pwc-archive/papers-with-abstracts huggingface dataset:
Minimum Distance Estimation of Quantile Panel Data Models
Authors: Blaise Melly, Martina Pons
Date: 2025-02-25 00:00:00
Tasks: quantile regression

We propose a minimum distance estimation approach for quantile panel data models where unit effects may be correlated with covariates. This computationally efficient method involves two stages: first, computing quantile regression within each unit, then applying GMM to the first-stage fitted values. Our estimators apply to (i) classical panel data, tracking units over time, and (ii) grouped data, where individual-level data are available, but treatment varies at the group level. Depending on the exogeneity assumptions, this approach provides quantile analogs of classic panel data estimators, including fixed effects, random effects, between, and Hausman-Taylor estimators. In addition, our method offers improved precision for grouped (instrumental) quantile regression compared to existing estimators. We establish asymptotic properties as the number of units and observations per unit jointly diverge to infinity. Additionally, we introduce an inference procedure that automatically adapts to the potentially unknown convergence rate of the estimator. Monte Carlo simulations demonstrate that our estimator and inference procedure perform well in finite samples, even when the number of observations per unit is moderate. In an empirical application, we examine the impact of the food stamp program on birth weights. We find that the program's introduction increased birth weights predominantly at the lower end of the distribution, highlighting the ability of our method to capture heterogeneous effects across the outcome distribution.
Conditional Triple Difference-in-Differences
Authors: Dor Leventer
Date: 2025-02-22 00:00:00

Triple difference-in-differences designs are widely used to estimate causal effects in empirical work. Surveying the literature, we find that most applications include controls. We show that this standard practice is generally biased for the target causal estimand when covariate distributions differ across groups. To address this, we propose identifying a causal estimand by fixing the covariate distribution to that of one group. We then develop a double-robust estimator and illustrate its application in a canonical policy setting.
Certified Decisions
Authors: Isaiah Andrews, Jiafeng Chen
Date: 2025-02-25 00:00:00
Tasks: Decision Making

Hypothesis tests and confidence intervals are ubiquitous in empirical research, yet their connection to subsequent decision-making is often unclear. We develop a theory of certified decisions that pairs recommended decisions with inferential guarantees. Specifically, we attach P-certificates -- upper bounds on loss that hold with probability at least $1-\alpha$ -- to recommended actions. We show that such certificates allow "safe," risk-controlling adoption decisions for ambiguity-averse downstream decision-makers. We further prove that it is without loss to limit attention to P-certificates arising as minimax decisions over confidence sets, or what Manski (2021) terms "as-if decisions with a set estimate." A parallel argument applies to E-certified decisions obtained from e-values in settings with unbounded loss.
Binary Outcome Models with Extreme Covariates: Estimation and Prediction
Authors: Laura Liu, Yulong Wang
Date: 2025-02-22 00:00:00
Tasks: Prediction

This paper presents a novel semiparametric method to study the effects of extreme events on binary outcomes and subsequently forecast future outcomes. Our approach, based on Bayes' theorem and regularly varying (RV) functions, facilitates a Pareto approximation in the tail without imposing parametric assumptions beyond the tail. We analyze cross-sectional as well as static and dynamic panel data models, incorporate additional covariates, and accommodate the unobserved unit-specific tail thickness and RV functions in panel data. We establish consistency and asymptotic normality of our tail estimator, and show that our objective function converges to that of a panel Logit regression on tail observations with the log extreme covariate as a regressor, thereby simplifying implementation. The empirical application assesses whether small banks become riskier when local housing prices sharply decline, a crucial channel in the 2007--2008 financial crisis.
The effect of minimum wages on employment in the presence of productivity fluctuations
Authors: Asahi Sato
Date: 2025-02-25 00:00:00

Traditionally, the impact of minimum wages on employment has been studied, and it is generally believed to have a negative effect. Yet, some recent studies have shown that the impact of minimum wages on employment can sometimes be positive. In addition, certain recent proposals set a higher minimum wage than the wage earned by some high-productivity workers. However, the impact of minimum wages on employment has been primarily studied on low-skilled workers, whereas there is limited research on high-skilled workers. To address this gap and examine the effects of minimum wages on high-productivity workers' employment, I construct a macroeconomic model incorporating productivity fluctuations, incomplete markets, directed search, and on-the-job search and compare the steady-state distributions between the baseline model and the model with a minimum wage. As a result, binding minimum wages increase the unemployment rate of both low and high-productivity workers.
Enhancing External Validity of Experiments with Ongoing Sampling
Authors: Chen Wang, Shichao Han, Shan Huang
Date: 2025-02-25 00:00:00
Tasks: Survival Analysis

Participants in online experiments often enroll over time, which can compromise sample representativeness due to temporal shifts in covariates. This issue is particularly critical in A/B tests, online controlled experiments extensively used to evaluate product updates, since these tests are cost-sensitive and typically short in duration. We propose a novel framework that dynamically assesses sample representativeness by dividing the ongoing sampling process into three stages. We then develop stage-specific estimators for Population Average Treatment Effects (PATE), ensuring that experimental results remain generalizable across varying experiment durations. Leveraging survival analysis, we develop a heuristic function that identifies these stages without requiring prior knowledge of population or sample characteristics, thereby keeping implementation costs low. Our approach bridges the gap between experimental findings and real-world applicability, enabling product decisions to be based on evidence that accurately represents the broader target population. We validate the effectiveness of our framework on three levels: (1) through a real-world online experiment conducted on WeChat; (2) via a synthetic experiment; and (3) by applying it to 600 A/B tests on WeChat in a platform-wide application. Additionally, we provide practical guidelines for practitioners to implement our method in real-world settings.
Determinants of the Spousal Age Gap in India: Analysis of Indian Microdata
Authors: Praveen, Suddhasil Siddhanta, Anoshua Chaudhuri
Date: 2025-02-24 00:00:00
Tasks: Selection bias

This study examines the determinants of the spousal age gap (SAG) in India, utilizing data from the 61st and 68th rounds of the National Sample Survey (NSSO). We employ regression analysis, including instrumental variables, to address selection bias and account for unobservable factors. We hypothesize an inverted U-shaped relationship between educational assortative mating and SAG, where, keeping the husband's education constant at the graduation level, the SAG first widens and then narrows as the wife's education level increases from primary to postgraduate. This pattern is shaped by distinct socio-economic factors across rural and urban settings. In rural India, increasing prosperity, changes in family structure, and educational hypergamy contribute to a wider age gap, with the influence of bride squeeze further exacerbating this disparity. Conversely, in urban areas, while the growth of white-collar jobs initially contributed to a narrowing of the SAG in 2004-05, this trend did not persist by 2011-12. Specifically, the influence of income on SAG becomes nonlinear, showing declining trends beyond the 7th income quantile, reflecting limited marriage mobility opportunities for females and hinting at a possible threat to the institution of marriage among the urban upper class. To the best of our knowledge, this is the first study to provide empirical evidence on how specific social, economic, and cultural dynamics influence the spousal age gap in Indian society. This increasing and persistent spousal age gap has significant implications for the treatment of women, power dynamics, and violence within marriage.
A data-driven econo-financial stress-testing framework to estimate the effect of supply chain networks on financial systemic risk
Authors: Jan Fialkowski, Christian Diem, András Borsos, Stefan Thurner
Date: 2025-02-24 00:00:00

Supply chain disruptions constitute an often underestimated risk for financial stability. As in financial networks, systemic risks in production networks arises when the local failure of one firm impacts the production of others and might trigger cascading disruptions that affect significant parts of the economy. Here, we study how systemic risk in production networks translates into financial systemic risk through a mechanism where supply chain contagion leads to correlated bank-firm loan defaults. We propose a financial stress-testing framework for micro- and macro-prudential applications that features a national firm level supply chain network in combination with interbank network layers. The model is calibrated by using a unique data set including about 1 million firm-level supply links, practically all bank-firm loans, and all interbank loans in a small European economy. As a showcase we implement a real COVID-19 shock scenario on the firm level. This model allows us to study how the disruption dynamics in the real economy can lead to interbank solvency contagion dynamics. We estimate to what extent this amplifies financial systemic risk. We discuss the relative importance of these contagion channels and find an increase of interbank contagion by 70% when production network contagion is present. We then examine the financial systemic risk firms bring to banks and find an increase of up to 28% in the presence of the interbank contagion channel. This framework is the first financial systemic risk model to take agent-level dynamics of the production network and shocks of the real economy into account which opens a path for directly, and event-driven understanding of the dynamical interaction between the real economy and financial systems.
Bounded Foresight Equilibrium in Large Dynamic Economies with Heterogeneous Agents and Aggregate Shocks
Authors: Bilal Islah, Bar Light
Date: 2025-02-23 00:00:00

Large dynamic economies with heterogeneous agents and aggregate shocks are central to many important applications, yet their equilibrium analysis remains computationally challenging. This is because the standard solution approach, rational expectations equilibria require agents to predict the evolution of the full cross-sectional distribution of state variables, leading to an extreme curse of dimensionality. In this paper, we introduce a novel equilibrium concept, N-Bounded Foresight Equilibrium (N-BFE), and establish its existence under mild conditions. In N-BFE, agents optimize over an infinite horizon but form expectations about key economic variables only for the next N periods. Beyond this horizon, they assume that economic variables remain constant and use a predetermined continuation value. This equilibrium notion reduces computational complexity and draws a direct parallel to lookahead policies in reinforcement learning, where agents make near-term calculations while relying on approximate valuations beyond a computationally feasible horizon. At the same time, it lowers cognitive demands on agents while better aligning with the behavioral literature by incorporating time inconsistency and limited attention, all while preserving desired forward-looking behavior and ensuring that agents still respond to policy changes. Importantly, in N-BFE equilibria, forecast errors arise endogenously. We measure the foresight errors for different foresight horizons and show that foresight significantly influences the variation in endogenous equilibrium variables, distinguishing our findings from traditional risk aversion or precautionary savings channels. This variation arises from a feedback mechanism between individual decision-making and equilibrium variables, where increased foresight induces greater non-stationarity in agents' decisions and, consequently, in economic variables.
The Endurance of Identity-Based Voting: Evidence from the United States and Comparative Democracies
Authors: Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman
Date: 2025-02-23 00:00:00

This study demonstrates the persistent dominance of identity based voting across democratic systems, using the United States as a primary case and comparative analyses of 19 other democracies as counterfactuals. Drawing solely on election data from the Roper Center (1976 through recent cycles), we employ OLS regression, ANOVA, and correlation tests to show that race remains the strongest predictor of party affiliation in the US (p < 0.001), with White voters favoring Republicans and Black voters consistently supporting Democrats (85% since 1988). Income, education, and gender exemplified by gaps like 10 points in 2020 further shape voting patterns, yet racial identity predominates. Comparative evidence from majoritarian (e.g., India), proportional (e.g., Germany through 2025), and hybrid (e.g., South Korea with a 25 point gender gap) systems reveals no democracy where issue based voting fully supplants identity based voting. Digital mobilization amplifies this trend globally. These findings underscore identity enduring role in electoral behavior, challenging assumptions of policy driven democratic choice.
De facto Openness to Immigration
Authors: Ljubica Nedelkoska, Diego Martin, Alexia Lochmann, Ricardo Hausmann, Dany Bahar, Muhammed A. Yildirim
Date: 2025-02-23 00:00:00

Various factors influence why some countries are more open to immigration than others. Policy is only one of them. We design country-specific measures of openness to immigration that aim to capture de facto levels of openness to immigration, complementing existing de jure measures of immigration, based on enacted immigration laws and policy measures. We estimate these for 148 countries and three years (2000, 2010, and 2020). For a subset of countries, we also distinguish between openness towards tertiary-educated migrants and less than tertiary-educated migrants. Using the measures, we show that most places in the World today are closed to immigration, and a few regions are very open. The World became more open in the first decade of the millennium, an opening mainly driven by the Western World and the Gulf countries. Moreover, we show that other factors equal, countries that increased their openness to immigration, reduced their old-age dependency ratios, and experienced slower real wage growth, arguably a sign of relaxing labor and skill shortages.
Sticky information and price controls: Evidence from a natural experiment
Authors: Doron Sayag, Avichai Snir, Daniel Levy
Date: 2025-02-21 00:00:00

We test the predictions of the sticky information model using a survey dataset by comparing shoppers accuracy in recalling the prices of regulated and comparable unregulated products. Because regulated product prices are capped, they are sold more than comparable unregulated products, while their prices change less frequently and vary less across stores and between brands, than the prices of comparable unregulated products. Therefore, shoppers would be expected to recall the regulated product prices more accurately. However, we find that shoppers are better at recalling the prices of unregulated products, in line with the sticky information model which predicts that shoppers will be more attentive to prices that change more frequently.
Carbon financial system construction under the background of dual-carbon targets: current situation, problems and suggestions
Authors: Yedong Zhang, Han Hua
Date: 2025-02-19 00:00:00

Under the guidance of the dual-carbon target, the development of the carbon financial system is of great significance to compensate for the gap between green and low-carbon investment. Considering the current state of the development of carbon financial system, China has initially formed a carbon financial system, including participants, carbon financial products and macro and micro operation structures, but the system is still in the initial development stage. Given the current restrictions on the development of carbon finance, it can be seen that there are still problems such as unreasonable economic structure, insufficient market construction, single product category, low utilization rate and urgent construction of relevant judicial guarantee system. Therefore, the system should be improved at the economic level and the legal level. The economic level includes adjusting the layout of economic development structure, strengthening the construction of market infrastructure, encouraging the diversification of carbon financial products and strengthening publicity and education promotion strategies. The legal level includes improving the top-level design, formulating judicial interpretation to promote carbon financial trading, promoting commercial law amendment, and promoting the linkage mechanism between specialized environmental justice and carbon finance and other safeguard measures. Finally, improving the carbon finance system is required to promote and protect the orderly development of carbon finance. To promote the reform of the pattern of economic development, the concept of ecological and environmental protection in the financial sector needs to be implemented to form an overall pattern of pollution reduction, carbon reduction and synergistic efficiency improvement.
Pareto-undominated strategy-proof rules in economies with multidimensional single-peaked preferences
Authors: Agustin G. Bonifacio
Date: 2025-02-24 00:00:00

In the problem of fully allocating a social endowment of perfectly divisible commodities among a group of agents with multidimensional single-peaked preferences, we study strategy-proof rules that are not Pareto-dominated by other strategy-proof rules. Specifically, we: (i) establish a sufficient condition for a rule to be Pareto-undominated strategy-proof; (ii) introduce a broad class of rules satisfying this property by extending the family of "sequential allotment rules" to the multidimensional setting; and (iii) provide a new characterization of the "multidimensional uniform rule" involving Pareto-undominated strategy-proofness. Results (i) and (iii) generalize previous findings that were only applicable to the two-agent case.
A Theory of Chaordic Economics: How Artificial Intelligence and Blockchain Transform Businesses, Economies and Societies
Authors: Horst Treiblmaier
Date: 2025-02-23 00:00:00

Dee Hock, the founder of Visa, coined the term 'chaordic' to describe simultaneously chaotic and ordered systems. Based on his reasoning, we introduce the Theory of Chaordic Economics to explain how economic systems are transformed by two disruptive technologies: namely Artificial Intelligence and Blockchain. Artificial intelligence can generate novel output through algorithmic yet rather unpredictable processes. Blockchain creates deterministic results without central authorities and relies on elaborated protocols that prescribe how consensus can be reached within a network of peers. The amalgamation of chaos and order produces chaordic economic systems and can yield hitherto unthinkable economic structures.
Pricing Valid Cuts for Price-Match Equilibria
Authors: Robert Day, Benjamin Lubin
Date: 2025-02-21 00:00:00
Tasks: valid

We use valid inequalities (cuts) of the binary integer program for winner determination in a combinatorial auction (CA) as "artificial items" that can be interpreted intuitively and priced to generate Artificial Walrasian Equilibria. While the lack of an integer programming gap is sufficient to guarantee a Walrasian equilibrium, we show that it does not guarantee a "price-match equilibrium" (PME), a refinement that we introduce, in which prices are justified by an iso-revenue outcome for any hypothetical removal of a single bidder. We prove the existence of PME for any CA and characterize their economic properties and computation. We implement minimally artificial PME rules and compare them with other prominent CA payment rules in the literature.
URO-Bench: A Comprehensive Benchmark for End-to-End Spoken Dialogue Models
Authors: Ruiqi Yan, Xiquan Li, Wenxi Chen, Zhikang Niu, Chen Yang, Ziyang Ma, Kai Yu, Xie Chen
Date: 2025-02-25 00:00:00
Tasks: Instruction Following

In recent years, with advances in large language models (LLMs), end-to-end spoken dialogue models (SDMs) have made significant strides. Compared to text-based LLMs, the evaluation of SDMs needs to take speech-related aspects into account, such as paralinguistic information and speech quality. However, there is still a lack of comprehensive evaluations for SDMs in speech-to-speech (S2S) scenarios. To address this gap, we propose URO-Bench, an extensive benchmark for SDMs. Notably, URO-Bench is the first S2S benchmark that covers evaluations about multilingualism, multi-round dialogues, and paralinguistics. Our benchmark is divided into two difficulty levels: basic track and pro track, consisting of 16 and 20 datasets respectively, evaluating the model's abilities in Understanding, Reasoning, and Oral conversation. Evaluations on our proposed benchmark reveal that current open-source SDMs perform rather well in daily QA tasks, but lag behind their backbone LLMs in terms of instruction-following ability and also suffer from catastrophic forgetting. Their performance in advanced evaluations of paralinguistic information and audio understanding remains subpar, highlighting the need for further research in this direction. We hope that URO-Bench can effectively facilitate the development of spoken dialogue models by providing a multifaceted evaluation of existing models and helping to track progress in this area.
Balancing Speech Understanding and Generation Using Continual Pre-training for Codec-based Speech LLM
Authors: Jiatong Shi, Chunlei Zhang, Jinchuan Tian, Junrui Ni, Hao Zhang, Shinji Watanabe, Dong Yu
Date: 2025-02-24 00:00:00
Tasks: Automatic Speech Recognition, Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Speech Synthesis, Speech-to-Speech Translation, Speech-to-Text, Speech-to-Text Translation, text-to-speech, Text to Speech, Translation

Recent efforts have extended textual LLMs to the speech domain. Yet, a key challenge remains, which is balancing speech understanding and generation while avoiding catastrophic forgetting when integrating acoustically rich codec-based representations into models originally trained on text. In this work, we propose a novel approach that leverages continual pre-training (CPT) on a pre-trained textual LLM to create a codec-based speech language model. This strategy mitigates the modality gap between text and speech, preserving the linguistic reasoning of the original model while enabling high-fidelity speech synthesis. We validate our approach with extensive experiments across multiple tasks, including automatic speech recognition, text-to-speech, speech-to-text translation, and speech-to-speech translation (S2ST), demonstrating that our model achieves superior TTS performance and, notably, the first end-to-end S2ST system based on neural codecs.