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Democrats Seem to Have a Religion Problem

Published by Anonymous (not verified) on Wed, 02/12/2020 - 11:00pm in

Photo Credit: Joaquin Corbalan P/Shutterstock.com In a previous essay I demonstrated that Democrats have been consistently losing ground with both people of...

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Victims of the “the overwhelming might of the state”

Published by Anonymous (not verified) on Tue, 17/11/2020 - 1:07pm in

This is a quote from Rishi Sunak, who is profiled in ‘Tatler’, which although I feel sure absolutely all our readers subscribe to, I will offer the link nonetheless. When your Chancellor is in ‘Tatler’ you have to realise that he has arrived – even if none of the rest of us has. (The article... Read more

The Effects of the Pandemic on Journal Submissions

Published by Anonymous (not verified) on Fri, 09/10/2020 - 1:38am in

Journal editors: how has the pandemic been affecting submissions to your journals over the past eight months?

Has there been an increase or decrease overall? Have there been differential effects on submission rates for different demographics (gender, race, etc.) or type of institutional affiliation? Have there been other changes you’ve noticed?

Some research has shown that women’s journal submission rates across academia have decreased during the pandemic; it would be useful to hear about the extent to which this trend and others are apparent in philosophy.

[Update: In light of some of the initial comments on this post, we might also ask about whether journals are publishing less as a result of pandemic-related obstacles to processing, reviewing, editing, and publishing articles.]

While we await replies from journal editors, here’s a beguiling music video of graphs of the movement of a variety of objects:

The post The Effects of the Pandemic on Journal Submissions appeared first on Daily Nous.

Some Good News, Some Bad News in the APA’s State of the Profession Report (guest post)

Published by Anonymous (not verified) on Thu, 01/10/2020 - 12:59am in

The American Philosophical Association (APA) today released a new report, “State of the Profession 1967-2017 and Beyond: Institutions and Faculty.”

The report, drafted by Debra Nails (Michigan State University, emeritus) and John Davenport (Fordham University), “gives a detailed picture of the profession of academic philosophy—that is, the makeup of philosophy departments and philosophy faculty in North America—and how it has evolved over five decades.”

Its information comes from past editions of the Directory of American Philosophers published by the Philosophy Documentation Center (PDC) and other data from the PDC, the National Center for Education Statistics (NCES), the APA, and departmental sites.

The report, its authors say, “provides more detail on the state of the profession than has previously been available, including more specific information on gender, institutional types and affiliations, and regional differences among philosophy programs” and “explores the types of institutions that teach ad hoc courses and those that offer degrees, identifying where faculty in contingent positions are most concentrated, and where women philosophers are most concentrated.” It also provides information on the types of positions philosophy professors hold.

The full report is available here. In the following guest post*, Carolyn Dicey Jennings (UC Merced) and Eric Schwitzgebel (UC Riverside) cover some of its key findings.

Some Good News, Some Bad News in the APA’s State of the Profession Report
by Carolyn Dicey Jennings and Eric Schwitzgebel

We were recently provided with a report from the APA that recounts work by Debra Nails and John Davenport to collect, organize, and analyze available data on the discipline over the past 50 years, including data from the Philosophy Documentation Center, the National Center for Education Statistics, and the APA itself. We are grateful for the efforts of Nails and Davenport in creating this important report on the state of the profession. As colleagues on the Data Task Force, we have some insider knowledge of how challenging this task was, and how much time it required between 2016 and now. In reviewing the report, a few threads stood out: good news, bad news, and supporting news. Let’s start with the good.

Contingent Faculty

While some use the language of “adjunct” or “part-time” faculty, we follow the report in using “contingent,” since it is possible for adjunct and part-time positions to be permanent ones. The national issue of increasing contingent labor in academia has come up many times at the APA Blog and Daily Nous, and there was recently an APA session dedicated to the topic. As one report puts the problem:

For many part-time faculty, contingent employment goes hand-in-hand with being marginalized within the faculty. It is not uncommon for part-time faculty to learn which, if any, classes they are teaching just weeks or days before a semester begins. Their access to orientation, professional development, administrative and technology support, office space, and accommodations for meeting with students typically is limited, unclear, or inconsistent. Moreover, part-time faculty have infrequent opportunities to interact with peers about teaching and learning. Perhaps most concerning, they rarely are included in important campus discussions about the kinds of change needed to improve student learning, academic progress, and college completion. Thus, institutions’ interactions with part-time faculty result in a profound incongruity: Colleges depend on part-time faculty to educate more than half of their students, yet they do not fully embrace these faculty members. Because of this disconnect, contingency can have consequences that negatively affect student engagement and learning.

So far this sounds like bad news, but we want to be sure that we do not overlook the real issues contingent faculty face in communicating the good. Namely, that the percentage of contingent faculty in philosophy is low and stable. As Nails and Davenport explain, around 73% of all faculty nationwide are in contingent or “unranked” positions, whereas only 22% of philosophy faculty had contingent positions in 2017. Moreover, there is a lower percentage of contingent philosophy faculty now than there was in the 1960s. While the APA membership numbers have suggested this for some time (with around 20% of its members reporting contingent status), a reasonable concern about that estimate was that contingent faculty might be underrepresented among APA members. This new report suggests that the numbers just are low in our discipline. That’s good news, under the plausible assumption that it is best for the discipline if a large majority of faculty are tenured or tenure track.

While we are heartened by these findings, we see a couple of reasons to stay vigilant about the status of contingent faculty.

First, we don’t know the reason that there are fewer contingent faculty in philosophy. It may be, for example, that philosophers are less likely to teach the types of courses that are typically offered to contingent faculty, such as general education and writing courses. In that case, this wouldn’t be a reason to celebrate philosophy’s success on the issue.

Second, the report raises the possibility that contingent positions have recently been replacing assistant professor positions: “Since 1987, there has been a steady increase in the number of faculty hired outside the tenure system compared to entry-level positions inside it.” We note only that the numbers show that the ratio of assistant professor positions to contingent positions has shifted from about 1:1 in 1987 to about 4:3 in 2017, representing a gain of about 500 contingent positions while the number of assistant professor positions remains about the same (see “a” in Figure 5).

It is unclear what we should conclude from this, since the overall ratio of contingent to non-contingent faculty hasn’t really changed: there are also nearly 500 extra professor and associate professor positions since 1987 (“b” in Figure 5). This could be due to faculty staying in the profession for longer than in past decades, but it could be for some other reason. Zooming in on the difference between 2007 and 2017, Nails and Davenport note that “the drop in assistant professors and rise in associate professors may indicate a decline in entry-level hires since 2007. Universities that hired new faculty into contingent positions in the wake of the Great Recession have not yet made tenure lines available to those who, under normal circumstances, would have been hired as assistant professors.” But here, too, the additional numbers of those in associate and professor positions could explain the difference (“c” in Figure 5). It may be, for example, that those in more recent years are achieving promotion faster than in past years, leaving fewer people at the assistant rank relative to ranked positions, overall. So it is unclear what to take from this data, but we may want to be cautious, given the possibility Nails and Davenport raise.

Alright, how about some bad news?

Decline

The bad news is that philosophy is represented at about 100 fewer institutions in 2017 than in 1967 (1669 colleges and universities in 1967 and 1552 in 2017). This appears to represent a decline of the discipline in academia that has been the subject of numerous blog posts.

Surprisingly, the report particularly notes a decline in philosophy at (non-Catholic) religious institutions, both at the undergraduate and graduate level. Whereas around 16% of all public institutions offer no philosophy degree, this is true of 27% of non-Catholic religious institutions (but only 11% of Catholic colleges and universities). We don’t know the root of this decline of philosophy in religious institutions. It might be due to the especially atheistic culture of philosophy and its writings, or due to such institutions having comparatively stronger religious studies or theology programs competing for majors, or due to the relatively left-wing politics of many academic philosophers.

In addition, a striking 78% of historically Black colleges and universities (HBCU) offer no philosophy degree. Given the historical racism in philosophy, it seems likely that this is also connected to cultural issues. It would be in the interest of philosophy to further explore the  matter. (Interested readers might start with this interview with Brandon Horgan at Howard University, an HBCU.)

We did note one reason for optimism with the overall numbers: while the numbers of institutions with a degree in philosophy has declined, the number of faculty at these institutions has increased, from around 6,000 in 1967 to around 9,000 in 2017. One can see how this played out at most institutions through the median number of faculty: whereas the median number of faculty for programs offering a PhD in philosophy was around 13 in 1967, it was around 19 in 2017. Similarly, those offering a Master’s went from 6 to 11, those offering Bachelor’s went from 3 to 5, and those offering courses only went from 1 to 2 (Figures 6a-b and 7a-b).

The report also provided some numbers that support other findings, which we called “supporting news” above. We focus here on the supporting news regarding gender diversity. (The authors of the report were unable to explore race/ethnicity, disability, LBGTQ status, or other aspects of diversity.)

Gender Diversity

The APA now collects some demographic data from its members, including gender, race/ethnicity, LGBT status, and disability status. Among the 1874 APA members who reported gender, 505 (27%) answered “female”, 1363 (73%) answered “male”, and 6 (<1%) answered “something else.” Other recent research has suggested that women constitute about 30% of recent philosophy PhDs and new assistant professors in the U.S., about 20% of full professors, and about 25% of philosophy faculty overall (plus or minus a few percentage points). However, most of this previous research is either a decade out of date or is limited to possibly unrepresentative samples, such as APA member respondents, faculty at PhD-granting programs, or recent PhDs.

The current report finds generally similar numbers, in a larger and more representative sample (all faculty in the Directory of American Philosophers from 2017). Overall, 26% of philosophy faculty were women, including 34% of assistant professors and 21% of full professors. (Associate professors and contingent faculty are intermediate at 28% and 26%, respectively.)

We note that the authors of the report relied on the DAP’s binary gender classifications of faculty, which were generally reported by department heads or other department staff. And where faculty gender was not specified, the report’s authors searched websites and CVs for gender designations. Thus, the data do not include non-binary gender and some classification errors are possible.

The tendency for women to be a smaller percentage of full professors than assistant professors could reflect either a cohort effect, a tendency for women to advance more slowly up the ranks than men, or a tendency for women to exit the profession at higher rates than men. On the possibility of a cohort effect, since professors often teach into their 70s, the lower percentage of women among professors might to some extent reflect the fact that in the 1970s and 1980s, 17% and 22% of philosophy PhDs in the U.S. were awarded to women. By the 1990s, it was 27%, which is closer to the numbers for recent graduates. However, cohort effects might not be a complete explanation, since 21% might be a bit on the low side for a group that should reflect a mix of people who earned their PhDs from approximately the 1970s through the early 2000s. In the NSF data, women received 23% of all philosophy PhDs from the year 1973 through 2003—the approximate pool for full professors in 2017.

The report also explores gender by institution type, highest degree offered, and region. One notable result is that philosophy departments offering at least Bachelor’s degrees had on average higher percentages of women than departments not offering Bachelor’s degrees. Faculty were 27% women in departments offering the PhD, 28% in departments offering a Master’s but no PhD, 27% in departments offering a Bachelor’s degree, 22% in departments offering a minor but no Bachelor’s, 23% in departments offering an Associate degree but no minor, and 20% in departments offering philosophy courses but no degrees. (A chi-square test shows that this is unlikely to be statistical chance: 2×6 chi-square = 24.3, p < .001, lowest expected cell count = 104.) We are unsure what would explain this phenomenon.

Editorial note: readers may also be interested in the report’s data concering the number of graduate and undergraduate programs in philosophy, represented in Figures 11a and 11b, below. Nails and Davenport write, “the total number of doctoral programs has continued its slow increase since 2007, despite the soft job market for faculty positions in the tenure system.”

The post Some Good News, Some Bad News in the APA’s State of the Profession Report (guest post) appeared first on Daily Nous.

Investigating the Effect of Health Insurance in the COVID-19 Pandemic

Published by Anonymous (not verified) on Fri, 25/09/2020 - 9:00pm in

Rajashri Chakrabarti, Maxim Pinkovskiy, Will Nober, and Lindsay Meyerson

Investigating the Effect of Health Insurance in the COVID-19 Pandemic

Does health insurance improve health? This question, while apparently a tautology, has been the subject of considerable economic debate. In light of the COVID-19 pandemic, it has acquired a greater urgency as the lack of universal health insurance has been cited as a cause of the profound racial gap in coronavirus cases, and as a cause of U.S. difficulties in managing the pandemic more generally. However, estimating the effect of health insurance is difficult because it is (generally) not assigned at random. In this post, we approach this question in a novel way by exploiting a natural experiment—the adoption of the Affordable Care Act (ACA) Medicaid expansion by some states but not others—to tease out the causal effect of a type of health insurance on COVID-19 intensity.

The Oregon Health Insurance Experiment—in which Medicaid in Oregon was assigned at random to some eligible individuals—found that the Medicaid recipients faced much lower financial pressure and had better mental health than nonrecipients, but did not find improvement on any short-term metrics of physical health. However, another literature has noted that when people are sent to the hospital in serious condition shortly after they turn sixty-five (and qualify for Medicare) rather than shortly before, their health outcomes, including their probability of survival, tend to improve. Therefore, whether health insurance improves health is still an open question, and the answer may vary with the exact nature of health insurance and the exact definition of “health."

We exploit the fact that Medicaid expansion under the ACA was up to the states, and many states did not adopt the expansion. While the nonadopting states are clearly different from the adopting states (they are in the South and West, and are more conservative and more rural than the expansion states, on average), the counties on the borders of these states probably differ little from their neighboring counties whose state governments did expand Medicaid.

We illustrate these similarities in the maps below. The map on the left shows all states expanding and not expanding Medicaid as of January 2020; the regional tilt of the Medicaid expansion is apparent. The map on the right shows counties on both sides of the borders between Medicaid-expanding states and Medicaid-nonexpanding states in red and dark blue. By definition, the bordering counties are next to each other, so restricting our analysis to this sample of counties removes the bias arising from differing geography that we would have if we were to compare expanding and non-expanding states. We have also observed that many variables that are correlated with COVID-19 incidence—such as minority share, population density, public transit use, and home crowding—are continuous across the Medicaid expansion border, and we show some of these results later in the post.

Investigating the Effect of Health Insurance in the COVID-19 Pandemic

To make our natural experiment more convincing, we consider plots of the outcomes of interest (health insurance rates, coronavirus cases per capita, potential confounders) against distance to the border between Medicaid-nonexpanding and Medicaid-expanding states. Places within Medicaid-nonexpanding states are assigned a negative value for distance while places in Medicaid-expanding states are assigned a positive value for distance. Although there is much spatial variation in outcomes that we care about, this variation should evolve continuously across space without major jumps unless some policy factor that affects these variables should change abruptly from one state to another. Our main assumption in this analysis is that the only reason for the value of any outcome to jump at the Medicaid expansion border is that states on one side of the border have expanded Medicaid and states on the other side of the border have not. If there are other systematic policy differences between states that have expanded Medicaid and states that have not, our assumption would be violated. However, as can be seen from the maps, the Medicaid expansion border largely runs within the South and the West (for example, between Louisiana and Mississippi, or between Kentucky and Tennessee, or South Dakota and Nebraska) and therefore does not coincide with major political divisions between red and blue states.

The chart below shows the fundamental reason for using our empirical strategy, by plotting county uninsurance rates against distance from the Medicaid expansion border. The gray region around the dots represents the statistical uncertainty around the estimates. We see that the fraction of uninsured in counties that have not expanded Medicaid is between 13 and 15 percent close to the border, at which point it drops precipitously to about 10 percent and remains there for most counties in states that have expanded Medicaid. Therefore, which side of the Medicaid expansion border someone lives on materially affects their chances of being insured. While counties far away from the Medicaid expansion border in the nonexpanding region are very different from counties far away from the border in the expanding region, counties very close to the border are likely similar (as evidenced above in the continuity of observable characteristics at the border). Therefore, we can compare outcomes between nearby counties on different sides of the border as though they have been randomly assigned. It is important to note, though, that all of our comparisons are representative of the areas around the Medicaid expansion border and effects in other parts of the country may be different.

Investigating the Effect of Health Insurance in the COVID-19 Pandemic

If health insurance improves health, and in particular, weakens the COVID-19 pandemic, we should expect that COVID-19 cases and deaths per person would show the same pattern: continuous evolution with distance to the border and a precipitous drop as one goes from the Medicaid-nonexpanding to the Medicaid-expanding region. The first two panels of the chart below show that this is not the case. Instead, both cases and deaths per person evolve continuously through the Medicaid expansion border, with no jumps at all. The statistical uncertainty around the estimates is modest, suggesting little potential for Medicaid expansion—and therefore, uninsurance rates—to affect reported COVID-19 intensity. The third panel of the chart shows the plot for the fraction of the county’s residents who are white, which is an important correlate of COVID-19 intensity given that minorities have been hit harder by the pandemic than whites. We see that it is also continuous through the COVID-19 border. More generally, all the potential confounders that we have considered—population density, use of public transit, pollution, the number of people per room, comorbidities such as hypertension and obesity, ICU bed availability, as well as many others—have not exhibited any jumps at the Medicaid expansion border.

Investigating the Effect of Health Insurance in the COVID-19 Pandemic

Based on this evidence, we would be tempted to conclude that the availability of health insurance, most specifically Medicaid, does not affect the intensity of COVID-19. This result would be consistent with the Oregon Health Insurance Experiment described above, which shows no short-term effects of Medicaid on physical health. However, we are not completely convinced. The problem is that reported COVID-19 case and death counts suffer from considerable underreporting—studies of excess deaths suggest that as many as half of the deaths attributable to COVID-19 may be classified as coming from another disease, while cases may be underreported by as much as ten times, and this underreporting may be differential across states and counties. It is likely that with such staggering rates of underreporting, some of this underreporting may be correlated with the availability of Medicaid.

The last panel of the chart above presents a plot of the number of doctor visits for COVID-19 symptoms against distance to the Medicaid expansion border. This data is obtained from Carnegie Mellon’s COVID Tracker database and is available for a smaller set of counties, which includes most areas east of the Mississippi. We find that there is a small upward jump in doctor visits for COVID-19 symptoms as one goes from the Medicaid-nonexpanding to the Medicaid-expanding states. Does Medicaid then cause coronavirus? The answer is more prosaic: people with health insurance are much more likely to avail themselves of the medical system than people without insurance (a robust finding of the Oregon Health Insurance Experiment). Individuals in the Medicaid-expanding region were more likely to have Medicaid and to use it to have their symptoms checked out. However, this behavior would also make them more likely to be diagnosed as a coronavirus case (and, potentially, as a coronavirus death) than people just on the other side of the border who did not have Medicaid. Therefore, it is possible that 1) Medicaid reduced true COVID-19 cases and deaths but 2) increased the reporting rate of COVID-19 for individuals who were infected, 3) making the overall effect appear to be zero.

We are currently exploring this hypothesis by applying for access to data on digital thermometers distributed across the United States by Kinsa Inc. This data has been used in various papers to document the influence of public health measures such as lockdowns on COVID-19. Finding no discontinuity in Kinsa thermometer temperatures across the Medicaid expansion boundary would be convincing evidence that Medicaid really does not do anything to reduce true COVID-19 prevalence, while finding a discontinuity could help us disentangle the medical and utilization effects of Medicaid on reported COVID-19 rates.

We look forward to updating readers on what we find as our research progresses.

Rajashri Chakrabarti
Rajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Maxim Pinkovskiy
Maxim Pinkovskiy is a senior economist in the Research and Statistics Group.

Will Nober was a senior research analyst in the Research and Statistics Group.

Lindsay Meyerson was an economics student at Columbia University.

How to cite this post:

Rajashri Chakrabarti, Maxim Pinkovskiy, Will Nober, and Lindsay Meyerson, “Investigating the Effect of Health Insurance in the COVID-19 Pandemic,” Federal Reserve Bank of New York Liberty Street Economics, September 25, 2020, https://libertystreeteconomics.newyorkfed.org/2020/09/investigating-the-....




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Debt Relief and the CARES Act: Which Borrowers Face the Most Financial Strain?

Published by Anonymous (not verified) on Wed, 19/08/2020 - 9:00pm in

Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw

 Which Borrowers Face the Most Financial Strain?

In yesterday's post, we studied the expected debt relief from the CARES Act on mortgagors and student debt borrowers. We now turn our attention to the 63 percent of American borrowers who do not have a mortgage or student loan. These borrowers will not directly benefit from the loan forbearance provisions of the CARES Act, although they may be able to receive some types of leniency that many lenders have voluntarily provided. We ask who these borrowers are, by age, geography, race and income, and how does their financial health compare with other borrowers.

Who is Without Mortgage and Student Debt?

To understand the distribution of borrowers who will be ineligible for the debt relief provisions of the CARES Act, we draw on the New York Fed’s Consumer Credit Panel—a nationally representative sample of Equifax credit report data. Our data set for this post includes a representative 1 percent sample of the nation’s adults with credit records in anonymized form.

The map below shows the (adult) population-weighted geographic (zip code) distribution of the share of the population with neither mortgages nor student debt. Each bin represents 1/4 of the U.S. adult population, so one quarter of adults live in zip codes where less than 60 percent have a student loan or mortgage, while another quarter lives in zip codes in which more than 75 percent have neither type of debt. The darkest-colored bin, where more than 75 percent of people are borrowers with neither a student loan nor mortgage, dominates by land area: 40 percent of all zip codes fall in the top quartile, indicating that these zip codes are less populated than those with higher rates of mortgage and student debt. Spatially, the borrowers without mortgages and student debt largely live in rural areas and they constitute a higher share of the borrowers in rural areas: 74 percent of Americans living in rural areas are in this category, versus 68 percent of people living in MSAs (urban areas).

LSE_2020_debt-relief-heterogeneity-Part 2_map1_art-01

Borrowers with neither mortgages nor student debt skew older than those who have either form of debt more generally. These older borrowers have either paid off their mortgages or never owned a home to begin with. Also they are less likely to have originated a student loan when they were younger. The median age of these borrowers is 56, while the median age of mortgage holders is 51 and that of student debt borrowers is 34. Although the median age of borrowers without any mortgage and student debt, and the median age of mortgage holders are not very different, the age distributions are different.

Partitioning zip codes by income (low-income, mid-income, high-income) and by race (majority-Black, majority-Hispanic, majority-white and mixed) as defined in our previous post, we investigate where these borrowers are concentrated (See the table below). First, distinguishing neighborhoods by income, we find that these borrowers are more concentrated in low-income neighborhoods: 72 percent of the borrowers in low-income neighborhoods have neither mortgage nor student debt whereas in mid-income and high-income neighborhoods these numbers are, respectively, 63 percent and 56 percent.

Distinguishing neighborhoods by race, we find that these borrowers are more concentrated in majority Hispanic and majority Black neighborhoods with 71 percent of borrowers in majority-Hispanic neighborhoods and 67 percent of borrowers in the majority Black neighborhoods. This compares to 62 percent in majority white neighborhoods. This analysis reveals that a large share of borrowers in low income, majority Hispanic, and majority Black neighborhoods will not receive direct relief from the debt moratorium provisions of the CARES Act.

 Which Borrowers Face the Most Financial Strain?

Borrowers who carry loans other than mortgages or student debt have lower credit scores (as captured by Equifax risk scores) than other borrowers, as we see in the table below. The median Equifax risk score for this group is 669 while the median risk score of all individuals with any debt is 700. To further understand the state of financial health of this group of borrowers, below we present the proportions of borrowers in this category who are delinquent on credit card payments by at least 90 days or in collection, conditional on having credit card debt. We find that 11.6 percent of this group of borrowers are more than 90 days delinquent on their credit card debt (last row). This compares with an overall delinquency rate, of 9.9 percent in our sample for credit card debt.

We find that older borrowers with loans other than mortgages and student debt are in better financial health than working-age borrowers. This reflects the fact that older people have often paid off their mortgages and student debt, while younger people without mortgages or student loans often had never owned a home. Regardless, these borrowers will not receive debt relief under the CARES Act forbearance provisions, although they can receive relief from its other provisions, such as direct stimulus checks.

Differentiating by neighborhood income, borrowers without any mortgage or student debt who live in low-income neighborhoods are considerably more likely to be in financial distress than those from high- or mid-income neighborhoods—as captured by both risk score and credit card delinquency. Differentiating by race, we find that borrowers without mortgage and student debt from Black neighborhoods are in markedly worse financial health than those coming from other neighborhoods. The risk score of these borrowers who reside in Black neighborhoods is 574 and their credit card delinquency and collection rate is at 21.8 percent. These contrast with delinquency rates of 16.4 percent and 9.8 percent for the group residing in majority Hispanic and majority white neighborhoods, respectively.

 Which Borrowers Face the Most Financial Strain?

In this post, we have focused on borrowers who carry debt other than mortgages and student loans, and therefore, will not directly benefit from the debt payment moratorium provisions under the CARES Act. We find that these borrowers are likely to be older than those holding mortgage and student debt, and more likely to be concentrated in low income, majority Black and majority Hispanic neighborhoods. They are more likely to be in financial distress than other borrowers, as captured by credit card delinquency rates. Further, as revolving debts are important for smoothing gaps in income, borrowers with already-delinquent credit card accounts are unlikely to be able to lean on these accounts to smooth consumption. Given this, relief from other parts of the Act, such as unemployment insurance and stimulus checks, will therefore be particularly important in mitigating the crisis' impact on the financial health of these borrowers.

Rajashri Chakrabarti

Rajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Andrew Haughwout

Andrew F. Haughwout is a senior vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Donghoon Lee

Donghoon Lee is an officer in the Bank’s Research and Statistics Group.

Joelle Scally

Joelle Scally is a senior data strategist in the Bank's Research and Statistics Group.

Wilbert van der Klaauw

Wilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw. “Debt Relief and the CARES Act: Which Borrowers Face the Most Financial Strain?" August 19, 2020, https://libertystreeteconomics.newyorkfed.org/2020/08/debt-relief-and-th....

Additional heterogeneity posts on Liberty Street Economics:

Heterogeneity: A Multi-Part Research Series




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Debt Relief and the CARES Act: Which Borrowers Benefit the Most?

Published by Anonymous (not verified) on Tue, 18/08/2020 - 9:00pm in

Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw

 Which Borrowers Benefit the Most?

COVID-19 and associated social distancing measures have had major labor market ramifications, with massive job losses and furloughs. Millions of people have filed jobless claims since mid-March—6.9 million in the week of March 28 alone. These developments will surely lead to financial hardship for millions of Americans, especially those who hold outstanding debts while facing diminishing or disappearing wages. The CARES Act, passed by Congress on April 2, 2020, provided $2.2 trillion in disaster relief to combat the economic impacts of COVID-19. Among other measures, it included mortgage and student debt relief measures to alleviate the cash flow problems of borrowers. In this post, we examine who could benefit most (and by how much) from various debt relief provisions under the CARES Act.

Data and Definitions

In addition to direct stimulus to individuals and corporations, the CARES Act provides for debt forbearance (that is, a temporary break from debt service payments) for various types of loans. FHA- and GSE-backed mortgages are eligible for a 180-day forbearance period, which can be extended to 360 days, but the borrower needs to contact the mortgage servicer to request forbearance. There was also a moratorium on foreclosure for 60 days after March 18. Federal student debt borrowers can defer payments until September 30, with interest waived. This forbearance is administrative and does not have to be negotiated. The Act also suspends involuntary collections, which includes wage garnishment and the reduction of tax refunds or other federal benefits, for qualifying federal student debt borrowers who are in default. While private student debt makes up a small share (approximately 8 percent) of total outstanding student debt, our data do not enable us to differentiate between federal and private student debt. The small subset of the student debt borrowers who have only private student loans will not be eligible for CARES Act forbearance relief. For simplicity, we will consider all student debt borrowers as being eligible for student debt forbearance in this post.

To understand who may benefit (and by how much) from the mortgage and student debt relief proposed, we draw on the New York Fed’s Consumer Credit Panel—an anonymized, nationally representative sample of Equifax credit report data. Our data set for this post covers a representative 1 percent sample of the nation’s adults with credit records, showing payments, balances, and delinquencies for various types of debt, including student loans, mortgages, auto loans, and credit cards. We focus on mortgage and student debt in this post because the relief under the CARES Act pertained to these two kinds of consumer debt.

To understand who the potential beneficiaries of debt relief are, we examine differences in forbearance relief across income, age and racial lines. Specifically, we split zip codes into equal-population quartiles of median household income (pre-tax); we refer to the bottom quartile as “low income,” (with median income below $46,310) the two middle quartiles as “middle income,” and the top quartile as “high income” (with median income above $78,303). We also look at zip codes that are “majority Black,” “majority Hispanic,” “majority white,” and “mixed.” We define majority Black zip codes (neighborhoods) as those in which Black residents make up at least 50 percent of the population, and define majority Hispanic and majority white zip codes (neighborhoods) similarly. We group all other neighborhoods together into a fourth category, “mixed” neighborhoods. For all income and race data, we use the 2014-18 Five-Year American Community Survey. We investigate the extent of mortgage and student debt relief faced by each of these neighborhoods: low income, middle income, high income, majority Black, majority Hispanic, majority white, and mixed.

At the end of December 2019, the majority of borrowers (63 percent) in our sample have neither mortgage nor student debt, but 21 percent have a mortgage but no student debt and 12 percent have student debt but no mortgage. Only 4 percent of adults have both mortgage and student debt. The median student debt borrower is 34 years old while the median age of mortgagors is 51. Thus, while the student debt relief will potentially benefit younger borrowers, the mortgage relief will potentially benefit relatively older borrowers.

Who Can Benefit from CARES Act Debt Relief?

Borrowers who have student debt or mortgage debt (and hence may qualify for CARES Act debt moratoria) fall into three groups: those with student debt but no mortgage, those with mortgage but no student debt, and those with both types of debt. In the table below, we investigate what share of the adult (above 18) population in each type of neighborhood has student debt but no mortgage (column 1), mortgage but no student debt (column 2), and both mortgage and student debt (column 3), and hence will potentially be eligible for corresponding student debt and/or mortgage debt relief. Differentiating across neighborhoods by income, we find in column 1 that similar shares of the adult population will potentially be eligible for assistance from only the student debt relief provisions of the CARES Act across the three neighborhoods (18 percent), but a markedly higher share (more than double) can be eligible for mortgage relief in the high income neighborhoods relative to low income neighborhoods (column 2). The share of the adult population that may benefit from only mortgage relief is also considerably larger in middle income neighborhoods (1.6 times) than in low income neighborhoods. Column 3 reveals that the share of adult population respectively in high and middle income neighborhoods that can benefit from both the CARES Act mortgage and student debt relief is double the corresponding share in low income neighborhoods.

Differentiating by race, column 1 shows that a significantly larger share (20 percent) of the adult population in majority Black neighborhoods can be eligible for assistance from only the student debt relief provisions of the CARES Act compared to such shares in the majority Hispanic, majority white, and mixed neighborhoods. In contrast, columns 2 and 3 find that a substantially larger share in majority white neighborhoods will be potentially eligible for only mortgage relief or both mortgage and student debt relief compared to the shares in majority Black, majority Hispanic, and mixed neighborhoods.

 Which Borrowers Benefit the Most?

Is There Heterogeneity in the Expected Benefit from the CARES Act Student Debt Forbearance?

To further understand who may benefit and the extent of the potential cash flow assistance (driven by funds released by deferral of payments), we look at a neighborhood type in the table below and examine what share of the adult population in that neighborhood will be eligible for any student debt assistance and how much assistance they may receive based on their debt profile at the end of 2019. Differentiating by income, we find in the first column that a slightly higher share of the adult population in high and middle income neighborhoods can benefit from student debt relief than in the low income neighborhood. Unlike column 1 of the first table in this post, this column accounts for any student debt relief, regardless of whether the borrower holds both mortgage and student debt or holds student debt but no mortgage debt. The higher shares in this table (in contrast to the earlier table) are driven by increased incidence of borrowers who hold both student and mortgage debt in the high and middle income neighborhoods.

 Which Borrowers Benefit the Most?

Turning to the amount of potential forbearance, we find that the median scheduled monthly payments per borrower (those eligible for forbearance) in low income neighborhoods are markedly smaller than those in high income neighborhoods; at least half of the student loan borrowers in low income neighborhoods had a scheduled payment of zero before the onset of the pandemic. These may be due to a number of factors: smaller loan sizes in these neighborhoods, larger incidence of in-school deferment, or higher participation in income-driven repayment programs in these neighborhoods. In column 4, we find that the mean scheduled payment per adult (and hence the potential assistance per adult) in high income neighborhoods is more than double that in low income neighborhoods. Annualizing the payments and comparing mean scheduled payment to the median household income of the zip code the person lives in, we find that the relief is actually a higher share of median income in these low income neighborhoods, despite the smaller forbearance amount (column 5).

By race, we continue to find that majority Black zip codes have markedly higher concentrations of student debt borrowers relative to the other neighborhoods. 23 percent of the adult population of majority Black neighborhoods is eligible for student debt relief versus 14 percent in majority Hispanic and 17 percent in majority white and mixed neighborhoods. However, as in the case of low income neighborhoods, more than 50 percent of borrowers in majority Black zip codes have no regular monthly scheduled payment, and thus would not benefit from forbearance. We find in column 3 that the mean scheduled payment per borrower is higher in majority white neighborhoods and significantly lower in majority Black and majority Hispanic neighborhoods. In column 4, we find that the mean scheduled payment per adult is broadly similar across majority white, majority Black and mixed neighborhoods, while it is perceptibly lower in Hispanic neighborhoods. The difference in patterns between columns 3 and 4 is driven by the fact that majority white neighborhoods are considerably more populous than majority Black neighborhoods (column 4 of the first table in this post). Interestingly, we once again find in the last column that the potential forbearance amount will constitute a higher share of median household income in majority Black neighborhoods than in other neighborhoods. In summary, we find that larger shares of borrowers from majority Black neighborhoods can benefit from the student debt relief provision, although the expected per-borrower relief to these communities is smaller. Regardless, this relief will address a higher debt burden (as share of income) in these neighborhoods.



Understanding Heterogeneity in the CARES Act Mortgage Debt Forbearance Relief

We can repeat this analysis for mortgage debt. Remember, not all mortgages are FHA or GSE-backed and hence eligible for forbearance. The table below shows that the highest concentrations are in majority white and higher-income zip codes, as qualifying for a mortgage requires a relatively high credit score and steady stream of income. Mortgagors in high income zip codes also pay much more per month than those in other areas, indicating higher home value and mortgage balance on average. We find from column 3 that the monthly scheduled payment of mortgagors (and hence the potential forbearance amount per mortgagor) is higher for those from high income, mixed, and majority white neighborhoods, and smallest for those from low income and majority Black neighborhoods. Looking at mean scheduled payment per adult in the various neighborhoods, the indicator of average per-capita forbearance dollars to a neighborhood, once again we find that high income, majority white, and mixed neighborhoods can expect higher mortgage forbearance relief, while this relief is lowest for low income, majority Black, and majority Hispanic neighborhoods (column 4). Nevertheless, turning to the mean payment as a share of median income in the neighborhood, we find that this relief amount again constitutes higher relative debt burdens in low income, majority Black, and majority Hispanic neighborhoods, largely because of lower median income in these neighborhoods.

 Which Borrowers Benefit the Most?

To summarize, we have investigated who may benefit (and the expected forbearance amounts) from the various debt relief provisions in the CARES Act. We find that while student debt relief may be expected to reach a larger share of borrowers in majority Black neighborhoods, the dollar value of expected student debt relief per borrower will be perceptibly less in low income, majority Black, and majority Hispanic neighborhoods. Unlike student debt relief, mortgage relief may be concentrated in high income and majority white neighborhoods, both in terms of dollar amounts and share of borrowers that will be potentially assisted. It is worth emphasizing that in this post we have outlined who may benefit from the mortgage and student debt relief provisions of the CARES Act. In other words, we have focused on the supply of this relief to different neighborhoods. Who will actually benefit and the amount of relief obtained will be determined by a combination of supply and demand factors. Since, low income and majority minority neighborhoods have been affected more negatively by this pandemic, residents in these neighborhoods may have the highest take-up rate. Moreover, mortgage benefits are not automatic; mortgagors must actively seek out these benefits by contacting servicers and proving financial hardship. Thus, ultimately, who actually benefits and by how much will be determined by a combination of factors, a topic we will continue to study. This post starts the conversation by investigating the potential beneficiaries and the potential reach (in dollar terms) of the forbearance programs.

Rajashri Chakrabarti

Rajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

Andrew Haughwout

Andrew F. Haughwout is a senior vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Donghoon Lee

Donghoon Lee is an officer in the Bank’s Research and Statistics Group.

Joelle Scally

Joelle Scally is a senior data strategist in the Bank's Research and Statistics Group.

Wilbert van der Klaauw

Wilbert van der Klaauw is a senior vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti, Andrew Haughwout, Donghoon Lee, William Nober, Joelle Scally, and Wilbert van der Klaauw. “Debt Relief and the CARES Act: Which Borrowers Benefit the Most?" August 18, 2020, https://libertystreeteconomics.typepad.com/libertystreetecontest/2020/08....

Additional heterogeneity posts on Liberty Street Economics:

Heterogeneity: A Multi-Part Research Series




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

Are Financially Distressed Areas More Affected by COVID-19?

Published by Anonymous (not verified) on Mon, 17/08/2020 - 9:00pm in

Rajashri Chakrabarti, William Nober, and Maxim Pinkovskiy

Editor’s note: When this post was first published, the columns in the second table were mislabeled; the table has been corrected. (August 19, 9:30 a.m.)

Are Financially Distressed Areas More Affected by COVID-19?

Building upon our earlier Liberty Street Economics post, we continue to analyze the heterogeneity of COVID-19 incidence. We previously found that majority-minority areas, low-income areas, and areas with higher population density were more affected by COVID-19. The objective of this post is to understand any differences in COVID-19 incidence by areas of financial vulnerability. Are areas that are more financially distressed affected by COVID-19 to a greater extent than other areas? If so, this would not only further adversely affect the financial well-being of the individuals in these areas, but also the local economy. This post is the first in a three-part series looking at heterogeneity in the credit market as it pertains to COVID-19 incidence and CARES Act debt relief.

We use county-level data, on numbers of cases and deaths, compiled by the New York Times (NYT) and the New York City Department of Health (Department of Health) for our analysis. The New York Times compiles a daily series of confirmed cases and deaths for almost every county in the United States. Its data set aggregates New York City, which consists of five counties, into a single entity. To get a breakdown of deaths and cases by New York City’s boroughs, we use data from the Department of Health.

Because race and income data for affected individuals are not available in all states, we match our county-level COVID-19 data with county-level data on race, median household income, urban status, and population density from the 2014-18 five-year American Community Survey (ACS). We define percent minority as the percentage of people in a county that is Hispanic or non-Hispanic Black. We define majority-minority counties as those in which at least half the population is Hispanic or non-Hispanic Black. We split counties into equal-population quartiles of median household income; we refer to the counties that fall in the bottom quartile as “low-income” counties.

For measures of financial health, we use the New York Fed’s Consumer Credit Panel (CCP), a nationally representative sample of Equifax credit report data. Our data set for this analysis includes roughly 1 percent of the nation’s adults with credit records in anonymized form. We see their payments, balances, and delinquencies for various types of debt, including 1) auto loans, 2) mortgages, 3) credit cards, and 4) student loans. For each county and for each of these four types of loans, we compute delinquency measures that constitute the share of borrowers who are 90+ days past due on that type of loan. In addition, we create an overall delinquency measure that captures the share of borrowers in a county who are 90+ days past due on any type of loan. For each of these five delinquency measures (overall, auto, mortgage, credit card, student loan) we define high-delinquency (High DQ) counties as those in the (population-weighted) top quartile of that delinquency rate (High DQ, High Auto DQ, High Mortgage DQ, High CC DQ, High SL DQ). All analysis uses data from the fourth quarter of 2019.

The chart below presents bin-scatter plots representing the descriptive relationship between overall delinquency and neighborhood characteristics: percent minority and median household income. We find that areas with a greater level of delinquency are also those that have lower household income and larger minority populations.

Are Financially Distressed Areas More Affected by COVID-19?

Next we investigate whether high delinquency counties faced different case and death rates due to COVID-19. We find that as of mid-July, high-delinquency counties had a mean of 4.3 cases/1,000, while other counties had 2.8 cases/1,000. Death rates have been higher as well: 16/100,000 in high-delinquency counties and 10/100,000 elsewhere. To investigate whether this relationship continues to hold within communities that are relatively homogeneous in terms of income, race, urban status, and population density—factors that have been found to correlate with COVID-19 incidence—we conduct a multivariate regression analysis.

We start by regressing case rate on a dummy for high delinquency. All regressions in this post control for fixed characteristics of states, observable and unobservable. We find in column 1 of the table below that more financially vulnerable counties have had more severe COVID-19 spreads. Specifically, High DQ counties have had 3.65 more cases/1,000 than others (column 1). Given the high correlation between delinquency and household income and minority status (in the chart above), it is likely that some of this association between high delinquency and COVID-19 incidence is explained by higher minority and low-income populations in these counties.

To investigate to what extent this relationship between delinquency and case rates is accounted for by inherent characteristics of these counties (low income, majority minority status, urban status, population density), we control for these variables in column 2. We can explain some of the relationship between High DQ and case rate by the demographic factors (column 2), but even after including these variables, we find that High DQ counties still have 0.59 more cases/1,000 than counties that are not High&nbspDQ. High-delinquency counties also have more deaths after controlling for the same factors (column 3): four more per 100,000 than other counties. This analysis suggests that even if we look within low income or majority-minority or dense areas, places with higher delinquency also suffered higher COVID-19 incidence.

Next, we examine whether High DQ counties that are also low income faced a higher incidence of cases. In column 4 of the table below, we observe that high-delinquency counties that are also low-income have had a worse spread of COVID-19 than High DQ counties that are not low income—almost three more cases/1,000. In fact, increased virus spread in High DQ counties appears to be concentrated exclusively in those that are also low-income.

Are Financially Distressed Areas More Affected by COVID-19?

Next, in the table below, we examine relationships between delinquency in specific kinds of debt and COVID-19 spread. We find that counties that have high mortgage delinquency (High mortgage DQ) have higher COVID-19 incidence as captured by both case and death rates. Counties that have high student loan delinquency (High SL DQ) also have higher death rates. Counties with high auto loan delinquency (High Auto DQ) have higher case rates than those that do not have high auto loan delinquency. The High Auto DQ counties also have higher death rates, but this estimate is not statistically different from zero at conventional levels. It is worth noting that although High Mortgage DQ and High SL DQ areas were relatively adversely affected by COVID-19 incidence, and this is expected to further increase financial distress in these areas, the borrowers in these areas will potentially receive some relief from the CARES Act mortgage and student debt forbearance provisions which to some extent will ameliorate their increased financial distress. In contrast, the CARES Act does not include provisions for auto loan payment relief although these borrowers may be able to obtain some relief on a case-by-case basis by contacting the lenders. However, this relief is considerably more uncertain than CARES Act relief for student loans and mortgages. Consequently, the higher incidence of COVID-19 in High Auto DQ areas (relative to those that do not have high auto loan delinquency) may lead to larger subsequent increases in financial hardships in these areas.

Are Financially Distressed Areas More Affected by COVID-19?

What have we learned? We have seen that there is a strong relationship between COVID-19 cases and pre-COVID delinquency rates at the county level and this correlation cannot be easily explained by some known sources of heterogeneity in COVID-19, such as income, minority status, and population density. This suggests that the harms from COVID-19—the loss of life and health, the decline in employment, the destruction of businesses and the surge in medical expenses—will fall on counties particularly ill-suited to bearing them. The much higher per-capita case counts in places with high delinquency rates and low income portend a disproportionate financial impact on those who can least afford it. Why do we observe this relationship? While the precise mechanisms are beyond the scope of this work, volatility of income may be associated with both financial stress and higher risk of COVID-19, potentially because of greater reliance on essential work in such communities and corresponding difficulty of social distancing. Further research is needed to understand these factors.

Rajashri Chakrabarti<
Rajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

William Nober was a former a senior research analyst in the Bank’s Research and Statistics Group.

Maxim Pinkovskiy
Maxim Pinkovskiy is a senior economist in the Bank’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti, William Nober, and Maxim Pinkovskiy, “Are Financially Distressed Areas More Affected by COVID-19?,” Federal Reserve Bank of New York Liberty Street Economics, August 17, 2020, https://libertystreeteconomics.newyorkfed.org/2020/08/are-financially-di....

Additional heterogeneity posts on Liberty Street Economics.

Heterogeneity: A Multi-Part Research Series




Disclaimer

The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

The French will have to build a wall …

Published by Anonymous (not verified) on Sun, 09/08/2020 - 5:30pm in

If Brexit in fact is actually happening – and so far it seems it is – then unsurprisingly, its spirit is gradually dying of its own contradictions. Immigration is just one area when taking back control means actually relying on the co-operation of the er, French. True we already rely on French co-operation, but that... Read more

Introduction to Heterogeneity Series III: Credit Market Outcomes

Published by Anonymous (not verified) on Tue, 07/07/2020 - 10:00pm in

Rajashri Chakrabarti

 Credit Market Outcomes

Average economic outcomes serve as important indicators of the overall state of the economy. However, they mask a lot of underlying variability in how people experience the economy across geography, or by race, income, age, or other attributes. Following our series on heterogeneity broadly in October 2019 and in labor market outcomes in March 2020, we now turn our focus to further documenting heterogeneity in the credit market. While we have written about credit market heterogeneity before, this series integrates insights on disparities in outcomes in various parts of the credit market. The analysis includes a look at differing homeownership rates across populations, varying exposure to foreclosures and evictions, and uneven student loan burdens and repayment behaviors. It also covers heterogeneous effects of policies by comparing financial health outcomes for those with access to public tuition subsidies and Medicare versus those not eligible. The findings underscore that a measure of the average, particularly relating to policy impact, is far from complete. Rather, a sharper picture of the diverse effects is essential to understanding the efficacy of policy.

Here is a brief look at each post in the series:

Inequality in U.S. Homeownership Rates by Race and Ethnicity1. Inequality in U.S. Homeownership Rates by Race and Ethnicity

Andrew Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw investigate racial gaps in homeownership rates and, importantly, explore the reasons behind these differences. They find:

  • The Black-white and Black-Hispanic homeownership gaps widened after the Great Recession, markedly more so after 2015.
  • The foreclosure crisis disproportionately affected areas with majority Black or Hispanic populations.
  • Explanations for the homeownership gap may include differential effects of tightening underwriting standards across areas with a majority Black or Hispanic population versus those with a majority white population, differences in labor market outcomes across these areas during and following the Great Recession, and larger incidence of student debt in these areas.

Who Has Been Evicted and Why?2. Who Has Been Evicted and Why?

Andrew Haughwout, Haoyang Liu, and Xiaohan Zhang explore the reasons behind evictions, who is more likely to be evicted, and the possibility of owning a home and gaining access to credit following evictions. Their findings reveal:

  • Large shares of low-income households have been evicted.
  • Income or job loss and change in building ownership are important reasons behind evictions.
  • Renters with a past eviction history are less likely to have access to credit cards and auto loans.

Measuring Racial Disparities in Higher Education and Student Debt Outcomes3. Measuring Racial Disparities in Higher Education and Student Debt Outcomes

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw investigate whether (and how) differences in college attendance rates and types of college attended may lead to student debt borrowing and default. The key takeaways include:

  • There are noticeable disparities in college attendance and graduation rates between majority white, majority Black, and majority Hispanic neighborhoods, with graduation rates the lowest in majority Black neighborhoods.
  • Students from majority Black neighborhoods are more likely to hold student debt and in larger amounts.
  • Borrowers from majority Black neighborhoods are more likely to default, and this pattern is more prominent for borrowers from two-year colleges than those from four-year colleges.

Do College Tuition Subsidies Boost Spending and Reduce Debt? Impacts by Income and Race4. Do College Tuition Subsidies Boost Spending and Reduce Debt? Impacts by Income and Race

Rajashri Chakrabarti, William Nober, and Wilbert van der Klaauw investigate the effect of tuition subsidies, specifically merit-based aid, on other debt and consumption outcomes. The main findings include:

  • Cohorts eligible for these tuition subsidies have higher credit card balances and higher delinquencies in their early-to-mid-twenties. These patterns are more evident for borrowers from low-income and predominantly Black neighborhoods.
  • Eligible cohorts are more likely to own cars (as captured by auto debt originations) in their early-to-mid-twenties. This pattern is more prominent for borrowers from low-income and predominantly Black neighborhoods.
  • The patterns indicate substitution away from student debt (as net tuition declines) to other forms of consumer debt for eligible cohorts in college-going ages, a pattern more prominent for borrowers from low-income and Black neighborhoods.

Medicare and Financial Health across the United States5. Medicare and Financial Health across the United States

Paul Goldsmith-Pinkham, Maxim Pinkovskiy, and Jacob Wallace investigate the effect of access to health insurance programs, as captured by Medicare eligibility, on financial health of individuals. They find:

  • Medicare eligibility markedly improves financial health, as captured by declines in debt in collections.
  • Access to Medicare drastically reduces geographic disparities in financial health.
  • The improvements in financial health are most evident in areas with a high share of Black, low-income, and disabled residents and in areas with for-profit hospitals.

As these posts will demonstrate in greater detail tomorrow, the average outcome doesn’t provide a full picture of credit market outcomes. There is considerable heterogeneity in different segments of the credit market both in terms of outcomes, as well as the in the effects of specific policies. Outcomes vary by a range of factors, such as differences in race, income, age, and geography. We will continue to study and write about the importance of heterogeneity in the credit market and other segments of the economy.

Chakrabarti_rajashriRajashri Chakrabarti is a senior economist in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:

Rajashri Chakrabarti, “Introduction to Heterogeneity Series III: Credit Market Outcomes,” Federal Reserve Bank of New York Liberty Street Economics, July 7, 2020, https://libertystreeteconomics.newyorkfed.org/2020/06/introduction-to-he....

Related Reading:

Series One

Introduction to Heterogeneity: Understanding Causes and Implications of Various Inequalities

Series Two
Introduction to Heterogeneity: Labor Market Outcomes




Disclaimer

The views expressed in this post are those of the author and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the author.

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