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Is China Running Out of Policy Space to Navigate Future Economic Challenges?

Published by Anonymous (not verified) on Mon, 26/09/2022 - 9:00pm in

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Credit

 Chinese shoppers lining up to buy shopping carts full of items within a store

After making progress slowing the pace of debt accumulation prior to the pandemic, China saw its debt levels surge in 2020 as the government responded to the severe economic slowdown with credit-led stimulus. With China currently in the midst of another sharp decline in economic activity due to its property slump and zero-COVID strategy, Chinese authorities have responded again by pushing out credit to soften the downturn despite already high levels of debt on corporate, household, and  government balance sheets. In this post, we revisit China’s debt buildup and consider the growing constraints on Chinese policymakers’ tools to navigate future economic challenges.

China’s Waves of Debt

Previous posts have explored China’s credit boom and rise of household debt. China’s policy response to the global financial crisis in 2009 unleashed nearly ten years of uninterrupted growth in debt, with credit to the nonfinancial sector exploding by almost 150 percent of GDP, according to data from the Bank for International Settlements—one of the largest increases in modern history. It was not until 2018 that Chinese authorities were able to briefly stabilize these debt ratios through a hard-fought “deleveraging campaign.”

China’s debt ratio resumed its upward trend in 2019, but then exploded again in 2020 as China was the first country in the world to respond to the pandemic. China’s run-up in credit in 2020 totaled nearly 29 percentage points of GDP but was short-lived, as the credit ratio contracted modestly in 2021. As COVID-19 spread globally, other countries’ policy responses were also associated with rising debt ratios, with China’s increase comparable to that of other countries, as illustrated in the chart below. However, while other major economies in the world are now tightening their monetary policies, expectations are for overall debt in China to rise again in 2022 to stabilize growth. China’s repeated reliance on credit-driven stimulus raises questions about the buildup of risks in the financial system and the extent to which rising debt levels in all three sectors—discussed below—are sustainable and could ultimately hamstring Chinese authorities’ policy options.

China Has Seen a Sharp Run-Up in Debt Levels

Bar chart showing the percentage point change in the credit-to-GDP ratios of China, Japan, the euro area, the U.S., and other emerging markets between 2007 and 2021, in particular highlighting the change from the end of 2019 through the end of 2021. Source: Bank for International Settlements via CEIC.

One Step Forward, Two Steps Back

As illustrated in the chart below, China’s total nonfinancial sector credit was almost 290 percent of GDP at the end of 2021. Borrowing by the corporate sector—the largest part of China’s total debt—is equivalent to approximately 153 percent of GDP, a figure that is among the highest in the world. China’s deleveraging campaign was effective in curbing runaway growth in shadow credit, but growth in corporate leverage resumed with pandemic-related stimulus. While estimates vary, state-owned enterprises (SOEs) account for around 50 to 60 percent of total corporate debt, with the remainder held on Chinese privately owned corporate balance sheets. Entities known as local government financing vehicles are also classified as corporate debt in China, although a large portion of these debts are assumed to be implicit government debt, as discussed in the next section. Repayment concerns involving corporate debt in China primarily relate to lending to inefficient SOEs and distressed real estate developers, with the latter the focus of authorities’ efforts over the past two years to reduce leverage in the property sector.

Corporate, Household, and Government Debt Have All Increased Notably

Area chart showing increases in China’s government debt and corporate and household credit as a percent of GDP since 2006. Increases in debt have occurred across all three components.Source: Bank for International Settlements via CEIC.

Household debt accounts for 62 percent of GDP in China and has grown rapidly in recent years, raising concerns around increasingly stretched household balance sheets. China’s household debt has risen to levels that are quite high by developing country standards but remain broadly comparable to those of developed economies. As illustrated in the two charts below, the ratio of household debt to income in China is currently estimated to be in a range above the median for the economies in the OECD, while household debt service ratios have increased steadily and now exceed those in the United States, approaching even Korea.

Household Debt, in Particular, Has Surged

Two-panel chart with a bar chart on left showing the ratio of household debt to disposable income in China, the U.S., the OECD, and Korea, in percent, and a line chart on the right showing the household debt service to income ratios of Australia, Korea, the U.S., and China. As illustrated in the two charts, the ratio of China’s household debt to income is currently estimated to be in a range above the median for the economies in the OECD, while China’s household debt service ratios have increased steadily and now exceed those in the U.S., and possibly even Korea.Source: Authors’ calculations, based on data from the Organisation for Economic Co-operation and Development and the Bank for International Settlements (BIS) via CEIC.
Notes: The estimation range shown in the left panel uses household debt as reported by the BIS in the numerator and disposable income as reported by the sum of compensation of labor and property income in the flow of funds (lower range) and the household survey (upper range). The lower and upper ranges shown in the right panel use disposable income reported in the flow of funds (labor compensation and property income) and the household survey, respectively.

Mortgage loans make up roughly 63 percent of China’s total household debt (39 percent of GDP). Chinese authorities’ recent focus on curbing excesses in the property sector—and intermittent COVID-related lockdowns—have slowed mortgage growth notably, to under 10 percent year over year as of July after averaging more than twice that pace over the past six years. Property developers’ struggles to complete construction of pre-sold properties have likely added to the debt burdens of Chinese households, who are waiting to move into new properties yet still making rental or mortgage payments on current residences. In response, an increasing number of home buyers in China have threatened to suspend mortgage payments on undelivered homes, increasing financial risks to banks and developers.

Officially recognized central and local government debt in China is moderate by international standards, at about 50 percent of GDP. However, estimates of “augmented” fiscal debt are much higher, at up to 100 percent of GDP for year-end 2021, according to IMF estimates. The sizable gap between these numbers represents debt that has been issued for fiscal purposes—typically categorized as corporate loans—and likely requires implicit fiscal assistance to be serviced or repaid, or that could be recognized as official debt under some circumstances. This “hidden” government debt is almost entirely borne by local governments, which are highly reliant on the property sector for financing and have much less fiscal flexibility than the central government.

Is China Heading for a Financial Crisis?

International experience suggests that rapid buildup of debt is often followed by financial crises or at least extended periods of much slower economic growth. Thus far, China has managed to avoid a severe day of reckoning, and Chinese authorities are still viewed as having considerable policy tools to manage the nation’s economy and associated financial risks

These tools stem from unique features of the Chinese political and financial system. For example, China’s government maintains direct and indirect control of the country’s financial and nonfinancial sectors at the central and local level, including through ownership of most of the banks in the financial system and a significant portion of nonfinancial corporate firms. In addition, China’s domestic economy is shielded from external shocks by its current account surplus, large stock of foreign exchange reserves, and capital controls. Finally, China possesses ample scope to use monetary, credit, and central government fiscal policies to dampen economic fluctuations, as reflected in the response to the pandemic.

Despite this unique array of policy tools, China has not been immune to financial turbulence over the past decade. China experienced an interbank market crisis in 2013, equity market busts in 2007 and 2015, massive capital outflows in 2015-16, a spate of bank failures in 2019, and most recently a crisis in its property sector accompanied by additional pressures on parts of its banking sector.

Against such a backdrop, there are strong reasons to be watchful for signs of a sustained downshift in China’s historical pattern of economic performance. First, there is evidence that China’s credit-driven growth model is facing serious diminishing returns, as shown, for example, in the high and steadily increasing incremental capital to output ratio (shown in the chart on left below) and rising credit intensity (shown in chart on right). The decline in the “GDP bang for the credit buck” suggests that the old playbook of turning on the credit spigots will be less effective than in the past, while leading to the potential for increases in bad debt. Our colleague, Matthew Higgins, has written more extensively on the self-limiting nature of capital accumulation as a growth driver in China.

Is China’s Credit-Driven Growth Model Now Pushing on a String?

Q2 in trillions of renminbi. Source: Authors’ calculations, based on data from the National Bureau of Statistics of China and the People’s Bank of China, via CEIC.
Notes: Left panel shows five-year moving average. Incremental capital to output ratio (ICOR) is calculated as the ratio of gross fixed capital formation to GDP divided by real GDP growth. In right panel, data shown for 2022 are through June. Credit intensity of GDP is the ratio of the incremental increase in credit over the incremental increase in GDP on a three-year moving average.

Second, fiscal and monetary policies appear to face political and institutional constraints that may not be readily apparent from the data. On the fiscal side, even though official debt levels appear quite manageable, local governments are experiencing rapidly increasing debt burdens that remain hidden in local government affiliated enterprises and financial institutions. Based on China’s historical precedent, addressing these issues will likely take years of reform and fiscal tightening, which will create an additional drag on growth. On the monetary side, the authorities likely face constraints on cutting interest rates and reserve requirements for fear of sparking capital outflows or weakening banks’ profitability and encouraging additional buildups of risky borrowing.

Finally, China faces other important challenges that will represent a sharp departure from the conditions it has experienced since Deng Xiaoping launched the country on its path of economic reform roughly four decades ago. Most profoundly, China’s demographic profile is aging quickly, which will greatly increase old-age dependency and lead to a reduction in the working population. Moreover, as its share of global trade stops rising, China’s export engine eventually will downshift to a growth rate similar to that of world trade, or perhaps even lower. Against this backdrop, the medium- to long-term outlook for China’s economy will likely hinge more than ever on the quality of its economic and institutional policies.

 portrait of Hunter Clark

Hunter L. Clark is an international policy advisor in International Studies in the Federal Reserve Bank of New York’s Research and Statistics Group. 

 portrait of Jeff Dawson

Jeffrey B. Dawson is an international policy advisor in International Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:
Hunter Clark and Jeff Dawson, “Is China Running Out of Policy Space to Navigate Future Economic Challenges?,” Federal Reserve Bank of New York Liberty Street Economics, September 26, 2022, https://libertystreeteconomics.newyorkfed.org/2022/09/is-china-running-o....

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

RIP Nigel Dodd, an eminent money scholar

Published by Anonymous (not verified) on Tue, 06/09/2022 - 11:40pm in

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Obituary, Credit, money

by LSE Department of Sociology It is with great sadness that we share news of the death of Professor Nigel Dodd on 12 August 2022. Nigel Dodd was Professor of Sociology at the LSE. He obtained his PhD from the University of Cambridge in 1991 on the topic of Money in Social Theory, and lectured at […]

Small Business Recovery after Natural Disasters

Published by Anonymous (not verified) on Tue, 06/09/2022 - 9:02pm in

The first post of this series found that small businesses owned by people of color are particularly vulnerable to natural disasters. In this post, we focus on the aftermath of disasters, and examine disparities in the ability of firms to reopen their businesses and access disaster relief. Our results indicate that Black-owned firms are more likely to remain closed for longer periods and face greater difficulties in obtaining the immediate relief needed to cope with a natural disaster.

How Often and How Long Do Small Businesses Close After Disasters?

The Federal Reserve’s 2021 Small Business Credit Survey (SBCS) asked disaster-affected firms: “Did your business temporarily close because of this natural disaster?” Amongst firms that responded yes, the survey also asked them to estimate the length of time for which they were temporarily closed. These responses likely represent lower bounds for closure since a firm that was closed temporarily at the time of survey completion may have ended up remaining closed for longer than reported.

Sixty-three percent of small businesses that reported natural disaster-related losses were forced to close temporarily. Although the fraction of firms that temporarily closed is relatively similar between Black- and white-owned firms, Hispanic-owned firms were more likely to be forced to temporarily shut their doors (see left panel of the chart below). There are also pronounced disparities in the length of closures for impacted firms (see right panel of the chart below). For example, 34 percent of Black-owned firms and 23 percent of Hispanic-owned firms were forced to keep their doors shut for greater than three months as compared to only 16 percent of white-owned and 6 percent of Asian-owned small businesses.

Part of this disparity may be explained by the finding in our previous post that losses from natural disasters make up a greater share of total revenue for firms owned by people of color. More generally, the severity of a disaster’s impact can be compounded by existing disparities in access to financial resources available to business owners prior to a disaster (for example, because Black and Hispanic small business owners have a lower level of starting wealth).

Firms Owned by People of Color Remain Closed for Longer

Two-panel bar charts showing 1) the percentage of white and minority-owned firms that closed temporarily after a disaster, as grouped  by the race/ethnicity of their owner and 2) disparities in the length of closures for the impacted firms. The left chart shows the fraction of firms that closed temporarily was relatively similar between Black- and white-owned firms but was the biggest for Hispanic-owned firms. The right chart shows that more Black-owned firms and Hispanic-owned firms were forced to keep their doors shut for greater than three months compared to white-owned and Asian-owned small businesses.Source: Federal Reserve Banks, 2021 Small Business Credit Survey.
Notes: The left panel includes only firms that reported disaster-related losses. For respondents in each race/ethnicity category, the bars plot the percentage of firms that responded yes to the question: “Did your business temporarily close because of this natural disaster?” The panel on the right further limits the sample to firms that temporarily closed because of a natural disaster. For each race/ethnicity category this panel shows the percentage of firms that were closed for the length indicated on the x-axis at the time of survey completion. A firm is considered Black-, Hispanic-, or Asian-owned if at least 51 percent of the firm’s equity stake is held by owners identifying with the group. A firm is defined as white-owned if at least 50 percent of the firm’s equity stake is held by non-Hispanic white owners. Race/ethnicity categories are not mutually exclusive. An observation is excluded from the sample if it is missing a response to the question or if the owner’s race is not observed. The sample pools employer and nonemployer firms. Responses by employer and nonemployer firms are weighted separately on a variety of firm characteristics to match the national population of employer and nonemployer firms, respectively. To construct a pooled weight, we use the employer (nonemployer) weight if the firm is an employer (nonemployer). Fielded September-November 2021.

What Sources Can Small Businesses Turn to for Relief?

In the aftermath of a disaster, small businesses experience an increase in demand for funding to replace damaged properties and replace lost revenues while they are temporarily closed or operating at reduced capacity. Immediately after a natural hazard, firms can tap into existing cash reserves or emergency funds. According to research by the JPMorgan Chase Institute (JPMCI), the median small business holds a cash buffer large enough to support twenty-seven days of their typical outflows. However, this number does not account for funds needed to repair or replace property and physical assets damaged in a disaster. Moreover, firms in labor intensive and low-wage industries have fewer buffer days relative to high-technology or professional service enterprises.

Property insurance can help firms repair and replace direct physical damages, and business disruption insurance can cover lost incomes and operating expenses that continue while the business is closed. Previous research has documented that only 30-40 percent of small businesses have business disruption insurance.

Firms whose losses are not fully covered by insurance can turn to funding from the federal government if located in a Federal Emergency Management Agency (FEMA)-designated disaster area. The Small Business Association (SBA) provides long-term, low-interest loans to repair or replace damaged property. The SBA also offers Economic Injury Disaster Loans (EIDLs) of up to $2 million to meet expenses the business would have paid if the disaster had not occurred. FEMA provides recovery grants to small businesses, but only through referral upon completion of the SBA loan application. State and local relief programs intended for small businesses are limited, and state governments often appropriate emergency funds only after a disaster declaration is made, which delays immediate assistance.

Beyond these sources, firms with additional need can take on debt, loans, or lines of credit from banks, online lenders, or public private partnerships, and natural disasters are associated with higher demand for credit from such lenders. Securing a loan or line of credit of moderate size requires collateral, but a disaster can limit the ability of firm-owners to pledge their homes that are damaged in disasters.

How Do Funding Sources Vary Across Owner Race and Ethnicity?

More limited access to financial relief following a disaster may drive the longer closure periods for small businesses owned by people of color. For example, lower home values can make it relatively more difficult for them to put up adequate collateral for loans. And disparities in the allocation of government aid to affected firms may make it more difficult for firms owned by people of color to reopen their doors and recover revenues following a disaster. However, if these firms apply for government aid at a high rate, their take-up of these loans could be substantial even with low approval rates.

In 2021, the SBCS asked respondents that reported disaster losses to indicate the source(s) that they relied on to cope with their losses. Firms could select from multiple options as shown in the table below. A higher fraction of white-owned firms (12 percent) relied on disaster insurance funds compared to Black-owned firms (6 percent). This gap may be driven by a lower fraction of firms owned by people of color possessing insurance; younger, smaller, and financially constrained firms are less likely to insure—a profile that often fits firms owned by people of color. Further, conditional on having insurance, agencies may be less likely to pay claims of businesses owned by people of color, and so the latter may rely less on this source of funding.

Disparities in Funding Sources to Assist with Disaster Relief

Funding Source(s) Relied On:(1)
All(2)
WhiteRace/Ethnicity

(3)
Black (4)
Hispanic(5)
Asian Insurance0.110.120.060.100.17 Federal disaster relief (e.g.,
FEMA, SBA) 0.140.130.220.110.25 State/local government disaster
relief funds0.090.080.060.060.31 Donations, crowdfunding, or
nonprofit grants0.040.030.050.090.03 Debt/loans (other than gov’t
loans)0.150.150.120.100.27 Other0.030.030.060.030.00 Did not rely on external funds0.600.620.580.590.35Observations1,687902469182112Source: Federal Reserve Banks, 2021 Small Business Credit Survey.
Notes: This table includes only firms that reported disaster-related losses. The SBCS asks these firms: “Which of the following sources of funding did your business rely on to cope with these losses? Select all that apply.” The options are listed in the left column of the table. For each race/ethnicity category, the table reports the fraction of firms that relied on a particular source of funding. The columns do not sum to one because survey respondents had the option to select multiple sources. A firm is considered Black-, Hispanic- , or Asian-owned if at least 51 percent of the firm’s equity stake is held by owners identifying with the group. A firm is defined as white-owned if at least 50 percent of the firm’s equity stake is held by non-Hispanic white owners. Race/ethnicity categories are not mutually exclusive. An observation is excluded from the sample if it is missing a response to the question or if the owner’s race is not observed. The sample pools employer and nonemployer firms. Responses by employer and nonemployer firms are weighted separately on a variety of firm characteristics to match the national population of employer and nonemployer firms, respectively. To construct a pooled weight, we use the employer (nonemployer) weight if the firm is an employer (nonemployer). Fielded September-November 2021.

Among disaster-affected firms, Black-owned businesses disproportionately relied on government programs from FEMA, the SBA, and other agencies: 22 percent of Black-owned firms relied on federal disaster relief funds, compared to only 13 percent of white-owned companies. Previous research and news reports have documented evidence of racial disparities in approvals of SBA disaster loans and FEMA disaster relief. Further, the SBA has acknowledged that in disaster loan approvals, they strongly consider credit scores that may be affected by biases in scoring models. Even among firms that ultimately receive federal relief, application and disbursement can occur with long delays, limiting their effectiveness right after a disaster when funding is most needed. Our results imply that firms owned by people of color apply for government aid at a greater rate so that they have a higher take-up of these loans, despite being approved at lower rates.

A slightly higher fraction of white-owned firms did not rely on any external relief to cope with disaster losses (see table above), consistent with our finding in the first post of this series that disaster-related losses make up a smaller share of total revenues for white-owned firms. This gap could also be explained by differences in the size of firms’ cash reserves as, according to research by the JPMorgan Chase Institute, small businesses in majority-Black and majority Hispanic communities have fewer cash buffer days relative to majority-white areas.

Final Words

Relative to white-owned firms, Black-owned businesses are more likely to remain closed for longer and rely disproportionately on less immediate forms of relief funding. These results underscore the importance of accessing affordable relief after disasters to businesses owned by people of color. 

Recently, state and local governments have established partnerships with the private sector to make disaster relief more accessible. For example, the New York Forward Loan Fund leveraged public funds with private dollars to provide low-interest working capital loans to help small businesses and non-profits—especially firms that typically lack access to credit—cope with the COVID-19 pandemic. Similarly, the California Rebuilding Fund (CARF) aggregated funding from private, philanthropic, and public sector sources to help small business reopen and recover during the pandemic. The fund dispersed loans through community development financial institutions (CDFIs) that have experience working with traditionally underserved borrowers as well as Fintechs. Expanding these approaches to include disaster relief may enable vulnerable businesses to access the funding needed to reopen their doors and rebuild their revenues following disasters.

Martin Hiti was a summer research intern in the Federal Reserve Bank of New York’s Research and Statistics Group.

Claire Kramer Mills is a Communication Development Research Manager in the Federal Reserve Bank of New York’s Communications and Outreach Group.

Asani Sarkar is a financial research advisor in Non-Bank Financial Institution Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:
Martin Hiti, Claire Kramer Mills, and Asani Sarkar, “Small Business Recovery after Natural Disasters,” Federal Reserve Bank of New York Liberty Street Economics, September 6, 2022, https://libertystreeteconomics.newyorkfed.org/2022/09/small-business-rec....

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

How Do Natural Disasters Affect U.S. Small Business Owners?

Published by Anonymous (not verified) on Tue, 06/09/2022 - 9:00pm in

Recent research has linked climate change and socioeconomic inequality (see here, here, and here). But what are the effects of climate change on small businesses, particularly those owned by people of color, which tend to be more resource-constrained and less resilient? In a series of two posts, we use the Federal Reserve’s Small Business Credit Survey (SBCS) to document small businesses’ experiences with natural disasters and how these experiences differ based on the race and ethnicity of business owners. This first post shows that small firms owned by people of color sustain losses from natural disasters at a disproportionately higher rate than other small businesses, and that these losses make up a larger portion of their total revenues. In the second post, we explore the ability of small firms to reopen and to obtain disaster relief funding in the aftermath of climate events. 

What Factors Contribute to Disaster Vulnerabilities?

Disaster vulnerability, defined as the susceptibility to severe climate events, is linked to economic, social, and locational factors. For example, people of color and those with low incomes are more likely to reside in high-risk flood zones. And in states like Florida, a growing preference for high elevation is increasing housing prices in areas with lower flood risk that were traditionally inhabited by people of color. To the extent that firms owned by people of color are more likely to be located in communities of color, these trends imply that they may be priced out of areas with lower climate risk.

Disparities in the impact of natural disasters, among those who are exposed to them, are also related to existing inequalities. Practices like “redlining” have continued to keep home values in low-income and predominantly Black areas lower. This may reduce the capacity of communities to finance disaster-resilient infrastructure if, for example, government programs favor areas with higher property values in the allocation of disaster mitigation grants. Indeed, individuals living in formerly redlined districts—many of whom are people of color—remain vulnerable to greater flood risk as compared to non-redlined districts.

Are Small Businesses Owned by People of Color More Likely to Report Disaster-Related Losses?

We use data from the SBCS for the period 2019-21 to document the impact of natural disasters on small businesses. The annual survey provides detailed information on the operations and financial conditions of businesses with fewer than 500 employees and records the demographics of firm owners. Notably, this information allows us to relate climate outcomes to race directly, rather than to the racial profile of geographic areas, as in some existing research.

The 2019, 2020, and 2021 surveys included 9,315, 15,234, and 18,190 respondents, respectively. The natural disaster module of the survey asks respondents whether their business sustained any direct or indirect losses from a natural disaster in the past twelve months. The fraction of firms experiencing disaster-related losses rose from 7 percent in 2019 to 14 percent in 2021 (see left panel of the chart below). The racial disparities in these losses increased as well. While there were few disparities in 2019, in 2021, 19 percent of Black-owned firms, 21 percent of Hispanic-owned firms, and 17 percent of Asian-owned firms reported disaster-related losses while only 12 percent of white-owned firms did (see right panel below).

The incidence and racial disparity of losses appear to move together, a pattern that exists even outside our sample period. For example, in 2017 (a year with widespread hurricanes and severe storms), there were large disparities between Hispanic- and white-owned firms in reported losses among SBCS respondents.

Fraction of Firms with Losses and Disparities in Losses Have Both Increased since 2019

Two-panel trend chart showing the percentage of all firms with disaster-related losses on the left and the percentage of Asian-owned, Black-owned, Hispanic-owned, and white-owned firms with disaster-related losses on the right, from 2019 to 2021. Both the fraction of all firms with losses and the disparities in losses between white-owned and minority-owned firms increase over the period. Source: Federal Reserve Banks, 2021, 2020, and 2019 Small Business Credit Surveys.

Notes: For respondents in each year and race/ethnicity category, the lines show the percentage of firms who answered yes to the question “Within the past 12 months, did your business sustain direct or indirect losses from a natural disaster other than COVID-19 (e.g., hurricane, wildfire, earthquake, etc.)?” A firm is considered Black-, Hispanic-, or Asian-owned if at least 51 percent of its equity stake is held by owners identifying with the group. A firm is defined as white-owned if at least 50 percent of its equity stake is held by non-Hispanic white owners. Race/ethnicity categories are not mutually exclusive. An observation is excluded from the sample if it is missing a response to the question or if the owner’s race is not observed. The sample pools employer and nonemployer firms. Responses by employer and nonemployer firms are weighted separately on a variety of firm characteristics to match the national population of employer and nonemployer firms, respectively. To construct a pooled weight, we use the employer (nonemployer) weight if the firm is an employer (nonemployer).

Among those in disaster-related areas, more firms owned by people of color face damages than white-owned firms. We show this by focusing on the subsample of small businesses located in counties designated as disaster-affected by the Federal Emergency Management Agency (FEMA) in the period of the survey. We find that 24 percent of Black-owned firms, 23 percent of Hispanic-owned firms, and 22 percent of Asian-owned firms reported disaster-related losses in 2021, compared to 17 percent of white-owned firms.

Existing disparities, such as the location of communities of color in low-lying areas with poor disaster-resilient investments, can vary within counties. Using county fixed effects regressions, we find that in 2021, Black-owned small businesses were 5 percentage points more likely than their white-owned counterparts to report disaster-related losses, supporting the disparity in climate effects even within relatively small geographic areas.

Are Business Owners in Some States More Vulnerable to Disaster-Related Losses?

States and cities located in the Southern U.S. are particularly susceptible to disasters, as they have older infrastructure and are disproportionately located in floodplains; as the map below shows, the fraction of firms reporting natural disaster-related losses in 2021 is especially high in states along the Gulf Coast. States in the Middle Atlantic (New Jersey and New York) and on the West Coast also have a high fraction of small businesses reporting disaster-related losses. In 2020 Census data, the states with the highest concentration of African Americans—Mississippi, Georgia, and Louisiana, and also Washington, D.C.—overlap with these high-risk areas. This suggests that businesses owned by people of color (also concentrated in these four localities, according to the SBCS) may be vulnerable due to their concentration in particularly susceptible states. When looking within Census Divisions, we find that a greater fraction of Black-owned firms report disaster-related losses than white-owned firms, and this disparity has increased between 2019 and 2021. Thus, regional disparities have increased pari passu with national disparities.

Fraction of Firms Reporting Disaster-Related Losses by State, 2021

Heat map showing the fraction of firms reporting disaster-related losses, by state, in 2021. States along the Gulf Coast are shown as having the highest fraction of firms that suffered losses. States in the Northeast and on the West Coast also show high fractions.Source: Federal Reserve Banks, 2021 Small Business Credit Survey.

Notes: The heat map shows the fraction of firms in a given state that answered yes to the question “Within the past 12 months, did your business sustain direct or indirect losses from a natural disaster other than COVID-19 (e.g., hurricane, wildfire, earthquake, etc.)?” All observations that are missing a response to the question are excluded from the sample. The sample pools employer and nonemployer firms. Responses by employer and nonemployer firms are weighted separately on a variety of firm characteristics to match the national population of employer and nonemployer firms, respectively. To construct a pooled weight, we use the employer (nonemployer) weight if the firm is an employer (nonemployer). The survey was fielded September-November 2021.

Do Firms Owned by People of Color Suffer Larger Disaster-Related Losses?

The 2020 and 2021 surveys ask respondents that report disaster-related losses to estimate the value of those losses. We note that, since responses are voluntary (with 78 percent of eligible respondents opting in), firms with lower losses may be less likely to complete the climate-related questions, implying an upward bias in the reported losses. However, there is no reason to think that less-impacted firms owned by people of color are more likely to skip these questions than less-impacted white-owned firms.

We normalize these losses as a percentage of a firm’s total revenue in the year prior. Because people of color faced greater revenue losses as a result of the COVID-19 pandemic, we rely on disaster-loss data from the 2020 survey, which is normalized by total revenues from 2019, before the onset of the pandemic.

For most small businesses of color, disaster-related losses were a large share of their revenues. For example, 20 percent of Black-owned businesses reported losses that amount to more than 60 percent of 2019 revenue, while just 4 percent of such firms had losses of 0-5 percent of 2019 revenue (see chart below). In contrast, for most white-owned businesses, disaster-related losses were a relatively small share of revenues. For example, 25 percent of white-owned firms experienced disaster-related losses of 0-5 percent of 2019 revenue and 39 percent had losses of 5-30 percent, while only 13 percent had losses of more than 60 percent of total revenue.

Black-Owned Firms Have Higher Revenue Shares of Disaster-Related Losses

This Liberty Street Economics bar chart shows disaster-related losses as a share of revenue for Black-owned firms and white-owned firms, using 2020 revenue losses normalized by total revenue in 2019. It shows larger shares of white-owned firms than Black-owned firms with losses of 0-5% and 5-30%, and larger shares of Black-owned firms than white-owned firms with losses of 30-60% and more than 60%.Source: Federal Reserve Banks, 2020 Small Business Credit Survey.

Notes: Among firms that reported disaster-related losses, the 2020 SBCS asks “What is the estimated value of your business’s losses as a result of the natural disaster?” Respondents can select from six categories. Firms are also asked to report their total revenues from 2019 by selecting from eight ranges. To compute the normalized revenue loss, we divide the midpoint of the disaster-related losses range by the midpoint of the firm’s revenue range. The normalized losses are grouped into four bins, which are shown on the x-axis. The bars show the percentage of firms in each race/ethnicity category with normalized disaster-related losses in a given bin. A firm is considered Black-owned if at least 51 percent of its equity stake is held by owners identifying as Black. A firm is defined as white-owned if at least 50 percent of its equity stake is held by non-Hispanic white owners. Race/ethnicity categories are not mutually exclusive. An observation is excluded from the sample if it is missing a response to the question or if owner race is not observed. The sample pools employer and nonemployer firms. Responses by employer and nonemployer firms are weighted separately on a variety of firm characteristics to match the national population of employer and nonemployer firms, respectively. To construct a pooled weight, we use the employer (nonemployer) weight if the firm is an employer (nonemployer). The survey was fielded September-October 2020.

It is important to note that, when we compare disaster-related losses on a dollar basis, rather than as a percentage of revenues, there is little evidence of racial disparities. This suggests that our result is driven by the lower revenues of Black-owned firms, implying that natural disasters are a greater burden for firms owned by people of color through their interaction with existing racial disparities that have a negative effect on small business revenues. For example, relative to white-owned firms, Black-owned businesses are younger, have less access to startup capital, employ fewer people, have a harder time accessing credit, and lack experience in family businesses—all of which are associated with lower revenues. 

Looking Ahead

Our findings suggest that small businesses owned by people of color and located in particular geographic areas are especially vulnerable to natural disasters. Moreover, these disparities have increased over the three years in our sample, in tandem with the frequency and severity of disaster events. These disparate outcomes are likely to be closely linked to the broader challenges faced by small businesses of color in accessing credit as well as to underinvestment in climate infrastructure in areas where low-income and high-minority communities live. As such, addressing these challenges may prove especially effective in ameliorating disparities in climate outcomes. In our next post, we examine the resources that small businesses can rely on to cope with losses following disasters, such as access to disaster relief.

Martin Hiti was a summer research intern in the Federal Reserve Bank of New York’s Research and Statistics Group.

Claire Kramer Mills is a Communication Development Research Manager in the Federal Reserve Bank of New York’s Communications and Outreach Group.

 portrait of Asani Sarkar

Asani Sarkar is a financial research advisor in Non-Bank Financial Institution Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:
Martin Hiti, Claire Kramer Mills, and Asani Sarkar, “How Do Natural Disasters Affect U.S. Small Business Owners?,” Federal Reserve Bank of New York Liberty Street Economics, September 6, 2022, https://libertystreeteconomics.newyorkfed.org/2022/09/how-do-natural-dis....

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

Consumer Scores and Price Discrimination

Published by Anonymous (not verified) on Mon, 11/07/2022 - 9:00pm in

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Credit

 Woman shopping online with smartphone and making a payment

Most American consumers likely are familiar with credit scores, as every lender in the United States uses them to evaluate credit risk. But the Customer Lifetime Value (CLV) that many firms use to target ads, prices, products, and service levels to individual consumers may be less familiar, or the Affluence Index that ranks households according to their spending power. These are just a few among a plethora of scores that have emerged recently, consequence of the abundant consumer data that can be gathered online. Such consumer scores use data on age, ethnicity, gender, household income, zip code, and purchases as inputs to create numbers that proxy for consumer characteristics or behaviors that are of interest to firms. Unlike traditional credit scores, however, these scores are not available to consumers. Can a consumer benefit from data collection even if the ensuing scores are eventually used “against” her, for instance, by enabling firms to set individualized prices? Would it help her to know her score? And how would firms try to counteract the consumer’s response?

Concerns about “Scoring”

A distinguishing feature of these scores is that the data brokers that produce them also sell them to companies for market-segmentation strategies. Thus, these scores do not merely affect a consumer’s interaction with a single firm: the information carried by the score creates links across interactions with different firms and industries over time. The argument in favor is that data collection adds value by creating gains from trade, and scores are a convenient way of packaging data. But adverse welfare effects can arise. For example, if a consumer makes a big purchase, leading her “profitability” score to increase, she may face higher prices tomorrow.

In a recent paper, we developed a model of score-based price discrimination. Our model shuts down any value creation to isolate the mechanisms by which consumers can be harmed by data collection, and the focus on price discrimination stems from the increasingly granular e-commerce targeting and product-steering techniques that make de facto discriminatory pricing a real possibility. In our setup, a consumer interacts with a sequence of firms, and her willingness to pay for the firms’ products is her private information. Because purchases carry information about willingness to pay, and the latter is positively correlated over time, firms use scores that are based on signals of past purchases to set prices. In this context, our analysis examines how consumer welfare is affected by the interplay between different degrees of consumer sophistication (does the consumer know about the scores and the links they create?) and of score transparency (can consumers check their current score?).

Harms and Benefits

Price discrimination unambiguously harms naïve consumers—that is, those who do not recognize the links across transactions—but it can benefit strategic consumers. Specifically, in the naïve case, consumer welfare falls with the quality of the signals available to the firms. Firms in turn are better off. More strikingly, compressing data into a score does not protect consumers at all. This is because firms can aggregate data about purchases in the form of a score belonging to the class that we study, without any loss in predictive power. This class is parametrized by the relevance that each score gives to past signals of behavior, so that a large weight on the past leads to a score with high persistence.

By contrast, a strategic consumer can benefit from the presence of scores even if firms ultimately use them against her, since she can reduce her quantity demanded to manipulate her score. Consider the figure below, depicting a traditional monopoly problem between a consumer with downward-sloping demand and a single firm, say Firm 1. If there is only one interaction, the consumer does not adjust her behavior, resulting in an outcome with Q units purchased at price P. But suppose now that a second firm interacts with the consumer tomorrow after seeing a signal of the first-period purchase. Because the consumer recognizes the impact of her first-period choice on the second period price, she will attempt to reduce Firm 2’s signaland hence her score—by adopting a lower demand, which reduces her purchases to Q’.

Gains and Losses from Strategic Demand Reduction

Source: Bonatti and Cisternas. 2020. “Consumer Scores and Price Discrimination.” Review of Economic Studies 87, no. 2 (March): 750-91.

The consumer suffers because she buys less (with the loss represented by the red area). And while not depicted, she also suffers from future price discrimination due to information about her willingness to pay (that is, the intercept of her demand function) getting transmitted to Firm 2. However, Firm 1 is forced to lower its price (P’ in the figure) after the strategic demand reduction occurs. If the consumer has high willingness to pay, the benefit of this discount applied to many units is such that she wants to be tracked (the blue areaa benefit—grows as the intercept of demand increases).

Managing Consumers’ Strategic Response

The strategic demand reduction implies that purchases are less sensitive to changes in willingness to pay. Thus, signals lose informativeness, and price discrimination with scores is less effective. These losses cannot be eliminated: if firms use scores that are best predictors in an ex-post sense, that is, given the available data, strategic consumers will adjust their behavior making the data less informative in the first place. A complex “cat and mouse” situation emerges, with consumers attempting to “hide” as firms seek to estimate their preferences.

Our first contribution consists of uncovering that firms choose a suboptimal use of the available data to improve the quality of the underlying data. Specifically, firms can mitigate their losses if they commit to persistent scores—those that give excessive importance to past information. This may seem counterintuitive, as the long-term consequences of a very persistent score suggest consumers might become more scared of revealing information and facing high prices for a long time. But a score that overweighs the past also correlates less with current willingness to pay, so prices initially react less to changes in the score. Therefore, scores that are more persistent than those that arise in a cat and mouse world can be more profitable, because they incentivize consumers to signal more of their information.

Score “Transparency” Is Critical

Our second contribution consists of showing that the possibility of data collection benefiting consumers via lower prices relies heavily on making scores transparent. To make this point, we assess the current market paradigm whereby the score is hidden to the consumer.

When signals of purchases are imperfect, a strategic consumer will not know her score just by knowing her past behavior. But prices will convey information. Specifically, the observation of a high price today tells the consumer that firms think she has a high willingness to pay, and hence that prices will remain high in the future due to the score’s persistence. If the consumer then expects to purchase relatively few units, she is less inclined to reduce her demand due to the discount being applied to a few units only. Thus, the consumer becomes less price sensitive relative to the case in which the score is observable. (In this latter case, the consumer would be able to identify “abnormally” high prices as those above what her score dictates, enabling her to forgo bad offers.)

With a reduced sensitivity, firms make prices more responsive to the score. While this exacerbates the demand reduction and results in lower purchases, prices are nevertheless higher, which ends up hurting consumers. What is more, strategic consumers to whom scores are hidden can be worse off than their naïve counterparts. Our results can inform policy: consumer awareness of the potential for price discrimination and score transparency have complementary roles, and one without the other may be detrimental to welfare.

Alessandro Bonatti is a professor of applied economics at the MIT Sloan School of Management.

 portrait of Gonzalo Cisternas

Gonzalo Cisternas is a financial research advisor in Non-Bank Financial Institution Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.  

How to cite this post:
Alessandro Bonatti and Gonzalo Cisternas, “Consumer Scores and Price Discrimination,” Federal Reserve Bank of New York Liberty Street Economics, July 11, 2022, https://libertystreeteconomics.newyorkfed.org/2022/07/consumer-scores-an....

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

Did Changes to the Paycheck Protection Program Improve Access for Underserved Firms?

Published by Anonymous (not verified) on Wed, 06/07/2022 - 9:00pm in

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Credit

Editors’ note: When this post was first published the x-axis labels on the final chart were incorrect. The chart has been corrected.  9:10 a.m. ET, July 6.

 African American woman business owner with COVID mask putting up sign for business reopening

Prior research has shown that many small and minority-owned businesses failed to receive Paycheck Protection Program (PPP) loans in 2020. To increase program uptake to underserved firms, several changes were made to the PPP in 2021. Using data from the Federal Reserve Banks’ 2021 Small Business Credit Survey, we argue that these changes were effective in improving program access for nonemployer firms (that is, businesses with no employees other than the owner(s)). The changes may also have encouraged more applications from minority-owned firms, but they do not appear to have reduced disparities in approval rates between white- and minority-owned firms.

Changes in the Paycheck Protection Program in 2021

Critics have cited various reasons for underserved firms’ lack of access to PPP funds. Some have faulted the intermediated nature of the program, and the resulting incentives for banks to prioritize existing borrowers as well as larger firms in their approval processes. Others have argued that certain program rules—especially the requirement that nonemployer firms calculate eligible loan amounts using net profit (rather than gross income)—were especially disadvantageous to the smallest firms. Nonemployer firms also had delayed access to PPP at the onset of the program. For nonemployers and other small firms, a lack of program awareness and concerns about eligibility for the loan and subsequent forgiveness likely acted as barriers to program take-up. Finally, racial discrimination faced by some applicants for PPP loans may also have played a part.

In response to evidence of underserved firms’ lack of access to PPP, Congress and the Small Business Administration (SBA) implemented several important changes prior to the 2021 round of PPP. As part of the Economic Aid Act of 2020, Congress pre-allocated large amounts of PPP funds for businesses located in low- and moderate-income communities, those with at most ten employees, and first-time PPP borrowers. The bill also set aside funds for lenders who typically provide credit to underserved borrowers, such as Community Development Financial Institutions (CDFIs), Minority Depository Institutions (MDIs), small banks, and credit unions. To effectively implement these and other changes, the SBA instituted a two-day exclusivity window at the very start of the 2021 program during which only Community Financial Institutions (CFIs) were permitted to submit applications to the SBA.

To further increase access to PPP funds, the SBA and the Biden administration announced additional changes in late February. The most important of these were: (i) an exclusive, fourteen-day borrowing window for firms with at most twenty employees; and (ii) allowing nonemployer firms to base loan amount calculations on gross income.

PPP Application Take-up in 2021

Were these initiatives effective in increasing application take-up by nonemployer firms and small employer firms? The SBA’s PPP reports indicate a clear increase in funding for smaller businesses in the 2021 phase of the PPP. However, it remains unclear whether this increased take-up is driven primarily by changes in application behavior or by changes in approval rates. Furthermore, almost 71 percent of PPP borrowers chose not to report race/ethnicity information on their PPP applications, making it difficult to draw conclusions about PPP access for minority-owned businesses. To resolve these issues, we turn to data from the Federal Reserve’s 2021 Small Business Credit Survey, which contains detailed demographic information about the owners of small businesses.

In the chart below, we examine nonemployer application rates from 2020 and 2021, relative to the analogous rates for firms with employees on payroll. Application rates were lower across the board in 2021 than in 2020, but there was a much smaller drop for nonemployer firms than for any other size class of firms. Moreover, 36 percent of 2021 nonemployer applicants were first-time PPP applicants, compared to just 12 percent of 2021 employer applicants. Thus, the program changes appear to have increased application take-up by nonemployer firms, with the caveat that employer firms may have had a larger drop in demand for PPP funds between 2020 and 2021 than did nonemployer firms.

PPP Application Rates by Firm Size

Source: Federal Reserve Banks, 2021 Small Business Credit Survey.

Notes: Fielded September-November 2021. For respondents in each size group (where size is determined by number of full- and part-time employees), the blue bars show the fraction of respondents who reported applying for a PPP loan in 2020. Similarly, the gold bars show the fraction of respondents who reported applying for a PPP loan in 2021. Responses by nonemployer (employer) firms are weighted on a variety of firm characteristics in order to match the national population of nonemployer (employer) firms. See the methodology section of the 2022 Report on Employer Firms for more information.

The chart also suggests that initiatives targeted toward small employer firms had mixed effects. We do not observe a clear effect of the fourteen-day exclusivity period for firms with at most 20 employees, as application rates for firms with 1-19 employees and with at least 20 employees feature similar declines. However, the pre-allocated funding for firms with at most 10 employees may have encouraged firms to apply: 19 percent and 8 percent of firms with 1-4 and 5-9 employees, respectively, were first-time applicants in 2021, compared to just 6 percent and 2 percent for firms with 10-19 and at least 20 employees, respectively.

PPP Application Take-up by Race and Ethnicity

We next study changes in PPP application rates by owner race/ethnicity from 2020 to 2021. In the following chart, we observe noticeably smaller decreases in application rates for Black- and Hispanic-owned businesses than for white-owned businesses. Indeed, the application rate for Black-owned businesses exceeded that of white-owned firms in 2021. Furthermore, we find that 33 percent and 30 percent of Black- and Hispanic-owned employer firms applying in 2021, respectively, were first-time borrowers, relative to just 11 percent of white-owned employer firms.

PPP Application Rates by Race/Ethnicity

Source: Federal Reserve Banks, 2021 Small Business Credit Survey.

Notes: Fielded September-November 2021. For respondents in each Race/Ethnicity category, the blue bars show the fraction of respondents who reported applying for a PPP loan in 2020. Similarly, the gold bars show the fraction of respondents who reported applying for a PPP loan in 2021. Race/ethnicity categories are mutually exclusive. Responses by nonemployer (employer) firms are weighted on a variety of firm characteristics in order to match the national population of nonemployer (employer) firms. See the methodology section of the 2022 Report on Employer Firms for more information.

PPP Approvals in 2021

Did the 2021 initiatives alleviate gaps in approval rates documented for the 2020 phase of the program? In the chart below, we plot the fraction of PPP applicants in each size category that successfully obtained at least some PPP funding. Nonemployer firms are the only category for which the 2021 approval rate was higher than the 2020 approval rate. In contrast, approval rates across all other groups were slightly lower in 2021 than in 2020. This suggests that, at least for employer firms, changes made to the PPP in 2021 did not improve the success of applications.

PPP Approval Rates by Firm Size

Sources: Federal Reserve Banks. 2021 Small Business Credit Survey.

Notes: Fielded September-November 2021. For respondents in each size group (where size is determined by number of full- and part-time employees), the blue bars show the fraction of 2020 PPP applicants who reported receiving a PPP loan in 2020. Similarly, the gold bars show the fraction of 2021 PPP applicants who reported receiving a PPP loan in 2021. Responses by nonemployer (employer) firms are weighted on a variety of firm characteristics in order to match the national population of nonemployer (employer) firms. See the methodology section of the 2022 Report on Employer Firms for more information.

Which changes may have had uniquely positive, albeit small, effects on approval outcomes for nonemployer firms? The guidance allowing nonemployer firms to calculate loan amounts using gross income rather than net profit likely played a large role in increasing approval rates. Additionally, nonemployer firms applying for PPP in 2021 were more likely than employer firms to be applying for first-draw loans, which did not require attestation and supporting documentation of at least a 25 percent drop in revenues in at least one quarter of 2020 (relative to the same quarter in 2019). News articles have documented additional obstacles to getting second-draw loans (for example, applications stalled when first-draw loans were flagged by the SBA’s internal review of the 2020 program) that may have disproportionately impacted employer firms.

PPP Approvals in 2021 by Race and Ethnicity

Our final chart plots 2020 and 2021 approval rates by owner race/ethnicity. Within-group approval rates are generally similar between 2020 and 2021, further supporting the idea that determinants of PPP approval outcomes were largely unaffected by the 2021 changes to the PPP. An important exception is an almost 9 percent drop (from 74.1 percent to 65.4 percent) in approval rates for Black-owned employer firms from 2020 to 2021. This exception is consistent with the relatively strong take-up of PPP applications by Black-owned employer firms in that more underserved firms may have been less likely to apply successfully for PPP funds.

PPP Approval Rates by Race/Ethnicity

 ppp approved rates by race/ethnicitySource: Federal Reserve Banks, 2021 Small Business Credit Survey.

Notes: Fielded September-November 2021. For respondents in each race/ethnicity category, the blue bars show the fraction of 2020 PPP applicants who reported receiving a PPP loan in 2020. Similarly, the gold bars show the fraction of 2021 PPP applicants who reported receiving a PPP loan in 2021. Race/ethnicity categories are mutually exclusive. Responses by nonemployer (employer) firms weighted on a variety of firm characteristics in order to match the national population of nonemployer (employer) firms. See the methodology section of the 2022 Report on Employer Firms for more information.

Final Words

Our findings suggest that changes made to the PPP in 2021 succeeded in increasing credit access for nonemployer firms. For minority-owned firms, these initiatives appear to have improved application take-up, particularly for Black-owned employer firms. However, approval rate gaps between white-owned and Black-/Hispanic-owned firms were not attenuated. Understanding the causes of persistent gaps in PPP approvals between employer and nonemployer firms, as well as between white- and minority-owned firms, remains an important avenue for future research.

Nathan Kaplan is a research analyst in Money and Payment Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

 portrait of Claire Mills

Claire Kramer Mills is a manager and director of community development analysis in the Bank’s Communications and Outreach Group.

Asani Sarkar is a financial research advisor in Non-Bank Financial Institution Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:
Nathan Kaplan, Claire Kramer Mills, and Asani Sarkar, “Did Changes to the Paycheck Protection Program Improve Access for Underserved Firms?,” Federal Reserve Bank of New York Liberty Street Economics, July 6, 2022, https://libertystreeteconomics.newyorkfed.org/2022/07/did-changes-to-the....

Related reading:
Who Benefited from PPP Loans by Fintech Lenders? (May 27, 2021)
Who Received PPP Loans by Fintech Lenders? (May 27, 2021)
Small Business Credit Survey: 2021 Report on Employer Firms

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

What Is Corporate Bond Market Distress?

Published by Anonymous (not verified) on Thu, 30/06/2022 - 12:00am in

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Credit

 Ticker sign with the word Bond lit up.

Corporate bonds are a key source of funding for U.S. non-financial corporations and a key investment security for insurance companies, pension funds, and mutual funds. Distress in the corporate bond market can thus both impair access to credit for corporate borrowers and reduce investment opportunities for key financial sub-sectors. In a February 2021 Liberty Street Economics post, we introduced a unified measure of corporate bond market distress, the Corporate Bond Market Distress Index (CMDI), then followed up in early June 2022 with a look at how corporate bond market functioning evolved over 2022 in the wake of the Russian invasion of Ukraine and the tightening of U.S. monetary policy. Today we are launching the CMDI as a regularly produced data series, with new readings to be published each month. In this post, we describe what constitutes corporate bond market distress, motivate the construction of the CMDI, and argue that secondary market measures alone are insufficient to capture market functioning.

Market distress involves both primary and secondary markets

Justice Potter Stewart famously said “I know it when I see it” in the 1964 Jacobellis vs Ohio Supreme Court ruling. So what should policymakers pay attention to in determining whether a market is in distress?  The Emergency Relief and Reconstruction Act of 1932  states that, in order to supply backstop lending, that:

…the Federal Reserve Bank shall obtain evidence that such individual, partnership or corporation is unable to secure adequate credit accommodations from other banking institutions.

Similarly, the BIS Market Committee highlights that:

Market dysfunction has the potential to disrupt the flow of credit to the economy, thereby impacting real activity and price stability and, as a result, attainment of central banks’ monetary goals.

In the context of debt capital markets, access to credit will be impaired when there is a significant slowdown in primary markets where credit flows from investors to businesses.

Primary markets, however, do not exist in isolation, and secondary market conditions may affect primary market functioning if, for example, underwriters face uncertain prospects of placing new issuance. A well-functioning secondary market is one in which transactions can take place rapidly and with little impact on price, the size of transaction volume can be absorbed without undue influence on prices, execution is immediate, and prices return quickly to “normal” levels after temporary order imbalances. An important feature of episodes of market distress—or “liquidity black holes”—is that large price changes alone are not sufficient to assess market functioning as large price changes can instead indicate a smoothly functioning market incorporating new information quickly.

How do we recognize distress?

The descriptions above highlight that market distress is multifaceted and that measures of any one aspect of market functioning will likely present an incomplete picture of distress. To construct the CMDI, we coalesce information on seven aspects of market functioning—primary market volumes, primary market pricing, secondary market volumes, secondary market liquidity, secondary market pricing, secondary market default-adjusted pricing, and quoted pricing on non-traded bonds—using insights from the machine learning literature on image recognition and language processing to construct a unified measure of market functioning. As described in technical detail in our staff report, the CMDI flags market distress as periods when metrics of more aspects of market functioning are signaling distress, rather than when metrics of individual features of market distress are higher on average.

To understand the difference between these two concepts of coalescing information from multiple individual measures, consider the following simple example. Suppose that we only used bid-ask spreads and primary market volume. Which of the following two situations is more distressed?

  • The bid-ask spread is in its top tenth historical percentile, so that liquidity in the market is poor, while primary market volume is also in its top tenth percentile, so that issuance is nonetheless good.
  • Both the bid-ask spread and issuance volume are at their corresponding historical medians.

From the perspective of a single measure that averages across metrics, market distress is at the same level in both situations, as the average metric is in the center of the distribution in both cases. A measure that instead identifies distress as distress along a greater number of features will perceive the second situation as being potentially more concerning as the bid-ask spread and issuance volume provide conflicting signals in the first example.

The chart below plots the time series of the CMDI together with the first principal component (PCA)—a sophisticated way of averaging across metricsof the seven features of market functioning that underlie the CMDI. The chart shows that, while both approaches correctly identify periods of extreme distress—when both the average feature indicates distress and the plurality of measures indicate distress—the PCA overidentifies market distress when market conditions are relatively calm. Our simple example illustrates this intuition: the averaging approach is more likely to be influenced by extreme observations of any one measure. In the staff report, we show that the CMDI is a better predictor of both future realizations of other measures of financial conditions and future real activity realizations than the PCA, suggesting that the CMDI does indeed provide a “cleaner” signal of market distress.

The PCA approach overidentifies market distress during periods of relative calm

Sources: Mergent FISD; FINRA Trade Reporting and Compliance Engine; ICE; authors’ calculations. Grey shaded areas correspond to NBER recessions.
Notes: CMDI is Corporate Bond Market Distress Index. PCA is principal component analysis.

Why not just use secondary market measures?

If distress in the secondary market coincides with or even precedes distress in the primary market, then measures of flow of credit may be superfluous in the measurement of market distress. The next chart plots the time series of monthly changes in the amount of corporate debt outstanding, together with two popular measures of secondary market stress: the excess bond premium (EBP) of Gilchrist and Zakrajšek (2012) and the investment-grade credit default swap-bond (CDS-bond) basis. The chart shows that, at least contemporaneously, neither of these secondary market metrics is related to changes in debt amount outstanding, so that months with slowdowns in (net) corporate bond issuance are rarely months with high EBP or months with large dislocations between the corporate bond and CDS markets.

Secondary market measures don’t capture primary market conditions

Sources: Mergent FISD; Haver Analytics; J.P. Morgan.
Notes: EBP is excess bond premium. IG is investment grade. CDS is credit default swap. RHS is right-hand side.

A “preponderance of metrics” approach may be useful in a variety of applications

Overall, the staff report shows that taking the “preponderance of metrics” approach to measuring corporate bond market distress produces an index that correctly identifies periods of market distress and predicts future realizations of commonly used measures of market distress. While we focus on quantifying corporate bond market distress, the similarity-based approach to summarizing information about different aspects of activity is potentially applicable in a variety of economic settings, including measuring financial vulnerabilities and identifying recessions or financial crises, as described in a previous Liberty Street Economics post.

 portrait of Nina Boyarchenko

Nina Boyarchenko is the head of Macrofinance Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

 Portrait of Richard K. Crump

Richard K. Crump is a financial research advisor in Macrofinance Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

 portrait of Anna Kovner

Anna Kovner is the director of Financial Stability Policy Research in the Bank’s Research and Statistics Group.

 portrait of Or Shachar

Or Shachar is a financial research economist in Capital Markets Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

How to cite this post:
Nina Boyarchenko, Richard Crump, Anna Kovner, and Or Shachar, “What Is Corporate Bond Market Distress?,” Federal Reserve Bank of New York Liberty Street Economics, June 29, 2022, https://libertystreeteconomics.newyorkfed.org/2022/06/what-is-corporate-....

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

The First Global Credit Crisis

Published by Anonymous (not verified) on Mon, 27/06/2022 - 9:00pm in

Tags 

Credit, liquidity

 Amsterdam stock exchange in 17 and 18 centuries; source Wikimedia

June 2022 marks the 250th anniversary of the outbreak of the 1772-3 credit crisis. Although not widely known today, this was arguably the first “modern” global financial crisis in terms of the role that private-sector credit and financial products played in it, in the paths of financial contagion that propagated the initial shock, and in the way authorities intervened to stabilize markets. In this post, we describe these developments and note the parallels with modern financial crises.

The Evolution of the Credit Crisis

The 1772-3 crisis was global in scope, with failures spread across Great Britain and the Netherlands, the other main European financial centers, and as far afield as St. Petersburg and the West Indian and North American colonies (as covered in a previous Liberty Street Economics post). Over the course of a year, it disrupted credit markets, adversely affecting both banks and non-bank borrowers.

There were two waves of failures. Sparked by the flight of the Scottish banker and speculator Alexander Fordyce, panic broke out on June 9, 1772, in London, with the experimental Ayr Bank in Scotland an important casualty soon after. Another round of failures hit Amsterdam over the winter of 1772-3; most notable among these was the ancient bank of Clifford, held by contemporaries to be the second most important bank in Europe.

Since the role of fast-changing private credit markets was crucial in precipitating and propagating the crisis, we start with an overview of the private credit instruments prevailing at the time.

Bills of Exchange Helped Transmit Contagion

The bill of exchange was the primary credit tool fueling trade in this era: a promise to pay money (usually foreign currency) in a defined place and at a certain time. It was effectively an IOU that a merchant or bank could “accept” or ask a third party with stronger credit to accept (underwrite) on its behalf. Depending on the distance that the bill or related shipments might need to travel, the bill would typically have a maturity of up to a year, though three-six months was more common.

Although originally created to support short-term trade, a bill could (and did) become endorsed to third parties as payment of debts before its maturity, in effect serving as a paper money surrogate. All parties (including endorsers) undersigning a bill were jointly and serially liable for the debt, thus diversifying credit risk in normal times. During times of distress, however, the bill’s credit liability characteristics  served as an avenue of financial contagion since all undersigned parties were at equal risk to be called upon for the full debt.

The bill of exchange was also increasingly used in long-term finance by “rolling” an expiring bill with a matching bill on the same date, in a process known as swiveling. This helped merchants to secure working capital, but also allowed speculators to finance long-dated, higher-risk asset purchases, such as commodities or equities. The “rollover” risk inherent in this process is similar to that underlying the global financial crisis of 2007-9.

Mortgage Lending

Innovations in mortgage lending in the mid-eighteenth century stand out for their contributions to financial instability in the run-up to 1772 and the failures of that year. The mortgages themselves became more speculative as they included riskier loans, such as those collaterized by West Indian plantations managed on the behalf of absentee owners. As the loans were pooled and sold as mortgage-backed securities (MBS), they distributed the underlying risks to investors broadly.

MBS (negotiaties in Dutch) were issued on a massive scale in the Netherlands in the 1760s. They were sold to well-to-do retail investors, often in increments of 1,000 guilders, a sum about six to eight times the annual income of a typical citizen. The plantation sector in the Caribbean provided the fuel for the boom, with mortgages on Dutch and Danish plantations in the West Indies used as collateral for over 40 million guilders in new loans (or about 22 percent of the GDP of Holland) in the years 1766-72 alone. By the end of the decade, the volume of new loans exceeded the productive investment opportunities.

Margin Lending

Speculation in the stock markets, then as today, relied to a significant extent on margin lending. Notaries and other intermediaries had long used the pledge of securities as the basis for short-term loans. In the Amsterdam market, these loans were typically for six months, with an option for renewal should both parties consent. A haircut on the pledged securities helped to ensure that in case of a borrower default, there would be more than enough value in the collateral to cover losses.

This investing was often conducted cross-border, with the Dutch acting as major financiers of speculation in British shares and debt securities. Increasingly, sophisticated investors lent via arrangements similar to those used today by prime brokers when they lend to hedge funds. These lenders ensured that they could re-margin their loans in response to market movements and thus were able to avoid losses, even as a credit crisis gripped the market.

Not all lenders, however, showed this level of sophistication. Some lent against illiquid securities, such as negotiaties. Others failed to secure legal control of collateral, and disputes about who was entitled to what share of recovered creditor funds continued for many years afterward.

Cleaning Up the Mess

To quell the panic and to ensure that the commercial economy did not collapse, authorities employed tools familiar to modern readers: collateralized lending facilities and lender-of-last-resort powers.

In Amsterdam, civic authorities set up a collateralized lending facility open to anyone with qualifying collateral to pledge. Loans backed by various warehoused commodities (Beleningskamer loans), and provided at standardized advance rates, replaced some of the lending capacity that had been lost. While these loans were relatively modest in size, the very existence of the facility stopped the downward spiral of forced commodity liquidations and helped bring private lenders back into the market. These loans, combined with the arrival of precious metal shipments called in from other European financial centers, ensured that markets had resumed normal functions by mid-1773, although investors absorbed large losses.

The Bank of England provided last-resort lending starting in 1772 (although the term itself was not coined until three decades after the crisis). The Bank provided liquidity by increasing the volume of its discounts. Because of usury laws, the Bank was obliged to credit ration these loans instead of raising its discount rate as Bagehot would later suggest. But the Bank did not hesitate in deploying additional containment resources, such as supporting the biggest bill acceptors in London with targeted short-term loans, through which they, in turn, could support their clients.

Final Words

Intense as the twin panics of 1772-73 were, authorities were able to stabilize markets and restore confidence in the economy. These events resulted in a larger role for the institutional infrastructure of finance, focused around central banks and other state institutions, and created a set of financial stabilization techniques that are still in use today. The availability of these new tools was fortuitous, since Europe was entering the period of the most profound changes in economic growth and capital investment in human history. 

Stein Berre is a director in the Federal Reserve Bank of New York’s Supervision Group.

Paul Kosmetatos is a lecturer in International Economic History at the University of Edinburgh.

Asani Sarkar is a financial research advisor in Non-Bank Financial Institution Studies in the Federal Reserve Bank of New York’s Research and Statistics Group.

Disclaimer
The views expressed in this post are those of the author(s) 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(s).

Credit, crises and inequality

Published by Anonymous (not verified) on Tue, 03/05/2022 - 6:00pm in

Jonathan Bridges, Georgina Green and Mark Joy

Any distributional effects on credit of macroprudential policies are only one part of the distributional story. Relatively little is known about how such policies affect the income distribution in the longer term via their role in preventing crises or mitigating their severity. Our paper helps to fill that gap in the literature by looking at the impact of past recessions and crises on inequality, and the amplifying roles of credit and capital within that. This helps to shed light on the distributional implications of not intervening – in the form of an amplified recession. We find that inequality rises following recessions and that rapid credit growth prior to recessions exacerbates that effect by around 40%.

To shed light on this issue we extend findings that link measures of the financial cycle – such as credit growth – with the probability and severity of macroeconomic tail events. We use a cross-country data set spanning the five decades prior to the Covid-19 pandemic to investigate whether rapid credit growth in the lead-up to a downturn is associated with an amplification of any subsequent impact on inequality. To our knowledge, we are the first to extend those findings into distributional space.

Recessions and financial crises in our sample

Our data are annual in frequency and cover 26 advanced economies since the 1970s. Our final sample covers around 100 recessions, of which just over 20% are financial crises. We identify a recession as two consecutive quarters of negative real GDP growth (based on OECD and national statistics websites). When a recession is accompanied by a banking crisis – defined by Laeven and Valencia as the recession being within one year of a systemic banking crisis – we call it a ‘financial’ recession. When there is no banking crisis, we call these ‘normal’ recessions. Recessions are well represented across the five decades but financial recessions are mainly concentrated around the global financial crisis (GFC).

Measuring inequality

Our data source is the Standardised World Income Inequality Database. We focus on market income inequality and use the Gini coefficient as our headline measure. This captures the extent to which the Lorenz curve – which reflects the proportion of overall income assumed by different income shares ordered from lowest to highest – sags below the 45-degree line of ‘perfect equality’. If during recessions those at the bottom of the distribution bear the brunt of the shock we might expect the Lorenz curve to shift down and the gini coefficient to increase.

So what does the Gini coefficient look like in our sample? Income inequality has trended upwards over the past 50 years growing by around 20% since the 1970s (Chart 1). This trend has been the focus of a growing body of work looking at how rising inequality may have set the conditions for the GFC. But our interest is actually in the reverse of this – the effect of recessions on inequality, and not in the trend but in variation around that trend (also called cyclical variation).

Chart 1: The path of market income inequality in our sample

Source: Authors’ calculations, based on SWIID data. The red line represents the median. The blue shaded area represents the interquartile range.

Empirical approach

To explore the relationship between recessions and inequality we use a local projections approach, where we regress lead observations (up to five years ahead) for income inequality on recession dummies. Because the dependent variable leads our explanatory variables, this helps to address endogeneity concerns ie the worry that inequality might impact the likelihood of a recession taking place.

To focus on cyclical dynamics we de-trend our dependent variable directly, subtracting the full panel average trend. Alongside that, we also control for any country and time-specific trends. This allows us to abstract from any slow-moving effects driven, for example, by different structural changes in a given country in a given decade.

We include country fixed effects to control for any bias in our estimates caused by unobserved, time-invariant variables across countries. And we also control for the domestic macroenvironment in the period before each recession, by including inflation, the size of the current account, the central bank policy rate and the output gap.

The effect of recessions on inequality

Our baseline regression reveals that income inequality rises following recessions. Recessions are associated with a significant increase in the cyclical component of income inequality three to five years out, rising to 2.7% after five years (Chart 2). When we split our sample into normal and financial recessions we find the response of the Gini to financial recessions builds to nearly 4% by year 5 and is more than 50% larger than for normal recessions (Chart 3).

Our findings are robust to a variety of alternative specifications: alternative approaches to de-trending; dropping overlapping recession episodes; dropping our macro controls; and the country-specific trend.

Chart 2: Cumulative change in de-trended Gini index (%) following recessions

Chart 3: Cumulative change in de-trended Gini index (%) following ‘financial’ and ‘normal’ recessions

Notes to Charts 3 and 4: Solid line gives the mean response of the Gini coefficient to a recession. Shaded areas represent 95% confidence intervals around the mean.

We might expect that a large amount of this rise in inequality is accounted for by a rise in unemployment. Low-income earners are most likely to lose their jobs in a recession as they’re often less skilled and more likely to be employed in cyclical industries. They are also more likely to be young with less secured job contracts. There is also an indirect link via wages, as high unemployment also weakens the bargaining power of workers, resulting in weaker wage growth which may particularly impact wages of the lowest paid.

To gauge the relative importance of the unemployment channel in driving the overall link between recessions and inequality, we control for the contemporaneous move in unemployment. This specification moves away from our baseline local projection approach, which is careful to only include explanatory variables observable in the year preceding the onset of each recession. Here we rely on reduced-form accounting rather than claiming causality.

We find that the increase in income inequality is partially accounted for by the increase in unemployment that accompanies recessions. This suggests there is a skewed impact on the income of those remaining in work, consistent with shocks loading most heavily on lower-paid workers.

The amplifying role of credit

To look at the role of credit growth as an amplifier we interact our recession dummies with credit growth. We find that a one standard deviation increase in credit growth (a 15 percentage point increase in the credit to GDP ratio in the three years prior to the crisis) is associated with around a 1 percentage point additional rise in the Gini, which is a 40% amplification by year 5. When we split our sample we find that the amplifying role of credit growth is strongest (and most statistically significant) for financial recessions (Chart 4). We find that the primary mechanism through which the rise in inequality appears to be amplified by rapid credit growth does appear to be through the unemployment channel.

Chart 4: Cumulative change in de-trended Gini index (%) following financial recessions preceded by high credit growth

Notes: Solid line gives the mean response of the Gini to a financial recession. Dashed line shows the amplified effect of a 1 standard deviation credit boom prior to the crisis. The shaded areas gives the 95% confidence interval.

Chart 5: Cumulative change in de-trended Gini index (%) following recessions preceded by low bank capital

Notes: Solid line gives the mean response of the Gini to a recession. Dashed line shows the amplified effect of 1 standard deviation lower capital prior to the recession. The shaded area gives the 95% confidence interval.

Extension: the role of bank capital

We extend our analysis to explore the role low bank capital ahead of a downturn plays in the inequality fallout that follows. Our capital data is only available for a subset of countries so we group recessions together given the more limited sample size. We include bank capital in the regression by interacting it with the recession dummy. We find that a country entering a recession with a banking sector where the aggregate tangible common equity ratio is one standard deviation (1.4 percentage points) lower, experiences around a 55% amplification of the rise in inequality that follows (Chart 5). Our preliminary results suggest that this may operate through the wage distribution of those remaining in work, rather than through the direct impact of unemployment on inequality. This is consistent with channels whereby ‘resilience gaps’ in the financial system can increase the likelihood and costs of macroeconomic tail events.

Policy implications

Our findings provide potential insights for a holistic assessment of the distributional implications of various macroprudential policy options. In particular, they highlight that any consideration of distributional effects needs to consider other aspects, beyond the immediate effect on credit allocation. These include: i) the distributional effects arising from crisis prevention; ii) the role of credit growth in exacerbating post-crisis inequality; and iii) the effect of greater bank capital on post-crisis inequality. All of these work in the ‘opposite direction’ to the effect on credit allocation of macroprudential measures.

Jonathan Bridges works in the Bank’s Market Intelligence and Analysis Division, Georgina Green works in the Bank’s Macro-financial Risks Division and Mark Joy works in the Bank’s Global Analysis Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

What Might Happen When Student Loan Forbearance Ends?

Published by Anonymous (not verified) on Fri, 22/04/2022 - 10:27pm in

Federal student loan relief was recently extended through August 31, 2022, marking the sixth extension during the pandemic. Such debt relief includes the suspension of student loan payments, a waiver of interest, and the stopping of collections activity on defaulted loans. The suspension of student loan payments was expected to help 41 million borrowers save an estimated $5 billion per month. This post is the first in a two-part series exploring the implications and distributional consequences of policies that aim to address the student debt burden. Here, we focus on the uneven consequences of student debt relief and its withdrawal. With the end-date of the student loan relief drawing near, a key question is whether and how the discontinuation of student debt relief might affect households. Moreover, will these effects vary by demographics?

Student debt forbearance relief during the pandemic has been instrumental in staving off student loan delinquencies and defaults, which previous research has linked to delays in homebuying and other measures of financial stress.  Private student loans are not eligible for this relief while most federal student loans are. Exceptions are certain FFEL, Perkins and HEAL loans. Will delinquency rates climb to pre-pandemic levels as student debt relief ends? Using data collected from student loan borrowers in the New York Fed’s Survey of Consumer Expectations, we seek to understand who obtained student loan forbearance relief, who continues to be in forbearance, and what do they expect to happen if the relief were to end. The second post in this series explores the demographic differences of alternative student debt forgiveness policies.

Insight from the Survey of Consumer Expectations

In this post, we leverage data from the May 2021 Survey of Consumer Expectations (SCE). In May 2021, federal student loan borrowers were also eligible for forbearance relief and it was set to expire on September 30 of that year. Currently, the student loan borrowers face a similar situation, with student loan forbearance expected to end at the end of August 2022. From this perspective, we hypothesize that the survey responses on expectations of future repayment and delinquencies can shed light on current expectations about future repayment and delinquencies. Since June 2013, this monthly survey has collected information on the economic expectations, choices, and behavior of household heads. The SCE covers about 1,300 nationally representative U.S. households and, in addition to monthly core questions, special modules focusing on specific topics are fielded frequently.

Here, we use data collected both as part of the May 2021 core survey as well as from a special module in that month that focused on debt relief during the pandemic. As part of the special module, we asked respondents about debt repayment, receipt of debt relief, the type of relief received, and their expectations that they will miss a debt payment in the next three months. Additionally, we asked respondents to consider a counterfactual scenario under which student debt relief is discontinued forthwith and elicited their expectation that they will miss a payment in the next three months under this scenario.

Student Debt and Likelihood of Any Missed Debt Payment

We begin by investigating households’ expected likelihood of missing any debt payment (that is, any of the minimum required payments on credit and retail cards, auto loans, student loans, mortgages, or any other debt) in the next three months. The average probability of missing a payment is 9.7 percent for the 1,232 households that answered this question. Student debt holders expected a 13.5 percent chance of missing a minimum debt payment in comparison to 8.7 percent for those who did not have student debt. Thus, even with most having an automatic pause on their student debt payments and thereby avoiding delinquency on such loans, student loan borrowers perceived a higher risk of missing a minimum required payment on other outstanding debts. This is consistent with the lower average age, income, and credit score of student loan borrowers—a difference that existed even before the pandemic. These groups are the ones that experienced the greatest financial hardship during the pandemic, with younger workers disproportionally working in the hardest hit sectors of the economy, seeing larger increases in their unemployment rate, and a lower share of younger workers being eligible for unemployment insurance expansions.

Student debt holders who had an income-driven repayment (IDR) plan were less likely to expect that they will be delinquent in the next three months (13.0 percent versus 14.6 percent) on any debt payment. Thus, we find that student debt holding is associated with a higher perceived probability of delinquency while having an IDR plan reduces this risk, likely by restricting payment to a relatively manageable share of their income.

Focusing on borrowers who reported receiving some type of debt relief (other than student debt relief) during the pandemic, we investigate whether such “ever-relief borrowers” had a different perceived risk of being past due on a loan in the near future. We find that debt relief recipients (those receiving fee and interest waivers, debt forgiveness, or a deferral or delay on credit card, auto loan, or mortgage payments) are more likely to expect that they will be delinquent, with an average probability of missing a payment of 23.2 percent compared to 7.8 percent for those who never received debt relief. This suggests that borrowers who received temporary relief are financially worse off and more likely to have needed the financial support. They remain financially more vulnerable and expect a higher level of financial insecurity in the future.

Although Debt Relief Recipients Perceived a Higher Likelihood of Delinquency; Those with IDR Perceived a Lower Likelihood of Such Distress

Average percent chance of not being able to make a debt payment in the next three months

Overall
9.7

No Student Loan
8.7

Student Loan
13.5

No IDR
14.6

IDR
13

Never Debt Relief (except student loan)
7.8

Ever Debt Relief (except student loan)
23.2

Observations
1232

Source: New York Survey of Consumer Expectations, May 2021.
Note. “Ever Debt Relief” refers to borrowers who have ever obtained debt relief (other than for student loans) during the pandemic. “Never debt relief” refers to borrowers who never obtained such relief.

We next focus our attention on borrowers who received student loan forbearance. The first column in the table below focuses on the 159 borrowers in our sample who have ever received student debt forbearance through the administrative forbearance of federal student loans. On average, these borrowers expect an 11.8 percent likelihood of missing any debt payment in the next three months, slightly smaller than the likelihood faced by an average student loan borrower (13.5 percent). Note that this contrasts with the finding from the above table where borrowers with other kinds of debt relief expected a higher probability of delinquency than those who did not seek and obtained relief. This difference can be explained by two factors. First, borrowers with federal student debt automatically entered forbearance whereas borrowers with other debt sought out relief, likely when they were financially more vulnerable. Second, federal student debt holders who never received forbearance mostly hold FFEL and Perkins loans which were associated with higher delinquencies during this period.

Among borrowers who have ever received student loan forbearance, there is considerable heterogeneity in perceived future delinquency risk by demographics as well as IDR status. We find that less educated, lower-income, female, non-white and middle-aged borrowers expect a higher risk of such delinquency. Consistent with the finding above, we find that borrowers who do not have an IDR plan perceive a higher likelihood of financial distress (as captured by delinquency).

The second column of the table below focuses on the 116 borrowers who in May 2021 remained in student loan forbearance. Borrowers who remained in forbearance have a slightly higher expected delinquency rate than those who ever received forbearance (12.9 percent versus 11.8 percent), although this difference is only marginally statistically significant. Differentiating by demographics, we find the same patterns as above, except that average delinquency probabilities generally are somewhat higher. These results go beyond our earlier findings indicating that student loan borrowers who availed themselves of the option and continue to remain in forbearance perceive a significant risk of missing a debt payment over the next three months, even after pausing their student loan payment and able to continue doing so over the next three months.

Among Forborne Borrowers, Less Educated, Women, and Racial Minority Borrowers Have Higher Expected Delinquency Rates as Do Those Without an IDR

Ever Received Student Loan Forbearance
Currently in Forbearance

Overall
11.8
12.9*

No College
17.2
19.2*

College
8.1
7.8

<60k (US dollars)
13.7
15.1*

>60k (US dollars)
10.9
11.6

Male
10.2
11.4*

Female
12.7
13.8

Non-white
15.3
17.2*

White
10.8
11.4

<40 yrs old
9.9
10.7

41-60 yrs old
15.9
17.7*

>60 yrs old
2.6
2.6

No IDR
13.3
14.5

IDR
9.5
10.5

Observations
159
116

Source: New York Survey of Consumer Expectations, May 2021 Survey.
Stars denote whether column 2 is statistically different from column 1. * p<0.1, ** p<0.05,
*** p<0.01

Finally, we conduct a hypothetical exercise where we ask respondents who continue to avail themselves of student debt forbearance to consider a scenario in which all student debt forbearance is discontinued at the end of the month. Then we asked respondents about their expected likelihood of student loan delinquency in the next three months. We find that, under this scenario the borrowers expect a 16.1 percent risk of delinquency on their student debt. Differentiating by demographics, we find that lower-income, less educated, non-white and female borrowers expect a higher likelihood of delinquency, should the relief be discontinued. Consistent with the above results, discontinuation of student debt forbearance will more adversely affect borrowers without an IDR plan with an almost 50 percent higher expected delinquency rate compared to those who do.

Discontinuation of Student Debt Relief Will Hit the Less Educated, Women, Racial Minorities, and Middle-Aged Borrowers the Hardest.

Currently in Forbearance

Overall
16.1

No College
18.7

College
14.1

<60k (US dollars)
18.7

>60k (US dollars)
14.7

Male
8.3

Female
21.5

Non-white
13.6

White
17

<40 yrs old
13.1

41-60 yrs old
21.5

>60 yrs old
11.9

No IDR
16.5

IDR
15.7

Observations
116

Source: New York Survey of Consumer Expectations, May 2021 Survey.
Note: Sample of student debt borrowers who are in administrative forbearance.

Our analysis suggests that the scheduled discontinuation of student debt forbearance on August 31 will likely increase financial hardship and delinquency rates. Borrowers currently availing themselves of student debt forbearance expect a 16 percent chance of delinquency if relief is discontinued. Assuming a zero-delinquency rate among those not currently receiving student debt relief, this suggests an overall borrower delinquency rate of 10 percent, a return to two-thirds of the pre-pandemic student loan delinquency rate of 15.6 percent of borrowers. Our 10 percent estimate is clearly a lower bound as we have assumed that those that are not currently in forbearance have a zero percent expected delinquency rate, thus implying that we cannot rule out that the delinquency rate at relief withdrawal could reach or even surpass the pre-pandemic rate.

We also find that this hardship in repayments will not affect all borrowers evenly. Rather, we find that lower-income, less educated, non-white, female and middle-aged borrowers will struggle more in making minimum payments and in remaining current. Borrowers who do not have an IDR plan are expected to be relatively worse off, likely because the IDR allows payments to be more manageable amid income fluctuations. Finally, our analysis suggests likely consequences for repayment of debts other than student loans, with student loan borrowers reporting high average risks of delinquency on other debts even while their student loan payments were paused.

Rajashri Chakrabarti is a senior economist in the Bank’s Research and Statistics Group.

Jessica Lu is a senior research analyst in Bank’s Research and Statistics Group.

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