Amir Sufi

Capital Structure and Debt Structure

Using a novel data set that records individual debt issues on the balance sheets of public firms, we demonstrate that traditional capital structure studies that ignore debt heterogeneity miss substantial capital structure variation. Relative to high credit quality firms, low credit quality firms are more likely to have a multi-tiered capital structure consisting of both secured bank debt with tight covenants and subordinated non-bank debt with loose covenants. We discuss the extent to which these findings are consistent with existing theoretical models of debt structure in which firms simultaneously use multiple debt types to reduce incentive conflicts.

Creditor Control Rights, Corporate Governance, and Firm Value

We provide evidence that creditors play an active role in the governance of corporations well outside of payment default states. By examining the Securities and Exchange Commissions filings of all U.S. nonfinancial firms from 1996 through 2008, we document that, in any given year, between 10% and 20% of firms report being in violation of a financial covenant in a credit agreement. We show that violations are followed immediately by a decline in acquisitions and capital expenditures, a sharp reduction in leverage and shareholder payouts, and an increase in CEO turnover. The changes in the investment and financing behavior of violating firms coincide with amended credit agreements that contain stronger restrictions on firm decision-making; changes in the management of violating firms suggest that creditors also exert informal influence on corporate governance. Finally, we show that firm operating and stock price performance improve post-violation. We conclude that actions taken by creditors increase the value of the average violating firm. The Author 2012. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.

What Explains the 2007-2009 Drop in Employment?

We show that deterioration in household balance sheets, or the housing net worth channel, played a significant role in the sharp decline in U.S. employment between 2007 and 2009. Counties with a larger decline in housing net worth experience a larger decline in non‐tradable employment. This result is not driven by industry‐specific supply‐side shocks, exposure to the construction sector, policy‐induced business uncertainty, or contemporaneous credit supply tightening. We find little evidence of labor market adjustment in response to the housing net worth shock. There is no significant expansion of the tradable sector in counties with the largest decline in housing net worth. Further, there is little evidence of wage adjustment within or emigration out of the hardest hit counties.

The Great Recession: Lessons from Microeconomic Data

We highlight how a micro-level analysis of the Great Recession provides us with important clues to understand the origins of the crisis, the link between credit and asset prices, the feedback effect from asset prices to the real economy, and the role of household leverage in explaining the downturn. We hope that our discussion also serves as an example of the usefulness of incorporating microeconomic data and techniques in answering traditional macroeconomic questions.

Dynamic Risk Management

Both financing and risk management involve promises to pay that need to be collateralized, resulting in a financing versus risk management trade-off. We study this trade-off in a dynamic model of commodity price risk management and show that risk management is limited and that more financially constrained firms hedge less or not at all. We show that these predictions are consistent with the evidence using panel data for fuel price risk management by airlines. More constrained airlines hedge less both in the cross section and within airlines over time. Risk management drops substantially as airlines approach distress and recovers only slowly after airlines enter distress. JEL classification: D92; E22; G32

Explaining Corporate Capital Structure: Product Markets, Leases, and Asset Similarity

Better measurement of the output produced and capital employed by firms substantially improves the ability to explain capital structure variation in the cross section. For every firm, we construct the set of other firms producing the same output using the set of product market competitors listed in the firms public Securities and Exchange Commission filings. In addition, we improve measurement of capital structure by explicitly accounting for leased capital. These two steps increase the explanatory power of the average capital structure of other firms producing similar output on a firms capital structure by 50% compared to using only the average unadjusted debt ratio of other firms in the same three-digit Standard Industrial Classification (SIC) code. We provide evidence that the large explanatory power of the capital structure of other firms producing similar output is related to the assets used in the production process. Our findings suggest that what a firm produces and the assets used in production are the most important determinants of capital structure in the cross section. Copyright 2011, Oxford University Press.

House Price Gains and U.S. Household Spending from 2002 to 2006

We examine the effect of rising U.S. house prices on borrowing and spending from 2002 to 2006. There is strong heterogeneity in the marginal propensity to borrow and spend. Households in low income zip codes aggressively liquefy home equity when house prices rise, and they increase spending substantially. In contrast, for the same rise in house prices, households living in high income zip codes are unresponsive, both in their borrowing and spending behavior. The entire effect of housing wealth on spending is through borrowing, and, under certain assumptions, this spending represents 0.8% of GDP in 2004 and 1.3% of GDP in 2005 and 2006. Households that borrow and spend out of housing gains between 2002 and 2006 experience significantly lower income and spending growth after 2006.

Fraudulent Income Overstatement on Mortgage Applications during the Credit Expansion of 2002 to 2005

Academic research, government inquiries, and press accounts show extensive mortgage fraud during the housing boom of the mid-2000s. We explore a particular type of mortgage fraud: the overstatement of income on mortgage applications. We define “income overstatement” in a zip code as the growth in income reported on home-purchase mortgage applications minus the average IRS-reported income growth from 2002 to 2005. Income overstatement is highest in low credit score, low income zip codes that Mian and Sufi (2009) show experience the strongest mortgage credit growth from 2002 to 2005. These same zip codes with high income overstatement are plagued with mortgage fraud according to independent measures. Income overstatement in a zip code is associated with poor performance during the mortgage credit boom, and terrible economic and financial economic outcomes after the boom including high default rates, negative income growth, and increased poverty and unemployment. From 1991 to 2007, the zip code-level correlation between IRS-reported income growth and growth in income reported on mortgage applications is always positive with one exception: the correlation goes to zero in the non-GSE market during the 2002 to 2005 period. Income reported on mortgage applications should not be used as true income in low credit score zip codes from 2002 to 2005.

Household Debt and Defaults from 2000 to 2010: The Credit Supply View

During the first decade of the 21st century, the United States witnessed a dramatic rise in household debt followed by a severe default crisis. In this study, we review the existing literature and provide new evidence supporting the credit supply view of the episode, which holds that an increase in credit supply unrelated to fundamental improvements in income or productivity was the shock that initiated the household debt boom and bust. The credit supply view is supported by four facts: First, from 2002 to 2005, there was an expansion of mortgage credit supply that was independent of improved economic circumstances. This can most easily be seen in the increased number of mortgages for home purchase originated to marginal households, or households that previously were routinely denied mortgage credit. Second, the expansion in mortgage credit supply increased house prices. Third, existing homeowners responded to rising house prices by borrowing aggressively out of home equity; such borrowing was prevalent in all but the top 20% of the credit score distribution and was the primary driver of the rise in aggregate household debt. Fourth, the default crisis was driven mainly by lower credit score individuals. The view that credit played only a passive role in explaining the rise in household debt and the subsequent default crisis is inconsistent with the evidence.

Household Debt and Defaults from 2000 to 2010: Facts from Credit Bureau Data

We use individual level credit bureau data to document which individuals saw the biggest rise in household debt from 2000 to 2007 and the biggest rise in defaults from 2007 to 2010. Growth in household debt from 2000 to 2007 was substantially larger for individuals with the lowest initial credit scores. However, initial debt levels were lower for individuals in the lowest 20% of the initial credit score distribution. As a result, the contribution to the total dollar rise in household debt was strongest among individuals in the 20th to 60th percentile of the initial credit score distribution. Consistent with the importance of home-equity based borrowing, the increase in debt is especially large among individuals in the lowest 60% of the credit score distribution living in high house price growth zip codes. In contrast, the borrowing of individuals in the top 20% of the credit score distribution is completely unresponsive to higher house price growth. In terms of defaults, the evidence is unambiguous: both default rates and the share of total delinquent debt is largest among individuals with low initial credit scores. The bottom 40% of the credit score distribution is responsible for 73% of the total amount of delinquent debt in 2007, and 68% of the total in 2008. Individuals in the top 40% of the initial credit score distribution never make up more than 15% of total delinquencies, even in 2009 at the height of the default crisis.