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December 2, 2021
Atlanta Fed Conference Investigates Inequalities in the Financial System
A commitment to an inclusive society also means a commitment to an inclusive economy. Such an economy would represent a rebuke of systemic racism and other exclusionary structures. It would represent a true embrace of the principles that all are created equal and should enjoy unburdened life, liberty, and the pursuit of happiness.
— Raphael Bostic, president and CEO of the Federal Reserve Bank of Atlanta, from the essay A Moral Imperative and Economic Imperative to End Racism
Despite the nation's progress toward a more inclusive economy over the last half century, vestiges of structural discrimination are still entrenched in US institutions, and often manifest themselves in the form of disparities in economic outcomes. To promote the topic of racial equality, the Atlanta Fed has partnered with other organizations, cohosting, hosting, or participating in several recent events, including the Atlanta Fed-Princeton University conference on Racial Justice and Finance in September 2020 and the Racism and the Economy webinar series, which began in 2020 and will conclude in early 2022.
On October 14–15, 2021, the Atlanta Fed kicked off the inaugural conference on Racial Inequality and Disparities in Financial Markets to further promote research on inequalities in the financial system. The conference, which was virtual, included presentations and discussions of six papers on racial or gender disparities in various financial markets, including credit markets for mortgages and automobiles, labor markets, and the academic finance profession.
Paula Tkac, associate director of the Atlanta Fed's Research Department, gave the opening remarks. She began by describing how researchers face a dearth of high-quality data in this area, but shared she is hopeful the increased interest in research on racial inequality will spur the effort needed to bring together better data sets. Tkac then called for "a deeper understanding of the 'whys', and insight into potential paths forward toward full economic inclusion" as research progresses. She stressed that this research is crucial in the context of the Fed's monetary policy mission, as a fuller understanding of the barriers stopping individuals from participating to their full potential in labor and financial markets is necessary for good policymaking. Tkac concluded her opening remarks with a quote from Atlanta Fed president Raphael Bostic from last year's Racial Justice and Finance conference:
The influence of race is multidimensional and persists over time. We must look "under the hood" at our institutions to see and truly understand their design and its implications...In your research, think about how you ask questions, particularly how you incorporate historical and institutional realities into your research designs. Examine the role played by institutions and structures and explore how the burdens they impart have contributed to inequities that are still with us...With such an understanding, we can then find more creative and accurate ways to incorporate race into our models, estimation approaches, and narratives. This, I hope, will yield better insights and result in a set of policy prescriptions that can truly create meaningful and lasting change.
Three papers in the conference examined racial disparities in mortgage lending. In "Racial Disparities in Mortgage Lending: New Evidence Based on Processing Time," authors Bin Wei (Atlanta Fed) and Feng Zhao (University of Texas–Dallas) looked at racial disparities in mortgage processing time, which is the time needed for a loan to be processed. Looking at the period prior to the 2008 global financial crisis, they found that Black borrowers experienced significantly longer processing times (about five days more) than did White borrowers for mortgages securitized by the government-sponsored enterprises. In contrast, processing times for the privately securitized mortgages were much shorter for Black borrowers, driven by the fact that Black borrowers were more likely to use nonstandard mortgage financing channels where fast-processing lenders and loan products proliferate.
In the second paper focusing on the mortgage market, "Mortgage Prepayment, Race and Monetary Policy," Kris Gerardi (Atlanta Fed), Paul Willen (Boston Fed), and David Zhang (Harvard) found that Black and Hispanic borrowers pay significantly higher mortgage interest rates than do White and Asian borrowers, and that the primary reason for the large gap in rates is due to differences in refinancing behavior. Minority borrowers are significantly less likely to have refinanced their loans in response to declines in mortgage interest rates, and as a result, they benefited less from lower interest rates.
In the third mortgage paper, "Mortgage Policies and Their Effects on Racial Segregation and Upward Mobility," Nirupama Kulkarni (Centre for Advanced Financial Research and Learning) and Ulrike Malmendier (University of California–Berkeley) noted that housing policies aimed at reducing racial disparities in home ownership can have unintended adverse consequences. Exploiting variation in the ease of mortgage financing created by the 1992 GSE Act, which explicitly targeted underserved neighborhoods, they showed that, while Black home ownership increased in targeted neighborhoods, white families moved out. As a result, segregation increased and the upward mobility of Black children deteriorated. They pointed to declining house prices, reduced education spending, and lower school quality in targeted areas as plausible channels for the decline in upward mobility.
The paper "Testing Models of Economic Discrimination Using the Discretionary Markup of Indirect Auto Loan" by Jonathan Lanning (Philadelphia Fed) examined racial discrimination in auto lending. Lanning presented some compelling empirical evidence for taste-based discrimination in the auto loan market. Auto loans are typically the largest consumer loans after mortgages. More than 80 percent of these loans are indirect, meaning they are arranged by a dealer on the borrower's behalf. The dealer has the discretion to mark up an indirect auto loan by as much as 250 basis points over the rate at which the lender is willing to extend credit. In exchange for the rate increase, the dealer receives additional compensation from the lender. Lanning found that the average markup for Black borrowers is about 14 basis points higher than for White borrowers. More importantly, the racial disparity in the markup is shown to be consistent with the taste-based discrimination theory developed by Becker (1957).
The final two papers in the conference examined the gender gap in the academic finance profession and in the labor market. In the paper "Diversity, Inclusion, and the Dissemination of Ideas: Evidence from the Academic Finance Profession," authors Renee Adams (University of Oxford) and Michelle Lowry (Drexel University) examined how diversity relates to variation in career outcomes within the academic finance profession. They conducted their research based on a survey they administered to current and recent past members of the American Finance Association (AFA) on the professional climate in the field of finance. The survey had 1,628 respondents, about 30 percent of them female. Survey results suggest that female finance faculty members in general have a lower satisfaction level than do their male counterparts. The authors found that gender discrimination is one of the most important causes for this discrepancy.
In the final paper, "Hidden Performance: Salary History Bans and Gender Pay Gap," Jesse Davis (University of North Carolina), Paige Ouimet (University of North Carolina) and Xinxin Wang (University of California–Los Angeles) looked at how salary history bans affected the wage gap between male and female workers. These bans prevent employers from requesting and using a job candidate's prior salary information. Many states have adopted these bans with the explicit intent of reducing the gender pay gap. The idea is that historical pay discrimination against women is propagated if employers are allowed to use past salary information to set pay for new female hires. Presumably, imposing bans should prevent the perpetuation of past discrimination. However, the bans have the additional, negative consequence of preventing potential employers from observing a signal of worker productivity. So the overall effect of salary history bans on the gender gap is unclear. Using a large-panel data set of disaggregated wages covering all public sector employees in 36 states, the authors do not find evidence that salary history bans significantly decrease the gender pay gap.
All in all, the conference proved to be a memorable event. Papers incorporated high-quality micro data and state-of-the-art empirical methods that uncovered evidence of racial and gender inequalities across a variety of financial markets. The paper presentations and thoughtful discussant presentations spurred a lot of dialogue and debate around the nature of the disparities and their implications for future policy. We hope to hold similar conferences in the future, perhaps on an annual or biennial frequency, to continue to promote and raise awareness of this topic.
June 30, 2014
The Implications of Flat or Declining Real Wages for Inequality
A recent Policy Note published by the Levy Economics Institute of Bard College shows that what we thought had been a decade of essentially flat real wages (since 2002) has actually been a decade of declining real wages. Replicating the second figure in that Policy Note, Chart 1 shows that holding experience (i.e., age) and education fixed at their levels in 1994, real wages per hour are at levels not seen since 1997. In other words, growth in experience and education within the workforce during the past decade has propped up wages.
The implication for inequality of this growth in education and experience was only touched on in the Policy Note that Levy published. In this post, we investigate more fully what contribution growth in educational attainment has made to the growth in wage inequality since 1994.
The Gini coefficient is a common statistic used to measure the degree of inequality in income or wages within a population. The Gini ranges between 0 and 100, with a value of zero reflecting perfect equality and a value of 100 reflecting perfect inequality. The Gini is preferred to other, simpler indices, like the 90/10 ratio, which is simply the income in the 90th percentile divided by the income in the 10th percentile, because the Gini captures information along the entire distribution rather than merely information in the tails.
Chart 2 plots the Gini coefficient calculated for the actual real hourly wage distribution in the United States in each year between 1994 and 2013 and for the counterfactual wage distribution, holding education and/or age fixed at their 1994 levels in order to assess how much changes in age and education over the same period account for growth in wage inequality. In 2013, the Gini coefficient for the actual real wage distribution is roughly 33, meaning that if two people were drawn at random from the wage distribution, the expected difference in their wages is equal to 66 percent of the average wage in the distribution. (You can read more about interpreting the Gini coefficient.) A higher Gini implies that, first, the expected wage gap between two people has increased, holding the average wage of the distribution constant; or, second, the average wage of the distribution has decreased, holding the expected wage gap constant; or, third, some combination of these two events.
The first message from Chart 2 is that—as has been documented numerous other places (here and here, for example)—inequality has been growing in the United States, which can be seen by the rising value of the Gini coefficient over time. The Gini coefficient’s 1.27-point rise means that between 1994 and 2013 the expected gap in wages between two randomly drawn workers has gotten two and a half (2 times 1.27, or 2.54) percentage points larger relative to the average wage in the distribution. Since the average real wage is higher in 2013 than in 1994, the implication is that the expected wage gap between two randomly drawn workers grew faster than the overall average wage grew. In other words, the tide rose, but not the same for all workers.
The second message from Chart 2 is that the aging of the workforce has contributed hardly anything to the growth in inequality over time: the Gini coefficient since 2009 for the wage distribution that holds age constant is essentially identical to the Gini coefficient for the actual wage distribution. However, the growth in education is another story.
In the absence of the growth in education during the same period, inequality would not have grown as much. The Gini coefficient for the actual real wage distribution in 2013 is 1.27 points higher than it was in 1994, whereas it's only 0.49 points higher for the wage distribution, holding education fixed. The implication is that growth in education has accounted for about 61 percent of the growth in inequality (as measured by the Gini coefficient) during this period.
Chart 3 shows the growth in education producing this result. The chart makes apparent the declines in the share of the workforce with less than a high school degree and the share with a high school degree, as is the increase in the shares of the workforce with college and graduate degrees.
There is little debate about whether income inequality has been rising in the United States for some time, and more dramatically recently. The degree to which education has exacerbated inequality or has the potential to reduce inequality, however, offers a more robust debate. We intend this post to add to the evidence that growing educational attainment has contributed to rising inequality. This assertion is not meant to imply that education has been the only source of the rise in inequality or that educational attainment is undesirable. The message is that growth in educational attainment is clearly associated with growing inequality, and understanding that association will be central to the understanding the overall growth in inequality in the United States.
By Julie L. Hotchkiss, a research economist and senior policy adviser at the Atlanta Fed, and
Fernando Rios-Avila, a research scholar at the Levy Economics Institute of Bard College
August 21, 2008
The “What’s Fair” contest
At Café Hayek, George Mason’s Russell Roberts opens up a brand new “Inequality Chart Contest.” The chart in question is based on work by Thomas Piketty (professor, Paris School of Economics) and Emmanuel Saez (professor, University of California Berkeley), the essence of which is that the rich have gotten richer and everyone else not so much. (You can find a link to the Piketty-Saez paper, as well as updated data and executive summaries, on Emmanuel Saez’ homepage. Russell links to more information from the Center on Budget and Policy Priorities.)
Here’s the picture…
… and the contest is to construct “ONE sentence explaining ONE thing that is wrong with concluding that these numbers are evidence that the U.S. economy has become more tilted toward the rich at the expense of the poor.”
In the spirit of prompting reflection on issues of inequality and fairness, I invite you to think about the following three pictures, generated from Internal Revenue Service (IRS) tax data through 2006:
Let’s focus on the 1 percent of income-earners (by IRS defined Adjusted Gross Income, or AGI). If you look at the average federal tax rate paid by this group—that is, taxes paid divided by AGI—it did fall substantially over the period from 2000–2006. The average tax rates for other income groups fell as well, but not as dramatically.
If you instead prefer to look at taxes paid, the share the top 1 percent forked over to the federal government rose from 37.4 percent in 2000 to 39.9 percent in 2006. The share paid by the next highest 4 percent rose only slightly over this period, and the share paid by all other groups actually fell or stayed roughly the same.
On the other hand, concentrating on the share of taxes paid relative to the share of income earned by each group would lead you to the conclusion that not much had changed between the year 2000 and 2006.
So here’s the contest: Explain in one sentence which one of those pictures tells us whether the federal income-tax system has become more or less “fair.”
February 13, 2007
Poverty In The Suburbs
Once prized as a leafy haven from the social ills of urban life, the suburbs are now grappling with a new outbreak of an old problem: poverty. Currently, 38 million Americans live below the poverty line, which the federal government defines as an annual income of $20,000 or less for a family of four. But for the first time in history, more of America's poor are living in the suburbs than the cities—1.2 million more, according to a 2005 survey. "The suburbs have reached a tipping point," says Brookings Institution analyst Alan Berube, who compiled the data.
Cleveland, as it turns out, is chosen as the poster city for suburban poverty:
Six years ago, Brian Lavelle moved out of the city of Cleveland to the nearby suburb of Lakewood for what he thought would be a better life... Then, three years ago, the steel mill closed and Lavelle found that the life he dreamed of was just that, a dream. The suburbs, he quickly learned, are a tough place to live if you're poor.
... five years ago, a Hunger Network food pantry in Bedford Heights, a struggling suburb of Cleveland, served 50 families a month. Now more than 700 families depend on it for food.
Howard and Jane Pettry, of Middleburg Heights, Ohio [another Cleveland suburb], see themselves as working-class—just facing hard times. In December, Jane was laid off from her job at a local supermarket, and a week later Howard had a heart attack and missed a month of work from his job at a grain mill. Now Jane's collecting unemployment and they're staring at the poverty line as they struggle to pay the mortgage and the bills.
Is it really the case that the suburbs are increasingly poverty-ridden? I'm suspicious. Not too long ago, my colleagues Mark Schweitzer and Brian Rudick examined the case of Cleveland specifically:
Cleveland is the poorest big city in the United States, according to the Census Bureau, with nearly a third of the city’s residents living in poverty. The city’s poverty rate also rose since it was last measured. These numbers have received a lot of attention since they were released, but unfortunately, they are easily misinterpreted.
The numbers tell us that Cleveland has many poor people. But the numbers don’t tell us that Clevelanders have become worse off, that the region’s economy has deteriorated, or even that there are more poor people in the city than before.
A closer look at the American Community Survey results suggests that Cleveland’s poverty rate reflects the fact that the region’s poor are concentrated in the central city, while the wealthier live in the suburbs. The rate may have risen because people are moving out of the city, and those that leave are disproportionately better-off than those that stay behind. Neither of these facts on its own says anything about the economic health of the region, but they do say something about the relative desirability of living in the city versus its outskirts.
The definition of the Cleveland MSA has recently changed to exclude Ashtabula County, but under either the old or new definition, poverty is far lower outside the city than inside it.
... Looking at the poverty rate of the MSA [metropolitan statistical area] reveals that Cleveland (officially the Cleveland-Lorain-Elyria MSA) is not too different from other U.S. cities. The poverty rate of the Cleveland MSA is just a little above the average for all metropolitan areas in the United States. And like other cities in the country, Cleveland’s suburbs have far lower poverty than the city itself.
...the 2005 rise in metro area poverty figures seen in this figure is almost entirely accounted for by the increase in the number of poor people in the city of Cleveland, rather than in the suburbs. In the 2004-2005 shift that boosted the poverty rate for the MSA, for example, the number of poor people in the MSA grew by 43,540, but 36,991 of that increase occurred in the city.
It is true that the number of poor people in the suburbs is larger than the number of poor people in the city -- according to Schweitzer and Rudick "about two-thirds of the metro area’s poor live outside the city of Cleveland" -- but this is simply a function of the fact that the population of the MSA is heavily concentrated outside of the city. In terms of poverty trends, there just isn't that much happening in the suburbs:
If one wants to use Cleveland to tell a story about poverty -- especially concentrated poverty -- the action is still in the central city.
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