What Employees Value Most
What’s better than a pay raise? Benefits and perks. Glassdoor does a quarterly survey on such issues, and this quarter’s survey found these are the specific benefits and perks employees would prefer:
- Healthcare insurance (e.g., medical, dental): 40 percent
- Vacation/Paid time off: 37 percent
- Performance bonus: 35 percent
- Paid sick days: 32 percent
- 401(k) plan, retirement plan and/or pension: 31 percent
- Flexible schedule (e.g., work from home): 30 percent
- Office perks (e.g., free lunch, casual dress): 19 percent
- Employee development programs (e.g., on-the-job training, professional development): 19 percent
- Tuition reimbursement: 18 percent
- Employee discounts: 17 percent
- Gym membership or wellness programs: 16 percent
- Stock, stock options and/or equity: 16 percent
- Paid parental leave (e.g., maternity leave, adoption assistance): 13 percent
- Childcare assistance (e.g., on-site childcare, financial assistance): 13 percent
- Commuter assistance (e.g., company shuttle, commuter checks): 9 percent
- Diversity program: 3 percent
Field-Data Study Finds No Evidence of Racial Bias in Predictive Policing
While predictive policing aims to improve the effectiveness of police patrols, there is concern that these algorithms may lead police to target minority communities and result in discriminatory arrests. A computer scientist in the School of Science at Indiana University-Purdue University at Indianapolis (IUPUI) conducted the first study to look at real-time field data from Los Angeles and found predictive policing did not result in biased arrests.
“Predictive policing is still a fairly new field. There have been several field trials of predictive policing where the crime rate reduction was measured, but there have been no empirical field trials to date looking at whether these algorithms, when deployed, target certain racial groups more than others and lead to biased stops or arrests,” said George Mohler, an associate professor of computer and information science in the School of Science at IUPUI.
Mohler, along with researchers at UCLA and Louisiana State University, worked with the Los Angeles Police Department to conduct the experimental study. A human analyst made predictions on where officers would patrol each day, and an algorithm also made a set of predictions; it was then randomly selected which set was used by officers in the field each day.
The researchers measured the difference in arrest rates by ethnic groups between the predictive policing algorithm and maps of hot spots created by LAPD analysts that were in use prior to the experiment.
“When we looked at the data, the differences in arrest rates by ethnic group between predictive policing and standard patrol practices were not statistically significant,” Mohler said.
The study examined data both at the district level and within the LAPD officers’ patrol areas and found there was no statistically significant difference between arrest rates by ethnic group at either geographical level. Finally, researchers looked at arrest rates overall in patrol areas and found that they were statistically higher in the algorithmically selected areas, but when adjusted for the higher crime rate in those areas, the arrests were lower or unchanged. “The higher crime rate, and proportionally higher arrest rate, is what you would expect since the algorithm is designed to identify areas with high crime rates,” Mohler said.
Mohler said that in the developing field of predictive policing, there continue to be lessons learned from each study and implementation. A recent simulation study of predictive policing with drug arrest data from Oakland, California, showed there is potential for bias when these algorithms are applied in certain contexts. Mohler hopes the Los Angeles study is a starting point to measure predictive policing bias in future field experiments.
“Every time you do one of these predictive policing deployments, departments should monitor the ethnic impact of these algorithms to check whether there is racial bias,” Mohler said. “I think the statistical methods we provide in this paper provide a framework to monitor that.”
“Does Predictive Policing Lead to Biased Arrests? Results from A Randomized Control Trial” is published in the journal Statistics and Public Policy.
Key Drivers of High U.S. Healthcare Spending Identified
Key takeaways:
- In 2016, the U.S. spent nearly twice as much as other high-income countries on healthcare, yet had poorer population health outcomes.
- The main drivers of higher healthcare spending in the U.S. are generally high prices–for salaries of physicians and nurses, pharmaceuticals, medical devices, and administration.
- Contrary to commonly held beliefs, high utilization of healthcare services and low spending on social services do not appear to play a significant role in higher U.S. healthcare costs.
- In addition, despite poor population health outcomes, quality of healthcare delivered once people are sick is high in the U.S.
These new findings, from Harvard T.H. Chan School of Public Health, the Harvard Global Health Institute, and the London School of Economics, suggest that common explanations as to why healthcare costs are so high–such as the notions that the U.S. has too many doctor visits, hospitalizations, procedures, and specialists, and spends too little on social services that could mitigate healthcare needs–may be wrong.
The study appears in JAMA (Journal of the American Medical Association).
“We know that the U.S. is an outlier in healthcare costs, spending twice as much as peer nations to deliver care. This gap and the challenges it poses for American consumers, policymakers, and business leaders was a major impetus for healthcare reform in the U.S., including delivery reforms implemented as part of the Affordable Care Act,” said senior author Ashish Jha, K.T. Li Professor of Global Health at Harvard Chan School and Director of the Harvard Global Health Institute (HGHI). “In addition, the reasons for these substantially higher costs have been misunderstood: These data suggest that many of the policy efforts in the U.S. have not been truly evidence-based.”
Using international data primarily from 2013-16, the researchers compared the U.S. with 10 other high-income countries–the United Kingdom, Canada, Germany, Australia, Japan, Sweden, France, Denmark, the Netherlands, and Switzerland–on approximately 100 metrics that underpin healthcare spending.
The study confirmed that the U.S. has substantially higher spending, worse population health outcomes, and worse access to care than other wealthy countries. For example, in 2016, the U.S. spent 17.8% of its gross domestic product on healthcare, while other countries ranged from 9.6% (Australia) to 12.4% (Switzerland). Life expectancy in the U.S. was the lowest of all 11 countries in the study, at 78.8 years; the range for other countries was 80.7-83.9 years. The proportion of the U.S. population with health insurance was 90%, lower than all the other countries, which ranged from 99%-100% coverage.
But commonly held beliefs for these differences appear at odds with the evidence, the study found. Key findings included:
Belief: The U.S. uses more healthcare services than peer countries, thus leading to higher costs.
Evidence: The U.S. has lower rates of physician visits and days spent in the hospital than other nations.
Belief: The U.S. has too many specialists and not enough primary care physicians.
Evidence: The primary care versus specialist mix in the U.S. is roughly the same as that of the average of other countries.
Belief: The U.S. provides too much inpatient hospital care.
Evidence: Only 19% of total healthcare spending in the U.S. is spent on inpatient services–among the lowest proportion of similar countries.
Belief: The U.S. spends too little on social services and this may contribute to higher healthcare costs among certain populations.
Evidence: The U.S. does spend a bit less on social services than other countries but is not an outlier.
Belief: The quality of healthcare is much lower in the U.S. than in other countries.
Evidence: Overall, quality of care in the U.S. isn’t markedly different from that of other countries, and in fact excels in many areas. For example, the U.S. appears to have the best outcomes for those who have heart attacks or strokes, but is below average for avoidable hospitalizations for patients with diabetes and asthma.
What does explain higher spending in the U.S. is administrative complexity and high prices across a wide range of healthcare services. For example, the findings showed that:
- Administrative costs of care–activities related to planning, regulating, and managing health systems and services–accounted for 8% of total healthcare costs, compared with a range of 1%-3% for other countries.
- Per capita spending for pharmaceuticals was $1,443 in the U.S., compared with a range of $466 to $939 in other nations. For several commonly used brand-name pharmaceuticals, the U.S. had substantially higher prices than other countries, often double the next highest price.
- The average salary for a general practice physician in the U.S. was $218,173, while in other countries the salary range was $86,607-$154,126.
“As the U.S. continues to struggle with high healthcare spending, it is critical that we make progress on curtailing these costs. International comparisons are very valuable–they allow for reflection on national performance and serve to promote accountability,” said first author Irene Papanicolas, visiting assistant professor in the Department of Health Policy and Management at Harvard Chan School.