pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Treatment Allocation under Constraints
Authors: Torben S. D. Johansen
Date: 2024-04-28 00:00:00

In optimal policy problems where treatment effects vary at the individual level, optimally allocating treatments to recipients is complex even when potential outcomes are known. We present an algorithm for multi-arm treatment allocation problems that is guaranteed to find the optimal allocation in strongly polynomial time, and which is able to handle arbitrary potential outcomes as well as constraints on treatment requirement and capacity. Further, starting from an arbitrary allocation, we show how to optimally re-allocate treatments in a Pareto-improving manner. To showcase our results, we use data from Danish nurse home visiting for infants. We estimate nurse specific treatment effects for children born 1959-1967 in Copenhagen, comparing nurses against each other. We exploit random assignment of newborn children to nurses within a district to obtain causal estimates of nurse-specific treatment effects using causal machine learning. Using these estimates, and treating the Danish nurse home visiting program as a case of an optimal treatment allocation problem (where a treatment is a nurse), we document room for significant productivity improvements by optimally re-allocating nurses to children. Our estimates suggest that optimal allocation of nurses to children could have improved average yearly earnings by USD 1,815 and length of education by around two months.
Testing for Asymmetric Information in Insurance with Deep Learning
Authors: Serguei Maliar, Bernard Salanie
Date: 2024-04-28 00:00:00
Tasks: Deep Learning

The positive correlation test for asymmetric information developed by Chiappori and Salanie (2000) has been applied in many insurance markets. Most of the literature focuses on the special case of constant correlation; it also relies on restrictive parametric specifications for the choice of coverage and the occurrence of claims. We relax these restrictions by estimating conditional covariances and correlations using deep learning methods. We test the positive correlation property by using the intersection test of Chernozhukov, Lee, and Rosen (2013) and the "sorted groups" test of Chernozhukov, Demirer, Duflo, and Fernandez-Val (2023). Our results confirm earlier findings that the correlation between risk and coverage is small. Random forests and gradient boosting trees produce similar results to neural networks.
Sequential monitoring for explosive volatility regimes
Authors: Lajos Horvath, Lorenzo Trapani, Shixuan Wang
Date: 2024-04-27 00:00:00

In this paper, we develop two families of sequential monitoring procedure to (timely) detect changes in a GARCH(1,1) model. Whilst our methodologies can be applied for the general analysis of changepoints in GARCH(1,1) sequences, they are in particular designed to detect changes from stationarity to explosivity or vice versa, thus allowing to check for volatility bubbles. Our statistics can be applied irrespective of whether the historical sample is stationary or not, and indeed without prior knowledge of the regime of the observations before and after the break. In particular, we construct our detectors as the CUSUM process of the quasi-Fisher scores of the log likelihood function. In order to ensure timely detection, we then construct our boundary function (exceeding which would indicate a break) by including a weighting sequence which is designed to shorten the detection delay in the presence of a changepoint. We consider two types of weights: a lighter set of weights, which ensures timely detection in the presence of changes occurring early, but not too early after the end of the historical sample; and a heavier set of weights, called Renyi weights which is designed to ensure timely detection in the presence of changepoints occurring very early in the monitoring horizon. In both cases, we derive the limiting distribution of the detection delays, indicating the expected delay for each set of weights. Our theoretical results are validated via a comprehensive set of simulations, and an empirical application to daily returns of individual stocks.
A Nonresponse Bias Correction using Nonrandom Followup with an Application to the Gender Entrepreneurship Gap
Authors: Clint Harris, Jon Eckhardt, Brent Goldfarb
Date: 2024-04-26 00:00:00
Tasks: Attribute, Selection bias, Survey

We develop a nonresponse correction applicable to any setting in which multiple attempts to contact subjects affect whether researchers observe variables without affecting the variables themselves. Our procedure produces point estimates of population averages using selected samples without requiring randomized incentives or assuming selection bias cancels out for any within-respondent comparisons. Applying our correction to a 16% response rate survey of University of Wisconsin-Madison undergraduates, we estimate a 15 percentage point male-female entrepreneurial intention gap. Our estimates attribute the 20 percentage point uncorrected within-respondent gap to positive bias for men and negative bias for women, highlighting the value of within-group nonresponse corrections.
Generative AI Usage and Exam Performance
Authors: Janik Ole Wecks, Johannes Voshaar, Benedikt Jost Plate, Jochen Zimmermann
Date: 2024-04-30 00:00:00

This study evaluates the impact of students' usage of generative artificial intelligence (GenAI) tools such as ChatGPT on their exam performance. We analyse student essays using GenAI detection systems to identify GenAI users among the cohort. Employing multivariate regression analysis, we find that students using GenAI tools score on average 6.71 (out of 100) points lower than non-users. While GenAI may offer benefits for learning and engagement, the way students actually use it correlates with diminished exam outcomes. Exploring the underlying mechanism, additional analyses show that the effect is particularly detrimental to students with high learning potential, suggesting an effect whereby GenAI tool usage hinders learning. Our findings provide important empirical evidence for the ongoing debate on the integration of GenAI in higher education and underscores the necessity for educators, institutions, and policymakers to carefully consider its implications for student performance.
Internal migration after a uniform minimum wage introduction
Authors: Alexander Moog
Date: 2024-04-30 00:00:00

Internal migration is an essential aspect to study labor mobility. I exploit the German statutory minimum wage introduction in 2015 to estimate its push and pull effects on internal migration using a 2% sample of administrative data. In a conditional fixed effects Poisson difference-in-differences framework with a continuous treatment, I find that the minimum wage introduction leads to an increase in the out-migration of low-skilled workers with migrant background by 25% with an increasing tendency over time from districts where a high share of workers are subject to the minimum wage (high-bite districts). In contrast the migration decision of native-born low-skilled workers is not affected by the policy. However, both native-born low-skilled workers and those with a migrant background do relocate across establishments, leaving high-bite districts as their workplace. In addition, I find an increase for unemployed individuals with a migrant background in out-migrating from high-bite districts. These results emphasize the importance of considering the effects on geographical labor mobility when implementing and analyzing policies that affect the determinants of internal migration.
Analysis of Proximity Informed User Behavior in a Global Online Social Network
Authors: Nils Breitmar, Matthew C. Harding, Hanqiao Zhang
Date: 2024-04-29 00:00:00

Despite the earlier claim of "Death of Distance", recent studies revealed that geographical proximity still greatly influences link formation in online social networks. However, it is unclear how physical distances are intertwined with users' online behaviors in a virtual world. We study the role of spatial dependence on a global online social network with a dyadic Logit model. Results show country-specific patterns for distance effect on probabilities to build connections. Effects are stronger when the possibility for two people to meet in person exists. Relative to weak ties, dependence on proximity is looser for strong social ties.
Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners Guide
Authors: W. Benedikt Schmal
Date: 2024-04-29 00:00:00
Tasks: Econometrics, Sociology, Time Series, Time Series Analysis

Natural language processing tools have become frequently used in social sciences such as economics, political science, and sociology. Many publications apply topic modeling to elicit latent topics in text corpora and their development over time. Here, most publications rely on visual inspections and draw inference on changes, structural breaks, and developments over time. We suggest using univariate time series econometrics to introduce more quantitative rigor that can strengthen the analyses. In particular, we discuss the econometric topics of non-stationarity as well as structural breaks. This paper serves as a comprehensive practitioners guide to provide researchers in the social and life sciences as well as the humanities with concise advice on how to implement econometric time series methods to thoroughly investigate topic prevalences over time. We provide coding advice for the statistical software R throughout the paper. The application of the discussed tools to a sample dataset completes the analysis.
Exit Spillovers of Foreign-invested Enterprises in Shenzhen's Electronics Manufacturing Industry
Authors: Hanqiao Zhang
Date: 2024-04-27 00:00:00

Neighborhood characteristics have been broadly studied with different firm behaviors, e.g. birth, entry, expansion, and survival, except for firm exit. Using a novel dataset of foreign-invested enterprises operating in Shenzhen's electronics manufacturing industry from 2017 to 2021, I investigate the spillover effects of firm exits on other firms in the vicinity, from both the industry group and the industry class level. Significant neighborhood effects are identified for the industry group level, but not the industry class level.
Revisiting the Resource Curse in the Age of Energy Transition: Cobalt Reserves and Conflict in Africa
Authors: Weihong Qi
Date: 2024-04-26 00:00:00

This study reevaluates the traditional understanding of the "political resource curse" by examining the unique impact of energy transition metals, specifically cobalt, on local-level conflicts in Africa. Contrary to previous studies that primarily focus on high-value minerals and their political outcomes resulted from substantial economic revenues, this study investigates cobalt's influence on local conflict. Despite its strategic importance, cobalt's limited commercial value presents a unique yet critical case for analysis. Different with the prevailing view that links mineral reserves with increased conflict, this research finds that regions rich in cobalt experience a reduction in conflict. This decrease is attributed to enhanced government security measures, which are implemented independently of the economic benefits derived from cobalt as a commodity. The study utilizes a combination of georeferenced data and a difference-in-difference design to analyze the causal relationship between cobalt deposits and regional conflict. The findings suggest that the presence of cobalt deposits leads to enhanced security interventions by governments, effectively reducing the likelihood of non-governmental actors taking control of these territories. This pattern offers a new perspective on the role of energy transition metals in shaping conflict and governance, highlighting the need to reassess theoretical frameworks related to the political implications of natural resources with the ongoing energy revolution.
Level-$k$ Reasoning, Cognitive Hierarchy, and Rationalizability
Authors: Shuige Liu
Date: 2024-04-30 00:00:00

We employ a unified framework to provide an epistemic-theoretical foundation for Camerer, Ho, and Chong's (2003) cognitive hierarchy (CH) solution and its dynamic extension, using the directed rationalizability concept introduced in Battigalli and Siniscalchi (2003). We interpret level-$k$ as an information type instead of specification of strategic sophistication, and define restriction $ \Delta^\kappa$ on the beliefs of information types; based on it, we show that in the behavioral consequence of rationality, common belief in rationality and transparency of $\Delta^\kappa$, called $\Delta^\kappa$-rationalizability, the strategic sophistication of each information type is endogenously determined. We show that in static games, the CH solution generically coincides with $\Delta^\kappa$-rationalizability; this result also connects CH with Bayesian equilibrium. By extending $\Delta^\kappa$ to dynamic games, we show that Lin and Palfrey's (2024) dynamic cognitive hierarchy (DCH) solution, an extension of CH in dynamic games, generically coincides with the behavioral consequence of rationality, common strong belief in rationality, and transparency of (dynamic) $\Delta^\kappa$. The same framework can also be used to analyze many variations of CH in the literature.
Reputation and Risk in Regimes
Authors: Daniel Luo
Date: 2024-04-29 00:00:00

How valuable is reputation to a regime seeking to deter uprisings? I show the answer depends almost entirely on players' strategic uncertainty over other players actions. Without higher-order uncertainty, reputational effects are overshadowed by stage-game strategic complementarities: any payoff attainable in the complete-information repeated game remains attainable in the corresponding reputation game. However, when agents face globalized uncertainty over their payoffs, the regime's value for reputation rises sharply. In particular, the regime can leverage globalized strategic uncertainty to successful develop a reputation and secure their Stackelberg payoff in all pure equilibria of the reputation game, even as that same globalized strategic uncertainty forces them to their minimax payoff in the associated complete-information repeated game. Moreover, the globalized reputation game selects a unique pooling Markov equilibria among the continuum of Markov equilibria present in the complete-information game, refining predictions about equilibrium play. To establish these results, I develop new tools for analyzing non-normalized discounted repeated games with endogenous exit, where commitment actions vary with the regime's patience.
Persuasion in Networks: Can the Sender Do Better than Using Public Signals?
Authors: Yifan Zhang
Date: 2024-04-29 00:00:00

Political and advertising campaigns increasingly exploit social networks to spread information and persuade people. This paper studies a persuasion model to examine whether such a strategy is better than simply sending public signals. Receivers in the model have heterogeneous priors and will pass on a signal if they are persuaded by it to take sender's preferred action. I show that a risk neutral or risk loving sender prefers to use public signals, unless more skeptical receivers are sufficiently more connected in the network. A risk averse sender may prefer to exploit the network. These results still hold when the network exhibits homophily.
Diversification for infinite-mean Pareto models without risk aversion
Authors: Yuyu Chen, Taizhong Hu, Ruodu Wang, Zhenfeng Zou
Date: 2024-04-29 00:00:00

We study stochastic dominance between portfolios of independent and identically distributed (iid) extremely heavy-tailed (i.e., infinite-mean) Pareto random variables. With the notion of majorization order, we show that a more diversified portfolio of iid extremely heavy-tailed Pareto random variables is larger in the sense of first-order stochastic dominance. This result is further generalized for Pareto random variables caused by triggering events, random variables with tails being Pareto, bounded Pareto random variables, and positively dependent Pareto random variables. These results provide an important implication in investment: Diversification of extremely heavy-tailed Pareto profits uniformly increases investors' profitability, leading to a diversification benefit. Remarkably, different from the finite-mean setting, such a diversification benefit does not depend on the decision maker's risk aversion.
Nonlinear Domar aggregation over transforming production networks
Authors: Satoshi Nakano, Kazuhiko Nishimura
Date: 2024-04-28 00:00:00

An economy-wide production network, as a form of monetary input--output coefficients, becomes unstable during the general equilibrium propagation of sectoral productivity shocks, when the substitution elasticities of sectoral production are nonneutral. The associated network transformation hence leads to nonlinearity in aggregating sectoral productivity shocks into the economy's output, i.e., in Domar aggregation. In this study, we demonstrate the possibility of production networks being transformed into a singular one. We also explore the conditions under which productivity shocks are synergistic in Domar aggregation.
Bertrand oligopoly in insurance markets with Value at Risk Constraints
Authors: Kolos Csaba Ágoston, Veronika Varga
Date: 2024-04-27 00:00:00

Since 2016 the operation of insurance companies in the European Union is regulated by the Solvency II directive. According to the EU directive the capital requirement should be calculated as a 99.5\% of Value at Risk. In this study, we examine the impact of this capital requirement constraint on equilibrium premiums and capitals. We discuss the case of the oligopoly insurance market using Bertrand's model, assuming profit maximizing insurance companies facing Value at Risk constraints. First we analyze companies' decision on premium level. The companies strategic behavior can result positive as well as negative expected profit for companies. The desired situation where competition eliminate positive profit and lead the market to zero-profit state is rare. Later we examine ex post and ax ante capital adjustments. Capital adjustment does not rule out market anomalies, although somehow changes them. Possibility of capital adjustment can lead the market to a situation where all of the companies suffer loss. Allowing capital adjustment results monopolistic premium level or market failure with positive probabilities.
Disappointment concordance and duet expectiles
Authors: Fabio Bellini, Tiantian Mao, Ruodu Wang, Qinyu Wu
Date: 2024-04-27 00:00:00

We introduce an axiom of disappointment-concordance (disco) aversion for a preference relation over acts in an Anscombe-Aumann setting. This axiom means that the decision maker, facing the sum of two acts, dislikes the situation where both acts realize simultaneously as disappointments. Our main result is that, under strict monotonicity and continuity, the axiom of disco aversion characterizes preference relations represented by a new class of functionals belonging to the Gilboa-Schmeidler family, which we call the duet expectiled utilities. When the outcome space is the real line, a duet expectiled utility becomes a duet expectile, which involves two endogenous probability measures. It further becomes a usual expectile, ,a statistical quantity popular in regression and risk measures, when these two probability measures coincide. We discuss properties of duet expectiles and connections with fundamental concepts including probabilistic sophistication, risk aversion, and uncertainty aversion.
PSTAIC regularization for 2D spatiotemporal image reconstruction
Authors: Deepak G Skariah, Muthuvel Arigovindan
Date: 2024-04-28 00:00:00
Tasks: Image Reconstruction

We propose a model for restoration of spatio-temporal TIRF images based on infimal decomposition regularization model named STAIC proposed earlier. We propose to strengthen the STAIC algorithm by enabling it to estimate the relative weights in the regularization term by incorporating it as part of the optimization problem. We also design an iterative scheme which alternatively minimizes the weight and image sub-problems. We demonstrate the restoration quality of this regularization scheme against other restoration models enabled by similar weight estimation schemes.
LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising
Authors: Menghua Xia, Huidong Xie, Qiong Liu, Bo Zhou, Hanzhong Wang, Biao Li, Axel Rominger, Quanzheng Li, Ramsey D. Badawi, Kuangyu Shi, Georges El Fakhri, Chi Liu
Date: 2024-04-27 00:00:00
Tasks: Denoising, Image Denoising

Deep learning-based positron emission tomography (PET) image denoising offers the potential to reduce radiation exposure and scanning time by transforming low-count images into high-count equivalents. However, existing methods typically blur crucial details, leading to inaccurate lesion quantification. This paper proposes a lesion-perceived and quantification-consistent modulation (LeqMod) strategy for enhanced PET image denoising, via employing downstream lesion quantification analysis as auxiliary tools. The LeqMod is a plug-and-play design adaptable to a wide range of model architectures, modulating the sampling and optimization procedures of model training without adding any computational burden to the inference phase. Specifically, the LeqMod consists of two components, the lesion-perceived modulation (LeMod) and the multiscale quantification-consistent modulation (QuMod). The LeMod enhances lesion contrast and visibility by allocating higher sampling weights and stricter loss criteria to lesion-present samples determined by an auxiliary segmentation network than lesion-absent ones. The QuMod further emphasizes quantification accuracy for both the mean and maximum standardized uptake value (SUVmean and SUVmax) across multiscale sub-regions throughout the entire image, thereby reducing biases of denoised results relative to high-count references. Experiments conducted on large PET datasets from multiple centers and vendors, and varying noise levels demonstrated the LeqMod efficacy across various denoising frameworks. Compared to frameworks without LeqMod, the integration of LeqMod reduces the lesion SUVmax bias by 5.92% on average and increases the peak signal-to-noise ratio (PSNR) by 0.36 on average, when denoising images across participating sites.