Supply Chain Lending and Accounting Conservatism

Transcription

1 Supply Chain Lending and Accounting Conservatism Guojin Gong Smeal College of Business Pennsylvania State University University Park, PA Shuqing Luo Business School National University of Singapore 1 Business Link, Singapore [email protected] March 2014 Comments Welcome We thank workshop participants at National University of Singapore and the 2013 American Accounting Association Annual Meeting for helpful comments and suggestions.

2 Supply Chain Lending and Accounting Conservatism ABSTRACT: Economic interdependence between suppliers and customers generates proprietary information useful for credit risk assessment. Such proprietary information can mitigate information asymmetry between borrowers and lenders, and substitutes for conservative accounting numbers in debt contracting. We hypothesize that a pre-existing lending relationship with a borrower s customer(s) grants the lender access to proprietary supply-chain information and reduces the lender s demand for, and therefore the supply of, accounting conservatism by the borrowing supplier. Consistently, we document a negative effect of the pre-existing lendercustomer lending relationship on the borrowing suppliers accounting conservatism at loan origination. This negative effect is robust to instrumental variable estimation, propensity score matching approach, and a placebo test. Further, this negative effect becomes stronger when the suppliers and customers operate in an opaque information environment, when the customers represent important business partners and have long-lasting relationship with their lenders, and when the suppliers extend sizeable trade credit to their customers. In additional analysis, we find less use of accounting-based covenants when lenders have a pre-existing lending relationship with the borrower s customer(s). Our findings demonstrate the informational value of proprietary supply-chain information for lenders and its impact on borrowers reporting practices. Keywords: Supply-chain relationship; Information asymmetry; Debt contracting; Conservatism. Data Availability: Data are available from public sources indicated in the text.

3 I. INTRODUCTION Business relationships between suppliers and customers create significant operational and financial interdependence within supply chains. Such economic interdependence generates valuable information about supply-chain partners financial health and business risks. 1 Prior literature documents considerable influences of supply-chain information over corporate managers and equity market participants. 2 Yet, the implication of supply-chain information for lenders, one important stakeholder group, has received little attention. As supply-chain partners typically engage in non-arm s-length transactions and collaborative activities, supply-chain information is often proprietary in nature and unavailable to the public. 3 Lenders, as the dominant source of corporate financing, can potentially access such proprietary information when contracting with supply-chain partners. In this study, we examine the informational value of supply-chain information in lenders decision making and its associated effect on borrowers financial reporting practices. Specifically, we examine whether a pre-existing lending relationship with a borrower s major customer(s), a situation where the lender is granted access to proprietary supply-chain information, reduces the lender s demand for, and therefore the supply of, conservative accounting by the borrowing supplier at loan origination. During a lending process, borrowers are better informed than lenders about their own credit quality, and lenders are more sensitive to borrowers losses than they are to borrowers profits. The information asymmetry between borrowers and lenders, coupled with lenders asymmetric payoffs, leads to agency cost of debt (Jensen and Meckling 1976). An extensive literature 1 See Cooper, Ellram, Gardner and Hanks (1997), Blackwell and Blackwell (1999), Lambert and Cooper (2000), Lee, So, and Tang (2000), Baiman and Rajan (2002a, 2002b), and Kulp, Lee, and Ofek (2004). 2 See, for example, Kulp, Lee, and Ofek (2004), Hertzel, Li, Officer, and Rodgers (2008), Cohen and Frazzni (2008), and Murfin and Njoroge (2011). 3 Examples of proprietary supply-chain information include customers purchase plans, demand forecasts, collaborative product design and development, and other relationship-specific investments. 1

4 proposes that lenders demand conservative accounting numbers to reduce deadweight agency costs, because conservatism can generate verifiable lower bound measures of net assets that assist lenders in screening potential borrowers and in monitoring borrower s creditworthiness after loan issuance (e.g., Leftwich 1983; Watts and Zimmerman 1986; Watts 1993, 2003a, 2003b; Ahmed, Billings, Morton, and Stanford-Harris 2002; Zhang 2008). 4 Consistently, prior evidence suggests that higher lender-borrower information asymmetry leads to more conservative accounting by the borrowers (e.g., Ball, Kothari, and Robin 2000; Ball and Shivakumar 2005; Gormley, Kim, and Martin 2011; Erkens, Subramanyam, and Zhang 2013). Through a pre-existing lending relationship with major customer(s), a lender can communicate with the customer(s) privately and request timely financial disclosures, covenant compliance information, financial projections, and even strategic plans that may or may not well aligned with the interests of the suppliers (e.g., Standard and Poor's 2007; Sufi 2007). Proprietary information obtained from the borrowing customer(s) has important implications for the supplier s business operations and financial health. The lending experience with major customer(s), therefore, enhances the lender s understanding of the supplier s business risks and creditworthiness. This tends to reduce the reliance on conservative accounting numbers when the lender makes the loan-granting decision and deliberates potential monitoring costs (hereafter, we term the lender who has prior lending experience with the customer when granting loans to the supplier as a supply-chain lender ). As conservatism in conjunction with debt contracting is costly to implement (e.g., Beneish and Press 1993; Gigler, Kanodia, Sapra and Venugopalan 2009), suppliers that borrow from supply-chain lenders adopt less conservative accounting 4 Throughout the paper, we use conservatism to refer to the timely incorporation of economic losses into accounting earnings (Basu 1997; Watts 2003a), which is also called asymmetric timeliness of loss recognition or conditional conservatism. From a debt contracting perspective, conditional and unconditional conservatism are substantially different concepts. While unconditional conservatism seems at best neutral and possibly inefficient, conditional conservatism can increase the efficiency of debt contracting (Ball and Shivakumar 2005). 2

5 choices to save associated costs. 5 In contrast, a non-supply-chain lender who does not have prior lending experience with major customer(s) has less information to assess the supplier s credit quality prior to loan initiation. Therefore, to mitigate agency costs, the lender is likely to demand more conservative financial reporting when granting loans to suppliers. Accordingly, we hypothesize that suppliers borrowing from supply-chain lenders report less conservatively than suppliers borrowing from non-supply-chain lenders. We note that our hypothesis is not entirely straightforward. Proprietary supply-chain information often involves forward-looking events (e.g., customers planned purchase orders and product development plans), and the credibility of such soft information remains a concern. Conservative accounting numbers presumably are more credible than soft information due to extensive public scrutiny of financial statements. It is possible that lenders consider proprietary supply-chain information and conservative accounting numbers as complementary sources of information, and do not reduce the demand for conservatism despite the possession of customers confidential information. Furthermore, the economic interdependence between a supplier and its major customer(s) leads to positively correlated business risks along the supply chain (e.g., Hertzel et al. 2008). By lending to members of the same supply chain, a lender faces more concentrated credit risk and has incentive to more diligently monitor borrowers, heightening the demand for accounting conservatism. We also note that demands for conservatism come from multiple sources including other lenders, 6 corporate boards, auditors and managers legal 5 The costs of conservative accounting include the adverse effect that covenant violations have on firms (which are exacerbated by false alarms ), the potential negative effect on managers investment decisions, and possible inefficiencies for equity valuation (e.g., Gigler, Kanodia, Sapra, and Venugopalan 2009; Guay and Verrecchia 2006; Roychowdhury 2010; Barth, Caprio, and Levine 2004). 6 It is possible that other lenders conservatism demand is muted due to the certification role of supply-chain lenders, because the fact that a supply-chain lender is willing to initiate or continue the loan indicates that the borrowing supplier does not face immediate and serious concern about credit quality. In untabulated tests, we compare loan terms between non-supply-chain lenders and concurrent non-supply-chain lenders for suppliers also borrowing from supply-chain lenders. We find that the use of accounting-based covenants is significantly less prevalent when the 3

6 liability concerns, and shareholders (e.g., Watts 2003a, 2003b). The effect of supply-chain lending experience might be outweighed by conservatism demands from other sources. Whether the supply-chain lending experience affects the lender s demand for accounting conservatism remains an empirical question. We start by examining the association between the existence of lender-customer lending relationship and suppliers accounting conservatism at loan origination. We measure accounting conservatism using firm-specific C-Score developed in Khan and Watts (2009). Results show that at loan origination, suppliers borrowing from supply-chain lenders report less conservatively than those borrowing from non-supply-chain lenders, after controlling for credit quality and financial health of both the suppliers and their customers. This finding is consistent with the notion that proprietary supply-chain information, by mitigating lender-borrower information asymmetry, substitutes for conservative accounting numbers in debt contracting. Extending credit to members of the same supply chain is an endogenous decision, and omitted variables that lead to this decision may correlate with lenders demand for, as well as borrowers supply of, accounting conservatism. To alleviate the endogeneity concern, we employ an instrumental variable approach, a propensity score matching approach, and a placebo test. We continue to find a negative effect of lender-customer lending relationship on the borrowing supplier s conservatism at loan origination, suggesting that endogeneity is unlikely to be the primary driver of our finding. Admittedly, in addressing our research question we face the empirical challenge of being unable to directly observe the flows of proprietary information from customers to their lenders. To alleviate this concern and further explore the impact of proprietary supply-chain information, borrowing supplier also has a supply-chain lender. This result suggests that, conditional on the presence of supplychain lenders, other (non-supply-chain) lenders recognize the reduced conservatism by the borrowing suppliers and accordingly adjust the covenant structure to reduce the reliance on accounting numbers. 4

7 we conduct several cross-sectional analyses. First, the access to propriety supply-chain information should be more valuable when the supplier s credit quality is more difficult to assess. Consistently, we find a more pronounced negative relation between the pre-existing lendercustomer relationship and borrowing suppliers conservatism when the supplier and its major customer(s) operate in an opaque information environment, where informational opacity is proxied by low quality analyst forecasts and large magnitude of discretionary accruals. Second, we expect that strong economic tie between supply-chain members and long-lasting lendercustomer lending relationship would generate more valuable proprietary information relevant for evaluating the supplier s credit quality. As expected, results show that greater customer importance, proxied by the proportion of major customers purchases in their suppliers total sales, and longer lender-customer lending relationship, proxied by the number of years that a lender has had lending experience with the customers, strengthens the negative relation between lender-customer lending relationship and borrowing suppliers conservatism. Third, among various types of information obtainable from major customers, credit-relevant information is the most important to assess the creditworthiness of their suppliers. Supporting this conjecture, we find that larger trade credit extended to major customer(s), an important source of credit-relevant information, strengthens the negative relation between lender-customer lending relationship and borrowing suppliers conservatism. In additional analyses, we find that supply-chain lenders accept fewer accounting-based covenants than non-supply-chain lenders. This finding echoes existing evidence of fewer debt covenants due to acquisition of private information by lenders (Ciamarra 2012; Bharath, Dahiya, 5

8 Saunders, and Srinivasan 2011; Engelberg, Gao, and Parsons 2012), 7 thus providing further support for our hypothesized effect of proprietary supply-chain information on lender-borrower information asymmetry. This result also speaks to the use of conservatism in debt contract design. Beatty, Weber, and Yu (2008) point out that lenders who in spite of having the authority to prescribe the financial information that they want, typically include at most very crude adjustment to their contracts. Our evidence indicates that better informed lenders do make modifications in the design of loan covenants in accordance with a lower demand for conservatism. We contribute to the literature examining economic consequences of supply-chain network. Prior studies document that economic interdependence and information integration between supply-chain partners affect operating profit (Kulp et al. 2004), financial distress (Hertzel et al. 2008), corporate investment (Murfin and Njoroge 2011), opportunistic financial reporting (Ramam and Shahrur 2008), and cross-firm return predictability (Cohen and Frazzni 2008; Pandit, Wasley, and Tzachi 2011). A few recent studies also examine how various features of the supplier-customer relationship affect loan contract terms (Koh, Teoh, and Tham 2011; Li and Yang 2011; Files and Gurun 2011). The literature overlooks the potential interplay between lenders and supply-chain partners in the debt contracting process. Lenders, being the major capital provider, can privately communicate with borrowing customers and influence borrowing suppliers financial reporting practices. Our study employs a novel setting, supply-chain lending relationship, to offer new evidence concerning the role of proprietary supply-chain information (obtained over the course of a prior lending relationship) in affecting lenders deliberations and borrowers financial reporting practices. 7 These studies find fewer restrictive debt covenants when lender-borrower information asymmetry is mitigated due to lenders acquisition of private information through board representation (Ciamarra 2012), relationship lending (Bharath et al. 2011), or personal connections between employees of banks and firms (Engelberg et al. 2012). 6

9 Our study is also closely related to the literature examining the impact of information asymmetry on accounting conservatism. Several studies suggest that lower lender-borrower information asymmetry, achieved through private communication, geographical connection, board representation, or relationship lending, leads to lower accounting conservatism by the borrowers (Ball et al. 2000; Ball and Shivarkumar 2005; Gormley et al. 2011; Erkens et al. 2013). Relatedly, LaFond and Watts (2008) document a lower level of conservatism after a decrease in information asymmetry between equity investors. 8 Our evidence complements these studies by identifying supply-chain lending as one important information channel through which lenders obtain proprietary information from borrowers stakeholders (in particular, major customers). Such information appears to substitute for borrowers financial reporting (in particular, conservative accounting numbers) during the debt contracting process. We also document an indirect effect of proprietary supply-chain information on debt contract design such information, by substituting for conservative accounting numbers in debt contracting, reduces the reliance on accounting numbers in the design of debt covenants. The rest of the paper proceeds as follows. We develop testable hypotheses in Section II. Section III describes the sample selection process and presents the sample. Section IV reports and discusses empirical findings. We conclude in Section V. II. HYPOTHESIS DEVELOPMENT Proprietary supply-chain information and credit risk assessment Proprietary supply-chain information has significant implications for suppliers business operations and financial health. A lender can use such information directly as a source of new information, in addition to the information received from other sources (such as public SEC 8 Conversely, accounting conservatism can affect information asymmetry. Wittenberg-Moerman (2008) shows that greater conservatism lessens the information asymmetry between credit investors, and improves the efficiency of the secondary trading of debt securities. 7

10 filings, press releases, and even inside information provided by the supplier at loan origination), to improve credit assessment. The lender can also use the proprietary supply-chain information indirectly to verify information regarding the supplier s business prospects and facilitate information processing in credit analysis. Both direct and indirect use of proprietary supplychain information can enhance lenders understanding of the suppliers creditworthiness. In a supply-chain network, suppliers often request a significant amount of proprietary information from customers to facilitate collaborative activities and enhance investment efficiency. In particular, suppliers require their customers production plans and demand forecasts to effectively allocate resources and manage operations. From lenders perspective, it is difficult to ascertain and verify customers demand information solely from publicly available information, as customers may not fully reveal their product development and expected demand to the public and even their suppliers due to concerns about the competitive harm of disclosures (Baiman and Rajan 2002b). 9 Proprietary information about customer demand, obtained over the course of the lender-customer lending relationship, can enhance supply-chain lenders understanding of the suppliers business prospects and operational stability. 10 Moreover, through prior lending experience with major customers, supply-chain lenders can glean insights concerning the incentive alignment between customers and their suppliers. Misaligned incentives along the supply chain can impede timely innovation and the development of relationship-specific investments, reducing the suppliers potential cash flows (Baiman and 9 Although information sharing is common between customers and suppliers (Lee et al. 2000; Baiman and Rajan 2002a; Kulp et al. 2004), it does not preclude partial disclosure or selective disclosure along the supply chain (Baiman and Rajan 2002b). For instance, the potential for hold-up in supply chains can impede information sharing between business partners (Schloetzer 2012). 10 As one example, a customer s new product development plan tends to bring in higher and more stable future revenues for its supplier. The customer refrains from publicly disclosing this information in fear of intense competition from peers, but may supply this information to its lender to obtain favorable loan terms. The lender thus enjoys an information advantage over outsiders (including non-supply-chain lenders) when evaluating the supplier s debt capacity and debt repayment ability. 8

11 Rajan 2002a). Therefore, supply-chain lenders have greater ability to project the level and persistence of cash flow streams by the suppliers, which facilitates the credit granting process. Besides implications for business operations, proprietary supply-chain information also reveals important aspects regarding suppliers financial health and liquidity risk. Suppliers often extend a substantial amount of trade credit to customers (Petersen and Rajan 1997). Accounts receivable (resulting from trade credit) serves as an important collateral in loan contracts (Mian and Smith 1992), and failure to timely collect accounts receivable can lead to adverse consequences such as default on loans and cutting investment (Murfin and Njoroge 2011). Proprietary information regarding customers financial health can shed light on the liquidity risk exposure of suppliers to their customers. The information can also help lenders evaluate the reasonableness of sales forecasts and/or cash flow forecasts provided by the supplier s management team. This can help supply-chain lenders better evaluate the risk of extending credit to the suppliers. Hypotheses As discussed above, through prior lending experience with customers, supply-chain lenders gain proprietary information valuable for credit risk assessment of suppliers. Proprietary supplychain information can reduce lenders demand for conservatism by the borrowing supplier through two channels. First, supply-chain lending experience improves lenders ability to screen potential borrowers. Prior to loan origination, proprietary supply-chain information can facilitate supply-chain lenders to identify high quality suppliers and price their loans accordingly; this reduces the need for conservative accounting numbers to protect against potential downside risk. In contrast, non-supply-chain lenders face greater information disadvantage in assessing 9

12 borrowing suppliers, strengthening their demand for reporting conservatism. 11 Second, after loan origination, supply-chain lending experience facilitates lenders monitoring of borrowers. Prior literature suggests that conservative reporting allows lenders to more closely monitor borrowers creditworthiness on a timely basis, and thus limits managerial opportunism (e.g., Watts and Zimmerman 1986; Watts 1993). 12 Proprietary supply-chain information offers lenders valuable and timely signals about the borrowing suppliers cash flow steams and credit risk, and thus can reduce the reliance on conservative accounting numbers in the lender monitoring process. Anticipating this monitoring benefit, supply-chain lenders can permit less conservative reporting before granting loans to the borrowing suppliers. While non-supply-chain lenders can also learn about the supply-chain network from publicly available information and/or requesting proprietary information directly from the borrowing suppliers at loan origination, without any private communication with the customers, non-supply-chain lenders need to incur greater costs verifying and processing acquired information. Hence, it is more cost effective for non-supply-chain lenders to rely on hard information such as conservative accounting numbers when contracting with the supplier. Nonsupply-chain lenders, therefore, demand more conservative financial reporting to mitigate potential downside risks. To the extent that practicing conservative reporting involves nontrivial 11 Our hypothesis is complicated by the fact that proprietary supply-chain information may reveal poor credit quality about the borrowing suppliers and lead to more informed rejection of loan applications. To the extent that borrowing suppliers selected by supply-chain lenders differ systematically from the suppliers borrowing from non-supply-chain lenders, our evidence may be confounded by correlated omitted variables that affect borrowers credit quality and lenders loan-approval decisions. To mitigate this endogeneity concern, we control credit quality for both the supplier and its customer(s) in the empirical models. We also use a propensity score matching approach and an instrumental variable approach to address the concern of correlated omitted variables. 12 After loan origination, lenders do not have direct control over borrowers accounting policy. Nevertheless, lenders can influence borrowers to maintain or adopt more conservative accounting choices through lender-initiated loan modification/renegotiation and covenant violation, and thus limit the scope for managerial opportunism. 10

13 costs, 13 borrowing suppliers respond to the lower conservatism demand by supply-chain lenders to save associated costs. This reasoning leads to the following hypothesis, stated in the alternative form. H1: A pre-existing lending relationship with the borrower s customer is associated with less accounting conservatism by the borrower. We further postulate several factors that may affect the informational value of propriety supply-chain information in debt contracting. First, when customers operate in an opaque information environment, gaining access to customers inside information allows supply-chain lenders to enjoy greater information advantage than non-supply-chain lenders. In a similar vein, the suppliers information opacity can also increase the potential benefits of supply-chain lending because information opacity makes it more costly for non-supply-chain lenders to acquire and analyze credit-relevant information when assessing the suppliers creditworthiness. Hence, we expect that information opacity for both customers and borrowing suppliers increases the information advantage of supply-chain lenders and further lowers their demand for accounting conservatism. H2a: Information opacity of the customer strengthens the negative effect of lender-customer lending relationship on the borrowing supplier s accounting conservatism. H2b: Information opacity of the supplier strengthens the negative effect of lender-customer lending relationship on the borrowing supplier s accounting conservatism. Second, the benefits of gaining access to propriety supply-chain information should be greater when a supplier s business relies more heavily on its customers, as greater business reliance produces more and higher quality proprietary information for credit risk assessment. Relatedly, supply-chain lenders are able to acquire more high-quality proprietary information 13 While it is debatable whether conservatism improves debt-contracting efficiency (Gigler et al. 2009; Caskey and Hughes 2012), our paper is agnostic regarding this theoretical debate. In particular, our arguments only require nontrivial costs associated with conservative accounting, and do not imply lower debt-contracting efficiency for supply-chain lenders. 11

14 when they have repeated lending experience with the customers and hence are more familiar with the customers business risks. Endowed with more high-quality proprietary information, supply-chain lenders are likely to further reduce their reliance on conservative accounting numbers in the lending process. H3a: The importance of the customer in the supplier s business strengthens the negative effect of lender-customer lending relationship on the borrowing supplier s accounting conservatism. H3b: The duration of prior lending experience with the customer strengthens the negative effect of lender-customer lending relationship on the borrowing supplier s accounting conservatism. Finally, of all types of information accessible to supply-chain lenders, credit-relevant information is most important to their decision making. Trade credit extended to customers represents an important source of credit-relevant information because it directly reveals the supplier s credit exposure to customers. Supply-chain lenders can evaluate the collectability of a supplier s accounts receivable by assessing its customers ability to repay trade credit in a timely manner. As accounts receivable often serves as important collateral in loan contracting (Mian and Smith 1992), a large amount of trade credit extended to the customers (i.e., greater credit exposure to the customers) indicates greater informational value of proprietary supply-chain information. Consequently, the amount of trade credit extended to major customers tends to strengthen the negative effect of supply-chain lending on the borrowing supplier s accounting conservatism. H4: The amount of trade credit extended to the customer strengthens the negative effect of lender-customer lending relationship on the borrowing supplier s accounting conservatism. 12

15 III. SAMPLE, VARIABLE MEASUREMENT, AND DESCRIPTIVE STATISTICS Sample To build the sample, we start by collecting information on public firms and their disclosed major customers. Our data collection is facilitated by Regulation S-K, which requires public issuers (with the exception of small businesses filing 10-KSB) to reveal the identity of their major customers as well as SFAS 14, which requires public companies to disclose the amount of revenues from each major customer. 14 These disclosures are collected and compiled by Compustat Segment files, from which we identify a sample of 27,041 supplier-customer-year observations with valid firm identifier (GVKEY) for both the suppliers and their customers between 1988 and We then exclude 6,464 observations if the suppliers and/or the customers belong to utility industry (SIC code ) or financial industry (SIC code ). These procedures yield a sample of 20,577 supplier-customer-year observations, representing 15,339 unique supplier-years. For each supplier-customer-year observation, we search whether the supplier initiated new loan(s) during the year. We do so in the Loan Pricing Corporation s DealScan database which has a comprehensive coverage of the global loan markets, providing information on the identity of lenders and borrowers, loan characteristics, and loan contract terms. For each supplier-lender pair, we then search whether the same lead arranger has a lending relationship with the supplier s major customers prior to (and remain outstanding during) the year that the lender initiated loans 14 Accordingly to SFAS 14 (paragraph 39), a major customer is defined as any customer whose purchases represent at least ten percent of an issuer s consolidated revenue and if the loss of the customer would have a material adverse effect on the issuer and its subsidiaries. While SFAS 14 has been superseded by SFAS 30 in 1979 and SFAS 131 in 1997, the disclosure requirement about major customers remains. 15 We start our sample with year 1988 as this is the year Dealscan starts to have a more continuous coverage of global loan markets. 13

16 to the supplier (i.e., supply-chain lender). 16 We find that 16.7% of the suppliers (2,560 of 15,339) initiated a lending relationship during a supplier-customer-year when their major customers have outstanding loans in the year. Among these suppliers 41.7% (1,068 of 2,560) share at least one lead arranger with their major customers during a supplier-customer-year. We further retrieve financial data from Compustat annual files, stock trading information from CRSP daily and monthly files to construct accounting conservatism measures and control variables included in the regression analyses. The final sample includes 1,135 supplier-customeryear observations, 61.7% of which (700 of 1,135) share a common lead arranger with their major customers in the same year. Variable measurement Supply-chain lending relationship We create an indicator variable CHAIN_LENDING to denote the existence of a lendercustomer lending relationship. CHAIN_LENDING equals one if a supplier borrows from a lead arranger who also serves as a lead arranger of an outstanding loan to the supplier s major customer(s) (i.e., supply-chain lender), and equals zero if a firm borrows from a lead arranger who does not have outstanding lending relationship with the supplier s major customer(s) (i.e., non-supply-chain lender). Accounting conservatism Prior research suggests that lenders demand accounting conservatism, in particular asymmetric timeliness of loss recognition, in the loan contracting process (e.g., Watts 2003a, 16 Note that 74.7% (11,455 of 15,339) of the suppliers have reported only one single major customer. When supplier firms have reported more than one major customers in a fiscal year, we define supply chain lenders based on whether a lender acts as a lead arranger in an outstanding lending relationship with any one of the supplier s major customers prior to initiating loans to the suppliers. In subsequent analysis where we examine how customer characteristics affect lenders demand for suppliers accounting conservatism, we retain the largest customer (who makes the greatest amount of purchases from the supplier) for each supplier-year observation. 14

17 2003b). To measure asymmetric timeliness of loss recognition, we follow Khan and Watts (2009) to construct a firm-year measure C-Score (CSCORE). CSCORE stems from Basu s (1997) asymmetric timeliness metric, but allows inter-temporal and cross-sectional variations in accounting conservatism. To measure C-Score, Khan and Watts (2009) begin with Basu (1997) piece-wise linear regression, described below as Equation (1.1), and propose an augmented regression, described below as Equation (1.2). X i,t = β 1 + β 2 D i,t + β 3 R i,t + β 4 D i,t R i,t + ε i,t (1.1) X i,t = β 1 + β 2 D i,t + R i,t (μ 1 + μ 2 ln(mve) i,t + μ 3 MB i,t + μ 4 LEV i,t ) + D i,t R i,t (λ 1 + λ 2 ln(mve) i,t + λ 3 MB i,t + λ 4 LEV i,t ) + (δ 1 ln(mve) i,t + δ 2 MB i,t + δ 3 LEV i,t + δ 4 D i,t ln(mve) i,t + δ 5 D i,t MB i,t + δ 6 D i,t LEV i,t ) + ε i,t (1.2) In both equations, i indexes firm and t indexes year. X is income before extraordinary items divided by lagged market value of equity. R is 12-month compounded return starting nine months before the year end. D is an indicator variable that equals one when R is negative, and zero otherwise. In Equation (1.2), ln(mve) is the logarithm transformation of market capitalization, MB is market-to-book ratio, and LEV is book leverage. In Equation (1.1), the coefficient β 3 reflects the sensitivity of earnings to good news, and the coefficient β 4 captures the incremental sensitivity of earnings to negative news. Following Khan and Watts (2009), for each year we specify β 3 and β 4 as a linear function of firm characteristics that have been shown to significantly affect the degree of conservatism (e.g., LaFond and Roychowdhury 2008; LaFond and Watts 2008), including firm size, market-to-book ratio, and book leverage. C-Score (CSCORE) is specified as CSCORE = β 4 = λ 1 + λ 2 ln(mve) i,t + λ 3 MB i,t + λ 4 LEV i,t. We estimate Equation (1.2) for each year during our sample period using data from the intersection of CRSP and COMPUSTAT. The coefficient estimates are applied to the above 15

18 specification for C-Score to generate the firm-year measure of accounting conservatism. A higher value of CSCORE represents more conservative financial reporting. Control variables We control for firm size (ln(mve)) and firm age (ln(firmage)), because larger firms and more matured firms are likely to have richer information environment, which diminish the importance of conservatism in debt contracting (Khan and Watts 2009). We also include investment cycle (INVCYCLE), market-to-book ratio (MB), 17 return volatility (RETVOL), sales volatility (SALEVOL), and bid-ask spreads (BID_ASK), as firms having longer investment cycle, more growth opportunities, greater return and sales volatility, and larger bid-ask spreads tend to suffer from greater information asymmetry, and hence are subject to higher demand for conservative reporting. Since conservatism increases in credit risk, we include leverage (LEV) and Altman s Z-score (ALTMAN) in the regression analysis. We add auditor choice (BIG_FOUR) since prior studies suggest that big-four auditors demand a higher level of accounting conservatism due to greater bargaining power and reputation concern (Basu et al. 2001; Krishan 2005). We also identify industries that involve high litigation risk (LITIGATION), as firms exposing to higher litigation risk tend to recognize bad news promptly to preempt lawsuits. We further control for customer fundamentals, customer-supplier relationship, and customer importance since these factors may affect the supplier s accounting conservatism. Specifically, we include the customers performance (CU_ROA), leverage (CU_LEV), and Altman s Z-score (CU_ALTMAN). In general, suppliers face lower business risks when their major customers exhibit higher profitability, lower leverage, and lower bankruptcy risk, which reduce lenders potential losses and necessitate a lower level of accounting conservatism. We also include the 17 Although Khan and Watts (2009) predict a positive effect of MB on CSCORE, they also point out that buffer problem (Roychowdhury and Watts 2007) may make the effect hard to observe. 16

19 number of years that the supplier-customer relationship has existed (ln(duration)) and the supplier s sales to the specific customer divided by the supplier s total sales (CU_IMPT). A closer customer-supplier relationship and a heavier reliance on major customers imply greater bargaining power by major customers, leading to greater conservatism demand (Hui et al. 2012). Detailed definitions for these control variables are provided in the Appendix. Finally, we include industry and year fixed effects to account for cross-industry variations and inter-temporal trends in accounting conservatism. Descriptive statistics Table 1, Panel A reports the number of suppliers that have lending relationship with supplychain lenders versus those with non-supply-chain lenders by loan origination year. On average, about 62% (700 of 1,135) suppliers borrow from supply-chain lenders and 38% (435 of 1,135) borrow from non-supply-chain lenders. The number of observations declines after 2007, reflecting the adverse consequence of financial crisis on corporate debt financing activities. Table 1, Panel B presents the sample by Fama-French industry classification (excluding utility and financial industries). We find that suppliers borrowing from supply-chain lenders spread broadly across all industries, with the fraction ranging from as low as 3.3% in the consumer durables industry to as high as 25.9% in the manufacturing industry. [Insert TABLE 1 Here] Table 2 reports summary statistics for observations with supply-chain lenders and those with non-supply-chain lenders. Consistent with H1, CSCORE for suppliers borrowing from supplychain lenders (mean = 0.11 and median = 0.12) is significantly lower (p-value < 0.01) than the corresponding CSCORE for suppliers borrowing from non-supply-chain lenders (mean = 0.25 and median = 0.24). In addition, suppliers that borrow from supply-chain lenders are larger in 17

20 market capitalization, have longer company history, shorter investment cycle, higher market-tobook ratio, lower stock return volatility and sales volatility, smaller bid-ask spreads, higher leverage, and higher bankruptcy risk. These suppliers are also more likely to employ reputable auditors and operate in litigious industries. Furthermore, customers from these suppliers tend to have higher leverage, higher bankruptcy risks, longer business relationship with their suppliers, and represent more important partners to their suppliers. [Insert TABLE 2 Here] The Pearson and Spearman correlations, presented in Table 3, confirm the univariate evidence in Table 2. In particular, our variable of interest, CHAIN_LENDING, is negatively and significantly correlated with CSCORE (Pearson and Spearman correlations are both -0.29, p- value < 0.01). We next turn to multivariate analyses to further study the relation between these two variables. [Insert TABLE 3 Here] IV. EMPIRICAL RESULTS The effect of lender-customer lending relationship on borrowers accounting conservatism To examine whether a pre-existing lending relationship with major customer(s) is associated with lower accounting conservatism by the borrowing supplier, we estimate the following regression model at the time that the lender originates loans to the supplier: CSCORE i,t = β 1 + β 2 CHAIN_LENDING i,t + β 3 CV i,t (2) where i indexes firm and t indexes year. The dependent variable, CSCORE, measures the degree of accounting conservatism by the borrowing suppliers. The variable of interest, CHAIN_LENDING, intends to capture the effect of lender-customer lending experience with major customer(s). CV is a vector of control variables on firm and industry characteristics, as 18

21 described in the previous section. Robust standard errors are clustered at the firm level and adjusted for heteoskedasticity. Table 4 reports the regression results. Consistent with H1, the coefficient estimate on CHAIN_LENDING is negative and significant (coefficient = , p-value < 0.01). This result suggests that relative to suppliers borrowing from non-supply-chain lenders, supplier firms that borrow from supply-chain lenders would report less conservatively (measured by CSCORE) by 0.041, the magnitude of which accounts for 25.6% of the sample mean. [Insert TABLE 4 Here] Results on control variables are generally consistent with our predictions. Specifically, supplier firms in our sample report more conservatively when they have smaller market capitalization, shorter company history, higher leverage, lower growth, larger bid-ask spread, higher leverage, higher bankruptcy risk, and higher likelihood of employing reputable auditors. We also find that supplier firms report more conservatively when their customers have higher financial leverage and are of greater importance in the supplier s customer base. Identification strategy Although we include a battery of control variables in the estimation of Equation (2), we cannot rule out the possibility that the negative relation between the lender-customer lending relationship and suppliers conservatism is driven by omitted variables that correlate with both the existence of lender-customer lending relationship and suppliers conservatism. We address this endogeneity concern below. Instrumental variable approach We first adopt an instrumental variable (IV) approach that relies on an exogenous variation in supply-chain lending relationship due to the geographic proximity between a supplier and its 19

22 major customer(s). Prior literature proposes that geographic proximity can magnify the supplychain contagion effect that supply chain partners tend to make similar business decisions (McFarland, Bloodgood, and Payan 2008), as geographic proximity enhances firms opportunities to exchange ideas and be cognizant of other important information in their fields (Feldman 1994; Audretsch and Feldman 1996). Hence, a supplier that is in geographic proximity with its major customer(s) is likely to choose the same credit providers as its major customer(s). On the other hand, geographic proximity should not directly affect the supplier s choice of accounting conservatism other than through affecting the lender-customer lending relationship. 18 To implement the IV approach, we estimate the following two-stage model. CHAIN_LENDING i,,t = β 1 + β 2 DISTANCE i,t-1 + β 3 CV i,t-1 + ε i,t (3) CSCORE i,t = β 1 + β 2 FITTED_ CHAIN_LENDING i,t + β 3 CV i,t + ε i,t (4) In the first-stage probit regression, we endogenize the supply-chain lending decision by regressing CHAIN_LENDING on DISTANCE, the geographic distance between the supplier and its major customer(s), and CV, the same set of control variables as those included in Equation (2). The fitted value of CHAIN_LENDING is then used to explain supplier firms accounting conservatism in the second-stage regression. [Insert TABLE 5 Here] Table 5 reports the estimation results. In the first-stage regression results, we find that DISTANCE is negatively and significantly related with CHAIN_LENDING, consistent with geographic proximity increasing the probability that the supplier borrows from the same lender 18 A stream of finance research argues that a close geographic distance between borrowers and lenders mitigates lenders information acquisition and monitoring costs, thus reducing loan spread (Degryse and Ongena 2005) and fostering relationship banking (Berger, Miller, Petersen, Rajan, and Stein 2005; Bharath et al. 2011). Note that, our instrumental variable measures the distance between a supplier and its major customer(s), not the distance between a supplier and its lender (which may affect the information asymmetry between the supplier and the lender). We are unable to measure the supplier-lender distance, as Dealscan reports lenders headquarters, as opposed to the branches that actually initiated the loans to the borrowing suppliers. 20

23 as its major customer(s). 19 This finding supports that DISTANCE satisfies the relevance criterion as an IV. We also confirm the orthogonality criterion, as regressing the second-stage residual on DISTANCE yields a statistically insignificant adjusted R 2 of In terms of control variables, supplier firms with longer company history, shorter investment cycle, higher market liquidity, greater default risk, less profitable customers, higher leveraged customers, and longlasting supplier-customer relationship are more likely to borrow from the same lender as their major customers. 21 In the second-stage regression results, we find that the estimated coefficient on FITTED_CHAIN_LENDING is negative and significant (coefficient = , p-value = 0.07). The IV estimation results are consistent with a causal effect from supply-chain lending relationship to suppliers accounting conservatism. Propensity score matching approach We also conduct a propensity score matching (PSM) analysis to further address the endogeneity concern. PSM analysis allows us to identify a sample of suppliers that borrow from non-supply-chain lenders but are otherwise comparable to the sample of suppliers borrowing from supply-chain lenders. Specifically, we first estimate a probit model by regressing 19 Hui et al. (2012) show that customers have demand for the suppliers accounting conservatism. One particular concern about our IV is that customers conservatism demand may vary with the geographic distance between suppliers and customers. This concern is mitigated by the insignificant correlation between CSCORE and DISTANCE. Moreover, Hui et al. (2012) show that customers conservatism demand increases with the bargaining power the customer has over the supplier. To the extent that geographic proximity increases customers bargaining power (e.g., losing a local customer is more costly than losing a customer located further away), the possible effect of our IV on customer s conservatism demand should work against us finding predicted results. 20 We also follow Frank (2000) and Larcker and Rusticus (2008) to compute the Impact Threshold for a Confounding Variable (ITCV) to assess how closely an unobservable confounding variable would have to be correlated with CHAIN_LENDING and CSCORE in order to overturn the OLS results in Table 4. The results suggest we would need an omitted variable with an impact of at least 49% higher than that of any of our control variables to change our inferences. We believe it is unlikely that a confounding variable will overturn the negative relation between CSCORE and CHAIN_LENDING. 21 In untabulated results, we also control for customer-lender relationship, including the duration of lending experience with the customers and the importance of customer loans in lenders loan portfolio. Adding these controls does not qualitatively alter the results in Table 5, and these additional controls are statistically insignificant. 21

24 CHAIN_LENDING on firm and industry characteristics included in Equation (3). Next, for each supplier with CHAIN_LENDING = 1, we select one supplier with CHAIN_LENDING = 0 without replacement that has the closest predicted probability (i.e., propensity score) of borrowing from a supply-chain lender. This matching procedure yields 188 observations with CHAIN_LENDING = 1 and 188 observations with CHAIN_LENDING = 0. In Table 6, we report two diagnostic tests to verify the effectiveness of PSM procedures. Table 6 Panel A shows that the differences in observable characteristics between supply-chainlending observations and matched non-supply-chain-lending observations are mostly statistically insignificant. Table 6 Panel B reports that the difference in propensity scores between these two samples is minimal. Together, these statistics suggest that PSM procedures have effectively removed meaningful observable differences between the two samples and ensure that the difference in accounting conservatism is likely to be only caused by the lender-customer lending relationship. [Insert TABLE 6 about here] Table 6 Panel C presents the difference in CSCORE across the two samples. As shown, the average CSCORE for suppliers borrowing from supply-chain lenders is 0.183, which is significantly lower than the average CSCORE of for suppliers borrowing from non-supplychain lenders (difference = , p-value < 0.01). Hence, the propensity score matching analysis also support a negative effect of supply-chain lending relationship on suppliers accounting conservatism. A placebo test The endogeneity concern rests on the assumption that certain omitted variables are correlated with both the lender-customer lending relationship and the supplier s accounting 22

25 conservatism. To the extent that such omitted variables are stable over time, our baseline results should hold even when the lender-customer lending relationship is established in the periods after the lender originated loans to the supplier. In contrast, our hypothesis is built upon the argument that supply-chain lenders information advantage at loan origination comes from the proprietary information acquired via the pre-existing lending relationship with the borrowers customers. In other words, our argument is valid only when the lender-customer lending relationship is established before the loan origination to the borrowing suppliers. To implement the placebo test, we create an indicator variable, PSEUDO_CHAIN_LENDING, that equals one if a supplier borrows from a lender before the period that the same lender originates a loan to the supplier s major customer(s), and zero if a supplier borrows from a lender after a different lender originates a loan to its major customer(s) (i.e., the same control sample with CHAIN_LENDING = 0). We identify 374 supplier-year observations with PSEUDO_CHAIN_LENDING = 1 and 435 observations with PSEUDO_CHAIN_LENDING = 0. In Table 7, we re-estimate Equation (3) after replacing CHAIN_LENDING with PSEUDO_CHAIN_LENDING, and find an insignificant coefficient on PSEUDO_CHAIN_LENDING. This insignificant finding helps rule out the possibility that certain omitted time-invariant factors driving the supply-chain lending choice also affects the borrowing suppliers conservatism. [Insert TABLE 7 about here] Cross-sectional analyses We consider three factors that potentially affect the negative relation between supply-chain lending choice and suppliers accounting conservatism. First, we examine whether information opacity enhances the marginal effect of proprietary supply-chain information (obtained through a 23

26 pre-existing lender-customer relationship) on borrowers accounting conservatism. We use two proxies for information opacity: information cost (INFCOST) following Duchin, Matsusaka, and Ozbas (2010) and absolute value of discretionary accruals ( DACC ) based on Hutton, Marcus and Tehranian (2009). INFCOST is an index created by ranking firms on different dimensions of information acquisition costs including the number of analysts, analyst forecast dispersion, and analyst forecast error. Discretionary accruals are estimated from Modified Jones Model. Higher values of INFCOST and DACC indicate more opaque information environment. We then estimate Equation (2) separately for firms (either customers or suppliers) with above the median value of information opacity and those with below the median value. Table 8, Panel A presents the results based on information opacity of the customers, and Table 8, Panel B reports the results based on information opacity of the suppliers. Consistent with H2a and H2b, in both panels we find that the negative relationship between CHAIN_LENDING and CSCORE is statistically significant only in the subsamples with high information opacity. In both panels, Chow tests show that the differences in the coefficient on CHAIN_LENDING across subsamples are statistically significant. [Insert TABLE 8 about here] Next, we examine whether the quality of proprietary supply-chain information, proxied by customer importance in suppliers sales (CU_IMPT) and the duration of lender-customer lending relationship (CU_LYRS), strengthens the negative effect of supply-chain lending on the borrowers accounting conservatism. We estimate Equation (2) separately for firms with above median value of CU_IMPT or CU_LYRS and those with below median value. Table 9 presents regression results. Consistent with H3a and H3b, the negative coefficient on CHAIN_LENDING is only significant for the subsamples with greater customer importance or longer prior lending 24

27 experience with customers. Chow tests show that the differences in the coefficient on CHAIN_LEANDING across subsamples are statistically significant. [Insert TABLE 9 about here] Finally, we examine the importance of credit-relevant information to our baseline results. To measure credit-relevant information, we approximate the amount of trade credit extended to a major customer (TRADE_CREDIT) as the supplier s total accounts receivable multiplying the percentage of total sales made to this customer, scaled by the supplier s net worth. We estimate Equation (2) separately for firms with above median value of TRADE_CREDIT and firms with below median value. The results are reported in Table 10. Consistent with H4, the coefficient estimate on CHAIN_LENDING is negative and significant only in the subsample with larger amount of trade credit. Chow test shows that the difference in the CHAIN_LENDING coefficient across subsamples is statistically significant. [Insert TABLE 10 about here] Robustness check using an alternative measure of conservatism To lend further credence to our baseline results, we adopt an alternative measure of accounting conservatism by augmenting Basu s (1997) piece-wise linear regression, as specified in Equation (1.1), to include CHAIN_LENDING and its interaction term with D i,t R i,t. H1 predicts that prior lending experience with major customers reduces lenders demand for, and the supply of, borrowers accounting conservatism. Thus, we expect the coefficient on CHAIN_LENDING i,t D i,t R i,t to be negative. Table 11 reports the regression results. Confirming the main result, we find a negative and significant coefficient on CHAIN_LENDING i,t D i,t R i,t (coefficient = , p-value < 0.10) under column (1). In column (2), after adding control variables as in LaFond and Roychowdhury 25

28 (2008) and LaFond and Watts (2008), we find that the coefficient on CHAIN_LENDING i,t D i,t R i,t remains negative and significant. 22 [Insert TABLE 11 about here] The effect of lender-customer lending relationship on borrowers covenant design Our findings above support the notion that proprietary supply-chain information mitigates the information gap between lenders and borrowers, thereby leading to less conservative reporting by the borrowing suppliers. The access to proprietary supply-chain information, coupled with borrowers less conservative accounting practices, can reduce the reliance on accounting numbers in designing debt covenants. Accordingly, we may observe less frequent use of accounting-based or financial covenants in the lending agreements between supply-chain lenders and borrowing suppliers. Consistently, both univariate and multivariate results, reported in Table 12 Panel A and Panel B, show that suppliers borrowing from supply-chain lenders are subject to fewer financial covenants than suppliers borrowing from non-supply-chain lenders. [Insert TABLE 12 about here] Lenders often impose multiple monitoring mechanisms in the lending agreements, and financial reporting practices can alter lenders tradeoff of alternative monitoring mechanisms (Costello and Wittenberg-Moerman 2011). To further shed light on lenders decision making, we examine the relation between supply-chain lending experience and other loan terms, such as yield spread, collateral requirements, loan maturity, and general covenants. In untabulated results, we find that a lender s prior experience with the borrower s major customer(s) is associated with lower yield spread, less use of collateral requirements, and more general covenants. We find no evidence that supply-chain lending experience affects loan maturity. These results generally 22 Our baseline results are also robust to the asymmetric accrual-cash flow measure proposed by Ball and Shivakumar (2005). 26

29 support the beneficial role of proprietary supply-chain information in reducing lender-borrower information asymmetry, which indirectly benefits borrowing suppliers through cheaper debt financing costs and fewer restrictive collateral requirements. The effect of supply-chain lending relationship on customers accountingn conservatism While our hypotheses focus on the effect of supply-chain lending experience on suppliers accounting conservatism, our arguments imply a similar effect of supply-chain lending experience on customers accounting conservatism. Testing the latter effect, however, is empirically difficult due to the asymmetry disclosure requirements: firms are required to disclose their major customers, but not their major suppliers. Prior evidence suggests that suppliers, identified through the COMPUSTAT segment file, are not economically significant to their major customers (Fee, Hadlock and Thomas 2006). 23 Consequently, lenders information gains from accessing the suppliers private information are limited, and may not have a discernible effect on lenders demand for accounting conservatism by the borrowing customers. 24 To shed light on this issue, we identify whether a major customer originated loans with a lender when the same lender has an outstanding lending relationship with the major customer s supplier. Among 2,839 customer-originated loans, we find 60.1% of customers (1,706 of 2,839) originated loans from the same lender as their suppliers with whom the lender has outstanding loans. In contrast with our earlier results, Table 13 shows that the relationship between the preexisting lender-supplier lending relationship (CU_CHAIN_LENDING) and the customers conservatism is insignificant (coefficient on CU_CHAIN_LENDING = 0.002, p-value = 0.842). This result, however, does not necessarily imply that a pre-existing lending relationship with 23 For our sample firms, total sales that suppliers made to their major customers comprise only 2.1% of the total sales of those customers. 24 Our conjecture is in line with prior evidence. For example, Hertzel et al. (2008) find a stock market wealth effect for suppliers resulting from their customers bankruptcy, but fail to find similar effect for customers resulting from their suppliers bankruptcy. 27

30 suppliers has a minimal effect on lenders conservatism demand for borrowing customers, due to substantial measurement errors in identifying major suppliers under the current asymmetry disclosure requirements. [Insert TABLE 13 about here] V. CONCLUSION Supply-chain network generates economic interdependence across business partners. By lending to a supplier s major customer(s), the lender gains access to proprietary supply-chain information and enjoys a unique information advantage in assessing the supplier s creditworthiness. Given that accounting conservatism is costly to implement, we conjecture that proprietary supply-chain information lowers lenders demand for, and therefore the supply of, conservatism by substituting conservative accounting numbers in the loan contracting process. Consistent with our conjecture, we find that when lenders have prior lending experience with the suppliers major customers, borrowing suppliers practice less conservative reporting at loan origination. This finding is more pronounced when the customers and suppliers operate in an opaque information environment, and magnifies with customer importance, long-lasting lendercustomer relationship, and the significance of trade credit suppliers extend to major customers. Our findings support the importance of proprietary supply-chain information in reducing agency cost of debt and assist lenders decision making. The evidence also sheds light on the interaction between lenders and supply-chain partners, and its implications for borrowers accounting practices during the lending process. Future research may extend our analyses to examine the implications of proprietary supply-chain information for the secondary trading of debt securities and the potential spillover effect to equity market participants. 28

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34 APPENDIX Variable Definitions Supply-chain lending relationship CHAIN_LENDING it CU_CHAIN_LENDING it PSEUDO_CHAIN_LENDING it = A dummy variable that equals one if a supplier firm borrows from a supply-chain lender and zero if a supplier firm borrows from a nonsupply-chain lender, where a supplier chain lender refers to a lender who acts as a lead arranger in an outstanding lending relationship with any one of the supplier s major customers prior to initiating loans to the suppliers. = A dummy variable that equals one if a major customer firm borrows from a supply-chain lender and zero if a major customer firm borrows from a non-supply-chain lender, where a supply chain lender refers to a lender who acts as a lead arranger in an outstanding lending relationship with any one of the major customers supplier firm prior to initiating loans to the major customer firm. = A dummy variable that equals one if the lender of a supplier firm establishes a lending relationship with the supplier s major customer after lending to the supplier firm, and zero if a supplier firm borrows from a lead arranger who do not have a pre-existing lending relationship with the customers. Customer-supplier relationship DISTANCE it-1 = The distances (in 1,000 miles) between the corporate headquarter of the supplier firm i and that of the major customer in fiscal year t-1. ln(duration) it = Natural logarithm of one plus DURATION, the number of years that a supplier-major customer relationship has existed as of the end of fiscal year t. CU_IMPT it = The relative importance of the customer to the supplier firm i in fiscal year t, measured as the total sales made to the major customer during the year divided by the supplier s total annual sales (SALE). SUP_IMPT it = The relative importance of the supplier firm i to the customer firm in fiscal year t, measured as the total sales the supplier firm made to the major customer during the year divided by the total annual sales (SALE) of the customer firm in the year. Supplier characteristics (In Table 13, variables for the supplier s major customer(s) are prefixed with CU.) CSCORE it = The firm-year conservatism measure based on Khan and Watts (2009). The following annual cross-sectional model is first estimated: X i,t = β 1 + β 2 D i,t + R i,t (μ 1 + μ 2 ln(mve) i,t + μ 3 MB i,t + μ 4 LEV i,t ) + D i,t R i,t (λ 1 + λ 2 ln(mve) i,t + λ 3 MB i,t + λ 4 LEV i,t ) + (δ 1 ln(mve) i,t + δ 2 MB i,t + δ 3 LEV i,t + δ 4 D i,t ln(mve) i,t + δ 5 D i,t MB i,t + δ 6 D i,t LEV i,t ) + ε i,t. CSCORE for supplier firm i in fiscal year t is then computed as λ 1 + λ 2 ln(mve) i,t + λ 3 MB i,t + λ 4 LEV i,t.. ln(mve) it = Natural logarithm of market value of equity MVE (CSHO PRCC_F) measured at the end of fiscal year t for firm i. 32

35 ln(firm_age) = Natural logarithm of the age of supplier firm i at the end of year t, measured as the number of years supplier firm i has been listed by the Center for Research in Security Prices (CRSP). INVCYCLE it = Depreciation (DP) in fiscal year t divided by total assets (AT) for supplier firm i. MB it = Market value of equity, calculated as (CSHO PRCC_F), divided by book value of equity (CEQ) at the end of fiscal year t for supplier firm i. RETVOL it = The standard deviation of daily stock returns for supplier firm i in fiscal year t. SALEVOL it = The standard deviation of the natural log of revenues for supplier firm i measured from year t-5 to year t-1. BID_ASK it = The average daily closing bid-ask spread for firm i in fiscal year t. LEV it = Total book value of debt (DLTT + DLC) divided by total assets (AT) measured at the end of fiscal year t for supplier firm i. ALTMAN = Altman s bankruptcy score, calculated based on 1.2 (ACT-LCT)/AT (RE/AT) (OIADP/AT) * (PRCC_F CSHO)/LT + SALE/AT for supplier firm i in in year t. BIG_FOUR it = An indicator variable that equals 1 if the auditor is one of the big four (or five) auditing firms, and zero otherwise.at the end of fiscal year t for supplier firm i. LITIGATION it = A dummy variable that equals one if the supplier firm i s main operations in year t are in a highly litigious industry (biotechnology ( and ), computers ( and ), electronics ( ), and retail ( ) in fiscal year t, and zero otherwise (based on Rogers and Stocken 2005). ROA it = Net income before extraordinary items (NI) in fiscal year t, scaled by the total assets (AT) at the beginning of year t for supplier firm i. X it = Net income before extraordinary items (NI) in fiscal year t, scaled by the market value of equity at the beginning of year t for supplier firm i. R it = 12-month compounded returns starting 9 months before the end of fiscal year t for supplier firm i. D it = A dummy variable that equals one if R i,t is negative and zero otherwise. Additional variables used in partitioning analysis (In Table 8, variables for the supplier s major customer(s) are prefixed with CU.) INFCOST it = A measure of supplier firm i s information cost in fiscal year t. Following Duchin et al. (2010), we first create an information index by averaging the firm s reverse percentile rank of analyst coverage (i.e., the number of analysts following the firm), percentile rank of analyst forecast dispersion, and percentile rank of analyst forecast error in the sample firms of fiscal year t. We then scale the index to range from zero to one. DACC it = Prior three years sum of absolute value of the discretionary accruals for supplier firm i in year t, where the discretionary accruals is computed based on modified Jones model. CU_LYRS i,t = The number of months (divided by 12) that a lead bank has had lending experience with a supplier i s customer(s) when a new loan was initiated to the supplier i in year t. 33

36 TRADE_CREDIT it = The relative importance of the trade credit extended to the customer for supplier firm i, calculated as (the total sales made to the customer firm/ total sales of the supplier firm) (accounts receivable / net worth of the supplier firm), where net worth of the supplier firm is the total assets (AT) minus total liabilities (LT) minus the intangible assets (INTAN) of the supplier firm i. Loan characteristics FINCOV it = The number of financial covenants divided by the total number of covenants. ln(loan_size) it = Natural logarithm of the total loan amount for supplier i in year t from the lender. ln(maturity) it = Natural logarithm of a loan s maturity in number of months for supplier i in year t. SECURE_LOAN it = A dummy variable that equals one if a loan agreement for supplier i in year t has collateral and zero otherwise. RESOLVER it = A dummy variable that equals one if a loan agreement for supplier i in year t is on revolving basis and zero otherwise. SYNDICATED_LOAN it = A dummy variable that equals one if a loan for supplier i in year t is a syndicated loan by multiple lenders and zero otherwise. 34

37 TABLE 1 Sample Distribution Panel A: Sample distribution by loan initiation year Year CHAIN_LENDING = 1 CHAIN_LENDING = 0 N % N % % 6 1.4% % 5 1.2% % 6 1.4% % 7 1.6% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % % 7 1.6% % 0 0.0% Total % % Panel B: Sample distribution by Fama-French 12 industry Fama-French s 12 industries CHAIN_LENDING = 1 CHAIN_LENDING = 0 N % N % Consumer Non-Durables % % Consumer Durables % % Manufacturing % % Energy and Coal Extraction % % Chemicals and Allied products % % Business Equipment % % Wholesale, Retail, and Some Services % % Healthcare, Medical Equipment, and Drugs % % Total % % This table reports sample distribution for 1,135 supplier-year observations by new loan issuance year from 1988 to 2010 in Panel A and by industry in Panel B, conditional on whether a supplier borrows from a bank that has outstanding lending relationship with at least one of the supplier s major customer(s) (CHAIN_LENDING). 35

38 TABLE 2 Summary Statistics Variables CHAIN_LENDING = 1 CHAIN_LENDING = 0 t-test of mean z test of median difference difference Mean Median Mean Median CSCORE *** *** MVE (billion$) *** *** FIRM_AGE *** 9.23 *** INVCYCLE *** *** MB *** 3.19 *** RETVOL *** *** SALEVOL *** *** BID_ASK *** *** LEV *** 3.75 *** ALTMAN *** *** BIG_FOUR ** 2.12 ** LITIGATION ** ** CU_ROA CU_LEV *** 3.89 *** CU_ALTMAN *** *** DURATION *** 6.67 *** CU_IMPT *** *** DISTANCE (1,000 mile) *** ** This table reports the descriptive statistics of the variables used in the regression analyses of the effect of supply chain lending relationship on accounting conservatism of the borrowing suppliers. The sample includes 1,135 supplier-year observations that initiated a loan from 1988 to See the Appendix for variable definitions. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t (z) test statistics. 36

39 TABLE 3 Spearman/Pearson Correlation Matrix Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (1) CSCORE (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (0.15) (0.17) (<.01) (<.01) (0.49) (<.01) (0.43) (<.01) (<.01) (2) CHAIN_LENDING (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (0.03) (0.41) (0.12) (<.01) (0.42) (<.01) (0.01) (3) ln(mve) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (0.62) (0.56) (<.01) (0.01) (0.08) (0.01) (<.01) (<.01) (0.00) (4) ln(firm_age) (0.00) (0.00) (0.00) (0.00) (0.30) (0.00) (0.00) (<.01) (0.19) (<.01) (0.06) (0.39) (0.41) (0.26) (0.34) (<.01) (<.01) (5) INVCYCLE (<.01) (<.01) (<.01) (<.01) (<.01) (0.02) (<.01) (0.03) (<.01) (0.67) (0.13) (0.30) (0.19) (0.15) (0.13) (<.01) (<.01) (0.94) (6) MB (<.01) (0.01) (<.01) (0.13) (0.16) (<.01) (0.06) (<.01) (0.11) (<.01) (0.03) (<.01) (0.37) (<.01) (<.01) (0.69) (<.01) (7) RETVOL (<.01) (<.01) (<.01) (<.01) (<.01) (0.74) (<.01) (<.01) (0.03) (<.01) (<.01) (0.09) (0.01) (0.06) (0.47) (<.01) (<.01) (8) SALVOL (0.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (0.01) (<.01) (<.01) (<.01) (<.01) (<.01) (0.27) (<.01) (<.01) (9) BID_ASK (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (<.01) (0.01) (<.01) (0.14) (0.13) (<.01) (<.01) (10) LEV (0.06) (<.01) (0.04) (0.57) (0.61) (<.01) (0.67) (0.45) (0.63) (<.01) (0.80) (<.01) (0.13) (<.01) (<.01) (0.08) (0.04) (11) ATLMAN (0.65) (<.01) (0.38) (<.01) (0.55) (0.05) (<.01) (<.01) (<.01) (<.01) (0.02) (<.01) (0.08) (<.01) (<.01) (0.01) (<.01) (12) BIG_FOUR (<.01) (0.03) (<.01) (0.04) (0.08) (<.01) (0.05) (<.01) (<.01) (0.62) (0.12) (0.40) (0.39) (0.10) (0.11) (0.76) (<.01) (13) LITIGATION (<.01) (0.41) (<.01) (0.39) (0.84) (0.11) (0.39) (<.01) (0.09) (<.01) (<.01) (0.40) (0.36) (<.01) (<.01) (0.03) (<.01) (14) CU_ROA (0.11) (0.11) (0.55) (0.33) (0.15) (0.20) (<.01) (0.03) (<.01) (0.32) (0.71) (0.40) (0.99) (<.01) (<.01) (0.96) (0.02) (15) CU_LEV (<.01) (<.01) (0.10) (0.06) (0.98) (<.01) (0.11) (0.01) (0.25) (<.01) (<.01) (0.01) (<.01) (<.01) (<.01) (0.01) (0.10) (16) CU_ALTMAN (0.62) (0.06) (0.08) (0.09) (0.01) (0.76) (<.01) (0.75) (<.01) (<.01) (<.01) (0.11) (<.01) (<.01) (<.01) (<.01) (<.01) (17) ln(duration) (<.01) (<.01) (0.83) (<.01) (<.01) (0.07) (<.01) (<.01) (<.01) (0.10) (0.01) (0.62) (0.03) (0.93) (0.01) (<.01) (<.01) (18) CU_IMPT (<.01) (<.01) (0.01) (<.01) (0.10) (0.10) (<.01) (<.01) (<.01) (0.03) (<.01) (<.01) (0.10) (0.18) (0.20) (0.37) (0.06) This table shows Spearman and Pearson correlations among selected variables for 1,135 supplier-year observations that initiated a loan from 1988 to The upper (lower) right triangle of the matrix shows Spearman (Pearson) correlations. See the Appendix for variable definitions. Two-tailed p-values are reported in parentheses. 37

40 TABLE 4 Supply-Chain Lending and Accounting Conservatism Independent variables Predicted sign Dependent variable = CSCORE Intercept? 0.248*** (5.24) CHAIN_LENDING *** (-3.44) ln(mve) *** (-3.77) ln(firmage) * (-1.68) INVCYCLE (-0.89) MB? *** (-5.57) RETVOL (0.56) SALEVOL (-0.05) BID_ASK *** (8.24) LEV ** (2.47) ALTMAN * (-1.78) BIG_FOUR *** (4.25) LITIGATION (-1.43) CU_ROA (0.09) CU_LEV ** (2.26) CU_ALTMAN (-0.18) ln(duration) (-0.64) CU_IMPT? 0.107*** (2.92) Year fixed effects Included Industry fixed effects Included Number of observations 1,135 Adjusted R This table reports the OLS regression results on the effect of supply chain lending relationship on accounting conservatism for the borrowing suppliers. The sample includes 1,135 supplier-year observations that initiated a loan from 1988 to The dependent variable is CSCORE. See the Appendix for variable definitions. Year and industry fixed effects are included in the regressions, but their coefficients are not reported for brevity. Standard errors clustered by firm and adjusted for heteroskedasticity are reported in parentheses. *, **, and *** denote significant at 10%, 5%, and 1% respectively based on two-tailed t test statistics. See Appendix 3 for variable definitions. 38

41 TABLE 5 Supply-Chain Lending and Accounting Conservatism: Instrumental Variable Approach First-stage: Independent variables Second-stage: CSCORE CHAIN_LENDING Intercept 0.805*** (6.90) (-0.29) *** DISTANCE (-2.86) FITTED_CHAIN_LENDING * (-1.82) ln(mve) *** (0.07) (-2.95) ln(firmage) 0.003*** * (2.69) (-1.93) INVCYCLE ** (-2.01) (-0.44) MB *** (1.35) (-5.09) RETVOL (-1.33) (1.32) SALEVOL (-0.55) (0.24) BID_ASK ** 6.069*** (-6.09) (5.17) LEV ** (-0.45) (2.53) ALTMAN *** (-3.38) (-1.42) BIG_FOUR *** (0.29) (3.43) LITIGATION ** (0.98) (-2.34) CU_ROA * (-1.87) (-0.43) CU_LEV 0.308** 0.174*** (2.37) (2.60) CU_ALTMAN (0.79) (1.04) ln(duration) 0.011** * (2.47) (-1.71) CU_IMPT ** (-0.16) (2.49) Year fixed effects Yes Yes Industry fixed effects Yes Yes Number of observations 1,135 1,135 Adjusted R This table reports the two-stage regression results on the effect of supply chain lending relationship on accounting conservatism for the borrowing suppliers. The sample includes 1,135 supplier-year observations that initiated a loan from 1988 to The dependent variable in the first stage is CHAIN_LENDING. The dependent variable in the second stage is CSCORE. The predicted sign of the coefficient estimates is presented for first-stage/second-stage model respectively. See the Appendix for variable definitions. Year and industry fixed effects are included in the regressions, but their coefficients are not reported for brevity. Standard errors adjusted for heteroskedasticity are reported in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 39

42 TABLE 6 Supply-Chain Lending and Accounting Conservatism: Propensity Score Matching Panel A: Summary statistics of the matched samples Variables CHAIN_LENDING = 1 (N=188) Mean CHAIN_LENDING = 0 (N=188) Mean T-test of mean difference ln(mve) ln(firmage) INVCYCLE MB RETVOL SALEVOL BID_ASK ** LEV ALTMAN BIG_FOUR LITIGATION CU_ROA CU_LEV CU_ALTMAN ln(duration) *** CU_IMPT Panel B: Summary statistics of the estimated propensity score Propensity Scores No. of Obs. Min. P5 P50 Mean S.D. P95 Max. CHAIN_LENDING = CHAIN_LENDING = Pair Difference Panel C: Difference in CSCORE between the matched samples Two Groups Variable CHAIN_LENDING = 1 (N = 188) CHAIN_LENDING = 0 (N = 188) t-test of mean difference Mean Median Mean Median Wilcoxon test of median difference CSCORE *** 2.82*** This table reports the propensity score matching results on the effect of supply chain lending relationship on accounting conservatism for the borrowing suppliers. To construct the matched sample, we first estimate the first-stage regression, reported under Column (1) of Table 5, to generate a propensity score for each firm in the sample. For each of the 700 supplier firms having supply chain lending relationship ( treatment firm ), we match with a supplier firm without supply chain lending relationship ( control firm ) that has the same two-digit SIC code in the same year and has the closet propensity score as the treatment firm (matched without replacement). For 188 treatment firms, we are able to identify a matching firm from the control firms. Panel A reports the univariate comparisons between treatment and control firms, and their corresponding t-statistics for the mean differences. Panel B reports the distribution of estimated propensity scores for the treatment and control firms. Panel C reports the univariate comparisons in C-Score between treatment and control firms, and their corresponding t-statistics (Wilcoxon z statistics) for the mean (median) differences. See the Appendix for variable definitions. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t (z) test statistics. 40

43 TABLE 7 Supply-Chain Lending and Accounting Conservatism: A Placebo Test Independent variables Predicted sign Dependent variable = CSCORE Intercept? (-0.01) (-0.00) PSUDO_CHAIN_LENDING? (-1.64) ln(mve) (-0.57) (-0.57) ln(firmage) (-1.03) (-1.05) INVCYCLE * ** (-1.85) (-1.98) MB? *** *** (-2.79) (-2.79) RETVOL (-0.37) (-0.37) SALEVOL (-0.68) (-0.68) BID_ASK *** 5.004*** (6.99) (6.78) LEV (-0.44) (-0.37) ALTMAN ** ** (-2.52) (-2.52) BIG_FOUR *** 0.054*** (2.59) (2.59) LITIGATION (-0.36) (-0.34) CU_ROA (0.74) (0.62) CU_LEV *** 0.185*** (3.37) (3.50) CU_ALTMAN (-1.13) (-1.26) ln(duration) (-0.04) (-0.11) CU_IMPT ** 0.088** (1.98) (1.98) Year fixed effects Included Included Industry fixed effects Included Included Number of observations Adjusted R This table reports results on a placebo test of the effect of supply chain lending relationship on accounting conservatism of the borrowing suppliers. The sample has 809 supplier-year observations from 1988 to 2010, including 435 supplier-years that initiated a lending relationship with a non-supply chain lenders (CHAIN_LENDING = 0 and PSUDO_CHAIN_LENDING = 0) and 378 supplier-years that have their major customers borrow after the same lead arranger initiated a loan to the supplier firm (PSUDO_CHAIN_LENDING = 1). See the Appendix for variable definitions. Standard errors clustered by firm and adjusted for heteroskedasticity are reported in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 41

44 TABLE 8 Supply-Chain Lending and Accounting Conservatism: The Role of Information Opacity Panel A: Information Opacity for the Customers Dependent variable = CSCORE Predicted Independent variables HIGH LOW HIGH LOW sign CU_INFCOST CU_INFCOST CU_DACC CU_DACC Intercept? *** (-0.04) (1.60) (4.32) (0.56) CHAIN_LENDING ** *** (-2.56) (-0.05) (-3.13) (-0.21) ln(mve) *** * *** (-3.43) (-1.41) (-1.76) (-3.63) ln(firmage) * (-0.57) (-1.24) (-1.81) (-0.82) INVCYCLE ** (-2.19) (0.90) (-0.23) (-1.54) MB *** ** *** (-6.80) (-0.08) (-2.57) (-5.14) RETVOL (1.19) (0.54) (-0.06) (1.58) SALEVOL (-0.34) (0.81) (-0.32) (-0.18) BID_ASK *** 5.860*** 5.481*** 3.621*** (6.54) (4.97) (6.05) (4.34) LEV ** ** (2.58) (0.61) (0.82) (2.58) ALTMAN * (-0.85) (-0.97) (-1.94) (0.53) BIG_FOUR *** *** 0.054** (4.23) (0.44) (3.28) (2.39) LITIGATION (0.52) (-1.10) (-1.25) (0.17) CU_ROA (1.05) (-1.58) (-1.39) (0.64) CU_LEV *** (1.49) (0.34) (0.36) (3.06) CU_ALTMAN (-0.08) (-0.18) (-0.34) (1.12) ln(duration) (-0.65) (0.48) (0.14) (-1.42) CU_IMPT? 0.114** *** (2.36) (0.87) (1.46) (2.67) Year fixed effects Included Included Included Included Industry fixed effects Included Included Included Included Number of observations Adjusted R

45 Panel B: Information Opacity for the Suppliers Independent variables Predicted sign Intercept? CHAIN_LENDING - ln(mve) - ln(firmage) + INVCYCLE - MB - RETVOL - SALEVOL - BID_ASK + LEV - ALTMAN - BIG_FOUR + LITIGATION + CU_ROA - CU_LEV + CU_ALTMAN - ln(duration) + CU_IMPT - Dependent variable = CSCORE HIGH INFCOST LOW INFCOST HIGH DACC LOW DACC 0.258*** *** 0.316*** (4.02) (-3.77) (4.34) (-0.14) *** *** (-3.59) (1.37) (-4.01) (-0.50) *** ** *** (-3.14) (-2.15) (-0.53) (-4.36) ** (-1.97) (-1.45) (-1.45) (-1.17) (-1.14) (-0.03) (-0.95) (-0.48) *** *** *** (-5.64) (-1.10) (-3.58) (-5.14) (-0.07) (1.23) (1.37) (-0.75) * (-0.54) (1.77) (-0.89) (0.39) 5.237*** 4.200*** 3.490*** 6.299*** (6.62) (4.03) (4.05) (7.47) 0.120*** * 0.102** (3.15) (-0.72) (1.83) (2.23) * (-1.01) (-1.73) (-1.04) (-0.41) 0.109*** *** 0.066** (4.71) (0.53) (3.06) (2.46) (-0.69) (1.04) (-1.24) (-0.17) (-0.17) (-0.51) (-0.77) (0.47) *** (1.21) (2.99) (0.94) (1.28) (0.07) (-0.96) (0.12) (-0.06) (0.22) (-0.97) (-0.02) (-1.03) 0.119** *** (2.43) (1.45) (1.51) (3.11) Year fixed effects Included Included Included Included Industry fixed effects Included Included Included Included Number of observations Adjusted R This table reports the OLS regression results on the effect of supply chain lending relationship on accounting conservatism of the borrowing suppliers, conditional on informational opacity for the customers (Panel A) and their suppliers (Panel B). Information opacity is measured using informational cost (INFCOST) and the absolute value of total accruals ( DACC ). The sample includes 1,135 supplier-year observations from 1988 to See the Appendix for variable definitions. Standard errors clustered by firm and adjusted for heteroskedasticity are in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 43

46 TABLE 9 Supply-Chain Lending and Accounting Conservatism: The Role of Information Amount/Quality Independent variables Dependent variable = CSCORE Predicted HIGH LOW HIGH LOW sign CU_IMPT CU_IMPT CU_LYRS CU_LYRS Intercept? 0.258*** *** (3.66) (-0.82) (-1.62) (3.98) *** *** CHAIN_LENDING - (-3.39) (-1.50) (-3.36) (-1.21) ln(mve) *** *** * (-3.47) (-1.62) (-4.11) (-1.81) ln(firmage) (-0.66) (-1.55) (-1.23) (-1.20) INVCYCLE (-1.26) (0.19) (-1.55) (0.03) MB? *** *** *** *** (-3.63) (-3.61) (-4.83) (-3.99) RETVOL (-0.32) (0.69) (0.94) (0.30) SALEVOL (-0.74) (0.80) (-0.03) (-0.01) BID_ASK *** 5.203*** 3.833*** 5.548*** (5.98) (5.36) (4.30) (6.40) LEV * 0.158*** (1.30) (1.81) (2.71) (0.75) ALTMAN ** (-2.14) (-0.72) (-0.92) (-0.86) 0.072*** 0.072** 0.073*** 0.049* BIG_FOUR - (3.59) (2.02) (3.10) (1.66) LITIGATION (-0.35) (-1.34) (-0.01) (-1.35) CU_ROA (0.73) (-0.57) (0.36) (-0.52) CU_LEV ** (1.58) (0.61) (2.53) (0.96) CU_ALTMAN (0.23) (-0.40) (0.91) (-0.99) ln(duration) (0.31) (-0.78) (-0.54) (-0.35) CU_IMPT ** *** (1.51) (2.38) (1.37) (2.93) Year fixed effects Included Included Included Included Industry fixed effects Included Included Included Included Number of Adjusted observations R This table reports the OLS regression results on the effect of supply chain lending relationship on accounting conservatism of the borrowing suppliers, conditional on customer importance (CU_IMPT) and the duration of customerlender relationship (CU_LYRS). The sample includes 1,135 supplier-year observations from 1988 to See the Appendix for variable definitions. Standard errors clustered by firm and adjusted for heteroskedasticity are in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 44

47 Independent variables TABLE 10 Supply-Chain Lending and Accounting Conservatism: The Role of Trade Credit Predict ed sign Intercept? CHAIN_LENDING - ln(mve) - ln(firmage) + INVCYCLE? MB + RETVOL - SALEVOL + BID_ASK + LEV - ALTMAN - BIG_FOUR + LITIGATION + CU_ROA - CU_LEV + CU_ALTMAN - ln(duration)? CU_IMPT - Dependent variable = CSCORE HIGH TRADE_CREDIT LOW TRADE_CREDIT 0.325*** (4.82) (0.07) *** (-2.85) (-0.73) * *** (-1.75) (-3.30) (-1.44) (-0.46) (0.18) (-1.20) ** *** (-2.08) (-5.71) (1.13) (-0.90) (-0.14) (-0.21) 4.214*** 6.575*** (5.33) (6.53) *** (0.29) (2.60) (-1.41) (-0.89) 0.091*** (3.72) (1.63) (-0.12) (-0.94) (-1.01) (0.72) *** (0.20) (3.69) (-1.35) (1.48) (-0.74) (-0.22) 0.105* 0.094* (1.74) (1.79) INTERCEPT Included Included Industry fixed effects Included Included Number of observations Adjusted R This table reports the OLS regression results on the effect of supply chain lending relationship on accounting conservatism of the borrowing suppliers, conditional on the importance of trade credit in suppliers net worth (TRADE_CREDIT). The sample includes 1,135 supplier-year observations from 1988 to See the Appendix for variable definitions. Standard errors clustered by firm and adjusted for heteroskedasticity are in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 45

48 TABLE 11 Supply-Chain Lending and Accounting Conservatism: Alternative Measure of Conservatism Independent variables Predicted sign Dependent variable = X (1) (2) Intercept? ** (-2.04) *** (-2.84) D (1.09) *** (-3.05) R (0.45) 0.228*** (3.77) D R ** (2.12) 0.255*** (3.49) CHAIN_LENDING ** (-2.04) (-0.21) CHAIN_LENDING D (1.31) (-0.24) CHAIN_LENDING R (-0.68) (-0.06) CHAIN_LENDING D R * (-1.77) * (-1.89) ln(mve) *** (5.13) ln(mve) D *** (-3.26) ln(mve) R *** (-4.83) ln(mve) D R (-0.76) LEV *** (-6.04) LEV D (-0.21) LEV R *** (4.68) LEV D R (-0.68) MB? (1.09) MB D? * (-1.92) MB R *** (-2.67) MB D R? *** (-3.09) LITIGATION *** (-3.42) LITIGATION D (-0.77) LITIGATION R (0.64) LITIGATION D R (-1.48) Number of observations 1,135 1,135 Adjusted R This table reports the OLS regression results on the effect of supply chain lending relationship on accounting conservatism of the borrowing suppliers, using Basu (1997) piece-wise linear regression. The sample includes 1,135 supplier-year observations that initiated a new loan from 1988 to See the Appendix for variable definitions. 46

49 Standard errors clustered by firm and adjusted for heteroskedasticity are in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 47

50 Panel A: Summary statistics for loan contract terms Variables TABLE 12 Supply-Chain Lending and Covenant Design CHAIN_LENDING = 1 (N=515) CHAIN_LENDING = 0 (N=343) Mean Median Mean Median t test of mean difference z test of median difference FINCOV *** *** SPREAD *** *** LOAN_SIZE (million$) *** *** MATURITY *** 4.67 *** SECURE_LOAN *** *** RESOLVER SYNDICATED_LOAN *** *** This table reports summary statistics of loan contract terms. The sample includes 858 supplier-year observations that initiated a new loan from 1988 to 2010, after excluding observations with missing loan contract terms. See the Appendix for variable definitions. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t (z) test statistics. 48

51 Panel B: Supply-chain lending and the relative use of financial covenants versus general covenants Independent variables Dependent variable = FINCOV Intercept 1.313*** (4.70) CHAIN_LENDING *** (-3.74) SPREAD ** (-2.19) ln(loan_size) *** (-3.31) ln(maturity) (0.71) SECURE_LOAN (-0.77) RESOLVER (0.71) SYNDICATED_LOAN *** (-3.67) ln(mve) *** (-2.82) ROA 0.302*** (4.35) LEV 0.104* (1.74) MB * (-1.89) ALTMAN (1.33) Industry fixed effects Included Year fixed effects Included Number of observations 822 Adjusted R This table reports the OLS regression results on the effect of supply chain lending relationship on the use of financial covenants in loan contracts. The sample includes 822 supplier-year observations that initiated a new loan from 1988 to 2010, after excluding observations with missing loan contract terms. The dependent variable is FINCOV. See the Appendix for variable definitions. Standard errors clustered by firm and adjusted for heteroskedasticity are in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 49

52 TABLE 13 Supply-Chain Lending and Customers Accounting Conservatism Independent variables Predicted sign Dependent variable = CU_CSCORE Intercept? 7.183*** 7.238*** (3.88) (3.91) CU_CHAIN_LENDING? (0.20) CU_ln(MVE) *** 0.001*** (9.69) (9.61) CU_ln(FIRMAGE) *** *** (-4.28) (-4.31) CU_INVCYCLE *** (-2.75) *** (-2.71) CU_MB? (0.88) (0.88) CU_RETVOL *** 0.765*** (4.72) (4.72) CU_SALEVOL *** (-2.77) *** (-2.77) CU_BID_ASK *** *** (14.58) (14.57) CU_LEV *** 0.201*** (4.94) (4.94) CU_ALTMAN (-0.93) (-0.91) CU_BIG_FOUR (-0.27) (-0.27) CU_LITIGATION *** *** (-6.48) (-6.47) ROA (0.65) (0.61) LEV (0.80) (0.76) ALTMAN (1.43) (1.43) ln(duration) *** *** (-3.68) (-3.67) SUP_IMPT (0.19) (0.16) Year fixed effects Included Included Industry fixed effects Included Included Number of observations 2,835 2,835 Adjusted R This table reports the OLS regression results on the effect of supply chain lending relationship on accounting conservatism for the borrowing major customers. The sample includes 2,836 customer-year observations that initiated a loan from 1988 to The dependent variable is CSCORE for the major customers. The regression variables are measured for the customers with the prefix CU, and the variables ROA, LEV, and ALTMAN are measured for the customer s supplier firm. See the Appendix for variable definitions. Year and industry fixed effects are included in the regressions, but their coefficients are not reported for brevity. Standard errors clustered by firm and adjusted for heteroskedasticity are reported in parentheses. *, **, and *** denote significance levels at 10%, 5% and 1% respectively based on two-tailed t test statistics. 50