Employment Challenges for Persons with Serious Mental Illness

This post is comprised of excerpts from testimony by Dr. Gary Bond, Professor of Psychiatry, Dartmouth Psychiatric Research Center to the EEOC at a March 15, 2011 hearing on employment of persons with disabilities. Please click on this link for the complete written version of the testimony, together with all references.

There are many benefits of employment—work enhances skills such as communication, socialization, academics, physical health, and community skills; it factors into how one is perceived by society; it promotes economic well-being; it leads to greater opportunity for upward mobility; and it contributes to greater self-esteem. Yet only 15 percent of those with a mental disability are in the labor market

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People with serious mental illness are a very heterogeneous group that has included Nobel Prize winners, American Presidents, artists, and other famous persons, as well as many who live in poverty and isolation. You cannot judge a person by their diagnosis. While the public has many negative stereotypes about this group, the take-home message from this testimony is that the research strongly demonstrates that full recovery from mental illness is possible. Working is a crucial element in this recovery process.

What is Serious Mental Illness?

This testimony concerns the population of people with serious mental illness, defined by three criteria: 

(a) Diagnosis: a psychiatric diagnosis of schizophrenia, bipolar disorder, or other major psychiatric disorder;

(b)Disability: significant role impairment, in areas such as independent living, interpersonal functioning, and employment;

(c) Duration: extended involvement with the mental health system (such as admission to psychiatric hospitals, supervised group homes, and mental health case management services). 

This is a large segment of the disability population. For example, over one-third of people of the Social Security disability roles have a serious mental illness.

Employment of People with Serious Mental Illness

Employment rates for people with serious mental illness are very low. Surveys have found that only 10% - 15% of people with serious mental illness receiving community treatment are competitively employed. Rates are even lower, typically less than 5%, in follow-up surveys of people discharged from psychiatric hospitals National and international surveys of community samples, which include respondents with less serious disorders, have reported employment rates of 20% - 25% for people with schizophrenia and related disorders.

The employment rate for people with serious mental illness is less than half the 33% rate for other disability groups Both rates are of course much lower than for the general population. Even during the height of the current recession, the national employment rate for adults in the general population was 72%, according to U.S. Bureau of Labor statistics.

Moreover, people with serious mental illness who are working are often underemployed. Nearly twice as many workers with mental illness earn at or near minimum wage as workers without disabilities. Non-standard jobs (such as temporary employment, independent contracting, and part-time employment) are common among workers with serious mental illness. Such jobs pay lower wages with fewer benefits. Among those employed, people with serious mental illness are overrepresented in unskilled occupations, such as in the service industries and as laborers.

Most People with Serious Mental Illness Want to Work

Despite these dismal employment statistics, most people with severe mental illness want to work. Studies indicate that approximately 2 out of every 3 people with mental illness are interested in competitive employment Moreover, these rates may understate the interest in working in this population, because many mental health professionals discourage clients from pursuing employment goals.

Barriers to Getting and Keeping Jobs

Why, then, is there a wide disparity between employment rates and desire to work? The reason is not that people with serious mental illness cannot work. People with serious mental illness are capable of working if they are matched to appropriate jobs and receive appropriate supports. But attitudinal, service, and system barriers are challenges to their employment. According to a national survey, persons with serious mental illness reported the primary barriers to employment to be:

• stigma and discrimination (45%); 
• fear of losing benefits (40%);
• inadequate treatment of disability (28%);
• lack of vocational services (23%).

Regarding attitudinal barriers, psychiatric disability is the most stigmatizing of all disabilities. One national survey reported that only 19% of those polled were “very comfortable with people with mental illness,” compared to triple than rate for people with a physical disability (National Organization on Disability, 1991). People with serious mental illness experience discrimination and negative attitudes constantly in everyday life. Employers are less likely to hire someone whom they believe has a mental illness.

One major attitudinal barrier is the perception that people with mental illness are violent. News stories involving atrocities committed by people with mental illness reinforce this perception. Based on extensive epidemiological research over the past two decades, we have a much better understanding of the risk factors for violence in the psychiatric population. Violence is exceedingly rare among people with mental illness, and the rare instances that do occur are associated with other factors, such as active substance use or refusing to take medications. Being employed significantly reduces the possibility of violence even further. In sum, a very low proportion of people with mental illness have a history of violence and overall people with mental illness are no more likely to behave violently than people without mental illness.

Another barrier is the fear of losing Social Security disability benefits (MacDonald-Wilson, Rogers, Ellison, & Lyass, 2003) Many of these apprehensions are based on lack of information and misconceptions. In one survey, 85% of Social Security disability beneficiaries incorrectly believed that Medicaid benefits would be terminated if they went to work (MacDonald-Wilson, Rogers, Ellison et al., 2003). Fortunately, when provided accurate information about the impact of employment, beneficiaries substantially increase their employment earnings (Tremblay, Smith, Xie, & Drake, 2006).

Once employed, people with serious mental illness often need ongoing support and accommodations to succeed. But employers are far less willing to accommodate people with psychiatric disabilities than those with physical conditions. Workers with mental health conditions are half as likely to receive accommodations as those with other disabilities. This is true even though most accommodations for psychiatric disabilities cost very little or nothing, in contrast to technological and architectural changes required for other disabilities. According to one employer survey, the kinds of functional limitations they most commonly observe in workers with psychiatric disabilities are cognitive (e.g., following instructions, concentrating) and social (e.g., interacting, reading social cues), and to a lesser extent emotional (e.g., managing symptoms, tolerating stress) and physical (e.g., stamina).

The onset of serious mental illness often occurs in early adulthood, interfering with the completion of education. Over 30% of people with severe mental illness have not completed high school. Low educational attainment contributes to the underemployment of people with serious mental illness. The median annual income of the U.S. population without a high school diploma or equivalent is less than $20,000. Median income increases 33% with completion of high school and more than triples with the completion of a bachelor’s degree.

Conclusion

The dismal rate of employment among people with serious mental illness is a formidable challenge. Nonetheless, we have compelling reasons to be optimistic that people with serious mental health problems can work and that working helps them to recover from mental illness. The major barriers to employment are not immutable clinical or cognitive characteristics but rather attitudes and lack of access to support services and accommodations.

Even in the absence of professional support, employers can play a pivotal role promoting the employment of people with serious mental illness by approaching applicants and workers as individuals and applying sound employment practices. Work accommodations typically involve pragmatic and inexpensive modifications. Employment is a win-win for people with serious mental illness, for employers, and for society at large.

Algorithms: Deeply Human Choices Behind Cold Mechanisms

This post is comprised of excerpts and a graphic from Rethinking Personal Data: A New Lens for Strengthening Trust, a document published by the World Economic Forum and  prepared in collaboration with A.T. Kearney. The document addresses the key trust challenges facing the personal data economy, and offers a set of near-term and long-term insights for addressing these issues.  

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Complex and opaque, algorithms generate the predictions, recommendations and inferences for decision-making in a data-driven society. While easily dismissed as abstract empirical processes, algorithms are deeply human. They reflect the intentions and values of the individuals and institutions which design and deploy them. The ability for algorithms to augment existing power asymmetries gives rise to debates on their influence over data-driven policy-making.

The nature of these debates are complex, value-laden and give rise to some fundamental societal choices. Questions of individual autonomy, the sovereignty of individuals, digital human rights, equitable value distribution and free will are all a part of these conversations. There are no easy answers. Through this long-term lens on the impact of proactive computing, the focal point for discussion begins to shift away from personal data, per se, to computer-based profiles of individuals and groups of individuals. These profiles — fueled by fine-grained behavioral and sensor data — make it possible to monitor, predict and instrument social phenomena at the micro and macro levels.

The world of “smart” environments, where cars, eyeglasses and just about everything else coalesce into the Internet of Things, creates a sea change in how data will be processed. Rather than being based on “interactive” human-machine computing, smart environments rely upon “proactive computing”. By design, these proactive environments are one step ahead of individuals. Connected cars need to anticipate accidents before they happen. Evacuating flood prone areas needs to occur before major storms hit.

The emphasis on proactive computing will change the role of human intervention from a governance perspective. Lacking a full understanding of how complex systems work, the ability of humans to understand, make decisions and adapt can be too slow, incomplete and unreliable. In this brave new world, building trust from the “principles up” will be essential and require new forms of governance that are open, inclusive, self-healing and generative.

From a community and societal perspective, as civil “regulation-by-algorithm” begins to scale, incumbent interests and power asymmetries will play an increasing role in establishing who gets
access to an array of commercial and governmental services. As such, there is a need to ensure that the algorithms driving proactive and anticipatory decisions will be lawful, fair and can be explained intelligibly. Meaningful responses must be given “when individuals are singled out to receive differentiated treatment by an automated recommendation system”.

One emerging set of concerns is the institutional ability “to discover and exploit the limits of an individual’s ability to pursue their own self-interest.” Given that a majority of consumer interactions in the future will be mediated via devices and commercially oriented communications platforms, data-centric institutions will have the means and incentives to trigger “predictable irrationality”
from individuals.

With a vast trail of “digital breadcrumbs” accessible for companies to mine and tailor highly personalized experiences, a growing set of concerns is arising on how individuals could be profiled and targeted at moments of key vulnerability (decision fatigue, information overload, etc.) and limit their ability to act with agency and in their own self-interest. With the lives of individuals becoming increasingly mediated by algorithms, a richer understanding is needed for how people adapt their behaviors to empower themselves and gain more control over the manner of how profiles and algorithms shape their lives in areas such as credit scores, retail experiences, differential pricing, reputational currencies, insurance rates, etc.

One of the most strategic insights on strengthening trust is the concept of exploring ways to share intended consequences of data usage to individuals. For example, the 2012 Draft European Data Protection Act (section 20), calls for “the obligation for data controllers to provide information about the envisaged effects of such processing on the data subject”.

To address this emerging set of concerns, establishing a cross-disciplinary community of forward-looking experts, complexity scientists, biologists, policy-makers and business leaders with an appreciation of the long-term societal impact was identified as a priority. This group would proactively help design and test systems that balanced the commercial, legal, civil and technological
incentives shaping outcomes at the individual and social level. They would need to develop some form of legal protection to limit liabilities and provide a safe space to explore complex issues in a
real-world setting. One attribute of this safe space would be for it to be governed by an institutional review board where ethics and the interests of individuals could have a meaningful and relevant voice (similar to how they are used by the biomedical and behavioural science sectors). Institutions concerned about legal uncertainties, regulatory action or civil lawsuits could have a richer means for assessing ethical concerns using these approaches.

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.

Long Term Unemployment and its Costs

In the first quarter of 2012 (the 3-month period from January to March), approximately 29.5 percent of the nearly 13.3 million Americans who were unemployed had been jobless for a
year or more, according to data released by the U.S. Department of Labor’s Bureau of Labor Statistics (BLS). That percentage translates into 3.9 million  workers, slightly more than the population of Oregon. The total unemployment rate (short-term and long-term unemployed) is currently at 6.3%, but an unusually large one-third of those who are counted as unemployed have been out of work for more than six months.

According to Pew analysis of Current Population Survey (CPS) data from the BLS, the percentage
of jobless workers who had been unemployed for a year or more reached  a peak of 31.8 percent in the third quarter of 2011. Despite modest improvement in the first quarter of 2012, the rate of long-term unemployment among the jobless remained stubbornly high. In fact, it was more than triple the 9.5 percent rate that it was in the first quarter of 2008, the first quarter of the Great Recession (see following table).

As discussed in A Year or More: The High Cost of Long-Term Unemployment, a report released by the Pew Fiscal Analysis Initiative in April 2010, persistently high long-term unemployment has significant implications for families, government budgets, and the country’s overall economic and social health.

A 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 (i.e., SSDI and SSI) and by reducing taxable wages. Please see Costing Taxpayers Billions of Dollars.

Long-term unemployment also affects the federal budget on the other side of the fiscal ledger by reducing income tax revenue and the amount of money flowing into the unemployment insurance pool. Unemployment benefits are taxable, but people on the unemployment rolls are receiving only a fraction of the income they would be getting if they were working. As a result, they are paying only a fraction of the taxes.

On the Margins of the Labor Market

In “Are the Long-Term Unemployed on the Margins of the Labor Market?” Alan B. Krueger, Judd Cramer, and David Cho of Princeton University find that even after finding another job, reemployment does not fully reset the clock for the long-term unemployed, who are frequently jobless again soon after they gain reemployment. Even in good times, the long-term unemployed are on the margins of the labor market, with diminished job prospects and high labor force withdrawal rates, and as a result they exert little pressure on wage growth or inflation.

Justin Wolfers, a Senior Fellow, Economic Studies at Brookings Institution, the publisher of the paper, discusses the findings of the research paper in the video below:

Transitioning from Unemployment to Employment

The graphic below displays annual averages of monthly transition rates from unemployment to employment each year since 1994 for five duration of unemployment categories:

1. Unemployed less than 5 weeks
2. Unemployed for 5 to 14 weeks
3. Unemployed for 15 to 26 weeks
4. Unemployed for 27 to 52 weeks
5. Unemployed for 52 weeks and over

Probability of Transitioning from Unemployment to Employment by Duration of Unemployment

A few patterns are clear. First, the job-finding rate is lower for those with a longer duration of unemployment, with the long-term unemployed finding jobs at less than half the rate of those very short-term unemployed. Second, the cyclicality of job finding is clear in these data, with all rates declining during the recession of the early 2000s, and declining more dramatically during the Great Recession. Third, job finding rates for all groups remain well below their pre-Great Recession averages. Fourth, the job finding rate has risen for each group in the last four years, although it has barely increased for those unemployed longer than a year.
In 2013, just under 10 percent of those who had been unemployed for more than one year transitioned into employment in the average month. This rate, though higher than in many European countries, might overstate how well the long-term unemployed are faring due to measurement error and the fact that the long-term unemployed are more likely to take low-paying, part-time jobs and temporary jobs

On the Margins

The total unemployment rate (short-term and long-term unemployed) is currently at 6.3%, but an unusually large one-third of those who are counted as unemployed have been out of work for more than six months. The research paper indicates that the longer workers are unemployed the less they become tied to the job market, either because, on the supply side, they grow discouraged and search for a job less intensively or because, on the demand side, employers discriminate against the long-term unemployed, based on the (rational or irrational) expectation that there is a productivity-related reason that accounts for their long jobless spell.

The demand-side and supply-side effects of long-term unemployment can be viewed as complementary and reinforcing of each other as opposed to competing explanations, as statistical discrimination against the long-term unemployed could lead to discouragement, and skill erosion that accompanies long-term unemployment could induce employers to discriminate against the long-term unemployed.

As shown in the graphic below, even after finding another job, reemployment does not fully reset the clock for the long-term unemployed, who are frequently jobless again soon after they gain reemployment: only 11 percent of those who were long-term unemployed in a given month returned to steady, full-time employment a year later.

Comparing the Unemployed and Employed

If the long-term unemployed are compared to the short-term unemployed, a larger proportion of the long-term unemployed are over age 50.

African Americans and Hispanics are also over represented among the ranks of the unemployed compared with the employed. For example, African Americans comprise 22 percent of the long-term unemployed, compared with just 10 percent of the employed population.

If the unemployed as a whole are compared to the employed, notably larger shares of the unemployed are less well educated. For example, although about one third of employed workers have earned a bachelor’s degree, less than 20 percent of the unemployed have done so. By contrast, nearly 20 percent of the unemployed lack a high school diploma, which is twice the rate for the employed.

The long-term unemployed have problems finding work wherever they are, even in states with the lowest unemployment – Hawaii, Iowa, Kansas, Minnesota, Montana, Nebraska, New Hampshire, North Dakota, Oklahoma, South Dakota, Utah, Vermont, Virginia, and Wyoming – where the average unemployment rate was 4.4 percent (compared to 7.0 elsewhere). Even in those 14 states, long-term unemployment grew dramatically during the recession, reaching 4.5 times its historical average, suggesting that long-term unemployment will be a lingering problem even if the unemployment rate returns to normal.

Overall, there is little evidence to suggest that the long-term unemployed fare substantially better in the states with the lowest unemployment rates, consistent with the idea that the long-term unemployed are on the margins of the labor force, even where the economy is stronger.

Unlucky Subset 

The portrait of the long-term unemployed in the U.S. that emerges here suggests that, to a
considerable extent, they are an unlucky subset of the unemployed. Their diverse and varied set
of characteristics 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. Understanding the labor market and personal hurdles faced by the long-term unemployed should be a priority for future research in order to craft solutions to reduce long-term unemployment.

To the authors of the paper, the most important policy challenges involve designing effective interventions to prevent the long-term unemployed from receding into the margins of the labor market or withdrawing from the labor force altogether, and supporting those who have left the labor force to engage in productive activities. Overcoming the obstacles that prevent many of the long-term unemployed from finding gainful employment, even in good times, will require a concerted effort.

Primer on Big Data and Hiring: Chapter 7

This is the seventh chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 7: Optimize

Closed-Loop Optimization Constantly Analyzes and Refines Insights

According to Evolv, "closed-loop optimization is the process of using Big Data analytics to determine the outcomes of the assessments and other data collected, and then using the knowledge gained to make ever more effective assessments." Click on this link for an Evolv video that describes the closed-loop optimization process.

The challenge in using a closed-loop optimization process for hiring and employment decisions is that those decisions do not fit within a closed loop. Take for example the Evolv insight that living in close proximity to the job site are correlated with reduced attrition and better performance. Over time, the closed-loop optimization process for that insight means that a growing percentage of the workforce lives in close proximity to the job site. Excellent. Less attrition and better performance across jobsite.

That closed loop, however, does not account for factors like the element of time and the relative immobility of persons and companies. Businesses tend to be clustered; they are not evenly spread throughout the geography. If all businesses in a particular area focus on hiring applicants in close proximity, costs will increase (greater demand for the same number of applicants), employee turnover will increase (since the number of geographically-proximate employees changes slowly) and profitability will decrease (higher wage costs combined with greater turnover).

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

• A causes B
• B causes A
• A causes B at the same time as B causes A (a self-reinforcing system)
• Some third factor causes both A and B

The correlation is simple coincidence. It is wrong to assume any of these possibilities. Evolv, however, assumes that A (proximity to job site) causes B (reduced attrition and better performance). Therefore, employers should hire applicants who live closer to the job site. 

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.

As David Brooks wrote, "Data struggles with context. Human decisions are not discrete events. They are embedded in sequences and contexts. ... Data analysis is pretty bad at narrative and emergent thinking, and it cannot match the explanatory suppleness of even a mediocre novel."

Executives and managers frequently hear about some new software billed as the “next big thing.” They call the software provider and say, “We heard you have a great tool and we’d like a demonstration.” The software is certainly seductive with its bells and whistles, but its effectiveness and usefulness depend upon the validity of the information going in and how the people actually work with it over time. Having a tool is great, but remember that a fool with a tool is still a fool (and sometimes a dangerous fool).

Primer on Big Data and Hiring: Chapter 6

This is the sixth chapter of a primer on big data and hiring. The structure of the primer is based on the following graphic created by Evolv, a company that provides "workforce optimization" services. Evolv was selected not because it is sui generis; rather, it is emblematic of numerous companies, from start-ups to well-established companies that market "workforce science" services to employers.

The Evolv graphic below is intended to illustrate the process of workforce science.

Chapter 6: Evaluate and Act

Analyzed Data Reveals Insights That Drive Workforce Performance and Retention

The Impact of Insights Are Quantified and Used to Inform Decision-making

As noted in a prior post, prejudice does not rise 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:

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.

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., persons living closer to the job site tend to have longer tenure than persons living farther from the job site). The insight says 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. 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. 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 races.

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 to applicants, employees , employers  and society.