Data talks 2017 at Green Library
Friday, October 27, 2017
Green Library - SSRC Seminar Room 121A
with Jonathan Fisher and Emi Lesure at 2:00 pm
The Stanford Federal Statistical Research Data Center (FSRDC) allows qualified researchers to securely use restricted-access data from the U.S. Census Bureau, the National Center for Health Statistics (NCHS), the Agency for Healthcare Research and Quality (AHRQ), the Bureau of Economic Anlaysis (BEA), and theBureau of Labor Statistics.
Jonathan and Emi will discuss research that has used the FSRDC network and the specific data available for about 40 minutes and then take questions for the remaining time. Come learn about this valuable on-campus resource!
Jonathan Fisher is a Research Scholar at the Stanford Center on Poverty and Inequality. He is also a Senior Researcher on the American Opportunity Study (AOS), a joint project between Stanford University, the National Academies of Science, and the U.S. Census Bureau. AOS is an innovative data product that will help this and future generations understand socio-economic mobility in the United States along with providing the best infrastructure to study program evaluation. Jonathan’s current research focuses on inequality and intergenerational mobility using income, consumption, and wealth conjointly.
Emi Lesure is the Stanford FS-RDC administrator and is the primary contact for inquiries about the use of the RDC including proposal development, guidelines, access, and any other question surrounding the use of the FS-RDC.
with Sharad Goel at 3:00 pm
Millions of Americans are stopped by the police each year, but there is no comprehensive national database detailing such law enforcement activity. By filing a series of public records requests, we compiled and standardized data on over 130 million state patrol stops carried out in 31 states during the last 10 years. To illustrate the value of this resource for researchers and policymakers, we assess racial disparities in police interactions with the public in the 20 states that provided the most detailed stop data. Among stopped drivers -- and after controlling for age, gender, time, and location -- we find that blacks and Hispanics are more likely to be ticketed, searched, and arrested than white drivers. These disparities may reflect differences in driving behavior, and are not necessarily the result of bias. In the case of search decisions, we explicitly test for discrimination by examining both the rate at which drivers are searched and the likelihood searches turn up contraband. We find evidence that the bar for searching black and Hispanic drivers is lower than for searching whites. Finally, we find that legalizing recreational marijuana in Washington and Colorado reduced the total number of searches and misdemeanors for all race groups, though a race gap persists. We conclude by offering recommendations for improving data collection, analysis, and reporting by law enforcement agencies to facilitate data-driven decision making and policy evaluation.
Sharad Goel is an assistant professor at Stanford University in the Department of Management Science & Engineering, and holds courtesy appointments in Computer Science and Sociology. He looks at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary issues, including police practices, bail reform, political polarization, voter fraud, and online privacy. Before joining Stanford, Sharad was a senior researcher at Microsoft in New York City.
Introduction of High Performance Computing Resources at Stanford
with Mark Piercy, Research Computing Technical Liaison at 4:00 pm