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  • Arts, Humanities, & Social Sciences

    • Understanding poverty and data

    In the latest episode of Penn Today’s ‘Understand This …’ podcast series, Assistant Professor of Sociology Regina Smalls Baker and Assistant Professor Amy Castro Baker in the School of Social Policy & Practice discuss the use of data to understand poverty.
    A shiny dress shoe next to a ragged one

    Welcome to the fourth episode of “Understand This ...,” a Penn Today podcast. Designed as a journey to understand how to solve problems of the day—and of our time—by uniting minds from different disciplines, this episode explores the use of data to understand poverty, particularly in the unique moment of the COVID-19 pandemic. This podcast is in tandem with Penn Today’s "The role of data in a world reshaped by COVID-19” feature.

    Here, Regina Smalls Baker, assistant professor of sociology in the School of Arts & Sciences, and Amy Castro Baker, assistant professor in the School of Social Policy & Practice and director of the new Center for Guaranteed Income Research, talk about the use of data to inform recent economic relief bills, data gaps in assessing who is experiencing poverty, and the feasibility of solutions like universal basic income in alleviating poverty. 

    7:40: The state of poverty in the U.S.

    11:20: Housing’s role in developing wealth and perpetuating poverty.

    22:00: “You can’t resist and deconstruct what you can’t see,” says Castro Baker, explaining gaps in what big data shows about poverty.

    26:00: Smalls Baker discusses the changing economic experience of the middle class, especially during an economic recession and pandemic.

    41:00: How cash transfers like the one from this spring’s CARES Act affects the motivation of people to look for work. “People want to work,” Castro Baker explains.

    49:30: What gives them hope about the future in this moment. 

    Download the full podcast on Apple Music. 

    View Transcript

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  • Writers

    Brandon K. Baker

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  • Understand This...
    • Crowd-sourcing optogenetics data to tackle neurological diseases
    Two people in front of two computer monitors. The person moving the mouse is sitting. The other person stands watching.

    Sébastien Tremblay (front), a postdoctoral fellow in the Platt Labs, works in a specialized field of neuroscience called optogenetics, which shows clinical promise for treating conditions like epilepsy. To foster the open exchange of information, Tremblay spearheaded a crowd-sourced, free database that includes data from 45 labs in nine countries. (Pre-pandemic image: Kevin Monko)

    Crowd-sourcing optogenetics data to tackle neurological diseases

    The specialized field of neuroscience, optogenetics, shows clinical promise for conditions like epilepsy and Parkinson’s. But before human trials can get fully underway, the field must better understand a crucial intermediate step, aided by 45 labs in nine countries sharing information.

    Michele W. Berger

    Penn Medicine helps power international COVID-19 data consortium
    Doctor holding electronic tablet

    Penn Medicine helps power international COVID-19 data consortium

    An international consortium involving Penn researchers pools electronic health record data from around the world to discover clinical insights about COVID-19.

    From Penn Medicine News

    Coronavirus models aren’t crystal balls. So what are they good for?
    Microscopic coronavirus images superimposed over digital global map

    Coronavirus models aren’t crystal balls. So what are they good for?

    Epidemiologists and data scientists have been gathering data, making calculations, and creating mathematical models to answer critical questions about COVID-19, but math cannot account for the unpredictability of human behavior.

    Penn Medicine

    Childhood exposure to trauma costs society $458 billion annually
    A young child sits in a hallway burying their head in their arms on a rather dirty carpet

    Childhood exposure to trauma costs society $458 billion annually

    Bureaucratic hurdles block access to treatment services, so they tend to go unused. This leads to adverse outcomes that put stress on public systems like social services and law enforcement.

    Michele W. Berger

    What factors predict success?
    A person sitting at a desk covered in papers, with a computer screen in the background. Four people are blurry, in the foreground. They are all engaged in conversation.

    The findings of this latest work add to the canon of overall knowledge about what factors predict success. They also strengthen Duckworth’s original theories about grit and, at the same time, highlight other attributes key to long-term achievement.

    What factors predict success?

    New research from Angela Duckworth and colleagues finds that characteristics beyond intelligence influence long-term achievement.

    Michele W. Berger

    Removing human bias from predictive modeling
    rendering of the human head from three angles with visuals of networks reaching out from the center of the brain

    Removing human bias from predictive modeling

    Predictive modeling is supposed to be neutral, a way to help remove personal prejudices from decision-making. But the algorithms are packed with the same biases that are built into the real-world data used to create them. 

    Penn Today Staff