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Research articles and reports

Future of Work

Can financial markets make a difference to natural assets?
Lutfey Siddiqi, Sharmine Tan

LSE Business Review, published 17 December 2022

Abstract

Lutfey Siddiqi and Sharmine Tan argue that a holistic, nature-positive lens is the most effective means for private capital to help address climate change. Integrating net-zero initiatives with natural asset stewardship should lead to better outcomes for the planet, businesses, and communities.

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Documenting occupational sorting by gender in the UK across three cohorts: does a grand convergence rely on societal movements?
Warn N. Lekfuangfu, Grace Lordan

Published September 2022

Abstract

We consider the extent to which temporal shifts have been responsible for an increased tendency for females to sort into traditionally male roles over time, versus childhood factors. Drawing on three cohort studies, which follow individuals born in the UK in 1958, 1970 and 2000, we compare the shift in the tendency of females in these cohorts to sort into traditionally male roles compared to males, to the combined effect of a large set of childhood variables. For all three cohorts, we find strong evidence of sorting along gendered lines, which has decreased over time, yet there is no erosion of the gender gap in the tendency to sort into occupations with the highest share of males. Within the cohort, we find little evidence that childhood variables change the tendency for females of either the average or highest ability to sort substantively differently. Our work is highly suggestive that temporal shifts are what matter in determining the differential gendered sorting patterns we have seen over the last number of decades, and also those that remain today. These temporal changes include attitudinal changes, technology advances, policy changes and economic shifts.

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Return to work: a dictionary of behavioural biases
Julia Bladinieres-Justo, Aleesha Bruce, Anisah Ramli, Nichaphat Surawattananon, Chanya Trakulmaykee, Teresa Almeida, Jasmine Virhia, Grace Lordan

LSE Business Review, published 26 May 2022

Abstract

Firms are increasingly allowing their employees to decide if they work at home or in the office. But the return to work is fraught with many biases. The authors have put together a dictionary of biases in blended work. This is the second dictionary of the series. In this edition, the authors have compiled a list of the most important biases they perceive as impacting the future of work if left unchecked.

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Automation and the changing nature of work
Cecily Josten, Dr Grace Lordan

Published May 2022

Abstract

This study identifies the job attributes, and in particular skills and abilities, which predict the likelihood a job is recently automatable drawing on the Josten and Lordan (2020) classification of automatability, EU labour force survey data and a machine learning regression approach. We find that skills and abilities which relate to non-linear abstract thinking are those that are the safest from automation. We also find that jobs that require ‘people’ engagement interacted with ‘brains’ are also less likely to be automated. The skills that are required for these jobs include soft skills. Finally, we find that jobs that require physically making objects or physicality more generally are most likely to be automated unless they involve interaction with ‘brains’ and/or ‘people’.

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People versus machines: introducing the HIRE framework
Paris Will, Dario Krpan, Dr Grace Lordan

Published May 2022

Abstract

The use of Artificial Intelligence (AI) in the recruitment process is becoming a more common method for organisations to hire new employees. Despite this, there is little consensus on whether AI should have widespread use in the hiring process, and in which contexts. In order to bring more clarity to research findings, we propose the HIRE (Human, (Artificial) Intelligence, Recruitment, Evaluation) framework with the primary aim of evaluating studies which investigate how Artificial Intelligence can be integrated into the recruitment process with respect to gauging whether AI is an adequate, better, or worse substitute for human recruiters. We illustrate the simplicity of this framework by conducting a systematic literature review on the empirical studies assessing AI in the recruitment process, with 22 final papers included. The review shows that AI is equal to or better than human recruiters when it comes to efficiency and performance. We also find that AI is mostly better than humans in improving diversity. Finally, we demonstrate that there is a perception among candidates and recruiters that AI is worse than humans. Overall, we conclude based on the evidence, that AI is equal to or better to humans when utilised in the hiring process, however, humans hold a belief of their own superiority. Our aim is that future authors adopt the HIRE framework when conducting research in this area to allow for easier comparability, and ideally place the HIRE framework outcome of AI being better, equal, worse, or unclear in the abstract.

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Future of Work
Investors' Expectations of Ethical Artificial Intelligence in Human Capital Management 
Dr Christine Chow, Mark Lewis, Paris Will 

Published April 2022

Abstract

“To understand the causes of things, for the betterment of society.”

It is in this spirit that the three authors embarked on their journeys to write this paper, first conceptualised in August 2021. The paper is intended for investor engagement with companies on the responsible use of AI in hiring and workforce management. At the outset, this paper sought diverse and inclusive viewpoints from a range of stakeholders of the responsible investment ecosystem, for the paper is intended to provide investors’ expectations on ethical artificial intelligence (A.I.) applications in human capital management.

As such, across the paper, you will find expert opinion, insights based on independent surveys and the voices of diverse employees. We hope you enjoy reading this paper and find practical tips on applying human-centric AI. Ultimately, we invite you to join us in making workplace more transparent and inclusive.

Thank you.

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People versus machines: The impact of being in an automatable job on Australian worker’s mental health and life satisfaction
Dr Grace Lordan and Eliza-Jane Stringer

Published April 2022

Abstract

This study explores the effect on mental health and life satisfaction of working in an automatable job. We utilise an Australian panel dataset (HILDA), and take a fixed effects linear regression approach, to relate a person being in automatable work to proxies of their wellbeing. Overall, we find evidence that automatable work has a small, detrimental impact on the mental health and life satisfaction of workers within some industries, particularly those with higher levels of job automation risk, such as manufacturing. Furthermore, we find no strong trends to suggest that any particular demographic group is disproportionately impacted across industries. These findings are robust to a variety of specifications. We also find evidence of adaptation to these effects after one-year tenure on the job, indicating a limited role for firm policy.

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