Increasing savings at tax time

Increasing savings at tax time

Motivational prompts to save tax refunds and suggested savings amounts for the tax refund can increase saving among low- and moderate-income households, finds a new experimental study from the Brown School at Washington University in St. Louis.
WashU Expert: More must be done to address opioid crisis

WashU Expert: More must be done to address opioid crisis

Opioids, including heroin and prescription drugs, killed more than 33,000 people in 2015, more than any year on record, according to the CDC. President Trump’s proposed budget aims to address the crisis with a $500 million increase in prevention and treatment, but it isn’t enough to address the issue, says an expert on substance use disorder treatment.
Brown School honors distinguished alumni

Brown School honors distinguished alumni

Eight alumni and friends of the Brown School at Washington University in St. Louis were recognized March 6 at the 34th annual Distinguished Alumni Awards ceremony in the Clark-Fox Forum in Hillman Hall.
WashU Expert: Missouri SB 43 would weaken discrimination protections

WashU Expert: Missouri SB 43 would weaken discrimination protections

A bill pending in the Missouri Legislature would make it more difficult for workers who experience discrimination or lose their job because of whistleblowing to hold their employers responsible, says an expert on employment law at Washington University in St. Louis.
The public house as public forum

The public house as public forum

Without public spaces for debate and discussion, our ideas and our expressions stay in our private spaces and we don’t have opportunities to engage with each other, argues John Inazu, the Sally D. Danforth Distinguished Professor of Law & Religion.
WashU Expert: Hiring data creates risk of workplace bias

WashU Expert: Hiring data creates risk of workplace bias

American employers increasingly rely on large datasets and computer algorithms to decide who gets interviewed, hired or promoted. Pauline Kim, employment law expert, explains that when algorithms rely on inaccurate, biased or unrepresentative data, they may systematically disadvantage racial and ethnic minorities, women and other historically disadvantaged groups.
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