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...

We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data, we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF), a causal machine learning estimator for multiple programmes. While all...

Rapid technological progress in artificial intelligence (AI) has been predicted to lead to mass unemployment, rising inequality, and higher productivity growth through automation. In this paper we critically re-assess these predictions by (i) surveying the recent literature and (ii)...

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...

The economic impact of Articial Intelligence (AI) is studied using a (semi) endogenous growth model with two novel features. First, the task approach from labor economics is reformulated and integrated into a growth model. Second, the standard representative household assumption is rejected, so...

This study investigates the dose-response effects of making music on youth development. Identification is based on the conditional independence assumption and estimation is implemented using a recent double machine learning estimator. The study proposes solutions to two highly practically...

Using data on the near-universe of online US job vacancies collected by Burning Glass Technologies in 2016, we calculate labor market concentration using the Herfindahl-Hirschman index (HHI) for each commuting zone by 6-digit SOC occupation. The average market has an HHI of 3,953, or the...

In this paper, we study the crowdsourcing of innovation in Africa through a data science contest on an intermediated digital platform. We ran a Machine Learning (ML) contest on the continent's largest data science contest platform, Zindi. Contestants were surveyed on their motivations to take...

In this paper we propose the use of machine learning methods to estimate inequality of opportunity. We illustrate how our proposed methods—conditional inference regression trees and forests—represent a substantial improvement over existing estimation approaches. First, they reduce the risk...

Using panel data on immigrant populations from European, Asian and African countries the study estimates positive associations between the number of mobile applications in use aiming to facilitate immigrants' societal integration (m-Integration) and increased level of integration (Ethnosizer),...