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Michael Olenick: How Biden Could Tackle the Student Loan Crisis

Published by Anonymous (not verified) on Mon, 23/11/2020 - 10:19pm in

Biden could resort to a simple bankruptcy fix for the student loan mess. But would he?

I Suffered, Therefore So Must Others

Published by Anonymous (not verified) on Tue, 17/11/2020 - 7:56am in

I want to expand on this idea, ably put my Amal El-Mohtar here:

This idea seems right: the first is better than the second.

But the actual correct stance is:

This bad thing never happened to me BUT I can imagine how horrible it would be, so I want to make sure it doesn’t happen to other people.

None of us, no matter how bad our lives are, have experienced all the horrible things that can happen. Conservatives are notorious for being terrible except about one thing, you dig and it’s “My child got esophagal cancer so now I champion that,” OR “Someone I care about was shot with an assault rifle so now I’m against that.”

Imaginative empathy allows us to imagine being a blood diamond slave in the Congo, or there during a school shooting, or suffering from grinding poverty even if we’ve had good lives. It allows us to be disgusted and horrified by people cleaning out sewers by hand (Indians euphemistically call this “manual scavenging”) or what it’s like to suffer from anti-black racism or caste oppression. We don’t need to have suffered something either to say, “Others should suck it up,” or “Others shouldn’t have to go through what I did.”

This isn’t a call to removing all risk and stress from life. Not all unpleasant events are bad. The general rule, now well-supported by various studies, is that short term stress is good, and chronic stress is bad.

When I went to school, we had exam hell week: one before Christmas, one at the end of the year. The final exam week usually determined 50 percent of our marks.

(I am fundraising to determine how much I’ll write this year. If you value my writing and want more of it, please consider donating.)

This arrangement made for “good stress:” it was short-term and made you learn how to take high-impact tests. I’ve never feared a test since then: I assume I can pass any academic test, if given enough time to study, and my idea of enough time is a lot less than most people’s.

We don’t want to protect people from good stress — from short term challenges that teach them what they can do.

Chronic stress or traumatic stress, however, we do want to avoid. No one is improved by rape (prison administrators take note). No one is improved by being poor for years or even months on end. No one is improved by chronic hunger or fear.

The larger questions are why some people are unable to employ imaginative empathy: Why they must experience hell first-hand to realize “Oh! Hell is bad!” and why some can’t extrapolate this to “All hells are bad.”

Life is better with happy, healthy people. Heaven and Hell are both other people: if you’re surrounded by happy, loving people, odds are you’ll be happy. If you don’t start that way, you’ll almost certainly wind up that way. We shouldn’t want our fellow citizens to be subject to damaging long-term stress of traumatic events simply because we have to live with them.

The exception, alas, is that some people can’t learn that something is bad if it doesn’t happen to them. Their depraved indifference is a danger to everyone around them and a challenge to ethics. The people who need to be poor or spend time disabled or seriously sick are the people who think it’s no big deal. Some people, it seems, can only learn that “Hell is bad” if they or perhaps someone they love, spends time in Hell.

El-Mohtar’s tweet, of course, was about the possibility of Biden using an executive order to forgive $50k of student debt.

The good way to do it would be to get rid of the bankruptcy bill Biden pushed that made it impossible for student loans to be discharged in bankruptcy, which would sort the situation out fast (it is NEVER a good idea to make it so that creditors do not have to worry about non payment. NEVER.)

But Biden probably won’t have control of Congress, and this is better than doing nothing, even if some people who have paid off student loans feel it is “unfair.” It was unfair they had usurious loans, but just because they suffered doesn’t mean others should.

The best solution, of course, would be to go back to 60s-style universities where tuition is either cheap or non-existent. The cost is a lot less than any of the repeated bailouts of rich people and could be made even lower by doing something about university admin bloat (that’s an entire other article I may write one day) and a more complete solution would be to do something about credential inflation: Most jobs don’t need a degree and the idea that they do today is absurd.

But what Biden can do is forgive $50K with an admin order and he should. It’s a good thing he can do, and if it doesn’t relieve the suffering of people in the past, well, hopefully you are, at least, the sort of person who doesn’t want others to suffer like you did.

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Unsanitized: Student Debt Cancellation Now an Anti-Austerity Measure

Published by Anonymous (not verified) on Tue, 17/11/2020 - 4:06am in

The federal government directly issues almost all student debt, and has the discretion to reduce balances completely, or anything short of that. Continue reading

The post Unsanitized: Student Debt Cancellation Now an Anti-Austerity Measure appeared first on BillMoyers.com.

Is Fear of AOC Why Chuck Schumer Is Pumping a Non-Starter “Cancel Student Debt by Executive Order” Scheme?

Published by Anonymous (not verified) on Mon, 09/11/2020 - 9:12pm in

It’s odd to see Democrats talking about a Biden “100 days” when Obama did nothing like that when he actually did have a mandate to do so that he ignored. Did they not get the memo that their blue wave didn’t hit the beach? It’s even odder to see Chuck Schumer affecting a Damascene conversion, […]

It’s Time for a Debt “Jubilee”

Published by Anonymous (not verified) on Fri, 18/09/2020 - 7:59pm in

Households and businesses are overloaded with debt. Richard Vague offers a set of proposals for how to restructure it.

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.

Are American Colleges and Universities the Next Covid Casualties?

Published by Anonymous (not verified) on Thu, 23/07/2020 - 7:03pm in

Colleges and universities need to be saved, not only from financial ruin, but also, all too often, from themselves.

Trump Admin Retracts Rule that Would Have Cancelled Visas for Foreign College Students Who Only Study Online

Published by Anonymous (not verified) on Wed, 15/07/2020 - 8:50pm in

Trump backs down on plan too toss out foreign students who are forced to study on-line, as many universities circumscribe in-person classes,

Delaying College During the Pandemic Can Be Costly

Published by Anonymous (not verified) on Mon, 13/07/2020 - 9:00pm in

Jaison R. Abel and Richard Deitz

LSE_2020_college-costly_abel_460_art

Many students are reconsidering their decision to go to college in the fall due to the coronavirus pandemic. Indeed, college enrollment is expected to be down sharply as a growing number of would-be college students consider taking a gap year. In part, this pullback reflects concerns about health and safety if colleges resume in-person classes, or missing out on the “college experience” if classes are held online. In addition, poor labor market prospects due to staggeringly high unemployment may be leading some to conclude that college is no longer worth it in this economic environment. In this post, we provide an economic perspective on going to college during the pandemic. Perhaps surprisingly, we find that the return to college actually increases, largely because the opportunity cost of attending school has declined. Furthermore, we show there are sizeable hidden costs to delaying college that erode the value of a college degree, even in the current economic environment. In fact, we estimate that taking a gap year reduces the return to college by a quarter and can cost tens of thousands of dollars in lost lifetime earnings.

The Return to College during the Pandemic

Despite rising costs, we have shown that college has remained a good investment, at least for most people, when we weigh the costs against the benefits during normal times. The economic costs of college include direct costs, such as tuition and fees, as well as opportunity costs—the wages one gives up while in school. The economic benefit of college is the college wage premium—the extra wages one can expect to earn with a college degree compared to having only a high school diploma, summed up over an entire working career. Because the costs and benefits of college accrue over different time intervals, we calculate the internal rate of return to weigh the upfront costs against the lifetime benefits. Before the pandemic, during more normal times, we estimate the return to college at about 14 percent, easily surpassing the threshold for a good investment. Importantly, we can’t rule out the possibility that some of what we estimate as the return to college is not a consequence of the knowledge and skills acquired while in school, but rather is a reflection of the innate skills and abilities possessed by those who complete college. Our estimates are in line with an extensive body of research that is better able to correct for such possibilities.

However, nothing is normal in the current economic environment. Unemployment has skyrocketed, and there are signs that wages for some workers are falling. How does this change the return to college?

Let’s start with the costs. As we have shown before, opportunity costs are, by far, the largest cost of college. Importantly, however, these costs have fallen considerably due to the adverse shock to the labor market. Indeed, as the chart below shows, unemployment increased sharply after the pandemic hit, but particularly for those without a college degree. Indeed, about a quarter of young workers without a college degree did not have a job in the months immediately following the onset of the pandemic. The economic reasoning for the decline in opportunity costs is straightforward: if you can’t find a job, going to school is less costly. Another cost is, of course, tuition, which, if anything, may also decline next fall, as many schools have announced that they are cutting tuition in an effort to attract students, though perhaps only temporarily.

Artboard 1@2x-100

Turning to the benefits of college, despite greater uncertainty about the path of future earnings, there is little reason to think that the college wage premium will shrink. Though the wages of all workers may stagnate or even decline during recessions, they tend to fall as much or more for those without a college degree—and, importantly, it’s the gap that determines the college wage premium. This trend is likely to be especially pronounced during the pandemic, which has disproportionately reduced low-wage, high-contact jobs, the bulk of which are held by those without a college degree. Indeed, the increase in unemployment due to the pandemic was not nearly as steep for college graduates, as many of the jobs that typically require a college degree can more easily be done from home.

To re-estimate the return to college during the pandemic, we make one simplifying assumption: that workers with only a high school diploma are not able to find a job during the next year (we hold tuition constant to make a conservative estimate). This results in opportunity costs falling to zero for one year. As the chart below shows, this decline in opportunity costs alone increases the return to college to 17 percent, which is about 20 percent higher than in normal times. However, the chart also shows that taking a gap year during the pandemic reduces this return significantly, an issue we turn to next.

Artboard 2@2x-100

A Gap Year Can Be Costly

There are sizeable hidden costs to delaying college, even if only for a year. First, you give up a year’s worth of wages that could have been earned with a college degree had you graduated a year earlier. Second, if you enter the job market a year later, it damages your entire lifetime earnings profile because you miss out on the experience and the extra push that gives your wages over your working life, creating an earnings wedge each year. In essence, entering the job market a year later puts you behind for your entire career and you never really catch up.

Let’s take an example to illustrate this second point using our estimates. Someone who finishes a college degree in four years at the age of 22 would earn, on average, about $43,000 their first year on the job. By the time that person reaches the age of 25, they would earn an average of $52,000, having racked up three years of experience and commensurate raises. A graduate who delayed college by a year starts working at age 23, but would earn the same starting wage of a comparable graduate who took no delay. At the age of 25, a gap-year graduate would earn $49,000 compared to around $52,000 for the peer who graduated a year earlier, roughly $3,000 less. Being a year behind, these differences add up each and every year, so that those graduating later never catch up to those who graduated earlier. Together, these costs add up to more than $90,000 over one’s working life, which erodes the value of a college degree. We find taking a gap year cuts the return to college by a quarter to about 13 percent. No doubt, this return crosses the threshold for a good investment, but delaying college during the pandemic takes a financial toll.

Think Twice before Delaying

Of course, the return to college is not the only factor to consider when deciding whether to go to college, or the right time to do so. Health and safety are important considerations that our analysis does not take into account. In addition, if college is mostly online next year, the quality of instruction may be impacted and students might not build the skills they would with in-person classes. (Of course, this would not be a concern if college is just a signaling device, as some argue.) Moreover, recent research suggests at least some of the payoff to college comes from the network that is developed through personal relationships while in school, which could be damaged with extensive remote instruction. Furthermore, our analysis does not consider the consumption value of college—that is, missing out on the “college experience”—which may also be important for many students. And, finally, tuition may simply be out of reach for some families facing economic hardships during this time. Nonetheless, given the high cost of a gap year, perhaps some students will think twice before delaying.

Abel_jaisonJaison R. Abel is an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group.

Deitz_richardRichard Deitz is an assistant vice president in the Bank’s Research and Statistics Group.

How to cite this post:

Jaison R. Abel and Richard Deitz, “Delaying College During the Pandemic Can Be Costly,” Federal Reserve Bank of New York Liberty Street Economics, July 13, 2020, https://libertystreeteconomics.newyorkfed.org/2020/07/delaying-college-d....




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.

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