Trust and Customer-Supplier Relationships

Transcription

1 Trust and Customer-Supplier Relationships *Preliminary and Incomplete* Douglas (DJ) Fairhurst * Daniel Greene January 16, 2018 Abstract We examine the role of trust in coordination between firms in the supply chain. We use a survey based measure of trust and find that customers are more likely to select suppliers who are associated with higher levels of trust. Preliminary univariate evidence also suggests that one mechanism driving our result is the tendency of trusting suppliers to provide favorable contractual terms to customers, making them more likely to be selected from a menu of potential suppliers. The findings imply that trust enhances coordination between corporations, which may also allow for greater economic growth. Keywords: Trust, Customers and Suppliers, Trade Credit * Douglas Fairhurst is from the Department of Finance and Management Science, Washington State University, Pullman, WA, and can be reached at dj.fairhurst@wsu.edu and Daniel Greene is from the Department of Finance, Clemson University, Clemson, SC, and can be reached at dtg@clemson.edu and

2 1. Introduction Trust is essential in improving cooperation amongst economic entities in the presence of uncertainty. For example, increased trust is shown to both reduce bank borrowing costs (Hasan, Hoi, Wu, and Zhang, 2017) and increase participation by individuals in the equity markets (Guiso, Sapienza, and Zingales, 2008). However, capital market participants typically hold diversified portfolios. The risk of broken trust by a firm underlying one security is at least partially offset by the other securities in the portfolio. In other words, basing decisions on trust by a diversified economic agent may not be as risky as it would be for an economic entity with concentrated risk. In this paper, we consider how trust impacts the propensity of two firms in a supply chain to cooperate with one another by observing the probability that a specific supplier is selected from a menu of suppliers by a major customer. Customer-supplier relationships are an ideal setting to help extend our understanding of how trust improves cooperation in the economy for several reasons. First, in general, firms typically face more concentrated risk than banks and investors due to the benefits of focus in the product markets (e.g., Lang and Stulz, 1994). Further, a concentrated customer based introduces the potential for greater risk than if the firm had dispersed customers. For example, a change in the CEO at the customer firm, impacts the previously existing relationship between the customer and supplier (Intintoli, Serfling, and Shaikh, 2017). At the same time, maintaining a concentrated customer base also has significant potential advantages. For instance, operating efficiencies and, subsequently, profits increase with a concentrated customer base (Patatoukas, 2012; Cen, Dasgupta, and Sen, 2015). Further, high quality customers may provide a certification role of the quality of the suppliers leading to other benefits (Cen, Dagupta, Elkamhi, and Pungalya, 2016). These findings suggest that the potential benefits of aligning with a major customer may justify the 1

3 enhanced risk of a concentrated customer base. We empirically test whether trust affects the probability that a particular firm is selected as a supplier to a major customer. To measure trust, we use survey data provided by General Social Services (GSS). In this survey, respondents answer the question Generally speaking, would you say that most people can be trusted or that you can t be too careful in dealing with people? Responding participants can indicate whether they do or do not trust others. We aggregate responses to this question at the state level to capture a measure of state-level trust that varies in the time series and the cross-section. To test the empirical question, we run a selection model that includes up to 5 pseudo supplier-customer pairs for every actual supplier-customer pair. Pseudo suppliers are the firms in the same 4-digit industry as the actual supplier that are closest in sales to the actual supplier. In other words, we select a pool of pseudo suppliers that are similar to the actual supplier in size but are not necessarily located in the same state. We then run tests to determine whether trust alters the propensity of the supplier to be selected by the customer. We find that firms in states with relatively high trust are more likely to be selected to align with a major customer than firms in states with relatively low trust. Importantly, this affect does not appear to simply be an outcome of geographic proximity. Specifically, we control for whether the actual or pseudo supplier is in the same state or the bordering state as the customer. Our results suggest that major customers tend to be located close to the actual supplier, but the effect of trust is robust to holding this geographic effect constant. Our findings are also robust to controlling for customer and potential supplier characteristics as well as time and industry fixed effects. Further, we find that trust is a determinant of being selected as a supplier when controlling for customer-supplier-year fixed effects, suggesting that are results are driven by variation within actual supplier-customer pairs. This finding 2

4 provides support for the hypothesis that trust allows firms to cooperate in forming customersupplier relationships. A potential problem with our setting is that an omitted variable coinciding with trust could drive our results. To address this issue, we conduct a falsification exercise in which we randomly assign pseudo suppliers to home states and measure trust based on the randomly assigned state (Cornaggia, et al., 2015). We keep the distribution of states the same in the random sample as the actual data. Therefore, if there are unobservable factors correlated with the choice of supplier, those factors will still be represented in the distribution of the data. However, if those factors do not exist, then our randomized measure of trust should not be correlated with the choice of supplier. We repeat the falsification exercise 100 times and examine the distribution of coefficient estimates and standard errors of the control variables and randomly assigned trust measure. Across these tests, we continue to find that the control variables, on average, retain their sign and significance. However, the average coefficient on trust is indistinguishable from zero. As such, it appears that the relation between trust and being selected by a major customer is not spurious. The survey data used in our primary tests has a few empirical limitations. Specifically, the survey is not collected every year and, for years that it is collected, the number of respondents in a state may or may not be large as the survey is randomly sampled at the national level. We address these issues in our main findings by aggregating state-level responses over the most recent 10 years on a rolling basis. This approach ensures that we have a measure of state-level trust each year and that the measure includes a reliable number of respondents. However, this comes at the potential cost of basing our analysis on partially stale data. For robustness, we address this concern using a different approach to measure trust. 3

5 Specifically, using GSS survey data, we run a determinants model to capture factors that correlate with trust at the state level (education, religious preferences, crime statistics, etc.). This model is run for each year that we have survey responses, and factors included in the model are based on literature examining trust. We then use the coefficients from this model to predict trust at the state-level for all years. This approach allows us to measure trust each year and avoid potentially stale data. We note that our determinants model presents results consistent with other papers modeling factors that influence trust. As examples, trust is increasing in age, education, and income and decreasing in state-level violent crime rates and if an individual is divorced. We then use this predicted measure of trust and rerun our supplier-customer selection models. We continue to find that trust has a positive impact on the selection of a potential supplier. Further, similar to our main results, this result is robust to various control variables and combinations of fixed effects. In sum, our results do not appear to be driven by the use of historical survey responses as our measure of trust. We next consider mechanisms that might enhance the propensity for an alliance between a supplier and major customer. One potential mechanism is that trusting suppliers offer favorable contractual terms or that they re even willing to operate without a contract in place, enhancing the probability that they are selected by a large customer. An important operating decision by suppliers is the provision of trade credit to suppliers. If high trust at the supplier level incentivizes this supplier to offer more competitive (less restrictive) trade credit terms, then this may make them more attractive to the customer. This argument is parallel to the evidence in Hasan et al. (2017) that banks in trusting areas give more favorable terms on bank loans. In preliminary, univariate results we find evidence consistent with this. Specifically, as supplier trust increases, both supplier trade credit provided and 4

6 customer trade credit used increase. However, these results are significantly weaker in multivariate tests. We plan to pursue this line of research in future drafts. This work is in a preliminary stage. In future work, we plan to consider the robustness of the current mechanism and consider other potential mechanisms. Further, we also plan to test for the impact of trust on other ways that firms cooperate. For example, firms with relatively high trust may be more likely to form joint ventures despite the risk of sharing valuable strategic information with other firms. Our research contributes to two lines of literatures. Broadly, our findings are consistent with other papers that show the importance of trust in encouraging economic activity in the presence of uncertainty. Our findings add to this literature by documenting that trust encourages cooperation amongst economic agents even when the agents face undiversified risk. We also contribute to the literature on the tendency of firms to align themselves with major customers. Previous literature highlights both the potential for economic growth and significant risks faced when aligning with a major customer. Our findings highlight the role that trust plays in encouraging firms to chase this growth despite the inherent risks. The remainder of our paper proceeds as follows. Section 2 outlines the theoretical motivation for our paper. Section 3 discusses the data and the empirical methodology. Section 4 discusses the results of the empirical tests. Section 5 concludes. 2. Theoretical Motivation The tendency for firms to align a significant portion of their business introduces significant operating risk as the decisions of the customer firms have significant impact on the suppliers, which are typically much smaller. This risk has been shown to impact both the cost of equity (Dhaliwal, Judd, Serfling, and Shaikh, 2016) and both financial and non- 5

7 financial borrowing loan contract terms (Campello and Gao, 2017). Further, firms appear to shift financial policies to adjust for this risk. Specifically, firms with a concentrated customer base are less levered (Banerjee, Dasgupta, and Kim, 2008) and have relatively large cash holdings (Itzkowitz, 2013) Additional evidence supports the idea that decisions at the customer-level can disrupt suppliers operations. For example, a change in the CEO at the customer firm impacts the previously existing relationship between the customer and supplier (Intintoli, Serfling, and Shaikh, 2017) Of course, a willingness to bear these risks provide also provides opportunities to align with large, successful customers. For instance, operating efficiencies and, subsequently, profits increase with a concentrated customer base (Patatoukas, 2012; Cen, Dasgupta, and Sen, 2015). Further, high quality customers may provide a certification role of the quality of the suppliers leading to other benefits (Cen, Dagupta, Elkamhi, and Pungalya, 2016). The avoidance of aligning with a major customer potentially interrupts economic growth and may represent a loss to society. Theoretically, trust may allow suppliers to be selected from a menu of potential suppliers to receive the benefits of aligning with a major customer. Guiso, Sapienza, and Zingales (2004) note that developments in economic theory allow us to appreciate the intrinsic limitations agents face in contracting and the potential role social capital, and the trust it engenders, can play in reducing the deadweight loss generated by these limitations. Trust has specifically been shown to benefit and economic growth through large organizations (La Porta, Lopez-de-Silanes, Schleifer, and Vishny, 1997). A trusting potential supplier may offer favorable terms within operating contracts or even be more willing to operate without contracts in place. This would make the customer more willing to cooperate with this trusting potential supplier relative to other potential suppliers. As such, we predict 6

8 that trust within a potential supplier has a positive association with the likelihood that this supplier is selected. 3. Data and Empirical Methodology 3.1. Trust Measure We measure trust using data from the General Social Survey (GSS) from 1972 onwards. The survey asks respondents the question Generally speaking, would you say that most people can be trusted or that you can't be too careful in dealing with people?. The GSS is conducted annually from 1972 to 1994 and then every other year from 1994 onwards. We obtain the geographic location (state) of survey respondents from the GSS. Due to a relatively small number of survey respondents for some states, and the fact that the GSS is conducted every other year beginning in 1994, we aggregate state level data over a rolling ten year window. Our primary measure of trust in a state is the percentage of respondents who answer Can trust. (We exclude observations other than Can trust and Cannot trust such as Don t Know, Didn t Answer, etc.). For each firm in our sample, we obtain the level of trust based on the location of the firm s headquarters. For each customer-supplier pair, we have two measures of trust: Trust- Customer measures trust in the state in which the customer is headquartered and Trust- Supplier measures trust in the state in which the supplier is headquartered Customer-Supplier Data We begin with a dataset of suppliers and their major customers from 1981 to We identify key customer firms from the Compustat segment tapes. Customer names are linked to their corresponding Compustat based on a manual name match. This methodology has been used in a number of papers (e.g., Fee and Thomas, 2004; Kale and Shahrur, 2007). 7

9 To arrive at our final sample, we match PERMNOs of customers and suppliers to GVKEY. We exclude firms (either customers or suppliers) in the financial or utility industries (SICH , and , respectively). We obtain the historical state of firms headquarters from 1991 onwards using data from Lauren Cohen s website (Cohen, Coval, and Malloy, 2011). Prior to 1991, we use the 1991 historical headquarters as the headquarters. We delete observations missing total assets on Compustat or where we cannot calculate our trust measure. Our final sample is 34,887 customer-supplier-year observations where we have Compustat data and our trust measure for both customer and supplier. This data set contains 10,936 unique customer-supplier pairs, 1,717 unique customers, and 4,446 unique suppliers Customer-Supplier Data For each customer-supplier-year observation, we identify five potential suppliers following the methodology in Bena and Li. For each actual supplier, potential suppliers are the five firms closest in sales in the 4-digit SICH of the actual supplier. We use 3-digit and 2-digit level when we cannot find five firms at the 4-digit level. Thus, our dataset includes up to six observations for each customer-supplier-year: one with the actual supplier, and five with potential suppliers. The final dataset has 209,153 observations. Table 1 shows summary statistics for our sample. 4. Empirical Results 4.1. Choice of Supplier In Table 2, we estimate linear probability models on a sample of customers and pseudo suppliers. The dependent variable is Actual Supplier, a dummy variable which is set to 1 for actual suppliers and 0 for each pseudo supplier. Our key variable of interest is Trust in the 8

10 state of the potential supplier (Trust-Potential Supplier). We include controls for the geographic proximity of the two firms. The dummy variable Same State is set to 1 for pseudo suppliers who are headquartered in the same state as the customer. The dummy variable Border State is set to 1 for pseudo suppliers who are headquartered in a state which shares a border with the customer s state. In certain regression specifications, we control for customer and/or supplier characteristics including LN Sales, Operating Margin, Market-tobook Ratio, and Leverage. We also control for Customer Trade Credit Used (AP/COGS) and Supplier Trade Credit Provided (AR/SALE). We cluster standard errors by customer. Columns (1) and (2) include year fixed effects and supplier industry fixed effects. Columns (3) and (4) include fixed effects for each customer-supplier-year pair, thus the customer characteristics drop out of the regression. In each of the four columns of Table 2, the coefficient on Trust-Potential Supplier is positive and statistically significant at the 1% level. This result suggests that greater trust in the state of the supplier is associated with a greater likelihood of having a customersupplier relationship with the given customer. In each of the four columns, we find positive and statistically significant coefficients on Same State and Border State, indicating that geographic proximity is associated with a greater likelihood of forming a customer-supplier relationship. Among the supplier characteristics, we find that a larger size is positively associated with the likelihood of forming a customer-supplier relationship, and greater profitability and leverage is negatively associated with the likelihood of forming a customersupplier relationship. Suppliers that provide more trade credit are less likely to be chosen by customers. In Table 3, we run the same tests as in Table 2, but we use Predicted Trust-Potential Supplier as our key independent variable rather than Trust-Potential Supplier. Again, we 9

11 find a positive and statistically significant coefficient on our measure of trust. For example, in the specification in Column (4), which includes customer-supplier-year fixed effects as well as supplier characteristics, the coefficient on Predicted Trust-Potential Supplier is and significant at the 5% level (t-stat=2.006) Randomization Test A potential problem with our setting is that an omitted variable coinciding with trust could drive our results. To address this issue, we conduct a falsification exercise in which we randomly assign pseudo suppliers to home states and measure trust based on the randomly assigned state (Cornaggia, et al., 2015). We keep the distribution of states the same in the random sample as the actual data. Therefore, if there are unobservable factors correlated with the choice of supplier, those factors will still be represented in the distribution of the data. However, if those factors do not exist, then our randomized measure of trust should not be correlated with the choice of supplier. We repeat the randomization exercise 100 times and run our supplier selection models after each iteration. Table 4 presents the average coefficient and t-stat from the 100 replications. Column (1) replicates the specification in Table 2, Column (3). Column (2) replicates the specification in Table 2, Column (4). Columns (3) and (4) replicate the specification in Table 3, Columns (3) and (4), respectively. The geographic proximity variables and supplier characteristics retain their same sign and significance, on average, as our main models. However, the average coefficient on the randomized trust variable is not even close to significant. For example, in Column (1), the mean of the estimate of the coefficient on Randomized Trust-Potential Supplier is and the mean of the t-statistics is This compares to the estimate of the coefficient on Trust-Potential Supplier of

12 and t-statistic of in Table 2, Column (3). This finding suggests that our results are not driven by an omitted variable Trade Credit Tests We examine the use and provision of trade credit on a sample of customer-supplier pairs. Table 5 provides summary statistics for our sample of 34,887 customer-supplier pairs. In Table 6, we provide univariate tests. Panel A shows trade credit provided and used for customers and suppliers, by quartile of Trust-Customer. We find that both customers and suppliers provide more trade credit when Trust-Customer is in the top quartile compared to the bottom quartile. Panel B shows trade credit provided and used for customers and suppliers, by quartile of Trust-Supplier. We find that customers use more trade credit and suppliers provide more trade credit when Trust-Supplier is in the top quartile compared to the bottom quartile. We examine customer use of trade credit and supplier provision of trade credit in a multivariate specification in Tables 7 and 8, respectively. In Table 7, the dependent variable is Customer Trade Credit Used. The key independent variables are Trust-Customer and Trust-Supplier. We include controls for the relative size of the customer and supplier, customer characteristics, and supplier characteristics. The specification in Column (1) includes customer fixed effects and year fixed effects while column (2) includes customersupplier fixed effects and year fixed effects. The regression results document a negative association between trust in the customer s state and the use of trade credit. The coefficient on Trust-Customer is in Column (1), significant at the 1% level, and in Column (2), significant at the 5% level. Column (2) shows a negative association between Trust-Supplier and the use of trade credit by the customer. The coefficient is , and significant at the 1% level. The negative 11

13 coefficient on Relative Size indicates that customers use less trade credit when suppliers are larger relative to the customer. Table 8 examines the provision of trade credit from suppliers to customers. The setup of the table is similar to Table 7, except the dependent variable is Supplier Trade Credit Provided. The coefficient on Trust-Customer is statistically insignificant in both columns. The coefficient on Trust-Supplier is negative and statistically significant in Column (1), but positive and statistically insignificant in Column (2). The positive coefficient on Relative Size in Column (2) indicates that suppliers provide more trade credit when they are larger relative to the customer. 5. Conclusion Our findings suggest that trust plays an important role in coordination between firms in the supply chain. Customers are more likely to select suppliers with higher levels of trust. Preliminary evidence also suggests that one mechanism driving our result is the tendency of trusting suppliers to provide favorable contractual terms to customers, making them more likely to be selected from a menu of potential suppliers. The findings imply that trust enhances coordination between corporations, which may also allow for greater economic growth. 12

14 References Bena, Jan, and Kai Li, 2014, Corporate Innovations and Mergers and Acquisition, Journal of Finance 69, Cen, Ling, Sudipto Dasgupta, and Rik Sen, 2015, Discipline or Disruption? Stakeholder Relationships and the Effect of Takeover Threat, Management Science 62, Cen Ling, Sudipto Dagupta, Redouane Elkamhi, and Raunaq Pungalya, 2016, Reputation and Loan Contract Terms: The Role of Principal Customers, Review of Finance 20, Cohen, Lauren, Joshua Coval, and Christopher Malloy, 2011, Do Powerful Politicians Cause Corporate Downsizing?, Journal of Political Economy 119(6) Cornaggia, Jesse, Yifei Mao, Xuan Tian, and Brian Wolfe, 2015, Does Banking Competition Affect Innovation?, Journal of Financial Economics 115, Fee, Shawn and C. Edward Thomas, 2004, Sources of Gains in Horizontal Mergers: Evidence from Customer, Supplier, and Rival Firms, Journal of Financial Economics 74, Guiso, Luigi, Paola Sapienza, and Luigi Zingales, 2008, Trusting the Stock Market, Journal of Finance 63, Guiso, Luigi, Paola Sapienza, and Luigi Zingales, 2004, The Role of Social Capital in Financial Development, The American Economic Review 94, Hasan, Iftekhar, Chun Keung Hoi, Qiang Wu, and Hao Zhang, 2017, Social Capital and Debt Contracting: Evidence from Bank Loans and Public Bonds, Journal of Financial and Quantitative Analysis 52, Intintoli, Vincent, Matthew Serfling, and Sarah Shaikh, 2017, CEO Turnovers and Disruptions in Customer-Supplier Relationships, Journal of Financial and Quantitative Analysis 52, Itzkowitz, Jennifer, 2013, Customers and Cash: How Relationships Affect Suppliers Cash Holdings, Journal of Corporate Finance 19, Kale, Jayant and Husayn Shahrur, 2007, Corporate Capital Structure and the Characteristics of Suppliers and Customers, Journal of Financial Economics 83, La Porta, Lopez-de-Silanes, Schleifer, and Vishny, 1997, Trust in Large Organizations, American Economic Review Papers and Proceedings. Lang, Larry and René Stulz, 1994, Tobin s Q, Corporate Diversification, and Firm Performance, Journal of Public Economy 102, Patatoukas, Panos, 2012, Customer-Base Concentration: Implications for Firm Performance and Capital Markets, Accounting Review 87,

15 Table 1: Summary Statistics for Customer-Potential Supplier Matched Data Summary statistics for a sample of 209,153 customer-pseudo supplier pairs. For each Customer-Supplier- Year, we add 5 observations of 5 Potential Suppliers. Potential Suppliers are the 5 firms closest in sales in the 4-digit SICH of the actual supplier. We use 3-digit and 2-digit level when we cannot find 5 at the 4-digit level. Variable N mean sd p25 p50 p75 Customer Characteristics Assets 209, Sales 209, TC Provided 197, TC Used 209, Gross Profit Margin 209, Operating Profit Margin 207, Tangible Assets 209, Cash-to-Assets 209, ROA 209, Market-to-Book 208, Leverage 208, Trust 209, Predicted Trust 208, Supplier Characteristics Assets 209, Sales 209, TC Provided 205, TC Used 208, Gross Profit Margin 209, Operating Profit Margin 208, Tangible Assets 209, Cash-to-Assets 209, ROA 209, Market-to-Book 188, Leverage 208, Trust 209, Predicted Trust 209, Same State 209, Border State 209,

16 Table 2: Choice of Supplier and Trust Linear Probability Models for a sample of customers and potential suppliers. For each Customer-Supplier- Year, we add 5 observations of 5 Potential Suppliers. Potential Suppliers are the 5 firms closest in sales in the 4-digit SICH of the actual supplier. We use 3-digit and 2-digit level when we cannot find 5 at the 4- digit level. Dependent Variable is 1 for Actual Suppliers and 0 for Potential Suppliers (1) (2) (3) (4) VARIABLES Actual Supplier Trust-Potential Supplier 0.106*** 0.088*** 0.131*** 0.111*** (3.244) (2.602) (3.341) (2.745) Same State 0.099*** 0.107*** 0.145*** 0.156*** (10.415) (9.749) (11.062) (10.466) Border State 0.040*** 0.042*** 0.057*** 0.062*** (5.440) (5.163) (5.516) (5.166) Customer Characteristics LN Sales *** *** (-4.305) (-3.688) Operating Margin (0.380) Market-to-Book Ratio 0.002* (1.921) Leverage 0.015** (2.308) AP/COGS (TC Used) (-1.463) Potential Supplier Characteristics LN Sales 0.013*** 0.016*** 0.074*** 0.088*** (10.825) (10.463) (19.350) (18.650) Operating Margin *** *** (-7.456) (-8.480) Market-to-Book Ratio ** (0.984) (2.561) Leverage *** ** (-2.694) (-2.137) AR/Sales (TC Provided) *** *** (-3.293) (-3.615) Year Dummies Yes Yes No No Supplier 2-Digit Industry Dummies Yes Yes No No Customer-Supplier-FYEAR FE No No Yes Yes Observations 209, , , ,117 R-squared

17 Table 3: Choice of Supplier and Predicted Trust Linear Probability Models for a sample of customers and potential suppliers. For each Customer-Supplier- Year, we add 5 observations of 5 Potential Suppliers. Potential Suppliers are the 5 firms closest in sales in the 4-digit SICH of the actual supplier. We use 3-digit and 2-digit level when we cannot find 5 at the 4- digit level. Dependent Variable is 1 for Actual Suppliers and 0 for Potential Suppliers (1) (2) (3) (4) VARIABLES Actual Supplier Predicted Trust-Potential Supplier 0.197** 0.152* 0.255*** 0.201** (2.526) (1.794) (2.776) (2.006) Same State 0.101*** 0.108*** 0.147*** 0.157*** (10.356) (9.765) (10.907) (10.400) Border State 0.038*** 0.040*** 0.055*** 0.060*** (5.036) (4.818) (5.085) (4.811) Customer Characteristics LN Sales *** *** (-4.311) (-3.635) Operating Margin (0.377) Market-to-Book Ratio 0.002* (1.909) Leverage 0.015** (2.249) AP/COGS (TC Used) (-1.455) Potential Supplier Characteristics LN Sales 0.013*** 0.016*** 0.074*** 0.088*** (10.984) (10.629) (19.427) (18.717) Operating Margin *** *** (-7.517) (-8.481) Market-to-Book Ratio *** (1.047) (2.607) Leverage *** ** (-2.762) (-2.192) AR/Sales (TC Provided) *** *** (-3.364) (-3.684) Year Dummies Yes Yes No No Supplier 2-Digit Industry Dummies Yes Yes No No Customer-Supplier-FYEAR FE No No Yes Yes Observations 209, , , ,027 R-squared

18 Table 4: Randomize Trust Measure Falsification tests based on 100 replications where we randomly assign potential suppliers to home states and re-calculate trust measures based on the random home state. The table presents the average coefficient and t-stat from the 100 replications. Column (1) replicates the specification in Table 2, Column (3). Column (2) replicates the specification in Table 2, Column (4). Columns (3) and (4) replicate the specification in Table 3, Columns (3) and (4), respectively. (1) (2) (3) (4) VARIABLES Actual Supplier Randomized Trust-Potential Supplier Estimate, mean t-stat, mean (-0.041) (-0.055) Randomized Predicted Trust-Potential Estimate, mean Supplier t-stat, mean (0.122) (0.092) Same State Estimate, mean 0.143*** 0.154*** 0.143*** 0.154*** t-stat, mean (10.489) (10.040) (10.489) (10.040) Border State Estimate, mean 0.056*** 0.062*** 0.056*** 0.062*** t-stat, mean (5.268) (5.059) (5.267) (5.057) Potential Supplier Characteristics LN Sales Estimate, mean 0.074*** 0.087*** 0.074*** 0.087*** t-stat, mean (19.278) (18.699) (19.278) (18.699) Operating Margin Estimate, mean *** *** t-stat, mean (-8.448) (-8.448) Market-to-Book Ratio Estimate, mean 0.004** 0.004** t-stat, mean (2.619) (2.617) Leverage Estimate, mean ** ** t-stat, mean (-2.172) (-2.172) AR/Sales (TC Provided) Estimate, mean *** *** t-stat, mean (-3.676) (-3.676) Yes Yes Yes Yes Observations per replication 209, , , ,650 Number of replications

19 Table 5: Summary Statistics for Customer-Supplier Matched Data Summary statistics for 34,887 pairs of customers and suppliers. Variable N mean sd p25 p50 p75 Customer Characteristics Trust 34, TC Used 34, TC Provided 32, Cash-to-Assets 34, Assets 34, ROA 34, Leverage 34, Market-to-Book 34, Tangible Assets 34, Relative Size 34, Supplier Characteristics Trust 34, TC Used 34, TC Provided 34, Cash-to-Assets 34, Assets 34, ROA 34, Leverage 34, Market-to-Book 34, Tangible Assets 34,

20 Table 6: Trust and Trade Credit, Univariate Trade credit provided and used for customers and suppliers, by quartile of Trust-Customer (Panel A) and by quartile of Trust-Supplier (Panel B). Panel A: Customer Trust Quartile N Customer Trust Customer TC Provided Customer TC Used Supplier TC Provided Supplier TC Used 1 8, , , , (4)-(1) t-stat *** *** *** Panel B: Supplier Trust Quartile N Supplier Trust Customer TC Provided Customer TC Used Supplier TC Provided Supplier TC Used 1 8, , , , (4)-(1) t-stat *** 1.599*** 8.804*** *** 19

21 Table 7: Customer Trade Credit Used Multivariate analysis of customer trade credit used on a sample of 34,887 customer-supplier pairs. (1) (2) VARIABLES Customer TC Used (AP/COGS) Trust-Customer *** ** (-8.652) (-2.070) Trust-Supplier *** (-0.704) (-3.100) Relative Size ** ** (-2.245) (-2.570) Customer Characteristics LN Assets * (-1.697) (1.277) TC Provided (AR/SALES) 0.197*** 0.135*** (27.719) (16.946) Cash-to-Assets 0.015* *** (1.729) (-2.676) ROA (0.372) (0.751) Leverage * (0.202) (-1.789) Market-to-Book Ratio 0.010*** 0.006*** (11.306) (6.834) Tangible Assets 0.032*** (5.182) (-0.396) Supplier Characteristics TC Provided (AR/SALES) * (0.939) (1.954) Cash-to-Assets (-0.806) (-0.421) ROA ** (-2.297) (0.929) Leverage *** * (-4.848) (-1.866) Market-to-Book Ratio ** (1.607) (2.158) Tangible Assets * (-1.675) (-0.386) Customer FE Yes No Customer-Supplier FE No Yes FYEAR FE Yes Yes Observations 31,563 28,187 R-squared

22 Table 8: Supplier Trade Credit Provided Multivariate analysis of supplier trade credit provided on a sample of 34,887 customer-supplier pairs. (1) (2) VARIABLES Supplier TC Provided (AR/SALES) Trust-Customer (0.341) (-0.936) Trust-Supplier *** (-2.995) (0.327) Relative Size ** (0.159) (2.286) Customer Characteristics LN Assets ** (-0.718) (-2.043) TC Used (AP/COGS) 0.007* 0.014* (1.679) (1.883) Cash-to-Assets ** (-2.144) (-0.422) ROA *** (1.186) (3.300) Leverage (-0.528) (1.012) Market-to-Book Ratio *** * (-2.651) (-1.654) Tangible Assets ** (-2.070) (-1.034) Supplier Characteristics TC Used (AP/COGS) 0.106*** 0.096*** (18.628) (14.606) Cash-to-Assets *** *** ( ) ( ) ROA 0.018*** 0.019*** (7.502) (6.828) Leverage 0.006* 0.012*** (1.730) (3.258) Market-to-Book Ratio 0.001*** 0.001** (2.610) (2.301) Tangible Assets *** *** ( ) ( ) Supplier FE Yes No Customer-Supplier FE No Yes FYEAR FE Yes Yes Observations 33,227 29,965 R-squared