By Kate Randall 29 March 2013
A record number of Americans are using food stamps, known today as the Supplemental Nutrition Assistance Program (SNAP). Despite official proclamations that the recession has ended and an economic recovery is underway, families are turning to SNAP benefits in huge numbers. The working poor comprise a growing number of food stamp recipients, and about half of those receiving benefits are children.
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Enrollment in the food stamp program has increased by 70 percent since 2008, to 47.8 million people as of December 2012, the Wall Street Journal reported Thursday. The biggest factor driving the increase is the stagnating job market and a rising poverty rate. This means that a staggering 15 percent of the US population receives food stamp benefits, nearly double the rate of 1975.
In 2008, at the onset of the recession, 28.2 million people were enrolled in SNAP. While the official jobless rate, which peaked at 10 percent in 2009, had dipped slightly to 7.7 percent as of February this year, the SNAP program has continued to grow. The Congressional Budget Office (CBO) predicts that food stamp usage will drop only marginally, to 43.3 million people, by 2017. Even this estimate is predicated on the unemployment rate dropping to 5.6 percent over the next four years.
The number of people using food stamps roughly corresponds to the number of Americans living in poverty, which rose to just below 50 million people in 2011. Utilizing the Supplementary Poverty Measure (SPM), which factors in expenses for food, clothing, shelter, health care and other essentials, the US Census Bureau estimates that nearly one in six people in the US is living in poverty.
The average monthly benefit per person receiving SNAP benefits was only $133 last year. In order to qualify, a household’s income cannot be more than 130 percent of the poverty level, which is about $25,000 for a family of three, according to the Center on Budget and Policy Priorities (CBPP).
Enrollees receive benefits on a debit card, which can to be used to purchase cereal, meats, fruits, vegetables, bread, milk and other staples. When food is running low, recipients often seek out 24-hour grocery stores, waiting for 12 a.m. for their monthly benefits to kick in.
The fact that 15 percent of the population must rely on SNAP benefits has received little attention in the media or from politicians of either big business party. Earlier this week, President Obama signed a bill making permanent $85 billion in sequester cuts, which will slash billions of dollars from programs benefiting the poor, including Head Start, special education, housing and many other programs.
While SNAP technically evaded the sequester ax, other nutrition programs are facing deep cuts. The Special Supplemental Nutrition Program for Women, Infants, and Children, known as WIC, could be forced to cut almost 600,000 mothers, infants and children from its rolls. About half of all infants born in the US qualify for WIC benefits, and mothers use them to purchase food, formula and other vital necessities, as well as to access nutrition education and other services.
Due to the sequester cuts, about 4 million fewer meals will be delivered through Meals on Wheels programs, which provide daily meals to homebound seniors. For many recipients, it is not only their only hot meal of the day, but their sole connection to others in the community.
Millions of the long-term jobless—who have been forced to turn to food stamps—will also see an 11 percent cut to their extended unemployment benefits. The sequester cuts—which will constitute the baseline of future allocations of federal spending—come as the need for social programs benefiting working families is increasing at a rapid pace due to falling wages, unemployment and growing poverty.
The US government spent a record $74.6 billion on SNAP benefits last year, more than double the $30.4 billion spent on the program in 2007. Rules adopted under the Clinton administration allowed some leeway for states in allowing residents to qualify for benefits.
In 2001-2002, six states eased the income and asset requirements for SNAP benefits, making it somewhat easier for people to qualify if they had a low-wage job, or some savings. By 2009, in response to the recession, 17 states and US territories eased their eligibility requirements. Today, three out of four households receiving SNAP benefits include at least one person who is working.
The Obama administration’s 2009 stimulus bill expanded the SNAP program, raising the level of benefits recipients can receive, and allowing people to keep their benefits longer. This expansion is set to expire on October 31, and there are no moves afoot to extend it. The CBPP estimates that food stamp benefits will decrease by $8 per month per person with this expiration.
As of November 1, SNAP benefits will be returned to the level of the so-called Thrifty Food Plan, the lowest of four nutrition estimates calculated by the US Department of Agriculture. The four plans—Thrifty, Low-Cost, Moderate Cost, and Liberal—vary widely in cost. In February 2013, a family of four with two children on the “Thrifty” plan was expected to budget $636 a month for food at home, while the same family on the “Liberal” plan would spent $1,257—almost double the amount.
As with all aspects of social life in America, there is one standard for the working class and another for the wealthy. In this case the divide is between those who struggle to provide adequate nutrition for their families under conditions of rising costs for housing, utilities and other necessities, and the tiny elite who think nothing of splurging on a restaurant meal with a tab far in excess of the “Liberal” monthly budget for a family of four.
Almost half the children presently receiving SNAP benefits—some 10 million—already live in extreme poverty, which means household income is less than half the official poverty level, already set an unrealistically low level. Another 9 million receiving food stamps are elderly or have a serious disability. The cuts in SNAP benefits will quite literally take food off the table for millions of American families at a time of deepening poverty and burgeoning social inequality.
Minding the reality gap
Officially, unemployment in the US is declining. It’s fallen from a high of 9.1% a couple years ago, to 7.8% in recent months. This would be good news, if the official unemployment rate measured unemployment, in the everyday sense of the word. It doesn’t. The technical definition of “U3? unemployment, the most commonly reported figure, excludes people who’ve given up looking for work, those who’ve retired early due to market conditions, and workers so part time they clock in just one hour per week.
Most critically, unemployment excludes the 14 million American on disability benefits, a number which has quadrupled over the last 30 years. If you include just this one segment of the population in the official numbers, the unemployment rate would double. On Saturday, This American Life devoted their entire hour to an exploration of this statistic. Russ Robert’s, who’s podcast I’ve recommend in the past, discussed the same topic last year. Despite the magnitude of the program and the scale of the change, these are the only outlets I know of to report on the disability number, and on the implications it has for how we interpret the decline in U3 unemployment.
Targeting the number, not the reality
Statistics, in the sense of numerical estimates, are measures which attempt to condense the complex world of millions of people into a single data point. Honest statistics come with margins of error (the most honest indicate, at least qualitatively, a margin of error for their margin of error). But even the best statistical measures are merely symptoms of some underlying reality; they reflect some aspect of the reality as accurately as possible. The danger with repeated presentations of any statistic (as in the quarterly, monthly, and even hourly reporting of GDP, unemployment, and Dow Jones averages), is that we start to focus on this number by itself, regardless of the reality it was created to represent. It’s as if the patient has a high fever and all anyone talks about is what the thermometer says. Eventually the focus becomes, “How do we get the thermometer reading down?” All manner of effort goes into reducing the reading, irrespective of the short, and certainly long-term, health of the patient. When politicians speak about targeting unemployment figures, this is what they mean, quite literally. Their goal is to bring down the rate that gets reported by the Bureau of Labor Statistics, the number discussed on television and in every mainstream source of media.
Politicians focus on high profile metrics, and not the underlying realities, because the bigger and more complicated the system, the easier it is to tweak the method of measurement or its numeric output, relative to the difficulty of fixing the system itself. Instead of creating conditions which allow for growth in employment (which would likely require a reduction in politicians’ legislative and financial powers), the US has quietly moved a huge segment of its population off welfare, which counts against unemployment, and into disability and prisons — the incarcerated also don’t count in U3, whether they are slaving away behind bars or not.
How metrics go bad
Over time, all social metrics diverge from the reality they were created to reflect. Sometimes this is the result of a natural drift in the underlying conditions; the metric no longer captures the same information it had in the past, or no longer represents the broad segment of society it once did. For example, the number of physical letters delivered by the postal service no longer tracks the level of communication between citizens.
Statistics and the reality they were designed to represent are also forced apart through deliberate manipulation. Official unemployment figures are just one example of an aggressively targeted/manipulated metric. Another widely abused figure is the official inflation rate, or core Consumer Price Index. This measure excludes food and energy prices, for the stated reason that they are highly volatile. Of course, these commodities represent a significant fraction of nearly everyone’s budget, and their prices can be a leading indicator of inflation. The CPI also uses a complex formula to calculate “hedonics,” which mark down reported prices based on how much better the new version of a product is compared to the old one (do a search for “let them eat iPads”).
I don’t see it as a coincidence that unemployment and inflation figures are among the most widely reported and the most actively manipulated. In fact, I take the following to be an empirical trend so strong I’m willing to call it a law: the greater the visibility of a metric, the more money and careers riding on it, the higher the likelihood it will be “targeted.” In this light, the great scandal related to manipulation of LIBOR, a number which serves as pivot point for trillions of dollars in contracts, is that the figure was assumed to be accurate to begin with.
Often the very credibility of the metric, built up over time by its integrity and ability to reflect an essential feature of the underlying reality, is cashed in by those who manipulate it. Such was the case with the credit ratings agencies: after a long run of prudent assessments, they relaxed their standards for evaluating mortgage bundles, cashing in on the windfall profits generated by the housing bubble.
Why we don’t see the gaps
It might seem like the disconnect between a statistic and reality would cause a dissonance that, once large enough to be clearly visible, would lead to reformulation of the statistic, bringing it back in line with the underlying fundamentals. Clearly there are natural pressures in that direction. For example, people laid off at the beginning of a recession are unlikely to believe that the recovery has begun until they themselves go back to work. Their skepticism of the unemployment figure erodes its credibility. Unfortunately, two powerful forces work against the re-alignment of metric and reality: the first related to momentum and our blindness to small changes, the second having to do with the effects of reflexivity and willful ignorance.
In terms of inertia, humans have a built-in tendency to believe that what has been will continue to be. More sharply, the longer a trend has continued, the longer we presume it will continue — if it hasn’t happened yet, how could it happen now? Laplace’s rule of succession is our best tool for estimating probabilities under the assumption of a constant generating process, one that spits out a stream of conditionally independent (exchangeable) data points. But the rule of succession fails utterly, at times spectacularly, when the underlying conditions change. And underlying conditions always change!
These changes, when they come slowly, pass under our radar. Humans are great at noticing large differences from one day to the next, but poor at detecting slow changes over long periods of time. Ever walked by an old store with an awning or sign that’s filthy and falling apart? You wonder how the store owner could fail to notice the problem, but there was never any one moment when it passed from shiny and new to old and decrepit. If you think you’d never be as blind as that shop keeper, look down at your keyboard right now. As with our environment, if the gap between statistic and reality changes slowly, over time, we may not see the changes. Meanwhile, historical use of the statistic lends weight to it’s credibility, reducing the chance that we’d notice or question the change — it has to be right, it’s what we’ve always used!
The perceived stability of slowly changing systems encourages participants to depend on or exploit it. This, in turn, can create long term instabilities as minor fluctuations trigger extreme reactions on the part of participants. Throughout the late 20th century and the first years of the 21st, a large number of investors participated in the “Carry Trade,” a scheme which depended on the long term stability of the Yen, and of the differential between borrowing rates in Japan and interest rates abroad. When conditions changed in 2008, investors “unwound” these trades at full speed, spiking volatility and encouraging even more traders to exit their positions as fast as they could.
These feedback loops are an example of reflexivity, the tendency in some complex systems for perception (everyone will panic and sell) to affect reality (everyone panics and sells). Reflexivity can turn statistical pronouncements into self-fulfilling prophecies, at least for a time. The belief that inflation is low, if widespread, can suppress inflation in and of itself! If I believe that the cash in my wallet and the deposits in my bank account will still be worth essentially the same amount tomorrow or in a year, then I’m less likely to rush out to exchange my currency for hard goods. Conversely, once it’s clear that my Bank of Zimbabwe Bearer Cheques have a steeply declining half-life of purchasing power, then I’m going to trade these paper notes for tangible goods as quickly as possible, nominal price be damned!
Don’t look down
If perception can shape reality, then does the gap between reality and statistic matter? Clearly, the people who benefit most from the status quo do their best to avoid looking down, lest they encourage others to do the same. More generally, though, can we keep going forward so long as we don’t look down, like Wile E. Coyote chasing the road runner off a cliff?
The clear empirical answer to that questions is: “Yes, at least for a while.” The key is that no one knows how long this while can last, nor is it clear what happens when the reckoning comes. Despite what ignorant commentators might have said ex post facto, by 2006 there was wide understanding that housing prices were becoming un-sustainably inflated. In 2008, US prices crashed back down to earth. North of the border, in Canada, the seemingly equally inflated housing market stumbled, shrugged, then continued along at more level, but still gravity-defying trajectory.
The high cost of maintaining the facade
Even as the pressures to close the gap grow along with its size, the larger the divergence between official numbers and reality, the greater the pressures to keep up the facade. If the fictional single entity we call “the economy” appears to be doing better, politicians get re-elected and consumers spend more money. When the music finally stops, so too will the gravy-train for a number of vested interests. So the day of reckoning just keeps getting worse and worse as more and more resources go into maintaining the illusion, into reassuring the public that nothing’s wrong, into extending, pretending, and even, if need be, shooting the messenger.
It’s not just politicians and corporations who become invested in hiding and ignoring the gap. We believe official statistics because we want to believe them, and we act as if we believe them because we believe that others believe them. We buy houses or stocks at inflated prices on the hope that someone else will buy them from us at an even more inflated price.
My (strong) belief is that most economic and political Black Swans are the result of mass delusion, based on our faith in the quality and meaning of prominently reported, endlessly repeated, officially sanctioned statistics. The illustration at the beginning of this post comes from a comic I authored about a character who makes his living off just this gap between official data and the reality on the ground, a gap that always closes, sooner or later, making some rich and toppling others.
From Wikipedia, the free encyclopedia
In philosophy, reality is the state of things as they actually exist, rather than as they may appear or might be imagined. In a wider definition, reality includes everything that is and has been, whether or not it is observable or comprehensible. A still more broad definition includes everything that has existed, exists, or will exist.
Philosophers, mathematicians, and other ancient and modern thinkers, such as Aristotle, Plato, Frege, Wittgenstein, and Russell, have made a distinction between thought corresponding to reality, coherent abstractions (thoughts of things that are imaginable but not real), and that which cannot even be rationally thought. By contrast existence is often restricted solely to that which has physical existence or has a direct basis in it in the way that thoughts do in the brain.
Reality is often contrasted with what is imaginary, delusional, (only) in the mind, dreams, what is abstract, what is false, or what is fictional. The truth refers to what is real, while falsity refers to what is not. Fictions are considered not real.