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...

The study utilises the International Labor Organization's SMEs COVID-19 pandemic business risks scale to determine whether Artificial Intelligence (AI) applications are associated with reduced business risks for SMEs. A new 10-item scale was developed to capture the use of AI applications in...

The possibility that artificial extraterrestrial intelligence poses an existential threat to humanity is neglected. It is also the case in economics, where both AI existential risks and the potential long-term consequences of an AGI are neglected. This paper presents a thought experiment to...

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...

This paper argues that the level of financial services provision determines the risk management strategies among the poor. The paper estimates the determinants of the household's use of one, two or all three types of microfinancial services applying ordered probit models and additionally probit...

Childhood circumstances may impact senior health, prompting this study to introduce novel machine learning methods to assess their individual and collective contributions to health inequality in old age. Using the US Health and Retirement Study (HRS) and the China Health and Retirement...

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...

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...