Economics Insights @ CBS

This study implemented and evaluated a prediction-driven nurse staffing framework in a large adult emergency department. The framework leveraged a two-stage prediction model that forecasted patient volume and guided staffing decisions. Using a pre-post study design, we compared patient throughput (measured by door-to-evaluation time, active treatment time, boarding time, length of stay, and left-without-being-seen rate) and cost outcomes (measured as hourly nurse staffing costs) before and after implementation.

How has Wikipedia activity changed for articles with content similar to ChatGPT following its introduction? We estimate the impact using differences-in-differences models, with dissimilar Wikipedia articles as a baseline for comparison, to examine how changes in voluntary knowledge contributions and information-seeking behavior differ by article content. Our analysis reveals that newly created, popular articles whose content overlaps with ChatGPT 3.5 saw a greater decline in editing and viewership after the November 2022 launch of ChatGPT than dissimilar articles did.

In this letter, we summarize our recent work on the welfare impact of recommendation algorithms and propose questions for further study. We model recommendation algorithms as an information structure, which shapes how a third party takes actions that affect the welfare of different individuals in a population. Each recommendation algorithm thus induces a welfare profile, describing the expected payoffs of different individuals when the third party takes actions following the algorithm.

Any judgment about population heterogeneity depends on the definition of the sampling frame (1). In a recent paper, Holzmeister et al. (2) (HJBK hereafter) compare different sources of heterogeneity to population heterogeneity. They find that population heterogeneity is much smaller compared to design and analytic heterogeneity as a source of variation in effect sizes.

Multinational enterprises are at the centre of policy debates in low- and middle-income countries. As some of the most productive and innovative firms in the world, which are at the core of global supply chains, multinational enterprises (MNEs) can accelerate development in the countries hosting them, both directly with their presence, and indirectly through linkages to local economic actors.