Gender differences in overconfidence are well documented in the empirical literature, but their impact on labor market outcomes remains underexplored. We provide new insights into how behavioral biases interact with career dynamics by presenting a theoretical analysis of how men's relatively...

This paper provides insights into the apparent seesaw between the generation of theoretical versus empirical economics research over time. A dynamic model considers the incentives of researchers to focus on empirical versus theoretical papers. It yields the main characteristics of the...

Using a promotion signaling model in which wages are realistically shaped by market forces, we analyze how male overconfidence combined with competitive workplace incentives affects gender equality in the labor market. Our main result is that overconfident workers exert more effort to be...

Gender differences in overconfidence have been extensively documented in the empirical literature, but the implications for labor market outcomes are not well understood. In this paper, we analyze how men's relatively higher overconfidence, combined with competitive job incentives, affects...

A literacy-targeted approach to economic instruction draws on insights from cognitive science. It highlights that students process complex economic information by constructing and modifying schemas that represent economic material. Following this approach, we developed a set of instructional...

As part of the green transition, the European cap-and-trade scheme for CO2 emissions will be extended to cover consumer durables. We propose a New Keynesian model that features both, "brown" and "green" durable goods and show that if monetary policy follows a business-as-usual approach, the...

We discuss the timing and strength of the Fed's reaction to the recent inflation surge within an estimated macroeconomic model where long-run inflation expectations are heterogeneous and can lose their anchoring to the target. The resulting inflation scare worsens the real cost of disinflation....

This paper investigates the long-run nexus between wealth inequality and aggregate output using a DSGE model in which wealth inequality endogenously affects individual entrepreneurship incentives, thereby influencing aggregate output. Our model passes the indirect inference test against the UK...

This paper investigates the determinants of inflation target credibility (ITC) using a unique survey we designed to measure the credibility of the ECB's inflation target. Containing over 200,000 responses from German consumers collected between January 2019 and November 2024, our dataset enables...

We develop a quantitative macroeconomic framework with heterogeneous financial intermediaries and active liquidity management. In the model, banks manage uninsured, idiosyncratic deposit withdrawal risk through an iterative over-the-counter interbank market with endogenous intensive and...

This report estimates the "social rate of time preference" (SRTP) for New Zealand public policy appraisal using a Ramsey equation. It uses the Weitzman (1998) method to derive a declining discount rate schedule of 2% (real) for years 1-30, 1.5% for years 31-100, and 1% for years 101 on. The...

This study explores the pivotal role of theories of economics in shaping the multifaceted concept of sustainable development and integrates economic, social, and environmental dimensions. It traces the intellectual trajectory of classical, neoclassical, and contemporary economic paradigms,...

This paper identifies the salient features of feedforward control and the advantages that it provides vis-à-vis feedback control. These advantages are especially salient in strategic control situations and also during periods of environmental turbulence and extreme strategic discontinuity....

Most social problems are "wicked", meaning that they are highly complex, intractable, open-ended, and multi-dimensional. In wicked learning environments, information is ambiguous, feedback may be slow, or causes and effects are difficult to ascertain. Using the insights from the Bloomington...

Convolutional Neural Networks (CNNs) are a specialized class of deep neural networks tailored for processing high-dimensional data, excelling in tasks like image classification, object detection, and facial recognition. Their architecture is built on convolutional layers interspersed with...