From What Distance is Discrimination Acceptable?

Some 60% of American workers earn hourly wages. Of these, about half change jobs each year, so firms that employ lots of entry-level workers, such as call centers, supermarkets, home improvement stores and fast-food chains, have to vet million of applications every year.

Xerox Evolv(ing)

A recent article in MIT Technology Review reports that Xerox is screening tens of thousands of applicants for low-wage jobs in its call centers using software from a startup company called Evolv. According to its website, Evolv “is a workforce science software company that harnesses big data, predictive analytics and cloud computing to help businesses improve workplace productivity and profitability” and its customers include 20 of the Fortune 100.

Working with Xerox, Evolv found that one of the best predictors that a customer-service employee will stick with a job is that he lives nearby and can get to work easily.  As Evolv states in its Q3 2013 Workforce Performance Report:

The distance that employees live from work affects how long they choose to stay at a job. Unsurprisingly, employees that live 0-5 miles from their place of work have the longest median tenure. They remain at their jobs 20% longer than employees with the shortest median tenure.

Although it still sells photocopiers, Xerox has also become one of the world’s largest outsourcing companies. It provides services like running customer service centers, handling health claims, and processing credit-card applications that brought in $11.5 billion in revenue last year.

That business relies on a huge workforce of 54,000 customer service agents, and because of high attrition in hourly jobs, Xerox will have to replace 20,000 of them this year, says Teri Morse, vice president for recruiting at Xerox Services.

The Dictatorship of Data

Morse says Xerox today won’t even look at resumes of those who score in the “red” category of Evolv’s initial behavioral assessment, a 30-minute online exam that workers fill out at home. Early on, while piloting the system, Morse says Xerox still hired against the advice of the data. Now, she says, “people who do poorly we no longer hire.”

As stated by the authors in Big Data: A Revolution That Will Transform How We Live, Work, and Think:

We are more susceptible than we may think to the “dictatorship of data” — that is, to letting the data govern us in ways that may do as much harm as good. The threat is that we will let ourselves be mindlessly bound by the output of our analyses even when we have reasonable grounds for suspecting something is amiss. Or that we will become obsessed with collecting facts and figures for data’s sake. Or that we will attribute a degree of truth to the data which it does not deserve.

For more and more companies, like Xerox, the hiring boss is an algorithm. Jobs that were once filled on the basis of work history and interviews are left to personality tests and data analysis. The new hiring tools are part of a broader effort to gather and analyze employee data.

The risks to employers of utilizing online personality tests in their employment application process have been set out in a number of prior posts, including What Are the Issues, Courts Find Tests to be Illegal, The Next Asbestor? The Next FLSA?, Damages and Indemnification Challenges to Employers, and Welcomed as Customers; Rejected as Employers. The remainder of this post sets out the employment discrimination litigation risks to employers (and society) of blindly following the "insights" of Kenexa and Evolv in distance from job site and housing mobility.

Interestingly, while Evolv now touts the distance from job insight for use by its clients, the company expressed a different view in a 2012 Wall Street Journal article, which reads;

Evolv is cautious about exploiting some of the relationships it turns up for fear of violating equal opportunity laws. While it has found employees who live farther from call-center jobs are more likely to quit, it doesn't use that information in its scoring in the U.S. because it could be linked to race.

From What Distance is Discrimination Acceptable?

Kenexa, purchased by IBM in December 2012, will test approximately 40 million applicants this year for thousands of clients. Kenexa believes that a lengthy commute raises the risk of attrition in call-center and fast-food jobs. It asks applicants for call-center and fast-food jobs to describe their commute by picking options ranging from "less than 10 minutes" to "more than 45 minutes."

The longer the commute, the lower their recommendation score for these jobs, says Jeff Weekley, who oversees the assessments.Applicants also can be asked how long they have been at their current address and how many times they have moved. People who move more frequently "have a higher likelihood of leaving," Mr. Weekley said.

Painting with the broad brush of distance from job site, commute time and moving frequency results in well-qualified applicants being excluded, applicants who might have ended up being among the longest tenured of employees. The Kenexa and Evolv findings are generalized correlations (i.e., persons living closer to the job site tend to have longer tenure than persons living farther from the job site). The insights say nothing about any particular applicant.

As a consequence, employers will pass over qualified applicants solely because they live (or don't live) in certain areas. Not only does the employer do a disservice to itself and the applicant, they increase the risk of employment litigation, with its consequent costs. 

Distance From Jobsite

A recent New York Time article, "In Climbing Income Ladder, Location Matters," reads, in part:

Stacey Calvin spends almost as much time commuting to her job — on a bus, two trains and another bus — as she does working part-time at a day care center.  ...

Her nearly four-hour round-trip [job commute] stems largely from the economic geography of Atlanta, which is one of America’s most affluent metropolitan areas yet also one of the most physically divided by income. The low-income neighborhoods here often stretch for miles, with rows of houses and low-slung apartments, interrupted by the occasional strip mall, and lacking much in the way of good-paying jobs

This geography appears to play a major role in making Atlanta one of the metropolitan areas where it is most difficult for lower-income households to rise into the middle class and beyond, according to a new study that other researchers are calling the most detailed portrait yet of income mobility in the United States.

The dearth of good-paying jobs in low-income neighborhoods means that residents of those neighborhoods have a longer commute. The 2010 Census showed that poverty rates are significantly higher for blacks and Hispanics. Consequently, hiring decisions predicated on distance from job site, intentionally or not, discriminate against certain races.

Housing Mobility

As shown in the table below, poor and near-poor families tend to move much more frequently than their higher income neighbors and the general population.

According to a 2011 study by the Center for Public Housing, entitled "Should I Stay or Should I Go? Exploring the Effects of Housing Instability and Mobility on Children," a wide range of often complex forces appears to drive frequent mobility, and residential instability in general — the formation and dissolution of households, an inability to afford one’s housing costs, the loss of employment, the lack of a safety net, lack of quality housing or a safer neighborhood.

Correlation Is Not Causation

When two variables, A and B, are found to be correlated, there are several possibilities:

1. A causes B
2. B causes A
3. A causes B at the same time as B causes A (a self-reinforcing       system)
4. Some third factor causes both A and B

The correlation is simple coincidence. It is wrong to assume any of these possibilities.

Kenexa, Evolv and their clients, however, assume that A (proximity to job site) causes B (reduced attrition and better performance). That assumption leads them to disfavor otherwise qualified applicants who do not live within a five-mile radius.

The correlation could also demonstrate B (reduced attrition and better performance) is caused by C (proximity of job site to applicants homes). Instead of being a hiring insight, the correlation might function better as being a job site location insight. Given the relative immobility of persons and companies, locating a job site (call center, etc.) close to communities with high numbers of lower-income persons could lead to a more sustainable competitive advantage.

Per Kenexa, the correlation between (A) persons who move and (B) shorter job tenure is that A causes B. However, per the 2011 study by the Center of Public Housing, it may well be that (B) shorter job tenure causes (B) persons to move. As shown in the table below, taken from the 2011 study, more than 76% of involuntary moves were a result of job loss.

Although data does give rise to information and insight, they are not the same. Data's value to business relies on human intelligence, on how well managers and leaders formulate questions and interpret results. More data doesn't mean you will get "proportionately" more information. In fact, the more data you have, the less information you gain as a proportion of the data (concepts of marginal utility, signal to noise and diminishing returns).

Maybe It's the Work, Not the Workforce, That Need Analysis ...

Some 60% of American workers earn hourly wages. Of these, about half change jobs each year. The cost to U.S. businesses of worker attrition and lost productivity is $350 billion annually. How much is that?

What should an employer do? Increase starting pay? Since most people work, at least in part, for the money - giving them more money might encourage them to stay longer and work harder. It seems to work reasonably well with corporate executives.  Top executive compensation averaged $9.4 million last year at the 50 largest employers of low-wage workers.

What do many employers do? Retain "workforce science" companies like Evolv and Kenexa to administer personality tests and analyze applicant data in order to address the employee turnover issue.

As noted above, working with Xerox, Evolv found that one predictor that a customer-service employee will stick with a job is that s/he lives nearby and can get to work easily. Kenexa had a similar "insight," and added that people who move more frequently have a higher likelihood of leaving.

Are there any groups of people who might live farther from the work site and may move more frequently than others? Yes, lower-income persons, disproportionately women, black, Hispanic and the mentally ill. They can't afford to live where the jobs are and move more frequently because of an inability to afford housing or the loss of employment.

So, not only are low-income persons poorly paid, many are electronically redlined from hiring consideration. What type of “workforce science” fails to take into account the most important variable (pay) and yet offers “solutions” based on this flawed science?

Are Employer Referral Programs Encouraging Discriminatory Hiring Practices?

According to Evolv, it recently “used rich data on hundreds of thousands of employees to demonstrate that referred workers show measurably better productivity and retention.” The research showed that 65 percent of companies have a referral program and 36 percent filled their last opening through an employee referral. A primary insight from Evolv is that, when it comes to retention, referred workers were around 13 percent less likely to quit.

As noted above, previous insights of Evolv and Kenexa – distance from work and housing mobility – lead to workforce selection processes that discriminate against blacks and Hispanics. Combining the employer referral program insight with the distance from work and housing mobility insights likely exacerbates the discriminatory impact of workforce science and its use in the hiring process.

About 40 percent of white Americans and about 25 percent of non-white Americans are surrounded exclusively by friends of their own race, according to an ongoing Reuters/Ipsos poll. Even looking at a broader circle of acquaintances to include coworkers as well as friends and relatives, 30 percent of Americans are not mixing with others of a different race, the poll showed.

The workforce insights regarding distance from job and employee mobility results in fewer blacks and Hispanics being hired. Consequently, if 36% of job openings are filled by referrals from employees and 30% of those employees do not have friends or relatives of another race, blacks and Hispanics will be underrepresented in the workforce hired as a result of referrals.

Big Data and Employment Testing: Correlation Is Not Causation

Imagine you are watching at a railway station. More and more people arrive until the platform is crowded, and then — hey presto — along comes a train. Did the people cause the train to arrive (A causes B)? Did the train cause the people to arrive (B causes A)? No, they both depended on a railway timetable (C caused both A and B).

Some 60% of American workers earn hourly wages. Of these, about half change jobs each year. So firms that employ lots of unskilled workers, such as supermarkets, home improvement stores and fast-food chains, have to vet million of applications every year. Making the process more efficient could yield big payoffs.

Algorithms and big data are powerful tools. Wisely used, they can help match the right people with the right jobs. But they must be designed and used by humans, otherwise they can go terribly wrong.

Big Data

Big Data is a tool in the right hands which can yield insight, help determine paths and alternatives which are more likely to be successful, and lead to improved conditions. But Big Data is just one of a set of tools which can be used to develop successful paths and alternatives. It is best when it is used in conjunction with other tools: intuition, inductive reasoning, statistical analysis to name a few.

One of the reasons why Big Data is in the forefront today is with the advent of new tools, very large data flows, and advanced computing techniques there are real opportunities to manage and use huge data sets.

The theory of big data is to have no theory, at least about human nature. One just gathers huge amounts of information, observes the patterns and estimates probabilities about how people will act in the future. One does not address causality.

As the authors of Big Data state, “Contrary to conventional wisdom, such human intuiting of causality does not deepen our understanding of the world." Instead, they aim to stand back nonjudgmentally and observe linkages: “Correlations are powerful not only because they offer insights, but also because the insights they offer are relatively clear. These insights often get obscured when we bring causality back into the picture.”

But are correlations relatively clear? The authors of Freaknomics discuss correlation and causation in the video below; specifically, the view of medical professionals in the first half of the 20th century that polio was caused by ice cream consumption (since disproved).

When two variables, A and B, are found to be correlated, there are several possibilities:

1. A causes B
2. B causes A
3. A causes B at the same time as B causes A (a self-reinforcing system)
4. Some third factor causes both A and B

The correlation is simple coincidence.

It’s wrong to assume any one of these possibilities. Correlation is a (perhaps strong) hint that there may be a relationship, but identifying the exact nature of that relationship requires more - i.e., a controlled experiments or proper statistical analysis. One needs to examine all of the variables that may influence the relationship and look for evidence supporting or rejecting the influence of each. One also needs to find a mechanism that explains any causal relationship. 

Bad Data

"Bad data" is data that has not been collected accurately or consistently or the data has been defined differently from person to person, group to group and company to company. The huge amount of "bad" data that is regularly served up for analysis may make it irresponsible for one to just "stand back nonjudgmentally and observe linkages," especially in the pre-employment testing process.

In the recruitment and hiring context, unproctored online personality tests are used to allow an applicant to take the test anywhere and anytime. That freedom creates conditions ripe for obtaining "bad' data. As stated by Jim Beaty, PhD, and Chief Science Officer at testing company Previsor:

Applicants who want to cheat on the test can employ a number of strategies to beat the test, including logging in for the test multiple times to practice or get the answers, colluding with another persons while completing the test, or hiring a test proxy to take the test.

And what about the accuracy of tests responses from those who are hired? Analyzing a sample of over 31,000 employees, a data analytic company's researchers found that employees who said they were most likely to follow the rules left the job on average 10% earlier, were 3% less likely to close a sale and were actually not particularly good at following rules.

Bad data exposes a vexing problem for employers. Applicants and employees seek to tell employers what they believe employers want to hear, and employers tend to ask questions that lead applicants employees to answer these questions in the “right” way.

A Simple Want of Careful, Rational Reflection

As noted in a prior post, prejudice rises not from malice or hostile animus alone. It may result as well from insensitivity caused by simple want of careful, rational reflection.

For example, take two insights from Evolv, a data analytics company:

1. Living in close proximity to the job site and having access to reliable transportation—are correlated with reduced attrition and better performance; and
2. Referred employees have 10% longer tenure than non-referred employees and demonstrate approximately equal performance.

An employer confronted with these two insights might well determine that (i) applicants living beyond a certain distance from the job site (i.e., retail store) should be excluded from employment consideration and (ii) preference in hiring should be extended to applicants referred by existing employees. Such a determination may end up being penny wise and pound foolish.

Painting with the broad brush of distance from job site will result in well-qualified applicants being excluded, applicants who might have ended up being among the longest tenured of employees. Remember that the Evolv insight is a generalized correlation (i.e., the pool of persons living closer to the job site tend to have longer tenure than the pool of persons living farther from the job site). The insight says nothing about any particular applicant or employee.

As a consequence, employers will pass over qualified applicants solely because they live (or don't live) in certain areas. Not only does the employer do a disservice to itself and the applicant, they increase the risk of employment litigation, with its consequent costs (attorney fees, damages, reputational harm, etc.). How?

A recent New York Time article, "In Climbing Income Ladder, Location Matters," reads, in part:

Her nearly four-hour round-trip [job commute] stems largely from the economic geography of Atlanta, which is one of America’s most affluent metropolitan areas yet also one of the most physically divided by income. The low-income neighborhoods here often stretch for miles, with rows of houses and low-slung apartments, interrupted by the occasional strip mall, and lacking much in the way of good-paying jobs

The dearth of good-paying jobs in low-income neighborhoods means that residents of those neighborhoods have a longer commute. As to the demographic makeup of low-income families, the 2010 Census showed that poverty rates are much higher for blacks and Hispanics. Consequently, hiring decisions predicated on distance, intentionally or not, discriminate against certain protected classes.

Similarly, an employer extending a hiring preference to referrals of existing employees may be further exacerbating the discriminatory impact of its hiring process. Those referrals tend to be persons from the same neighborhoods and socioeconomic backgrounds of existing employees, meaning that workforce diversity, broadly considered, will decline.

With the huge amounts of "bad" data that get generated and stored daily, the failure to understand how to leverage the data in a practical way that has business benefit will increasingly lead to shaky insights and faulty decision-making, with significant costs both to the employer and society.

A Fool With A Tool Is Still A Fool

Most companies have vast amounts of HR data (employee demographics, performance ratings, talent mobility data, training completed, age, academic history, etc.) but they are in no position to use it. According to Bersin by Deloitte, an HR research and consultancy organization, only 20% believe that the data they capture now is highly credible and reliable for decision-making in their own organization.

Research shows that the average large company has more than 10 different HR applications and their core HR system is over 6 years old. So it will take significant effort and resources (read funding) to bring this data together and make sense of it.

With the huge amounts of "bad" data that get generated and stored daily, the failure to understand how to leverage the data in a practical way that has business benefit will increasingly lead to shaky insights and faulty decision-making, with significant costs both to the employer and society.

As stated by Jim Stikeleather on the Harvard Business Review blog:

Machines don't make the essential and important connections among data and they don't create information. Humans do. Tools have the power to make work easier and solve problems. A tool is an enabler, facilitator, accelerator and magnifier of human capability, not its replacement or surrogate. That's what the software architect Grady Booch had in mind when he uttered that famous phrase: "A fool with a tool is still a fool." 

Understand that expertise is more important than the tool. Otherwise the tool will be used incorrectly and generate nonsense (logical, properly processed nonsense, but nonsense nonetheless). 

Although data does give rise to information and insight, they are not the same. Data's value to business relies on human intelligence, on how well managers and leaders formulate questions and interpret results. More data doesn't mean you will get "proportionately" more information. In fact, the more data you have, the less information you gain as a proportion of the data (concepts of marginal utility, signal to noise and diminishing returns).