Demand Shock, Liquidity Management, and Firm Growth during the Financial Crisis

Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. Demand Shock, Liquidity Management, and Firm Growth during the Financial Crisis Vojislav Maksimovic, Mandy Tham, and Youngsuk Yook 2015-096 Please cite this paper as: Maksimovic, Vojislav, Mandy Tham, and Youngsuk Yook (2015). “Demand Shock, Liquidity Management, and Firm Growth during the Financial Crisis,” Finance and Economics DiscussionSeries2015-096. Washington: BoardofGovernorsoftheFederalReserveSystem, http://dx.doi.org/10.17016/FEDS.2015.096. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

Demand Shock, Liquidity Management, and Firm Growth during the Financial Crisis Vojislav Maksimovic, Mandy Tham, and Youngsuk Yook∗ October 2015 ABSTRACT Weexaminethetransmissionofliquidityacrossthesupplychainduringthe2007–09 financial crisis, a period of financial market illiquidity, for a sample of unrated public firms with differential demand shocks. We measure differential demand by comparing firms that primarily supply to government customers with those that primarily supply to corporate customers. A difference-in-difference analysis shows little evidence that relatively high demand firms provide more or less liquidity to their own suppliers. The main determinant of the usage of short-term financing is a product market shock. Firms withrelativelyhighdemandhavehigherraw-materialinventoryandusemoretradecredit. Thereislittleevidencethattheamountofcreditusageperunitofraw-materialinventory changeswithfirms’demandshocks. Theseoutcomesareconsistentwiththeoriesoftrade creditthatstresstheuseoftradecreditinfinancinginputsratherthanprovidingefficient monitoringofcreditorsbysuppliers. Thelackofliquidityprovisiontosuppliersbyhigh demand firms is likely due to the high opportunity costs they face: We show that such firmsbecomemoreinvestment-constrainedoverthecrisisandengageinmoreacquisition activitiesoncetheliquiditycrunchdissipates. Keywords: FinancialCrisis;DemandShock;LiquidityManagement;TradeCredit;Inventory ∗University of Maryland, Nanyang Technological University, and Federal Reserve Board of Governors, respectively. Maksimovic can be reached at vmaksimovic@rhsmith.umd.edu. Tham can be reached at atmtham@ntu.edu.sg. Yookcanbereachedatyoungsuk.yook@frb.gov. Theviewsexpressedinthisarticleare thoseoftheauthorsandnotnecessarilyoftheFederalReserveSystem. WethankRickOgdenforhisresearch assistance.

1. Introduction Weinvestigatetheeffectofamacro-levelliquidityshockanddifferentiatedproduct-market shocks on the product market and financing outcomes of firms. In particular, we ask whether firms hit by the recent financial crisis but facing comparatively high demand used their comparatively stronger product-market advantage to provide additional financing to their suppliers. Such a strategy is potentially profitable for the firms taking on the role of a provider of capital at a time of scarcity. We further examine whether firms with strong product market demand used the liquidity crisis as an opportunity to strengthen their position relative to their competitors. The 2007–09 financial crisis started with a severe macro-level liquidity shock accompanied by observable differential product-market shocks–some firms saw the demand for their products fall, while others saw demand from a reliable and observable customer hold steady and even increase. We measure the latter group by identifying firms that primarily supply to government departments and agencies. We term this group of firms government suppliers and compare their financial policies during the financial crisis and its aftermath to those of what we call corporate suppliers, firms that primarily supply to corporations. We take a difference-in-differenceapproachtocomparegovernmentsuppliersinpre-crisisandcrisisperiods relative to corporate suppliers. This approach alleviates the concern that the ability to acquirehigh-demandcustomersisendogenous. We find that firms that experienced a positive demand shock during the crisis did not act as liquidity providers to their own suppliers. On the contrary, such firms increased accounts payable relative to their cost of goods sold while increasing outputs, suggesting that they were, on balance, relying more heavily on vendor financing. Contemporaneously, these firms increased their short-term debt financing only marginally, which most likely came from liquidity-constrainedfinancialintermediaries. In contrast, the pattern of short-term financing by firms that mainly supplied to corporations presents a mirror image. These firms increased their reliance on short-term debt substantially, but simultaneously lowered their reliance on vendor financing. The contrast in 1

financingoutcomesbetweengovernmentandcorporatesuppliersmightsuggestthatcorporate supplierswereunabletoobtainfinancingfromtheirownsuppliersandwereforcedtoborrow fromfinancialintermediaries,whereasfirmswithreliablecustomerswereabletoborrowfrom their suppliers. However, we show that these patterns of short-term financing are most likely drivenbydifferencesinthefirms’product-marketshocks. Governmentsuppliersbuiltuprawmaterial inventory to service increases in demand, whereas corporate suppliers facing falling demand built up finished-goods inventory. As suggested by models of trade credit financing that argue that vendors have a comparative advantage in financing raw material inventories, raw-material inventory is usually financed by vendors, whereas finished-goods inventory is morenaturallyfinancedbyfinancialintermediaries. Over the crisis period, government suppliers grew faster in terms of revenues than corporate suppliers. While this growth was accompanied by a smaller drop in capital investment than for corporate suppliers, there is evidence that government suppliers’ investment did not keepupwiththeincreaseintheirgrowthopportunities. Directevidenceforthisisthatoverthe crisisperiod,theindexofinvestmentdelayconstraints,derivedfromHobergandMaksimovic (2015),showsageneralincreaseforgovernmentsupplierswhereasthatofcorporatesuppliers decreases. Bytheendofthecrisis,governmentsuppliershadsubstantiallyincreasedthemarketshare in their industries relative to corporate suppliers. However, the constraint in their ability to obtainlong-termfinancingduringthecrisisimpliedthattheirsales-to-fixedassetsratioissuboptimal. Their cash holdings also remained lower than corporate counterparts. We show that asaresult,constrainedfirmsaremorelikelytoengageinmergeractivitypostcrisis. Our results illustrate several points: First, firms experiencing positive demand shocks in a financial crisis did not offer liquidity to other firms through trade channels. On balance, theywerenetusersofcashinthecorporatesector. Thisfindingimpliesthat,whilesuchfirms will have a positive effect on the expected income of their supplier firms by transmitting the demand shocks, they do not provide indirect additional financing through the trade channel. Thus,theydonotsubstitutefortheliquidityshocksofthefinancialsector. 2

Second, both firms experiencing positive demand shocks and firms experiencing negative shocksincreasedtheirdemandforshort-termfinancing. Theformerborrowedmorefromsuppliersinordertofinancethepurchaseofrawmaterialsforproduction,andthelatterborrowed more from financial intermediaries in part to finance finished-goods inventory. This suggests that the optimal provision of short-term financing may be more closely tied to the type of shocksfacedbyfirms. Third,firmswithpositivedemandshocksbecamefinanciallyconstrainedduringthefinancial crisis. Thus, during the recession, their opportunity cost of liquidity increased, making themlesslikelytoprovideliquidityfortheirownsuppliersorotherfirms. Fourth, the firms who were doing comparatively well did not fully convert their increases in market share into stronger real-asset positions until the general liquidity constraints were reduced. At that time, they increased their fixed assets through acquisitions. Thus, as suggested by Harford (2005), periods of low liquidity created demand for asset reallocations that occurredonceliquiditywasrestored. Ourpaperisrelatedtoastrandintheinternationalfinanceliteraturethatfollowsthework of Calvo, Izquierdo, and Talvi (2006) (CIT) and examines recovery in emerging market crisis episodes characterized as Systemic Sudden Stops (3S episodes) where the liquidity of the financial sector and demand decline sharply. CIT observe that in their sample of 3S episodes, output in the economy collapses with bank lending to commercial customers but that output bounces back to full recovery before bank lending is restored to prior levels. They argue that the provision of working capital between firms is the mechanism by which the economy recovers, calling a macro recovery of this type a Phoenix recovery.1 Our evidence suggests that this channel is unlikely to be effective in financing firms facing negative demand shocks. In the absence of a quick financial sector recovery, firms facing negative demand shocks are notabletorelyontradecredittofinancerecovery. Instead,weobservethatsuchfirmsrelyon bankfinancing,particularlyshort-termdebt. 1Claessens,Kose,andTerrones(2009;2012),Abiad,DellAricia,andLi(2011)andAyyagari,Demirguc-Kunt andMaksimovic(2011)alsoevaluatePhoenixrecoveries. 3

Our paper is also related to the literature on trade credit. The literature has examined factors such as the informational advantages of vendor financing over bank financing (Biais and Gollier (1997)). In this paper, we have a clean natural experiment in which external liquidityfromintermediariesexogenouslybecamemoredifficulttoobtain. Weshowthatgovernment suppliers, who experienced observable positive shocks, increased reliance on trade credit when compared to corporate suppliers experiencing negative demand shocks, which increased reliance on bank financing. This finding suggests that short-term trade credit financing is unlikely to be driven by informational advantages and risk sharing by a borrowing firm’s suppliers, and is more likely to be driven by the composition of the firm’s short-term assets. Thus, our results suggest that trade credit financing is the likely response to demand shocks resulting in the accumulation of raw-material inventory while financing by financial intermediariesisthelikelyresponsetoshocksthatincreasefinished-goodsinventory. Wilner (2000) and Cunat (2007) examine the role of trade credit in transmitting liquidity from suppliers to customers. In Cunat’s model, suppliers have an incentive to insure their customers against adverse shocks and will provide liquidity in cases where banks will not allow late payments on sales in the form of increasing accounts payables on customers’ balancesheets. Oursetupexaminesareversecase. Wehavegovernmentsuppliers,whoseoutput demandisdifferentiallyshockedduringaliquiditycrisis. Byobservingwhetherornotgovernment suppliers pay their suppliers faster than corporate suppliers, as measured by the ratio of accounts payables to raw-material inventory, we can infer whether or not government suppliersareprovidingtheirsupplierswithmoreliquidity. Afallingratiowouldidentifyaplausible mechanismforthePhoenixrecoveries. Two studies have examined the role of trade credit as an alternative source of liquidity in themidstofliquiditycrunchesduringcrises. Examiningthe2007–08crisis,Garcia-Appendini andMontoriol-Garriga(2013)documentthatU.S.firmswithhighpre-crisisliquidityreserves initiallyextendedtradecreditfortheircustomers,thoughtheyretractedasthecrisisprogressed because they were unable to replenish their cash reserves. Similarly, examining six emerging markets during the 1997 Asian crisis and 1994 Mexican crisis, Love et al. (2007) document that trade credit provision increased immediately after a crisis broke but collapsed soon after and continued to contract for several years. They argue that, for redistribution to take place, 4

some firms first need to be able to raise external financing to pass on to less privileged firms, but that during a financial crisis, all sources of financing become scarce, leading to the shutdown of the redistribution channel. We add an important dimension, demand shock, to this setup,whichallowsustoinvestigatecrucialquestions: Arefirmswithpositivedemandshocks able to weather the storm despite the liquidity drought? Does liquidity spill over from these firmstoweakerlinks,revivingtheredistributionchannel? Finally, our paper has implications for the effect of government spending on firms. First, government spending enables the firms supplying to government to grow and expand their market share during the crisis. Cohen, Coval, and Malloy (2014) document that government suppliersgenerallyinvestlessinphysicalandintellectualcapitalandhavelowersalesgrowth than corporate suppliers. This finding leaves a puzzle of why firms choose to supply to the government when their expected performance is inferior to those supplying to corporate customers. Our study provides one explanation for this question by showing that government suppliers outperformed corporate suppliers in the crisis period–that is, while their upside potentialinnormaltimesmaybelimited,governmentsuppliersareprotectedfromthedownside riskduringcrisesbyhavingareliablecustomer. Second,governmentspendinghasaspill-over effect along the supply chain. Notable increase in government suppliers’ raw-material inventory during the crisis implies that positive demand shocks are transmitted to their suppliers, generatingrealeffects. 2. The Crisis and Financing of Firms The recent financial crisis provides a natural experiment setting for a sudden decline in liquidity in a developed economy. Figure 1 shows that the TED spread (the 3-month LI- BOR based on U.S. dollars minus the 3-month Treasury bill), which stayed below 1% in the pre-crisis period, climbed to 2.4% in August 2007, and to as high as 4.6% in October 2008. Similarly, the Federal Reserve Board’s Senior Loan Officer’s Opinion Survey indicates that banks started tighteninglending standards on commercialand industrial loans in 2007:Q3for mediumandlargefirmsandin2007:Q1forsmallerfirms(Mach(2014)). IvashinaandScharf- 5

stein(2010)andSantos(2011)showthatbankscutdownonloanoriginationsubstantiallyand chargedmuchhigherspreadswhentheyoriginatednewloans. Outputalsoplummeted(figure 1)andtheunemploymentratemorethandoubledasthecrisisprogressed.2 Forouranalysis,wedefine2007:Q3–2009:Q2asthecrisisperiodandcomparethisperiod tothepre-crisisperiodof2005:Q3–2007:Q2. Forsomeanalysis,wealsoextendthewindowto includethepost-crisisperiodof2009:Q3–2011:Q2. NotethatwhileNBERdefinesDecember 2007–June 2009 as a contractionary period, we view the crisis as starting in 2007:Q3; the signs of a liquidity crunch started appearing in 2007:Q3, and studies examining the recent crisis generally consider 2007:Q3 as the beginning of the crisis (for example, Duchin, Ozbas, andSensoy(2010)andGortonandMetrick(2012)). For our purposes, it is important that in the face of declining aggregate demand, the U.S. government maintained and increased spending, providing additional financing to jump start projects. Figure 2 shows that total government spending as a fraction of GDP steepened sharplyduringthecrisis.3 Governmentspendingduringthecrisiscreatedanaturalexperiment setting in which corporate suppliers, which did not receive government contracts, were likely to receive a negative demand shock while government suppliers, which received government contracts, were more likely to see demand maintained or increased. While the two groups of firms had differential product market shocks, they were both subject to the general liquidity shock. The liquidity shock had two effects. First, it made external financing more expensive to obtain. Second, it made it difficult for firms to obtain long-term financing, which we show below. 2.1. Supply of Liquidity by Firms in the Crisis A question of interest is whether firms experiencing a positive demand shock can act as conduitsofliquiditytootherfirmsintheeconomy,specificallytotheirsuppliers. Totheextent 2Theseasonally-adjustedunemploymentratesoaredfrom4.6%inJune2007to9.5%inJune2009. 3Newgovernmentcontractslikelystartedpickingupsoonerthansuggestedbythefigurebecausegovernment contractstendtotranslateintoactualexpenditurewithsomelags. Forexample,ittookanaverageof281days forunratedgovernmentsuppliersinoursampletobepaidbycustomersduringthecrisis,assumingallcustomers received100%credit. 6

that such firms have publicly observed positive demand shocks, they should be able to get financingduringthecrisisonbettertermsfrombanksandotherfinancialintermediariesrelative to those suffering from a negative demand shock. A straightforward comparative advantage argument suggests that this will lead these firms to use this advantage by negotiating terms with their suppliers–the firms facing positive demand shocks should be willing to speed up paymentstosuppliersinreturnforlowerinputprices. Theexpeditedpaymentschedulewould be financed either out of current cash flows or from a marginal increase in external financing. Thus, these considerations would suggest that, holding everything else constant, firms facing positivedemandshocksshould,comparatively,bereducingtheiraccountspayableand,tothe extentpossible,increasingtheirrelianceonshort-termdebt. Counteringthistendency,firmsfacingstrongdemandinacrisisarealsolikelytobefinancially constrained at the margin–that is, they may have a greater use for financing in order to facilitate their growth. To the extent that this tendency predominates, we would expect to see positivelyshockedfirmslengthentheirpaymentscyclestosuppliers,perhapsevensoakingup liquidityfromthematthemargin. Which of these tendencies predominates has significant macro-finance implications. If, in a crisis, positively shocked firms serve as a conduit for providing liquidity to their suppliers, then real sector recovery may occur even before the financial sector is fully restored, as suggested by CIT. By contrast, if positively shocked firms are net drains on the liquidity of their suppliers,thenageneralrecoverywillbeslower. Below, we examine this question in three steps. First, we exploit the natural experiment structure to examine whether or not firms with positive demand shocks supply liquidity to their suppliers by comparatively reducing accounts payable and increasing short-term debt. Second, we directly measure the financial constraints expressed by firms that have positive and negative real shocks during the crisis. Do positive demand shocks tighten or relax the firm’s constraints? Third, we consider the relation between a firm’s observed financial constraints and its financial policy. If the nature of product-market shocks predicts firm-level constraints, then an association between observed financial constraints and its provision of 7

liquidity will predict the extent to which firms facing positive shocks will act as de facto financialintermediariesinafinancialcrisis. 2.2. Effect of Shocks on Firm’s Short-Term Liabilities We term the hypothesis that firms with positive demand shocks transmit liquidity along the supply chain the liquidity hypothesis. There are two direct channels by which this can occur. First, positively shocked firms can expedite payments for inputs purchased from their suppliers. Second, they can offer more generous credit terms to their customers. Specifically, in our context, the liquidity hypothesis predicts that government suppliers will reduce their accounts payable. We may also expect government suppliers to increase their accounts receivable. However, this second effect is likely to be attenuated because we would expect government suppliers to extend additional financing only to their commercial customers, not tothegovernment. Wetestthesehypothesesbelow. The analysis above focuses on the effect of the overall level of liquidity faced by firms experiencing positive and negative demand shocks in the crisis. However, the way positive andnegativeshocksinteractwiththeproductionprocessoffirmsmayalsoaffecttheirfinancing choices. Sources of possible short-term financing are increases in short-term debt, usually from financial intermediaries, increases in accounts payable (that is, increases in credit from suppliers), and decreases in accounts receivable (that is, a reduction in financing to customers). Governmentsuppliersweresubjecttoaliquidityshockatthesametimeastheirneeds for financing–short-term financing of raw-material inventory and long-term financing of new investment capacity–were increasing. Corporate suppliers were facing potentially lower demand and the need to finance finished-goods inventory stemming from slowing sales. That is, firms facing negative supply shocks are likely to experience a revenue shortfall accompanied by a need to finance inventories of finished goods. By contrast, firms facing a positive demand shock are likely to need to build up production and inventories of raw materials and goods-in-process.4 4Bothshocksmayalsoleadtoincreaseddesiretofinancetradecredittocustomers,inthefirstcasetosupport decliningsalesandinthesecondcasetosupportincreasedsales. 8

Ifthetwosourcesofshort-termfinancing–fundsfromoperationsandshort-termloans–are close substitutes, we would expect both firms with positive shocks and firms with negative shocks to have similar responses to their shocks. Thus, we would observe similar changes in their short-term asset and liability positions. However, if trade and bank financing have different comparative advantages, then we would expect a tilt towards trade credit or shortterm debt financing of differentially shocked firms, holding all else constant. We consider severaldifferenttypesofpotentialcomparativeadvantages. First,tradecreditorshaveaninformationaladvantageoverintermediaries,assuggestedby Biais and Gollier (1997). Suppliers are able to monitor the order flow from their customers and may have detailed information about the quantity and type of inputs. Since they are also potential suppliers to their customers’ suppliers, they may be informed about the relative outputs of their customers and competitors. Biais and Gollier argue that these considerations make it efficient for firms to obtain short-term financing from both financial intermediaries and suppliers, since the willingness of suppliers to provide financing provides information to financialintermediaries. Thisinformationalvalueofcreditfromsuppliersissmallerwhenthe customer is reliable and when information about the status of the customer’s order book is publicly available. Firms with observable, creditworthy customers are likely to find it relativelycheapertoobtainshort-termdebtfrombanksratherthanfromtheirsuppliers. Thus,we wouldexpectatilttowardstrade-creditfinancingforfirmswhoseshockisaccompaniedbyan increaseindownsiderisk,holdingall elseconstant. Inourcontext,corporatesupplierswould facerelativelyhigheruncertaintyandprobabilityofdistressthangovernmentsuppliersduring the crisis. Thus, the information hypothesis would predict a comparative increase in accounts payable and a relative decrease in short-term debt financing for corporate suppliers relative to governmentsuppliers. Second, if different shocks lead to a different pattern of operational short-term holdings by firms and if trade and financial intermediaries have a comparative advantage in financing a specific type of assets or shortfalls, then we would expect to see differences in the pattern of short-term asset holdings. Specifically, firms facing negative supply shocks are likely to experiencearevenueshortfallaccompaniedbyaneedtofinanceinventoriesoffinishedgoods. 9

Bycontrast,firmsfacingapositivedemandshockarelikelytoneedtostepuptheirproduction andinventoriesofrawmaterialsandgoods-in-process. Thereareseveralreasonswhywemayseeadifferentialcomparativeadvantageofdebtor tradefinancingforshort-termassets. AsdiscussedbyFrankandMaksimovic(2005),suppliers have a comparative advantage in gaining security over the goods that they supply (that is, the firm’s raw materials). This advantage disappears as the goods are processed and turned into a finalproduct. Thus,weexpectsuppliers,ratherthanbanks,tofinanceinventories,particularly rawmaterialsorinputs. A similar prediction can be generated from arguments by Lee and Stowe (1993) and Kim and Shin (2012) that vendor financing is an implicit warranty offered to customers. That warranty is likely to extend until the goods are turned into a final product. To the extent that the warranty is tied to the size of raw-material inventory and work-in-progress, the amount of short-term credit will also depend on the composition of assets. Thus, we would expect thatifthecompositionofthefirm’sshort-termassetschangesbetweenassetsusedearlyinthe productioncycleandassetsusedlateintheproductioncycle,therewillbeadifferentpreferred mixofshort-termfinancing. Wetermthishypothesistheassetcompositionhypothesis. These arguments lead to sharply different predictions, which we test below, about the comparative behavior of government and corporate suppliers in the crisis. If the information hypothesisdominates,andtheprovisionoftradecreditisdrivenbythecomparativeadvantage of vendors in evaluating the viability of firms, we would expect to see corporate suppliers to receive greater injection of trade credit (accounts payable) than government suppliers. Trade financing would become relatively more valuable to corporate suppliers. Government suppliers, whose prospects are more certain as a result of having a more creditworthy customer, would gravitate to bank financing, which would become relatively less expensive to these customers, given that they would require less financing by suppliers, who themselves may befinanciallyconstrained. Thus,theinformationhypothesispredictsarelativeincreaseinaccountspayablebycorporatesuppliersandarelativeincreaseinshort-termdebtbygovernment suppliers. 10

In contrast, if the asset composition hypothesis drives the differential mix of short-term financing,thengovernmentsuppliers,whoaregrowingrelativelyfaster,willexperiencecomparatively larger raw-material inventories than corporate suppliers, will have less involuntary finished-goods inventory accumulation, and will thus experience relatively larger increases in accounts payable. The prediction of the asset composition hypothesis on the relative shortterm debt financing by government and corporate suppliers is similar. To the extent that corporate suppliers have general shortfalls in liquidity and a greater need to finance either their customers or final inventories, they will have a relative increase in short-term debt financing. However, given the drying up of long-term financing, this effect may be muted if governmentsupplierssubstituteshort-termforlong-termdebtfinancingtofundtheirrelativelyhigher growth. In principle, if the liquidity hypothesis dominates, there may be interactions between the predictions of that hypothesis and those of the asset composition hypothesis. We address thisbelowwhenreviewingtheempiricalresults. 2.3. Liquidity, Demand Demand Shocks, and Firm Growth Finally,weinvestigatehowthecommonliquidityshockaffectsfirms’financingandproductmarket positioning after the crisis. As noted above, firms differ in the product-market shock theyreceivearoundthetimeoftheliquidityshock. Givenlimitedaccesstofinancialmarkets, didthefirmsthatreceivedrelativelyfavorableproduct-marketshockstranslatetheirtemporary advantage into a more permanent product-market advantage? Firms also differ in their initial credit rating, which affects access to financial markets. To the extent that there is precautionary demand for financial flexibility, we would expect that those firms that had better credit ratingswereabletochangetheirlong-runcompetitiveposition. Didthisinfacthappen? 11

3. Identification Strategy and Data 3.1. Identification Strategy WetakethefinancialandoperatingstateofU.S.firmsinthepre-crisisperiodasexogenous with respect to the financial crisis. The crisis and the subsequent liquidity shock precipitated demand shocks across firms. We assume that the magnitude of the demand shocks was not anticipatedbyindividualfirmsandthattheyareexogenoustoanindividualfirm. However,we can only observe sales revenue at the firm level and not the exogenous shocks themselves. To measuretheshocks,werelyonthedifferentialresponseofgovernmentandprivatecustomers to the financial shock. Following the shock, the economy slowed substantially with real GDP growth falling to a negative territory. However, the U.S. government instituted a recovery programthatattemptedtoboosttheeconomybyincreasinggovernmentspending. Toobtainasampleoffirmsthatwerelesslikelythantheaveragefirmtoreceiveanegative shock in the crisis, we consider a sample of firms that had government as a major customer prior to the crisis, namely government suppliers. Using information from the 10-K filings thatwasrecordedintheCompustatBusinessSegmentFilesfromStandardandPoor’s(S&P), we identify firms that primarily supplied to government agencies in 2006 (Please see the next subsection for details on how we identify government and corporate suppliers).5 We rely on the assumption that a customer-supplier relationship tends to be long-term in nature, that a firm that has an on-going relationship with the government was better placed to win new orders from the government than a firm that did not, and that, in particular, such firm was unlikely to suffer from a negative demand shock which affected a significant portion of the remainingfirms. We note that firms that have material customers differ from a typical Compustat firm. Fee et. al (2006), for example, show these firms tend to be smaller and to have lower leverage. To provideacontrolsampleforgovernmentsuppliers,wealsoidentifycorporatesuppliers,firms that primarily supplied to corporate customers in 2006. The objective of identifying a control 52015S&P’sFinancialServicesLI.C.Allrightsreserved.Forintendedrecipientonly.Nofurtherdistribution and/orreproductionpermitted. 12

sample of corporate suppliers is to control for potentially unobservable characteristics that firmswithlargematerialcustomers,whethergovernmentorprivate,mayhaveincommon. Much of our analysis consists of difference-in-difference comparisons of financial and product-market policies of government and corporate suppliers in response to the exogenous crisis shock. We assume that neither the magnitude of the firm-level demand shocks from privatecustomersnorthegovernmentresponsewaspredictablein2006. Wealsodifferentiate betweenfirmsthathadcreditratingsin2006anddidn’t. Weexpectfirmsthatwereunratedto havehadlessaccesstoexternalfinancingduringthecrisis,andweexpecttoseealargereffect ofliquidityconstraintsandnegativedemandshocksinthosefirms. A key question in the measurement of the effects of the crisis on firms is the question of standardization. A firm’s accounts are interconnected through its balance sheet, so that the total quantity of an asset and liability may change in response to a shock, while the ratio of different accounts may not change, and vice versa. The appropriate scaling depends on the hypothesis being tested. It is customary to measure many of the key indicators of a firm’s liquidity by scaling with the firm’s contemporaneous assets or sales. This is reasonable when focusing on liquidity in a homogenous demand environment. It is more problematic in cases wherefirmsintwosubsamplesofinterestarefacingdifferentialdemandshocks. Accordingly, formuchofouranalyses,westandardizemeasuresofinterestbyeachfirm’spre-crisisaverage assets. This allows us to isolate the effect of the demand shocks on liquidity in a context analogous to an event study. In other cases, when comparing how sales or purchases are financed for different subsamples, we scale by relevant contemporaneous variables such as assets,sales,costsofgoodssold,andinventorylevels. 3.2. Customer Information We compile the customer information using supplier-customer relationships reported in the Compustat Business Segment Files. According to SFAS 14, public firms are required to disclose the names of customers that account for at least 10% of their total sales or whose purchase has a material impact on their businesses. We call these customers “principal cus- 13

tomers.”Inaddition,SFAS131issuedinJune1997requiresdisclosureoftheamountofsales generated from a principal customer.6 To identify corporate customers, we manually match the customer names to their corresponding GVKEYs in Compustat by closely following the approachinFeeetal. (2006). Forgovernmentcustomers,wemanuallycheckwhetherthecustomernameisaU.S.governmentagency. Wecomplementtheinformationusingthecustomer typevariablegivenintheCompustatBusinessSegmentFiles. Detailsonhowwehand-collect thecustomerinformationaredocumentedinAppendixC. To measure a firm’s dependence on each principal customer, we construct a customer reliance measure (CUSTREL), defined as the fraction of a firm’s sales that is attributed to a principal customer. In some cases, firms voluntarily report customers who contribute less than 10% of their revenue. To ensure consistency, we only retain firms whose CUSTREL is at least 10%.7 We classify a sample firm as a government supplier if its biggest principal customerisgovernment,andasacorporatecustomerifitsbiggestcustomerisacorporation.8 For our main tests, we identify our sample firms using their customer information as of 2006, and follow these firms over time to mitigate the selection bias. In unreported results, we repeat our tests by identifying our sample firms using their customer information every year to incorporate the changes in customer relationships over time. The results are little changed becausethecustomerrelationshiptendstobelonger-terminnature. 3.3. Summary Statistics For our sample of government suppliers and corporate suppliers, we obtain quarterly financial information between 2005:Q3 and 2011:Q2 for all firms available in the Compustat quarterly database. We exclude utilities and financial firms as well as observations with nonpositive values of assets. All our financial variables are winsorized at the 1% and 99% of their distributions. We also obtain S&P borrower credit ratings information (variable name: 6SeeAppendixCformoredetails. 7Toeliminatereportingerrors,wealsodropobservationswherethecustomerrelianceismorethan100%. 8Itisrareforafirmtohavebothgovernmentandcorporateprincipalcustomers,withonly2.7%ofoursample ofunratedfirmssellingtobothgovernmentandcorporateprincipalcustomersin2006. 14

SPLTICRM) from the Compustat ratings database.9 Appendix A provides the detailed descriptionofhowfinancialvariablesareconstructed. Panel A of Table 1 summarizes pre-crisis firm characteristics for all sample firms. Panel B split the sample to four subgroups according to sample firms’ rating information and customer types as of 2006. A firm is classified as rated if it is rated by S&P in 2006, and unrated otherwise. Unratedfirmsandratedfirmsare,respectively,furthersortedintogovernmentsuppliers (GOV) and corporate suppliers (CORP) according to their customer types. We observe 1,194 firms per quarter on average, of which unrated firms constitute about three quarters. Panel B shows unrated government suppliers and corporate suppliers have an average asset size(sales)of$300million($103million)and$511million($119million),respectively. Unrated firms are much smaller than an average Compustat firm, which has assets of $1,602 million and sales of $435 million in the corresponding period. Among unrated firms, government suppliers, which has less assets and sales than corporate counterparts, seem to rely more on one customer. The mean exposure of unrated government suppliers to their largest customers is higher at 47%. Unrated government suppliers have higher leverage, but lower capital expenditures-to-assets ratio (CAPEX/Assets), return on assets (ROA), and operating cash flows to assets ratio (OCF/Assets) than corporate suppliers. The market-to-book ratio (M/B)isroughlythesameforunratedgovernmentandcorporatesuppliers. Generally, the differences between rated government and corporate suppliers follow the same pattern. However, as expected, the rated suppliers as a group are larger and have higher average sales, leverage, ROA and OCF, but lower M/B than unrated suppliers as a group. Given the importance of ratings and size as a marker for access to financing (Faulkender and Peterson(2006)andHadlockandPierce(2010)),weanalyzethetwotypesoffirmsseparately andfocusourprincipalanalysesonunratedfirms. Next,weexaminehowunratedgovernmentandcorporatesuppliersfaredduringthecrisis. Figure 3 shows that government suppliers experienced robust growth in their assets over the crisis period. By contrast, the assets of corporate suppliers, while rising at the beginning of the crisis, started to decline when the crisis reached its vertex in mid-2008. This suggests 92015S&P’sFinancialServicesLI.C.Allrightsreserved.Forintendedrecipientonly.Nofurtherdistribution and/orreproductionpermitted. 15

that government suppliers were able to expand, despite the crisis, while corporate suppliers werenot. Whenweexaminesalesscaledbylaggedassets,weobservethatthisratiowaslittle changed for government suppliers in the crisis period while it declined a fair bit for corporate suppliers.10 Similarly, both the return on assets and the ratio of operating cash flow to assets, each standardized using average pre-crisis assets, show increases for government suppliers during the crisis period, but a steep decline for corporate suppliers during the vertex of the crisisinmid-2008. Table 2 shows the changes in a broader set of indicators from the pre-crisis (2005:Q3– 2007:Q2) to crisis (2007:Q3–2009:Q2) period for subsamples sorted on ratings information and customer types as of 2006. Note that, because firm size and sales changed substantially duringthecrisis,mostofthevariablesarescaledbypre-crisisaverageassetstoallowtotrack changes over time. We see that there has been an increase in sales and assets by all categories of firms during the crisis, with particularly striking and statistically significant growth for GOV firms. In subsequent analyses, we compare the changes for GOV and CORP firms usingadifference-in-differenceframework. AlsonotablearethelargeincreasesinCapexrelativetopre-crisisassets. WhileOCF increasedforbothGOV andCORPfirms,theincreaseis particularly notable for GOV firms. Consistent with this, ROA increased for GOV firms, and fell for CORP firms. Despite the observed increase in their investments and improved profitability, GOV firms’ PPE/Sales did not increase. These patterns raise the possibility, which we later investigate, that GOV firms may have had difficulty funding their desired expansion, whichwouldbeconsistentwiththesefirmsbeingfinancialconstrained. The patterns above are similar for both unrated and rated GOV andCORP firms, with the effects of the crisis for rated firms being less strong. These statistics are consistent with GOV firms receiving a differentially more favorable product market shock over the crisis period. For unrated firms, the effect on liquidity of the financial crisis appears stronger. Rated firms, whicharelargerandhavebetteraccesstoexternalfinancing,appearlessaffectedbythecrisis. Accordingly, in our analysis of the effects on the crisis on firms’ short-term financing, we focusonunratedfirms,usingratedfirmsforcomparisonwhereappropriate. 10Theratiodroppedabout7%forcorporatesuppliers,whichwasstatisticallysignificantatthe1%level. 16

4. Financing and Growth during the Crisis 4.1. Short-Term Assets and Liabilities We next consider the effect of the crisis on the short-term assets and liabilities of firms in our sample. Panel A of Table 3 shows the short-term asset and liability accounts scaled by the average quarterly pre-crisis assets. We observe a uniform increase across short-term asset categories (accounts receivables, total inventory, finished-goods and raw-material inventory, and cash) relative to their starting positions. Firms also increased their short-term liabilities (accounts payable and short-term debt). This is consistent with the finding in Table 2 that the ratioofsalestopre-crisisassetsincreased: Moreshort-termassetsandliabilitieswererequired tosupporttheincreasedlevelofactivityandsalesbyfirms. AdifferentpatternisobservedinpanelBofTable3,whereaccountsreceivablearescaled by current sales. Unrated GOV firms increased their accounts receivable to sales somewhat, albeitstatisticallyandeconomicallyinsignificant,whiletheaccountsreceivableofratedfirms (GOV andCORPinclusive)declined. Turningtothefinancingside,wescaleaccountspayable using cost of goods sold (COGS) rather than sales, as COGS captures input costs to finance production better than sales, which have a built-in profit margin.11 There is a clear distinction between unrated GOV and CORP firms. Unrated GOV firms had a significant increase in accounts payable, whereas unratedCORP firms did the opposite, decreasing their accounts payableconsiderably. Instead,theyincreasedrelianceonshort-termdebtrelativetosalesmore heavilythanunratedGOV firms. RatedCORPfirmsalsocutbackonaccountspayablesignificantlywhileratedGOV firmschangedlittle. Totheextentthatratedfirmshadcomparatively betteraccesstocapitalmarketsbutGOV firmshadstrongerdemand,theresultsraisethepossibility that these short-term financing patterns were caused by differentiated demand shocks. Below,wefurtherinvestigatewhetherthisdifferenceinshort-termfinancingcanbeexplained bydifferencesindemandshocks. The relation between demand shocks and short-term financing is further suggested by differential effects of inventories. Unrated GOV firms increased their inventories relative to 11SeeLoveetal(2007)formorediscussionsoftheseissues. 17

COGS significantly, and this increase was primarily driven by the increase in raw-material inventories, whereas the same ratios for unrated CORP firms remained largely unchanged. Cash holdings of CORP firms, both unrated and rated, declined significantly, whereas they changedlittleforratedandunratedGOV firms. In sum, there are significant differences in short-term asset and liability composition relative to contemporaneous sales or COGS of GOV and CORP firms. Evidence of financial constraints is stronger for unrated firms. However, the striking differences in the composition appear to be between GOV and CORP firms, rather than between rated and unrated firms. This pattern suggests that these differences are driven by demand shocks rather than liquidity constraints. Weinvestigatethisfurtherinthenextsection. 4.2. Short-Term Financing Wenowcompareshort-termfinancingresponsestothecrisisofgovernmentandcorporate suppliers in a regression framework. While the crisis decreased both types of firms’ access to external finance, the negative production shock most likely affected CORP firms. We use a difference-in-differenceapproachasfollows: y =α+β Crisis +β GOV +β Crisis ·GOV +X(cid:48)θ+ε , (1) it 1 t 2 i 3 t i it where the dependent variable is a short-term financing variable of interest for firm i at time t. GOV is an indicator variable that takes on the value of one if the firm is a government i supplier and zero if a corporate supplier. Crisis is an indicator variable that takes on the t value of one during the crisis period (2007:Q3–2009:Q2) and zero for the pre-crisis period (2005:Q3–2007:Q2). Industry fixed effects are also included, and standard errors are clustered at the firm-level. In our robustness tests, we add control variables (X), including the lagged values of the log of total assets (lag(logAssets)), cash holdings (lag(Cash/Assets)), cashflows(lag(OCF/Assets)),andquarterlysalesgrowth(lagSaleqg).12 Whiletherobustness 12Forcashregressions,wealsoincludelaggednetworkingcapitalscaledbyassets,sigma,andlaggedmarketto-bookratio. 18

tests show similar results, we do not report these results due to concerns that some of these controlvariablesmaythemselvesbeendogenous. Table 4 examines the short-term financing behavior of unrated firms. Column (1) indicates that, on average, GOV firms had more accounts receivable thanCORP firms before the crisis. This most likely reflects the governmental procurement practices and the fact that the government is at low risk of default. There are no significant changes to this ratio during the crisis, suggesting that unrated firms are not providing additional financing to their customers per dollar of sales during the crisis, nor are they cutting back on financing. Appendix B reports results for rated firms, which shows that the interaction term is likewise economically orstatisticallyinsignificant. Thus,column(1)providesnoevidenceinsupportoftheliquidity hypothesisthatGOV firmsprovideadditionalfinancingtotheircustomersduringthecrisis. Incolumn(2),weexaminehowunratedfirmsarepayingtheirownsuppliersbyexamining AP/COGS. Theratiodropsconsiderablyforcorporatesuppliersduringthecrisisasshownby a significant and negative loading on CRISIS (–0.049). The coefficient of CRISIS∗GOV is positive and statistically significant, indicating that AP/COGS for GOV firms increased duringthecrisis,differingsubstantiallyfromthatofCORPfirms. Thiscouldariseforoneoftwo reasons. First, GOV firms might have used increased market power during the crisis to obtain liquidity from their suppliers, whereasCORP firms might be paying their suppliers more quickly. This would suggest a reverse of the liquidity hypothesis in which firms with positive demand were extracting liquidity from weaker firms. It would also be inconsistent with the information hypothesis, because additional trade financing was going to government suppliers, which are less risky and more transparent in a financial crisis. Second, it is possible that government suppliers faced a different shock compared with that faced by corporate suppliers, leading to greater use of trade credit relative to COGS. In particular, ifCORP firms were facinglowerdemand,theymayhavesoldoutputfromtheirfinished-goodsinventory,whereas GOV firms, facing growing demand, are likely to have purchased new inputs that were still beingpaid. This,byitself,wouldcauseadivergencebetweentheratiosofaccountspayableto COGSofthetwotypesoffirms. Weinvestigatethesepossibilitiesfurtherinthenextsection. 19

As a point of comparison to the AP/COGS ratio, we examine STdebt/Sales of unrated firms in column (3). While bothCORP and GOV firms increased short-term debt relative to their sales during the crisis, the increase is much more pronounced forCORP firms with the coefficient of 0.193 compared to the increase of only 0.052 (=0.193–0.141) for GOV firms. Last,we examineCash/Assets incolumn(4). GOV firmsheld lesscashrelative toassets than CORP firms before the crisis. While CORP firms ran down their cash reserves more than GOV firmsdidduringthecrisis,differenceinthecashpolicybetweenthetwogroupsoffirms duringthecrisiswasnotstatisticallysignificant. Taken together, a divergence in the short-term financing pattern emerges. Corporate suppliers moved away from accounts payable and significantly increased their reliance on shorttermdebtinstead. Governmentsuppliers,ontheotherhand,steppeduptheirusageofaccounts payablewhileincreasingshort-termdebtonlymarginally. 4.3. Inventory Breakdown Next, we examine whether the observed differences in the short-term financing policy between GOV and CORP firms can be explained by changes in the composition of assets during the crisis. We focus on inventories for two reasons. First, CORP and GOV firms are differentially affected by demand shocks, which are most likely to be reflected in the changes to their inventories. Second, firms may match the maturity of their assets with that of their liabilities. Thus, particularly in a crisis when access to long-term financing is difficult, it is likelythataccesstoshort-termfinancingwilldependonthecompositionofshort-termassets. To better understand the changes to inventory, we break down inventory to raw materials, finishedgoods,andotherinventory. Column(1)ofTable5providesevidencethattheratioofraw-materialinventorytoCOGS for unrated GOV firms increased during the crisis relative to CORP firms. This pattern is consistent with firms increasing purchases of materials in anticipation of expected output 20

growth.13 As column (2) shows, there are no comparable divergences in the ratio of finishedgoodsinventorytoCOGS,althoughthisratioroseforbothtypesoffirmsduringthecrisis. Incolumns(3)and(4),wescaleaccountspayablebyraw-materialinventoryandfinishedgoodsinventory,respectively. AP/INVRM isofparticularinterestbecauseanychangesinthe terms of payment between suppliers and customers are likely to be reflected in this ratio. An increase(decrease)inthisratioindicatesthatsuppliersareprovidingmore(less)financingfor thepurchasesmadebytheircustomers. Column(3)showsthattheinteractiontermisinsignificant,suggestingthattherewasnodifferenceinthetradecreditpracticesbetweengovernment suppliers and corporate suppliers during the crisis. GOV firms’ increases in accounts payable areconsistentwithincreasesinpurchasesofinputsoncreditandthebuild-upofraw-material inventory. In the final two columns, we examine the relation between short-term debt and the composition of inventory. Examining STdebt/INVRM, column (5) shows the interaction term is negative and significant, suggesting that GOV firms significantly reduced the ratio of shorttermdebttoraw-materialinventoryrelativetoCORPfirmsduringthecrisis. Thus,thereisno evidencethattheincreaseinraw-materialinventoryofGOV firmswerefinancedbyshort-term debt. Rather,theevidencesuggeststhatshort-termdebtmovedinlinewithfinishedgoodsinventory. Corporatesuppliersincreasedtheirshort-termdebtrelativetoCOGSmuchmorethan government counterparts; STdebt/COGS for corporate suppliers increased by 0.206 from the pre-crisislevelof0.386whilethatforgovernmentsuppliersincreasedbyonly0.110fromthe pre-crisis level of 0.365. Despite differential changes in short-term debt, column (6) shows therelationbetweenshort-termdebtandfinished-goodsinventoryremainedunchangedduring the crisis for both GOV andCORP firms, providing support for the view that short-term debt wasusedtofinancefinished-goodsinventory. Overall, we have established that the short-term financing behavior of CORP and GOV firms differs in response to the crisis. The GOV firms’ increases in accounts payable are consistent with increases in input purchases on credit and the build-up of raw-material inven- 13Thesamplesizedropsbecausedataoninventorycomponentshavemanymissingvalues. Inaddition,observations with the inventory value of zero are removed from the test to allow to compare results using inventory variablesasdenominatorsandthoseusinginventoryvariablesasnumerators. 21

tory. In contrast, the relative increase of CORP firms is in short-term debt financing. This is consistent with a liquidity need caused by a demand shock. In unreported regressions, we find similar responses by rated firms, suggesting that these reactions are indeed caused by responsestodemandshocks,andarenotdrivenbydifferentialaccesstotradecreditorfinancing byfinancialintermediaries.14 4.4. Firm Growth during the Crisis Next, we examine whether the crisis affected the growth of GOV andCORP firms differently. Table 6 estimates specification (1) for investment-related dependent variables.15 The resultsshowtheeffectsofthecrisisonvariousformsofinvestmentforunratedfirms,including PPEscaledbysales,aswellascapitalexpenditure,R&Dexpenses,andacquisitionexpenses, all scaled by lagged PPE. Column (1) shows that GOV firms’ fixed investment measured by PPE/Sales did not increase during the crisis relative to that ofCORP firms. Examining different types of investment, we see that investment in capital expenditure and acquisition took a hit across the board for both GOV andCORP firms, as indicated by the negative and significant coefficients ofCRISIS. WhileCapex/lagPPE dropped for both GOV andCORP firms, the reduction in capital expenditure was much smaller for unrated GOV firms than forCORP firms; the coefficient ofCRISIS∗GOV is positive and significant, consistent with the higher growthratesofGOV firmsduringthecrisis. 4.5. Long-Term Financing Evidence so far suggests that government suppliers experienced a robust increase in sales relative to corporate suppliers during the crisis, without a corresponding increase in PPE relative to sales. Long-term financing was difficult to access during the crisis, suggesting that GOV firms became financially constrained. Direct evidence on this is shown by employing HobergandMaksimovic(2015)’smeasureofinvestmentdelayconstraints. Thismeasure,de- 14Unratedfirmsgenerallyhavedifficultiesaccessingpublicdebtmarkets,andmoresoduringacrisis. 15In unreported tests, we also run regressions including lagged values of M/B and OCF/Assets as control variables. 22

rived from textual analysis of 10-K reports of US listed firms, scores the MD&A sections of the10-Ksfordiscussionofexpecteddelaysininvestmentduetoaninabilitytoobtainfinancing for desired projects. Figure 4 shows that the delay-in-investment text measure increased over time for GOV firms but not forCORP firms, both rated and unrated, with the rated firms reporting lower mean levels of constraint.16 Initially, GOV firms reported being less constrainedthanCORPfirms,butasthecrisisevolved,theyreported,onaverage,becomingmore constrained. While GOV firms had higher positive revenue shocks thanCORP firms over the crisis, Figure 4 is consistent with these firms not having sufficient funding to fund desired investmentinlong-termassets. ThissuggeststhatGOV firmsdidnothaveadditionalliquidity to provide to their own suppliers (who themselves were also experiencing a positive demand shocktransmittedthroughtheGOV firms). Wereturntothisfurtherinthenextsection. We next confirm that GOV firms did not access long-term financing differentially compared with CORP firms during the crisis. Table 7 estimates specification (1) to evaluate changesinleverage. Thecontrolvariables(X)includelaggedvaluesofmarket-to-book,ROA, log(sales), and PPE/assets. The results show that changes in long-term debt and short-term debt during crisis, which are both scaled by lagged assets, were negative and positive, respectively. The results imply that both GOV and CORP firms struggled to access long-term financing during the crisis, with short-term debt partially substituting long-term debt. The interaction term (CRISIS∗GOV) is insignificant throughout, indicating that GOV firms did notobtainmorefinancingoverthisperioddespitethelargercomparativeincreaseintheirrevenue. Thatis,GOV firmsdidnotraiseadditionaldebtfinancinginproportiontotheirincrease in sales. Similar results are obtained if changes in debt are scaled by pre-crisis assets. These results show that during the crisis, a positive demand shock was not translated into differential long-term financing or investment policies, most likely due to constraints on external financing. Whilebothtypesoffirmshaddifficultyaccessinglong-termfinancing,earlierresultsshow GOV andCORP firms had striking differences in the short-term asset and liability composi- 16Thedelay-of-investmentmeasurepicksupfinancialconstraintswithrespecttonewinvestment,notfinancial distress. Thus,themeasureshowsthatGOV firmsbecameconstrained,butitdoesnotmeasuredirectlywhether theyweremoreorlessdistressedthanCORPfirms. 23

tions. GOV firms increased their ratio of accounts payable to COGS, whereas CORP firms relied more on their short-term debt. These differences are consistent with a differential pattern of demand shocks, with GOV firms increasing their raw-material usage to meet rising demand and CORP firms relying more on bank financing for increasing finished-goods inventory stemming from low demand. In the next section, we investigate whether GOV firms mightstillbeindirectlychannelingliquiditytotheirownsuppliersbychangingtermsoftrade, specifically,therateofpaymentmadetothem. 4.6. Accounts Payable and Raw Material inventory We first examine the determinants of the ratio of accounts payable to COGS. Panel A of Table 8 estimates specification (1) for subsamples of unrated firms. The first two columns show results for two subsamples sorted on the extent of a firm’s reliance on its largest principal customer (CUSTREL). For the next two columns, the sample is split based on the size of raw-material inventory relative to COGS. We find a stronger effect for GOV firms relative to CORP firms during the crisis when firms relied more on their customers and when they had more raw-material inventory. The first finding is predicted by a link between purchases of inputsoncreditandpositiveexogenousdemandshocks,theintensityofwhichismeasuredby the proportion of output sold to government customers. The second finding supports the hypothesis that the level of raw-materials inventory, which depends on demand shocks, predicts theratioofaccountspayabletoCOGS. We next show that there is not a differential relation between accounts payable and rawmaterialinventoryofGOV andCORPfirmsduringthecrisis. PanelBofTable8examineshow the ratio of accounts payable to raw-material inventory varies across the sample splits drawn fromPanelA.Theresultsshownoevidencethatthefinancingofraw-materialinventoryusing trade credit is related to a firm’s reliance on its customer or the size of raw-material inventory relativetoCOGS.Thus,theamountofsupplierfinancingperdollarofraw-materialinventory does not differ between GOV and CORP firms, implying that the differences in AP/COGS between GOV andCORP firms during the crisis do not stem from differential access to trade financing, but are rather due to differences in the level of raw-material inventory during the 24

crisis. Thus, the earlier finding is stable across these different subsamples of firms that the increaseintheuseofaccountspayablebyGOV firmsrelativetoCORPfirmsduringthecrisis is driven by relative changes in the level of raw-material inventory resulting from differential productionshocks,andnotbydifferencesintheuseoftradecredittofinancetheseinventories byGOV andCORPfirms. 5. Financial Crisis and Strategic Outcomes The previous sections suggest that the direct financial interactions between firms through trade credit during the crisis were driven by the nature of demand shocks experienced by the firms and did not lead to liquidity transfer through the medium of differential credit terms. There is also no evidence that increased demand caused government suppliers more than corporate suppliers to invest in other firms through acquisitions (Table 6, column 4). Thus, there is very limited evidence for compensatory financial interactions between firms to substitute for the drop in market liquidity during the financial crisis. There is, however, evidence of substantial product market effects of the crisis. Figure 5 shows that the relative market shares ofGOV andCORPfirmshadchangedsubstantiallybytheendofthecrisisperiod. This pattern is confirmed in Table 9, which shows that GOV firms’ market share increased substantially in the crisis period relative to CORP firms. The first column considers MSCRISIS, the logarithm of the ratio of a firm’s average quarterly market share during crisis to the corresponding pre-crisis market shares, where pre-crisis is defined as the period of 2005:Q2–2007:Q2 and crisis as the period of 2007:Q3–2009:Q2.17 The next column considers MSCRISIS2, which is calculated as the logarithm of the ratio of a firm’s market share in 2009:Q2 to the corresponding market share in 2007:Q2. Moreover, as shown in Figure 4, by the end of the crisis, unrated GOV firms were reporting a large increase in financial constraints–aninabilitytoobtainfinancingfordesiredinvestmentprojects.18 17Afirm’smarketshareiscomputedasthefractionoftheindustrysalesattributedtothefirm,whereindustry isdefinedaccordingtoFamaandFrench’s48industryclassification. 18Inunreporteddifference-in-differenceregressions,weshowthatthesedifferencesarestatisticallysignificant. 25

Somestudiesdocumentthatfirmswithlargercashholdingsorbetterbankaccessimproved theirpositionsduringthecrisis(Duchin,Ozbas,andSensoy(2010)andKahlandStulz(2013)) or that, more generally, cash holdings predict product market gains (Fresard (2010)). The effectthatweidentifyinthisstudyisdifferent;asshowninTable4,governmentsuppliershad lower cash holdings than corporate counterparts prior to the crisis, and their cash policy did notdifferesignificantlyfromthatofcorporatesuppliersduringthecrisis. However,thealtered marketshares inthe productmarketand increasedconstraints ofGOV firmsby theend ofthe crisis created a potential disequilibrium in the allocation of assets that has been identified by Harford (2005) as leading to reallocations through merger waves when the financial market conditions normalize. While Harford (2005) focuses on whether a potential disequilibrium arisingfromderegulationofindustriesleadstofutureassetreallocations,weexaminewhether a liquidity crisis does the same. The liquidity crisis, combined with differential shocks for GOV andCORPfirms,providesanaturalexperimentsettingforthishypothesis. Table 10 tests the proposition that GOV firms increase their post-crisis acquisition activities. The table reports regression results on post-crisis acquisition activities for unrated firms. The dependent variable is the value of a firm’s post-crisis acquisitions scaled by its pre-crisis assets, where post-crisis is defined as the period from 2009:Q3 to 2011:Q2. Columns (1) and (2) show that unrated firms that gained market share during the crisis indeed increased acquisitionsafterthecrisis. Adeepercausalinterpretationisprovidedincolumn(3)and(4),which show that firms identified as government suppliers in the pre-crisis period increased their acquisition activities three to four years after the crisis. Thus, a consequence of the liquidity crunchofthecrisisisadelayedadjustmentofcapacitytotheincreasedmarketshareoncethe financialmarkets’liquidityisrestored. Table 10 provides additional evidence that in the absence of fully functioning financial markets,directfinancialinteractionsacrossfirmshaveaverylimitedscopeforfacilitatingefficienttransferofresources. Although,inprinciple,firmsshouldbeabletomakeacquisitions by simply swapping stocks even when financial markets are under stress, as the crisis continued, there was an increase in reports of financial constraints by GOV firms and additional subsequent acquisition activities. This increase in constraints and misallocation is consistent withthelackofincreasedliquidityprovisionfromgovernmentsupplierstotheirownsuppliers 26

previouslydemonstrated,asitsuggeststhattheopportunitycostofsuchprovisionsistoohigh givengovernmentsuppliers’ownincentivestoinvest. 6. Robustness Checks We perform several robustness tests to check the consistency of our results. We test whether the pattern in the data is specific to the crisis period by repeating the regressions for pseudo-crisisperiods. Table11repeatstheregressionsinTable4forvariouspseudo-crisisperiods. Specification (A) treats 2003:Q3–2005:Q2 as a crisis period and 2001:Q3–2003:Q2 as apre-crisisperiod. Specification(B)treats2004:Q3–2006:Q2asacrisisperiodand2002:Q3– 2004:Q2 as a pre-crisis period. Finally, specification (C) considers a pseudo-crisis period of 2005:Q3–2007:Q2andapseudopre-crisisperiodof2003:Q3–2005:Q2. Allthreeregressions confirm that the patterns around the pseudo-crisis periods do not mimic the pattern of the actualcrisisfrom2007to2009. Table12providesadditionalrobustnesschecks. First,weaddresstheconcernthatthe10% cutoffforthereportingrequirementofprincipalcustomersmayintroduceaselectionbias. For this exercise,we redefinea principalcustomer as acustomer contributing20% ormore ofthe sample firm’s sales, and reconstruct the sample. The sample size drops to about 8,800 from around 14,000 in Table 4 due to the higher cutoff point. Specification (A) of Table 12 repeats theregressionsinTable4usingthissample,andreportsthecoefficientsoftheinteractionterm, Crisis∗GOV. Second, we add quarter dummy variables (specification (B)). Third, we cluster standard errors simultaneously at the firm- and time- (quarterly frequency) levels instead of clusteringatthefirmlevel(specification(C)).Inaddition,inunreportedresults,19 weperform the following robustness tests: (1) we select a control group of CORP firms using principal componentmatchingbasedonfirmsize,age,andindustryand(2)weuseacontinuoussample rather than a predetermined sample. That is, we sort sample firms according to their credit ratings and customer types each year rather than as of 2006. In every case listed above, the resultsaresimilartothosereportedinthetables. 19Availablefromtheauthorsuponrequest 27

7. Conclusion The 2007–09 financial crisis provides a natural experiment setting for examining the flow of liquidity between firms with differential demand shocks in a period of financial market illiquidity. We measure the differential demand by comparing firms that primarily supply to governmentwiththosesupplyingprimarilytocorporatecustomers. Usingasampleofunrated publicfirms,weobtainthreekeyresults. First, we find little evidence for a liquidity channel whereby firms with relatively high demandprovidemoreorlessliquiditytotheirownsuppliersthanfirmswithlowerdemand. The main determinant of the usage of financing is product-market shocks. Firms with relatively high demand have higher raw-material inventory and rely more on trade credit. However, we find little evidence that the amount of credit per unit of raw-material inventory changes with the firms’ demand shocks, which would indicate that firms were changing their trade credit paymenttermsaccordingtotheirstateofdemand. Second, these outcomes are consistent with theories of trade credit that stress its use in financing inputs rather than providing efficient monitoring of creditors by suppliers. Theories thatstresstheinformationaladvantagethatsuppliershaveoverfinancialintermediariespredict that corporate suppliers would receive more credit than government suppliers, but we do not find evidence supporting this view. Given that such theories do not have much predictive powerduringtimesofincreaseduncertainty,theyarenotlikelytobeabletoexplainthetimeseriesvariationintradecredit. Third, the opportunity cost of liquidity for unrated firms with high demand during the liquidity crisis is likely to be high. We show that they report an increasing inability to finance investmentsand,oncetheliquiditycrisisends,stepupacquisitionactivities. Thissuggeststhat evenhighdemandfirmsareunabletoobtainsufficientself-financingorshort-termfinancingto maintaintheirdesiredgrowthtrajectoryduringthecrisis. Thus,theymayhavelittleincentive toprovideliquiditytootherfirms. Overall, we find that the flow of trade credit is highly contingent on the type of demand shocks that firms face. The micro-evidence does not provide support for claims that trade 28

credit flows provide a financing channel that might promote recovery from a crisis by transferring liquidity from firms with positive demand shocks to other firms. Moreover, in this context, the theories that ascribe a monitoring role for trade credit do not have explanatory power. 29

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Appendix A: Variable Descriptions Variable Description AP Quarterlydollaraccountspayable(APQ). AQC Quarterlydollaracquisitionexpenses(AQCY –AQCY ). t t−1 AQCV Totalvalueofacquisitionssummedoverthepost-crisisperiod,scaledbypre-crisisassets. AR Quarterlydollaraccountsreceivable(RECTQ). Assets Quarterlydollartotalassets(ATQ). Capex Quarterlydollarcapitalexpenditures(CAPXY –CAPXY ). t t−1 Cash Quarterlydollaramountofcashandshort-terminvestments(CHEQ). COGS Quarterlydollarcostofgoodssold(COGSQ). CRISIS Indicatorvariablethattakesonunityduringthecrisisperiodof2007Q3to2009Q2,and zerointhepre-crisisperiodof2005Q3to2007Q2. CUSTREL Customerreliancecomputedasthepercentagesalesaccountedforbyaprincipalcustomer, whereaprincipalcustomerisdefinedasacustomercontributingatleast10%tothe samplefirm’sannualsales(SALES). Source: BusinessSegmentfilesofCompustatfor customersales;CompustatAnnualforSALES. Delaycon Anindexoffinancialconstraintsoninvestments,obtainedfromHobergand Maksimovic(2015). GOV Indicatorvariablethattakesonunityifasamplefirmisclassifiedasagovernmentsupplier, andzeroifclassifiedasacorporatesupplier,whereagovernment(corporate)supplierisa samplefirmwhosebiggestprincipalcustomerisagovernmentagency(corporation). INV Quarterlydollartotalinventory(INVQ). INVFG Quarterdollarfinished-goodsinventory(INVFGQ). INVRM Quarterdollarraw-materialinventory(INVRMQ). Leverage Quarterlyratioofthesumoflong-termdebt(DLTTQ)andcurrentliabilities(DLCQ)toassets. LTdebt Quarterlydollarlong-termdebt(DLTTQ). M/B Quarterlymarket-to-bookratiocomputedasthemarketvalueofassets(ATq–CEQq +PRCCq*CSHOq)dividedbythebookvalueofassets(ATQ). MS QuarterlymarketsharecomputedasthefractionoftheindustrysalesintheCompustat universeattributedtothefirm,whereindustryisdefinedaccordingtoFama-French’s 48industryclassification. MSCRISIS Logarithmoftheratioofafirm’squarterlymarketshareaveragedoverthecrisis (2007Q3–2009Q2)tothecorrespondingaveragequarterlypre-crisis(2005Q2–2007Q2) marketshare. MSCRISIS2 Logarithmoftheratioofafirm’smarketsharesin2009Q2tothecorrespondingmarketshare in2007Q2. NWC Quarterlynetworkingcapital(WCAPq–CHEq). OCF Quarterlydollarincomebeforedepreciation(OIBDPQ). Post-Crisis Theperiodfrom2009Q3to2011Q2. Pre-CrisisAssets Quarterlyassetvaluesaveragedoverthepre-crisisperiod. Pre-Crisis Theperiodfrom2005Q3to2007Q2. PPE Quarterlydollarproperty,plantandequipmentexpenses(PPENTQ). ProfitMargin Quarterlysales(SALEQ)minuscostofgoodssold(COGSQ),scaledbyCOGSQ. R&D Quarterlydollarresearchanddevelopmentexpenses(XRDQ)withmissingvaluesettozero. (cont’dinthenextpage) 33

Variable Description ROA Quarterlyreturnonassetscomputedasincomebeforeextraordinaryitems(IBY)scaled byassets(ATQ). Sales Quarterlydollarsales(SALEQ). Saleqg Quarterlysalesgrowthcomputedascurrentsalesminuspreviousquartersales,scaled bypreviousquartersales. Sigma Industrycashflowvolatility STdebt Short-termdebtmeasuredasquarterlycurrentliabilities(DLCQ.) *Note: Laggedvariablesareprecededbytheprefix“Lag”. Source: CompustatQuarterlyunless otherwisestated. 34

Appendix B: Regression Results on Short-Term Financing for all firms and rated firms Thistablereportsregressionresultsonvariousmeasuresofshort-termfinancingvariablesforallfirms(panel A)andratedfirms(panelB).Resultsforunratedfirmsarereportedintable4. Thedependentvariablesareaccounts receivable scaled by sales (AP/Sales) in column (1), accounts payable scaled by cost of goods sold (AP/COGS) in column (2), short-term debt scaled by sales (STdebt/Sales) in column (3), and cash scaled by assets(Cash/Assets)incolumn(4). CRISISisanindicatorvariablethattakesonunityduringthecrisisperiod (2007Q3–2009Q2),andzeroduringthepre-crisisperiod(2005Q3–2007Q2). GOV isanindicatorvariablethat takesonunityifthesamplefirmisclassifiedasagovernmentsupplier,andzeroifclassifiedasacorporatesupplier. Samplefirmsaresortedintorated/unratedandGOV/CORPaccordingtotheircreditratingsandcustomer typesasof2006. Allregressionsincludeindustryfixedeffects. Standarderrors,clusteredatthefirm-level,are reportedinbrackets. AllvariablesaredescribedinAppendixA. 35

PanelA:AllFirms (1) (2) (3) (4) VARIABLES AR/Sales AP/COGS STdebt/Sales Cash/Assets CRISIS -0.002 -0.059*** 0.168*** -0.018*** [0.007] [0.020] [0.028] [0.004] GOV 0.115*** -0.153*** 0.101** -0.048*** [0.027] [0.048] [0.047] [0.016] CRISIS*GOV 0.011 0.132*** -0.130** 0.011 [0.017] [0.043] [0.050] [0.009] Constant 0.795*** 1.388*** 0.826** 0.208*** [0.147] [0.429] [0.421] [0.065] Observations 17,357 18,415 18,011 18,414 R-squared 0.104 0.113 0.035 0.294 PanelB:RatedFirms (1) (2) (3) (4) VARIABLES AR/Sales AP/COGS STdebt/Sales Cash/Assets CRISIS -0.019** -0.095*** 0.064** -0.008* [0.009] [0.030] [0.028] [0.004] GOV 0.068 -0.234*** -0.013 -0.051*** [0.046] [0.081] [0.029] [0.017] CRISIS*GOV 0.005 0.077** -0.072** 0.014** [0.017] [0.034] [0.031] [0.007] Constant 0.819*** 0.426*** 0.015 0.045*** [0.004] [0.015] [0.014] [0.002] Observations 3,906 4,334 4,287 4,333 R-squared 0.149 0.235 0.041 0.258 36

Appendix C: Customer Information We consider a sample firm’s customer as the government if the sample firm reports its customer name (variable name: CNMS) as the U.S. or federal government, a state or local government in Compustat Business Segment Files. Some firms simply report its customer as government agencies or projects without specifying the exact identities of the government agencies. We then check whether the reporting firm is a U.S. company and the customer type (variable name: CTYPE) is GOVDOM. For government customers,CTYPE can take on one of four possible types, GOVDOM, GOVSTATE, GOVLOC, and GOVFRN, which refer to domestic,state,localandforeigngovernment,respectively. ForaU.S.firm,GOVDOM refers to the U.S. government and GOVFRN to a foreign government. Similarly, we check whether a firm reportingCNMS as Municipalities andCTYPE as GOVLOC is a U.S. firm to confirm thatitscustomerisaU.S.municipality. Among our sample of government suppliers, 98.5% sell to the U.S. government and only 1.5% sell to foreign governments. Among our unrated government suppliers, only 1.1% sells to foreign governments. The sheer majority of U.S. government customers are federal agencies,withonly1.9%ofourunratedsupplierscatertotheU.S.stateorlocalgovernmentagencies. Examples of state government include Arizona Department of Corrections, Arkansas Department of Revenue, California Department of Water Resources, State of Texas, etc. Examples of local government are Chicago Housing Authority, the cities and counties such as CityofToledo,WaynecountyofMichigan,etc.,oragovernmentagency. Amongthenamedgovernmentagencies: (i)48.3%ofthereportedfirm-yearrelationships are with the Department of Health and Human Services and its related agencies which include Medicare and Medicaid, National Institute of Allergy and Infectious Disease, National Institute of Health, SAMHSA, and Veterans Health Administration facilities; (ii) 40.3% are with the Department of Defense and defense-related units which include the U.S. Air Force, Army, Border Patrol, Coast Guard, Homeland Security, Intelligence Agency, Marine, Military, National Security, NASA, and the Navy; (iii) 2% are with the Department of Energy; (iv) 0.9% are with the Department of Education; and (v) the remaining 8.5% are with other U.S.governmentagencieswhichincludetheDepartmentofAgriculture,Commerce,Interior, 37

State, Transport, Veterans Affairs, Federal Bureau of Prisons, Federal Emergency Management Agency, Federal Job Corps, Federal Reserve Bank, General Services Administration, InternalRevenueServices,PublicStreetLightingandHighwayLighting,SocialSecurityAdministration, and U.S. Environmental Protection Agency, Forest Service, Immigration and CustomersEnforcement,NationalParkService,PostalService,andtheU.S.Treasury. In June 1997, SFAS 131 was issued which requires firms to disclose the sales to each principalcustomer,butnotthenameofthecustomer. Totheextentthatsmallandriskierfirms that are more likely to be affected by their customers’ actions choose not to disclose their customernames(Ellisetal.,2012),ourestimatesrepresentthelowerboundofthetrueimpact of customers actions on dependent suppliers. As a result, there is a significant number of firms in the Business Segment Files that are missing theCNMS variable but report the dollar amounts of sales (CSALE) to these customers. However, over our sample period, we do not havecaseswhereCTYPE indicatesagovernmentalcustomertypeandCNMS isunreported. To identify sample firms that supply to corporate customers, we start with the information in the Compustat Business Segment Files. As firms only report the names of their major customers,wemanuallymatchthecustomernamestotheircorrespondingGVKEYsinCompustat by closely following the approach in Fee et al. (2006). For customer names that are abbreviated, we use visual inspection and industry affiliation in our matches. For unmatched customers,wefurthersearchtheircorporatewebsitesortheDirectoryofCorporateAffiliation databasetodeterminewhetherthecustomerisasubsidiaryofalistedfirm,andassigntheparent GVKEY where applicable. To ensure accuracy of our matches, we retain only customers thatareunambiguouslymatchedtoGVKEYs. 38

Table1 Pre-CrisisFirmCharacteristics Thistablereportsquarterlyfirmcharacteristicsaveragedoverthepre-crisisperiodof2005Q3to2007Q2. Panel A reports the firm characteristics for all firms in our sample while Panel B partitions our sample firms into rated/unrated and GOV/CORP according to their credit ratings and customer types as of 2006, and report the firm characteristics for each subsample. DIFF measures the difference between GOV firm characteristics and CORPfirmcharacteristics. Reliance(CustREL)onsamplefirms’largestcustomersisreportedasoffiscalyear 2006. AllvariablesaredescribedinAppendixA. PanelA:AllFirms Variable Mean Median StdDev CustomerReliance(CustREL) 0.318 0.230 0.223 Assets($millions) 1,545 224 4,007 Sales($millions) 408 53 1,160 Leverage 0.215 0.144 0.253 Capex/Assets 0.016 0.007 0.025 ROA -0.014 0.008 0.082 OCF/Assets 0.009 0.026 0.071 M/B 2.296 1.748 1.717 Numberoffirmsperquarter 1,194 39

PanelB:Subsamples SampleFirms Variable CustomerTypes UnratedFirms RatedFirms CustomerReliance(CustREL) GOV 0.474 0.330 CORP 0.304 0.213 DIFF -0.170 -0.118 t-stat -9.82 -5.91 Assets($millions) GOV 300 6,004 CORP 511 4,651 DIFF 211 -1,353 t-stat 5.09 -4.08 Sales($millions) GOV 103 1,675 CORP 119 1,236 DIFF 15 -440 t-stat 1.30 -4.46 Leverage GOV 0.196 0.366 CORP 0.168 0.335 DIFF -0.028 -0.031 t-stat -3.94 -2.76 Capex/Assets GOV 0.011 0.011 CORP 0.016 0.019 DIFF 0.005 0.008 t-stat 6.92 7.10 ROA GOV -0.028 0.009 CORP -0.020 0.011 DIFF 0.009 0.002 t-stat 3.31 1.30 OCF/Assets GOV -0.004 0.034 CORP 0.001 0.037 DIFF 0.005 0.003 t-stat 1.97 2.92 M/B GOV 2.470 1.699 CORP 2.439 1.822 DIFF -0.031 0.123 t-stat -0.57 2.95 Numberoffirmsperquarter GOV 198 66 CORP 709 217 40

Table2 ChangesinFirmCharacteristicsDuringCrisis Thistablereportsquarterlyfirmcharacteristicsaveragedoverthepre-crisisperiodof2005Q3to2007Q2andthe crisisperiodof2007Q3to2009Q2,respectively,forfoursubsamples.Samplefirmsaresortedintorated/unrated andGOV/CORPaccordingtotheircreditratingsandcustomertypesasof2006. DIFFmeasuresthedifference between the pre-crisis firm characteristics and the crisis-period firm characteristics. *, **, and *** represent statisticalsignificanceatthe10%,5%,and1%level,respectively. AllvariablesaredescribedinAppendixA. UnratedFirms RatedFirms Variable Period GOV CORP GOV CORP Assets($millions) Pre-Crisis 300 511 6,004 4,651 Crisis 400 629 7,685 5,740 DIFF 100*** 118*** 1681*** 1090*** %change 33.33 23.07 28.00 23.44 Sales/Pre-CrisisAssets Pre-Crisis 0.326 0.261 0.283 0.263 Crisis 0.432 0.299 0.347 0.300 DIFF 0.106*** 0.039*** 0.064*** 0.037*** %change 32.52 14.94 22.63 14.07 Leverage Pre-Crisis 0.191 0.166 0.367 0.338 (scaledbypre-crisisassets) Crisis 0.261 0.248 0.464 0.436 DIFF 0.070*** 0.082*** 0.097*** 0.097*** %change 36.65 49.40 26.45 28.70 Capex/Pre-CrisisAssets Pre-Crisis 0.010 0.015 0.011 0.018 Crisis 0.015 0.018 0.014 0.023 DIFF 0.005*** 0.003*** 0.003*** 0.005*** %change 50.00 20.00 27.42 27.78 ROA Pre-Crisis -0.028 -0.020 0.008 0.009 (scaledbypre-crisisassets) Crisis -0.012 -0.028 0.013 0.000 DIFF 0.016*** -0.008*** 0.005** -0.009*** %change 57.10 -40.00 66.09 -100.00 OCF/Pre-CrisisAssets Pre-Crisis -0.002 0.002 0.034 0.037 Crisis 0.016 0.006 0.043 0.039 DIFF 0.018*** 0.004** 0.009*** 0.002* %change 900.00 200.00 26.73 5.44 M/B Pre-Crisis 2.470 2.439 1.699 1.822 Crisis 2.075 1.891 1.478 1.489 DIFF -0.396*** -0.548*** -0.221*** -0.334*** %change -16.03 -22.47 -13.01 -18.33 PPE/Sales Pre-Crisis 1.272 2.113 1.132 2.247 Crisis 1.191 2.412 1.101 2.487 DIFF -0.081 0.298*** -0.031 0.241* %change -6.36 14.10 -2.77 10.71 41

Table3 ChangesinShort-termFinancing This table reports quarterly short-term financing measures averaged over the pre-crisis period of 2005Q3 to 2007Q2 and crisis period of 2007Q3 to 2009Q2, respectively, for four subsamples. Sample firms are sorted intorated/unrated andGOV/CORPaccordingtotheircreditratingsandcustomertypesasof2006. DIFFmeasuresthedifferencebetweenthepre-crisisfirmcharacteristicsandthecrisis-periodfirmcharacteristics. PanelA scalesfinancingmeasuresbyaveragepre-crisisassets,quarterlyassetvaluesaveragedoverthepre-crisisperiod. PanelBscalesfinancingmeasuresbyvariouscontemporaneousvalues. ARandAParetheaccountsreceivable and accounts payable, respectively, while STdebt is the short-term debt. INV, INVRM, and INVFG are the totalinventory,raw-materialinventoryandfinished-goodsinventory,respectively. Allvariablesaredescribedin AppendixA. PanelA:VariablesScaledbyAveragePre-CrisisAssets UnratedFirms RatedFirms Variable Period GOV CORP GOV CORP AR/Pre-CrisisAssets Pre-Crisis 0.225 0.153 0.178 0.140 Crisis 0.306 0.178 0.213 0.155 DIFF 0.081*** 0.025*** 0.035*** 0.015*** %change 35.96 16.37 19.65 10.73 AP/Pre-CrisisAssets Pre-Crisis 0.101 0.090 0.072 0.099 Crisis 0.127 0.101 0.090 0.108 DIFF 0.026*** 0.011*** 0.018*** 0.010*** %change 25.74 12.19 24.86 10.15 STdebt/Pre-CrisisAssets Pre-Crisis 0.052 0.047 0.025 0.034 Crisis 0.066 0.066 0.029 0.048 DIFF 0.014*** 0.019*** 0.004 0.014*** %change 27.37 40.52 15.90 40.88 INV/Pre-CrisisAssets Pre-Crisis 0.148 0.142 0.099 0.119 Crisis 0.194 0.161 0.130 0.135 DIFF 0.046*** 0.019*** 0.031*** 0.016*** %change 31.13 13.37 31.28 13.45 INVRM/Pre-CrisisAssets Pre-Crisis 0.079 0.067 0.046 0.040 Crisis 0.108 0.074 0.054 0.043 DIFF 0.029*** 0.007*** 0.008** 0.004** %change 36.92 10.43 17.28 10.09 INVFG/Pre-CrisisAssets Pre-Crisis 0.058 0.070 0.028 0.067 Crisis 0.067 0.082 0.046 0.075 DIFF 0.008* 0.012*** 0.019*** 0.008*** %change 13.79 17.10 68.64 11.97 Cash/Pre-CrisisAssets Pre-Crisis 0.235 0.290 0.075 0.109 Crisis 0.302 0.324 0.101 0.116 DIFF 0.067*** 0.034*** 0.026*** 0.007 %change 28.53 11.72 34.82 6.30 42

PanelB:VariablesScaledbyContemporaneousDenominators UnratedFirms RatedFirms Variable Period GOV CORP GOV CORP AR/Sales Pre-Crisis 0.757 0.634 0.654 0.569 Crisis 0.771 0.634 0.641 0.549 DIFF 0.013 0.000 -0.013 -0.021** %change 1.76 0.00 -1.99 -3.69 AP/COGS Pre-Crisis 0.566 0.837 0.387 0.783 Crisis 0.664 0.790 0.379 0.690 DIFF 0.098*** -0.047** -0.008 -0.093*** %change 17.32 -5.62 -2.07 -11.88 STdebt/Sales Pre-Crisis 0.287 0.254 0.105 0.164 Crisis 0.335 0.442 0.095 0.229 DIFF 0.048 0.188*** -0.010 0.065*** %change 16.76 74.08 -9.31 39.70 INV/COGS Pre-Crisis 0.997 1.023 0.538 0.848 Crisis 1.112 1.052 0.567 0.835 DIFF 0.115** 0.029 0.029 -0.014 %change 11.54 2.83 5.39 -1.61 INVRM/COGS Pre-Crisis 0.522 0.483 0.268 0.277 Crisis 0.617 0.502 0.263 0.258 DIFF 0.095*** 0.019 -0.006 -0.019 %change 18.20 3.93 -2.24 -6.86 INVFG/COGS Pre-Crisis 0.384 0.484 0.167 0.483 Crisis 0.448 0.534 0.203 0.496 DIFF 0.064* 0.050*** 0.036** 0.013 %change 16.66 10.32 21.51 2.80 Cash/Assets Pre-Crisis 0.233 0.289 0.076 0.110 Crisis 0.219 0.264 0.083 0.101 DIFF -0.014 -0.024*** 0.007 -0.008* %change -6.01 -8.31 9.74 -7.30 43

Table4 RegressionResultsonShort-termFinancing Thistableestimatesthefollowingspecificationforunratedfirms: y =α+β Crisis +β GOV +β Crisis ·GOV +X(cid:48)θ+ε , it 1 t 2 i 3 t i it wherethedependentvariablesarevariousmeasuresofshort-termfinancingvariablesincludingaccountsreceivablescaledbysales(AP/Sales)incolumn(1),accountspayablescaledbycostofgoodssold(AP/COGS)incolumn(2),short-termdebtscaledbysales(STdebt/Sales)incolumn(3),andcashscaledbyassets(Cash/Assets) incolumn(4).CRISISisanindicatorvariablethattakesonunityduringthecrisisperiodof2007Q3to2009Q2, andzeroduringthepre-crisisperiodof2005Q3to2007Q2. GOV isanindicatorvariablethattakesonunityif thesamplefirmisclassifiedasagovernmentsupplier,andzeroifclassifiedasacorporatesupplier. Thesample firms are classified as government suppliers or corporate suppliers, and as rated or unrated according to their status in 2006. All regressions include industry fixed effects. Standard errors, clustered at the firm-level, are reportedinbrackets. ResultsforratedfirmsandallfirmsarereportedinappendixB.Allvariablesaredescribed inAppendixA.*,**,and***representstatisticalsignificanceatthe10%,5%,and1%level,respectively. (1) (2) (3) (4) Variable AR/Sales AP/COGS STdebt/Sales Cash/Assets CRISIS 0.002 -0.049** 0.193*** -0.022*** [0.009] [0.024] [0.035] [0.005] GOV 0.133*** -0.123** 0.136** -0.045** [0.031] [0.056] [0.060] [0.018] CRISIS*GOV 0.017 0.147*** -0.141** 0.008 [0.022] [0.055] [0.065] [0.011] Constant 0.781*** 1.528*** 0.962** 0.235*** [0.182] [0.467] [0.475] [0.071] Observations 13,385 14,011 13,654 14,011 R-squared 0.115 0.108 0.041 0.282 44

Table5 InventoryBreakdown This table regresses various measures of inventory for unrated firms. Dependent variables are raw-material inventoryscaledbycostofgoodssold(INVRM/COGS),finished-goodsinventoryscaledbycostsofgoodssold (INVFG/COGS),accountspayablescaledbyraw-materialinventory(AP/COGS)andscaledbyfinished-goods inventory(AP/INVFG), respectively, and short-termdebtscaledby raw-materialinventory(STdebt/INVRM) and scaled by finished-goods inventory (STdebt/INVFG), respectively. CRISIS is an indicator variable that takes on unity during the crisis period of 2007Q3 to 2009Q2, and zero during the pre-crisis period of 2005Q3 to 2007Q2. GOV is an indicator variable that takes on unity if the sample firm is classified as a government supplier, and zero if classified as a corporate supplier. All regressions include industry fixed effects. Standard errors,clusteredatthefirm-level,arereportedinbrackets. AllvariablesaredescribedinAppendixA.*,**,and ***representstatisticalsignificanceatthe10%,5%,and1%level,respectively. (1) (2) (3) (4) (5) (6) Variable INVRM/COGS INVFG/COGS AP/INVRM AP/INVFG Stdebt/INVRM STdebt/INVFG CRISIS 0.021 0.053*** 1.706** 0.596 1.844*** 0.134 [0.017] [0.017] [0.855] [0.412] [0.529] [0.232] GOV 0.071 -0.063 -1.188 2.913** 0.792 2.401*** [0.049] [0.052] [1.405] [1.299] [0.723] [0.888] CRISIS*GOV 0.075* 0.014 -0.720 2.988 -2.411*** -0.348 [0.039] [0.049] [1.145] [1.820] [0.768] [0.936] Constant 0.292 0.480** 8.763* 18.998 0.234 11.712 [0.262] [0.203] [4.899] [21.467] [0.755] [13.283] Observations 6,877 7,045 6,877 7,045 6,729 6,900 R-squared 0.154 0.075 0.111 0.060 0.067 0.081 45

Table6 FirmGrowthandInvestment Thistableregressesvariousmeasuresofinvestmentforunratedfirms. ThedependentvariablesarePPE/Sales incolumn(1),Capex/lagPPE incolumn(2), R&D/lagPPE incolumn(3)andacquisitionexpensesscaledby laggedPPE(AQC/lagPPE)incolumn(4). CRISISisanindicatorvariablethattakesonunityduringthecrisis periodof2007Q3to2009Q2,andzeroduringthepre-crisisperiodof2005Q3to2007Q2. GOV isanindicator variable that takes on unity if the sample firm is classified as a government supplier, and zero if classified as a corporatesupplier. Allregressionsincludeindustryfixedeffects. Standarderrors,clusteredatthefirm-level,are reportedinbrackets. AllvariablesaredescribedinAppendixA. (1) (2) (3) (4) Variable PPE/Sales Capex/lagPPE R&D/lagPPE AQC/lagPPE CRISIS 0.234** -0.024*** -0.051 -0.017* [0.104] [0.003] [0.045] [0.009] GOV 0.205 -0.003 0.107 0.018 [0.314] [0.007] [0.163] [0.025] CRISIS*GOV -0.303 0.013** -0.031 0.003 [0.280] [0.007] [0.103] [0.021] Constant 1.296 0.113*** 0.074 0.091 [0.809] [0.032] [0.150] [0.056] Observations 13,934 13,308 8,433 13,102 R-squared 0.290 0.041 0.139 0.014 46

Table7 ChangesinLeverage This table examines changes in leverage for unrated firms. In columns (1) and (3), the dependent variable is ∆LTdebt/lagAssets computed as long-term debt minus lagged long-term debt, scaled by lagged assets. In columns (2) and (4), the dependent variable is ∆STdebt/lagAssets computed as short-term debt minus lagged short-term debt, scaled by lagged assets. CRISIS is an indicator variable that takes on unity during the crisis periodof2007Q3to2009Q2,andzeroduringthepre-crisisperiodof2005Q3to2007Q2. GOV isanindicator variable that takes on unity if the sample firm is classified as a government supplier, and zero if classified as a corporatesupplier. Allregressionsincludeindustryfixedeffects. Standarderrors,clusteredatthefirm-level,are reportedinbrackets. AllvariablesaredescribedinAppendixA. (1) (2) (3) (4) Variable ∆LTdebt/lagAssets ∆STdebt/lagAssets ∆LTdebt/lagAssets ∆STdebt/lagAssets CRISIS -0.006*** 0.003*** -0.005*** 0.003*** [0.001] [0.001] [0.001] [0.001] GOV -0.001 0.003** -0.001 0.002 [0.002] [0.001] [0.002] [0.001] CRISIS*GOV 0.001 -0.002 0.000 -0.001 [0.002] [0.002] [0.002] [0.002] lagM/B 0.001*** 0.001** [0.000] [0.000] lagROA 0.009 -0.052*** [0.008] [0.013] lagLog(Sales) 0.000 -0.000 [0.000] [0.000] lag(PPE/Assets) 0.018*** 0.008*** [0.004] [0.003] Constant 0.003 0.001 -0.005 0.000 [0.005] [0.006] [0.005] [0.007] Observations 13,533 13,384 13,050 12,902 R-squared 0.009 0.003 0.013 0.014 47

Table8 SubsampleAnalysisonAccountsPayable This table presents various subsample regression results for unrated firms. The dependent variables are AP/COGS in Panel A and AP/INVRM in Panel B. In the first two columns, we sort unrated firms into two groups based on their sales-based reliance (CUSTREL) on their largest customers as of fiscal year 2006. In thelasttwocolumns, unratedfirmsaresortedaccordingtoINVRM/COGS, whereINVRM/COGS istherawmaterial inventory scaled by cost of goods sold. CRISIS is an indicator variable that takes on unity during the crisisperiodof2007Q3to2009Q2,andzeroduringthepre-crisisperiodof2005Q3to2007Q2. GOV isanindicatorvariablethattakesonunityifthesamplefirmisclassifiedasagovernmentsupplier,andzeroifclassified asacorporatesupplier. Allregressionsincludeindustryfixedeffects. Standarderrors,clusteredatthefirm-level, arereportedinbrackets. AllvariablesaredescribedinAppendixA. PanelA:AP/COGSasDependentVariable CUSTREL INVRM/COGS Variable >median <median >median <median CRISIS -0.071* -0.022 -0.022 0.027 [0.043] [0.027] [0.039] [0.027] GOV -0.116 -0.209*** -0.049 -0.032 [0.086] [0.067] [0.079] [0.064] CRISIS*GOV 0.193** 0.094* 0.186** 0.054 [0.079] [0.051] [0.088] [0.093] Constant 1.711*** 1.063** 0.975*** 0.648*** [0.600] [0.485] [0.096] [0.090] Observations 7,018 6,993 3,438 3,439 R-squared 0.155 0.092 0.119 0.065 PanelB:AP/INVRMasDependentVariable CUSTREL INVRM/COGS Variable >median <median >median <median crisis 3.763** 0.069 -0.032 3.505** [1.813] [0.665] [0.062] [1.665] gov -0.759 -3.232*** -0.046 -0.634 [2.301] [0.957] [0.133] [2.798] inter -2.436 0.34 0.051 -0.804 [2.099] [0.761] [0.114] [2.640] Constant 8.158 2.883*** 1.255*** 11.492*** [5.246] [0.000] [0.137] [4.129] Observations 3,163 3,714 3,438 3,439 R-squared 0.166 0.158 0.092 0.121 48

Table9 ChangesinMarketShares Thistablereportsregressionresultsonthemarketsharesofunratedfirms. ThefirstcolumnutilizesMSCRISIS, thelogarithmoftheratiooftheaveragequarterlymarketshareduringthecrisis(2007Q3–2009Q2)toaverage quarterly pre-crisis (2005Q2–2007Q2) market share, where market share is computed as the fraction of the industry sales attributed to the firm. The second column compares market share at the end of pre-crisis and market share at the end of crisis by using MSCRISIS2, the logarithm of the ratio of market share in 2009Q2 to market share in 2007Q2. GOV is an indicator variable that takes on unity if the sample firm is classified as a government supplier, and zero if classified as a corporate supplier. Robust standard errors are reported in brackets. AllvariablesaredescribedinAppendixA. DependentVariable Variable MSCRISIS MSCRISIS2 GOV 0.115** 0.162*** [0.046] [0.054] Constant 0.031 -0.000 [0.020] [0.027] Observations 776 739 R-squared 0.009 0.011 49

Table10 Post-CrisisAcquisitionActivities Thistablereportsregressionresultsonpost-crisisacquisitionactivitiesforunratedfirms.Thedependentvariable isthevalueofpost-crisisacquisitionsscaledbythefirm’spre-crisisassets(AQCV),wherepost-crisisisdefined as the period from 2009Q3 to 2011Q2. MSCRISIS is computed as the logarithm of the ratio of the average quarterly market share during the crisis (2007Q3–2009Q2) to average quarterly pre-crisis (2005Q2–2007Q2) marketshare, wheremarketshareiscomputedasthefractionoftheindustrysalesattributedtothefirm. GOV isanindicatorvariablethattakesonunityifthesamplefirmisclassifiedasagovernmentsupplier, andzeroif classifiedasacorporatesupplier. Robuststandarderrorsarereportedinbrackets. Allvariablesaredescribedin AppendixA. Variable (1) (2) (3) (4) MSCRISIS 0.058*** 0.047*** [0.013] [0.013] GOV 0.035** 0.041** [0.017] [0.018] M/B 0.001 0.003 [0.003] [0.003] OCF/Assets 0.384*** 0.473*** [0.072] [0.073] Constant 0.071*** 0.069*** 0.066*** 0.060*** [0.006] [0.008] [0.007] [0.008] Observations 758 734 758 734 R-squared 0.026 0.047 0.007 0.039 50

Table11 PlaceboCrisis This table repeats the regressions in table 4 for various pseudo crisis periods and report the coefficient of the interaction term,CRISIS∗GOV. Specification (A) considers a pseudo crisis period of 2003Q3–2005Q2 and a pseudo pre-crisis period of 2001Q3–2003Q2. Specification (B) considers a pseudo crisis period of 2004Q3– 2006Q2andapseudopre-crisisperiodof2002Q3–2004Q2. Specification(C)treats2005Q3–2007Q2asacrisis period and 2003Q3–2005Q2 as a pre-crisis period. The dependent variables are accounts receivable scaled by sales(AP/Sales)incolumn(1),accountspayablescaledbycostofgoodssold(AP/COGS)incolumn(2),shorttermdebtscaledbysales(STdebt/Sales)incolumn(3),andcashscaledbyassets(Cash/Assets)incolumn(4). CRISIS isanindicatorvariablethattakesonunityduringthepseudocrisisperiod, andzeroduringthepseudo pre-crisisperiod. GOV isanindicatorvariablethattakesonunityifthesamplefirmisclassifiedasagovernment supplier, and zero if classified as a corporate supplier. All regressions include industry fixed effects standard errorsareclusteredatthefirm-level. AllvariablesaredescribedinAppendixA. CoefficientsofCRISIS*GOV (1) (2) (3) (4) Specification AR/Sales AP/COGS STdebt/Sales Cash/Assets A.Pseudo-crisis: 2003Q3–2005Q2 -0.691 -0.090 0.337 -0.008 [0.587] [0.385] [0.607] [0.011] B.Pseudo-crisis: 2004Q3–2006Q2 -0.247 -0.628 -1.013 0.003 [0.357] [0.717] [0.841] [0.011] C.Pseudo-crisis: 2005Q3–2007Q2 1.295 -0.352 1.059 0.003 [1.197] [0.928] [1.001] [0.010] 51

Table12 AdditionalRobustnessChecks This table repeats the regressions in table 4 for three different specifications and report the coefficient of the interaction term. Specification (A) considers a sample where a principal customer is defined as a customer contributingatleast20%ofthesamplefirm’ssales. Specification(B)includesquarterdummyvariables. Specification(C)clustersstandarderrorssimultaneouslyatthefirmandtime(quarterlyfrequency)level. Dependent variablesareaccountsreceivablescaledbysales(AP/Sales)incolumn(1), accountspayablescaledbycostof goodssold(AP/COGS)incolumn(2),short-termdebtscaledbysales(STdebt/Sales)incolumn(3),andcash scaledbyassets(Cash/Assets)incolumn(4).CRISISisanindicatorvariablethattakesonunityduringthecrisis periodof2007Q3to2009Q2,andzeroduringthepre-crisisperiodof2005Q3to2007Q2. GOV isanindicator variable that takes on unity if the sample firm is classified as a government supplier, and zero if classified as a corporate supplier. All regressions include industry fixed effects standard errors are clustered at the firm-level unlessspecifiedotherwise. AllvariablesaredescribedinAppendixA. CoefficientsofCRISIS*GOV (1) (2) (3) (4) Specification AR/Sales AP/COGS STdebt/Sales Cash/Assets A.20%cutoffsample 0.024 0.203** -0.297** 0.006 [0.029] [0.083] [0.120] [0.013] B.Quarterdummyvariablesincluded 0.017 0.147*** -0.140** 0.008 [0.022] [0.055] [0.065] [0.011] C.St. errorsclusteredsimultaneouslyatfirmandtime 0.017 0.147*** -0.141** 0.008 [0.015] [0.046] [0.055] [0.007] 52

Figure1. CrisisStatistics ThisfigureplotsdailyvaluesoftheTEDspread(left)andquarterlyvaluesofrealGDP(right). TheTEDSpread (percent)iscalculatedasthespreadbetween3-MonthLIBORbasedonUSdollarsand3-MonthTreasuryBill. RealGDP($trillions)isaseasonallyadjustedannualrate. Source: FederalReserveBankofSt. Louis. 53

Figure2. GovernmentExpenditure/GDP ThisfigureplotstheratiooftotalgovernmentexpendituretonominalGDP.Bothtotalgovernmentexpenditure andGDPareseasonallyadjustedquarterlyvalues. Source: BureauofEconomicAnalysis. 54

Figure3. SampleFirmCharacteristics The figure plots average quarterly values of various firm characteristics of unrated firms. ROA and OCF are scaledbypre-crisisassets. AllvariablesaredescribedinAppendixA. 55

Figure4. InvestmentConstraints This figure plots average quarterly values of delaycon for unrated firms and rated firms, respectively, where delayconisanindexofinvestmentdelayconstraints,derivedfromHobergandMaksimovic(2015). 56

Figure5. MarketShareandAcquisitionActivities Thisfigureplotsthequarterlyvaluesofunratedfirms’averagemarketshare(left)andaverageacquisitionvalue scaled by average pre-crisis assets (right), where pre-crisis assets are quarterly asset values averaged over the pre-crisisperiod. 57