Bill Maher Blasts Spoiled Rich Kids During ‘New Rules’

Though something called the “death tax” sounds ominous, Bill Maher explained on “Real Time” why taxing inherited wealth is actually a good thing. Namely, he just called out some kids who will inherit fortunes for being “entitled jerks.”

Maher’s examples include Kylie Jenner texting while driving after Bruce Jenner’s deadly accident and Paris Hilton’s brother Conrad Hughes Hilton III needing to be restrained on a flight.

And while those stories are pretty shocking, we’re pretty sure Maher got the facts wrong when it came to saying Hilton’s proper title. But you can be the judge.

“Real Time with Bill Maher” airs Friday at 10:00 p.m. ET on HBO.

April Fools’ Festival Day: Our Fool Today Is David Brooks

Noah Smith: Americans Are Better Behaved: “David Brooks thinks… less affluent Americans are losing their morality….

Crime… teen drug and alcohol abuse… teen pregnancy… domestic violence… child molestation is way down…. David Brooks is cooking up off-the-cuff sociological theories to explain SOMETHING THAT ISN’T EVEN HAPPENING. And then he is recommending big changes in American culture and society…

“The relation between temptations and the level of consumption plays a key role in explaining the observed behaviors of the poor…

Abhijit V. Banerjee and Sendhil Mullainathan: The Shape of Temptation: Implications for the Economic Lives of the Poor: “The relation between temptations and the level of consumption plays a key role in explaining the observed behaviors of the poor…

…Temptation goods… generate positive utility for the self that consumes them, but not for any previous self that anticipates that they will be consumed…. The assumption… that the fraction of the marginal dollar that is spent on temptation goods decreases with overall consumption has a number of striking implications for the… behavior of the poor…. Predicted behaviors under the declining temptation assumption can help us explain some of the puzzling facts about the poor that have been emphasized in the recent literature.

How high levels of unemployment have held down wages, contributing to soaring corporate profits and a remarkable run-up in the stock market.

BY GERALD FRIEDMAN

PT I PDF | PT II PDF

 

Part I: Weak Employment, Stagnant Wages, and Booming Profits

 

The 2007-2010 recession was the longest and deepest since World War II. The subsequent recovery has been the weakest in the postwar period. While total employment has finally returned to its pre-recession level, millions remain out of work and annual output (GDP) is almost a trillion dollars below the economy’s “full-employment” capacity. This column explains how high levels of unemployment have held down wages, contributing to soaring corporate profits and a remarkable run-up in the stock market.

Output plunged and has not recovered. There was a sharp fall in output (GDP) at the onset of the Great Recession, down to 8% below what the economy could produce if labor and other resources were employed at normal levels (“full employment” capacity). Since the recovery began, output has grown at barely above the rate of growth in capacity, leaving the “output gap” at more than 6% of the economy’s potential—or nearly $1 trillion per year.

Figure 1: Output Gap (Actual GDP Minus Potential GDP), Inflation-Adjusted, 2000-2013

The drop in employment was much worse than in previous recessions. With eight million jobs lost, employment fell more sharply going into the Great Recession than it did in previous recessions. It took five years for the economy to recapture its pre-recession level of employment, a rate of recovery much slower than for previous post-World War II recessions. Had employment recovered at the average pace for pre-1991 recoveries, there would be more than 11 million additional jobs today.

Figure 2: Employment, 2009 Recovery vs. Earlier Recoveries

Productivity is growing; wages are not. The gap between rising productivity and stagnant wages—growing since the 1970s—has increased with the Great Recession. Real output per hour has continued to increase during the recession and recovery. Wages, however, have remained stuck at a level lower than in the early 1970s.

Figure 3: Hourly Earnings vs. Productivity, 1966-2014

Corporate profits are soaring. While output, employment, and wages have remained depressed, after-tax corporate profits have soared to about one-tenth of national income. While this continues a trend that goes back to the 1970s, the rising profit rate since the Great Recession reflects how high levels of unemployment allow employers to push employees to work harder and produce more—even without paying higher wages.

Figure 4: Corporate Profits, After-Tax, Percentage of GDP, 1964-2012

The stock market has surged. The combination of low interest rates (which keep down corporate borrowing costs and make bonds less attractive than stocks) and rising corporate profits has produced a surge in the stock market. Despite the weak recovery, the Dow Jones Industrial Average is now well past its pre-recession peak.

Figure 5: Dow Jones Industrial Average, 2007-2014

Part II: Government Policy and Why the Recovery Has Been So Slow

 

The recovery from the Great Recession has been so slow because government policy has not addressed the underlying problem: the weakness of demand that restrained growth before the recession and that ultimately brought on a crisis. Focused on the dramatic events of fall 2008, including the collapse of Lehman Brothers, policymakers approached the Great Recession as a financial crisis and sought to minimize the effects of the meltdown on the real economy, mainly by providing liquidity to the banking sector. While monetary policy has focused on protecting the financial system, including protecting financial firms from the consequences of their own actions, government has done less to address the real causes of economic malaise: declining domestic investment and the lack of effective demand. Monetary policy has been unable to spark recovery because low interest rates have not been enough to encourage businesses and consumers to invest. Instead, we need a much more robust fiscal policy to stimulate a stronger recovery.

The Fed has kept interest rates unprecedentedly low. Determined not to repeat what orthodox economists saw as the main cause of the Great Depression—a “tight” money supply—the Federal Reserve responded very aggressively to the crisis in 2007 and 2008. The Fed drove its main target short-term interest rate, the federal funds rate, down to an unprecedented near-zero level. Even at interest rates below zero in real (inflation-adjusted) terms, however, effective demand has been so depressed and so much unused productive capacity has remained that banks have found few borrowers.

Figure 1: Federal Funds Rate, 1999-2914

Corporate bond rates have continued to drift upward. The Federal Reserve tries to encourage economic expansion and job creation by driving down the interest rate on Treasury bills in order to indirectly force down the interest rates corporations pay on their borrowing. This policy has become less effective over time because investors have been insisting on a larger risk premium for lending to corporations. Since the 1970s, the rate on corporate bonds has been drifting up relative to treasury rates: Interest rates on corporate bonds have risen an average of 50 basis points (0.5 percentage points) each decade, relative to Treasury bills.

Figure 2: Interest Rate Spread, Corporate Baa Rate Minus Treasury Bill Rate, 1947-2012

Monetary policy has not spurred investment; banks have stockpiled cash. The lack of demand for borrowing, and banks’ concerns about the reliability of corporate borrowers, have undermined the ability of the Federal Reserve to spark economic recovery by increasing the money supply. Aggressive monetary policy has done little to promote increased investment as banks have stockpiled cash, hoarding almost $2 trillion in “excess reserves”—money they have deposited at the Fed over and above what is required by law. This is unprecedented; the only other time in the last 50 years when excess reserves were more than $10 billion was after the September 11, 2001, attacks. (Note: $2 trillion is equal to $2,000 billion.)

Figure 3: Excess Reserves of Depository Institutions, 1959-2013

Average household wealth has recovered, but consumer spending has not. The recovery from the 2001 recession was fueled by consumer spending—based on increasing debt and rising asset values. On the eve of the Great Recession, household debt payments were an unprecedented 18% of disposable personal income. As the crisis hit, household net worth plunged by over $13 trillion, led by falling home prices, and consumer spending fell accordingly. Since the recession, households have increased saving to bring down their debt levels. While this is good for individual balance sheets, it has meant reduced spending, lowering economy-wide demand and income. Average household wealth has since recovered, but the effect on consumer spending has been muted because most of the recovery has been in stocks, assets which are owned primarily by a small (and rich) portion of the U.S. population.

Figure 4: Increase in Household Financial Wealth, by Source, 2009-2013

Fiscal policy has been contractionary. As in past recessions, the federal government responded to higher levels of unemployment through deficit spending. Large increases in deficit spending in 2008 and 2009 helped to halt the economic collapse at the beginning of the Great Recession. From there, however, the all-government deficit (including state and local governments, which traditionally run surpluses) swung heavily toward surplus. Despite persistently high levels of unemployment, government fiscal policy has been contractionary every year since 2010, with rising surpluses slowing an already anemic economic recovery.

Figure 5: Change in Government Deficit, All Levels, 2005-2013

is a professor of economics at the University of Massachusetts-Amherst.

Bureau of Economic Analysis, “Real Gross Domestic Product Chained Dollars,” (bea.gov); Congressional Budget Office, “Real Potential Gross Domestic Product,” (cbo.gov); Economic Policy Institute (EPI), “Output Gap: Real GDP compared to potential GDP, 2000-2013” (stateofworkingamerica.org); Bureau of Labor Statistics, Employment Level (Series ID: LNS12000000) (bls.gov); Economic Report of the President 2013, Table B–47: Hours and earnings in private nonagricultural industries, 1966–2012, Table B-90: Corporate profits with inventory valuation and capital consumption adjustments, 1964–2012 (whitehouse.gov); Dow Jones Industrial Average (stockcharts.com); Board of Governors of the Federal Reserve System, series FEDFUNDS; Baa corporate bond yields minus 3-Month Treasury Bill: Secondary Market Rate (TB3MS); Board of Governors of the Federal Reserve System, series EXCRESNS; Federal Reserve Board, “Household Debt Service and Financial Obligations Ratios” for March 12, 2014 (federalreserve.gov); Household Financial Balance Sheet and Economic Recovery: Federal Reserve, Z.1 Statistical Release for June 5, 2014.

Two years, almost 750 job applications. Not one viable offer. This is what the American Dream has been reduced to

"My kids were embarrassed that their dad worked at Target": My American economy nightmare

(Credit: MoreISO via iStock)

Ron Dziuda’s family calls it “the black cloud.’’

When Ron applies for hundreds of jobs and no one calls back with an offer? The Black Cloud. When his daughter applies for her dream position and someone else gets it? The Black Cloud. Or when he and his wife Sue celebrate their anniversary with a dinner out and they suffer pangs of guilt over the bill?

Sue shakes her head and tries to explain this concept at her dining room table.

“There’s that black cloud again.’’

The black cloud – this nagging sense that things will go wrong and never get quite right again – appeared over the Dziudas’ life in July 2009 when he lost his sales and marketing job at industrial components-maker Misumi USA. At first, it didn’t seem that bad. This father of four had lost jobs before. His experience, smarts and can-do attitude always helped him bounce back.

But this was in the depths of America’s most punishing economic crisis since the Great Depression. Unemployment was rising fast and job-seekers of any age were having the toughest times of their lives finding work. And Ron wasn’t just any age – at 54, he started finding his gray hair and crow’s feet overshadowed his expertise and energy.

So the black cloud moved in to stay. For three years – 33 months, to be exact – Ron endured a drumbeat of humiliations: he lost his job, his savings, his confidence. He and Sue burned down their retirement accounts one by one; they borrowed from their own son to pay the mortgage; they racked up credit card debt to put food on the table. Ron applied for 800 jobs. One night he turned to Sue and confessed he didn’t think he’d ever get back on his feet.

He finally emerged in the spring of 2012 with a position selling sandblasters for Pangborn Group. At last he was out of the woods. But the black cloud barely budged. They shake their heads when I ask if they’ll ever feel secure again.

“Now to rebuild everything I had, and build from there and even try to build for retirement? It’s going to be incredible,’’ Ron says. “I mean all the credit cards are maxed out, so those got to be paid off, and there’s no equity in the house …’’

Sue is blunter.

“I’ll still live in fear,’’ she says. “I don’t know if I’ll ever feel comfortable.’’



The Great Recession took down millions of people like Dziuda, bringing devastation in very specific ways to older workers. It upended the final decade of their careers, drained their nest eggs and sentenced them to years of joblessness. It sapped the confidence of a generation of Americans on the cusp of a comfortable retirement. Above all, the recession has helped create a core group of Americans in their 50s and 60s who need to work many more years to make back what they lost.

Many may never get there.

The most immediate effect of the recession on older Americans was a sharp spike in joblessness. In May 2012, the unemployment rate for workers aged 55 and older stood at 6.5 percent—less than the overall rate that month of 8.2 percent, but double what it was for older workers five years earlier. By January 2015, it had come down to 5.1 percent, but still significantly higher than pre-recession rates.

And that might not be a disaster, if it didn’t take jobless 55-year-olds so long to get hired again. An AARP analysis of April 2014 labor numbers showed unemployed workers aged 55 and older were jobless for an average of 51 weeks, compared to 35 weeks for younger Americans.

So an older worker who loses a job can look forward to many months—or even years—struggling to get hired again.

That’s exactly what happened to Ron Dziuda.

* * *

The axe was about to fall. He could feel it.

It was July 2009 and the economy had bottomed out. Unemployment was at 9.5 percent and still headed south. Dziuda had been working for Misumi USA in Schaumburg, Illinois, about 40 miles north of his home in Plainfield, for more than five years, selling components like switches and sensors and ball screws.

But he had a bad feeling. In the spring, managers told him his performance was falling short. Dziuda thought that couldn’t be. To his mind, the problem was unrealistic expectations. Misumi’s sales targets were just too high, he argued. They weren’t convinced.

“It was coming,” he said. “The writing was on the wall.”

Still, Dziuda didn’t panic. After getting a night-school degree in marketing from University of Detroit in 1985, Dziuda—the oldest of five children—worked at a series of employers across the Midwest. Each job change usually meant something better.

In the late 1990s, he and his family settled into a four-bedroom home in a subdivision called Wesmere Estates. Portraits of the Dziudas and their four children hang in the wall along the carpeted stairs in their home. Stenciled in script above the photos is the legend, “All Because Two People Fell in Love.”

Still, Dziuda was subject to the ebb and flow of the economy. In 2001, he picked up a part-time job at Target to tide him over between jobs. Then Misumi came along in 2004 with an offer that fit perfectly. He liked them, they liked him, and Dziuda hoped the years of bouncing from job to job were over.

But by July 2009, his prospects had dimmed. One morning a human resources manager called him into a meeting where company officials told him it was over. He signed an agreement not to take a job with a competitor for a year in exchange for a three-month severance package.

He was 54, out of a job at the bottom of the worst recession since the 1930s, with one kid in college, another one about enter. And it would be a long time before he got back on his feet.

* * *

Dziuda could see he was part of a growing wave. From the outset of the recession in December 2007 until his last full month at Misumi, the number of unemployed 50-plus workers more than doubled, from 1.4 million to 3.2 million.

Thanks to previous bouts of unemployment, he knew the drill. He updated his resume. He signed up at job clubs like the one at the nearby St Mary Immaculate Church. He called up people he used to work with, applied for every job he could find. He registered on Internet job boards like Monster and Careerbuilder. He went to collect unemployment, despite the humilation; he had a family to feed.

Interviews were a little harder to come by than they had been in the past, but things were moving. He’d sold himself, communicated his ideas and the value he’d bring to the company. He’d met company officials over several hours in the morning, gone to lunch, parted with warm handshakes.

And then nothing happened.

By the opening of 2010, Dziuda could see the economy wasn’t going to roar back into shape anytime soon. Instead of getting better with the official end of the recession in June 2009, things seemed to be getting worse. Unemployment was higher than when Dziuda was laid off, edging up to 9.7 percent. He was plugging away part-time at Target at $13 an hour. When he wasn’t working, he pounded the pavement.

His family struggled to adjust. Sue loaded on the jobs. She subbed at the local school district and worked as a teachers’ aide. In the late afternoons she sold fragrances at the mall. After dinner, she gave fitness classes. Back home at 10 p.m., she worked till midnight marking standardized tests online.

“My fitness training was actually my sanity,’’ Sue recalls. “I needed to work out at that point – otherwise I would have cried.’’

Dziuda’s doubts began to gnaw at him as the months went by, and the Excel log of job applications he kept on his computer ran to the dozens, and then the hundreds. Before he knew it, he was part of that statistic he’d never expected to join: the long-term unemployed. Dziuda says Sue kept any doubts she had largely to herself, but he could almost hear the tape of thoughts running through her mind:

What do you think? You think you’re going to find something? Really? Do you really think you’re going to find something?

By the second half of 2010, Dziuda was in deep unemployment. He and Sue began to consume the nest eggs that had been meant to grow, hatch and mature in retirement. One by one, these keepsakes of Dziuda’s employment history were taken from the safe and sold off.

The $4,000 pension left over from from Panasonic.

Gone.

The $4,000 IRA.

Cashed in.

The $5,000 in stock from his days at GM and Detroit Edison.

Turned to dust when GM filed for bankruptcy.

Then came 2011. Another year of dwindling faith.

* * *

While his wife worked at the school, or in the aerobics studio, or at the mall selling fragrances, Dziuda was at home loading the washer or unloading the dryer, vacuuming the living room carpet. They bickered over the way he folded the laundry. The new job wasn’t materializing, and the money just wasn’t coming in. But the water bill arrived every month on time. And the electricity bill. And the gas bill. And the mortgage.

With their savings depleted, the couple took a deep breath and did what many desperate families of the Great Recession did: they went into debt. Dziuda turned to his own son, the eldest, a Purdue grad with an engineering job, for a $5,000 loan to make the house payment and avoid foreclosure. Then came a second mortgage on the house. They dipped into their daughter’s student loan fund and ended up owing her $3,000.

All the while, prospective employers seemed impossible to please. The companies were just too scared to hire, holding out for a sure thing, leaving hopefuls—by August 2011, 3.3 million 50-plus workers were jobless and looking for employment—to wallow for months while they deliberated. Or, Dziuda found, employers would hold his experience against him. You’re over-qualified, they told him, and even if we hire you, you’ll be out the door once something better comes along.

In September 2011, his unemployment insurance tapped out. Now the Dziudas were truly on their own. He would have to take whatever full-time work he could find to stay afloat.

Target, where he’d worked off and on since 2002, was the logical choice. The position was called Store Facilities Technician, but some of the duties—tending to clogged toilets and blown-out light bulbs—weren’t so technical. But Dziuda was desperate and willing to work hard for the sake of boosting his hourly pay by three bucks, from $13 to $16.

So he applied – and endured nine interviews for the sake of an annual salary of about $30,000—a fraction of what he’d made in his former life. That was above the poverty line for a family of four in 2011, but not by much. He filled in the gaps by substitute teaching at the local school district.

Rock bottom was not that far away. Dziuda had applied for 653 jobs in two years, fully documented on the family computer, with at least another 100 or so on top of that – with not a single viable offer. They had stopped buying anything beyond bedrock essentials, scraped by just to get clothes for the kids at Christmas. Retirement gone. The credit cards were close to maxed out; Sue was using them for food and gas.

Their children struggled to understand.

“My kids were embarrassed that their dad worked at Target and their daddy substitute taught and they were like, ‘Don’t come to my school, I don’t want anybody to know,’’’ said Sue. “It was very embarrassing.’’

Dziuda wondered: Was Target the best he could hope for? Was this the end of the road?

He turned to Sue.

“You know what?” he told her. “This might be it. I hate to say it, and I’ll never stop looking for something better. But this might be it.”

Sue shook her head.

“No, you’ve gone this far, you can’t stop,’’ she told him. “There’s something out there for you and you have to believe and you just have to keep going for it.’’

* * *

By early 2012 Dziuda was trying really hard to feel lucky. He was turning to the basics for sustenance: His 30-year marriage; the health of his four children.

And his faith. Dziuda had been a steadily practicing Catholic, carrying on the religion his family had brought with them from Poland in the early 20th century. He’d memorized a prayer for the unemployed which he recited to himself. And now, in his resignation, he and Sue were ready to put their fate in God’s hands.

Can God get you a job? Dziuda says He can. That’s how he explains the mystery that once he acknowledged his exhaustion and leaned on his faith for help, a door opened.

Dziuda came across a job ad on the Internet for selling sandblasters. Pangborn Group. Around since 1904. Then a guy at St. Mary pointed out the same job. Did you see this ad?Was this the sign Dziuda was waiting for? He applied.

Before he knew it, he was at a hotel in Chicago for an interview with two managers. Two guys his age, no impression that his gray hair or wrinkles around the eyes bothered them. Then a plane ride to headquarters in Atlanta for more talks.

Nothing happened for a while. Just like all the other times.

One day the phone rang.

One of the recruiters Dziuda had met in Atlanta was going up to Chicago for work and he asked to meet. He put on a tie and jacket, drove to a Longhorn Steakhouse off I-55. The recruiter took one look at Dziuda and shook his head. There was no reason for a tie. After all, the tough part was over: the recruiter was there to make Dziuda an offer.

Dziuda listened to the details, but his head was already drifting into the clouds. He got in his car and pulled carefully out of the parking lot, trying to contain his excitement. He drove home with the same thoughts over and over running through his head: My God, I got a job. I actually have a job. Oh my God, I never thought this would happen.

I have a job.

Three years of searching were at an end. The eating up the savings, the racking up of credit card debt, the sad, desperate move to borrow money from their own children. The dinners of instant ramen and hot dogs. But Dziuda says there were no tears of gratitude. Instead, he and Sue just sighed heavily with exhausted relief. They could start making the house payment again.

* * *

It was a few days later when I watched him stand up at the job group at St. Mary and make his announcement: after three years in the wilderness, he had finally landed a job. At 57, he was back in the game.

“I had to let go. That’s the toughest part. Let it go. Let God. We continue to use our God-given skills to find that job,’’  he said as heads around the room nodded.

“Continue to find that job – it’s out there.’’

The celebration was short. He had a long struggle ahead to dig his family out of a financial hole. Dziuda and his wife have started making payments on their mortgage from their income again, and their youngest child, a son, left the house for the University of Wisconsin, Madison, in August 2012. His children have resigned themselves to facing college debt loads when they go out into the work world.

The scars—financial and emotional—from his three-year battle with the job market are still there. They may never fade entirely. He’s kept his part-time job at Target. Will the job at Pangborn still be here next year? Can he learn shot-blast equipment fast enough and well enough to be successful? Will he be able to retire from this job, or will he again be on the job-search rollercoaster at an even older age?

Dziuda’s answer to all those questions is the same: I don’t know.

“I guess my biggest thing is: how long is this going to last?”

The world, in some ways, has dimmed. The past several years have been ones of disappointment and humiliation. The future is uncertain. He sees his kids graduating with loads of debt, moving into a shaky  job market. It doesn’t seem fair.

And the black cloud is still there, just over their shoulders. It hovered over them at Ron and Sue’s 30th wedding anniversary celebration in the fall of 2013, when they hesitated to buy themselves a nice dinner out, thinking about how much it would cost.

“There’s a guilt that comes with everything we do now, and my kids are like, ‘You gotta stop that,’’’ says Sue. “Because sometimes I think it’s too good, it’s gonna end. Is it gonna be taken away from us again? I don’t like that feeling, but that’s what this experience has done. It’s like I don’t ever feel secure.’’

At the same time, though, Dziuda is deeply thankful. He kept his house. He has a solid job.

But he’s clear about one thing: he won’t be retiring anytime soon. At this point, he’s hoping to stay healthy enough to work for at least for 15 years to rebuild—putting him in his 70s before he can downshift. For now, he’s putting all expectations of retirement out of his mind.

“In one way, shape or form, or another,” he said, “I will probably work for the rest of my life.”

Reprinted from “Unfinished Work: The Struggle to Build an Aging American Workforce”by Joseph Coleman, with permission from Oxford University Press, USA, ©Joseph Coleman, 2015. All rights reserved.

Joseph Coleman has been a journalist for more than two decades, spending most of that time as a foreign correspondent for Associated Press, including 11 years in Japan. He’s reported from more than 20 countries throughout Asia, Europe, and Latin America, covering stories ranging from the Colombian government’s battle with the Medellin drug cartel to the Kobe earthquake, the Asian tsunami, and global warming. A graduate of Vassar College and Columbia University, Coleman is the Roy W. Howard Professor of Practice in the Indiana University Media School.

Poor Us: An Animated History of Poverty

Do we know what poverty is? To find out more and get teaching resources, go to http://www.whypoverty.net

The poor may always have been with us, but attitudes towards them have changed. Beginning in the Neolithic Age, Ben Lewis’s film takes us through the changing world of poverty. You go to sleep, you dream, you become poor through the ages. And when you awake, what can you say about poverty now? There are still very poor people, to be sure, but the new poverty has more to do with inequality…

Director Ben Lewis
Producer Femke Volting & Bruno Felix
Produced by Subma­rine

Why Poverty?
http://www.whypoverty.net/en/video/24/

Video URL: http://youtu.be/TxbmjDngois

What Do Americans Prioritize When Picking a Place to Live?

According to a new poll, economic mobility and diversity are key components of a good city or town.

When you’re looking for a city or town to call home, there are lots of factors to consider. For some, it’s about being in an area that is economically vibrant, with plenty of job opportunities and growing infrastructure. For others, being in a city or town that is family-friendly, with access to good schools and activities for children may take precedence.

Though choosing a place to settle down includes many individual considerations, the most recent Allstate/National Journal Heartland Monitor poll suggests that there are a few things Americans agree on when it comes to picking a place to build a life.

More than 90 percent of respondents said that providing equal chances for all people to get ahead, through educational and economic opportunities, was one of the most important attributes that any community can have. And more than three-fourths of respondents thought that locations that included ethnic and racial diversity were a key factor for good communities. People cared less about living in an area that was politically or religiously homogenous, with only about half of respondents saying that they preferred living among those who shared the same religious or political affiliations.

In addition to asking people what elements made a city or town a good place to live, the poll also asked Americans how they felt their communities were performing on these measures. Seventy-four percent of Americans felt like their area was a place where all people had the opportunity for advancement. Twenty-eight-year-old Melanie Thompson would agree with that sentiment. She has lived in basically the same area, in Jackson, Michigan, for her entire life and says that the area provides resources and support for people of different income levels to enhance their educational attainment and careers.

“I’m currently going to the community college, I’ve been out of school for years and even when I was in school, there were lower-income kids who automatically got two free years [at the college],” she said. Thompson has four kids—ranging from 6-years-old to only 4 months in age—and she currently works at Burger King to help make ends meet. Though she says she’d be interested in a different job, she gives her towncredit for being family-friendly and providing opportunities to work—even if they aren’t the highest-paid jobs—while she pursues her nursing degree.

But some places didn’t score as high with residents. For example, 23-year-old Devin Townsend is adamant that his hometown of Cleveland, Ohio, is not the place to get ahead. “There aren’t that many opportunities. It’s actually getting worse: the job rate, and the crime rate due to the job rate,” he says. Townsend thinks that more jobs would be helpful, but he says that’s just not the reality, and that the opportunities that do exist aren’t spread evenly across all groups.

When it comes to the availability of economic opportunities in their communities, white respondents were slightly more likely to feel positively than black and Hispanic Americans. And not surprisingly, those who identified as being in a higher economic class were also more likely to have positive views of the economic opportunities available in their areas.

While there was a great deal of consensus about the importance of creating opportunities for everyone to get ahead, those who identified as minorities or Democrats were more likely to rate racial and ethnic diversity as important elements of their community than respondents who were white, or belonged to other political parties.

Melanie Thompson says that her once-homogeneous community in Jackson, Michigan, has gotten more diverse since she was a child. “When I was in school, it was mostly a white area but it’s changed. It’s quite diverse,” she said. Thompson says she’s all for the shift toward a more racially mixed town, but she doesn’t necessarily feel that it is critical. “It’s not huge deal, but it’s better. With every different ethnicity it’s more knowledge, the more you know the better off you are.”

Nearly three-quarters of Americans said that they felt like the places they lived were communities with people from lots of different ethnic and racial backgrounds. White Americans and Hispanics agreed with the statement that their communities were diverse at lower levels than black Americans, who were more likely to report that they lived around people of different races and ethnicities.


For more on the methodology of the Heartland Monitor Poll, see here.

The Economic Scars of Domestic Abuse

Joshua Lott/Reuters

 

The financial damage done to those in violent relationships can last for years—another reason it’s difficult for victims to just walk away.

When it comes to the subject of domestic abuse, accounts of crime and violence usually take center stage. But another element that’s not as often discussed are the financial losses suffered by the victims of domestic violence. It’s estimated that victims of domestic abuse lose 8 million work days each year that they would’ve had under safer circumstances. Further, the total economic impact of domestic abuse—adding up lost productivity with the expense of police, medical, and social services—is estimated to be as high as $7 billion.

The National Network to End Domestic Violence (NNEDV) calls the impact on a victim’s bank account the “hidden barrier,” and that the financial damage for those who escape abusive relationships can last for years. The NNEDV has found that financial abuse is a powerful way of trapping victims in their situations, and a survey of survivors showed that 98 percent of abusive relationships involve financial abuse. This happens in various ways, from draining assets and destroying a partner’s credit score, to jeopardizing a partner’s career.

A new University of Pittsburgh study looks at what happens to the finances of women who take action to put an end to their own abuse, by looking at their financial situations at the time they file a civil restraining order. The researchers looked at the financial data of nearly 4,000 women in Allegheny County, Pennsylvania, who filed for protective orders between 1996 and 1999.

In their data, they found that female domestic abuse victims had very low incomes to begin with, the median being $6,577 in 2014 dollars in the year before filing for a Protection from Abuse (PFA) order. Eventually their earnings went up (likely because they were living safer lives), but the researchers estimate that these women lost anywhere from $300 to $1,000 dollars in the year following filing for a petition. They concluded that the financial instability brought on by abuse also hinders women’s long-term earnings.

The study’s authors, Melanie Hughes and Lisa Brush, note that women in abusive relationships, particularly those in low-income situations, sometimes cannot afford to just leave their abusers. “Our study convincingly shows that women’s petitioning for a PFA does not come with either short- or long-term increases in earnings growth,” said Hughes. “We cannot offer women a restraining order as a tool to stop abuse and then walk away. We need to offer women other forms of support, especially economic ones, during this unstable time.”

How Low Can We Go? State Unemployment Insurance Programs Exclude Record Numbers of Jobless Workers

By Will Kimball and Rick McHugh

Introduction and executive summary

The Great Recession and its aftermath created severe challenges for unemployment insurance (UI) programs in the United States and for jobless workers relying upon them. In this briefing paper, we show that state UI programs are failing their critical goals of income replacement and supporting economic growth. The proportion of jobless workers receiving benefits from state programs, referred to as the UI recipiency rate, fell to 23.1 percent in December 2014—below the pre-Great Recession record low of 25.0 percent in September 1984.

Due to the expiration of federal emergency unemployment benefits at the end of 2013, jobless individuals were solely dependent upon state UI programs for support in 2014. While state UI benefit recipiency overall has declined due to the improving economy, these state programs in many cases failed to assist jobless workers. This brief focuses special attention on those states that have cut their potential available weeks of UI benefits to below the long-accepted norm of 26 weeks. Because state UI programs are mainly designed to address short-term unemployment, we focus our analysis on the short-term recipiency rate, which excludes people who have been unemployed for 27 weeks or more from the proportion of jobless workers receiving benefits from state programs.

The key findings of this brief include the following:

  • Since 2011, nine states have cut maximum durations of unemployment benefit recipiency: Arkansas, Florida, Georgia, Illinois, Kansas, Michigan, Missouri, North Carolina, and South Carolina.
  • Eight of these states have experienced faster-than-average declines in their short-term recipiency rates. The exception is Illinois, which cut available benefits by only one week for a single year (to 25 weeks for 2012). In four of the states (Florida, Georgia, North Carolina, and South Carolina), short-term recipiency rates declined by between 1.7 and 8.6 times as much as the U.S. average decline.
  • By cutting available weeks of benefits, these eight states’ already-low short-term recipiency rates fell even further below the recipiency rates of all other states. In 2014, Florida, Georgia, North Carolina, and South Carolina ranked in the bottom eight states in short-term (less than 26 weeks) recipiency rates.
  • In North Carolina, one of the states with the most severe cuts (cutting the duration of benefits from 26 weeks in 2013 to 14 weeks in 2014 as well as cutting the level of weekly benefit amounts), the decline in the short-term recipiency rate was 14.4 percentage points (or 8.6 times) greater than the nation’s average decline since the cuts went into effect in July 2013.

Expanding our analysis to the regular (versus short-term) UI recipiency rate, we find that jobless people exhausting state UI benefits in 2014 had less protection from income loss than any cohort of jobless individuals exhausting state UI benefits over the last few decades.

The current labor market situation

The damage caused by the Great Recession was extensive and prolonged. Further, the recovery that officially started in June 2009 has been characterized by historically tepid growth. Despite a more recent acceleration of employment growth, the labor market is far from a full recovery. As of December 2014, the unemployment rate was 5.6 percent, down from its peak of 10.0 percent in October 2010.

The drop in the official unemployment rate overstates the overall improvements made in the underlying labor market. The United States lost 7.8 million jobs between December 2007 and October 2010 but the working-age population continued to grow over that period. As a result, even with steady job growth in recent years, the current labor market is still short 5.6 million jobs needed to keep up with the growth in potential labor force (see Figure A). We are still far from a healthy labor market.

FIGURE A

Payroll employment and the number of jobs needed to keep up with the growth in the potential labor force, 2000–2014

Year Payroll employment – actual Jobs needed to keep up with growth in potential labor force
2000-03-01 131606
2000-04-01 131893
2000-05-01 132119
2000-06-01 132074
2000-07-01 132251
2000-08-01 132237
2000-09-01 132371
2000-10-01 132357
2000-11-01 132582
2000-12-01 132724
2001-01-01 132694
2001-02-01 132766
2001-03-01 132741
2001-04-01 132460
2001-05-01 132422
2001-06-01 132293
2001-07-01 132178
2001-08-01 132020
2001-09-01 131778
2001-10-01 131454
2001-11-01 131160
2001-12-01 130989
2002-01-01 130847
2002-02-01 130714
2002-03-01 130695
2002-04-01 130615
2002-05-01 130607
2002-06-01 130664
2002-07-01 130579
2002-08-01 130564
2002-09-01 130504
2002-10-01 130629
2002-11-01 130639
2002-12-01 130481
2003-01-01 130575
2003-02-01 130422
2003-03-01 130212
2003-04-01 130167
2003-05-01 130156
2003-06-01 130166
2003-07-01 130189
2003-08-01 130148
2003-09-01 130250
2003-10-01 130446
2003-11-01 130462
2003-12-01 130586
2004-01-01 130747
2004-02-01 130791
2004-03-01 131123
2004-04-01 131372
2004-05-01 131679
2004-06-01 131753
2004-07-01 131785
2004-08-01 131917
2004-09-01 132079
2004-10-01 132425
2004-11-01 132490
2004-12-01 132619
2005-01-01 132753
2005-02-01 132992
2005-03-01 133126
2005-04-01 133489
2005-05-01 133664
2005-06-01 133909
2005-07-01 134282
2005-08-01 134478
2005-09-01 134545
2005-10-01 134629
2005-11-01 134966
2005-12-01 135125
2006-01-01 135402
2006-02-01 135717
2006-03-01 135997
2006-04-01 136179
2006-05-01 136202
2006-06-01 136279
2006-07-01 136486
2006-08-01 136670
2006-09-01 136827
2006-10-01 136829
2006-11-01 137039
2006-12-01 137210
2007-01-01 137448
2007-02-01 137536
2007-03-01 137724
2007-04-01 137802
2007-05-01 137946
2007-06-01 138017
2007-07-01 137984
2007-08-01 137968
2007-09-01 138053
2007-10-01 138135
2007-11-01 138253
2007-12-01 138350 138350
2008-01-01 138365 138441
2008-02-01 138279 138532
2008-03-01 138199 138623
2008-04-01 137985 138714
2008-05-01 137803 138805
2008-06-01 137631 138896
2008-07-01 137421 138987
2008-08-01 137162 139078
2008-09-01 136710 139169
2008-10-01 136236 139261
2008-11-01 135471 139352
2008-12-01 134774 139444
2009-01-01 133976 139535
2009-02-01 133275 139627
2009-03-01 132449 139718
2009-04-01 131765 139810
2009-05-01 131411 139902
2009-06-01 130944 139994
2009-07-01 130617 140086
2009-08-01 130401 140178
2009-09-01 130174 140270
2009-10-01 129976 140362
2009-11-01 129970 140454
2009-12-01 129687 140546
2010-01-01 129705 140638
2010-02-01 129655 140731
2010-03-01 129811 140823
2010-04-01 130062 140915
2010-05-01 130578 141008
2010-06-01 130456 141100
2010-07-01 130395 141193
2010-08-01 130353 141286
2010-09-01 130296 141378
2010-10-01 130537 141471
2010-11-01 130674 141564
2010-12-01 130745 141657
2011-01-01 130815 141750
2011-02-01 130983 141843
2011-03-01 131195 141936
2011-04-01 131517 142029
2011-05-01 131619 142123
2011-06-01 131836 142216
2011-07-01 131942 142309
2011-08-01 132064 142403
2011-09-01 132285 142496
2011-10-01 132468 142590
2011-11-01 132632 142683
2011-12-01 132828 142777
2012-01-01 133188 142871
2012-02-01 133414 142964
2012-03-01 133657 143058
2012-04-01 133753 143152
2012-05-01 133863 143246
2012-06-01 133951 143340
2012-07-01 134111 143434
2012-08-01 134261 143528
2012-09-01 134422 143623
2012-10-01 134647 143717
2012-11-01 134850 143811
2012-12-01 135064 143906
2013-01-01 135261 144000
2013-02-01 135541 144095
2013-03-01 135682 144189
2013-04-01 135885 144284
2013-05-01 136084 144378
2013-06-01 136285 144473
2013-07-01 136434 144568
2013-08-01 136636 144663
2013-09-01 136800 144758
2013-10-01 137037 144853
2013-11-01 137311 144948
2013-12-01 137395 145043
2014-01-01 137539 145138
2014-02-01 137761 145211
2014-03-01 137964 145283
2014-04-01 138268 145356
2014-05-01 138497 145428
2014-06-01 138764 145501
2014-07-01 139007 145574
2014-08-01 139210 145646
2014-09-01 139481 145719
2014-10-01 139742 145792
2014-11-01 140095 145864
2014-12-01 140347 145937
Number of jobs (thousands)Jobs needed to keep up with growth in potential labor forcePayroll employment – actual2000201020052015125,000130,000135,000140,000145,000150,000

5.6 million job shortfall

Note: The potential labor force is the actual labor force plus the “missing workers,” potential workers who, because of weak job opportunities, are neither employed nor actively seeking a job.  How EPI calculates missing workers can be found here: http://www.epi.org/publication/missing-workers/

Source: EPI analysis of Bureau of Labor Statistics’ Current Employment Statistics public data series and Current population Survey public data series

Background on UI programs

Unemployment insurance (UI) is a federal-state program that provides income support for jobless workers in economic downturns.

A recent EPI report provides a succinct overview of how the federal-state system has historically worked:

In the United States, the federally supported but state-administered unemployment insurance (UI) system typically provides someone who has lost a job through no fault of his or her own with unemployment benefits for up to 26 weeks. In states that have experienced a sharp rise in unemployment rates, the extended benefit (EB) program kicks in, providing an additional 13 to 20 weeks of jobless benefits. And in times of severe economic distress, Congress routinely votes to provide extra weeks of aid beyond EB. The most recent Emergency Unemployment Compensation (EUC) program was authorized by Congress in June 2008. … It was allowed to lapse in December 2013. (Bivens, Smith, and Wilson 2014)

The EUC program authorized in 2008 lasted 66 months and provided at its maximum 63 weeks of additional benefits.

While the federal government fulfills a more central role than states in providing benefit extensions during recessions, state governments have responsibility for setting the main parameters of regular state UI programs, including eligibility rules, benefit amounts, and weeks of benefits available. (Detailed descriptions of how UI programs changed during and after the Great Recession are provided by Bradbury (2014) and Isaacs (2012).)

The principal goals of UI programs are providing involuntarily unemployed workers with adequate, temporary income replacement while maintaining consumer spending levels during an economic downturn. Related goals include supporting the job search of unemployed individuals by permitting them to find work that matches their prior experience and skills, as well as enabling employers to retain experienced workers during layoffs (Advisory Council on Unemployment Compensation 1995, 27–30). One important performance indicator for state UI programs is the proportion of unemployed individuals who get UI benefits, or the benefit recipiency rate. Both the income support and economic stimulus objectives of UI programs are better fulfilled with higher levels of benefit recipiency (Vroman 2011).

Viewing UI as an important social insurance program, this brief assumes that falling recipiency rates are a troubling development. Admittedly, many economists concentrate on the moral hazard risks posed by unemployment benefits. These theoretical concerns (that UI benefits encourage unemployment) are undercut by more recent research finding empirical evidence that UI benefits improve job-matching (the fit between a worker’s skills and earnings experience and the pay provided and skills required by the new job) and support work search. Bivens, Smith, and Wilson (2014) provided an overview of recent economic research concerning the relationship between duration of unemployment and UI, casting doubt on traditional perspectives that concentrate largely upon moral hazard. The focus on moral hazard has been criticized for making unrealistic assumptions about how jobless individuals value work, avoid unemployment, and conduct job searches (Altman 2014; Howell and Azizoglu 2011). Significantly, in addition to the works cited in Bivens, Smith, and Wilson, there is new empirical evidence based upon labor market and administrative data that UI benefits and extensions improve job matching and help long-term unemployed individuals remain connected to the labor market.1

State UI benefit recipiency rates reach historic lows in 2014

Figure B presents our calculated benefit recipiency rate for regular state UI programs from 1977 through December 2014. It does not include federal extensions to UI often instituted during recessions.2 It is notable that during the prior lowest point for benefit recipiency in the early 1980s, there were federal benefit extensions available. Given the phase-out of federal benefit extensions at the end of 2013, this figure indicates that UI benefit recipiency is now lower than the previous historical low in 1984. By December 2014, only 23 out of every 100 jobless workers were getting state UI benefits. Because there were federal benefit extensions in place in 1983 and 1984, this means that those exhausting UI benefits in 2014 had less protection from income loss than any cohort of jobless individuals exhausting state UI benefits for several decades.

FIGURE B

U.S. unemployment insurance (UI) recipiency rate, 1977–2014

Date U.S. recipiency rate (12-month moving average)
1977-01-01 34.4
1977-02-01 34.4
1977-03-01 34.2
1977-04-01 33.9
1977-05-01 33.8
1977-06-01 33.6
1977-07-01 33.3
1977-08-01 33.3
1977-09-01 33.1
1977-10-01 33.0
1977-11-01 32.9
1977-12-01 32.7
1978-01-01 32.6
1978-02-01 32.7
1978-03-01 32.7
1978-04-01 32.7
1978-05-01 32.6
1978-06-01 32.6
1978-07-01 32.5
1978-08-01 32.7
1978-09-01 32.6
1978-10-01 32.8
1978-11-01 32.8
1978-12-01 32.5
1979-01-01 32.8
1979-02-01 32.6
1979-03-01 32.5
1979-04-01 32.6
1979-05-01 32.9
1979-06-01 32.9
1979-07-01 33.3
1979-08-01 33.3
1979-09-01 33.5
1979-10-01 33.7
1979-11-01 34.1
1979-12-01 34.5
1980-01-01 35.0
1980-02-01 35.5
1980-03-01 35.6
1980-04-01 36.1
1980-05-01 36.3
1980-06-01 36.8
1980-07-01 37.6
1980-08-01 37.8
1980-09-01 38.3
1980-10-01 38.6
1980-11-01 38.2
1980-12-01 38.5
1981-01-01 37.9
1981-02-01 37.4
1981-03-01 37.1
1981-04-01 36.7
1981-05-01 36.0
1981-06-01 35.4
1981-07-01 34.7
1981-08-01 34.0
1981-09-01 33.4
1981-10-01 32.6
1981-11-01 32.4
1981-12-01 32.3
1982-01-01 32.1
1982-02-01 32.1
1982-03-01 32.4
1982-04-01 32.5
1982-05-01 32.7
1982-06-01 33.0
1982-07-01 32.9
1982-08-01 33.2
1982-09-01 33.3
1982-10-01 33.4
1982-11-01 33.6
1982-12-01 33.5
1983-01-01 33.3
1983-02-01 33.1
1983-03-01 33.0
1983-04-01 32.6
1983-05-01 32.3
1983-06-01 31.8
1983-07-01 31.3
1983-08-01 30.8
1983-09-01 30.1
1983-10-01 29.5
1983-11-01 29.0
1983-12-01 28.3
1984-01-01 27.7
1984-02-01 27.4
1984-03-01 26.5
1984-04-01 26.0
1984-05-01 25.8
1984-06-01 25.4
1984-07-01 25.3
1984-08-01 25.1
1984-09-01 25.0
1984-10-01 25.3
1984-11-01 25.4
1984-12-01 25.4
1985-01-01 25.7
1985-02-01 25.8
1985-03-01 26.0
1985-04-01 26.4
1985-05-01 26.3
1985-06-01 26.3
1985-07-01 26.7
1985-08-01 26.8
1985-09-01 27.0
1985-10-01 27.1
1985-11-01 27.1
1985-12-01 27.5
1986-01-01 27.6
1986-02-01 27.5
1986-03-01 27.5
1986-04-01 27.6
1986-05-01 27.7
1986-06-01 27.9
1986-07-01 28.1
1986-08-01 28.1
1986-09-01 28.3
1986-10-01 28.4
1986-11-01 28.3
1986-12-01 28.5
1987-01-01 28.1
1987-02-01 28.2
1987-03-01 28.4
1987-04-01 28.3
1987-05-01 28.2
1987-06-01 28.4
1987-07-01 28.3
1987-08-01 28.2
1987-09-01 28.1
1987-10-01 27.8
1987-11-01 27.8
1987-12-01 27.5
1988-01-01 27.4
1988-02-01 27.4
1988-03-01 27.5
1988-04-01 27.3
1988-05-01 27.3
1988-06-01 27.4
1988-07-01 27.1
1988-08-01 27.3
1988-09-01 27.3
1988-10-01 27.3
1988-11-01 27.6
1988-12-01 27.4
1989-01-01 27.5
1989-02-01 27.5
1989-03-01 27.6
1989-04-01 27.6
1989-05-01 27.9
1989-06-01 27.8
1989-07-01 28.0
1989-08-01 28.2
1989-09-01 28.3
1989-10-01 28.7
1989-11-01 28.9
1989-12-01 29.0
1990-01-01 29.6
1990-02-01 29.9
1990-03-01 30.0
1990-04-01 30.3
1990-05-01 30.6
1990-06-01 30.8
1990-07-01 31.2
1990-08-01 31.2
1990-09-01 31.2
1990-10-01 31.5
1990-11-01 31.7
1990-12-01 31.9
1991-01-01 32.4
1991-02-01 32.8
1991-03-01 33.1
1991-04-01 33.9
1991-05-01 34.2
1991-06-01 34.2
1991-07-01 34.6
1991-08-01 34.7
1991-09-01 34.9
1991-10-01 34.9
1991-11-01 34.7
1991-12-01 34.9
1992-01-01 34.6
1992-02-01 34.2
1992-03-01 34.1
1992-04-01 33.7
1992-05-01 33.0
1992-06-01 32.7
1992-07-01 32.2
1992-08-01 31.8
1992-09-01 31.6
1992-10-01 31.2
1992-11-01 30.9
1992-12-01 30.6
1993-01-01 29.7
1993-02-01 29.2
1993-03-01 28.9
1993-04-01 28.3
1993-05-01 28.0
1993-06-01 27.9
1993-07-01 27.6
1993-08-01 27.5
1993-09-01 27.4
1993-10-01 27.2
1993-11-01 27.4
1993-12-01 27.4
1994-01-01 27.6
1994-02-01 27.9
1994-03-01 28.1
1994-04-01 28.2
1994-05-01 28.6
1994-06-01 28.9
1994-07-01 29.0
1994-08-01 29.3
1994-09-01 29.4
1994-10-01 29.7
1994-11-01 29.9
1994-12-01 29.9
1995-01-01 30.2
1995-02-01 30.4
1995-03-01 30.5
1995-04-01 30.4
1995-05-01 30.5
1995-06-01 30.4
1995-07-01 30.5
1995-08-01 30.6
1995-09-01 30.6
1995-10-01 30.8
1995-11-01 30.8
1995-12-01 30.8
1996-01-01 31.1
1996-02-01 31.4
1996-03-01 31.2
1996-04-01 31.7
1996-05-01 31.6
1996-06-01 31.5
1996-07-01 31.7
1996-08-01 31.6
1996-09-01 31.7
1996-10-01 31.7
1996-11-01 31.4
1996-12-01 31.6
1997-01-01 31.3
1997-02-01 30.9
1997-03-01 30.8
1997-04-01 30.6
1997-05-01 30.4
1997-06-01 30.4
1997-07-01 30.3
1997-08-01 30.1
1997-09-01 30.1
1997-10-01 30.0
1997-11-01 30.1
1997-12-01 30.2
1998-01-01 29.9
1998-02-01 30.0
1998-03-01 30.1
1998-04-01 30.2
1998-05-01 30.2
1998-06-01 30.3
1998-07-01 30.5
1998-08-01 30.7
1998-09-01 30.8
1998-10-01 30.7
1998-11-01 30.9
1998-12-01 30.9
1999-01-01 31.1
1999-02-01 31.2
1999-03-01 31.7
1999-04-01 31.6
1999-05-01 31.8
1999-06-01 32.1
1999-07-01 31.9
1999-08-01 32.1
1999-09-01 32.2
1999-10-01 32.4
1999-11-01 32.6
1999-12-01 32.5
2000-01-01 32.5
2000-02-01 32.7
2000-03-01 32.2
2000-04-01 32.1
2000-05-01 32.1
2000-06-01 31.8
2000-07-01 31.9
2000-08-01 31.8
2000-09-01 31.8
2000-10-01 32.2
2000-11-01 32.3
2000-12-01 32.4
2001-01-01 33.1
2001-02-01 33.3
2001-03-01 33.5
2001-04-01 34.1
2001-05-01 34.8
2001-06-01 35.3
2001-07-01 36.3
2001-08-01 36.8
2001-09-01 37.1
2001-10-01 37.7
2001-11-01 37.9
2001-12-01 38.4
2002-01-01 38.8
2002-02-01 39.0
2002-03-01 39.2
2002-04-01 39.5
2002-05-01 39.4
2002-06-01 39.2
2002-07-01 39.0
2002-08-01 38.7
2002-09-01 38.7
2002-10-01 38.5
2002-11-01 38.1
2002-12-01 38.0
2003-01-01 37.5
2003-02-01 37.2
2003-03-01 37.3
2003-04-01 37.1
2003-05-01 36.8
2003-06-01 36.7
2003-07-01 36.5
2003-08-01 36.4
2003-09-01 36.3
2003-10-01 36.1
2003-11-01 36.0
2003-12-01 36.0
2004-01-01 35.7
2004-02-01 35.6
2004-03-01 35.4
2004-04-01 34.9
2004-05-01 34.6
2004-06-01 34.5
2004-07-01 33.9
2004-08-01 33.8
2004-09-01 33.4
2004-10-01 32.9
2004-11-01 32.9
2004-12-01 32.2
2005-01-01 32.1
2005-02-01 31.6
2005-03-01 31.3
2005-04-01 31.0
2005-05-01 31.0
2005-06-01 30.8
2005-07-01 30.8
2005-08-01 30.8
2005-09-01 30.6
2005-10-01 30.9
2005-11-01 30.9
2005-12-01 30.8
2006-01-01 31.1
2006-02-01 31.1
2006-03-01 31.0
2006-04-01 31.0
2006-05-01 31.1
2006-06-01 31.0
2006-07-01 31.0
2006-08-01 30.9
2006-09-01 31.0
2006-10-01 31.0
2006-11-01 31.0
2006-12-01 31.1
2007-01-01 31.1
2007-02-01 31.3
2007-03-01 31.3
2007-04-01 31.7
2007-05-01 31.8
2007-06-01 31.8
2007-07-01 32.0
2007-08-01 32.0
2007-09-01 32.0
2007-10-01 32.0
2007-11-01 31.9
2007-12-01 31.9
2008-01-01 31.8
2008-02-01 31.9
2008-03-01 32.1
2008-04-01 32.3
2008-05-01 32.0
2008-06-01 32.1
2008-07-01 32.0
2008-08-01 31.6
2008-09-01 31.9
2008-10-01 31.7
2008-11-01 31.6
2008-12-01 32.3
2009-01-01 32.5
2009-02-01 32.8
2009-03-01 33.9
2009-04-01 34.5
2009-05-01 35.2
2009-06-01 36.0
2009-07-01 36.3
2009-08-01 36.8
2009-09-01 36.8
2009-10-01 36.5
2009-11-01 36.5
2009-12-01 36.0
2010-01-01 35.3
2010-02-01 34.6
2010-03-01 33.7
2010-04-01 32.6
2010-05-01 31.7
2010-06-01 30.7
2010-07-01 29.6
2010-08-01 28.9
2010-09-01 28.1
2010-10-01 27.6
2010-11-01 27.1
2010-12-01 26.4
2011-01-01 26.1
2011-02-01 25.8
2011-03-01 25.3
2011-04-01 24.9
2011-05-01 24.8
2011-06-01 24.3
2011-07-01 24.1
2011-08-01 23.8
2011-09-01 23.6
2011-10-01 23.5
2011-11-01 23.4
2011-12-01 23.3
2012-01-01 23.3
2012-02-01 23.3
2012-03-01 23.0
2012-04-01 23.1
2012-05-01 23.0
2012-06-01 22.9
2012-07-01 23.0
2012-08-01 22.8
2012-09-01 22.9
2012-10-01 23.0
2012-11-01 22.9
2012-12-01 22.9
2013-01-01 22.7
2013-02-01 22.5
2013-03-01 22.5
2013-04-01 22.5
2013-05-01 22.4
2013-06-01 22.4
2013-07-01 22.4
2013-08-01 22.4
2013-09-01 22.4
2013-10-01 22.3
2013-11-01 22.1
2013-12-01 22.3
2014-01-01 22.4
2014-02-01 22.5
2014-03-01 22.6
2014-04-01 22.7
2014-05-01 22.7
2014-06-01 23.0
2014-07-01 22.9
2014-08-01 22.9
2014-09-01 23.1
2014-10-01 23.1
2014-11-01 23.1
2014-12-01 23.1
25.023.11980199020002010202530354045

Note: The UI recipiency rate is the share of unemployed workers receiving benefits from regular state programs, and is calculated by dividing the number of weeks compensated by the total number of unemployed persons. It is presented as a 12-month moving average. Shaded bars denote recessions.

Source: EPI analysis of Department of Labor (DOL) administrative data and Current Population Survey basic monthly data

The shaded months of recessions in the figure illustrate the cyclical nature of recipiency in UI programs. Overall, when unemployment peaks around recessions, UI recipiency rises (Vroman, 2011).3 In general, more claims are filed and individuals draw benefits for longer periods as unemployment rises. When unemployment falls, there are fewer workers to file claims and fewer workers with qualifying earnings.4 For these reasons, there is a clear cyclical pattern in benefit recipiency.

Compared with previous recessions, the U.S. labor market in 2014 is at a point in the recovery when we would expect benefit recipiency rates to fall from their peak levels. What stands out about the current low recipiency levels is the degree of their decline and the extremely low levels to which some individual states’ recipiency rates have fallen. We now examine some details regarding these recent declines.

Most short-term unemployed workers do not get state unemployment benefits

Unemployment benefits are paid by trust funds maintained for individual states in the U.S. Treasury and financed with employer payroll taxes (three states also use employee contributions).

Bivens, Smith, and Wilson (2014) explain how the federal-state UI system is designed to work:

The federal-state UI system is designed to enable states to increase the balances of their unemployment trust fund accounts during periods of economic prosperity and low unemployment so that the accounts maintain solvency during economic downturns, when the unemployment rate increases. Conversely, during recessions, when unemployment rises and growth slows, expenditures increase while revenues decrease. In this manner, during economic downturns, the federal and state governments replace a portion of the economic activity that is lost though decreased wages by injecting money in the form of UI benefits into the economy. Moreover, during times of prosperity, states pay less in benefits than they receive in revenues, allowing them to build up account balances … which tend to decline during downturns, when states must pay out more in benefits than they receive in taxes.

But a failure to adequately fund state trust fund accounts during the economic recovery and expansion between 2001 and 2007 set them up for insolvency when the Great Recession hit (Bivens, Smith, and Wilson 2014).

As early as 2011—when the year-round unemployment rate averaged 8.9 percent—some states reacted to record benefit payments from their unemployment trust funds by enacting restrictive legislation designed to cut UI benefit costs. In particular, between 2011 and 2014, nine states abandoned a long-established national pattern of providing at least 26 weeks of potential duration for a state unemployment claim. At the end of 2013, with long-term unemployment still more thandouble what it had been when the most recent federal unemployment extension program was adopted in mid-2008, the federal government let all emergency extensions expire, leaving jobless workers wholly dependent upon state UI programs. As noted, this combination of state cuts and federal inaction meant that at the close of 2014, levels of UI benefit recipiency in the U.S. were the lowest in the program’s history.

The costs of UI benefits are driven by three main factors: the unemployment rate, the benefit recipiency rate, and the share of prior wages replaced by UI benefits. Of these three factors, recipiency rates have shown the biggest variation over the post-World War II life of UI programs (Vroman 2011). Indeed, state UI legislation is often passed during and following recessions and is explicitly designed to cut benefit costs. Not unexpectedly, this legislation then plays a role in falling state UI program recipiency rates (GAO 1993). In this briefing paper, we present clear evidence that recent legislative cuts in duration of unemployment benefits have led to significant declines in benefit recipiency rates in those affected states which exceed the overall declines in recipiency observed in other states.

Many studies have attempted to provide explanations for the observed long-run decline of recipiency during the early 1980s, and these studies provide useful insight into why variations exist between states’ recipiency rates. A report commissioned by the Department of Labor and produced by the Lewin Group (Wittenburg et al. 1999) provided a summary of many such analyses to that time, analyses that primarily focused on explaining the rapid decline of the national recipiency rate from the late 1970s to the 1980s. Among those that studied labor force or unemployment compositions, Blank and Card (1991) found that the decline of unionization explained a substantial portion—25 percent—of the decline between 1977 and 1987, 25 percent. Other labor market variables, such as industry or demographic profiles of the unemployed, had negligible effects according to Burtless and Saks (1984) and Wittenberg’s own analysis. Another substantive explanation was the federal taxation of UI benefits, first enacted in 1978, which significantly reduced the recipiency rates by reducing the incentive to file claims (Anderson and Meyer 1997).

Benefit recipiency rates vary across states. While labor market and other factors contribute to variation in UI recipiency, public policy decisions have played a major role in recipiency declines. The most recent comprehensive assessment of recipiency rates was undertaken by Wayne Vroman in a report for the Department of Labor in 2001. After reviewing prior research, Vroman (2001) combined a regression analysis with site visits to eight states falling on the high and low ends of the range of state recipiency rates. Vroman’s paper found that no single factor explained the differences between low and high recipiency states, but that state laws and administrative practices made “an important contribution” to differences in UI recipiency. In his later 2011 paper, Vroman observed that UI recipiency rates and benefit-wage replacement rates are “strongly influenced by state UI statutes and program administration.” Similarly, Wittenburg et al. (1999) concluded that administrative and policy changes can explain a “large portion” of recipiency declines, but these were difficult to measure as independent factors.

In this paper, we focus on states that have passed restrictive legislative packages that have included cuts in available weeks of state benefits to levels below the customary 26 weeks. We find significant reductions in recipiency that exceed the overall declines in recipiency accompanying falling unemployment rates in other states in recent years.

Given that state UI programs are mainly designed to address short-term unemployment, for this portion of our paper we refine our consideration of recipiency by focusing on benefit receipt by the short-term unemployed (those unemployed 26 weeks or less). These jobless individuals are properly considered the target population for state UI programs. Our calculation of short-term recipiency rates uses a 12-month moving average of weeks compensated divided by a 12-month moving average of the number of short-term unemployed (26 weeks or less) in each state.5 This calculation gives us a short-term recipiency rate reported for each state in Figure C, with those states that cut maximum durations highlighted with red bars. Strikingly, these short-term recipiency levels indicate that regular state UI programs are failing jobless workers who need income replacement and support of work search.

FIGURE C

Short-term UI recipiency rate, by state (ordered from highest to lowest), 2014

State Short-term recipiency rate, ordered from highest to lowest
NJ 65.7%
CT 62.3%
DC 62.1%
MA 58.0%
PA 54.9%
AK 52.0%
MN 48.8%
NY 47.4%
VT 47.4%
IL 44.9%
CA 43.8%
HI 42.7%
WV 41.8%
WI 41.6%
ND 39.9%
RI 39.9%
DE 39.5%
MT 39.5%
MD 37.9%
NM 37.5%
NV 36.9%
IA 36.3%
OR 35.3%
US 34.7%
WA 34.3%
ME 33.0%
KS 32.7%
WY 32.4%
ID 31.6%
MI 31.0%
CO 30.1%
AR 29.9%
OH 29.1%
KY 28.1%
NH 26.9%
TX 26.6%
NE 25.6%
MO 24.4%
UT 24.0%
AL 23.9%
MS 23.9%
IN 23.4%
OK 22.8%
VA 22.3%
NC 20.5%
TN 19.9%
AZ 19.7%
GA 18.8%
FL 18.7%
LA 16.9%
SD 15.0%
SC 14.8%
65.7%62.3%62.1%58.0%54.9%52.0%48.8%47.4%47.4%44.9%43.8%42.7%41.8%41.6%39.9%39.9%39.5%39.5%37.9%37.5%36.9%36.3%35.3%34.7%34.3%33.0%32.7%32.4%31.6%31.0%30.1%29.9%29.1%28.1%26.9%26.6%25.6%24.4%24.0%23.9%23.9%23.4%22.8%22.3%20.5%19.9%19.7%18.8%18.7%16.9%15.0%14.8%NJCTDCMAPAAKMNNYVTILCAHIWVWINDRIDEMTMDNMNVIAORUSWAMEKSWYIDMICOAROHKYNHTXNEMOUTALMSINOKVANCTNAZGAFLLASDSC010203040506070%

Source: EPI analysis of Department of Labor (DOL) administrative data and Current Population Survey basic monthly data

Many critics of UI programs wrongly assume that the lion’s share of jobless workers get benefits. This is plainly wrong over the history of UI and especially in the more restrictive states.6 The U.S. short-term recipiency rate (black bar) was 34.7 percent in 2014, meaning that over 65 percent of short-term jobless workers did not get state UI benefits. Figure C shows the short-term recipiency rates for 2014 ranged from a low of 14.8 percent in South Carolina to a high of 65.7 percent in New Jersey. In 21 states, 70 percent or more of short-term jobless workers did not get UI benefits in 2014.

Cuts in weeks of available benefits cause significant recipiency declines

From the late 1960s to 2011, all states paid regular benefits for at least 26 weeks. One important potential reason for the recent decline in benefit recipiency is that since 2011 nine states have cut the maximum available number of weeks of regular UI benefit duration: Arkansas, Florida, Georgia, Illinois, Kansas, Michigan, Missouri, North Carolina, and South Carolina. Except Illinois, all these states made other legislative changes to their programs which may have reduced benefit recipiency. Our study does not try to distinguish among the variety of state law changes which accompanied the cuts in weekly benefit duration in these nine states. As a shorthand, we refer only to the cuts in available weeks of benefits and we focus on those states with these cuts in this section of our analysis.

The timing, approach, and severity of the cuts in benefit duration in these nine states have varied.Table 1 shows the new maximum durations in each state and the effective dates of the cuts in weeks. Arkansas and Illinois enacted relatively minor cuts to duration, reducing the maximum from 26 weeks to 25 weeks. Arkansas passed other restrictions along with cutting available weeks. In the case of Illinois, the cut of one week was effective only in calendar year 2012. Michigan, Missouri, and South Carolina all reduced their maximum duration from 26 to 20 weeks. Effective in January 2014, Kansas adopted a sliding scale formula that reduces benefits from 26 weeks to 20 weeks, and finally to 16 weeks as the state’s unemployment rate falls. Kansas paid 20 weeks on claims in 2014 and will pay only 16 weeks in 2015. Finally, the most severe cuts were made in Florida, Georgia, and North Carolina, where the maximum duration depends on a sliding scale formula based upon each state’s unemployment rate, with the variable number of weeks potentially available ranging from 12 to 20 across the three states. With falling state unemployment rates, the impact on available weeks of benefits was significant in these formula-based states. In December 2014, Florida paid a maximum 16 weeks of benefits, Georgia provided 15 weeks, and North Carolina’s limit was 14 weeks. Starting for claims in January 2015, Florida is providing a maximum of 14 weeks, Georgia is providing at most 17 weeks, and North Carolina is providing 15 weeks.

TABLE 1

Maximum weeks of unemployment insurance benefits, by states that cut benefits duration in the aftermath of the Great Recession

State Current maximum duration (compared with prior maximum of 26 weeks) Effective date of cut
Arkansas 25 March 30, 2011
Florida Sliding scale, 12 to 23 January 1, 2012
Georgia Sliding scale, 14 to 20 July 1, 2012
Illinois 25 to 26 January 1, 2012 (expired December 2012)
Kansas Sliding scale, 16, 20, or 26 January 1, 2014
Michigan 20 January 1, 2012
Missouri 20 April 13, 2011
North Carolina Sliding scale, 12 to 20 July 1, 2013
South Carolina 20 June 14, 2011

Note: The number of weeks of benefits available to recipients in states with sliding scales is “determined by the state’s unemployment rate.” In Illinois, the criteria for the duration cut were met in 2012 but not 2013 (meaning 26 weeks were available in 2013). In North Carolina, labor market conditions at the time of implementation were such that the maximum fell to 19 weeks.

Source: EPI analysis of Isaacs (2012) and state laws

Quite predictably, those states that cut available weeks of benefits have experienced faster-than-average declines in UI recipiency rates since the cuts. Figure D compares  the short-term UI recipiency rates in eight of the nine states that cut their maximum available weeks (excluding Illinois as its temporary cut did not negatively impact recipiency) with the average UI rate across the 41 states (plus the District of Columbia) that did not cut benefit weeks.

FIGURE D

Percentage-point difference between short-term UI recipiency rates in duration-cutting states and states that did not cut duration of benefits

AR FL GA KS MI MO NC SC
0 -4.7 -10.8 -13.2 -3.8 -1.1 -8.8 -1.3 -9.9
1 -6.0 -11.0 -13.2 -5.0 -1.3 -9.2 -1.5 -10.6
2 -7.1 -11.1 -13.3 -4.8 -0.4 -8.2 -1.7 -10.9
3 -6.7 -11.6 -12.9 -4.5 -0.7 -7.5 -2.0 -11.1
4 -7.0 -12.0 -13.4 -3.7 -0.6 -7.2 -2.1 -10.9
5 -7.1 -11.9 -14.7 -5.2 -0.1 -6.9 -3.0 -11.0
6 -7.6 -12.9 -14.1 -6.2 -0.1 -7.1 -4.1 -10.7
7 -7.4 -12.7 -14.3 -5.5 -0.3 -6.5 -4.9 -12.0
8 -5.8 -13.2 -14.8 -4.6 -0.9 -6.2 -7.2 -12.3
9 -5.5 -13.2 -14.6 -4.2 -1.3 -3.9 -8.5 -12.3
10 -5.8 -13.0 -14.9 -5.3 -1.7 -4.3 -9.6 -12.8
11 -4.8 -12.5 -14.7 -4.9 -2.0 -4.7 -10.5 -13.5
12 -4.2 -11.7 -14.8 -0.7 -5.2 -12.1 -13.9
13 -4.6 -11.8 -14.8 -0.9 -4.6 -13.1 -14.4
14 -3.3 -12.2 -14.9 -1.3 -4.4 -14.4 -15.4
15 -4.6 -12.3 -15.1 -2.2 -5.6 -15.0 -16.0
16 -3.7 -11.8 -15.3 -2.5 -6.1 -16.1 -17.7
17 -2.9 -12.3 -15.0 -3.4 -5.6 -17.1 -18.6
18 -3.9 -12.3 -15.9 -3.4 -6.2 -19.0
19 -3.5 -12.5 -15.9 -3.7 -7.0 -19.6
20 -4.4 -12.3 -15.7 -4.4 -6.5 -20.0
21 -5.7 -12.8 -16.5 -5.2 -6.0 -20.2
22 -5.6 -13.6 -16.9 -6.0 -6.3 -20.4
23 -5.9 -14.2 -17.4 -7.2 -6.0 -19.8
24 -5.8 -15.2 -17.5 -8.6 -5.2 -19.5
25 -5.3 -15.3 -18.1 -9.3 -5.0 -19.3
26 -5.0 -15.4 -18.5 -9.7 -5.5 -18.9
27 -3.9 -15.7 -18.6 -9.5 -5.0 -18.7
28 -3.3 -16.3 -18.7 -9.6 -4.9 -18.0
29 -3.3 -16.9 -18.8 -10.0 -5.4 -17.6
30 -3.8 -17.1 -10.1 -4.1 -18.0
31 -5.2 -17.8 -9.6 -3.8 -18.1
32 -6.4 -18.4 -9.1 -4.2 -17.6
33 -7.3 -18.6 -8.3 -6.0 -17.8
34 -7.5 -18.3 -7.6 -6.2 -18.3
35 -7.5 -18.9 -6.6 -7.6 -18.6
36 -8.3 -8.6 -19.0
37 -8.2 -8.8 -9.6 -19.6
38 -9.0 -11.0 -20.2
39 -9.1 -11.2 -21.4
40 -9.1 -11.3 -21.9
41 -9.7 -11.7 -22.2
42 -8.6 -12.0 -22.8
43 -7.3 -12.6
44 -7.7 -13.2
Months since cuts went into effectPercentage-point difference from all other states*-18.9-18.8-4.9-8.8-17.1-22.8-7.7-13.2FLGAKSMINCSCARMO0510152025303540-25-20-15-10-50

*The figure charts the benefit-cutting states’ short-term UI rates relative to the average short-term UI rate across all other states. Illinois was excluded from the all-other-state average but is not included in this graph because its temporary cut did not negatively impact recipiency.

Source: EPI analysis of Department of Labor (DOL) administrative data and Current Population Survey basic monthly data

Because each state enacted its cuts at a different time, the x-axis in Figure D reflects the months since the cuts became effective in each state. Because we use 12-month averages and the cuts have a delayed effect on new claimants, the effects of these cuts to available weeks typically begin to impact calculated recipiency rates from six to 12 months after the cuts went into effect.7 While these states’ recipiency rates were below the average recipiency rate even before their cuts, all of them now display an even steeper deviation from the rates of all other states. Arkansas, which reduced the duration of UI to 25 weeks, saw a milder drop from 4.7 percentage points below overall rates to 7.7 percentage points below. Kansas’s legislation became effective in January of 2014, leaving only a small sample to observe, but its deviation from the norm dropped from 3.8 percentage points to 4.9 percentage points after the first 12 months. Michigan and Missouri, two states that cut their durations to 20 weeks, which initially kept recipiency rates relatively flat, experienced drops that brought them to 6.6 and 13.2 percentage points below the overall recipiency rate of other states. In January 2015, Kansas’s duration formula dropped its maximum duration to 16 weeks, which will only accelerate its decline in recipiency.

States with more dramatic cuts to their durations also saw rapid declines in their recipiency rates. South Carolina, the fourth state that cut its durations from 26 to 20 weeks, saw one of the biggest declines. South Carolina’s recipiency rate fell from 9.9 to 22.8 percentage points below all other states’ recipiency rates, a decline of 12.9 percentage points. (This likely reflects the fact that South Carolina’s legislative package included several other restrictive measures.) Among the group of states with the most severe cuts in available weeks, both Florida and Georgia already had recipiency rates far below those of all other states. Still, their cuts to maximum durations did correlate with similarly large declines: 8.1 and 5.5 percentage points, respectively. North Carolina, a state whose recipiency rate was only 1.3 percentage points below the average recipiency rate of all other states, saw its rate fall sharply, by 15.7 percentage points so that it now stands 17.1 percentage points below the short-term recipiency rate of all other states that had no cuts to durations. This was the fastest and largest drop of any state.

Further analysis of individual state changes shows that the percentage-point declines in recipiency rates are greatest in those states with the biggest cuts in available weeks of benefits. Table 2compares the changes in the recipiency rate for each of the nine states that cut maximum durations to changes in the the U.S. average recipiency rate over the same timeframes as those cuts took effect in each state. The table breaks down these changes and shows how the magnitude of the rate cuts in nine states with cuts in available weeks depend upon the severity of the duration cuts.

TABLE 2

Percentage-point change in short-term UI recipiency rates for states that cut maximum durations and the corresponding changes in the U.S. average

State Current maximum duration (compared with prior maximum of 26 weeks) Effective month* of cut Percentage-point change in state’s short-term recipiency rate Percentage-point change in U.S. short-term recipiency rate Ratio of state’s change to U.S. average
Minimal cuts
Arkansas 25 April 2011 -10.8 -9.8 1.1
Illinois 25 to 26 January 2012 (expired in December 2012) -1.6 -2.7 0.6
Medium cuts
Kansas 16, 20, or 26 January 2014 -1.4 -1.1 1.3
Michigan 20 January 2012 -10.7 -6.9 1.6
Missouri 20 April 2011 -12.2 -9.8 1.2
South Carolina 20 June 2011 -19.8 -8.6 2.3
Biggest cuts
Florida Sliding scale, 12 to 23 January 2012 -13.3 -6.9 1.9
Georgia Sliding scale, 14 to 20 July 2012 -9.4 -5.4 1.7
North Carolina Sliding scale, 12 to 20 July 2013 -16.3 -1.9 8.6

* Effective month defined as the first month in which the cuts are effective for the majority of the month. Unless otherwise indicated, changes extend to the most recent month available, December 2014.

Source: EPI analysis of Isaacs (2012), state laws, Current Population Survey Outgoing Rotation Group microdata, and Department of Labor administrative data

As shown in Table 2, short-term recipiency rates in Arkansas and Illinois, the states that cut maximum durations least, experienced percentage-point declines that were about the same as or below the decline in the average U.S. decline. Illinois actually showed a slower-than-average decline (1.6 percentage points versus 2.7 nationally) over the one year for which it cut maximum durations. Since the enactment of their maximum duration cuts, Kansas, Michigan, Missouri, and South Carolina all experienced higher-than-average declines in their recipiency rates of 1.4, 10.7, 12.2, and 19.8 percentage points, respectively. Compared with the national average, these declines were 1.3, 1.6, 1.2 and 2.3 times the overall national average declines. These results show cuts to available weeks meant a decline in the recipiency rate. Increasingly severe cuts to the duration of benefits also meant more severe declines in the recipiency rate.

Figure E graphically presents these changes as reductions in the share of unemployed workers receiving short-term jobless benefits since the duration cuts went into effect and compares those reductions to the fall in short-term UI recipiency in the U.S. overall over the identical time periods. With the exception of Illinois, all states cutting available weeks of benefits below the norm of 26 have experienced larger reductions in short-term UI recipiency than the national average reduction in recipiency.

FIGURE E

Percentage-point change in the short-term recipiency rate in duration-cutting states compared with national average*

State National average
SC -19.8 -8.6
NC -16.3 -1.9
FL -13.3 -6.9
MO -12.2 -9.8
AR -10.8 -9.8
MI -10.7 -6.9
GA -9.4 -5.4
IL -1.6 -2.7
KS -1.4 -1.1
-19.8-16.3-13.3-12.2-10.8-10.7-9.4-1.6-1.4-8.6-1.9-6.9-9.8-9.8-6.9-5.4-2.7-1.1StateNational averageSCNCFLMOARMIGAILKS-25-20-15-10-50

* From effective date of legislation in each state

Source: EPI analysis of Department of Labor (2015) and Current Population Survey Outgoing Rotation Group microdata

States cutting weeks of UI have saved some money by reducing the number of weeks of benefits they paid out, but when spread across all covered employees, these savings are minimal, especially when compared with the importance that those benefits have for UI recipients. Bivens, Smith, and Wilson (2014) projected the costs of paying jobless benefits to workers affected by the cuts to duration, excluding Illinois, Kansas, and North Carolina because cuts there had been in effect for a limited time. For the other six states (Arkansas, Florida, Georgia, Michigan, Missouri, and South Carolina) however, the analysis found that the cuts saved $0.37 per covered worker per week on average.8 These savings are small compared with the average weekly benefit amount of $251.61 lost due to these cuts. Bivens, Smith, and Wilson also showed that those states that cut maximum durations did not experience anything resembling a boost in the labor market as some theorize. Rather, both employment growth and employment attachment (as measured by the prime-age employment-to-population ratio) remained about average in those states.

Can we expect states to maintain robust UI programs?

States play a large role in U.S. unemployment insurance programs, especially when it comes to operating regular state UI programs and setting their eligibility rules. Nearly 80 years into the life of UI as a federal-state program, there is considerable evidence that a significant portion of states fall short in terms of benefit recipiency, even when measured by the “low bar” of short-term recipiency rates. While there are economic and demographic differences among the states, from a policy perspective it is hard to defend state autonomy when jobless workers in South Carolina and other stingy states are denied benefits while those in more generous states get UI benefits under identical circumstances. Permitting states to operate restrictive UI programs undercuts the goals of UI and creates competitive pressures on those states with decent programs to engage in a “race to the bottom.” Both these factors undercut the economic and policy rationales for UI.

Other serious problems with state UI programs are easily identified. Although this brief has focused on falling recipiency rates, many states failed to sufficiently build UI trust fund reserves in advance of the Great Recession (Evangelist 2012), requiring them to borrow federal loans and raise payroll taxes while providing a rationale for the restrictive cuts examined here. In particular, larger states have failed to adequately finance their programs, and over time inadequate financing bodes ill for the welfare of UI programs under state control over financing (Vroman 2012).

Adequate wage replacement rates are key to achieving the goals of UI. Some states are unable or unwilling to keep UI programs at reasonable levels. Many states pay low benefits. There were 11 states with maximum weekly benefit levels of $350 or less in 2014 (U.S. Department of Labor 2014), meaning that workers earning more than $700 a week (well below the median weekly earnings) do not get half their prelayoff wages replaced by UI benefits. Average benefits overall were only $315 a week in 2014 (U.S. Department of Labor, 2015a) with average weekly benefits below poverty levels in the poorly performing states.

The recently released FY 2016 federal budget contains one possible federal policy response to the erosion in state UI program performance. Budget details released in early February 2015 by the Labor Department revive the concepts found in the 2009 UI modernization proposal, but reconceive the approach to address many pressing problems with states’ stewardship over UI programs (U.S. Department of Labor, 2015b). Under the first UI modernization program, states were offered federal financial incentives to adopt modest eligibility measures. Over $4 billion in payments were made to state trust funds during the lifetime of the program (March 2009 to August 2012).

Under the proposed UI modernization program, in order to gain access to a portion of the $5 billion in federal incentives available, states would have to agree to five administrative measures. For purposes of this brief, the most critical administrative requirement is that states must pay at least 26 weeks of regular state program benefits. In addition to the required administrative measures, the new version of UI modernization asks states to select two among a range of eligibility options and two options from a set of “work connection” strategies. Regardless of its short-term political viability, the inclusion of the renewed UI modernization concept in the FY 2016 budget proposal offers a potential pathway toward encouraging states to adopt sensible options which will, at a minimum, help states better resist the temptation to adopt cuts like those seen in the worst-performing states. Based upon state responses to the initial version of UI modernization prior to 2012, financial incentives may prove insufficient to improve policy in states that regard UI as a program which is best kept as small and stingy as possible. In that case, a campaign for federal standards for benefits and financing—advocated for decades by supporters of robust state UI programs—should be a central aspect of ongoing UI reform efforts.

Conclusion

While the proportion of jobless workers receiving UI benefits has varied over business cycles and varies between states, in 2014 the overall benefit recipiency rate for state UI programs reached its lowest level in post-WWII U.S. history. Further, this low level of benefit recipiency has come at a time when the U.S. labor market remains unambiguously slack, with significantly more unemployed workers than available job openings. Until 2011 all states provided at least 26 weeks of available benefits in their regular state UI programs. Nine states broke this national pattern beginning in 2011, and our analysis shows that this change had a negative and substantial impact on the UI benefit recipiency rate in eight of those states.

Focusing only on the short-term recipiency rate, we find that (with the exception of Illinois) those states cutting available weeks below 26 weeks had greater reductions in recipiency than the national average decline in recipiency. The states with the most severe cuts (Florida, Georgia, and North Carolina) were also among the four states whose recipiency declines most exceeded the national average. Reducing the duration of benefit receipt is especially detrimental to the unemployed. Clearly, these states ignore the proven advantages of UI programs in providing a countercyclical stimulus, protecting families against income loss, and supporting work searches by jobless individuals.

About the authors

Rick McHugh is an attorney and policy advocate who has worked on unemployment insurance for more than 35 years. He has testified before congressional and state legislative committees, litigated many cases in courts and before administrative agencies, and speaks frequently on UI topics. This is his second publication for EPI. McHugh has a bachelor’s degree from Wabash College and a J.D. degree from the University of Michigan.

Will Kimball joined EPI in 2013. As a research assistant, he supports the research of EPI’s economists on topics such as wages, labor markets, macroeconomics, international trade, and health insurance. Prior to joining EPI, Kimball worked at the Center on Budget and Policy Priorities and the Center for Economic and Policy Research.

Endnotes

1. A recent working paper by Nekoei and Weber (2014) suggests that moderate extensions of UI benefits can improve job quality for those individuals receiving benefits while searching for work. The authors found that increased earnings and revenue from taxing these higher-earning positions more than overcame the increased costs of extended benefits as well as the moral hazard issue arising from any increased duration of unemployment. Bradbury (2014), examining data from 2005 through 2013, finds that the main impact of UI on job transitions is to delay the transition from unemployment to nonparticipation in the labor market, while the transitions from unemployment to employment were “virtually unaffected” by UI program extensions. As a result, the overall effect of extensions in recent years was to raise unemployment rates by keeping jobless individuals from dropping out of the labor market. According to this study, job-finding rates by benefit recipients were distributed across the weeks of unemployment and not closely related to benefit exhaustion. Finally, three authors of papers reviewed in Bivens, Smith, and Wilson (2014), collaborated on a follow-up review of more recent data and found “little or no effect on job-finding but a reduction in labor force exits due to benefit availability” (Farber, Rothstein, and Valletta 2015).

2. Data are as reported by the U.S. Department of Labor. We have calculated benefit recipiency rates using the monthly number of those getting benefits, reported as “weeks compensated,” divided by the monthly number of total unemployed individuals. We opt to use weeks compensated by the alternative weeks claimed because weeks compensated reflect all benefit weeks that are actually paid out, rather than including those claiming benefits who are not paid because they are claiming during a waiting week or other ineligibility period. The monthly figures are smoothed by using 12-month moving averages. While there are other measures of UI recipiency, including the ratio of insured unemployed to the total unemployed, or weeks claimed compared with job losers, these measures show a similar pattern to our measure. Our use of 12-month moving averages shifts the peaks and troughs a few months later in our time series as compared with those reported by others.

3. A large group of nonrecipients of UI programs are those who do not apply for benefits. The reasons for significant non-application rates in state UI programs are not well understood. In this paper, non-applicants looking for work fall into the number of total unemployed in the denominator as with other past analyses of UI recipiency.

4. Within this broad cyclical pattern for recipiency, there are other labor market interactions with UI rules influencing benefit recipiency. Soon after a recession, recipiency increases as laid-off workers with recent wages file UI claims and fewer unemployed workers have quit their jobs. These freshly unemployed workers are more likely to be eligible for benefits compared with those unemployed later in the recovery period. Later in a recovery a higher share of jobless workers may have experienced a prior period of unemployment (reentrants), as well as those entering the labor market (new entrants and nonparticipants).These groups lack the sufficient wage history benefit eligibility as compared with those unemployed earlier in a downturn. And, when unemployment levels are lower, more workers in the ranks of the jobless leave their jobs and some of these job leavers are disqualified for voluntarily quitting.

5. We adopt this short-term recipiency approach from H. Luke Shaefer and Michael Evangelist (2014) who employed it in an analysis of the impact of Michigan’s restrictive 2011 legislation.

6. Vroman (2001, p. 9-10), calculated centered 5-year average recipiency rates using two alternate measures and showed that from 1949 until 1997, recipiency declined from peaks in the early 50s to a trough in the early 80s, then rose modestly in the 90s. Figure B (above) shows that higher recipiency levels in the 90s did not persist, and as this brief has shown, overall recipiency is now below the 80s trough. See also McKenna (2015).

7. Restrictive legislation impacts only new claims beginning on those effective dates. As a result, the effects of the cuts occur only four to six months after the effective date (depending on the severity of the cut in available weeks). Additionally, the use of 12-month averages in the calculation of our recipiency rates has a smoothing effect.

8. A covered worker is an employee on whom the employer is required to pay UI-dedicated state taxes (also known as State Unemployment Tax Acts or SUTA taxes).

References

Advisory Council on Unemployment Compensation. 1995. Unemployment Insurance in the United States:Benefits, Financing, Coverage. U.S. Department of Labor, Report.

Altman, Morris. 2014. “Insights from behavioral economics on how labor markets work.” School of Economics and Finance, Victoria University of Wellington (NZ), Working Paper.

Anderson, Patricia M., and Bruce D. Meyer. 1997. “Unemployment Insurance Takeup Rates and the After-Tax Value of Benefits.” The Quarterly Journal of Economics, 1997, vol. 112, no. 3. 913–937.

Bivens, Josh, Joshua Smith, and Valerie Wilson. 2014. State Cuts to Jobless Benefits Did Not Help Workers or Taxpayers. Economic Policy Institute, Briefing Paper #380.

Blank, Rebecca, and David Card. 1991. “Recent Trends in Insured and Uninsured Unemployment: Is There an Explanation?” The Quarterly Journal of Economics, 1991, vol. 106, no. 4, 1157–1189.

Bradbury, Katharine. 2014. “Labor Market Transitions and the Availability of Unemployment Insurance.” Federal Reserve Bank of Boston Working Paper No. 14-2.

Burtless, Gary, and Daniel H. Saks. 1984. The Decline in Insured Unemployment During the 1980s. The Brookings Institution. Report prepared for the U.S. Department of Labor, Employment and Training Administration.

Evangelist, Mike. July 2012. Lessons Left Unlearned: Unemployment Insurance Financing After the Great Recession. National Employment Law Project.

Farber, Henry S., Jesse Rothstein, and Robert G. Valletta. January 2015. “The Effect of Extended Unemployment Insurance Benefits: Evidence from the 2012-2013 Phase-Out.” Princeton University Working Paper #586.

Government Accountability Office (GAO). 1993. Unemployment Insurance: Program’s Ability to Meet Objectives Jeopardized. Report.

Howell, David R., and Bert M. Azizoglu. 2011. “Unemployment Benefits and Work Incentives: The U.S. Labor Market in the Great Recession.” Oxford Review of Economic Policy vol. 27, no. 2, 221-240.

Isaacs, Katelin P. August 2012. Unemployment Insurance: Consequences of Changes in State Unemployment Compensation Laws. Congressional Research Service.

McKenna, Claire. February 2015. The Job Ahead: Advancing Opportunity for Unemployed Workers. National Employment Law Project, Report.

Nekoei, Arash, and Andrea Weber. 2014. “Does Extending Unemployment Benefits Improve Job Quality?” Job Market Paper.

Shaefer, H. Luke, and Michael Evangelist. April 2014. The Impact of the 2011 Changes to Michigan’s Unemployment Insurance Program on Unemployed Workers and Their Families. Michigan Unemployment Insurance Project, Report.

U.S. Department of Labor, Employment and Training Administration. 2014. “Significant Provisions of State Unemployment Insurance Laws, July 2014.”

U.S. Department of Labor, Employment and Training Administration. 2015a. “Latest Statistics.”

U.S. Department of Labor. 2015b. “FY 2016 Congressional Budget Justification, Employment and Training Administration, State Unemployment Insurance and Employment Service Operations.”

Vroman, Wayne. 2001. Low Benefit Recipiency in State Unemployment Insurance Programs. The Urban Institute.

Vroman, Wayne. 2011. Unemployment Insurance: Problems and Prospects. National Academy of Social Insurance, Brief #2.

Vroman, Wayne. August 2012. Financing Unemployment Insurance After the Great Recession. Urban Institute Brief # 7.

Wittenburg, David C., Michael Fishman, David Stapleton, Scott Scrivner, and Adam Tucker. 1999. Literature Review and Empirical Analysis of Unemployment Insurance Recipiency Ratios. The Lewin Group, for the Division of Research and Policy of the U.S. Department of Labor Unemployment Insurance Service.

Wolf Richter: The US Oil Bust Just Got Worse

Wolf was early to point out the disconnect between declining rig counts, which the mainstream media has touted as proof that the oil glut was about to end, and rising production. That pattern has not abated.

By Wolf Richter, a San Francisco based executive, entrepreneur, start up specialist, and author, with extensive international work experience. Originally published at Wolf Street.

The price of oil did today what it has been doing for a while: it waits for a trigger and plunges. As I’m writing this, West Texas Intermediate is down 4.4%, trading at $44.99 a barrel, less than a measly buck away from this oil bust’s January low. It’s down over 20% from the peak of the most recent sucker rally.

US oil drillers have been responding by slashing capital expenditures, including drilling, in a deceptively brutal manner. In the latest week, drillers idled 56 rigs that were classified as drilling for oil, according to Baker Hughes. Only 866 rigs were still active, down 46.2% from October, when they’d peaked at 1,609. In the 22 weeks since, drillers have taken out 743 rigs, the most dizzying cliff dive in the data series, and probably in history:

US-rig-count_1988_2015-03-13oil bust

 

You’d think this sort of plunge in drilling activity would curtail production. Eventually it might. But for now, the industry has focused on efficiencies, improved drilling technologies, and the most productive plays. Drillers are trying to raise production but with less money so that they can meet their debt payments. Thousands of wells have been drilled recently but haven’t been completed and aren’t yet producing. This is the “fracklog,” a phenomenon that has been dogging natural gas for years.

So US oil production hit another record of 9.366 million barrels per day for the week ended March 6, according to the Energy Information Administration’s latest estimate. This chart shows how the rig count (red) has plunged while production (black) continues to soar:

US-oil-production-rig-count-2014-2015-Mar13

 

But demand is not living up to the level of production and imports. As an inevitable result, US crude oil inventories are piling up. Excluding the Strategic Petroleum Reserve, crude oil stocks, according to the EIA, rose by 4.5 million barrels in the latest reporting week, to a record 448.9 million barrels. A more modest rise than in prior weeks, but the ninth week in a row of increases. Crude oil stocks are now 78.9 million barrels, or 21.3%, higher than at this time last year. Note the beautiful spike:

US-crude-oil-stocks-2015-01-30

 

So when is US storage capacity going to be full? That event would cause all sorts of havoc in the oil markets, including a terrible plunge in price. With no place to put their oil, some production companies would have to turn off the tap and leave the oil in the ground. That would bring production down in a hurry, but it would add to the pent-up supply, the “fracklog,” thus dragging out the bust even further.

How likely is this scenario?

Last week, the EIA released estimates that crude oil stocks nationwide, as of on February 20, were at 60% of “working storage capacity,” up from 48% last year at that time. In critical Cushing, Oklahoma, which accounts for 14% of the national total and is the delivery point for WTI futures contracts, storage facilities were 67% full.

Given a storage capacity of 521 million barrels, if weekly increases amount to an average of 5 million barrels going forward, it would take about 3 months to fill the remaining capacity. Cushing would be full sooner, which would pose its own set of problems.

But we’re not biting our nails just yet. The largest US refinery strike in 30 years that impacted 12 refineries and a fifth of US refining capacity appears to be settled. A tentative agreement has been reached between the United Steelworkers union and oil companies. Once these refineries are fully operational again, more crude will head their way. The driving season will start soon. SUVs and pickups and even fuel misers have a prodigious appetite collectively and can burn through a lot of gasoline in a hurry. And imports could be throttled back further.

So there is a very good chance that storage capacity will disappear as a death trap for the price of oil this year. But US oil production is likely to continue to rise, leaving the industry to face an even bigger oil glut and even more price mayhem next year. Yet production won’t start declining until the money runs out.

Some smaller oil and gas companies are already running out of money. For them, “restructuring” and “bankruptcy” are suddenly the operative terms. Read…  “Default Monday”: Oil & Gas Companies Face Their Creditors