Redact to Protect?

Customers’ Incentive to Protect Information and Suppliers’ Disclosure Strategies

Gary Chena, Xiaoli (Shaolee) Tianb*, and Miaomiao Yuc

a College of Business Administration, University of Illinois at Chicago, Chicago, IL 60607

b McDonough School of Business, Georgetown University, Washington, DC 20057 c EJ Ourso College of Business, Louisiana State University, Baton Rouge, LA 70808

August 2019

Abstract

Protecting value-relevant information is becoming increasingly important and challenging as we

move towards a knowledge-based economy and partners along the supply chain are more closely

intertwined. We document three customer-supplier characteristics (i.e., customer R&D intensity,

patent cross-citations, customer size) that vary directly with suppliers’ likelihood of redacting

mandated disclosures after controlling for supplier’s own proprietary cost concerns. We further

use customers’ possession of trade secrets and nondisclosure agreements to capture customers’

known preference for information withholding and find that their suppliers are more likely to

redact. The increased likelihood of redaction is concentrated in supply chain and R&D-related

information. Overall, our findings highlight that customers’ incentive to protect information,

particularly knowledge or value intensive information, can have a significant influence on

suppliers’ disclosure strategies.

Keywords: Trade secrecy, Supply Chain, Customer, Supplier, Disclosure, Redaction

JEL Classification Codes: G14, G30, M41, D23

*Corresponding author: xt51@georgetown.edu , McDonough School of Business Georgetown University 420 Rafik

B Hariri, Washington, DC 2005

This paper has benefited from valuable comments and suggestions by Ke Bin, Rui Dai, Shushu Jiang, Yupeng Lin,

Rajesh P. Narayanan, James C. Nordlund, Gord Richardson, Jee-Eun Shin, Junbo L Wang, Kelly Wentland, workshop

participants at George Mason University, Louisiana State University, National University of Singapore, and the

University of Toronto, and conference participants at the 2018 Annual Washington Area Research Symposium, the

2019 Conference on the Convergence of Financial and Managerial Accounting Research, and the 2019 China

International Conference in Finance. We further thank Naaser Mohammed and SeekEDGAR for research assistance.

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1. Introduction

Over the last few decades, the United States has transitioned into an information-based

economy where knowledge has become a primary driver of value creation for firms (Shapiro and

Varian 1998).1 Around the same time, effective firm management has also shifted from managing

within the firm towards managing the supply chain as a single entity. As a result, information

management among supply chain partners has become an increasingly important and yet

challenging aspect in their business relationships. Beyond directly shared information, principal

customers may also have the incentive and power to manage the information provided by suppliers,

such as the deals suppliers have with their other business partners (Anand and Goyal 2009, Li,

Ragu-Nathan, Ragu-Nathan and Rao 2006, Kwon and Suh 2004, Zhang 2002). Thus, we

investigate whether customer’s incentive to manage and protect value-relevant information extend

to the disclosure strategies of dependent suppliers.

Major customers account for a substantial portion of their suppliers’ revenue. As a result,

major customers can often leverage their position to exercise significant influence over suppliers’

important strategic decisions (Blois 1972). Existing literature documents that major customers can

often demand favorable contracting terms (e.g. Fishman 2003; Fee and Thomas 2004; Murfin and

Njoroge 2014; Loten 2012). They can also influence supplier decisions in operations, marketing,

and advertising (e.g. Allen and Phillips 2000; Ziobro and Ng 2015) as well as lean on suppliers to

help conduct earnings management and demand more conservative financial reporting (e.g. Lanier,

Wempe, and Swink 2019; Hui, Klasa, and Yeung 2012). Given major customers’ power over their

dependent suppliers, we posit that a major customer’s incentives to manage and protect value-

1 It is estimated that intangible assets contribute to 17 percent of S&P 500 companies’ total value in 1975. By 2015,

the percentage had increased to 85 percent (Keller 2015).

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relevant information can also affect their suppliers’ disclosure behavior after controlling for

suppliers’ other incentives.

On the one hand, major customers can strategically protect the value of their information

by providing greater disclosure because disclosure can serve as a signaling mechanism. In

particular, disclosing information can signal future firm prospects and help to obtain better terms

for external financing. It can also signal the firms’ commitment and ability to compete, and thus,

deter rivals (Bhattacharya and Ritter 1983, Gill 2008, Baker and Mezzetti 2005, Anton and Yao

2004). 2 If a customer prefers to use disclosure to protect the value of information, we posit that its

dependent suppliers will likely be more transparent in disclosures. After all, investors and

customers, two of the most important stakeholders, will have similar preferences for higher

transparency and less information asymmetry under such a scenario.

One the other hand, major customers might prefer to retain an information advantage by

keeping information private. For instance, trade secrets, or any valuable information that firms

wish to keep proprietary, rely exclusively on nondisclosure to retain its value (e.g., Bhattacharya

and Ritter 1983, Hall, Helmers, Rogers and Sena 2014, Gill 2008, Zaby 2010).3 This line of

argument is closely related to the arguments on the proprietary costs of disclosure, but focuses

more on information that can generate significant future value (e.g., Verrecchia 1983, Li, Lin, and

Zhang 2018, Huang, Jennings, and Yu 2017). 4 If a major customer prefers nondisclosure,

balancing the benefits of appeasing the major customer with the costs of less information for the

2 For instance, firms can use IBM’s Technical Disclosure Bulletin, Xerox’s technical journal or websites such as

IP.com and researchdisclosure.com as venues for defensive publishing of information (Henkel and Pangerl 2008,

Baker and Mezzetti 2005). 3 Even for patents, these studies argue that firms may need to rely on withholding proprietary information to protect

research-in-progress prior to obtaining a patent. Moreover, a firm may choose to patent some parts of an innovation

while leaving other parts as a trade secret. 4 For instance, disclosures on segment profitability can have significant proprietary costs (by attracting competition)

but the information in of itself does not generate future profits, in contrast to other types of information such as trade

secrets.

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investors becomes a more pressing issue for dependent suppliers. After all, existing literature

suggests that a dependent suppliers’ investors may be more sensitive to declines in the information

environment because having a concentrated customer base can lead to risks in large revenue losses

(Dhaliwal, Judd, and Serfling, and Shaikh 2016, Campello and Gao 2017). Nonetheless, anecdotal

evidence suggests that suppliers may withhold disclosures to cater to their major customers. For

instance, GT Advanced Technologies Inc. filed a set of heavily redacted contracts after it became

a dependent supplier for Apple. It was only during bankruptcy proceedings for Advanced

Technologies Inc. that the details of the contracts were publicly disclosed (Robinson, Higgins, and

Renick 2014).

To examine the influence that customer-supplier relationships may have on a supplier’s

information strategies, we use redacted contracts to capture suppliers’ decision to withhold

valuable information. A key advantage of this setting is that redactions capture known information

withholding. This is important because it is hard to distinguish whether nondisclosure is caused by

no information to disclose or by the information strategies of a firm in a voluntary setting (Tian

and Yu 2018, Hribar 2004, Guo, Lev, and Zhou 2004). Perhaps, more important and specific to

our setting, registrants are required to file material contracts with the Securities and Exchange

Commission (SEC). Major customers are often big firms.5 Naturally, it is harder for an individual

contract to pass the materiality threshold for large firms, giving major customers opportunities to

avoid filing many of their contracts. This makes it difficult for outsiders to access and infer

information from customers’ filings. Dependent suppliers, however, are often much smaller

compared to their major customers. As a result, each of their contracts is more likely to be material.

These filed contracts from dependent suppliers, including contracts with parties other than

5 The average size of a major customer in our sample has $19 billion in total assets, which is about 71 times larger

than the average size of a supplier at $266 million.

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principal customers, may reveal valuable information to their major customers’ competitors (see

Appendix A for examples of redacted contracts). 6

In our first set of tests, we explore three relationship factors that likely strengthen

customers’ influence on suppliers’ disclosure strategies: customers’ research and development

(R&D) intensity (CustomerR&D), customers’ and supplier’s cross citations of each other’s patents

(Crosscite), and customers’ size (CustomerSize). Each supplier may have multiple major

customers and thus higher sales can indicate greater influence that a customer may have over its

suppliers. Therefore, we scale all of these factors by the proportion of sales to each customer.

Among these factors, R&D-related information might be of particular interest because of its

potential to generate large profits among all value-relevant information. For instance, the 2017

World Intellectual Property Report estimates that, on average, one third of the price paid for a

product is for the value from intellectual property. Thus, customers who are more research

intensive may care more about how to preserve the value of their information. Additionally,

customers and suppliers who cross cite each other’s patents likely have a tighter connection at the

R&D level and leaked information may be more harmful. Consequently, suppliers’ decision to

redact their filings may be more influenced by their customers when CustomerR&D and Crosscite

are higher. Lastly, CustomerSize may amplify customers’ influence on suppliers’ disclosure

strategies because larger customers will likely have greater power and individual contracts are less

likely to be considered material for them. Consequently, suppliers’ decision in redacting contracts

will likely be more important and reflective of their customers’ preference.

6 We include several examples of contracts between suppliers and customers as well as between suppliers and a third

party in Appendix A. For instance, the first example is between Adaptimmune (supplier) and Life Technologies (a

third party). The specific product information is redacted. The product information at the supplier level might be

useful at making inferences about customers. The third example is a contract between Whole Foods (customer) and

United Natural Foods (supplier). The information related to Whole Foods’ expansion strategies is redacted in this

example. All of these are examples of redacted information that can generate value, thus, we refer them as value-

relevant information.

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Our first set of findings indicate that dependent suppliers are more likely to redact when

CustomerR&D, Crosscite, and CustomerSize are higher after controlling for suppliers’ other

incentives to redact. These baseline results suggest that major customers’ preference can lead to a

higher likelihood of redaction for dependent suppliers. The findings on CustomerR&D and

Crosscite also highlight that supplier’s disclosure strategies are more likely affected when

customers have knowledge-intensive information to protect.

Next, to establish a tighter link between customers’ preferences and suppliers’ disclosure

strategies, we employ two specific settings where customers are known to have preferences for

nondisclosure to protect valuable information. First, a trade secret has arguably the most reliance

on secrecy to protect its value among all protection strategies. We posit and find a positive

association between major customers’ possession of trade secrets and suppliers’ likelihood of

redactions. This finding confirms that suppliers are more likely to redact when they have customers

exhibit preferences for information withholding. In the second setting, we use nondisclosure

agreements, also referred to as confidentiality agreements/clauses, to identify major customers that

likely prefer information withholding. A nondisclosure agreement is a legal contract between at

least two parties where the parties agree not to disclose information covered by the agreement. The

presence of nondisclosure agreements likely reflects firms’ efforts to use legally binding

procedures to enforce their preference for information withholding. Thus, we expect to observe a

higher likelihood of redaction for suppliers when their major customers use nondisclosure

agreements. Our findings are consistent with this prediction.

We further perform robustness checks and additional analyses to rule out alternative

explanations and to support our main findings. First, our results can be confounded if suppliers

withhold information to hide their favoritism towards a particular principal customer. This concern

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is particularly acute for scenarios when pricing information is redacted and/or when suppliers have

multiple principal customers. In these cases, suppliers may not want other principal customers to

learn about the favorable pricing (or contract terms) and use this information as leverage in their

negotiations. To alleviate this concern, we re-run our tests by using observations with only one

principal customer. We also run a robustness check excluding observations where pricing

information is redacted. Our results remain robust.

Second, to further establish support for our main findings, we classify filed contracts into

five categories based on their key objectives and content: Employment/Incentive, Credit/Leasing,

Research & Development/License (R&D/License), Purchases and Sales (P&S), and Investment

and Other contracts. Given our research question, our focus is on P&S and R&D/License contracts

because R&D/License contracts likely contain more value-relevant information while P&S

contracts might be more directly related to principal customers as these are supply chain related

contracts. The findings are consistent with these predictions.

Third, we re-run our trade secrecy test using customers subject to the Inevitable Disclosure

Doctrine (IDD) as an exogenous shock to trade secrecy at the customer level (Li et al. 2018, Leuz

and Wysocki 2016). We find that suppliers whose major customers are headquartered in states that

enact IDD (or prior to the subsequent rejection of this rule) are more likely to redact. We also re-

run our tests using customers’ horizontal mergers and acquisitions (M&A) at the industry-level as

an instrument. Our results remain robust. Another potential concern is that a supplier’s own

proprietary cost concerns can influence a supplier’s decision to redact its disclosures and confound

with our testing variables. While we control for suppliers’ own incentives to redact their

disclosures across all of our tests, we do recognize that competitive pressure can be multi-faceted.

Using various alternative proxies to capture different aspects of competition, we find that our

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results generally do not differ statistically or economically for suppliers facing high versus low

competition. We also run a battery of additional tests to further rule out this and other alternative

explanations. We discuss the details in Section 5.

Our study makes at least two incremental contributions. First, prior studies find that

relationships with major customers can impact a supplier’s capital structure (Kale and Shahrur

2007, Banerjee, Dasgupta, and Kim 2008), costs of external financing (Dhaliwal, Judd, Serfling,

and Shaikh 2016, Campello and Gao 2017), cash holdings (Itzkowitz 2015), and operating

performance (Patatoukas 2012, Irvine, Park, and Yildizhan 2016, Kalwani and Narayandas 1995).

Our findings suggest that proprietary costs for major customers can travel up the supply chain and

affect disclosure strategies of their dependent suppliers. Furthermore, our findings can also have

policy implications. The SEC is currently considering a proposal to allow companies to redact

filings without the need to formally submit a confidential treatment request application (Brehaeny

et al. 2017, Alsop et al. 2017). Our findings suggest that major customers’ preferences may not

align with that of suppliers’ shareholders when major customers emphasize more on information

withholding. Such a policy, should it pass, may change the information equilibrium if customers

use it to leverage their influence on dependent partners. This development can have a significantly

negative impact on the information environment of dependent suppliers, which are typically

smaller firms that may already have a more opaque information environment.

2. Hypothesis Development

As we move into a knowledge-based economy, protecting the value of information

becomes increasingly important and yet difficult due to technological advances in how

information-based assets can be acquired and disseminated. At the same time, effective supply

chain management becomes an essential component of successful firm management. For instance,

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to mitigate production and distribution problems, such as the bullwhip effect, a greater demand for

transparency and information sharing between the suppliers and customers is required. This

sharing of information has been shown to lead to more efficient operations (Ha and Tong 2008,

Lee, So and Tang 2000). However, increased information sharing also raises the risk of

information leakage. According to the ASIS 2002 survey, suppliers and vendors are ranked as one

of the top risk factors associated with the loss of their proprietary information for medium to large

firms.7 Moreover, information that is not directly shared among supply chain partners may also

have value implications. Thus, revelations of such information might also be costly for supply

chain partners. For instance, the first and second examples from Appendix A captures such

scenarios while the third and fourth examples are contracts between a supplier and its principal

customer. In summary, to effectively protect the value of their information, customers have

incentives to manage or influence their suppliers’ disclosure strategies. This is particularly true for

dependent suppliers that rely on a small set of customers where value-relevant information across

the supply chain can be more easily inferred.

Several mechanisms exist to protect value-relevant information. These methods include:

disclosure, trade secrets, and formal intellectual property rights (e.g., patents, trademarks, or

copyrights). Some of these methods naturally focus more on R&D and knowledge intensive

information. The latter two methods rely on a certain degree of information withholding. This

contrasts with the first method that relies on disclosing value-relevant information to protect its

value. Specifically, withholding information can help protect research-in-progress until it reaches

a patentable stage or until formal intellectual property rights are granted. Moreover, a firm may

patent some parts of an invention while keeping other parts a trade secret. Trade secrets consist of

7 “Trends in Proprietary Information Loss,” ASIS International, September 2002.

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all proprietary information that can generate economic value and can be of many forms. For

instance, a secret formula, process, code, or customer list, are all examples of trade secrets. Among

all three methods that rely on information withholding, trade secrecy is perhaps the one that relies

the most on nondisclosure to protect its value because the information details must be kept

proprietary in order to receive legal protection as a trade secret.

In contrast, some existing studies predict that firms can also disclose value-relevant

information to protect its worth. This set of studies model disclosure as a signaling mechanism to

either the financial market or a firm’s competitors. Bhattacharya and Ritter (1983) model a set of

firms that are competing on the innovation dimension. In their model, obtaining knowledge to

create an innovation is costly. Thus, firms have incentives to disclose invention-related

information in order to obtain better financing terms in order to support their research and

development activities. In other signaling studies, disclosure can serve as a deterrent signal to

competitors. Firms have incentives to disclose because disclosure can help them signal their

capability and commitment to engage in aggressive competition with rivals (Hall et al. 2014, Gill

2008, Baker and Mezzetti 2005, Anton and Yao 2004).

Prior literature documents that investors generally prefer transparency and reward

transparency with lower cost of capital and higher stock liquidity (Beyer et al. 2010, Easley and

O’Hara 2004, Leuz and Verrecchia 2000, Bertomeu et al. 2011). Given that investors have

preferences for transparency, we should expect that dependent suppliers will have a higher

likelihood of being transparent with their disclosures if major customers rely on disclosures to

profit from their valuable information. Holding suppliers’ other incentives constant, dependent

suppliers can enjoy the benefits of transparent disclosures while pleasing their customers by

committing to the same disclosure strategy of transparency as their major customers.

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On the other hand, if major customers depend on information withholding to appropriate

value from their information/knowledge, the leakage of proprietary information can have adverse

effects on both the major customers and their dependent suppliers. After all, disruptions from

major customers can lead to a significant loss in sales for dependent suppliers (Intintoli et al. 2017,

Cen, Dasgupta, and Sen 2016). To reduce the likelihood of such losses, suppliers may have

incentives to protect proprietary information that others might use to infer information about their

major customers. Furthermore, prior research indicates that major customers can leverage their

bargaining power to demand more favorable contract terms such as lower prices, more generous

trade credit, and flexible product delivery schedules (Blois 1972, Fee and Thomas 2004, Murfin

and Njoroge 2014, Allen and Phillips 2000). Ellis, Fee, and Thomas (2012)

Anecdotal evidence also suggests that major customers can influence their dependent

suppliers’ information strategies. For instance, consider the case of GT Advanced Technologies.

On November 4, 2013, GT Advanced announced a multi-year supply agreement with Apple Inc.

Shares of GT Advanced surged as high as 139 percent, as investors welcomed the news. On

November 7, 2013, GT Advanced submitted a set of heavily redacted contracts, including the

supply agreement itself and a leasing agreement, related to its business deal with Apple as part of

its 10-Q filing.8 It was only in bankruptcy proceedings that details about the relationship between

Apple and GT Advanced were disclosed to the public (Robinson et al. 2014). In filings made with

the SEC, Apple’s agreements with GT Advanced do not appear in concurrent or subsequent 8-K,

10-Q, or 10-K filings for 2013 or 2014 because of immateriality for Apple. If withholding

information is important for major customers to protect their valuable information, then we posit

8 The following agreements are submitted by GT Advanced:

https://www.sec.gov/Archives/edgar/data/1394954/000110465913082405/a13-19507_1ex10d1.htm

https://www.sec.gov/Archives/edgar/data/1394954/000110465913082405/a13-19507_1ex10d3.htm

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that they are also likely to demand their dependent suppliers to engage in information withholding

to help protect valuable information.

However, dependent suppliers may not comply with such demands because withholding

information can increase information asymmetry for investors (Armstrong, Core, Taylor, and

Verrecchia 2011). For instance, Verrecchia and Weber (2006) document a decline in the stock

liquidity of redacting firms as evidenced by higher bid-ask spreads and deteriorating market depth

and share turnover. Furthermore, prior literature has documented that suppliers who are dependent

on a small set of key customers bear the risk of losing large revenue streams from customers

(Campello and Gao 2017; Dhaliwal, Judd, Serfling, and Shaikh 2016). To the extent that

transparency can mitigate the negative effects of such risk, suppliers may be incentivized to be

more transparent and be more forthcoming in their disclosures if customers exert no influence on

suppliers’ disclosure strategies. Given the above discussion, we make no directional prediction on

whether or not major customers’ incentive to protect value-relevant information can influence

suppliers’ disclosure strategies.

3. Sample Selection and Descriptive Statistics

3.1. Sample selection and data

3.1.1 Data on Suppliers and Major Customers

Consistent with prior research (e.g., Pandit, Wasley, and Zach 2011, Patatoukas 2012,

Campello and Gao 2017), we use the Compustat segment files to identify a supplier’s major

customers. Data on major customers is based on the SEC and FASB requirement that U.S. public

companies disclose the existence of major customers and the revenue derived from each major

customer (Regulation S-K and SFAS 131). This disclosure is made on a firm's 10-K filings and is

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recorded in the Compustat Segment file. The Compustat Segment file contains customer and sales

information on nearly all publicly traded firms in the U.S. and Canada as well as foreign firms

with American depositary receipts (ADRs) traded on U.S. exchanges. However, the information

contained in the Compustat Segment files is not immediately usable. Specifically, major customers

in the dataset are only identified by name. Furthermore, the customer name may be abbreviated,

the customer may be a subsidiary of another firm (e.g. "IBM Global Services" may be reported

instead of IBM), or the customer has changed names over time (such as through a merger or

acquisition). Customer names also do not follow any convention. For example, American Airlines

can be reported as "American Air Lines", "American Airlines, Inc", or "American".

We link major customers with identifiers (gvkey) in Compustat using the following

procedure. We begin by removing all observations which reference major customers that are not

public U.S. companies. This includes all private firms, public foreign firms, government entities,

and entries that do not reveal a customer's identity (e.g. entries that report "5 customers", "US and

Asian companies", or "not reported"). We next lower case the names of all companies in both the

Compustat Segment files and Merged Fundamental Annual file and do an exact name match by

calendar year between the two data sets in order to match a customer name with its Compustat

permanent identifier (gvkey). For the observations in which there is no exact match, we utilize a

fuzzy string-matching algorithm (spelling distance of 25) which returns a set of companies and

their identifiers in the Compustat Merged Fundamentals Annual file that are similar to the name

reported in the segment file and then visually match the gvkeys. We only include matches which

we are reasonably sure as a conservative estimate. Specifically, we exclude cases where the match

is unclear or the name abbreviation matches multiple firms. SEC Regulation S-K Item 101

mandates that suppliers must disclose customers who represent at least 10% of total sales.

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Suppliers, though, often voluntarily disclose customers below this threshold. Following Campello

and Gao 2017 and Patatoukas (2012), we exclude these customers in our analyses.

3.1.2 Data and Background on Redactions

Regulation S-K Item 601 requires the disclosure of material contracts. These contracts are

filed as exhibits attached as part of a firm’s 8-K, 10-Q, 10-K, or various other filings (e.g. S-4,

DEF 14A, etc.). However, SEC filers can request for confidential treatment governed by Rule 406

under the Securities Act of 1933 and Rule 24b-2 under the Securities Exchange Act of 1934. To

apply for confidential treatments under these rules, firms must submit a paper filing to the SEC to

request for confidential treatment while they submit a redacted version of the mandatory filings on

EDGAR. The paper filing needs to include a confidential treatment request application and a

complete copy of the unredacted mandatory filing with the redacted portion clearly marked.

Under Item 601, the SEC requires firms to maintain an exhibit index of all material

contracts for the corresponding period on periodical reports (e.g. 10-K, 10-Q). The index is

required to be displayed before the signature page. For quarterly reports, the index is generally

listed as Item 6 and for annual reports it is generally listed as Item 16. Redacted contracts are

clearly denoted in the exhibit index. We use the Stage One 10-X Parse files provided by Bill

McDonald to obtain a set of 10-K and 10-Q filings that are cleaned of extraneous materials

(including HTML and XBRL tags, embedded PDFs and images, etc.).9 To identify filings with

redacted disclosures, we search all 10-K and 10-Q filings starting in 1996 for the following

keywords: “confidential treatment”, “confidential portion”, “rule 24b2”, “rule 406”, “redacted”

and “agreement” or “exhibit” within 20 words, or “confidential information” together with

variations of star marks (e.g, [*], (**), et al. ) to identify portions of the contract that were redacted.

9 https://sraf.nd.edu/data/stage-one-10-x-parse-data/

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Our sample period begins in 1996 because that was the first year when EDGAR electronic filings

became mandatory across all public companies.10

3.1.3 Sample Selection

Panel A of Table 1 describes our sample selection procedure. We begin with 231,531 firm

year observations corresponding to 27,440 firms across Compustat from 1996-2015. We exclude

67,270 firm-year observations where the firm had nonpositive assets, sales or equity. We further

exclude 44,404 observations from financial firms (SIC 6000-6999), utilities (SIC 4900-4999), and

public administration companies (SIC 9000-9999). Observations with insufficient data for

computing the control variables are also excluded from the sample. Finally, firms that do not have

available data in the Compustat Segment database or identifiable major customers are further

excluded. Our final sample consists of 12,404 firm-year observations from 2,897 suppliers.

Panels B and C of Table 1 provides descriptive statistics on the industry and year

distribution of the observations in our sample. As shown in Panel B of Table 1, the majority of the

suppliers are in manufacturing with 67.90% of the firm-year observations coming from that

industry. Services is the next largest industry comprising of 15.55% the sample. Panel C of Table

1 provides the distribution of observations by year. As the panel shows, the observations are spread

relatively evenly throughout all years of the sample.

< INSERT TABLE 1 >

3.2. Descriptive statistics

Table 2 provides descriptive statistics about the sample. Panel A of Table 2 provides the

number of observations, means, standard deviations, and quartile values of the variables used in

our main analyses. Redaction, is set to one if a firm has at least one redacted material contract, and

10 https://www.sec.gov/edgar/aboutedgar.htm

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zero otherwise. The mean value of Redaction is 0.298 indicating that firms redacted one or more

agreements in their filings across 29.8% of our firm-year observations. Size has a mean of 5.584,

indicating that the average supplier in our sample has $266 million in total assets. In contrast, the

major customers that they serve have an average of $19.13 billion in total assets, suggesting that

major customers are substantially larger than their suppliers. Panel B of Table 2 provides the

correlation table between all of the variables in our sample. We use three measures to capture the

relationship characteristics between a supplier and its major customer. The univariate correlations

between Redaction and our three proxies for customer influence (CustomerR&D, Crosscite, and

CustomerSize) are positive, which provide some preliminary evidence that major customer

influence can affect the information strategies of suppliers.

< INSERT TABLE 2 >

4. Empirical Design and Findings

4.1 The effects of customer-supplier relationship characteristics on suppliers’ likelihood of

redaction

We use the following multivariate probit model to test whether customer-supplier

relationship characteristics affect suppliers’ likelihood of redaction:

Redaction = α0 + α1Customer Supplier Relation + ∑αiControls + ε (1)

We use three proxies to capture the characteristics of Customer Supplier Relation for which

customers may have the strongest incentive or power to influence their suppliers’ disclosure

strategies. Our first proxy is CustomerR&D. It is the sales-weighted research and development

intensity across all major customers. Higher values of CustomerR&D indicate greater dependency

on fewer major customers who have higher R&D. If customers with higher R&D intensity have

higher incentives to protect the value of their obtained knowledge/information, then they may put

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more pressure or emphasis on their suppliers’ disclosure strategy to help with information

protection.

Next, we use patent cross citations (Crosscite) to capture whether a dependent supplier is

technologically closely linked to their customers.11 Higher patent citation intensity between a

supplier and its customers may indicate higher information exchange and sharing between them.

This may generate additional incentives for suppliers to protect their major customers’ value-

relevant information. This argument is along the same line as existing findings that customers have

greater influence on suppliers when customers and suppliers are more closely linked through

shared research and technology (Chu, Tian and Wang 2018).

The last factor is customers’ firm size weighted by the proportion of sales generated from

that customer (CustomerSize). Higher values of CustomerSize indicate that the firm depends more

heavily on a smaller set of larger customers. This measure intends to capture the notion that larger

customers may have greater influence over their suppliers because bigger customers are more

likely to have greater power (Hui, Liang and Yeung 2018, Campello and Gao 2017). Moreover,

the larger the customer is, the less likely an individual contract will be material for them. Thus,

big customers can avoid including contracts with these suppliers in their filings. In these scenarios,

suppliers’ disclosures may become a more important information source for customers’

competitors. As a result, suppliers’ information strategy (i.e. redact versus not redact) will likely

be more important and reflective of their customers’ preferences.

Controls refer to a set of control variables intended to capture supplier’s other incentives

to redact. Consistent with prior literature (e.g., Verrecchia and Weber 2006, Boone et al. 2016,

Ettredge et al. 2016, Tian and Yu 2018), we control for firm size (Size). We include Market-to-

11 We thank Noah Stoffman for providing the patent data through his website: https://kelley.iu.edu/nstoffma/

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book to control for the impact that firm growth and expansion can have on redactions (Ettredge et

al. 2016). In addition, we control for firm performance through ROA and firm competition through

Total_Similarity since these factors may influence the decision to redact a disclosure (Verrecchia

and Weber 2006, Hoberg and Phillips 2016). Prior research also indicates that proprietary costs

may influence the disclosures provided by a supplier (Ellis et al. 2012). Furthermore, prior work

highlights the role that external financing activities can play in a firm’s propensity to redact

disclosures (e.g., Verrecchia and Weber 2006). Thus, we control for the amount of debt held by

the firm (Leverage) as well as the propensity of the firm to issue bonds (Debt issue) and equity

(Equity issue). We also control for research and development expenditure (R&D) and firm age

(Age) since these factors can influence the disclosures provided by a firm (Boone et al. 2016,

Ettredge et al. 2016, Tian and Yu 2018). Consistent with Verrecchia and Weber 2006, we further

include the number of exhibits (Num_exhibits) to control for the association between redaction and

the number of exhibits as the likelihood of redaction may naturally rise when firms have more

contracts. Appendix B contains details on how variables are measured.

Table 3 reports the results for Equation (1). Columns (1) through (3) present results using

the three characteristics of customer-supplier relationships (CustomerR&D, Crosscite, and

CustomerSize). In column (1), the coefficient of CustomerR&D is 0.071 and is significant at the

1% level. This result suggests that the research intensity of major customers has a significant

influence over the redactions made by their suppliers. In terms of the magnitude of the effect, a

one standard deviation increase from the mean of CustomerR&D increases the probability of

supplier redaction by 2.98%. Among the control variables, firm size (Size) is positively associated

with redaction as evidenced by the positive coefficient and statistically significant. ROA has a

negative and statistically significant coefficient suggesting that poorer performing firms are more

18

likely to redact their disclosures. Firms that issue equity (Equity issue) and those that are more

highly levered (Leverage) are less likely to redact their disclosures. This result is consistent with

the notion that firms prefer to be more transparent in order to lower the cost of capital and attract

external financing from public sources (e.g., Lang and Lundholm 2000). Consistent with ex-ante

expectations, firms with greater research activity (as proxied by R&D) are more likely to redact

their filings. Age has a statistically significant, negative coefficient indicating that younger firms

have a greater propensity to redact disclosures. Furthermore, firms facing higher competitive threat

(as proxied by Total_similarity) are more likely to redact their filings. Overall, the control variable

coefficients generally conform to ex-ante expectations and existing literature (Boone et al. 2016,

Verrecchia and Weber 2006, Glaeser 2017, Tian and Yu 2018).

The remaining columns present results for the other factors. As shown in columns (2) and

(3), Crosscite, and CustomerSize have positive coefficients of 0.711 and 0.059 with p-values of

less than 1% indicating that these customer-supplier relationship factors are significantly

associated with the likelihood of a redaction made by a supplier. The marginal effects analyses

indicate that a one standard deviation increase from the mean values of Crosscite and

CustomerSize increases the probability of supplier redaction by 2.87%, and 4.40%, respectively.

The results from these columns indicate that suppliers with research connections to their major

customers and size of a supplier’s major customers are factors in the determination of filing

redactions by a supplier. Overall, this table provides support for our main hypothesis and suggests

that major customers can influence their suppliers’ information strategies.

< INSERT TABLE 3>

19

4.2 Customer trade secrets and suppliers’ likelihood of redaction

Our baseline results from Table 3 indicate that suppliers’ likelihood of redaction increases

when customers are likely to have greater influence on suppliers’ information strategies. These

findings suggest that customers generally prefer to withhold information to protect its value and

such preferences can influence their dependent suppliers’ information strategies. To provide

further evidence on this inference, we identify two settings where customers are known to prefer

information withholding. Our first setting identifies customers that have trade secrets. We posit

that customers who use trade secrecy to protect value-relevant information (i.e. innovations, secret

chemical, business strategies) are more likely to favor nondisclosure strategies. If customers’

preferences can influence their dependent suppliers’ information strategies as our baseline findings

suggest, we should observe a higher likelihood of redaction for suppliers when their major

customers protect proprietary information through trade secrets.

The United States Patent and Trademark Office (USPTO) defines a trade secret as

proprietary information that can include a “formula, pattern, compilation, program, device, method,

technique or process…… used in business” and gives “an economic advantage over competitors

who do not know or use it.” Simply stated, a trade secret contains information that have two basic

characteristics: proprietary and commercially valuable.12 As our society continues to shift to an

information-based economy, firms are relying more on secrecy to protect their “know-how” and

intangible assets (Almeling 2012).13 In fact, surveys suggest that secrecy is almost always ranked

12 Some experts believe that every reasonable sized firms have trade secrets given this broad definition (Rowe and

Sandeen 2012) 13 It is estimated that intangible assets contribute to 17 percent of S&P 500 companies’ total value in 1975. By 2015,

the percentage increased to 85 percent (Keller 2015).

20

as the top mechanism to protect returns to innovation (Cohen, Nelson, Walsh 2000, Marsh and

Liberty International Underwriters 2011).14

Compared with other forms of legal property rights protection, trade secrecy has lower

enforceability and thus less protection. However, it also has many advantages. For instance, the

application for a patent grant requires detailed disclosures of the invention. On the other hand,

trade secrecy requires no such disclosures. Thus, even though the existence of trade secrets is

generally public knowledge, the details of the invention is kept secret. In theory, trade secrecy

protection can last forever while patents generally last up to 20 years (Cohen et al. 2000, Glaeser

2017, Schwartz 2013, Yeh 2016).

Using possession of trade secret to capture customers’ general preference for information

witholding we test whether their suppliers are also more likely to redact:

Redaction = α0 + α1Customer Trade Secret + α2Supplier Trade Secret Indicator

+ ∑αiControls + ε

(2)

Customer Trade Secret is a sales-weighted count of whether trade secrets are discussed in the

major customers’ 10-K filings. Supplier Trade Secret Indicator is an indicator variable set to one

if a supplier’s 10-K filings mention trade secrets and zero otherwise. To receive protection, firms

need to establish that they have trade secrets while the details of the secret is not publicly revealed.

Thus, firms will discuss the existence of trade secrets in their annual reports (i.e., 10-K filings)

without disclosing their details. Following Glaeser (2017), we identify trade secret discussions by

searching for the keywords “trade secret” or “trade secrecy” across all 10-K filings. If a 10-K filing

mentions either of these keywords at least once, we set the trade secret indicator for the customer

equal to one. We then use the customers’ sales to get a weighted measure of trade secrets across

14 In 2016, the Defend Trade Secrets Act was signed into federal law by President Obama, mark yet another

acknowledgement that protecting trade secrets is an important and pressing issue faced by corporate America.

21

all of a supplier’s major customers to compute Customer Trade Secret. Controls refer to the same

set of control variables as used in equation (1). If customers’ incentive to protect trade secrets

increases the suppliers’ likelihood to withhold material information, then we expect α1 > 0.

Table 4 provides the results. Column (1) of Table 4 provides preliminary results for

Customer Trade Secret excluding controls for supplier trade secrets. As shown in the column, the

coefficient of Customer Trade Secret is positive and statistically significant at the 1% level

(coefficient = 0.570, p-value = 0.000). Column (2) of the same table further controls for supplier

trade secrets through the indicator Supplier Trade Secret Indicator. As expected, the coefficient of

Supplier Trade Secret Indicator is positive and statistically significant (coefficient = 0.310, p-value

= 0.000). More importantly, the coefficient of Customer Trade Secret remains positive and

statistically significant, suggesting that major customer trades secrets are an increment factor

above and beyond the influence of supplier trade secrets on a supplier’s decision to redact its

disclosures. A one standard deviation increase from the mean value of Customer Trade Secret

increases the probability of supplier redaction by 4.60%. Overall, this table provides support for

the notion that major customers’ incentive to protect their trade secrets can lead to a higher

likelihood of supplier redaction. The protection of trade secrets can incentivize a supplier’s major

customers to influence their dependent suppliers from providing disclosures that can potentially

divulge those secrets.

< INSERT TABLE 4 >

4.3 Customers’ nondisclosure agreements and suppliers’ information strategies

A non-disclosure agreement is also frequently referred to as a confidentiality agreement. It

is a legal contract that firms sign with their employees or business partners which generally

requires the involved parties to keep certain information proprietary. It is also frequently referred

22

to as a confidentiality clause. Firms can use this type of agreement when they have valuable

information that they do not want to reveal or disclose. We predict that major customer

confidentiality agreements can also influence the information strategies of dependent suppliers.

Suppliers may be more likely to redact their disclosures if major customers utilize confidentiality

agreements. To test this prediction, we specify the following regression model:

Redaction = α0 + α1Customer Nondisclosure + α2Supplier Nondisclosure Indicator

+ ∑αi Controls + ε

(4)

Customer Nondisclosure is defined as the sales-weighted count of whether nondisclosure

agreements are discussed in the major customer’s filings. Supplier Nondisclosure Indicator is set

to one if the supplier discusses about nondisclosure agreements in its filings, and zero otherwise.

To determine whether a firm enters into confidentiality agreements, we search 10-K and 10-Q

filings for the keywords "confidentiality agreement", "confidentiality obligation", "nondisclosure

agreement", or "non-disclosure agreement". If a filing contains at least one of these keywords, we

identify that 10-K or 10-Q filing as mentioning a confidentiality agreement. Controls refer to the

same set of control variables as in equation (1). We expect customers’ use of nondisclosure

agreement will lead to higher likelihood of redaction from suppliers (α1 > 0).

Table 5 provides the results for equation (4). The presence of nondisclosure agreements

made by major customers can influence a supplier to curtail its disclosures. Column (1) of Table

5 provides the results for Customer Nondisclosure excluding controls for suppliers’ confidentiality

agreements. Column (2) of the same table further controls for Supplier Nondisclosure Indicator.

The findings from both columns indicate that the magnitude and significance of Customer

Nondisclosure remains very similar and significant regardless of whether one controls for Supplier

Nondisclosure Indicator or not. A one standard deviation increase from the mean value of

23

Customer Nondisclosure increases the probability of supplier redaction by 3.81%. Overall, this

table provides supportive evidence that a supplier is more likely to redact its disclosures when a

firm’s major customers protect their proprietary information through nondisclosure agreements.

< INSERT TABLE 5>

5. Supplemental Analyses and Robustness Tests

5.1 The role of bargaining power and/or hiding favoritism

An alternative story may be that suppliers are redacting their disclosures to increase their

bargaining power when negotiating with other major customers. By withholding contract terms

from public filings, a supplier may prevent its other major customers from using that information

in its negotiations with the supplier. This concern is accentuated when the redacted information is

price-related. In such scenario, the supplier might want to hide a favorable pricing term to one of

the principal customer so that other principal customers will not demand similar pricing terms. To

examine if this alternative drives our findings, we investigate the subsample of suppliers that have

only one major customer. If suppliers are redacting disclosures to maintain bargaining power over

their other major customers, we should expect our main results to disappear when a supplier has

only one major customer. Table 6 presents the results for suppliers with only one principal

customer. Overall, our results remain positive and statistically significant, suggesting that

bargaining power and/or hiding favoritism is unlikely to be driving our main results.

< INSERT TABLE 6>

Next, to determine whether a redacted information is pricing related, we do a keyword

search for ‘$’, followed by up to three spaces, and a variation of star marks indicating portions of

24

the contract that are redacted. The variants of star marks include [*], [**], [* *], [***], [* * *],

along with the above patterns but replacing ‘[’ and ‘]’ with ‘(’ and ‘)’. Table 7 presents the results

excluding pricing related redactions. In other words, if a redaction is pricing related then we

redefined it as if no redaction has happened. As shown across the five columns, the results remain

robust.

< INSERT TABLE 7>

5.2 Exploring the effects on different contract types

In this section, we further explore what type of information redactions are more likely

affected by customer-supplier relations. If customers influence suppliers’ information strategies to

protect value relevant information for customers, then we expect customers to care the most about

R&D/License and supply chain related information. Supply chain information can be directly

related to customers while R&D/license information may be more value-relevant. We use different

contract groups to capture the type of redacted information. In particular, we search through

separately filed exhibits 10.XXX, 2.XXX, 4.XXX and 99.XXX from 10-K and 10-Q filings and

classify the type of each agreement. The SEC requires that material contracts to be filed as exhibit

10.XXX. However, firms often file material contracts in exhibit 2.XXX, 4.XXX or 99.XXX.15 We

classify each agreement into one of five categories based on their key objectives and content: (1)

Employment/Incentive, (2) Credit/Leasing, (3) Research and Development/License

15 Tian and Yu (2018) hand collect redacted contracts that are identified through confidential treatment orders from

2008 to 2016. We used their sample and find that approximately 10% of redacted contracts in their sample are from

exhibit 2.XXX 4.XXX or 99.XXX. To conduct robustness checks, we also restrict our tests to those with 10.XXX

and report the results in appendix C.

25

(R&D/License), (4) Purchases and Sales of services or products (P&S), and (5) Investment and

others (Investment &Other).

Employment/Incentive includes contracts of salary, severance, compensation, long- and

short-term incentive plans, and bonuses. Credit/Leasing includes contracts on loans, pledge,

collateral, covenant, lease, and rent. Research and Development/License includes contracts related

to research development, intellectual property, patent, license, and co-

development/collaboration/alliance/cooperation. Purchases and Sales contracts are related to

purchase or sale of products or services. They are defined based on key tasks involving key words

of purchase, sell, advertising, delivery, supplier, customer, etc. Investment and the others involves

contracts for capital expenditures such as asset or equipment purchases or transfers, mergers and

acquisitions, and financial asset investments such as security purchases. The remaining contracts

are categorized as others included in a fifth category.

To classify contracts based on their types, we search the title and content of each exhibit

for a set of keywords. The keywords for each contract type are based on the manual collection of

key tasks for each contract in Tian and Yu (2018). For each keyword, we retain a count of that

keyword across the entire exhibit. We then sum the counts of each keyword by the contract

category. The contract is then classified based on which contract category has the highest count of

keywords. For instance, if “employee bonus” appears five times, “salary” appears five times, and

“loan” appears one time. The contract is classified as an employment/incentive contract because

employment/incentive keywords appear 10 times in the contract. To determine whether the

contract was redacted, we use the same set of keywords as those in the filing search.

Table 8 Panel A to E report the results for CustomerR&D, CrossCite, CustomerSize,

Customer Trade Secret, and Customer Nondisclosure respectively. As expected, the contract

26

categories that are consistently significant across most measures of customer-supplier relations are

R&D/License and P&S contracts across the panels. These results are consistent with the notion

that major customers can influence the redactions of supplier contracts in order to protect their

own value-relevant information.

< INSERT TABLE 8>

5.3 Shock to customers’ trade secrecy and instrumental variable approach

In our trade secrecy test from section 4.2, we use a keyword search across financial reports

to identify trade secrets discussed by major customers. The benefit of this approach is that it helps

us to identify a relatively large sample of firms with trade secrets. However, one might argue that

this identification suffers from endogeneity issues (Li et al. 2018, Leuz and Wysocki 2016). In

particular, major customers may choose to work with suppliers who are more likely to redact their

disclosures independent of customer influence. Thus, we examine how the staggered adoption, and

subsequent rejection in some cases, of the inevitable disclosure doctrine (IDD) by U.S. state courts

can impact major customers’ incentives to influence supplier information strategies. The IDD is a

legal doctrine through which a firm can prevent a former employee from working for a rival firm

if the new job would lead the former employee to reveal the trade secrets of the firm to the rival.

The adoption of IDD eliminates a primary mechanism through which product market rivals can

obtain proprietary information about a company. Thus, it increases the incremental value of using

nondisclosure to protect proprietary information (Li et al. 2018). In our setting, this indicates that

the value of using nondisclosure increases for those customers who are subject to IDD. As a result,

suppliers may be more likely to redact when their major customers are subject to the IDD shocks.16

16 See Li et al. (2018) and Klasa et al. (2018) for more details about the Inevitable Disclosure Doctrine (IDD).

27

Thus, we predict that the adoption of IDD in states where major customers are headquartered can

influence the propensity for a supplier to redact its disclosures.

Table 9 provides the results. Because the shock is applied at the customer level, the model

departs from traditional difference-in-differences designs. The average relationship between a

customer and supplier is about two to three years. Thus, we restrict our sample to those suppliers

that have at least one principal customer keeping the relationship with them for at least two years.

If major customers subject to IDD exert significant influence on its dependent suppliers to curtail

the information that rivals can obtain through their supplier’s disclosures then we expect

IDD_Treated_Post to be positive, which is measured as sales-weighted indicators whether

customers are headquartered in IDD state and during the periods of three years post (pre) the

adoption (rejection) events. The findings in Table 9 confirm this expectation.17

< INSERT TABLE 9>

We further reexamine the main hypothesis using an instrumental variables (IV) approach.

Following Campello and Gao (2017), we use horizontal customer merger and acquisitions (M&A)

in the customers’ industries (CustomerM&A) as an instrument for customer-supplier relationship

characteristics. Higher M&A activity within a customer’s industry implies greater concentration

and fewer customers that a supplier can potentially do business with. Campello and Gao (2017)

demonstrate two important effects of M&A in the customers’ industries on their suppliers. First,

sales to acquirer customers increase quickly following a merger. Second, suppliers’ risk of losing

a major customer increases significantly after a merger or acquisition. As a result, the buyer’s

17 Note, the shock is applied at the customer level, the model departs from traditional dynamic difference-in-

differences designs.

28

position is strengthened after an M&A among customers. These characteristics make customers’

industry-level M&A a valuable instrument for our study.

Moreover, the instrumental approach assumes that M&A activities among customers will

only affect suppliers’ information strategies through the relation between a supplier and its

customers. This assumption is plausible as there is no clear relation between horizontal customer

M&A at the industry level and the redactions made by suppliers. Customer M&A should only

influence the information strategies of suppliers through the business relation between the supplier

and its customers (otherwise, known as the exclusion restriction). Thus, a merger (or acquisition)

between customers and suppliers might influence redaction in related contracts. To address this

concern, we only use horizontal M&As. Specifically, we exclude suppliers who are in the same

industry as their customers. We require that acquirers do not buy their suppliers. Essentially, we

exclude all possibility that an M&A is between a supplier and their customers.

Table 10 Panel A to E provides the results for CustomerR&D, CrossCite, CustomerSize,

Customer Trade Secret, and Customer Nondisclosure, respectively. The first stage results are

presented in column (1) of each panel. We also report the Kleinberg-Paap statistics in each panel

and the statistics all rejects the null hypothesis of under-identification. Column (2) of each panel

reports the results for the second stage IV results and it indicates that our findings are robust to the

IV method.

< INSERT TABLE 10 >

5.4 Confounding effects of supplier’s proprietary costs and other robustness checks

One concern is that the documented impact of customers’ influence on supplier redactions

is driven by greater proprietary costs and peer competition faced by these suppliers. First, in our

tests, we try to control for suppliers’ competitive pressure as supplier’s own proprietary cost

29

concern may also lead them to redact. However, we recognize that it is not possible to control for

all aspects of competitive pressure. To alleviate this concern, we further test in subsamples for

suppliers who face high versus low competition. We use three variables to try to capture different

aspect of competition: industry concentration (Herfindahl-Hirschman Index), product fluidity from

Hoberg, Phillips, and Prabhala (2014) and Total Similarity. Using sample median as a cutoff for

higher versus low competition, we examine all of our testing variables in the two subsamples:

CustomerR&D, CrossCite, CustomerSize, Customer Trade Secret, and Customer Nondisclosure.

Untabulated findings indicate that none of these coefficients is significantly larger for suppliers

that face high competition.18 This indicates that it is unlikely that suppliers’ proprietary cost

concern is the primary driver for our findings.

Moreover, we also examine the effect of customer-supplier relationship length. If our main

results are driven by suppliers’ own proprietary costs concerns then we would expect suppliers to

worry less about losing a customer to its competitors as they build closer relationships with their

customers. Untabulated result indicate that relationship length has a positive effect on suppliers’

likelihood of redaction, which suggest that suppliers are more likely to cater to their long-term

customers. Lastly, in our main tests, we require the customer identity to obtain customer variables

such as assets and R&D. To check if customers’ influence apply to a more general sample, we

relax this restriction and examine all suppliers with a principal customer as reported on the

Compustat Segment files. Untabulated findings indicate that the percentage of sales from principal

customers, principal customers’ sales concentration, and the log number of principal customers

are all positive and significantly associated with the likelihood of suppliers’ redaction. These

18 All untabulated findings are available upon request.

30

findings suggest that our main findings are unlikely driven by our sample choices from the need

to identify major customers.

6. Conclusion

In order to protect value-relevant information, a firm can choose to disclose that

information in order to signal future firm prospects and deter rivals. However, a firm may also

choose to withhold that information in order to retain its value. How a firm chooses to protect its

value-relevant information is likely to influence the information strategies of dependent suppliers.

We show that customer-supplier relationships (in the form of customer R&D intensity, shared

research and technology, and customer size) can influence the propensity for a supplier to redact

its disclosures.

We also show that the presence of trade secrets and confidentiality agreements in the

disclosures of major customers are positively associated with supplier disclosure redactions. Using

a shock to customer propriety costs, in the form of the Inevitable Disclosure Doctrine, we find that

the adoption of IDD in states where customers are incorporated is positively associated with

supplier redactions. In a battery of robustness tests, we examine and alleviate concerns regarding

alternative stories that may explain our findings.

Furthermore, using the type of contract to capture redaction content, we examine how

customer-supplier relations can influence the type of contracts redacted by a supplier. We find that

R&D/License and Purchases and Sales contracts are likely to be redacted consistent with major

customers using their influence on dependent suppliers to curtail innovation and supply chain

related information. We find similar results using the presence of customer trade secrets and

confidentiality (non-disclosure) agreements. To further alleviate concerns regarding endogeneity

in the choice of major customers, we also use an instrumental variables approach and provide

31

results consistent with our main findings. Overall, this study sheds light on the role that major

customers can play in the information strategies of suppliers.

32

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36

Appendix A

Examples of Redacted Disclosures in Material contracts

Examples of redacted disclosures from contracts that suppliers sign with a third party

1. Supplier: Adaptimmune Therapeutics; Major customer(s): GlaxoSmithKline plc

Third Party: Life Technologies Corporation

Exhibit 10.1, 8-K filed by Adaptimmune Therapeutics on 6/21/2016

(filed and redacted by the supplier)

https://www.sec.gov/Archives/edgar/data/1621227/000110465916128496/a16-

13526_1ex10d1.htm

This Supply Agreement is made and entered into with effect from June 1 2016 (“Effective Date”)

by and between:

Life Technologies Corporation of 5791 Van Allen Way, Carlsbad, California, 92008, U.S.A.

(“Life”); and

Adaptimmune Limited of 101 Park Drive, Milton Park, Abingdon, Oxon, OX14 4RX, England

(“Customer”),

Development Phase Purchasing Obligation: The minimum purchasing obligation applicable

during the Development Phase shall be as follows: (a) Adaptimmune shall purchase and receive

*** ; and, assuming that the Commercial Phase has not commenced prior to 31 December 2019,

(b) Adaptimmune shall purchase and receive *** . If the Transitional Phase commences prior to

or during 2019 then the Development Phase Purchasing Obligation shall continue to apply unless

the Commercial Phase commences prior to 31 December 2019. In the event that the Commercial

Phase has commenced prior to 31 December 2019, the Minimum Purchasing Obligation shall

apply for 2019 and each subsequent calendar year. Life also acknowledges the purchase and receipt

of *** under the Letter Agreement.

Minimum Purchasing Obligation: The minimum purchasing obligation between the Effective

Date and 31 December 2019 is defined by the Development Phase Purchasing Obligation. The

minimum purchasing obligation applicable during the Commercial Phase shall be mutually agreed

during the Transitional Phase with both Parties acting in good faith but shall be no less than ***

in the Commercial Phase.

Minimum Order Volume: *** during Commercial Phase and *** during Development Phase

and Transitional Phase.

Minimum Delivery Size: *** during Commercial Phase and *** during Development Phase and

Transitional Phase.

2. Supplier: Synacor Inc.; Major customer(s): Alphabet Inc

Third Party: Maxit Technology Inc

Exhibit 10.2.1, 10-Q filed by Synacor Inc. on 5/14/2013

(filed and redacted by the supplier)

37

https://www.sec.gov/Archives/edgar/data/1408278/000140827813000022/sync-

3312013xexx1021.htm

This JOINT VENTURE AGREEMENT (this “Agreement”) is made as of March 11, 2013, by and

among Synacor, Inc., a Delaware corporation (“Synacor”), Maxit Technology Incorporated, a

company incorporated under the laws of the British Virgin Islands (“Maxit”), and Synacor China,

Ltd., a company incorporated under the laws of the Cayman Islands (the “Company”).

WHEREAS, the parties hereto intend that (i) the Company shall directly wholly own a company

limited by shares incorporated under the law of the Hong Kong Special Administrative Region of

the People’s Republic of China (the “PRC”), which the parties intend, subject to applicable Law,

to name XingMai Technology (HK) Limited (the “HK Sub”); and (ii) the HK Sub shall directly

wholly own a limited liability company organized and existing under the law of the PRC, which

the parties intend, subject to applicable Law, to name Beijing XingMai Technology, Ltd. (the

“WFOE”), which WFOE shall operate all of the business of the Company in the PRC;

(vi) [*]. Synacor shall be satisfied that all rights to that certain [*] (the “[*] Agreement”) have been

legally and validly transferred to the WFOE on terms and

conditions satisfactory to Synacor and that all Governmental Approvals or other Third Party

Approvals in connection therewith shall have been obtained.

3. HK Sub and WFOE.

3.1 Formation of HK Sub and WFOE. As soon as reasonably practicable following the First

Closing, the Company shall take all necessary actions to form the HK Sub and the WFOE,

including, without limitation, in relation to the formation of the WFOE, obtaining the approval of

the Ministry of Commerce of the PRC or its relevant local branches and business licenses issued

by the State Administration of Industry and Commerce of the PRC or its relevant local branches.

3.2 [*]

Examples of redacted disclosure from contracts between customers and suppliers

3. Supplier: United Natural Foods (UNFI); Customer: Whole Foods Markets (WFM)

Exhibit 10.9, 10-K filed by WFM on 12/8/2006

(filed and redacted by both WFM and UNFI)

https://www.sec.gov/Archives/edgar/data/865436/000119312506249376/dex109.htm

UNFI agrees to (i) use commercially reasonable efforts to increase its distribution capacity in

[*CONFIDENTIAL*] and (ii) establish a new distribution center in [*CONFIDENTIAL*]. If

UNFI fails to provide fully functional UNFI DCs capable of servicing the applicable WFM

Locations in the [*CONFIDENTIAL*] and [*CONFIDENTIAL*] (in each case, the “Online

Date”), UNFI will be charged a penalty fee. The penalty fee begins on the applicable UNFI DCs

Online Date and continues until the applicable UNFI DC is fully functional and is equal to

38

[*CONFIDENTIAL*]. If there is an event of Force Majeure that prevents UNFI from meeting the

applicable Online Date, the parties agree to negotiate a new Online Date…….

Exhibit B

[*CONFIDENTIAL*]

[*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*]

-

[*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*] [*CONFIDENTIAL*]

4. Supplier: Republic Airways Holdings, Inc.; Customer: United Air Lines, Inc.

Exhibit 10.3, 10-Q filed by Republic Airways Holdings, Inc. on 7/26/2004

(filed and redacted by the supplier)

https://www.sec.gov/Archives/edgar/data/1159154/000110465904020861/a04-

8199_1ex10d3.htm

When executed by both parties, this letter agreement (this “Agreement”) shall amend and

supersede the letter agreement dated February 13, 2004 (the “Prior Agreement”) between Republic

Airways Holdings, Inc. (“RJET”) and United Air Lines, Inc. (“United”) pursuant to which RJET

agreed to provide United a [*] for United Express flights operated by RJET’s subsidiary,

Chautauqua Airlines, Inc. (“Chautauqua”). Upon the execution of this Agreement, the Prior

Agreement shall be null and void and of no force or effect.

In consideration of United entering into the United Express Agreements dated as of February 9,

2004 with Republic Airline, Inc. (“Republic”) and dated as of February 13, 2004 with Chautauqua,

in each case as amended (collectively, the “United Express Agreements”), and in consideration of

United agreeing to amend and supersede the Prior Agreement and to forego [*] to which it is or

would have be entitled thereunder, RJET shall provide United with [*] as provided herein. The

[*] shall be [*] for each aircraft [*] aircraft that is operated during an [*] in revenue service (i.e.

excluding spares) by Chautauqua or Republic under a [*] under the United Express Agreements.

The [*] no later than the 10th day of [*]. By way of example, if during a [*], Chautauqua and

Republic operated a total of [*] aircraft, including [*] spare aircraft, under the United Express

Agreements, RJET would be required to [*] by the 10th day of [*].

39

Appendix B: Variable Definitions

Redaction

= an indicator variable. It equals one if the firm files at least one

redacted agreement for a given year, and zero otherwise.

Determination of redacted agreements are based on the search across

all 10-K/10-Q filings using the following keywords: “confidential

treatment”, “confidential portion”, “rule 24b2”, “rule 406”, “redacted”

and “agreement” or “exhibit” within 20 words, or “confidential

information” together with variants of star marks indicating portions

of the contract that were redacted. The variants of star marks include

[*], [ * ], [**], [ ** ], [ * * ], [* *], [***], [ *** ], [ * * * ], [* * *],

along with the above patterns but replacing ‘[’ and ‘]’ with ‘(’ and ‘)’.

CustomerSize

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝑆𝑖𝑧𝑒𝑗

𝑛𝑗=1 , where Salei is total sales for firm i, Salej,i is sales

from customer j to firm i, and n is the total number of major customers

for firm i. Sizej is the natural logarithm of the book value of total assets

for customer j. Major customer is defined as a customer accounting for

at least 10% of total sales as reported in Compustat. Computation of

CustomerSize follows Campello and Gao (2017).

CustomerR&D

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝑅&𝐷𝑗

𝑛𝑗=1 , where Salei is total sales for firm i, Salej,i is sales

from customer j to firm i, and n is the total number of major customers

for firm i. R&Dj is the natural logarithm of one plus R&D expenditures

for customer j. Major customer is defined as a customer accounting for

at least 10% of total sales.

Crosscite

= ∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝐶𝑢𝑠𝑡𝑜𝑚𝑒𝑟_𝑆𝑢𝑝𝑝𝑙𝑖𝑒𝑟 𝐶𝑟𝑜𝑠𝑠𝑐𝑖𝑡𝑒 𝑖,𝑗

𝑛𝑗=1 , where Salei is

total sales for firm i, Salej,i is sales from customer j to firm i, and n is

the total number of major customers for firm i.

Customer_Supplier_Crosscitei,j equals one if customer j (firm i) has

cited one or more of firm i (customer j)’s patents within the past three

years and zero otherwise. Major customer is defined as a customer

accounting for at least 10% of total sales.

Customer Trade Secret

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝑇𝑟𝑎𝑑𝑒𝑠𝑒𝑐𝑟𝑒𝑡𝑗

𝑛𝑗=1 , where Salei is total sales for firm i,

Salej,i is sales from customer j to firm i, and n is the total number of

major customers for firm i. Tradesecretj equals one if customer j states

a trade secret in its 10-K filing and zero otherwise. 10-K filings

containing the keywords of “trade secret” or “trade secrecy” are

classified as containing one or more trade secrets following Glaeser

(2017). Major customer is defined as a customer accounting for at least

10% of total sales.

40

Supplier Trade Secret

Indicator

=dummy variable that equals one if the firm states a trade secret in its

10-K filing and zero otherwise. 10-K filings containing the keywords

of “trade secret” or “trade secrecy” are classified as containing one or

more trade secrets following Glaeser (2017).

Customer Nondisclosure

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝑁𝑜𝑛𝑑𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒𝑗

𝑛𝑗=1 , where Salei is total sales for firm i,

Salej,i is sales from customer j to firm i, and n is the total number of

major customers for firm i. Nondisclosurej equals one if customer j’s

10-K/10-Q filing discusses or contains a nondisclosure/confidentiality

agreement and zero otherwise. 10-K/10-Q filings containing the

keywords of “confidentiality agreement”, “confidentiality obligation”,

“nondisclosure agreement” or “non-disclosure agreement” are

classified as discussing or containing a confidentiality agreement.

Major customer is defined as a customer accounting for at least 10%

of total sales.

Supplier Nondisclosure

Indicator

=dummy variable that equals one if the firm states a

nondisclosure/confidentiality agreement in its 10-K/10-Q filing and

zero otherwise. 10-K/10-Q filings containing the keywords of

“confidentiality agreement”, “confidentiality obligation”,

“nondisclosure agreement” or “non-disclosure agreement” are

classified as discussing or containing a confidentiality agreement.

Size = the natural logarithm of total book value of assets (AT) at the end of

the year.

Market-to-book

= total assets minus book value of common equity (CEQ) plus market

value of common equity (shares outstanding times fiscal year-end

stock price (CSHO*PRCC_F)), all divided by total assets.

ROA = Income before extraordinary item (IB), scaled by total assets at the

beginning of the year (ATt-1).

Equity_issue

= sale of common and preferred stock (SSTK), minus purchase of

common and preferred stock (PRSTKC), all divided by total assets at

the beginning of the year (ATt-1). If SSTK or PRSTKC are missing, we

set them to zero.

Debt_issue

= long-term debt issuance (DLTIS), minus long-term debt reduction

(DLTR), all divided by total assets at the beginning of the year (ATt-

1). If DLTIS or DLTR are missing, we set them to zero.

Leverage

= total debt divided by total market value of assets, where total debt is

the sum of long-term debt (DLTT) and debt in current liability (DLC).

Total market value of assets is total book value of assets (AT), minus

book value of common equity (CEQ) plus market value of common

41

equity (shares outstanding times fiscal year-end stock price

(CSHO*PRCC_F))

R&D = Research and development (XRD) divided by total assets at the

beginning of the year (ATt-1).

Age

= natural logarithm of one plus firm age. Firm age is calculated as

current year minus the first fiscal year of available accounting data in

COMPUSTAT.

Num_Exhibits = natural logarithm of number of exhibits filed with form 10-K or

10-Q

Total_Similarity

= the sum of pairwise similarity scores defined in Hoberg and Phillips

(2016). Specifically, the pairwise similarity between firm i and its peer

at year t is a “cosine” similarity between a firm’s own product word

vector at year t and its counterparts’ product word vector at the same

year.

CustomerM&A

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛𝑗

𝑛𝑗=1 , where Salei is total sales for firm i, Salej,i

is sales from customer j to firm i, and n is the total number of major

customers for firm i. Acquisitionj is the past five-year average of

acquisition activity for customer j’s industry, where industry is

classified based on two-digit SIC. For each industry-year, we first

calculate the ratio of total deal value within the industry over the sum

of acquirors’ sales, then calculate the ratio average over the past five

years. 𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛 =1

5∑ (

∑ 𝐷𝑒𝑎𝑙 𝑣𝑎𝑙𝑢𝑒𝑘𝑚𝑘=1

∑ 𝑆𝑎𝑙𝑒𝑠𝑘𝑚𝑘=1

)𝑡−5𝑡=−1 , where m is the

number of acquisitions within the industry. (Campello and Gao, 2017)

IDD_Treated_Pre

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝐼𝐷𝐷_𝑃𝑜𝑠𝑡𝑗

𝑛𝑗=1 , where Salei is total sales for firm i, Salej,i

is sales from customer j to firm i, and n is the total number of major

customers for firm i. IDD_Prej is a dummy variable set to one if the

major customer j’s headquarter state is an IDD state and the year is

within the three years after the rejection year (subsequent to adoption)

or within three years before the adoption year. Otherwise, it is equal to

zero. IDD states include AR, CT, FL, GA, IA, IN, KS, MO, OH, TX,

UT, WA, DE, IL, MA, MN, NC, NJ, NY, PA, or MI. The state adopted

or subsequently rejected the Inevitable Disclosure Doctrine (IDD) and

the corresponding adoption or rejection years are listed in Table 1 of

Klasa and et al. (2018).

42

IDD_Treated_Post

=∑𝑆𝑎𝑙𝑒𝑗,𝑖

𝑆𝑎𝑙𝑒𝑖× 𝐼𝐷𝐷_𝑃𝑜𝑠𝑡𝑗

𝑛𝑗=1 , where Salei is total sales for firm i, Salej,i

is sales from customer j to firm i, and n is the total number of major

customers for firm i. IDD_Postj is a dummy variable set to one if the

major customer j’s headquarter state is an IDD state and the time

period is within the three years after the adoption year or within three

years before the rejection year (subsequent to adoption). Otherwise, it

is equal to zero. IDD states and the corresponding adoption or rejection

years are listed in Klasa and et al. (2018).

Supplier_IDD_Treated_Pre =dummy variable that equals to one if the supplier’s headquarter state

is an IDD state and the year is during the three years after the rejection

year or the year is three years before the adoption year. Otherwise it is

equal to zero.

Supplier_IDD_Treated_Post =dummy variable that equals to one if the supplier’s headquarter state

is an IDD state and the year is during the three years after the adoption

year or three years before the rejection year. Otherwise it is equal to

zero

43

Appendix C:

Information redaction across different contract types when contract search are restricted

to exhibit 10.XXX

This table presents the Probit regression results for five different types of contracts that have redacted

disclosures using CustomerR&D, CrossCite, and CustomerSize as measures of customer-supplier relations

and Customer Trade Secret, Customer Nondisclosure indicating whether customers use trade secret or

nondisclosure agreement to protect value relevant information. The five redacted agreement categories are

1) Employment/Incentive, 2) Credit/Leasing, 3) Research and Development/License, 4) Purchase and Sale,

5) Investment and other. All contracts are restricted to those reported with exhibit number 10.XXX. All

variable definitions are detailed in Appendix B. Year and industry fixed effects are included across all

specifications. P-values based on firm-clustered robust standard errors are reported in parentheses below

the coefficients.

Panel A

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

CustomerR&D -0.005 0.031 0.056 0.054 0.009

(0.874) (0.149) (0.012) (0.010) (0.793)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 5,912 4,926 1,914 4,400 1,581

Pseudo R-squared 0.0739 0.128 0.207 0.117 0.137

Panel B

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

CrossCite 0.480 0.350 0.544 0.245 -0.519

(0.103) (0.143) (0.029) (0.274) (0.289)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 5,416 4,557 1,759 3,989 1,490

Pseudo R-squared 0.0791 0.128 0.186 0.101 0.147

Panel C

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

CustomerSize 0.014 0.018 0.044 0.060 -0.005

44

(0.547) (0.225) (0.009) (0.000) (0.845)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 5,912 4,926 1,914 4,400 1,581

Pseudo R-squared 0.0743 0.128 0.208 0.121 0.137

Panel D

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

Customer Trade Secret 0.175 0.029 0.484 0.758 0.192

(0.445) (0.860) (0.004) (0.000) (0.469)

Supplier Trade Secret Indicator 0.086 0.063 0.053 0.199 0.298

(0.438) (0.381) (0.568) (0.002) (0.029)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 5,912 4,926 1,914 4,400 1,581

Pseudo R-squared 0.0752 0.127 0.209 0.127 0.144

Panel E

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

Customer Nondisclosure -0.003 -0.031 0.259 0.617 0.276

(0.988) (0.855) (0.136) (0.000) (0.313)

Supplier Nondisclosure Indicator 0.206 0.267 0.210 0.306 0.463

(0.047) (0.000) (0.013) (0.000) (0.000)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 5,912 4,926 1,914 4,400 1,581

Pseudo R-squared 0.0782 0.133 0.208 0.127 0.155

45

Table 1 : Sample Selection and Distribution

This table panel A presents the sample-selection procedure. Panel B and panel C present sample distribution

across industries and years.

Panel A. Sample selection

Firms Observations

All firms on Compustat from 1996-2015 27,440 231,531

Less:

Observations with nonpositive assets, sales or equity (67,270)

Financial firms (SIC 6000-6999), utilities (SIC 4900-

4999), Public administration (SIC 9000-9999) (44,404)

Observations with insufficient data for computing the

control variables (51,216)

Observations without available data in Segment

Database (32,585)

Observations without identifiable major customers (23,652)

Total observations 2,897 12,404

Panel B. Industry distribution of suppliers in the sample

SIC Codes Industry # of Suppliers # of supplier-years Percent

0100-0999 Agriculture, Forestry and Fishing 13 59 0.48

1000-1499 Mining 241 956 7.71

1500-1799 Construction 26 114 0.92

2000-3999 Manufacturing 1786 8422 67.90

4000-4999 Transportation, Communications, and

Sanitary Service 137 502 4.05

5000-5999 Wholesale and Retail Trade 117 422 3.40

7000-8999 Services 577 1929 15.55

2,897 12,404 100%

46

Panel C: Sample Distribution by Year

Year No. of Observations Percent of Total Observations

1996 645 5.2

1997 665 5.36

1998 580 4.68

1999 550 4.43

2000 704 5.68

2001 693 5.59

2002 733 5.91

2003 674 5.43

2004 667 5.38

2005 680 5.48

2006 678 5.47

2007 687 5.54

2008 643 5.18

2009 633 5.1

2010 593 4.78

2011 569 4.59

2012 511 4.12

2013 512 4.13

2014 525 4.23

2015 462 3.72

Total: 12,404 100%

47

Table 2: Descriptive Statistics

This table provides the variable descriptive statistics over the sample period of 1996 through 2015. All variables are defined in Appendix B.

Panel A

Variable N Mean Std Dev 1st Quartile Median 3rd Quartile

Redaction 12404 0.298 0.457 0 0 1

CustomerSize 12404 3.153 2.241 1.469 2.390 4.165

CustomerR&D 12404 1.293 1.711 0 0.777 1.892

Crosscite 11417 0.039 0.122 0 0 0

Size 12404 5.584 1.917 4.182 5.411 6.882

Market-to-book 12404 2.155 2.467 1.126 1.539 2.353

ROA 12404 -0.045 0.297 -0.088 0.028 0.085

Debt_issue 12404 0.024 0.146 -0.015 0 0.009

Equity_issue 12404 0.155 0.692 -0.001 0.003 0.026

Leverage 12404 0.132 0.157 0.001 0.072 0.214

R&D 12404 0.094 0.152 0 0.028 0.127

Age 12404 2.631 0.729 2.079 2.639 3.135

Num_exhibits 12134 2.765 0.870 2.197 2.944 3.401

Total_similarity 12404 4.882 6.684 1.260 2.048 5.266

Customer Trade Secret 12404 0.118 0.195 0 0 0.170

Supplier Trade Secret Indicator 12404 0.531 0.499 0 1 1

Customer Nondisclosure 12404 0.100 0.179 0 0 0.150

Supplier Nondisclosure Indicator 12404 0.524 0.499 0 1 1

CustomerM&A 8403 0.027 0.029 0.010 0.018 0.035

IDD_Treated_Pre 7816 0.002 0.022 0 0 0

IDD_Treated_Post 7816 0.003 0.032 0 0 0

Supplier_IDD_Treated_Pre 12404 0.012 0.109 0 0 0

Supplier_IDD_Treated_Post 12404 0.017 0.130 0 0 0

Employment/Incentive 12404 0.009 0.095 0 0 0

48

Panel A (continued)

Variable N Mean Std Dev 1st Quartile Median 3rd Quartile

Credit/Leasing 12404 0.028 0.164 0 0 0

R&D/License 12404 0.077 0.267 0 0 0

P&S 12404 0.076 0.266 0 0 0

Investment&Other 12404 0.011 0.102 0 0 0

Panel B: Pearson \ Spearman correlations. Bold indicates statistical significance at the 5% level (or lower).

Redaction CustomerSize CustomerR&D Crosscite

Customer Trade

Secret

Customer

Nondisclosure

Redaction 1 0.16 0.16 0.08 0.17 0.12

CustomerSize 0.19 1 0.49 0.10 0.29 0.24

CustomerR&D 0.20 0.70 1 0.32 0.29 0.16

Crosscite 0.10 0.24 0.38 1 0.12 0.05

Customer Trade Secret 0.21 0.45 0.39 0.18 1 0.49

Customer Nondisclosure 0.16 0.40 0.28 0.10 0.60 1

Size 0.01 -0.09 -0.12 0.08 -0.03 -0.09

Market-to-book 0.09 0.06 0.10 0.02 0.06 0.06

ROA -0.14 -0.11 -0.18 -0.04 -0.06 -0.05

Debt_issue 0.01 0.01 -0.02 -0.01 -0.01 -0.02

Equity_issue 0.02 0.07 0.10 -0.03 0.01 0.03

Leverage -0.13 -0.06 -0.14 -0.05 -0.12 -0.09

R&D 0.25 0.19 0.34 0.14 0.16 0.13

Age -0.11 -0.10 -0.13 0.02 -0.01 -0.06

Num_Exhibits 0.16 0.05 -0.01 -0.01 0.15 0.08

Total similarity 0.28 0.31 0.37 0.09 0.24 0.23

49

Panel B (continued)

Size

Market-to-

book ROA Debt_issue Equity_issue Leverage R&D Age Num_Exhibits

Total

similarity

Redaction 0.01 0.15 -0.16 0.02 0.14 -0.14 0.29 -0.11 0.16 0.23

CustomerSize -0.07 0.04 -0.09 0.00 0.08 -0.05 0.10 -0.07 0.05 0.12

CustomerR&D -0.11 0.09 -0.16 -0.02 0.18 -0.15 0.34 -0.11 -0.03 0.24

Crosscite 0.15 0.04 -0.05 -0.01 0.00 -0.05 0.25 0.07 -0.01 0.11

Customer Trade Secret 0.01 0.08 -0.05 0.00 0.04 -0.12 0.19 0.02 0.18 0.11

Customer Nondisclosure -0.06 0.06 -0.05 -0.01 0.07 -0.11 0.13 -0.03 0.08 0.12

Size 1 -0.03 0.28 0.12 -0.29 0.29 -0.19 0.30 0.42 0.05

Market-to-book -0.10 1 0.21 0.02 0.32 -0.44 0.40 -0.14 0.02 0.26

ROA 0.26 -0.15 1 0.00 -0.21 -0.08 -0.25 0.19 0.06 -0.16

Debt_issue 0.10 -0.01 -0.06 1 -0.05 0.07 -0.01 0.01 0.09 0.07

Equity_issue -0.14 0.34 -0.47 0.00 1 -0.19 0.30 -0.37 -0.16 0.26

Leverage 0.22 -0.28 0.04 0.21 -0.14 1 -0.46 0.11 0.05 -0.21

R&D -0.23 0.38 -0.53 0.00 0.49 -0.33 1 -0.17 -0.04 0.41

Age 0.33 -0.17 0.22 -0.05 -0.28 0.07 -0.26 1 0.32 -0.23

Num_Exhibits 0.39 -0.07 0.11 0.04 -0.16 0.00 -0.09 0.30 1 -0.01

Total similarity 0.00 0.21 -0.23 0.04 0.16 -0.13 0.48 -0.18 0.06 1

50

Table 3: Customer-supplier relationship characteristics and suppliers’ likelihood of

redaction

This table presents the Probit regression results testing the relation between customer-supplier

characteristics (through CustomerR&D, Crosscite, or CustomerSize) and likelihood of supplier redaction

(Redaction). Coefficient estimates are reported to the left, while p-values, based on firm-clustered robust

standard errors, are reported to the right of each variable. All variables are defined in Appendix B.

Note the number of observations for column (2) is different because patent data is only available up to 2010

and we define Crosscite as an indicator whether supplier (customer) cited its customer’s (supplier’s) patents

within the past three years.

(1) (2) (3)

Variables Redaction Redaction Redaction

coeff. p-value coeff. p-value coeff. p-value

CustomerR&D 0.071 0.000

Crosscite 0.711 0.000

CustomerSize 0.059 0.000

Size 0.047 0.002 0.029 0.064 0.051 0.001

Market-to-book -0.000 0.955 -0.002 0.806 -0.001 0.877

ROA -0.252 0.000 -0.244 0.001 -0.262 0.000

Debt issue 0.024 0.818 0.024 0.822 0.008 0.936

Equity issue -0.093 0.001 -0.079 0.004 -0.099 0.000

Leverage -0.364 0.025 -0.317 0.058 -0.364 0.024

R&D 1.444 0.000 1.341 0.000 1.515 0.000

Age -0.384 0.000 -0.406 0.000 -0.380 0.000

Num_Exhibits 0.509 0.000 0.521 0.000 0.508 0.000

Total similarity 0.015 0.000 0.033 0.000 0.016 0.000

Constant -1.483 0.000 -1.490 0.000 -1.650 0.000

Industry & Year Fixed Effects Yes Yes Yes

Firm Cluster Yes Yes Yes

Observations 12,404 11,417 12,404

Pseudo R-squared 0.199 0.199 0.201

51

Table 4: Customers’ trade secrets and suppliers’ likelihood of redaction

This table presents the Probit regression results examining the association between the presence of

customers’ trade secrets (Customer Trade Secret) and the propensity that a supplier redacts its disclosures

(Redaction). Coefficient estimates are reported to the left, while p-values, based on firm-clustered robust

standard errors, are reported to the right of each variable. All variables are defined in Appendix B.

(1) (2)

Variables Redaction Redaction

coeff. p-value coeff. p-value

Customer Trade Secret 0.570 0.000 0.546 0.000

Supplier Trade Secret Indicator 0.310 0.000

Size 0.044 0.003 0.043 0.004

Market-to-book -0.002 0.774 -0.002 0.781

ROA -0.258 0.000 -0.225 0.002

Debt issue 0.006 0.957 -0.003 0.980

Equity issue -0.097 0.001 -0.057 0.038

Leverage -0.361 0.025 -0.315 0.051

R&D 1.556 0.000 1.459 0.000

Age -0.388 0.000 -0.376 0.000

Num_Exhibits 0.507 0.000 0.490 0.000

Total similarity 0.017 0.000 0.016 0.000

Constant -1.415 0.001 -1.358 0.001

Industry & Year Fixed Effects Yes Yes

Firm Cluster Yes Yes

Observations 12,404 12,404

Pseudo R-squared 0.199 0.206

52

Table 5: Customers’ nondisclosure agreement and suppliers’ likelihood of redaction

This table presents the Probit regression results examining the association between customer’s effort to

protect value-relevant information, proxied by nondisclosure/confidentiality agreement stated in a

customer’s 10-K/10-Q (Customer Nondisclosure), and the propensity for a supplier to redact its disclosures

(Redaction). Coefficient estimates are reported to the left, while p-values, based on firm-clustered robust

standard errors, are reported to the right of each variable. All variables are defined in Appendix B.

(1) (2)

Variables Redaction Redaction

coeff. p-value coeff. p-value

Customer Nondisclosure 0.447 0.000 0.453 0.000

Supplier Nondisclosure Indicator 0.399 0.000

Size 0.046 0.002 0.043 0.004

Market-to-book -0.002 0.775 -0.001 0.892

ROA -0.261 0.000 -0.211 0.003

Debt issue 0.014 0.890 -0.010 0.924

Equity issue -0.099 0.000 -0.070 0.011

Leverage -0.365 0.024 -0.306 0.057

R&D 1.562 0.000 1.480 0.000

Age -0.387 0.000 -0.367 0.000

Num_Exhibits 0.505 0.000 0.485 0.000

Total similarity 0.018 0.000 0.016 0.000

Constant -1.433 0.000 -1.445 0.000

Industry & Year Fixed Effects Yes Yes

Firm Cluster Yes Yes

Observations 12,404 12,404

Pseudo R-squared 0.197 0.210

53

Table 6: For suppliers with only one principal customer

This table presents the Probit regression results testing the relation between customer-supplier

characteristics (through CustomerR&D, Crosscite, or CustomerSize) and likelihood of supplier redaction

(Redaction) among suppliers with only one principal customer in Columns (1)-(3). Columns (4)-(5) present

the results testing the effect of the presence of customer trade secrets (Customer Trade Secret) and

customer’s effort to protect value-relevant information, proxied by nondisclosure/confidentiality agreement

(Customer Nondisclosure) on likelihood of supplier redaction (Redaction). P-values, in parenthesis, are

based on firm-clustered robust standard errors. All variables are defined in Appendix B.

(1) (2) (3) (4) (5)

VARIABLES Redaction Redaction Redaction Redaction Redaction

CustomerR&D 0.096

(0.000) Crosscite 0.887

(0.000) CustomerSize 0.062

(0.000) Customer Trade Secret 0.439

(0.005) Customer Nondisclosure 0.348

(0.028)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 4,158 3,904 4,158 4,158 4,158

Pseudo R-squared 0.214 0.213 0.213 0.214 0.219

54

Table 7: Results for excluding redactions of price information

This table presents the Probit regression results examining the relation between customer-supplier

characteristics (through CustomerR&D, Crosscite, or CustomerSize) and likelihood of supplier redaction

(Redaction) excluding redactions of price information. In other words, redacting of pricing information is

not counted as redactions. We present the effect of the presence of customer trade secrets (Customer Trade

Secret) or customer’s nondisclosure/confidentiality agreement (Customer Nondisclosure) on likelihood of

supplier redaction (Redaction) excluding redactions of price information in columns (4)-(5). To exclude

pricing information redaction, specifically, for a firm-year if redaction is related to pricing information,

then we redefine redaction indicator as zero. We identify pricing information redaction by searching

exhibits attached to 10-K/10-Q filings based on the key words of “$” followed up to three spaces with

variants of [*]. The P-values, in parenthesis, are based on firm-clustered robust standard errors. All

variables are defined in Appendix B.

(1) (2) (3) (4) (5)

VARIABLES Redaction Redaction Redaction Redaction Redaction

CustomerR&D 0.052

(0.000)

Crosscite 0.623

(0.000)

CustomerSize 0.038

(0.000)

Customer Trade Secret 0.338

(0.000) Customer Nondisclosure 0.297

(0.003)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 12,404 11,417 12,404 12,404 12,404

Pseudo R-squared 0.127 0.125 0.128 0.133 0.135

55

Table 8: Information redaction across different contract types

This table presents the Probit regression results for five different types of contracts that have redacted

disclosures using CustomerR&D, CrossCite and CustomerSize as measures of customer-supplier

relationship characteristics in Panel A to Panel C. Panel D presents results for the presence of customer

trade secrets (Customer Trade Secret). Panel E presents results for the presence of customer nondisclosure

agreements (Customer Nondisclosure). The five redacted agreement categories are 1)

Employment/Incentive, 2) Credit/Leasing, 3) Research and Development/License, 4) Purchase and Sale, 5)

Investment and other. All variable definitions are detailed in Appendix B. In all specifications, we control

for year and industry fixed effects. P-values based on firm-clustered robust standard errors are reported in

parentheses below the coefficients.

Panel A: Detailed contract results for CustomerR&D

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

Variables (1) (2) (3) (4) (5)

CustomerR&D -0.006 0.025 0.056 0.058 0.001

(0.836) (0.236) (0.008) (0.004) (0.965)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 6,189 5,509 2,399 5,000 2,664

Pseudo R-squared 0.076 0.122 0.254 0.117 0.166

Panel B: Detailed contract results for CrossCite

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

Variables (1) (2) (3) (4) (5)

CrossCite 0.509 0.305 0.529 0.322 -0.094

(0.077) (0.189) (0.022) (0.134) (0.803)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 5,689 5,109 2,193 4,558 2,548

Pseudo R-squared 0.080 0.124 0.234 0.106 0.170

56

Panel C: Detailed contract results for CustomerSize

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

Variables (1) (2) (3) (4) (5)

CustomerSize 0.012 0.020 0.046 0.059 0.010

(0.601) (0.172) (0.003) (0.000) (0.643)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 6,189 5,509 2,399 5,000 2,664

Pseudo R-squared 0.076 0.122 0.255 0.121 0.166

Panel D: Detailed contract results for Customer Trade Secret

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

Customer Trade Secret 0.201 0.089 0.517 0.750 0.246

(0.362) (0.569) (0.001) (0.000) (0.269)

Supplier Trade Secret Indicator 0.101 0.101 0.016 0.166 0.219

(0.359) (0.139) (0.853) (0.006) (0.046)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 6,189 5,509 2,399 5,000 2,664

Pseudo R-squared 0.077 0.123 0.256 0.126 0.171

Panel E: Detailed contract results for Customer Nondisclosure

Employment/

Incentive

Credit/

Leasing

R&D/

License P&S

Investment&

Other

VARIABLES (1) (2) (3) (4) (5)

Customer Nondisclosure -0.024 0.043 0.337 0.593 0.349

(0.914) (0.789) (0.045) (0.000) (0.131)

Supplier Nondisclosure Indicator 0.221 0.250 0.224 0.282 0.499

(0.033) (0.000) (0.005) (0.000) (0.000)

Controls Yes Yes Yes Yes Yes

Industry & Year Fixed Effects Yes Yes Yes Yes Yes

Firm Cluster Yes Yes Yes Yes Yes

Observations 6,189 5,509 2,399 5,000 2,664

Pseudo R-squared 0.080 0.127 0.256 0.126 0.187

57

Table 9: Major customers subject to the Inevitable Disclosure Doctrine and suppliers’

likelihood of redactions

This table presents the Probit regression results examining how major customers subject to the Inevitable

Disclosure Doctrine (IDD) influences a supplier’s propensity to redact its disclosures (Redaction).

IDD_Treated_Post is a sales-weighted sum of indicators across all major customers that is equal to one if

the major customer’s headquarter state is an IDD state and the year is during the three years after the

adoption year or three years before the rejection year (for states that enacted IDD and subsequently rejected

it). Otherwise it is equal to zero. IDD_Treated_Pre is a sales-weighted sum of indicators across all major

customers that is equal to one if the major customer’s headquarter state is an IDD state and the year is

during the three years after the rejection year or three years before the adoption year. Otherwise it is equal

to zero. Coefficient estimates are reported to the left, while p-values, based on firm-clustered robust

standard errors, are reported to the right of each variable. All variables are defined in Appendix B.

(1)

Variables Redaction

coeff. p-value

IDD_Treated_Post 0.736 0.005

IDD_Treated_Pre 0.441 0.207

Supplier_IDD_Treated_Post 0.060 0.638

Supplier_IDD_Treated_Pre -0.341 0.020

Size 0.069 0.001

Market-to-book -0.004 0.771

ROA -0.343 0.004

Debt issue -0.075 0.601

Equity issue -0.135 0.036

Leverage -0.445 0.030

R&D 1.818 0.000

Age -0.428 0.000

Num_Exhibits 0.558 0.000

Total similarity 0.014 0.014

Constant 5.830 0.000

Industry & Year Fixed Effects Yes

Supplier State Fixed Effect Yes

Firm Cluster Yes

Observations 7,048

Pseudo R-squared 0.223

58

Table 10: Instrument Variable Estimation

This table presents the results of the instrumental variables (IV) estimation examining the role of major

customer relationship characteristics (through CustomerR&D, Crosscite, and CustomerSize) on a supplier’s

propensity to redact its disclosures (Redaction) in Panel A to Panel C. Panel A presents the results for

CustomerR&D. Panel B presents the results for CrossCite. Panel C presents the results for CustomerSize.

The table presents the results of the instrumental variables (IV) estimation for the presence of customers’

trade secrets (Customer Trade Secret) and customers’ nondisclosure agreement (Customer Nondisclosure)

in Panel D and Panel E. Columns (1) and (2) present the first stage and second stage results, respectively.

CustomerM&A is a sales-weighted measure of the average acquisition activity across all major customer

industries. Coefficient estimates are reported to the left, while p-values, based on firm-clustered robust

standard errors, are reported to the right of each variable. All variables are defined in Appendix B.

Panel A: Instrumental variable results for CustomerR&D

First-Stage Second-Stage

(1) (2)

Variables CustomerR&D Redaction

coeff. p-value coeff. p-value

CustomerM&A 13.827 0.000 CustomerR&D 0.047 0.011

First-Stage F-test 29.41 0.000

Kleibergen_Paap LM Stat 112.87 0.000 Controls Yes Yes

Industry Fixed Effects Yes Yes

Year Fixed Effects Yes Yes

Observations 8,403 8,403

Panel B: Instrumental variable results for CrossCite

First-Stage Second-Stage

(1) (2)

Variables CrossCite Redaction

coeff. p-value coeff. p-value

CustomerM&A 0.638 0.000 CrossCite 1.252 0.004

First-Stage F-test 18.82 0.000

Kleibergen_Paap LM Stat 31.58 0.000 Controls Yes Yes

Industry Fixed Effects Yes Yes

Year Fixed Effects Yes Yes

Observations 7,792 7,792

59

Panel C: Instrumental variable results for CustomerSize

First-Stage Second-Stage

(1) (2)

Variables CustomerSize Redaction

coeff. p-value coeff. p-value

CustomerM&A 38.751 0.000 CustomerSize 0.017 0.013

First-Stage F-test 74.94 0.000

Kleibergen_Paap LM Stat 234.07 0.000 Controls Yes Yes

Industry Fixed Effects Yes Yes

Year Fixed Effects Yes Yes

Observations 8,403 8,403

Panel D: Instrumental variable results for Customer Trade Secret

First-Stage Second-Stage

(1) (2)

Variables Customer Trade Secret Redaction

coeff. p-value coeff. p-value

CustomerM&A 1.499 0.000 Customer Trade Secret 0.417 0.019

First-Stage F-test 57.27 0.000

Kleibergen_Paap LM Stat 99.32 0.000 Controls Yes Yes

Industry Fixed Effects Yes Yes

Year Fixed Effects Yes Yes

Observations 8,403 8,403

Panel E: Instrumental variable results for Customer Nondisclosure

First-Stage Second-Stage

(1) (2)

Variables Customer Nondisclosure Redaction

coeff. p-value coeff. p-value

CustomerM&A 1.222 0.000 Customer Nondisclosure 0.497 0.015

First-Stage F-test 26.88 0.000

Kleibergen_Paap LM Stat 85.99 0.000 Controls Yes Yes

Industry Fixed Effects Yes Yes

Year Fixed Effects Yes Yes

Observations 8,403 8,403