New articles on Economics

[1] 2405.17682

Managing Financial Climate Risk in Banking Services: A Review of Current Practices and the Challenges Ahead

The document discusses the financial climate risk in the context of the banking industry, emphasizing the need for a comprehensive understanding of climate change across different spatial and temporal scales. It highlights the challenges in estimating physical and transition risks, specifically extreme events and limitations of current climate models. The document also reviews current gaps in assessing physical and transition risks, including the development, improvement of modeling frameworks, highlighting the need for detailed databases of exposed physical assets and climatic hazard modeling. It also emphasizes the importance of integrating financial climate risks into financial risk management practices, particularly in smaller banks and lending organizations.

[2] 2405.17762

Tuition too high? Blame competition

We develop a feedback theory that includes reinforcing and balancing feedback effects that emerge when colleges compete for reputation, applicants, and tuition revenue. The feedback theory is replicated in a formal duopoly model consisting of two competing colleges. An independent ranking entity determines the relative order of the colleges. College applicants choose between the two colleges based on the rankings and the financial aid offered by the colleges. Contrary to the conventional wisdom that competition lowers prices and benefits consumers, our simulations show that competition between academic institutions for resources and reputation leads to tuition escalation that negatively affects students and their families. Four of the five scenarios -- rankings, a capital campaign, facilities improvements, and an excellence campaign -- increase college tuition, institutional debt, and expenditures per student; only the scenario of ignoring the rankings decreases these measures. By referring to the feedback structure of academic competition, the article makes several recommendations for controlling tuition inflation. This article contributes to the literature on the economics of higher education and illustrates the value of feedback economics in developing economic theory.

[3] 2405.17787

Dyadic Regression with Sample Selection

This paper addresses the sample selection problem in panel dyadic regression analysis. Dyadic data often include many zeros in the main outcomes due to the underlying network formation process. This not only contaminates popular estimators used in practice but also complicates the inference due to the dyadic dependence structure. We extend Kyriazidou (1997)'s approach to dyadic data and characterize the asymptotic distribution of our proposed estimator. The convergence rates are $\sqrt{n}$ or $\sqrt{n^{2}h_{n}}$, depending on the degeneracy of the H\'{a}jek projection part of the estimator, where $n$ is the number of nodes and $h_{n}$ is a bandwidth. We propose a bias-corrected confidence interval and a variance estimator that adapts to the degeneracy. A Monte Carlo simulation shows the good finite performance of our estimator and highlights the importance of bias correction in both asymptotic regimes when the fraction of zeros in outcomes varies. We illustrate our procedure using data from Moretti and Wilson (2017)'s paper on migration.

[4] 2405.18089

Semi-nonparametric models of multidimensional matching: an optimal transport approach

This paper proposes empirically tractable multidimensional matching models, focusing on worker-job matching. We generalize the parametric model proposed by Lindenlaub (2017), which relies on the assumption of joint normality of observed characteristics of workers and jobs. In our paper, we allow unrestricted distributions of characteristics and show identification of the production technology, and equilibrium wage and matching functions using tools from optimal transport theory. Given identification, we propose efficient, consistent, asymptotically normal sieve estimators. We revisit Lindenlaub's empirical application and show that, between 1990 and 2010, the U.S. economy experienced much larger technological progress favoring cognitive abilities than the original findings suggest. Furthermore, our flexible model specifications provide a significantly better fit for patterns in the evolution of wage inequality.

[5] 2405.17753

Regression Equilibrium in Electricity Markets

Renewable power producers participating in electricity markets build forecasting models independently, relying on their own data, model and feature preferences. In this paper, we argue that in renewable-dominated markets, such an uncoordinated approach to forecasting results in substantial opportunity costs for stochastic producers and additional operating costs for the power system. As a solution, we introduce Regression Equilibrium--a welfare-optimal state of electricity markets under uncertainty, where profit-seeking stochastic producers do not benefit by unilaterally deviating from their equilibrium forecast models. While the regression equilibrium maximizes the private welfare, i.e., the average profit of stochastic producers across the day-ahead and real-time markets, it also aligns with the socially optimal, least-cost dispatch solution for the system. We base the equilibrium analysis on the theory of variational inequalities, providing results on the existence and uniqueness of regression equilibrium in energy-only markets. We also devise two methods for computing the regression equilibrium: centralized optimization and a decentralized ADMM-based algorithm that preserves the privacy of regression datasets.

[6] 2405.17924

Generative AI Enhances Team Performance and Reduces Need for Traditional Teams

Recent advancements in generative artificial intelligence (AI) have transformed collaborative work processes, yet the impact on team performance remains underexplored. Here we examine the role of generative AI in enhancing or replacing traditional team dynamics using a randomized controlled experiment with 435 participants across 122 teams. We show that teams augmented with generative AI significantly outperformed those relying solely on human collaboration across various performance measures. Interestingly, teams with multiple AIs did not exhibit further gains, indicating diminishing returns with increased AI integration. Our analysis suggests that centralized AI usage by a few team members is more effective than distributed engagement. Additionally, individual-AI pairs matched the performance of conventional teams, suggesting a reduced need for traditional team structures in some contexts. However, despite this capability, individual-AI pairs still fell short of the performance levels achieved by AI-assisted teams. These findings underscore that while generative AI can replace some traditional team functions, more comprehensively integrating AI within team structures provides superior benefits, enhancing overall effectiveness beyond individual efforts.