Exacerbating Long-Term Unemployment: Big Data and Employment Assessments

A recent Brookings Institution paper states that the “diverse and varied set of characteristics [of the long-term unemployed] implies that a broad array of policies will be needed to substantially lower the long-term unemployment rate and stem labor force withdrawal, as concentrating on any single occupation, industry, demographic group or region is unlikely to have a substantial impact reducing long-term unemployment by itself." Please see On the Margins of the Labor Market.

There is, however, a common employment factor that can be linked to numerous occupations, industries, demographic groups and regions -- online job application processes that require individuals (i) to provide "location-based information" (i.e., distance from job site, commute time, household relocation) and (ii) to complete personality assessments.The screening elements in these processes exclude or penalize persons with lower socioeconomic status - disproportionately Blacks, Hispanics, persons with mental illness, and the less well-educated. The same groups (ex persons with mental illness) that the recent Brookings Institution paper found to comprise a disproportionate percentage of the long-term unemployed.

Jobs that were once filled on the basis of work history and interviews are left to personality tests, data analysis and algorithms. The new hiring tools are part of a broader effort to gather and analyze employee data.  Use of online assessments has grown exponentially over the past 10-15 years, with assessment companies like Kronos now having a database of hundreds of millions of job applicant and employee information. To provide a sense of scale, one major big box retailer processes more than nine million job applications a year.

Personality tests are “growing like wildfire,” said Josh Bersin, president and CEO of Bersin & Associates, an Oakland, Calif., research firm. Bersin estimated that this kind of pre-hire testing has been growing by as much as 20 percent annually in the past few years. Industries that are flooded with resumes such as retail, food service and hospitality are among the ones that use such tests most often, he said.

Employment Redlining: Location-Based Discrimination

Kenexa, an assessment company purchased by IBM in December 2012 for $1.3 billion, will test tens of millions of 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, said 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 otherwise well-qualified applicants being excluded, applicants who might have ended up being among the longest tenured of employees. The Kenexa  findings are generalized correlations; the insights say nothing about any particular applicant. Please see From What Distance is Discrimination Acceptable.


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.

Spatial  Mismatch and its Institutionalization

An NBER study published in April 2014, "Job Displacement and the Duration of Joblessness: The Role of Spatial Mismatch, finds that better job accessibility significantly decreases the duration of joblessness among lower-paid displaced workers. Blacks, females, and older workers are more sensitive to job accessibility than other subpopulations.

The so-called “spatial mismatch hypothesis,” which originally grew out of research on the effects of segregated housing markets, has been debated among economists and social scientists since the 1960s. But while there’s general agreement that “job accessibility” has some impact on unemployment duration, researchers have disagreed about how important it is and for which groups of workers.

Although the study was limited to the 2000-05 period, its conclusion — that “a worker with locally inferior access to jobs is likely to have worse labor market outcomes” — could help explain the current situation. What we know for sure is that as of March 2014, more than a third (35.7%) of all unemployed Americans had been out of work for more than 26 weeks, according to the BLS. Blacks and Asians are most likely to experience extended joblessness: Last month, 44% of unemployed blacks and about as many unemployed Asians had been out of work longer than 26 weeks, versus a third of unemployed whites and 32% of unemployed Hispanics. Please see Long-Term Unemployment and its Costs.

With the "location-based" scoring "insights" provided by companies like Kenexa, spatial mismatch has been institutionalized over the past 5-10 years. If a job applicant has a long commute - whether due to the lack of effective mass transit where the applicant lives or to the lack of access to personal transportation, that applicant may never be interviewed, let alone offered a job.

Mental Illness and Socioeconomic Status

One of the most consistently replicated findings in the social sciences has been the negative relationship of socioeconomic status (SES) with mental illness: The lower the SES of an individual is, the higher is his or her risk of mental illness.

As an example, for the period from 2005-2010, the Centers for Disease Control found that among adults 20–44 and 45–64 years of age, depression was five times as high for those below poverty, about three times as high for those with family income at 100%–199% of poverty, and 60% higher for those with income at 200%–399% of poverty compared with those at 400% or more of the poverty level.

According to a 2001 study, lower income Americans had a higher prevalence of 1 or more psychiatric disorders (51% vs 28%): mood disorders (33% vs 16%), anxiety disorders (36% vs 11%), and eating disorders (10% vs 7%). Consequently, pre-employment assessments using these location-based "insights" screen out persons with mental illness.

Mental Illness and Disability

The prevalence of mental disorders in the U.S. population remained unchanged between 1990 and 2003.  In that same interval, the rate of treatment of mental illness substantially increased—which in turn should have contributed to improved work-readiness among individuals coping with mental illness. The combination of the prevalence of mental disorders remaining unchanged and substantially increased rates of treatment should have resulted in a decline in the percentage of persons receiving SSDI awards who are diagnosed with mental illness. That has not been the case. Please see Costing Taxpayers Billions of Dollars Each Year.

People with psychiatric impairments constitute the largest and most rapidly growing subgroup of Social Security disability beneficiaries. In 2011, 47.5 percent of persons receiving SSI and 31.0 percent of persons receiving SSDI had a mental disorder. These percentages keep growing, in part because beneficiaries with psychiatric impairments are generally younger than other beneficiaries when they become ill and therefore remain on the Social Security rolls much longer.

Some analysts contend that rising disability awards for mental illness reflect a “broken” system that provides benefits to those who should not receive them; others point out that income support makes it easier for persons with mental illness to live in the community. These conflicting conclusions reflect an ongoing debate over whether increasing awards for mental illness represent a policy success because they reach needy individuals or failure because the increased awards reflect moral hazard.

The income support programs may be working as designed, but those programs did not anticipate the impact of the widespread use of pre-employment assessments and the resulting material increase in the absolute number and percentage of unemployed persons with mental disabilities seeking SSDI and SSI benefits as a consequence of the use of potentially  illegal assessments.

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Persistently high long-term unemployment has significant implications for families, government budgets, and the country’s overall economic and social health. The high rate of long-term unemployment has had a direct impact on the federal budget by prompting the extension of normal unemployment benefits, ratcheting up spending on other government safety-net programs (including, indirectly, SSDI, SSI and Medicare) and by reducing taxable wages. Martin Feldstein in a recent article in the Wall Street Journal, draws on the Brookings Institution paper to suggest that those who have been out of work for six months or more do not affect wage inflation and that since the unemployment rate among those out of work for less than six months was only 4.1%, wage inflation may soon begin to rise more rapidly.

The growing and widespread use of employment assessments and applicant data collection processes over the past ten years has likely had an impact on the growth of the long-term unemployed in the U.S. labor market.  Persons with lower socioeconomic status, disproportionately Black, Hispanic, persons with mental illness, and the less well-educated, risk becoming a permanent underclass of the unemployed and underemployed.

Some of the most profound challenges revealed by the recent White House Report "Big Data: Seizing Opportunities, Preserving Values" concern how big data analytics may lead to disparate inequitable treatment, particularly of disadvantaged groups, or create such an opaque decision-making environment that individual autonomy is lost in an impenetrable set of algorithms. Please see White House: Big Data's Role in Employment Discrimination.

Workforce assessment systems, designed in part to mitigate risks for employers, have become sources of material risk, both to job applicants and employers. The systems create the perception of stability through probabilistic reasoning and the experience of accuracy, reliability, and comprehensiveness through automation and presentation. But in so doing, technology systems draw  attention away from uncertainty and partiality. Moreover, they shroud opacity—and the challenges for oversight that opacity presents—in the guise of legitimacy, providing the allure of shortcuts and safe harbors for actors both challenged by resource constraints and desperate for acceptable means to demonstrate compliance with legal mandates and market expectations.

Gut Check: How Intelligent Is Artificial Intelligence?

In 6 Ways to Create a Smarter Workforce in 2014, Tim Geisert, Kenexa's Chief Marketing Officer, writes:

Use science, precision and data to hire the right people for the job the first time. According to a 2012 IBM study, 71 percent of CEOs surveyed cited human capital as their greatest source of sustained economic value. So, why does HR continue to rely on gut instinct alone to make such important decisions?

Perhaps Mr. Geisert should have spoken with Rudy Karsan, one of Kenexa's founders and its CEO, who wrote in Listening to Your Gut Feeling:

On the big decisions I have gone against my gut on a couple of occasions and it’s been a train wreck. My gut is made up of my instinct, my faith, my intuition, my experiences and data that is currently inaccessible because it’s tucked away in the deep recesses of my brain.

Then again, perhaps Mr. Karsan should have spoken with Troy Kanter, President of Human Capital Management for Kenexa, who stated in a press release:

Now, instead of making hiring decisions based on 'gut' feelings and personal likes and dislikes, hiring managers and HR can select candidates based on objective data, which also prevents potential legal ramifications and mitigates risk in the hiring process."

So, two of the Kenexa executives believe that going with your gut is bad idea, but the most senior Kenexa executive believes that going against your gut is an accident waiting to happen. Who's right?

What Does Data Tell Us?

The benefits provided by the use of pre-employment assessments, whether called workforce science, talent analytics or any other name, should be readily apparent and quantifiable. For example, has the rising use of pre-employment assessments over the past 10-15 years resulted in greater employee engagement?

Gallup has measured employee engagement since 2000 and it defines “engaged” employees as those who are involved in, enthusiastic about, and committed to their work and contribute to their organization in a positive manner. The 2013 Gallup report shows that 70% of American workers are “not engaged” or “actively disengaged” and are emotionally disconnected from their workplaces.

Is the employee engagement data from the 2013 report an anomaly? No. As shown in the following chart taken from the 2013 Gallup report, there has been little change workplace engagement levels since 2000.

Contrast the lack of change in employee engagement with the marketing of pre-employment assessments, like this selection from the Kronos website:

Your employees are the face of your brand and the most vital asset of your business. They drive your productivity and profitability. What’s more important than selecting the right ones? Take the guesswork out of employee selection with industry-specific, behavioral-based assessments and interview guides [from Kronos].

Gallup’s research shows that employee engagement is strongly connected to business outcomes essential to an organization’s financial success, including productivity, profitability, and customer satisfaction. Yet, as the report states, "workplace engagement levels have hardly budged since Gallup began measuring them in 2000."

Brain vs Computer

In "Thinking In Silicon," a December 2013 article in the MIT Technology Review, Tom Simonite writes:

Picture a person reading these words on a laptop in a coffee shop. The machine made of metal, plastic, and silicon consumes about 50 watts of power as it translates bits of information—a long string of 1s and 0s—into a pattern of dots on a screen. Meanwhile, inside that person’s skull, a gooey clump of proteins, salt, and water uses a fraction of that power not only to recognize those patterns as letters, words, and sentences but to recognize the song playing on the radio. 

All today’s computers, from smartphones to supercomputers, have just two main components: a central processing unit, or CPU, to manipulate data, and a block of random access memory, or RAM, to store the data and the instructions on how to manipulate it. The CPU begins by fetching its first instruction from memory, followed by the data needed to execute it; after the instruction is performed, the result is sent back to memory and the cycle repeats. Even multicore chips that handle data in parallel are limited to just a few simultaneous linear processes. 

Brains compute in parallel as the electrically active cells inside them, called neurons, operate simultaneously and unceasingly. Bound into intricate networks by threadlike appendages, neurons influence one another’s electrical pulses via connections called synapses. When information flows through a brain, it processes data as a fusillade of spikes that spread through its neurons and synapses. You recognize the words in this paragraph, for example, thanks to a particular pattern of electrical activity in your brain triggered by input from your eyes. Crucially, neural hardware is also flexible: new input can cause synapses to adjust so as to give some neurons more or less influence over others, a process that underpins learning. In computing terms, it’s a massively parallel system that can reprogram itself.

Okay, but what about computing at the bleeding edge, like the "cognitive computing" of IBM's Watson?

Not So Elementary

According to a January 9, 2014 article in CIO.com, IBM says cognitive computing systems like Watson are capable of understanding the subtleties, idiosyncrasies, idioms and nuance of human language by mimicking how humans reason and process information.

Whereas traditional computing systems are programmed to calculate rapidly and perform deterministic tasks, IBM says cognitive systems analyze information and draw insights from the analysis using probabilistic analytics. And they effectively continuously reprogram themselves based on what they learn from their interactions with data.

Said IBM CEO Ginni Rometty, "In 2011, we introduced a new era [of computing] to you. It is cognitive. It was a new species, if I could call it that. It is taught, not programmed. It gets smarter over time. It makes better judgments over time." "It is not a super search engine," she adds. "It can find a needle in a haystack, but it also understands the haystack."

This "new species" of computing has its challenges. According to "IBM Struggles to Turn Watson Computer Into Big Business," a recent Wall Street Journal article:

Watson is having more trouble solving real-life problems than "Jeopardy" questions, according to a review of internal IBM documents and interviews with Watson's first customers. 

For example, Watson's basic learning process requires IBM engineers to master the technicalities of a customer's business—and translate those requirements into usable software. The process has been arduous.

Klaus-Peter Adlassnig is a computer scientist at the Medical University of Vienna and the editor-in-chief of the journal Artificial Intelligence in Medicine. The problem with Watson, as he sees it, is that it’s essentially a really good search engine that can answer questions posed in natural language. Over time, Watson does learn from its mistakes, but Adlassnig suspects that the sort of knowledge Watson acquires from medical texts and case studies is “very flat and very broad.” In a clinical setting, the computer would make for a very thorough but cripplingly literal-minded doctor—not necessarily the most valuable addition to a medical staff.

As Hector J. Levesque, a professor at the University of Toronto and a founding member of the American Association of Artificial Intelligence, recently wrote:

 "As a field, I believe that we tend to suffer from what might be called serial silver bulletism, defined as follows:

the tendency to believe in a silver bullet for AI, coupled with the belief that previous beliefs about silver bullets were hopelessly naıve. 

We see this in the fads and fashions of AI research over the years: first, automated theorem proving is going to solve it all; then, the methods appear too weak, and we favour expert systems; then the programs are not situated enough, and we move to behaviour-based robotics; then we come to believe that learning from big data is the answer; and on it goes."

Similarly, assessment companies have marketed the benefits of "science, precision and data" over the past fifteen years under the guise of neural networks, artificial intelligence, big data and deep learning, yet what has changed? Employee engagement levels have hardly budged and employee turnover remains a continuing and expensive challenge for employers.

The more things change, the more they remain the same or, in deference to Monsieur Levesque "plus ça change, plus c'est la même chose."