In studying the role of creditor control in the resolution of financial distress ex-post, I focus on the probability of resolving financial distress out of court relative to filing for bankruptcy, and I estimate a probit model: Out-of-Court Restructuringi,t = 1 Y∗≥0 0 Y∗<0 (2.4)
where:
Y∗=β1(Creditor Control)i,t−1+β2(Creditor Control)i,t−1.(Collateral Intensity)i,t−1+β3Xi,t−1+εi,t εi,t∼N(0,1)
My main coefficient of interest is β2, which I expect to be positive from Prediction (1) of the previous
section. I also expectβ1 >0. In the theoretical benchmark, creditor control is a contract whereby the
entrepreneur or CEO is running the firm on behalf of the equity holders as long as outstanding debt is repaid, but control shifts to the creditors upon failure to repay. The empirical literature (Chava and Roberts, 2008; Roberts and Sufi, 2009a; Nini, Smith, and Sufi, 2012) has identified such creditors in control as the lenders that are protected by debt covenants in the context of private credit agreements. Upon violation of such covenants, these papers document that control shifts to creditors, as creditors typically threaten to accelerate the loan and as a result capital investment declines sharply following a financial covenant violation (Roberts and Sufi, 2009a).
A growing literature has established that such creditors exercise control in a variety of ways, not just in the case of a covenant violation (or technical default). For example, covenants are often renegotiated well before a violation occurs (Roberts and Sufi, 2009b; Denis and Wang, 2013). Therefore, covenants in private lending agreements are a contractual feature that makes sure that control shifts to creditors upon violation. Covenants in private lending agreements differ from those in public debt agreements in two major ways. First, evidence in Billett, King, and Mauer (2007) suggests that fewer than 5% of public bond indentures contain an explicit restriction on firm investments. Second, even when present, the covenants in public bond indentures are generally less tight than their counterparts in private credit agreements, in the sense that the distance between the covenant threshold and the actual accounting mea- sure is larger in public bond indentures (Kahan and Tuckman, 1995).
As a result of the above, I identify firms as having a “creditor control”contract if the firm in both the year prior to entering financial distress and the year of financial distress has 1) at least one outstanding private credit agreement, and 2) one of such private credit agreements has at least one covenant. I search the DealScan database of private credit agreements to determine if my sample firms fulfill these condi- tions, and I classify as creditor control 86 of my poor performers (out of 161), and 34 of my junk bond issuers (out of 93).
The other variable needed to estimate model (2.4) is the importance of collateral across industries, that is, collateral intensity. I proxy for collateral intensity by 1 minus the contract intensity measure con- structed by Nunn (2007), which in turn proxies for the importance of relationship-specific investments. My collateral intensity measure therefore captures the extent of the collateralizability of the firm’s assets,
Table 2.1
The Twenty Least and Twenty Most Collateral Intensive Industries
The table lists the twenty least and twenty most intensive industries in terms of collateral out of a pool of 281 industries in total from a sample of 4,598 distinct firms (34,655 firm-year observations) between 1996 and 2011. Coll.Int. refers to Collateral Intensity and is calculated as (1 - Contract Intensity) whereby Contract Intensity measures are taken from Nunn (2007).ATrepresents average asset tangibility by industry.
Least Collateral Intensive Most Collateral Intensive
SIC Coll.Int. Industry Description AT SIC Coll.Int Industry Description AT
3711 0.023 Motor Vehicles & Passenger
Car Bodies
0.166 2090 0.827 Miscellaneous Food Prepa-
rations - Coffee & Tea
0.278
3571 0.044 Electronic Computers 0.097 3390 0.838 Miscellaneous Primary
Metal Products
0.324
7372 0.053 Services-Prepackaged Soft-
ware
0.099 2070 0.856 Fats & Oils 0.517
3572 0.058 Computer Storage Devices 0.115 2810 0.862 Industrial Inorganic Chemi-
cals
0.496
6411 0.069 Insurance Agents, Brokers
& Service
0.069 3580 0.864 Refrigeration & Service In-
dustry Machinery
0.253
3575 0.084 Computer Terminals 0.166 2990 0.870 Miscellaneous Products of
Petroleum & Coal
0.236
3576 0.099 Computer Communications
Equipment
0.100 2090 0.875 Miscellaneous Food Prepa-
rations & Kindred Products
0.512
3721 0.107 Aircraft 0.200 3341 0.879 Secondary Smelting & Re-
fining of Nonferrous Metals 0.305
3663 0.109 Radio & TV Broadcasting
& Communications Equip- ment
0.138 3330 0.889 Primary Smelting & Refin-
ing of Nonferrous Metals
0.508
3812 0.112 Search, Detection, Naviga-
tion, Guidance, Aeronauti- cal Sys
0.129 3270 0.892 Concrete, Gypsum & Plaster
Products
0.504
2790 0.113 Service Industries For The
Printing Trade
0.213 2040 0.901 Grain Milling Products -
Rice
0.436
3760 0.115 Guided Missiles & Space
Vehicles & Parts
0.333 3334 0.908 Other Aluminum Rolling &
Drawing
0.528
3661 0.120 Telephone & Telegraph Ap-
paratus
0.122 5190 0.913 Wholesale-Miscellaneous
Non-durable Goods
0.453
3825 0.127 Instruments For Meas &
Testing of Electricity & Elec Signals
0.137 3350 0.917 Rolling Drawing & Extrud-
ing of Nonferrous Metals
0.409
3728 0.128 Aircraft Parts & Auxiliary
Equipment, NEC
0.219 3334 0.942 Primary Production of Alu-
minum
0.537
2750 0.128 Commercial Printing 0.136 3845 0.947 Electromedical & Elec-
trotherapeutic Apparatus
0.338
2451 0.146 Mobile Homes 0.197 2040 0.964 Grain Milling Products -
Wet corn
0.531
3931 0.146 Musical Instruments 0.307 2911 0.964 Petroleum Refining 0.586
2082 0.149 Malt Beverages 0.459 2040 0.976 Grain Mill Products - Flour 0.438
3760 0.150 Guided Missiles & Space
Vehicles & Parts
0.145 2015 0.976 Poultry Slaughtering and
Processing
both the physical and the non-physical ones, and varies by industry. It has a direct theoretical counterpart in my model parameter alpha. When collateral intensity is high then alpha is high and assets are easy to collateralize.
Importantly, unlike asset tangibility, collateral intensity is an industry characteristic, so it reflects the industry’s economic conditions and does not depend on the firm’s actual decisions. Table A3 in the Appendix shows that collateral intensity correlates positively with asset tangibility, but the coefficient is only 53%. Table 1 reports the twenty least and the twenty most collateral intensive industries. It shows that electronics and computers are among the least collateral intensive industries; and that petroleum re- fining and electromedical and electrotherapeutic are among the most collateral intensive ones. The table also reports average asset tangibility in those industries, clearly showing the lack of a perfect correspon- dence between the two measures. For example, the petroleum and coal products industry has low asset tangibility but high collateral intensity.