Food For Thought

Majority of U.S. public school students are in poverty

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More than half of children attending U.S. public schools come from low-income families, qualifying them for a free or reduced-price school lunch, a new report reveals.

The number of low-income school children has been steadily rising, going from 42% in 2006 to 49.6% in 2012. But 2013 marked the first year in recent history that a majority of schoolchildren – 51% – were low-income, according to the report from the Southern Education Foundation, drawing on state-level data from the National Center for Education Statistics.

Majority of students struggling with poverty

“No longer can we consider the problems and needs of low income students simply a matter of fairness,” the report stated. “Their success or failure in the public schools will determine the entire body of human capital and educational potential that the nation will possess in the future.”

The number of low-income students were concentrated in the South and West: Mississippi had the highest proportion of low-income students, at 71%, followed by New Mexico and Louisiana, according to the report. New Hampshire had the lowest proportion of low-income students at 27%.


The Rise Of Algorithmic HR

A group of researchers — Nathan R. Kuncel, Deniz S. Ones, and David M. Klieger — analyzed 17 studies of job applicant evaluations and found that a simple algorithm outperforms human decision-making by at least 25%. Why does this seem counterintuitive? Shouldn’t human experience and understanding of company culture have a higher predictive power?

It turns out that human beings are good at defining what a job is, and also good at getting information from candidates to help evaluate them. But people are simply bad at synthesizing that information, and making the right determination. Why? The researchers explain:

The problem is that people are easily distracted by things that might be only marginally relevant, and they use information inconsistently. They can be thrown off course by such inconsequential bits of data as applicants’ compliments or remarks on arbitrary topics—thus inadvertently undoing a lot of the work that went into establishing parameters for the job and collecting applicants’ data. So they’d be better off leaving selection to the machines.

Needless to say, there would be strong resistance to this idea. Surveys suggest that when assessing individuals, 85% to 97% of professionals rely to some degree on intuition or a mental synthesis of information. Many managers clearly believe they can make the best decision by pondering an applicant’s folder and looking into his or her eyes—no algorithm, they would argue, can substitute for a veteran’s accumulated knowledge. If companies did impose a numbers-only hiring policy, people would almost certainly find ways to circumvent it.

Other research has shown that human cognitive biases get in the way, too. People tend to lend more weight to experiences and background that they share with candidates, like attending the same schools, speaking the same dialect, or having a common religion.

As a result, more companies are leaving decisions about hiring to algorithms. For example, direct marketing company Harte-Hanks uses software from the company Cornerstone (formerly Evolv) to pick the best candidates for call center workers. This is based on what Cornerstone calls “workforce science,” a hard turn toward data-driven, algorithmic HR. Aki Ito reported on that in 2013, writing:

Harte-Hanks found call center agents selected by Evolv’s software had a 35 percent lower 30-day attrition rate, reported 29 percent fewer hours of missed work in the first six months and handled calls 15 percent more quickly than those hired through the company’s existing recruiting services provider at the time.

Algorithmic HR raises some legal questions — because people who aren’t rated a good fit are effectively blocked from the jobs — but the results are hard to argue with.

And the managers who believe that looking in a candidate’s eye still has a place may be interested in the current facial analysis program at the Milwaukee Bucks. The team has hired Dan Hill, a “facial coding” expert, to analyze draft picks, and to rate them psychologically. Hill is an exponent of Paul Ekman’s well-regarded FACS, or Facial Action Coding System, to determine which of the 43 facial muscles are working at any time. These translate to specific emotions, like the seven core emotions: happiness, surprise, contempt, disgust, sadness, anger and fear.