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Review of Accounting Studies
Published in: Review of Accounting Studies 1/2022

01-04-2021

Major government customers and loan contract terms

Authors: Daniel Cohen, Bin Li, Ningzhong Li, Yun Lou

Published in: Review of Accounting Studies | Issue 1/2022

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Abstract

We examine the relation between the presence of U.S. government as a major customer and a supplier firm’s loan contract terms, using major corporate customers as a benchmark. We find that firms with major government customers are associated with fewer covenants and a lower likelihood of having performance pricing provisions in their loan contracts. In contrast, we do not find such associations for firms with major corporate customers. Further, we find no evidence that the existence of major government customers is related to the supplier firm’s loan spread, security, or maturity. We conjecture that lenders benefit from the stricter monitoring of the government as a major customer and thus use fewer covenants and performance pricing provisions when lending to firms with major government customers than when lending to those with major corporate customers. We provide evidence consistent with this conjecture.
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Appendix
Available only for authorised users
Footnotes
1
Consistent with major government customers caring about the supplier firm’s downside risk, Hui et al. (2012) document that firms with major government customers have more conservative accounting.
 
2
The existence of a government customer may also reflect or lead to political connections. On the one hand, Houston et al. (2014) show that political connections reduce credit risk and thus the costs of bank debt and loan covenants. On the other hand, prior literature suggests that political connections may harm the quality of accounting information and the amount of voluntary disclosure, because there is less need to respond to the capital market’s demand for transparency (e.g., Chaney et al. 2011; Hung et al. 2018). Thus, lenders may demand greater protection when lending to government contractors.
 
3
We identify a customer as a major customer if it accounts for at least 10% of the supplier firm’s total sales (e.g., Dhaliwal et al. 2016).
 
4
The insignificant effect of major corporate customers could occur for the following reasons. On the one hand, customer base concentration could increase the supplier firm’s operating risk (e.g., Dhaliwal et al. 2016). On the other hand, major corporate customers have incentives and powers to monitor the supplier firm, due to their stake in the firm (e.g., Cornell and Shapiro 1987; Hui et al. 2012).
 
5
In an untabulated analysis, we find that having major government customers does not affect the supplier firm’s estimated default likelihood, CDS spreads, or credit rating and that the effect of major government customers does not differ from that of major corporate customers. This evidence further supports that it is unlikely that our findings for covenant intensity and performance pricing are due to the operating risk channel.
 
6
The reduced operating risk may also change covenant intensity because agency conflicts vary with the borrower’s default risk. However, if an economic factor affects only the agency conflicts but not the default risk, covenant intensity may change but loan spread may not.
 
7
This conclusion is at best conjectural. However, this does not dilute our contribution to the literature, because we focus on the effect of major government customers on the supplier firm’s loan contract terms, using major corporate customers as a benchmark.
 
10
There are two types of lawsuits under the FCA: non-qui tam cases and qui tam cases. Non-qui tam cases are government-initiated. In contrast, qui tam cases are initiated by whistle-blowers, and the government (i.e., the Department of Justice) can decide whether to join later.
 
11
See Sweet et al. (2017) for a list of commonly used federal laws that lead to criminal charges for violating government contracts.
 
13
When the monitoring benefits of covenants are lower, the contracting parties reduce the use of covenants because of the related costs. The direct costs of covenants include the costs of negotiating, implementing, and renegotiating the covenants. The indirect costs include the adverse effects of covenants on the borrowing firm’s investments, financing, and operations.
 
14
Our results are robust to using sales to major government customers as a percentage of total sales.
 
15
In addition to major corporate and government customers, a firm may have other major customers, such as individuals and non-profit organizations.
 
16
We match treatment observations pertaining to unrated firms with control observations from unrated firms.
 
17
A treatment observation can have multiple matched control observations. Similarly, a control observation can also be matched to multiple treatment observations.
 
18
Our results are qualitatively similar when we remove this requirement.
 
19
Consistent with new lenders delegating monitoring to existing lenders, Lou and Otto (2020) document that, when a firm has more covenants outstanding, its new loan contains fewer covenants. The presence of prior covenants is based on data for previously issued loans and bonds from Dealscan and Mergent FISD. Dropping the indicator Prior covenants from the regressions does not change our results.
 
20
Our results are robust to clustering the standard errors by industry.
 
21
A potential concern with the covenant data in Dealscan is that Dealscan does not report covenants for some loan packages, which may lead to measurement errors. However, to the extent that loans with missing covenant information represent covenant-lite loans, removing them would throw out useful information. Thus, following Costello and Wittenberg-Moerman (2011), we code loans with missing covenant information in Dealscan as having no covenants and include them in our sample. Nevertheless, we also conduct robustness tests using only loans with covenant information. We find qualitatively similar results.
 
22
For recent loans that are not covered by the linking table in Dealscan, we manually match them to Compustat by company names and addresses.
 
23
For a loan package with multiple facilities, we report the maturity and amount of the largest facility.
 
24
Our results are also robust to additionally controlling for loan spread and collateral requirement (untabulated).
 
25
When operating risk declines, loan spread will decrease because it is a function of default likelihood. When operating risk increases, it could also lead to more covenants, because risky borrowers tend to have more severe shareholder-creditor agency conflicts. Thus an increase in operating risk could lead to two possible outcomes: i) both loan spread and covenants increase, and ii) loan spread increases but covenants do not. Our key point is that an increased risk would lead to a higher spread, because covenants could not completely offset the increased risk.
 
26
According to Jensen and Meckling’s (1976) framework, when shareholder-creditor agency conflicts increase and nothing is done to address them, loan spread will increase because of creditors’ price protection. However, if some monitoring or bonding mechanisms (e.g., debt covenants) were implemented, this would restrict the agency issues. If these mechanisms fully addressed the agency issues, then the loan spread would not increase. If there were still some “residual” agency issues, loan spread would also increase but to a lesser extent.
 
27
Lou and Otto (2020) examine how debt heterogeneity in firms’ debt structure affects the use of covenants in their loan contracts and find that it is positively associated with covenant intensity but not significantly associated with loan spread. They argue that the finding is consistent with their theoretical argument because “the key argument underpinning our prediction that debt heterogeneity leads to more covenants (rather than higher interest spreads) is that debt heterogeneity increases the inefficiencies associated with liquidity defaults and that covenants can help reduce these inefficiencies.” This is because covenants “allow borrowers and creditors to increase total surplus they can share,” whereas “increasing the interest rate on the loan would not have the same effect” (p. 8 of their internet appendix).
 
28
In addition, to the extent that lenders have a larger stake in a loan at the beginning of the loan life (higher total future interest and possibly higher principal amount), compared to the later period, they may care more about the monitoring need at the beginning of the loan than later.
 
29
In an untabulated analysis, we calculate the fraction of the loan life that is expected to be covered by pre re-election and re-election years and use an indicator for high fraction as the interaction variable. We find similar results.
 
30
When we use Prior covenants as defined in Eq. (1), we obtain arguably stronger results: we find significant coefficients of SaleGov dummy × Prior covenants for both All covenants and General covenants (untabulated).
 
31
The recomputed Prior covenants is also included in Eq. (1) as a control variable.
 
32
We obtain the data on changes in congressional committee chairmanships from the supplemental material of Cohen et al. (2017) at https://​doi.​org/​10.​1086/​694203.
 
33
In an untabulated test, we also employ an instrumental variable analysis. Specifically, we follow Dhaliwal et al. (2016) and use the total government sales of each three-digit SIC industry scaled by total industry sales as an instrument variable for our treatment variable SaleGov dummy. We continue to find that the existence of major government customers is negatively related to the number of covenants (both general and financial covenants) and the use of performance pricing provisions.
 
Literature
Cameron, A. Colin., and Pravin K. Trivedi. 2005. Microeconometrics: Methods and applications. New York: Cambridge University Press.CrossRef
Cohen, Lauren, and Christopher Malloy. 2016. Mini west virginia: corporations as government dependents. Working paper.
Demski, Joel S., and Robert P. Magee. 1992. A perspective on accounting for defense contracts. The Accounting Review 67: 732–740.
Engstrom, David F. 2013. Public regulation of private enforcement: empirical analysis of DOJ of qui tam litigation under the False Claims Act. Northwestern University Law Review 107: 1689–1756.
Halchin, L. Elaine. 2012. Overview of the federal procurement process and resources. Current Politics and Economics of the United States, Canada and Mexico 14 (1): 1–8.
He, Jiapeng, Kevin Li, Ningzhong Li, and Weining Zhang. 2020. Proprietary information cost of contracting with the government. Working paper.
Madsen, Marcia G., and Roger V. Abbott. 2017. Government contracts: a regulated business relationship. In The False Claims Act and Government Contracts, eds. Hill, Brian A., Marcia G. Madsen and Gail D. Zirkelbach. American Bar Association.
Millman, Claude M. 2019. Avoiding litigation when auditing government contractors. The CPA Journal 89: 68–69.
Sansone, Alicia, and Allison Taylor. 2007. The handbook of loan syndications and trading. New York: McGraw-Hill.
Sweet, Mark B., Kara M. Sacilotto, and Kevin J. Maynard. 2017. Other consequences. In The False Claims Act and Government Contracts, eds. Hill, Brian A., Marcia G. Madsen and Gail D. Zirkelbach. American Bar Association.
Wooldridge, Jeffrey. 2002. Econometric analysis of cross section and panel data. Cambridge: MIT Press.
Metadata
Title
Major government customers and loan contract terms
Authors
Daniel Cohen
Bin Li
Ningzhong Li
Yun Lou
Publication date
01-04-2021
Publisher
Springer US
Published in
Review of Accounting Studies / Issue 1/2022
Print ISSN: 1380-6653
Electronic ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-021-09588-7

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