This paper develops a new equilibrium model of two-sided search where ex-ante heterogenous individuals have general payoff functions and vectors of attributes. The analysis applies to a large class of models, from the non-transferable utility case to the collective household case with...

In this paper, we describe a novel iterative procedure called SISTA to learn the underlying cost in optimal transport problems. SISTA is a hybrid between two classical methods, coordinate descent ("S"-inkhorn) and proximal gradient descent ("ISTA"). It alternates between a phase of exact...

Travel restrictions are often imposed to limit the spread of infectious diseases. As uniform restrictions can be inefficient and incur unnecessarily high costs, this paper examines the optimal design of restrictions that target specific travel routes. We propose a model with trade-offs between...

We examine the impact of Articial Intelligence (AI) on productivity in the context of taxi drivers. The AI we study assists drivers with finding customers by suggesting routes along which the demand is predicted to be high. We find that AI improves drivers' productivity by shortening the...

Predictions of whether newly unemployed individuals will become long-term unemployed are important for the planning and policy mix of unemployment insurance agencies. We analyze unique data on three sources of information on the probability of re-employment within 6 months (RE6), for the same...

In an era characterized by population aging and economic challenges in welfare states across the world, sustaining these welfare systems requires a large workforce. Many individuals outside the labor market aspire to work but encounter a labyrinth of obstacles. While Public Employment Services...

Background: Adult studies have shown that nursing overtime and unit overcrowding is associated with increased adverse patient events but there exists little evidence for the Neonatal Intensive Care Unit (NICU). Objectives: To predict the onset on nosocomial infections and medical accidents in a...

Using high-quality nation-wide social security data combined with machine learning tools, we develop predictive models of income support receipt intensities for any payment enrolee in the Australian social security system between 2014 and 2018. We show that off-the-shelf machine learning...

This study employs six Machine Learning methods - Logit, Lasso-Logit, Ridge-Logit, Random Forest, Extreme Gradient Boosting, and an Ensemble - alongside registry data on abortions in Spain from 2011-2019 to predict multiple abortions and assess monetary savings through targeted interventions. We...