This study explores how financial development and openness influence the effectiveness of fiscal and monetary policies. An analysis of data from about 100 countries between 1980 and 2018 reveals that both financial openness and development weaken the impact of monetary and fiscal policies. Our results further show that financial development in a country diminishes policy effectiveness depending on the country’s level of financial development; specifically, the more developed a country, the less effective the policies would be. Additionally, through a detailed examination employing a dynamic panel GMM approach, the study investigates the global repercussions of economic downturns in the US and how financial maturity shapes policy effectiveness during these times. We also discuss some policy implications that show that the positive impacts of monetary policy on output growth are lessened during crisis periods, and policymakers should act accordingly.

The credit constraint, an example of an incomplete credit market, provides an incentive to intensify the extensive and intensive margins related to labor force participation and work hours, respectively. This study uses the cross-section data from the Survey of Consumer Finance (SCF) and analyzes the impact of credit constraints on labor supply decisions, time to search for employment, and work hours. The empirical findings using the IV-probit and 2SLS models suggest that credit constraints and their various measures encourage households to increase both labor force participation and work hours to offset the negative impact of financial constraints. The intensity of working hours increases when we introduce both the alternate form of credit constraint and various age bands. Credit-constrained individuals effectively search for jobs and are most likely to accept employment in a short period, but their job search process takes more time than non-constrained individuals.

This article provides an educational demonstration of how transformer-based natural language processing models, specifically FinBERT, can be used to generate actionable sentiment indicators from financial news. The focus is on pedagogy, showing portfolio managers, risk officers, and students how unstructured textual data can be systematically converted into interpretable signals. The article outlines a replicable workflow for classifying firm-specific headlines, aggregating sentiment scores and aligning them with intraday stock price movements. Examples illustrate how negative or positive sentiment clusters correspond with short-term price behavior, making sentiment analysis an intuitive complement to established portfolio management tools. The goal is to demystify AI-driven sentiment analytics, providing a transparent and practical entry point for incorporating narrative-driven risks and opportunities into the investment process.