by Paige Marta Skiba, Vanderbilt University; Dalié Jiménez, University of California, Irvine; Michelle McKinnon Miller, Loyola Marymount University; Pamela Foohey, Indiana University, and Sara Sternberg Greene, Duke University
COVID-19 is exacerbating the existing challenges of accessing bankruptcy at a time when these vulnerable groups – who are bearing the brunt of both the economic and health impact of the coronavirus pandemic – may need its protections the most.
If Americans think about turning to bankruptcy for help, they will likely find a system that is ill-prepared for their arrival.
It’s a hard road
There are many benefits to filing bankruptcy.
For example, it can allow households to avoid home foreclosure, evictions and car repossession. The “automatic stay” triggered at the start of the process immediately halts all debt collection efforts, garnishments and property seizures. And the process ends with a discharge of most unsecured debts, which sets people on a course to regain some financial stability.
We know from our empirical research, however, that filing for bankruptcy comes with costs. In a Chapter 7 case, known as a liquidation when a debtor’s property is sold and distributed to creditors, households may be required to surrender some of their assets. The post-bankruptcy path to financial stability is often bumpy.
In the last 10 days of March, when states began issuing such orders, we found that Chapter 13 filings fell 45% compared with the last 10 days of March 2019, based on a docket search on Bloomberg Law. Filings in all of April – when most states were under lockdown – plunged 60%, while Chapter 7 filings were down 40%.
In somedistricts, only attorneys can file electronically, so people handling the process themselves must mail in their petition or find some other way of getting it to the courts, such as via physical drop boxes.
But such methods still assume access to technology. A computer, the internet and a printer are needed to access and print the petition. Libraries and other institutions that traditionally provide technology access for those who do not have it are, for the most part, closed.
Some courts are allowing initial email submission of the petition from those without attorneys, but petitioners are still required to follow up by sending original documents via the mail or drop boxes. Access to a computer, the internet and a printer remains necessary.
Finally many states require “wet signatures” on bankruptcy petitions. That is, people have to sign their names in ink, as opposed to using an electronic signature. To smooth filings while courts are physically closed, several states have waived this requirement for those using an attorney.
But even then, access issues still abound. People must first send their attorney the vast array of documents needed for filing – typically amounting to dozens of pages. Filers still need to be able to copy, scan and email documents. For those without computer access, they have to mail original documents, a somewhat risky proposition when important papers could get delayed, stolen or lost.
A bad time to file
In other words, the middle of a pandemic is not the best time to file for bankruptcy.
The legislation is an emergency intervention to provide paid leave and other support to millions of workers sidelined by school closures, quarantines and caregiving.
An obvious question you’re probably wondering is, “How will it affect me?”
The bad news is that the law does not provide blanket coverage for all workers. Instead, it’s a confusing mess – legislative Swiss cheese, full of exceptions and gradations that affect whether you are covered, for how long and how much pay you can expect to receive.
I study employment law and have combed through the bill to make sense of it. The law also provides emergency funding for unemployment insurance and subsidizes some employer health care premiums, but my focus here is on the core elements pertaining to sick and family leave.
Here’s what I learned.
Small, medium or large
To figure out whether you are covered, the first thing you’ll need to answer is how many people work at your company.
If your employer has 500 or more workers, it is excluded from the new law. Instead, workers at those companies will need to rely on any remaining sick leave benefits available under company policy or state law.
Several states, including New York, California and Washington, are also considering emergency legislation tied to the coronavirus pandemic and may offer some relief for workers at these bigger companies. These workers can also make use of the 1993 Family and Medical Leave Act, which provides for unpaid leave if the employee or a family member falls seriously ill.
In addition, some large employers have made new accommodations for their workers. Walmart, the nation’s largest employer, for example, has extended its sick leave benefits for hourly workers. And coffee chain Starbucks expanded its existing sick leave policy to provide paid leave of up to 26 weeks if an employee contracts COVID-19 and is unable to return to work.
If your company employs fewer than 500 people, you should be covered by the new law. But there’s another exception: Businesses with fewer than 50 employees can make use of a hardship exemption if providing leave might put them out of business.
Assuming your company is covered, the amount of leave available – and how much workers can expect to get paid – will depend on the reason you aren’t able to report to work or do your job remotely.
Here’s where it gets really complicated.
If you are stuck at home due to the closure of a child’s school or day care, you will be eligible for leave under two separate parts of the new law – paid sick leave and family and medical leave.
Congress seems to have structured the law to allow working parents sidelined by a school closure to use both forms of leave at once. Parents would request up to 12 weeks of leave as family and medical leave for a school closure. But since this part of the law doesn’t offer pay until the third week, parents could use the new sick leave provisions to receive income for the first two weeks.
Whether you’re using sick or family leave, you can expect to receive two-thirds of your usual pay, or up to US$200 per day. The money would come directly from your employer who will be reimbursed by tax credits.
Alternatively, people could use the sick leave for the first two weeks and then take 12 weeks under family leave, for a total of 14 weeks, but that would include two weeks that are unpaid.
If you have any available vacation or sick pay under your company’s policy, you may want to use that first since it typically provides full pay.
What happens if you get sick
Workers who are directly affected by the new coronavirus can expect more generous income replacement – but only briefly.
If you are under government-ordered quarantine or isolation, self-isolating at the instruction of a health care provider or experiencing COVID-19 symptoms and seeking a medical diagnosis, you can make use of the new federal sick leave law for up to two weeks. During this time, you should receive your usual pay, capped at $511 per day.
If you become seriously ill beyond two weeks, the new law does not offer additional paid leave. However, you may be eligible to take another 12 weeks of unpaid leave under the 1993 Family and Medical Leave Act. This covers only companies with more than 50 people and workers employed there for longer than 12 months. During this time, your job is protected, but you may be required to use any accrued sick leave or vacation available under company policy.
The rules are similar if you are caring for someone who is under government-ordered quarantine or isolation or has been ordered to self-isolate by a health care provider. The only difference is that your income would be only two-thirds of your usual pay, capped at $200 a day, for two weeks.
And again, if you are caring for a family member who becomes seriously ill, you may be able to take up to 12 weeks of unpaid leave under the 1993 act without losing your job.
In normal times, legislation like this would have been considered broad and ambitious, but as the crisis deepens, its exclusions will likely leave vulnerable workers exposed. With another stimulus bill in the works, Congress will have another chance to help Americans whose lives have been turned upside down by this pandemic.
Job prospects for young men who only have a high school diploma are particularly bleak. They are even worse for those who have less education. When young men experience joblessness, it not only threatens their financial well-being but their overall well-being and physical health.
Could a high quality and specialized technical education in high school make a difference?
Based on a study I co-authored with 60,000 students who applied to the Connecticut Technical High School System, the answer is: yes.
To reach this conclusion, we studied two groups of similar students: Those who barely were admitted to the Connecticut Technical High School System and those who just missed getting in. Students apply to these high schools and submit things such as test scores, attendance and discipline records from middle school. Then, applicants are ranked on their score and admitted in descending order until all seats are filled. We compared those whose score helped them get the last space in a school, to those who just missed being admitted because the school was out of space.
This enabled us to determine whether there was something special about Connecticut’s Technical High School System education that gave students an advantage over peers who also applied, but didn’t get into one of the system’s 16 technical schools across the state.
Connecticut Technical High School System is a popular choice for students – about 50% more students apply than can be admitted.
The system functions such that students can apply to attend a school in the tech system instead of their assigned public school. Statewide, the system schools – which offer specialized instruction in a variety of career fields – serve about 10% of the high school students. Most students who don’t get into the tech schools stay in their public high school.
What we found is that students who were admitted to the Connecticut Technical High School System went on to earn 30% more than those who didn’t get admitted. We also found that the tech school students were 10 percentage points more likely to graduate from high school than applicants who didn’t get in – a statistically significant finding.
Our research suggests that expanding a technical high school system like the one in Connecticut would benefit more students. I make this observation as one who examines outcomes associated with career and technical education.
Career and technical education does this without taking away from general learning in traditional subjects like math and English. But based on my experience, it has never been clear as to whether career and technical education makes a difference on a system-wide level rather than at just one or among a few select schools.
Our recent study finally answers that question because we studied an entire state technical high school system. Specifically, it shows that, yes, career and technical education can give students the same benefits that it has already been shown to give on a smaller level even if it’s scaled up. This has implications for school districts and states, especially as growing interest in what works in career and technical education.
The appeal of technical education in Connecticut
Once admitted into the Connecticut technical high school system, all students take career and technical education coursework instead of other electives, such as world languages, art or music. Typically, coursework is grouped into one of 10 to 17 programs of study, such as information technology, health services, cosmetology, heating ventilation and air conditioning, and production processes, among others. Traditional public high schools in the state, on the other hand, tend to offer at most four career and technical programs through elective courses.
In the Technical High School System schools in Connecticut, students explore various programs of study during their first year. Then – with help from an adviser – students select a program of study. Within these programs, students take at least three aligned courses and often more. They also have more opportunity to align academic and technical coursework materials, so that math and English content can often be integrated into technical courses. Chances for work-based learning and job exposure can also be enhanced in these settings, which may contribute to their impact.
To figure out if these technical schools were making a difference, we looked at admissions from 2006-2007 through 2013-2014 for 60,000 students.
We found that – compared to students who just missed being admitted – technical high school students had:
• Higher 10th grade test scores (like moving from the 50th to the 57th percentile, which is a significant jump for high school test scores)
• A greater likelihood of graduating from high school, about 85% versus 75% for those who just missed being admitted
• Higher quarterly earnings, over 30% higher
• While we found a lower likelihood of attending college initially, no differences were seen by age 23
As educators, elected officials and parents search for more effective ways to give young men in high school a better shot at being able to earn a living, our study suggests that Connecticut might have already figured it out.
Two years ago, ProPublica and The New York Times revealed that companies were posting discriminatory job ads on Facebook, using the social network’s targeting tools to keep older workers from seeing employment opportunities. Then we reported companies were using Facebook to exclude women from seeing job ads.
Experts told us that it was most likely illegal. And it turns out the federal government now agrees.
A group of recent rulings by the U.S. Equal Employment Opportunity Commission found “reasonable cause” to conclude that seven employers violated civil rights protections by excluding women or older workers or both from seeing job ads they posted on Facebook.
The agency’s rulings appear to be the first time it has taken on targeted advertising, the core of Facebook’s business. “It answers the question from the EEOC’s perspective,” former agency commissioner Jenny R. Yang said. “If you’re excluding older workers from seeing your ads for jobs it does violate” anti-discrimination laws. The EEOC declined to comment.
The decisions stem from complaints filed by the Communications Workers of America, the American Civil Liberties Union and plaintiff’s attorneys after our reporting. The agency made the rulings in July, but they are becoming public now as part of a separate pending class-action suit in federal court accusing companies of age discrimination.
The ads are all from 2018 or earlier. Since then, Facebook has agreed in a settlement to make sweeping changes to the way employers, landlords and creditors can target advertising. The changes are scheduled to take effect by the end of the year.
A Facebook spokesperson pointed to the company’s recent changes and said, “Helping prevent discrimination in housing, employment or credit ads is an area we believe we lead the advertising industry.”
In the latest rulings, the EEOC cited four companies for age discrimination: Capital One, Edwards Jones, Enterprise Holdings and DriveTime Automotive Group. Three companies were cited for discrimination by both age and gender: Nebraska Furniture Mart, Renewal by Andersen LLC and Sandhills Publishing Company. The companies can now work out a settlement with the EEOC or go to court.
Most of the companies did not immediately respond to requests for comment. Nebraska Furniture Mart declined to comment. A spokesperson for financial firm Edwards Jones said, “We strongly disagree with the claim that our firm engaged in discriminatory practices in advertising of job opportunities, recruiting or hiring.”
Dozens of other complaints have been filed with the EEOC about discrimination in targeted advertising on Facebook. Most of the allegations are still pending.
The EEOC’s batch of decisions are significant, attorney Peter Romer-Friedman of Outten & Golden says, because they are the first time companies besides Facebook have had to defend how they use Facebook’s tools to advertise jobs.
His firm also filed a suit against seven real estate companies last week for allegedly discriminating by age in housing ads. We first reported on discriminatory housing ads on Facebook three years ago. The company changed its process for screening housing ads after we retested the system two years ago and showed it was possible to buy dozens of ads that excluded people by gender, race, religion, national origin, age and other categories protected by civil rights laws.
Republished with permission under license from ProPublica.
Obama Sends Letter to Prisoner He Freed Who Turned Her Life Around
President Obama let Danielle Metz out of prison. Then she enrolled in college and made the dean's list. Obama heard about Metz's success and sent a letter telling her how proud he is of her for turning her life around and graduating college.
“I am so proud of you, and am confident that your example will have a positive impact for others who are looking for a second chance, Tell your children I say hello, and know that I’m rooting for all of you.”
Danielle Metz's full story about her journey from jail to college is below.
From prison to dean’s list: How Danielle Metz got an education after incarceration
by CASEY PARKS
NEW ORLEANS – The sun glowed gold, and a second line parade was tuning its horns just a few streets away. But Danielle Metz had missed half her life already, and she couldn’t spare the afternoon, even one as unseasonably warm as this mid-February Sunday.
She climbed the stairs to the shotgun house her mom had bought in uptown New Orleans more than half a century ago. Metz slipped through the screen door, then shut it tight enough to keep out the sun. Inside, she dug through a box next to her bed and pulled out the clothbound journal that a woman had given her in 1996, when they were both incarcerated in the Federal Correctional Institute in Dublin, California. Metz hadn’t kept much from the 23 years she spent in prison, but the journal had been too special to leave behind. She opened it and read the dedication as a reminder of what she hoped to accomplish now that she was out.
“To Danielle — There’s so many things we can’t get in here, but knowledge and education can’t be kept out by walls.”
Growing up, Metz had believed that college was for white kids and for “Huxtables” — black people she named after the upper-middle-class family in “The Cosby Show.” She knew, as she looked at the laptop screen, how improbable people might think earning a degree would be for her now. She’d dropped out of high school her junior year. At 26, a judge had sentenced Metz to three life sentences plus another 20 years for her role in her husband’s cocaine distribution. She’d thought she’d never see New Orleans again, let alone visit a university.
Even after President Barack Obama granted her clemency in 2016, Metz believed she couldn’t go to college. Nationwide, less than 4 percent of formerly incarcerated people have a bachelor’s degree, according to a report released last year. The chances seemed especially low in Metz’s home state. Louisiana had long held twin records, the world’s highest incarceration rate, and the country’s lowest rate of black college graduates. Put together, this meant tens of thousands of residents lacked a viable pathway to middle-class security.
But lawmakers had come to believe that a change was imperative for the state’s future. In 2017, Louisiana became the first state in the nation to “ban the box” on public college and university applications, prohibiting school officials from asking whether an applicant has a criminal record. Metz knew that people across the country were working to help people like her go to college after prison. Though Illinois and New York failed to pass “ban the box” measures for university applications, several other states are trying to follow Louisiana’s lead. And federal lawmakers from both parties are pushing to allow incarcerated people to access Pell Grants, financial aid that they’ve been barred from using since Metz first went to prison.
Metz was grateful for the legal shifts, but political momentum alone would not carry her through school. As the parade began its march through Uptown, she scrolled through the university’s website and hovered over the tab marked “current students.” She had no idea how long it would take or how much it might cost, but Metz didn’t care. She was going to college.
Metz grew up the youngest of nine children in a city barreling toward chaos. As a kid, she considered herself lucky. Both of her parents worked — her father as a cement finisher, her mother in a bakery — and together they earned enough to buy a home three miles away from the St. Thomas Projects, a public housing development where many other black families lived. St. Thomas was so poor and violent when Metz was young that Sister Helen Prejean described the neighborhood in the opening of her book “Dead Man Walking” as “not death row exactly, but close."
Even as a little girl, Metz knew people who’d gone to jail, but her neighborhood was quiet, and her parents were dreamers. For years, her father urged her to become a nurse. Metz knew the job required a college degree, but she didn’t know anyone who’d earned one. In 1980, the year Metz enrolled at Walter L. Cohen High School, more than half the city’s black adults didn’t have even a high school diploma, let alone a university credential.
Instead, Metz longed to become a hairstylist. She’d practiced since she was a little girl on her mom, whose locks grew in so straight that people speculated she must have white ancestors. But even that goal felt unreachable after Metz became pregnant in 1985, her junior year of high school. She dropped out and assumed she wouldn’t have a career. She’d be a mother instead.
Six months after Metz gave birth to her son, Carl, his father was murdered.
Metz became a single mother just as the state’s economy was collapsing. Louisiana had long been dependent on oil — profits from the natural resource accounted for nearly half of the state’s budget then. But the price per barrel began falling in 1981, and by the mid-1980s, one in eight Louisiana workers was unemployed, the highest rate in the nation. New Orleans lost nearly 10,000 jobs, leaving few openings for a teenage mother with no credentials or documentable skills.
Metz didn’t take time to grieve. Most black people in New Orleans knew someone who’d been killed, she said. Instead, she started looking for someone to help raise her child.
Glenn Metz had money. He’d grown up poor in the Calliope housing projects, one of the most violent neighborhoods in New Orleans, but he owned two tow-truck companies by the time Metz met him. At age 30, he possessed the kind of quiet maturity that Metz, then 18, thought would make him a good substitute father for Carl. Glenn Metz wore such nice clothes and jewelry the night Metz met him that she suspected he at least dabbled in drug-dealing, but she told herself his business had nothing to do with her.
According to federal prosecutors, Glenn Metz formed a drug ring just before he met the girl who would become his wife. Between 1985 and 1992, Glenn Metz and his crew came to dominate St. Thomas and Calliope, prosecutors said, distributing more than 1,000 kilos of cocaine and killing 23 rivals. Glenn Metz sat atop an organization manned by more than half a dozen enforcers, two of whom, prosecutors said, drove through town in an armor-plated pickup with the word “homicide” spelled out on the hood in gold letters.
Metz spent most of those years at home. “The Cosby Show” debuted the year she should have graduated high school, and she watched it and its college-based spin-off “A Different World” every week, dreaming of the life she wished she had. She took a few beauty school classes and occasionally cut hair in someone’s home, but Glenn Metz didn’t like when she left the house, she said. They married in 1989, and Metz soon gave birth to their daughter, Gleneisha. Metz didn’t have a social security number or any way to make money on her own. When Glenn Metz told her to ride with her aunt to deliver a few packages to Houston, Metz said, she did it.
Crack cocaine was spreading through black neighborhoods across the country then, and lawmakers blamed the drug for an increase in inner-city violence. New Orleans was especially hard hit. In 1990, the city topped 300 murders for the first time. Nearly every edition of The Times-Picayune that year carried news of cocaine busts. Police arrested scores of black men, including Metz’s older brother, Perry Bernard, for possession. As the city’s murder rate rose to the nation’s highest, investigators worked to take down Glenn Metz. His was the biggest and most violent drug ring in the city, prosecutors said. They indicted him and eight others, including Metz, in the summer of 1992.
Metz, who’d been temporarily living in Las Vegas with her husband before the indictment, fled to Jackson, Mississippi. She rented an apartment near Jackson State University and planned to enroll after the investigation concluded. When police arrested her there in January 1993, Metz figured she’d just get probation. Most people she knew went to jail “seasonally.” Her older brother had drifted in and out before a 1989 arrest netted him 13 years in a state prison.
After crack cocaine became popular, Congress adopted the Anti-Drug Abuse Act of 1986, establishing for the first time mandatory minimum sentences triggered by specific quantities of cocaine. The penalties were worse for defendants charged with possession or distribution of crack cocaine, favored by African-Americans, than for those accused of possessing or distributing the powder cocaine primarily used by white people.
But Metz, 25 then, had never had so much as a traffic ticket. She believed her involvement in her husband’s narcotics sales was minimal enough that prosecutors would let her go with a warning. Police did not find any drugs with her, and she was never implicated in any violence.
Instead, federal authorities charged Metz and her co-defendants under the Racketeer Influenced and Corrupt Organizations Act. Lawmakers created RICO in the 1970s under President Richard Nixon as a tool to combat the Mafia, but prosecutors increasingly used it in the 1980s to fight drug rings. The charges under RICO carried automatic sentences of life in prison without parole.
The U.S. attorneys who prosecuted her case presented witnesses who were major narcotics suppliers or small-time drug dealers. They testified that Metz had driven packages to Houston for her husband and, on occasion, accepted cash payments and wired money to suppliers. The jury decided she was guilty.
Four months later, in mid-December, U.S. District Judge A.J. McNamara sentenced Metz to three life sentences plus another 20 years in federal prison.
Earning a little more money may not automatically increase their standard of living if it boosts their income to the point where they lose access to some or all of those benefits. That’s because the value of those lost benefits may outweigh their income gains.
I have researched this dynamic, which experts often call the “cliff effect,” for years to learn why workers weren’t succeeding at retaining their jobs following job training programs. Chief among the one step forward, two steps back problems the cliff effect causes: Low-paid workers can become reluctant to earn more money due to a fear that they will get worse off instead of better.
“My supervisor wants to promote me,” a woman who gets housing assistance through the federal Section 8 housing voucher program, who I’ll call Josie, told me. “If my pay goes up, my rent will go up too. I don’t know if I’ll be able to afford my apartment,” Josie, a secretary at a Boston hospital, said.
These vouchers are available to Americans facing economic hardship, based on multiple criteria, including their income. Josie was worried that the bump up in pay that she’d get from the promotion would not make up for the loss of help she gets to pay her rent.
Given the possibility of a downside, many Americans in this situation decide it’s better to decline what on the surface looks like a good opportunity to escape poverty.
This uncertainty leads workers like Josie to forgo raises rather than take the risk of getting poorer while working harder. Having to stress out about potentially losing benefits that keep a roof over their heads and food on their table prolongs their own financial instability.
The pain isn’t just personal. Josie’s whole family misses out if she passes on an opportunity to earn more. The government loses a chance to stop using taxpayer dollars to cover benefits to someone who might not otherwise need them. The hospital can’t take full advantage of Josie’s proven talents.
Some low-paid workers do get farther behind when they should be getting ahead following a raise. But getting higher pay doesn’t always make anyone worse off. Whether it does or not depends on a lot of intersecting factors, like the local cost of living, the size of the raise, the size of the family and the benefits the worker receives.
The cliff effect is something social workers see their clients encounter all the time. And it’s maddeningly impossible to figure out for the people experiencing it and researchers like me alike.
But low-wage workers, such as those in food service, hospitality and retail have no way of knowing what to expect if they get SNAP benefits in combination with other government programs, such as housing vouchers and Medicaid.
At the heart of this problem is that the help millions Americans derive from the nation’s safety net comes from a fragmented system. Sorting out the repercussions of a higher income is nearly impossible because the safety net consists of a wide array of benefits programs administered by federal, state and local agencies. Each program and administrator has its own criteria, rules and restrictions.
Because that trepidation is sometimes unfounded, my colleagues at Project Hope Boston, a multi-service agency focused on moving the city’s families up and out of poverty, and I started to do something about it.
To help families assess risks tied to the cliff effect, we advised the Massachusetts Department of Transitional Assistance, which oversees state-administered safety net programs, to create a digital tool. Social workers are already using a preliminary version of it to show low-wage workers what they can probably expect to happen to their benefits if they earn more money.
The Commonwealth of Massachusetts plans to put this tool online for all to use by Summer of 2019.
After plugging information about variables like how many members are in the household, what benefits everyone receives, the costs of their regular expenses like rent, child care and medical bills, they become better able to make informed choices about their career opportunity based on their family’s personal financial situation.
But workers need more than just a tool, they need help getting over the cliff. We also help workforce development programs implement the state’s new Learn to Earn initiative, which gives low-income families the financial coaching they need to make educated decisions that could affect their bottom line.
But the reality is that even after some of the biggest minimum wage increases enacted at the state level lately, many families are not earning enough to pay for housing and other basic needs without help – for which they may no longer qualify. Several states, including Colorado and Florida, are seeking solutions.This complicated and frustrating challenge is just one symptom of an overarching problem. In addition to boosting wages, it will take major policy changes, like making child care more universally available and affordable, to offset the skyrocketing costs of living for American workers.
Automation threatens to replace some workers but can grow overall employment. The one sure thing is that technology will change how we labor.
By M. Mitchell Waldrop
Back in the 1990s, when US banks started installing automated teller machines in a big way, the human tellers who worked in those banks seemed to be facing rapid obsolescence. If machines could hand out cash and accept deposits on their own, around the clock, who needed people?
The banks did, actually. It’s true that the ATMs made it possible to operate branch banks with many fewer employees: 13 on average, down from 20. But the cost savings just encouraged the parent banks to open so many new branches that the total employment of tellers actually went up.
The robots are coming: SpaceX founder Elon Musk, and the late physicist Stephen Hawking both publicly warned that machines will eventually start programming themselves, and trigger the collapse of human civilization.
You can find similar stories in fields like finance, health care, education and law, says James Bessen, the Boston University economist who called his colleagues’ attention to the ATM story in 2015. “The argument isn’t that automation always increases jobs,” he says, “but that it can and often does.”
That’s a lesson worth remembering when listening to the increasingly fraught predictions about the future of work in the age of robots and artificial intelligence. Think driverless cars, or convincingly human speech synthesis, or creepily lifelike robots that can run, jump and open doors on their own: Given the breakneck pace of progress in such applications, how long will there be anything left for people to do?
That question has been given its most apocalyptic formulation by figures such as Tesla and SpaceX founder Elon Musk and the late physicist Stephen Hawking. Both have publicly warned that the machines will eventually exceed human capabilities, move beyond our control and perhaps even trigger the collapse of human civilization. But even less dramatic observers are worried. In 2014, when the Pew Research Center surveyed nearly 1,900 technology experts on the future of work, almost half were convinced that artificially intelligent machines would soon lead to accelerating job losses — nearly 50 percent by the early 2030s, according to one widely quoted analysis. The inevitable result, they feared, would be mass unemployment and a sharp upswing in today’s already worrisome levels of income inequality. And that could indeed lead to a breakdown in the social order.
Or maybe not. “It’s always easier to imagine the jobs that exist today and might be destroyed than it is to imagine the jobs that don’t exist today and might be created,” says Jed Kolko, chief economist at the online job-posting site Indeed. Many, if not most, experts in this field are cautiously optimistic about employment — if only because the ATM example and many others like it show how counterintuitive the impact of automation can be. Machine intelligence is still a very long way from matching the full range of human abilities, says Bessen. Even when you factor in the developments now coming through the pipeline, he says, “we have little reason in the next 10 or 20 years to worry about mass unemployment.”
So — which way will things go?
There’s no way to know for sure until the future gets here, says Kolko. But maybe, he adds, that’s not the right question: “The debate over the aggregate effect on job losses versus job gains blinds us to other issues that will matter regardless” — such as how jobs might change in the face of AI and robotics, and how society will manage that change. For example, will these new technologies be used as just another way to replace human workers and cut costs? Or will they be used to help workers, freeing them to exercise uniquely human abilities like problem-solving and creativity?
“There are many different possible ways we could configure the state of the world,” says Derik Pridmore, CEO of Osaro, a San Francisco-based firm that makes AI software for industrial robots, “and there are a lot of choices we have to make.”
Automation and jobs: lessons from the past
In the United States, at least, today’s debate over artificially intelligent machines and jobs can’t help but be colored by memories of the past four decades, when the total number of workers employed by US automakers, steel mills and other manufacturers began a long, slow decline from a high of 19.5 million in 1979 to about 17.3 million in 2000 — followed by a precipitous drop to a low of 11.5 million in the aftermath of the Great Recession of 2007–2009. (The total has since recovered slightly, to about 12.7 million; broadly similar changes were seen in other heavily automated countries such as Germany and Japan.) Coming on top of a stagnation in wage growth since about 1973, the experience was traumatic.
True, says Bessen, automation can’t possibly be the whole reason for the decline. “If you go back to the previous hundred years,” he says, “industry was automating at as fast or faster rates, and employment was growing robustly.” That’s how we got to millions of factory workers in the first place. Instead, economists blame the employment drop on a confluence of factors, among them globalization,the decline of labor unions, and a 1980s-era corporate culture in the United States that emphasized down-sizing, cost-cutting and quarterly profits above all else.
But automation was certainly one of those factors. “In the push to reduce costs, we collectively took the path of least resistance,” says Prasad Akella, a roboticist who is founder and CEO of Drishti,a start-up firm in Palo Alto, California, that uses AI to help workers improve their performance on the assembly line. “And that was, ‘Let’s offshore it to the cheapest center, so labor costs are low. And if we can’t offshore it, let’s automate it.’”
AI and robots in the workplace
Automation has taken many forms, including computer-controlled steel mills that can be operated by just a handful of employees, and industrial robots, mechanical arms that can be programmed to move a tool such as a paint sprayer or a welding torch through a sequence of motions. Such robots have been employed in steadily increasing numbers since the 1970s. There are currently about 2 million industrial robots in use globally, mostly in automotive and electronics assembly lines, each taking the place of one or more human workers.
The distinctions among automation, robotics and AI are admittedly rather fuzzy — and getting fuzzier, now that driverless cars and other advanced robots are using artificially intelligent software in their digital brains. But a rough rule of thumb is that robots carry out physical tasks that once required human intelligence, while AI software tries to carry out human-level cognitive tasks such as understanding language and recognizing images. Automation is an umbrella term that not only encompasses both, but also includes ordinary computers and non-intelligent machines.
AI’s job is toughest. Before about 2010, applications were limited by a paradox famously pointed out by the philosopher Michael Polanyi in 1966: “We can know more than we can tell” — meaning that most of the skills that get us through the day are practiced, unconscious and almost impossible to articulate. Polanyi called these skills tacit knowledge, as opposed to the explicit knowledge found in textbooks.
Imagine trying to explain exactly how you know that a particular pattern of pixels is a photograph of a puppy, or how you can safely negotiate a left-hand turn against oncoming traffic. (It sounds easy enough to say “wait for an opening in traffic” — until you try to define an “opening” well enough for a computer to recognize it, or to define precisely how big the gap must be to be safe.) This kind of tacit knowledge contained so many subtleties, special cases and things measured by “feel” that there seemed no way for programmers to extract it, much less encode it in a precisely defined algorithm.
Today, of course, even a smartphone app can recognize puppy photos (usually), and autonomous vehicles are making those left-hand turns routinely (if not always perfectly). What’s changed just within the past decade is that AI developers can now throw massive computer power at massive datasets — a process known as “‘deep learning.” This basically amounts to showing the machine a zillion photographs of puppies and a zillion photographs of not-puppies, then having the AI software adjust a zillion internal variables until it can identify the photos correctly.
Although this deep learning process isn’t particularly efficient — a human child only has to see one or two puppies — it’s had a transformative effect on AI applications such as autonomous vehicles, machine translation and anything requiring voice or image recognition. And that’s what’s freaking people out, says Jim Guszcza, US chief data scientist at Deloitte Consulting in Los Angeles: “Wow — things that before required tacit knowledge can now be done by computers!” Thus the new anxiety about massive job losses in fields like law and journalism that never had to worry about automation before. And thus the many predictions of rapid obsolescence for store clerks, security guards and fast-food workers, as well as for truck, taxi, limousine and delivery van drivers.
Meet my colleague, the robot
But then, bank tellers were supposed to become obsolete, too. What happened instead, says Bessen, was that automation via ATMs not only expanded the market for tellers, but also changed the nature of the job: As tellers spent less time simply handling cash, they spent more time talking with customers about loans and other banking services. “And as the interpersonal skills have become more important,” says Bessen, “there has been a modest rise in the salaries of bank tellers,” as well as an increase in the number of full-time rather than part-time teller positions. “So it’s a much richer picture than people often imagine,” he says.
Similar stories can be found in many other industries. (Even in the era of online shopping and self-checkout, for example, the employment numbers for retail trade are going up smartly.) The fact is that, even now, it’s very hard to completely replace human workers.
Steel mills are an exception that proves the rule, says Bryan Jones, CEO of JR Automation, a firm in Holland, Michigan, that integrates various forms of hardware and software for industrial customers seeking to automate. “A steel mill is a really nasty, tough environment,” he says. But the process itself — smelting, casting, rolling, and so on — is essentially the same no matter what kind of steel you’re making. So the mills have been comparatively easy to automate, he says, which is why the steel industry has shed so many jobs.
When people are better
“Where it becomes more difficult to automate is when you have a lot of variability and customization,” says Jones. “That’s one of the things we’re seeing in the auto industry right now: Most people want something that’s tailored to them,” with a personalized choice of color, accessories or even front and rear grills. Every vehicle coming down the assembly line might be a bit different.
It’s not impossible to automate that sort of flexibility, says Jones. Pick a task, and there’s probably a laboratory robot somewhere that has mastered it. But that’s not the same as doing it cost-effectively, at scale. In the real world, as Akella points out, most industrial robots are still big, blind machines that go through their motions no matter who or what is in the way, and have to be caged off from people for safety’s sake. With machines like that, he says, “flexibility requires a ton of retooling and a ton of programming — and that doesn't happen overnight.”
Contrast that with human workers, says Akella. The reprogramming is easy: “You just walk onto the factory floor and say, ‘Guys, today we’re making this instead of that.’” And better still, people come equipped with abilities that few robot arms can match, including fine motor control, hand-eye coordination and a talent for dealing with the unexpected.
All of which is why most automakers today don’t try to automate everything on the assembly line. (A few of them did try it early on, says Bessen. But their facilities generally ended up like General Motors’ Detroit-Hamtramck assembly plant,which quickly became a debugging nightmare after it opened in 1985: Its robots were painting each other as often as they painted the Cadillacs.) Instead, companies like Toyota, Mercedes-Benz and General Motors restrict the big, dumb, fenced-off robots to tasks that are dirty, dangerous and repetitive, such as welding and spray-painting. And they post their human workers to places like the final assembly area, where they can put the last pieces together while checking for alignment, fit, finish and quality — and whether the final product agrees with the customer’s customization request.
To help those human workers, moreover, many manufacturers (and not just automakers) are investing heavily in collaborative robots, or “cobots” — one of the fastest-growing categories of industrial automation today.
Collaborative robots: Machines work with people
Cobots are now available from at least half a dozen firms. But they are all based on concepts developed by a team working under Akella in the mid-1990s, when he was a staff engineer at General Motors. The goal was to build robots that are safe to be around, and that can help with stressful or repetitive tasks while still leaving control with the human workers.
To get a feel for the problem, says Akella, imagine picking up a battery from a conveyor belt, walking two steps, dropping it into the car and then going back for the next one — once per minute, eight hours per day. “I've done the job myself,” says Akella, “and I can assure you that I came home extremely sore.” Or imagine picking up a 150-pound “cockpit” — the car’s dashboard, with all the attached instruments, displays and air-conditioning equipment — and maneuvering it into place through the car’s doorway without breaking anything.
Devising a robot that could help with such tasks was quite a novel research challenge at the time, says Michael Peshkin, a mechanical engineer at Northwestern University in Evanston, Illinois, and one of several outside investigators that Akella included in his team. “The field was all about increasing the robots’ autonomy, sensing and capacity to deal with variability,” he says. But until this project came along, no one had focused too much on the robots’ ability to work with people.
So for their first cobot, he and his Northwestern colleague Edward Colgate started with a very simple concept: a small cart equipped with set of lifters that would hoist, say, the cockpit, while the human worker guided it into place. But the cart wasn’t just passive, says Peshkin: It would sense its position and turn its wheels to stay inside a “virtual constraint surface” — in effect, an invisible midair funnel that would guide the cockpit through the door and into position without a scratch. The worker could then check the final fit and attachments without strain.
Another GM-sponsored prototype replaced the cart with a worker-guided robotic arm that could lift auto components while hanging from a movable suspension point on the ceiling. But it shared the same principle of machine assistance plus worker control — a principle that proved to be critically important when Peshkin and his colleagues tried out their prototypes on General Motors’ assembly line workers.
“We expected a lot of resistance,” says Peshkin. “But in fact, they were welcoming and helpful. They totally understood the idea of saving their backs from injury.” And just as important, the workers loved using the cobots. They liked being able to move a little faster or a little slower if they felt like it. “With a car coming along every 52 seconds,” says Peshkin, “that little bit of autonomy was really important.” And they liked being part of the process. “People want their skills to be on display,” he says. “They enjoy using their bodies, taking pleasure in their own motion.” And the cobots gave them that, he says: “You could swoop along the virtual surface, guide the cockpit in and enjoy the movement in a way that fixed machinery didn’t allow.”
AI and its limits
Akella’s current firm, Drishti, reports a similarly welcoming response to its AI-based software. Details are proprietary, says Akella. But the basic idea is to use advanced computer vision technology to function somewhat like a GPS for the assembly line, giving workers turn-by-turn instructions and warnings as they go. Say that a worker is putting together an iPhone, he explains, and the camera watching from overhead believes that only three out of four screws were secured: “We alert the worker and say, ‘Hey, just make sure to tighten that screw as well before it goes down the line.’”
This does have its Big Brother aspects, admits Drishti’s marketing director, David Prager. “But we’ve got a lot of examples of operators on the floor who become very engaged and ultimately very appreciative,” he says. “They know very well the specter of automation and robotics bearing down on them, and they see very quickly that this is a tool that helps them be more efficient, more precise and ultimately more valuable to the company. So the company is more willing to invest in its people, as opposed to getting them out of the equation.”
This theme — using technology to help people do their jobs rather than replacing people — is likely to be a characteristic of AI applications for a long time to come. Just as with robotics, there are still some important things that AI can’t do.
Take medicine, for example. Deep learning has already produced software that can interpret X rays as well as or better than human radiologists, says Darrell West, a political scientist who studies innovation at the Brookings Institution in Washington, DC. “But we’re not going to want the software to tell somebody, ‘You just got a possible cancer diagnosis,’” he says. “You're still going to need a radiologist to check on the AI, to make sure that what it observed actually is the case” — and then, if the results are bad, a cancer specialist to break the news to the patient and start planning out a course of treatment.
Likewise in law, where AI can be a huge help in finding precedents that might be relevant to a case — but not in interpreting them, or using them to build a case in court. More generally, says Guszcza, deep-learning-based AI is very good at identifying features and focusing attention where it needs to be. But it falls short when it comes to things like dealing with surprises, integrating many diverse sources of knowledge and applying common sense — “all the things that humans are very good at.”
During the 2016 election campaign, to test Google’s Translate utility, he tried a classic experiment: Take a headline — “Hillary slams the door on Bernie” — then ask Google to translate it from English to Bengali and back again. Result: “Barney slam the door on Clinton.” A year later, after Google had done a massive upgrade of Translate using deep learning, Guszcza repeated the experiment with the result: “Hillary Barry opened the door.”
“I don’t see any evidence that we’re going to achieve full common-sense reasoning with current AI,” he says, echoing a point made by many AI researchers themselves. In September 2017, for example, deep learning pioneer Geoffrey Hinton, a computer scientist at the University of Toronto, told the news site Axios that the field needs some fundamentally new ideas if researchers ever hope to achieve human-level AI.
AI’s limitations are another reason why economists like Bessen don’t see it causing mass unemployment anytime soon. “Automation is almost always about automating a task, not the entire job,” he says, echoing a point made by many others. And while every job has at least a few routine tasks that could benefit from AI, there are very few jobs that are all routine. In fact, says Bessen, when he systematically looked at all the jobs listed in the 1950 census, “there was only one occupation that you could say was clearly automated out of existence — elevator operators.” There were 50,000 in 1950, and effectively none today.
On the other hand, you don’t need mass unemployment to have massive upheaval in the workplace, says Lee Rainie, director of internet and technology research at the Pew Research Center in Washington, DC. “The experts are hardly close to a consensus on whether robotics and artificial intelligence will result in more jobs, or fewer jobs,” he says, “but they will certainly change jobs. Everybody expects that this great sorting out of skills and functions will continue for as far as the eye can see.”
Worse, says Rainie, “the most worried experts in our sample say that we’ve never in history faced this level of change this rapidly.” It’s not just information technology, or artificial intelligence, or robotics, he says. It’s also nanotechnology, biotechnology, 3-D printing, communication technologies — on and on. “The changes are happening on so many fronts that they threaten to overwhelm our capacity to adjust,” he says.
Preparing for the future of work
If so, the resulting era of constant job churn could force some radical changes in the wider society. Suggestions from Pew’s experts and others include an increased emphasis on continuing education and retraining for adults seeking new skills, and a social safety net that has been revamped to help people move from job to job and place to place. There is even emerging support in the tech sector for some kind of guaranteed annual income, on the theory that advances in AI and robotics will eventually transcend the current limitations and make massive workplace disruptions inevitable, meaning that people will need a cushion.
This is the kind of discussion that gets really political really fast. And at the moment, says Rainie, Pew’s opinion surveys show that it’s not really on the public’s radar: “There are a lot of average folks, average workers saying, ‘Yeah, everybody else is going to get messed up by this — but I’m not. My business is in good shape. I can’t imagine how a machine or a piece of software could replace me.’”
But it’s a discussion that urgently needs to happen, says West. Just looking at what’s already in the pipeline, he says, “the full force of the technology revolution is going to take place between 2020 and 2050. So if we make changes now and gradually phase things in over the next 20 years, it’s perfectly manageable. But if we wait until 2040, it will probably be impossible to handle.”
Jessie Dean Gipson Simmons was full of optimism when she and her family moved from an apartment in a troubled area of Detroit to a new development in Inkster, Michigan in 1955.
With three children in tow, Jessie and her husband settled into a home on Colgate Street in a neighborhood known as “Brick City” – an idyllic enclave of single, working-class families with a shared community garden.
The plan was simple. Like many African Americans who left the South as part of the Great Migration, Jessie’s husband, Obadiah Sr., would find a stable factory job just outside of Detroit. Then Jessie would put to use the bachelor’s degree she had earned in upper elementary education from Grambling State University in the township of Taylor – just a few blocks from their new home.
But the plan went awry. Jessie first applied for a teaching position with the Taylor school district in April 1958, but was denied. The same thing happened in March 1959. And a third time in May 1959. The repeated denials may have set back Jessie’s plans, but they also set her up to fight an important battle for justice for black educators at a time when many were being pushed out of the teaching profession.
I interviewed Jessie’s family as part of my ongoing research into the history of black women teachers from the Reconstruction Era to the 21st century.
The battle began when Jessie filed a grievance with the Michigan Fair Employment Practices Commission, or MFEPC, on Sept. 1, 1959. Jessie’s grievance detailed her conversation with the superintendent Orville Jones in March 1958, in which he told her “there would be vacancies in 1959.”
In August 1958, the Taylor Township Board of Education – the body overseeing the school district where Jessie wanted to teach – took up the matter of employing Negro teachers at a board meeting. The reason the item was placed on the agenda? The Superintendent at the time, Orville Jones, “felt that any handicap” – he deemed race as a handicap – “be pointed out to the board.”
The chair of the school board, Mr. Randall, stated applications were “considered in the order of the dates they were received.” Since the Taylor school board was now on record regarding its hiring practices for teachers, Jessie used that statement in her grievance.
Jessie’s decision to file a grievance would be a costly one for her family. The couple had planned on two steady incomes. In 1959, now a mother of five children, Jessie took a job as a waitress and a cook in a cafe to make ends meet. Her job drew scorn from family members in Louisiana who knew she was severely underemployed. And though her children didn’t know it at the time, Jessie and her husband “gave up meals so the children could eat,” according to Jessie’s oldest son, Obidiah Jr.
In 1960 the MFEPC held a public hearing for the grievance filed by Jessie and Mary Ruth Ross – a second black teacher who was also denied employment by the Taylor board of education. According to the Detroit Courier, Jessie and Mary “were passed over for employment in favor of white applicants who lacked degrees.” Records uncovered by the MFEPC found that 42 non-degreed teachers hired between 1957 through 1960 were all white and “had a maximum of 60 hours of college credits.” Jessie and Mary, on the other hand, were both degreed teachers with some credits toward a graduate degree.
How the Brown decision hurt black teachers
While the 1954 Brown v. Board of Education decision is often celebrated and considered a legal victory, many scholars believe it had a harmful effect on black teachers. In 1951, scholars writing in the Journal of Negro Education rightly warned that Brown “might conceivably” impact “Negro teachers”. Nationwide, school district leaders pushed back against Brown in two ways.
First, school leaders slow-walked the implementation of Brown – for many school districts as late as the mid-1980s. Second, black teachers across the country lost their once-secure teaching jobs by the tens of thousands after Brown when black schools closed and black children integrated into white schools. In the South, for example, the number of black teachers had soared to around 90,000 pre-Brown. But by 1965 nearly half had lost their jobs. A 1965 report from the National Education Association, a leading labor union for teachers, concluded school districts had “no place for Negroes” in the wake of Brown. School officials railed against Brown and refused to hire black teachers like Jessie, turning them into what sociologist Oliver Cox described as “martyrs to integration.”
My own research confirms that the forced exodus of black women from the teaching profession was ignited by Brown. Discrimination by school leaders fueled the demographic decline of black teachers and remains one of the leading factors for their under-representation in the profession today.
First ruling of its kind
At the eight-day public hearing, Jones admitted that “the hiring of Negro teachers would be something new and different and something we had not done before.” He stated he felt that the Negro teachers were “not up to par.” The hearing eventually revealed that applications for “Negroes” were kept in distinct folders – separated from the submissions of the white applicants.
After more than a year, the MFEPC issued a ruling in Jessie’s case. The decision got a brief mention from Jet Magazine on Dec. 1, 1960:
In the first ruling of its kind, the MFEPC ordered the Taylor Township School Board to hire Mrs. Mary Ruth Ross and Mrs. Jessie Simmons, two Negro teachers, and pay them back wages for the school years of 1959-60 and 1960-61. FEPC Commissioner Allan A. Zaun said the teachers were refused employment on the basis of race.
The attorney for the Taylor board of education, Harry F. Vellmure, threatened to challenge the ruling in court – all the way “to the Supreme Court if necessary,” according to the Detroit Courier. The board stuck to its position that Jessie and Mary were given full and fair consideration for teaching jobs and simply lost out to better qualified teachers.
As a result of noncompliance with the MFEPC’s order, Carl Levin, future U.S. senator and general counsel for the Michigan Civil Rights Commission, filed a discrimination lawsuit against the Taylor school district on Jessie’s and Mary’s behalf. Even though the matter did not reach higher courts, Vellmure filed several appeals that effectively slowed down the commission’s order for seven years.
As the lawsuit dragged on, Jessie became an elementary school teacher with the Sumpter School District in 1961. By 1965, she left Sumpter for the Romulus Community School District. According to Jessie’s children, they would continue in the Taylor school district and were known as the kids “whose mother filed the lawsuit against the school district.”
In 1967, after seven years of fighting the Taylor school district in local court, Jessie and Mary prevailed. They were awarded two years back pay and teaching positions. Saddled by hurt feelings after a long fight with the Taylor school district, Jessie declined the offer and continued teaching in Romulus.
The Simmons moved into a larger, newly constructed home on Lehigh Avenue. Jessie gave birth to her sixth child, Kimberly, one month before moving in. Although the new home was only two blocks south of their old home on Colgate Avenue, Jessie’s four surviving children recall that their lifestyle improved and their childhood was now defined by two eras: “before lawsuit life and after lawsuit life.” And by 1968, Jessie earned a master’s degree in education from Eastern Michigan University.
Unsung civil rights hero
At her retirement in 1986, Jessie’s former students recalled that she was an effective teacher of 30 years who was known as a disciplinarian with a profound sense of commitment to the children of Romulus.
Jessie’s story is a reminder that the civil rights movement did not push society to a better version of itself with a singular, vast wave toward freedom. Rather, it was fashioned by little ripples of courage with one person, one schoolteacher, at a time.
The first group of borrowers who tried to get Public Service Loan Forgiveness – a George W. Bush-era program meant to provide relief to those who went into socially valuable but poorly paid public service jobs, such as teachers and social workers – mostly ran into a brick wall.
Of the 28,000 public servants who applied for Public Service Loan Forgiveness earlier this year, only 96 were approved. Many were denied in large part due to government contractors being less than helpful when it came to telling borrowers about Public Service Loan Forgiveness. Some of these borrowers will end up getting part of their loans forgiven, but will have to make more payments than they expected.
With Democrats having regained control of the U.S. House of Representatives in the November 2018 midterm elections, the Department of Education will likely face greater pressure for providing better information to borrowers, as it was told to do recently by the Government Accountability Office.
The Public Service Loan Forgiveness program forgives loans for students who made 10 years of loan payments while they worked in public service jobs. Without this loan forgiveness plan, many of these borrowers would have been paying off their student loans for 20 to 25 years.
In general, working for a government agency – such as teaching in a public school or a nonprofit organization that is not partisan in nature – counts as public service for the purposes of the program. For some types of jobs, this means that borrowers need to choose their employers carefully. Teaching at a for-profit school, even if the job is similar to teaching at a public school, would not qualify someone for Public Service Loan Forgiveness. Borrowers must also work at least 30 hours per week in order to qualify.
What types of loans and payment plans qualify?
Only Federal Direct Loans automatically qualify for Public Service Loan Forgiveness. Borrowers with other types of federal loans must consolidate their loans into a Direct Consolidation Loan before any payments count toward Public Service Loan Forgiveness. The failure to consolidate is perhaps the most common reason why borrowers who applied for forgiveness have been rejected, although Congress did provide US$350 million to help some borrowers who were in an ineligible loan program qualify for Public Service Loan Forgiveness.
In order to receive Public Service Loan Forgiveness, borrowers must also be enrolled in an income-driven repayment plan, which ties payments to a percentage of a borrower’s income. The default repayment option is not income-driven and consists of 10 years of fixed monthly payments, but these fixed payments are much higher than income-driven payments. The bottom line is it’s not enough to just make 10 years of payments. You have to make those payments through an income-driven repayment plan to get Public Service Loan Forgiveness.
Parent PLUS Loans and Direct Consolidation Loans have fewer repayment plan options than Direct Loans made to students, so borrowers must enroll in an approved income-driven repayment plan for that type of loan. Borrowers must make 120 months of payments, which do not need to be consecutive, while enrolled in the correct payment plan to receive forgiveness.
How can borrowers track their progress?
First of all, keep every piece of information possible regarding your student loan. Pay stubs, correspondence with student loan servicers and contact information for prior employers can all help support a borrower’s case for qualifying for Public Service Loan Forgiveness. Unfortunately, borrowers have had a hard time getting accurate information from loan servicers and the Department of Education about how to qualify for Public Service Loan Forgiveness.
The U.S. Government Accountability Office told the Department of Education earlier this year to improve its communication with servicers and borrowers, so this process should – at least in theory – get better going forward.
Borrowers should also fill out the Department of Education’s Employment Certification Form each year, as the Department of Education will respond with information on the number of payments made that will qualify toward Public Service Loan Forgiveness. This form should also be filed with the Department of Education each time a borrower starts a new job to make sure that position also qualifies for loan forgiveness.
Can new borrowers still access Public Service Loan Forgiveness?
Yes. Although congressional Republicans proposed eliminating Public Service Loan Forgiveness for new borrowers, the changes have not been approved by Congress. Current borrowers would not be affected under any of the current policy proposals. However, it would be a good idea for borrowers to fill out an Employment Certification Form as soon as possible just in case Congress changes its mind.
Are there other affordable payment options available?
Yes. The federal government offers a number of income-driven repayment options that limit monthly payments to between 10 and 20 percent of “discretionary income.” The federal government determines “discretionary income” as anything you earn that is above 150 percent of the poverty line, which would translate to an annual salary of about $18,000 for a single adult. So if you earn $25,000 a year, your monthly payments would be limited to somewhere between $700 and $1400 per year, or about $58 and $116 per month.
These plans are not as generous as Public Service Loan Forgiveness because payments must be made for between 20 and 25 years – instead of 10 years under Public Service Loan Forgiveness. Also, any forgiven balance under income-driven repayment options is subject to income taxes, whereas balances forgiven through Public Service Loan Forgiveness are not taxed.
Race-based discrimination is common in the hiring process.
For example, racial minorities are less likely than whites to receive a callback when they apply for a job. There are also wide earning gaps, with African-Americans and Latinos earning a fraction of what whites and Asians do.
Research has shown African-Americans get fewer job callbacks than whites. astarot/Shutterstock.com\
When analyzing these problems, researchers and others tend to focus on how the experiences of racial minorities compare with those of whites. Often missing is whether there are differences among individuals of the same racial group in terms of how they experience bias.
That is where my new study, which focuses on perceptions of others’ racial identities, comes in.
People have more than one identity, such as being a mom, a Muslim, an athlete, a scientist and so on.
Just as we commonly think about the importance of each of our identities to who we are – such as being a dad or very religious – we make the same assessments of other people. That is, we evaluate other people’s identities to understand which ones are most fundamental to who they are.
And it turns out, the conclusions we come to about each other’s “perceived identities” can have a big effect on how we interact with them.
As a researcher who has spent the last 19 years examining diversity and inclusion, I was interested in how perceptions of identity affected a racial minority’s prospects as a job applicant. More specifically, I wanted to know if the perception that an applicant has a strong racial identity affected her ability to get a job and how much she’d get paid.
Past research has shown that our inferences about others’ personal identities can influence how we interact with them.
In some cases, people might talk about how their identity is important to them, or how it reflects a critical part of who they are as a person. In other cases, we make assessments based on cues. For example, we might think someone strongly identifies as Latino when they are members of a Latino student organization. Or, we might infer a weak identity among people who engage in actions that are seemingly contrary to the interests of their group.
For example, psychologists Cheryl Kaiser and Jennifer Pratt-Hyatt found found that whites interact more positively with racial minorities they believe weakly identify with their race – and more negatively with those with stronger racial identifies. Specifically, whites expressed more desire to be their friends and offer favorable ratings of their personality.
Presumed identity and work
Drawing on their work, Astin Vick, a former student of mine, and I examined whether African-American women’s and Latinas’ presumed racial identity affect their job ratings.
Using an online data collection platform, we asked 238 white people who indicated that they currently or previously worked in the fitness industry to review the application of someone applying to be a club manager. They were told to review a job description, a hiring directive from the club owner, a summary of each applicant’s relevant background and a picture.
All applicants had the same experience, work history and education. The pictures were used to indicate an applicant’s race. Most importantly, we varied each applicant’s relevant affiliations and community service to suggest whether she had a strong identification to her racial group or a weak one.
For example, membership in the Latino Fitness Instructors Association or volunteering for former President Barack Obama’s campaign would signal a strong identification to an applicant’s Latina or black racial group. Belonging to the neutral-sounding Intercollegiate Athletics Coaches Association or volunteering for Obama’s opponent in the 2012 presidential campaign, Mitt Romney, would signal a weak one.
The participants then filled in a questionnaire to measure their perceptions of the applicant they reviewed, including work attributes such as “untested” or “expert,” hiring recommendation and suggested salary.
Our results showed that most people did in fact use cues from the application file to form views of the applicant’s racial identity, which in turn informed their hiring and salary recommendations. Essentially, as we expected, applicants perceived as identifying strongly with their racial group were less likely to be recommended for a job. And, when they were, received lower suggested salaries – on average US$2,000 less – than those signaling weak associations.
The story does not end there, though, since we also knew each participant’s gender. And we found that men showed a slightly different pattern than the one described above.
Men recommended roughly the same salaries for African-American women and Latinas who identified weakly with their racial groups. But for those with strong perceived identifies, they penalized Latinas far more than African-Americans. That is, they recommended the club pay Latinas with a strong racial identify about $5,000 less than African-Americans.
These small changes can add up over time. Over a 15-year tenure with a company, that difference results in $96,489 difference in inflation-adjusted earnings.
Our study illustrates several key points.
First, though racial minorities, as a collective, face bias in employment, there is considerable within group variability. An applicant’s specific race matters, as does her or his presumed racial identity.
Second, raters use cues on a resume to infer a job applicant’s racial identity. They then use this information in their decision-making. Aware of this pattern, some job seekers remove race-related activities on their resumes, what Sonia Kang, an associate professor of organizational behavior, refers to as racial whitening.
Finally, research has shown that diversity in the workplace leads to greater organizational performance and employee well-being. As such, employers would be wise to be on the lookout for biases like the one we found that are likely to lead to less diverse workforces and take steps to overcome them when hiring new workers.
Republished with permission under license from The Conversation under a Creative Commons license.