Research
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[Forthcoming] Artificial Intelligence and Geographic Inequality, The Brookings Institution
Research analyzing the impact of artificial intelligence and digitalization on geographic inequality in the US, with a focus on offering policy prescriptions to the Biden Administration in the service of boosting a more diffuse embrace of AI. Report will be co-authored with Brookings Senior Fellow and Policy Director Mark Muro. [Forthcoming] The Ethics and Economics of Personalised Medicine, University College London Faculty of Engineering Science
Research analyzing the impacts of AI-enabled personalized (precision) medicine on race inequality, income disparities, health inequity, climate change, privacy, and medical consent. (2022) Global Public Investor, OMFIF
Our research is the first of its kind to reveal how reserves managers – one of the world’s most influential investor groups – are reacting to a generational shift in the investment environment,’ said Clive Horwood, deputy CEO and managing editor of OMFIF. ‘We have moved incredibly quickly from a lower-for-longer rates environment to a higher-for-longer inflation outlook. These will be very challenging times for reserves managers. Read here. (2021): A Portrait of the Automation Susceptible Individual: Skills-Biased Technological Change and the American Conscience, The London School of Economics
This study considers the relationship between automation potential and the views, beliefs, traits, and experiences of a randomised sample of 26,311 Americans. Previous research on automation has largely attempted to consider the current and potential impact of labour-saving innovations (e.g. digitalisation) on macroeconomic and labour market conditions. There is currently limited literature tracing the views of individuals who are susceptible to automation. This study uses data from the American National Election Survey alongside 22-category automation potential estimates presented by The Brookings Institution. Findings are presented in two parts: the first summarises the time-varying characteristics of automation susceptible individuals along 224 characteristics and views; the second applies a fixed effects OLS model to study the potential impact of elevated automation potential on a narrower set of dependent variables. Additional robustness checks are conducted as well as additional tests with an experimental AI-exposure treatment variable. The chief findings of this approach are that automation susceptible Americans are more likely to be culturally conservative, economically left-leaning, anti-immigration, racist and intolerant, politically apathetic, politically pessimistic, cynical, and despairing. Read here. |
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(2019): Is This Time Different: Artificial Intelligence, Inequality and the Future of Work, Brown University
This project analyzes the economic consequences of modern digitally-enabled automation. Is today’s AI really such a momentous force, destined to render large numbers of Americans permanently redundant? Or will digitally-enabled automation follow previous patterns of historical automation, displacing some workers and increasing inequality only in the short run? This working project is currently 300 pages long and on track to be published as a full length book in 2022. |
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(2018): America's Most Digitally Inclusive Tech Cities, The Brookings Institution
I supported the drafting a development of this Brookings piece, which focused on studying the American cities with both a large tech industry and a high degree of race and gender economic inclusion. The report analyses the "metropolitan areas that, in different ways, offer glimpses of the promise tech holds for contributing to inclusive prosperity." Read |
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(2017): Pentagon Spending and Domestic Employment, The World Policy Institute
Utilized three novel data sets to model and regress the impacts of military supply chain outsourcing on US unemployment rates. Particular emphasis on analyzing the total sum of US-Saudi military contracts. |
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(2015): Cost of War Project, Center for International Policy
Design and help construct a novel dataset of US government military contracts, focusing on clients, costs, and impact on local wages. Specific areas of focus included Pennsylvania and Missouri states. |