games. We define the set of Rationalizable Conjecturally Stable matchings and show that it is always non empty. We also introduce the notion of rationalizability for matching and prove that every rationalizable conjecturally stable matching is indeed rationalizable. We show that rationalizability for matching is behaviorally implied by the epistemic condition of pairwise rationality and common belief in pairwise rationality, whereas rationalizable conjectural stability additionally requires belief correctness. Unlike our approach, traditional pairwise stability in matching with externalities depends on strong and implausible epistemic conditions.

ABSTRACT: This paper studies nepotism as both a form of favoritism and a tool for political bargaining. We focus on Brazilian municipalities, leveraging rich population data that link family ties, electoral candidates, and employment records. Using a regression discontinuity design based on close elections, we show that the relatives of narrowly elected mayors and councilors experience sizable labor market premiums. Relatives of mayors are 16 percent more likely to secure public employment relative to the control group mean, while relatives of councilors are 9 percent more likely. These effects are concentrated in public jobs, fade once the political term ends, and are strongest for less educated relatives, consistent with favoritism rather than improved screening. We then provide evidence that nepotism operates as a bargaining device: mayors appoint the relatives of councilors, including those in the opposition, with strongest effects when the mayor lacks a majority in the municipal council. Exploiting a second regression discontinuity design at the municipality level, we show that the premium for opposition councilors’ relatives increases when the opposition narrowly secures an additional seat, reinforcing the interpretation that appointments serve as political currency. Finally, we provide suggestive evidence that stricter regulation can help curb these nepotistic appointments.

ABSTRACT: Feature and trait allocation models are fundamental objects in Bayesian nonparametrics and play a prominent role in several applications. Existing approaches, however, typically assume full exchangeability of the data, which may be restrictive in settings characterized by heterogeneous but related groups. In this paper, we introduce a general and tractable class of Bayesian nonparametric priors for partially exchangeable trait allocation models, relying on completely random vectors. We provide a comprehensive theoretical analysis, including closed-form expressions for marginal and posterior distributions, and illustrate the tractability of our framework in the cases of binary and Poisson-distributed traits. A distinctive aspect of our approach is that the number of traits is a random quantity, thereby allowing us to model and estimate unobserved traits. Building on these results, we also develop a novel mixture model that infers the group partition structure from the data, effectively clustering trait allocations. This extension generalizes Bayesian nonparametric latent class models and avoids the systematic overclustering that arises when the number of traits is assumed to be fixed. We demonstrate the practical usefulness of our methodology through an application to the `Ndrangheta criminal network from the Operazione Infinito investigation, where our model provides insights into the organization of illicit activities.

ABSTRACT: This paper addresses the problem of running variable manipulation in the context of regression discontinuity designs. Leveraging the observation that manipulation can manifest as an asymmetry in the running variable's density around the cutoff, we identify this asymmetry using Benford's Law, a widely used data regularity property particularly in fraud detection. Our proposed test complements McCrary-type tests, offering the advantage of eliminating researcher-specified options and parameters that can affect the results. To do that, we first propose a new approach to determine a bandwidth consistent with Benford's Law, and then we propose two distinct but complementary tests that make use of predetermined acceptance/rejection threshold values as proposed by authoritative contributions in the literature, taking advantage of Benford's Law's main feature: its universality as a natural property of most empirical datasets. Finally, our approach overcomes a key limitation of the law itself by using probabilities and threshold values, rather than digits. Empirical examples and practical implementations are provided.

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ABSTRACT: Hedonic models suggest that real estate markets price natural disaster risk, particularly in the aftermath of catastrophic events. But can public salience alone—absent any actual disaster and with an underlying hazard already known and pre-existing—shift risk perception and tolerance enough to affect economic behavior and market outcomes? We answer this question by drawing on a natural experiment provided by the Phlegraean Fields, a large volcanic caldera near Naples, Italy. The area is home to over one million residents and is characterized by bradyseism, a distinctive geological phenomenon involving cyclic ground uplift often accompanied by seismic activity perceptible by the population. Combining granular sub-municipal data with an event-study design, we show that the bradyseismic crisis that began in 2023 significantly affected local real estate markets, despite minimal physical damage and no abrupt change in the long-known risks of living in the area. We find substantial heterogeneity both between and within affected zones, and the results suggest that the crisis is exacerbating pre-existing housing inequality in the most vulnerable areas.

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ABSTRACT: The COVID-19 pandemic unfolded alongside an unprecedented “infodemic” that reshaped public engagement with science, health, and authority. This study investigates how online infodemics translated into collective resistance and influenced population health through political mobilisation. Using dynamic structural equation models across six European countries, I conceptualise resistance—a latent construct measured by residential mobility and protests opposing vaccines, lockdowns, and public health mandates linked to populist radical right (PRR) movements—as the behavioural bridge between digital information environments and epidemic outcomes. Findings reveal a robust infodemic–resistance–epidemic pathway: exposure to higher levels of infodemic consistently predicts stronger opposition to non-pharmaceutical interventions (NPIs). This effect is most pronounced in Germany and Italy, where established PRR networks amplified the infodemic through narratives of “elite overreach” and “freedom under threat”, converting online discontent into organised mobilisation. In Austria, Belgium, and France, resistance was weaker and more pandemic-specific. By integrating informational, political, and epidemiological processes, the analysis shows how epidemics evolve into politicised collective behaviour that undermines compliance and sustains viral transmission. The results highlight the role of populist mobilisation as a social amplifier for epidemics and demonstrate that pandemic resistance reflects not cognitive failure but organised defiance. Effective responses must rebuild trust, depoliticise health communication, and address structural sources of populist grievance.

ABSTRACT: This paper examines the recent fragmentation of global trade, focusing on how purely-economic and geopolitical shocks have reshaped bilateral flows. Building on an atheoretical model of bilateral trade growth, we develop an empirical strategy (based on the contribution of Amiti and Weinstein, in the context of bank-firm lending) that extends beyond standard specifications of the inward and outward multilateral resistance terms by explicitly disentangling shocks related to belonging to different geopolitical trading blocs, in addition to the more standard economic blocs. The results reveal a growing segmentation of trade flows along geopolitical lines where geopolitical trading blocs have been important in determining world export growth after 2018, especially in the years of the first US-China trade war. The hegemons of the two main blocs seem to have a different weight. China has had a persistently higher role than the US in the export dynamics of its blocs (both defined as economic and geopolitical). The only exception is if we consider the USMCA as the US-referenced economic bloc where the US clearly dominates. 

ABSTRACT: Self-selection into social programs can lead to socially excessive or insufficient participation. We propose a framework to detect and address these inefficiencies, applying it to diabetes care, where individuals above a biomarker threshold receive nudges to seek care. Crossing the threshold increases healthcare utilization and improves health outcomes. However, those who opt into care—both compliers and beyond-compliers—are generally healthier and benefit less, indicating reverse selection on gains. Targeting based on observable characteristics reduces excessive participation and improves welfare, while outreach to individuals reluctant to seek care despite high potential benefits mitigates insufficient participation and may further raise welfare.

ABSTRACT: The purpose of this paper is to extend the CES model of monopolistic competition to the case where varieties are both horizontally and vertically differentiated. A distinctive feature of our model is the presence of a network externality, which operates through the number of varieties available at each quality level. We show that, depending on the quality gap, there can either be corner equilibria in which consumers purchase only high-quality or low-quality varieties, or an interior equilibrium, in which consumers are split between the two qualities. Differently from the standard CES model of monopolistic competition, the equilibrium can be inefficient and the market may even select the outcome that generates the lowest surplus.

ABSTRACT: Does geopolitical risk lead to financial fragmentation? We answer this question using fund-level data on international bond funds' portfolio allocations and investor flows. We find that fund managers persistently reduce the portfolio weights of countries where geopolitical risk increases. This is especially true for emerging market economies and when geopolitical risk is extreme. Increases in geopolitical risk spark financial fragmentation, with managers investing in fewer countries, holding portfolios that are more concentrated and tilted towards countries that are geopolitically aligned with the fund's home country. End investors also react adversely to geopolitical risk. Fund flows decline sharply when geopolitical risk increases but recover less than one quarter after the initial shock. These empirical findings are consistent with the predictions of a simple model of delegated portfolio management.

ABSTRACT: It is now widely acknowledged that the rise of populism around the world poses a serious challenge to liberal democracy. In response, a substantial research agenda has emerged which seeks to identify how liberal democracies can deal with this challenge, ranging from mainstream party strategies to media approaches to institutional design. But mostly missing from these discussions has been any attention to political finance: that is, the funding of parties and elections. In this paper we explore the potential of political finance as a counter-populism measure. In particular, we consider the problem of how to design (or reform) a political finance system with a view to minimising the threat of populism in a liberal democracy, particularly the challenges it poses to cross-party collegiality and to the mediating role of representative institutions. Our analysis integrates across normative and empirical literatures, and research agendas on political finance, populism and parties, to identify the most populism-resistant designs of a political finance system, with an eye to potential empirical implications.

ABSTRACT: Affirmative action policies in higher education aim to promote equity and enhance access for underrepresented groups. This study examines Portugal's recently introduced quota system, which reserves 2% of university seats for students from low-income households (ASE-A). Using detailed administrative data, the analysis evaluates the effectiveness of this policy in increasing university enrollment and access to selective academic programs among disadvantaged students. The findings indicate that, while the quota system improved admission rates and alignment with students' top preferences, only 43% of eligible students opted to utilize the quota. The paper investigates the factors contributing to this low uptake rate, revealing that quota users typically demonstrated higher GPAs, a stronger preference for competitive programs, and broader application strategies compared to non-users. Key barriers to quota utilization include insufficient information, financial constraints, and limited aspirations, which may reduce the policy's overall effectiveness. By situating these findings within the broader context of socioeconomic targeting, the study underscores the potential of affirmative action policies to foster diversity and mitigate educational inequities. The analysis also highlights the necessity of complementary interventions to address the structural barriers faced by low-income students, ensuring more comprehensive support for achieving equitable outcomes in higher education.

ABSTRACT: This paper investigates the impact of physical infrastructures on sectoral total factor productivity (TFP). In the first part, a neoclassical growth model with multiple productive sectors and public capital (in the form of infrastructures) uncovers a long-run relationship between infrastructures and sectoral TFP. In the second part, a panel-cointegration analysis evaluates the long-run impact of 4 distinct types of infrastructures − transport, energy, ICT, health − on the TFPs of 22 manufacturing sectors in a sample of 65 (developed and emerging) countries between 1995 and 2018. We find that infrastructures are a positive and significant determinant of sectoral TFPs. A panel error-correction model also confirms that causality runs from infrastructures to TFPs. These results are robust to further empirical analysis conducted on sub-samples segmented by several dimensions. Our findings can inform policy in designing targeted interventions and prioritizing investment.

ABSTRACT: We study which common factors drive downside risk across a large panel of U.S. macroeconomic variables. We consider a broad set of candidate downside risk factors, comprising of both observed factors constructed from macroeconomic, financial, and text data sources, as well as unobserved factors associated with the panel. We assess the relevance of the factors by evaluating how they contribute to improving the out-of-sample accuracy of downside risk forecasts. Factors are mapped into downside risk forecasts through quantile regression and location-scale regression. Results point to a single factor associated with macroeconomic volatility, which is most closely proxied by the macroeconomic uncertainty index (Jurado et al., 2015).

ABSTRACT: Eradicating organized criminal groups requires the efforts of both the state and ordinary citizens. But how do citizens react to collusion between public officials and criminal organizations? With two novel datasets and a generalized difference-in-differences design, I estimate that collusion episodes in Italy increase citizens’ participation in the activities of anti-mafia NGOs by about 25%. This positive effect persists for four years. I analyze the causal mechanisms behind this effect with an original survey experiment. Receiving information about the incidence of collusion increases citizens' concerns about organized crime, while decreasing their confidence in the state as an effective anti-mafia player. This makes them turn to civil society organizations as an alternative way to curb the influence of organized criminal groups. These findings complement the existing literature about the effects of corruption on citizens' behavior, showing that it can foster their engagement in non-traditional forms of political participation.