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    The worrisome return of a racist form of home lending

    The worrisome return of a racist form of home lending

    May 5, 2016

    Urban Institute | By Steven Brown, Ph.D. candidate in Sociology. How and why "contract for deed" is making a comeback and what it means for economic mobility and wealth accumulation for minority families.

    Where are the Jobs?, v. 2.0. Now with earnings data

    Where are the Jobs?, v. 2.0. Now with earnings data

    June 8, 2016

    Robert Manduca, Ph.D. candidate in Sociology & Social Policy, has released a new version of his 'Where are the Jobs?' data visualization, now with earnings data. These interactive maps depict nearly every single job in the United States, one dot per job. Each plotted job is color-coded by sector and by earnings, allowing exploration of the spatial distribution of employment and pay in fine detail. The new maps also include previously unavailable data for Massachusetts. Manduca's research interests in this area focus on local economic development—how cities and regions can promote sustainable and inclusive economic growth. Learn more about his work at his website.

    Alexandra Roulet

    Loi Sapin 2 : moderniser l'économie, comment l'entendez-vous?

    June 11, 2016

    France Culture | Alexandra Roulet, Ph.D. candidate in Economics, guests on 'L'Economie en questions' to discuss France's proposed Sapin 2 Law, which seeks tightened anti-corruption measures to enhance transparency and modernization of the economy. [Audio, in French, 29 minutes].

    How to Hire with Algorithms

    How to Hire with Algorithms

    October 17, 2016

    Harvard Business Review |  By Oren Danieli (Ph.D. candidate in Business Economics), Andrew Hillis, and Michael Luca (Assistant Professor of Business Administration). Algorithms have the potential to improve hiring and promotion decisions, the authors argue, but need to be managed.

    "We explored that potential in a recent study (American Economic Review, May 2016) on selecting teachers and policemen. We used machine learning algorithms to transform data about teacher and police characteristics – for example, educational background, surveys, and test performance – into predictions about their likely performance in the future. Our results demonstrate that students and communities alike could benefit from a more data-driven selection process. Algorithms can help with some of the nation’s most challenging personnel issues. For example, the data suggest that police departments can predict, at the time of hire, which officers are most likely to be involved in a shooting or accused of abuse."
    View the research

    Paying for Outcomes: Beyond the Social Impact Bond Buzz

    Paying for Outcomes: Beyond the Social Impact Bond Buzz

    October 28, 2016

    Inside Story (Australia) | By Matt Tyler (MPP '17) and Ben Stephens (MPP '17). Social impact bonds’ most valuable contribution could be to support the expansion of pay-for-success contracting to dramatically improve the lives of vulnerable Australians, write Tyler and Stephens.

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