Using a large longitudinal, nationally representative workplace-level dataset, we explore the productivity gains associated with computer use and organizational redesign. The empirical strategy involves the estimation of a production function, augmented to account for technology use and...

By exploiting establishment-level data, this paper sheds new light on the sources of the changes in the structure of production, wages, and employment that have occurred over the last several decades. We investigate the following two related hypotheses. First, that most of the recent increase in...

We investigate the relationship among staff engagement, job complementarities and labour supply in the hospital sector, where excessive turnover of the clinical staff (doctors and nurses) can be detrimental for quality of care. We exploit a unique and rich panel dataset constructed by combining...

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...

Using the entire population of USPTO patent applications published between 2002 and 2019, and leveraging on both patent classification and semantic analysis, this paper aims to map the current knowledge base centred on robotics and AI technologies. These technologies are investigated both as a...

In "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," Acemoglu and Restrepo (2018b) combine the task-based model of the labor market with an endogenous growth model to model the economic consequences of artificial intelligence (AI). This...

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...

A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of...

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...