Location and Inequality: Are the Income Gaps Real?

| Posted: December 6th, 2012

Debates about income inequality and the shrinking middle class have largely focused on globalization, the declining share of middle-wage jobs, the eroding role of unions, technological change that benefits more educated workers, tax policies, and the share of income going to the top 1 percent. Often ignored is the question of whether we’re really measuring inequality accurately.  Do standard measures of money income really capture inequalities in living standards?  Not really.  Because of differences in living costs across communities, higher incomes don’t necessarily translate into higher living standards.  Housing costs reduce purchasing power in some communities more than in others.  At the same time, income gaps can become more pronounced when low-wage workers are discouraged from moving to areas with high housing costs.  Where people live, it turns out, matters a lot in measuring and accounting for the inequality of living standards.

Analysts of poverty trends have long recognized that cash income does not tell the whole story, since it ignores the importance of noncash public benefits, such as food stamps, housing assistance, and health coverage. Indeed, the Census Bureau is now taking into account noncash benefits and differences in housing costs when measuring poverty. Recently, researchers have begun analyzing geographic differences to explain trends in income inequality.

Several mechanisms are potentially at work, as Enrico Moretti of the University of California, Berkeley points out. Living costs may increase faster in areas where high-income, highly educated people are concentrated. The rise in living costs may come from more rapid growth in housing prices and in the prices of other goods and services linked to rising land values. High-income people may have moved to metropolitan areas where housing costs are especially high. By contrast, low-wage, less-educated workers have been less likely to move to areas where they would earn higher wages but not higher living standards. Moretti finds all of these factors at work, showing that the rising inequality in money income didn’t completely translate into rising inequality in purchasing power. According to Moretti, more than 20 percent of the rising money advantage of college graduates over high school graduates between 1980 and 2000 did not represent an advantage in living standards.

Locational differences can make income inequality appear worse than the actual inequality of living standards.  Economists have long viewed local zoning requirements as harmful to low-income families by limiting their access to attractive suburban neighborhoods. By requiring large lot sizes, towns have priced low-income families out of their housing markets. Now, as highlighted by the New York Times Economix blog, Peter Ganong and Daniel Shoag of Harvard University have demonstrated that differential housing regulations are a major culprit in slowing the convergence of regional income gaps, thereby lessening the migration into high cash income, high cost areas communities and adding to the inequality in money incomes. High-wage areas used to attract all types of workers.  As the supply of workers, including low-skill workers, went up in high-wage metro areas and fell in low-wage metros, wage differences between metro areas declined.  In recent years, because high housing costs have increasingly offset higher wages, fewer workers have chosen to migrate within the United States. Thus, low-income families lose in two ways from restrictive regulations—those in highly regulated areas face higher prices because of limitations on supplies and those in other locations lose access to better paying jobs because they cannot afford the high-priced housing.  Housing subsidies can shield some low-income families from increased housing costs in high-priced areas, but most low- and middle-income families receive no housing benefits at all.

To see how money income fails to capture purchasing power differences, compare the ability of low- and middle-skill workers and of median-income families to buy homes in four high-priced and four low-priced metropolitan areas. To simplify, let’s look only at the burden of a 30-year mortgage at a 4 percent interest rate. As the table shows, workers at moderate education levels face enormous mortgage burdens trying to buy homes in the four high-priced metro areas. But, homes in low-priced areas are quite affordable even among workers without a college degree. The gaps in affordability are far less in the case of family income. Still, a median-income family would have to spend more than double their share of income on the median-priced home in the Los Angeles or San Francisco metro areas than in the four low-priced metro areas. As a result, family income inequality across cities looks far higher when we don’t account for differences in housing costs. Median family incomes are 40 percent higher in San Francisco than in Oklahoma City, but the gap in income after mortgage payments falls to 9 percent. Since other living costs are higher in San Francisco, the differences in purchasing power are even smaller. The figures illustrate how inequality in purchasing power is often lower than inequality in money incomes.

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Child poverty rates predicted to remain high through 2012

| Posted: December 5th, 2012

Children’s economic well-being has shown little improvement over the past year, according to several measures. One positive sign is that the number of children with an unemployed parent is modestly down from a year ago. On the other hand, the economy remains weak and the number of families applying for nutrition assistance continues to rise in most states.

Child poverty rates, which provide the most direct measure of children’s economic well-being, are available with a time lag: 2012 poverty statistics will be available next September. To track children’s well-being in a more timely manner, I have developed state-by-state predictions for current child poverty, based on partial-year data on unemployment and nutrition assistance, and lagged child poverty rates. My latest predictions, released yesterday and depicted in the map below, suggest that child poverty will increase in 4 states (Mississippi, Montana, New Jersey, and New York) and decrease in 7 states (Illinois, Indiana, Michigan, Minnesota, Nebraska, Ohio, and South Carolina). The remaining 40 states will not see significant changes in the child poverty rate (i.e., changes larger than the margins of error surrounding the estimates).

I predict that the national child poverty rate will remain close to the 22.5 percent rate reported in the 2011 American Community Survey. The model’s precise prediction is 22.4 percent; the actual rate may be half a percentage point higher or lower, judging from past experience. Over the past two years, the model has correctly predicted the general upward trend in child poverty, and its predictions have been within 0.4 percentage points of the national rate.

While child poverty rates appear to be leveling off in 2012, they remain much higher than before the recession. I predict that twice as many states will have child poverty rates of 20 percent or higher in 2012 as before the recession (28 vs. 14 states).

These predictions and other measures show that millions of children and families are worse off than they were five years ago. As policymakers debate broad budgetary packages of spending cuts and revenue increases, it is important to recognize the recession’s ongoing impact on children and families. In my view, this is not the time to make major cuts in nutrition assistance or other major elements of the social safety net, given the large number of families still struggling to regain the economic ground lost during the recession.

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Disaggregating the Monolithic Model Minority

| Posted: December 4th, 2012

Better data is the key to understanding the diverse and often ignored population of Asian Americans, Native Hawaiians, and Pacific Islanders (AANHPIs) living in the United States, according to the National Council of Asian Pacific Americans. Last week, the Council—a coalition of 30 AANHPI organizations—gave a briefing to Urban Institute researchers about policy issues salient to the community. They also suggested research specific to AANHPIs, the fastest growing racial/ethnic group in the United States.

Over 18.5 million AANHPIs live in the United States, representing 6 percent of the total population. They originate from more than 30 countries and speak over 100 languages and, yet, are often treated as one monolithic group. Individuals in this community are often cast as the Asian American “model minority:” highly educated, affluent, hard working, and self-sufficient, a constituency that has no need for government assistance. Moreover, its smaller size compared with the Latino and African American communities renders AANHPIs virtually invisible or ignored in the policymaking process and ultimately, in the allocation of resources.

Council presenters sought to dispel the myth and put the spotlight on economic, employment, housing, healthcare, education, civil rights, and immigration issues for AANHPIs. They argued that there is a general lack of data about Asian Americans, Native Hawaiians, and Pacific Islanders. When research is conducted, the information gathered is not disaggregated, thereby painting an inaccurate picture of the various ethnic groups that comprise the population. This stymies policy initiatives beneficial to AANHPIs and their families, resulting in little to no access to benefits and resources.

Taken together, for example, only 14 percent of AANHPIs experienced job loss since 2008, meaning they fared better than most Americans. When the data is broken down however, we learn that not all AANHPIs had the same experience. Seventeen percent of Chinese and 20 percent of Hmong experienced job loss since 2008; and close to a quarter of Cambodians lost their jobs.

Researchers interested in communities of color, and Asian Americans in particular, need to find ways to collect representative and adequate data on the various subgroups that comprise the Asian American, Native Hawaiian, and Pacific Islander community. They need to ensure that data is disaggregated when presented, especially to policymakers, in order for the disparate needs of the communities to be addressed.

Additional information on AANHPI policy issues and recommendations can be found in NCAPA’S 2012 Policy Platform.

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Child Care Subsidies: Family Copayments Vary Widely Across States

| Posted: December 3rd, 2012

The third in a three-part blog series about state child care subsidy policies from the CCDF Policies Database. Friday: Child care assistance during the job search. Thursday: Who's eligible for child care subsidies.

One of the most important sources of help for families struggling with child care expenses is the Child Care and Development Fund (CCDF), a federal block grant program that provides subsidized child care to low-income families. CCDF subsidies cover a portion of child care costs, with most families still required to pay part of the cost, often referred to as a family’s “copayment”. The amount families must pay, though, varies widely by state.

Federal guidelines require states to establish sliding fee scales to vary copayments by family size and income, but states have a lot of flexibility in determining the details of those policies. States determine exactly how much they will require families to pay at different income levels; how to vary the copayment for families with multiple children in care, children with special needs, or those only using part-time care; and whether to exempt some families from paying any copayment.

To compare state policies, let’s consider a family of three with a single parent earning the federal minimum wage and two children, ages two and four, receiving full-time, center-based care. Across the states and the District of Columbia, this family’s monthly copayment would range from $0 in nine states to $414 in Hawaii (about 33 percent of the family’s monthly income). The family’s average copayment across all states is equal to 6 percent of income.

If the same family has, instead, two parents earning minimum wage, their monthly copayment would range from $35 in Wyoming (about 1 percent of the family’s monthly income) to $1,035 in Hawaii (roughly 41 percent of the family’s monthly income), with the average copayment across states equal to almost 10 percent of income. Additionally, the family would be ineligible for child care assistance in 7 states.

It is important to consider these different copayment amounts in the full context of a state’s subsidy policies. For example, higher copayments may allow a state to stretch their subsidy dollars across more families. More detail about copayment policies and eligibility determination can be found in the CCDF Policies Database 2011 Book of Tables.

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Child Care Subsidies: Assistance for Unemployed Parents Searching for Jobs

| Posted: November 30th, 2012

The second in a three-part series about state child care subsidy policies from the CCDF Policies Database. Tomorrow: Family Copayments Vary Widely Across States. Yesterday: Who's eligible for child care subsidies.

While we typically think of child care needs in terms of working parents, unemployed parents may also need child care so they have time to look for a new job. Recognizing this need, many states offer some child care assistance to low-income unemployed families.

Given the rise in unemployment resulting from the Great Recession, job search policies in subsidized child care are particularly important to consider. Child care assistance can be considered part of the safety net for the unemployed, alongside unemployment insurance, Supplemental Nutritional Assistance Program benefits (“food stamps”), and Temporary Assistance for Needy Families (cash assistance).

Under federal Child Care and Development Fund (CCDF) guidelines, states can provide child care subsidies for parents seeking work, but the specifics of these policies vary widely. Fourteen states do not include job search as an eligible activity for CCDF subsidized child care. Among the states that do, policies differ in two major ways.

First, some states only approve these subsidies for parents who become unemployed while already participating in the child care subsidy program. This is referred to as job search for continuing eligibility only, which is the policy in 17 states. Nineteen states and the District of Columbia also approve care for parents newly entering the program who are unemployed and looking for work. This is referred to as job search for initial and continuing eligibility.

Second, states vary on the maximum length of time unemployed parents are allowed to use child care subsidies, as well as the number of times job search care can be approved in a given time period. For example, Iowa allows 30 days of child care assistance for job search per year, while New York allows six months. Fifteen states allow for more than one job search period in a year. DC has no time limitations on job search activities.

These policy differences can have a substantial effect on families. Consider a mother who loses a $40,000-a-year job, who was previously paying for child care without any subsidy. In 19 states and DC, she can apply for a subsidy to help with child care costs while she looks for a new job, but in most states, she is not eligible. Even when a subsidy is available for job search, it may not be available for a long enough time period to allow a parent to find new employment. If job search care is limited to once a year, a parent with irregular employment might not have his or her job search needs fully met. Of course, states must make many tradeoffs in determining how to allocate their finite amount of CCDF funding; a state with less expansive policies for unemployed families may be devoting more resources in other areas.

Many other policies, such as income eligibility and family copayment policies, also affect whether a particular family qualifies for child care assistance and how much support they will receive. We examined income limits in a previous post, and we’ll look at copayments in the next post

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Child Care Subsidies: Who's Eligible Varies by State

| Posted: November 29th, 2012

The first in a three-part series about state child care subsidy policies from the CCDF Policies Database. Tomorrow: Assistance for Unemployed Parents Searching for Jobs

The high cost of child care can be a significant obstacle to finding and keeping a job, especially for low-income parents. Child care subsidies can help low-wage parents pay for high-quality care, allowing them to continue working or looking for work, but whether subsidies are available and how much support they provide depends in part on which state families call home.

The Child Care and Development Fund (CCDF) provides federal block grant money to the states, territories, and tribes to subsidize child care costs for low-income families. In 2010, 1.7 million children received child care subsidized through CCDF, according to the most recent preliminary data from the Administration for Children and Families. CCDF is federally funded, but a family’s eligibility depends largely on state-specific rules. These state rules can have a significant impact on families, determining whether they qualify for any assistance, and if so, how much assistance they receive.

The federal guidelines for CCDF require that families have income below 85 percent of the state median income, have children who are under age 13 or who have special needs, and meet one of the defined reasons for needing care. Within these guidelines, states have substantial leeway to establish their own policies for determining eligibility, and state policies vary widely.

A key example is the wide range of state income eligibility limits, or the maximum amount of income a family can have and qualify for child care assistance. In 2011, the maximum allowable income for a family of three to qualify initially for CCDF subsidies ranged from $1,854 to $4,524 per month (35 to 81 percent of state median income). In 17 states, families already receiving a subsidy could have somewhat higher income and still retain subsidies. These “continuing eligibility” thresholds ranged from $185 to $2,451 higher than the initial eligibility thresholds for a family of three.

It is important to look at income limits in relation to a state’s other policies. A state with higher income limits is not necessarily serving a larger proportion of families. For example, the state could have stricter work requirements or limited funds that affect the number of families that can be served. Alternatively, the state may be serving more families, but requiring those families to pay a greater portion of child care costs. In the next two posts, we’ll look at how other childcare subsidy policies vary across states.

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Could Earlier Foreclosure Notices Help Borrowers Save Their Homes?

| Posted: November 27th, 2012

As part of our ongoing tracking of the foreclosure crisis in the Washington, D.C., region, NeighborhoodInfo DC examined Notice of Intent to Foreclose (NOI) filings in Prince George’s County, MD, from July 2011 through June 2012. Among other findings, our analysis revealed stark differences in when mortgage servicers —banks that collect monthly mortgage payments, administer the loan, and handle foreclosures—issue NOIs. These differences in servicer behavior could have profound implications for troubled homeowners and whether they can become current again on their mortgages and keep their homes.

In Maryland, mortgage servicers must file an NOI at least 45 days before initiating a foreclosure action in court. Filing an NOI triggers a number of subsequent steps that must be completed before a foreclosure sale can take place, including allowing the borrower to request mediation with the servicer within 25 days to negotiate a loan modification, short sale, or some other solution.

Bank of America and Wells Fargo are at opposite ends of the spectrum in the timing of their NOI filings. The median homeowner with mortgages serviced by Wells Fargo received NOIs much sooner, when they were less than two months delinquent in their payments, while Bank of America borrowers did not receive NOIs until they were nearly five months behind on payments. As a result, the median delinquent Bank of America borrower owed $9,300 on his mortgage at the time the NOI was issued, compared with $4,400 for the median Wells Fargo borrower. The difference in the amount owed could make it much harder for homeowners to catch up on payments and might result in many more foreclosed properties.

But does receiving an NOI sooner make it less likely that a homeowner will end up at a foreclosure sale? Our current data don’t tell us this because we cannot link NOIs to foreclosure sales nor can we account for other borrower or loan characteristics that might affect outcomes. While an NOI puts homeowners on a path that could result in losing their home, receiving a timelier NOI might motivate homeowners to seek help sooner, especially since the first page of the notice explains how to find housing counseling services. Research has shown that borrowers who seek solutions earlier, before they fall too far behind, are more likely to have successful outcomes.

Until we have better data, we are left wondering: Why do some servicers take longer than others to issue NOIs and what impact does that have on homeowners?

Want to see how the top six servicers—Bank of America, Wells Fargo, JPMorgan Chase, Citi, IndyMac Bank, and GMAC—compare? View Urban Institute maps of the foreclosure indicators by servicers.

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Diversity is Changing More Than Our Politics

| Posted: November 26th, 2012

The presidential election got everybody talking about our country’s growing diversity. But the changing makeup of America’s population has implications that go far beyond politics. Immigration, the aging of the baby-boom generation, growing tolerance of gays and lesbians, and evolving norms about marriage and childbearing are transforming American society. These changes fuel new sources of economic dynamism and opportunity, and they pose new challenges for equity and social mobility.

Urban areas—both cities and suburbs—are leading this demographic transformation, although some metros are much farther along the path of change than others.

In metropolitan America today, the average white American lives in a neighborhood where more than three-quarters of his neighbors (77 percent) are also white. Seven percent are black, 10 percent are Latino, and 4 percent are Asian. That's considerably more diverse than three decades ago, when the average white (metropolitan) American lived in a neighborhood that was 88 percent white, 5 percent black, 5 percent Latino, and 1 percent Asian.

City populations have rebounded in the past two decades as the number of people who value the density and diversity that cities offer has grown. They are mostly Millennials, who have delayed childbearing, marriage, and even household formation because of a combination of changing culture and economic necessity. But when they finally decide to settle down and form families, will cities offer the safety, good schools, and quality services necessary to keep them?

During the 2000s, growth in the number of children with immigrant parents offset a national decline in children with native-born parents. Were it not for the children of immigrants, the child population in the top 100 metros overall and in many metros would have declined in the last decade.

Many baby boomers are postponing retirement, in part because they need the income, but also because today’s older Americans are healthier than a generation ago. But working into one’s 70s is much more feasible for people like me whose work is intellectually engaging than for those working in physically demanding occupations.

In the months and years ahead, policymakers (not just politicians) should give their full attention to the new America we are becoming and respond to the opportunities and challenges of our demographic future.

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The Next Challenge for Public Housing

| Posted: November 15th, 2012

Over the past two decades, public housing in Chicago and other cities has undergone a remarkable transformation. Through the HOPE VI program, dilapidated developments that were blighting neighborhoods—and residents’ lives—have been replaced with new, mixed-income housing. Housing authorities have also made major investments in their traditional properties, improving management and security.

Our studies on the fate of the residents who used to live in the worst developments find that no matter what kind of housing they have now--whether they are living in a new development or in rehabilitated housing or are using a voucher to rent an apartment in the private market—most say they are now living in better housing in safer neighborhoods. The magnitude of this transformation is significant, reflecting profound changes in management and institutional capacity and evident in the more visible changes in the actual housing developments.

The question for housing authorities going forward is how to ensure a high standard of management and maintenance in the face of very real challenges to providing housing to the poorest, most needy households. In many housing markets, public housing is a major source of affordable multi-bedroom units; as a result, many developments are home to large numbers of children and youth. Further, many families who live in public housing face complex problems, such as serious mental and physical health conditions, substance abuse, and domestic violence—problems that simply improving their housing circumstances could not address. Both these factors can create serious management problems as it only takes a handful of problem tenants to create an atmosphere of fear for an entire development.

The Urban Institute’s HOST Demonstration, is testing whether providing intensive case management services to the most vulnerable families in public and mixed-income housing can help support the health of the entire community. The hope is that an investment in services like with wraparound case management, clinical and employment services for adults, and clinical support and positive activities for youth will pay off in terms of fewer management problems, reduced evictions, and greater safety and security overall.

In essence, we are hoping to demonstrate that using public housing as a platform for change can have a significant payoff as an asset management strategy for housing authorities. Beyond housing, we hope to demonstrate that this approach can reduce costs across systems—such as health care and criminal justice—much like the “housing first” approach to homelessness. Public housing transformation made significant strides in improving the way we provide assisted housing to the poorest households. But without substantial investments in human capital, we risk losing the hard-won gains of the past 20 years.

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Unemployment and the Presidential Election

| Posted: November 14th, 2012

During much of the presidential election campaign, Republican candidate Mitt Romney talked about the Obama administration’s failure to get unemployment back below the 8 percent mark. That theme stalled briefly in October, when the previous month’s 7.8 percent unemployment rate was announced. But for voters in many of the top 100 metros, Romney’s argument might not have rung true even earlier. Because by October, when the September unemployment figures were announced, unemployment was already below 8 percent and employment was growing in many states, especially in the battleground states that the two candidates were fighting so hard to win.

In four of the six battleground states, unemployment was well below 8 percent, and in the other two, (Colorado and Florida) employment was growing even though unemployment was still high.

In North Carolina and Arizona, both deemed by the New York Times to be “leaning red” (and, indeed, voted red), unemployment was well above the critical mark. The leaning blue (and voting blue) states are more of a mixed bag. In Minnesota and New Mexico, unemployment rates were below 8 percent, but unemployment was high in Pennsylvania and way above 8 percent in Michigan and Nevada.

Thirty-nine of the 100 top metros are in 13 states that aren’t solidly red or blue. And these metros present an even more interesting picture. In Ohio, for example, the unemployment rates were significantly below the national average in five of the seven top metros and employment was increasing in most of them.

In three states where the unemployment rate remained at 8 percent or above (Colorado, Michigan and Pennsylvania), some of their major metros were below the national average, including Denver (7.4), Grand Rapids (6.0), Lansing (6.2), and Pittsburgh (6.7). This does not appear to be an artifact of a shrinking labor force; the number of net new jobs appears to be increasing in those locales.

The fact that three of the “voting blue” states had high unemployment rates shows that gauging voting preferences solely by the official unemployment rate is too simple. The overall picture in these swing states would suggest that campaign ads about a stagnant economy did not trump the labor market reality that was closest to the voters.

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