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Assets and debts Archive

To Save or Spend: Meeting the Short-Term and Long-Term Consumption Needs of U.S. Households

Author: Gregory Mills

| Posted: March 16th, 2012

 

Two just-published reports provide useful insights on patterns of household saving in the United States. The analyses highlight the policy dilemma that comes during and after recessions. As American consumers, we tend to save more than we should during recessionary periods, when more spending would stimulate economic expansion. We then tend to fall back into myopic spending habits, when more saving would promote the economy’s long-run growth potential and help provide for our own long-term needs.

Amidst the statistical avalanche of the 2012 Economic Report of the President are the most recent numbers on personal saving (as a percent of disposable personal income) before, during, and after the Great Recession. From a pre-recession level of 2.4 percent in 2007, the saving rate more than doubled to above 5 percent in 2008–2010, exceeding 6 percent in some quarters during the recession. This reflects the collective reaction to the enormous drop in household wealth that was a result of plunging stock market values and housing prices. The total wealth decline was the equivalent of 1.8 years of income for the average household, the steepest drop since such data were first collected in the early 1950s.

During 2011 the saving rate then fell to below 4 percent as pent-up demand for consumer durables (especially cars) buoyed consumption. This has been welcome news for the economic recovery, but it suggests a return to a historical path of not saving, a matter of concern for our long-term economic health.

Barry Bosworth of the Brookings Institution focuses on this long-run horizon in his book The Decline in Saving. As Bosworth points out, our personal saving rate has declined over the past three decades. From an average of 7.7 percent in the 1970s, it dropped to 7.2 percent in the 1980s, 4.7 percent in the 1990s, and then 2.4 percent during 2000–2007. As explanations for this decline, Bosworth points to easier credit availability and financial innovations that enabled households to extract equity from homes and other assets, fueling a more consumption-oriented economy. He notes that Canada is an instructive comparison, observing that “the mortgage market innovations that led to the growth of the subprime mortgage market in the United States were largely absent from Canada.”

Whether American households under-save or over-save is a complex question from a macroeconomic perspective; it hinges importantly on the level of saving or dis-saving in the corporate and public sectors.  The evidence suggesting a return to long-term trends of under-saving in the household sector is discomforting, with discernible risks apparent as one moves down the income distribution. Do households have sufficient assets to weather financial emergencies, to withstand national economic downturns, and to meet their retirement needs? If upcoming data show continued low rates of personal saving, in a world where low interest rates do little to encourage thrift, we should heighten our focus on savings initiatives, especially targeted to low- and middle-income consumers, to break the pattern of saving too little.

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Filed under: Assets and debts, Government, Quality of Life, Urban Culture
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MetroTrends Week in Review

Author: MetroTrends staff

| Posted: March 3rd, 2012

 

Last week’s MetroTrends blogs dig deep – exploring trends and variations behind the national averages:

  • Graham MacDonald’s latest interactive map tracks flows of people moving between metro areas, both before and since the Great Recession.
  • Greg Mills highlights a new measure of “liquid asset poverty” and shows where it coincides with prolonged unemployment.
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Filed under: Assets and debts, Government, Quality of Life, Urban Culture
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“Liquid Asset Poverty” and Prolonged Joblessness: The Recession Ripples On

Author: Gregory Mills

| Posted: February 29th, 2012

 

On January 31, the Corporation for Enterprise Development released its annual Assets and Opportunity Scorecard. For the first time, the report included estimates of “liquid asset poverty”—the share of American households with insufficient liquid assets (e.g., bank accounts, stocks, mutual funds, and retirement accounts) to subsist at the poverty level for three months. In 2009 an estimated 43 percent of U.S. households did not have enough liquid assets to protect themselves from a major income loss or emergency expense. This represented a slight uptick from the pre-recession level of 41 percent in 2006. Among states, liquid asset poverty rates in 2009 varied by a factor of more than two, with Hawaii, New Hampshire, and Vermont below 25 percent, while Alabama, Georgia, Mississippi, and West Virginia each topped 55 percent.

At a time when the government’s official measure of income poverty has undergone serious review, we should also apply the same critical eye to asset poverty statistics. In light of the prolonged unemployment spells recently experienced in the American labor force, the three-month measure seems too limited. In 2009 more than half (51 percent) of the nation’s jobless were without work for 15 weeks or more.  This figure jumped to 59 percent in 2010, and these estimates do not count discouraged workers no longer looking for jobs.

In which states was the combination of pre-recession liquid asset poverty and subsequent prolonged joblessness particularly acute? The map below shows [with horizontal gridlines] the 21 states whose 2006 liquid asset poverty rate exceeded the 41 percent national average.

States With High Rates of Both Liquid Asset Poverty and Prolonged Unemployment Are Concentrated in the South

Source: Corporation for Enterprise Development and U.S. Bureau of Labor Statistics

The map also identifies [with vertical gridlines] the 19 states that experienced disproportionately high rates of longer-term joblessness, as evidenced by a 2009 median unemployment duration of 13 or more weeks (i.e., three months or longer) coupled with an unemployment rate at or above  that year’s 9.3 percent national average. Of the 10 states (in cross-hatch) where both liquid asset poverty and prolonged joblessness were prevalent, 8 were in the South. Workers in the South have clearly faced a dual challenge, with a risk of hardship greater than that confronted in other regions.

The focus on liquid asset poverty is long overdue. As more and more data become available on this important dimension of household economic security, we should be attentive to issues of measurement and geographic concentration.

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Getting American Households to Save: Lessons from Abroad

Author: Gregory Mills

| Posted: December 28th, 2011

 

American households save far less than their counterparts in other OECD countries.  In his insightful new book, Beyond Our Means: Why America Spends While the World Saves, Princeton historian Sheldon Garon puts our low net savings rate in perspective.  Yes, it trended upward during the 2000s, but it’s still less than half of the double-digit rates in such economically robust European countries as Austria, Belgium, France, Germany, Sweden, and Switzerland.

Net Household Saving Rates For Selected OECD Countries, 2000-2009 (Percent Of Disposable Household Income)

Source: Sheldon Garon, Beyond Our Means: Why America Spends So Much While The World Saves

As for why, Garon points to institutions that emerged in Britain, continental Europe, and the Far East during the 19th and early 20th centuries to promote small-dollar savings among younger generations and the working class.  Thrift was bred through savings banks in schools, post offices, and other community institutions.  We saw their like here in the Northeast and Upper Midwest, reinforced by patriotic savings campaigns during World Wars I and II.  Yet, our government has never nurtured saving behavior the way many others have.  As Professor Garon observes, instead of trying to help democratize saving, our policy has increasingly deregulated credit, especially during the 1980s and 1990s when credit card borrowing, home equity lines of credit, and subprime mortgage lending all exploded, sweeping  unwitting low-income borrowers into the feeding frenzy.

Now that we’re striving to spend our way through an economic recovery, let’s not allow the low-income population to get caught in the groundswell again.  Working families need savings incentives even stronger than those our tax system gives to middle- and upper-income families.  At a minimum, we shouldn’t allow deficit reduction to undermine the few savings programs designed to help the poor and near-poor.  It’s consoling that in the pending FY12 omnibus appropriation Congress didn’t gut individual development accounts (IDAs) under the Assets for Independence Act, which has provided matching funds for the savings accounts of more than 78,000 low-income households since 1998.  (The conference committee reduced this program by 16 percent, far less than a 63 percent cut earlier proposed in the House.)

As the economic recovery moves along, the short-term challenge in boosting personal saving will be to help households weather income shocks and meet emergency expenses as they regain liquidity.  Looking farther ahead, we need easier ways for would-be small-dollar savers to realize their good intentions.  School savings banks and postal savings banks seem antiquated now, but we should focus our energy on devising modern-day, higher-tech equivalents.

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MetroTrends Week in Review

Author: MetroTrends staff

| Posted: December 10th, 2011

 

Last week, MetroTrends bloggers delivered facts – some surprising – about challenges facing metros today:

  • Bob Lerman questions the new “supplemental” poverty measure – first arguing that it oversimplifies a complex problem and, second, that it mistakenly allows the poverty threshold to rise with living standards.
  • Juan Pedroza debunks the myth that immigrants are leaving states that passed punitive immigration laws.
  • Margery Turner explores which metros are good places for the “99 percent” to call home.
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Filed under: Assets and debts, Built Environment, People, Urban Culture, Washington DC
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Poverty Hits Home—So Poverty Policy Should Too

Author: Margaret Simms

| Posted: December 9th, 2011

 

In some ways, poverty is like the weather.  Everyone is willing to talk about it, but no one has wanted to do anything about it lately.  Now Congresswoman Gwen Moore and several of her colleagues say they are ready to take poverty on, at least for those who are eligible for Temporary Assistance for Needy Families (TANF).  On December 5, Congresswoman Moore introduced the Rewriting to Improve and Secure (RISE) an Exit Out of Poverty Act.  This bill proposes a fairly substantial overhaul to the TANF program.

Some of the bill’s interesting features relate to place.  One fundamental change, for instance, would require the federal government to adjust the state allocations.  All states would get an adjustment for inflation (which TANF has not provided since it morphed from AFDC into TANF in 1997), states with rising numbers of children living in poverty would get additional funds.

With this change, Congress would be acknowledging the substantial differences by state in the growth of the child population. Growth has been greatest in the South and West, with most increases  due primarily to increasing numbers of  Hispanics and other children of color.

Another change:  states would be required to tell the federal government annually their plans for prioritizing areas within their states with higher rates of poverty and unemployment and lower job-to-population ratios.   Here too, the new legislation bows to grim reality—in this case, strong racial and income segregation and concentration.  Making states address the consequences of such segregation in their attempts to reduce child poverty and help parents become more self-sufficient is essential  to  reducing poverty levels.  Without this direction it is always easier to focus on the easiest to serve.

Other place-based issues are worth thinking about too as the federal government redesigns programs to be more effective.  For example, the cost of living varies substantially across the country.  Work by the Urban Institute that uses the supplemental poverty measure to assess the effectiveness of state measures to reduce poverty through income and program supports shows that including state cost of living indices helps policymakers see each state’s challenge and progress in light of what it costs to live in the state.  For example, as Linda Giannarelli explained in an Urban Institute forum this week, looking at the impact of Georgia’s anti-poverty programs without taking account of its cost of living relative to some other states would suggest that the child poverty rate for 2008 was 16.7 percent, as opposed to the official figure of 19.3.  But after taking account of  the lower cost of living in Georgia (relative to Massachusetts, for example), the new measure shows that safety net policies brought the child poverty rate down to 13.8 percent (see figure below).  The cost of living is only one piece of the puzzle in understanding the impact of state policies.  Another is the impact on poor people in different age groups.  Giannarelli and her colleagues show that by changing the mix of programs in the safety net, a state can focus to good purpose on different age groups, with different outcomes.  So Massachusetts’ safety net helps seniors more than it helps children (which may serve a state with an aging population) while Georgia’s helps children more.  On balance, this suggests, federal   incentives for states to help children can reduce inter-generational poverty.

One feature of the RISE bill that isn’t very encouraging is that the program is to be permanent.  That suggests that the poor—like the weather-- “will always be with us.” Yet, if this bill or another one like it is effective, at least the same poor people won’t be poor year after year.

Effects of Safety net on SPM Poverty Rate: Children under 18, 2008

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DOES NOT COMPUTE? New Poverty Measure Counts More Benefits for Low-income People But Shows Poverty Rising

Author: Robert Lerman

| Posted: December 7th, 2011

 

Journalists and commentators parsing the U.S. Census Bureau’s new Supplemental Poverty Measure (SPM) when it debuted about a month ago missed one surprising result of what happens when the new measure is applied. As most news stories correctly pointed out, the SPM counts as income certain public benefits that the official measure didn’t. Chief among them are the Earned Income Tax Credit (EITC), food stamps (now Supplemental Nutrition Assistance Program-SNAP), and low-income housing assistance.  Under the official measure, the roughly $170 billion spent on these programs was totally under the radar, even though these three benefits amount to about $3,700 per low-income person. (That’s over $15,000 annually for a family of four).   Under the SPM, these and some other benefits are counted as income, though not fully because people report less in benefits than the government has paid out.

Several headlines highlighted the higher estimated poverty rate yielded by the SPM than by the official measure.  The Washington Posts Michael Fletcher, for instance, claims that the new Census measure “…painted a more dismal picture of the nation’s economic landscape than the official measure from September.”

So how can poverty go up if the new measure raises collective incomes by over $170 billion?  As some journalists and experts noted, children fared better when public benefits are counted. But why should extra spending on kids result in higher poverty among the elderly and other groups?

Let me oversimplify a bit to explain.  The official measure was set up as an absolute measure—the income needed to achieve a specific unchanging living standard. The threshold set was three times the cost of an economy food budget. Over time, that threshold has risen only to keep pace with inflation, not with rising living standards.  In contrast, the SPM threshold is a relative concept.  It equals what a family with two children at the 33rd percentile of spending devote to food, clothing, housing, and utilities plus another 20% of this amount.  This threshold is then adjusted for family size and local housing costs.

The percentile used to calculate the SPM threshold is somewhat arbitrary.  Choose a relatively high percentile (say 50%) and you get a high poverty threshold and higher level of measured poverty. Choose a lower one (say, 25%) and both drop.  By selecting 32-34%, the Census Bureau raised the income threshold so now it’s about 10% higher than under the official measure. That statistical move doesn’t mean that the poor’s living standards have dropped, so the SPM doesn’t really “…paint a more dismal picture ” so much as it creates a new benchmark based on a higher standard of living.

A second conceptual shift is that the SPM deducts from income what the Census Bureau terms “necessary expenses.”  These include taxes, work expenses, and the amount of child support individuals pay, all of which lower net income.  Also deducted are spending on child care and out-of-pocket health expenses.  Child care is usually a work expense, but people still have discretion over what quality they buy. Health spending is clearly consumption and differs from income or expenses necessary to generate income.  Health services are valuable—sensible uses of income—and more spending presumably raises an individual’s living standards.

Like the official measure, the SPM doesn’t count government-paid health services, even though they can greatly enhance living standards and life itself.  So, yes, older Americans spend more out-of-pocket on healthcare, which pushes up their poverty rate.  But, the presumed improvements in living standards financed by significant Medicare and Medicaid benefits go uncounted.  This approach raises the same concern that led to the SPM—the distorted picture you get when you don’t count government benefits aimed at alleviating hardship.

Certainly, health spending poses a quandary for counting poverty.  For an individual, paying more health expenses may reflect poorer health.  When unhealthy people must spend more of their own money to achieve the same health status as healthier individuals, they have fewer dollars to spend on everything else and thus have (non-health) living standards as low as individuals who have income levels below the poverty line.  On the other hand, when rising Medicaid and Medicare spending makes a population better off over time, they are surely enhancing living standards and should be counted.  Moreover, if the improvements from added health spending are not worth the costs, then policymakers should shift spending toward cash or other supports that would benefit recipients more.

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Filed under: Assets and debts, Government, Health Care, Urban Culture
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Reaching The Unbanked: What Would Willie Sutton Do?

Author: Gregory Mills

| Posted: November 28th, 2011

Now that Bank of America has pulled back its fees for debit-card holders, let’s turn to those who are really on the outside looking in at mainstream financial services: American consumers with no bank account at all. As measured by the FDIC’s January 2009 survey, 7.7 percent of U.S. households are unbanked. That’s 17 million adults.  Another 17.9 percent of households—some 43 million adults—are underbanked. They have bank accounts, but still make some use of payday loans, pawnshops, and other alternative financial products.  

The unbanked population is predominantly urban. Fully 81 percent of unbanked households reside in Metropolitan Statistical Areas (MSAs). If we look at the unbanked rates of very large MSAs that are also represented in the FDIC survey by at least 100 sample households, we find that 25 of these 69 metros have unbanked rates above the 7.7 percent national average.

All Regions Contain Large MSAs With High Unbanked Rates

Among these high-need areas, Memphis ranks highest at 17.3 percent.  Indeed, three-fourths of its Census tracts have unbanked rates exceeding the 7.7 percent benchmark, according to recent estimates by Corporation for Enterprise Development (CFED)—which has just released a powerful data tool for researching local patterns of bank use.

In the past five years CFED has also supported the creation of BankOn initiatives. These public-private partnerships promote access to mainstream retail financial services by negotiating with banks and credit unions to get them to offer starter or “second-chance” accounts. Among the 56 such programs up and running now, most focus on a core city or metro area while others are state or county entities.

How well do BankOn programs cover the neediest metro areas?  Quite simply, much has been accomplished and much remains to be done.  Of the 25 high-need MSAs, only 12 have a BankOn program.

BankOn Initiatives Are Widespread, But Do Not Reach Many High-Need MSAs

Among the 13 others are some with unbanked rates as high as Tulsa’s 12.6 percent.

Because most financial services providers work in a particular state or region, we need to develop strategies to better serve the unbanked residents of the high-need communities they serve. That means concentrating on the interior southwest (Riverside, Tucson, and Albuquerque), the central plains (Wichita, Tulsa, Oklahoma City, and Des Moines), the Great Lakes (Milwaukee, Buffalo, and Rochester), and the interior southeast (Birmingham, Atlanta, and Charlotte).  BTW: Charlotte is Bank of America’s corporate headquarters.

Willie Sutton robbed banks because “that’s where the money is.”  Banks, credit unions, and other mainstream institutions need to reach the underserved segments of their retail markets because that’s where the need and opportunity are.

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Alpha Testing and Randomized Control Trials: Improving on the Gold Standard

Author: Gregory Mills

| Posted: October 19th, 2011

In the world of policy research, experimental evaluations—or randomized control trials (RCTs)—are the gold standard.  Indeed, they are the most rigorous way to estimate a program’s effects on participants. That’s because the results for the participants—the “treatment group”—are measured against a randomly picked “control group” that doesn’t enter the program.

Increasingly, RCTs are being used to find out if programs providing financial services to low­-income people work. One recent large-scale example—evaluated by the Urban Institute—is a Treasury Department study of the effects of offering (through the Green Dot Corporation) prepaid debit cards to low-income tax filers so they can direct-deposit their federal tax refunds. In the 2011 tax filing season, 950,000 filers nationwide were randomly assigned to either a control group or one of eight other groups that received differing card offers.

A key element of any experimental study is the take-up rate of whatever is being offered to the treatment group. The closer the treatment group’s take-up rate to program expectations for an eventual operational roll-out, the more reliable the study’s assessment of program impacts.

Behavioral economics has taught us that our financial decision-making is deeply influenced by subtle contextual factors that frame our choices. Given the importance of this “choice architecture” and the pivotal role of the treatment group’s take-up, it’s surprising that so few program offers are pre-tested before they are evaluated. Ironically, government routinely requires pre-testing of the program evaluation questionnaires even though the program’s offer is rarely vetted in advance.

Social science should take a lesson from the computer industry. It uses “alpha tests”—small-scale short-term acceptance testing in an operational setting. Such testing goes beyond customer focus groups (which is what the Treasury study used), and a few funders are trying it. The StabilityFirst pilot test, conducted in 2010 by Harvard’s “ideas42” center on applied behavioral economics, enrolled 20 students at Central New Mexico Community College in Albuquerque into a prepaid debit card program. The students were interviewed at length both before and after to gauge their reactions to the program. A range of issues surfaced, including difficulty resolving customer service matters. Participants were reluctant to make calls to the customer service line, not wanting to commit scarce cellphone minutes for a possibly lengthy call with time spent being transferred or on hold.

Alpha tests like the one in Albuquerque can help researchers identify design features that inhibit take-up. And once these “blocking factors” are known, they can be corrected before an experimental evaluation is launched, making randomized trials far more useful and the outcomes sought more likely to come about.

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