The Effect of Financial Reporting on Strategic Investments:

Evidence from Purchase Obligations By

Suzie Noh

B.A. Economics & Mathematics Emory University, 2013

Master of Finance MIT Sloan School of Management, 2014

Master of Science in Management Research MIT Sloan School of Management, 2018

SUBMITTED TO THE SLOAN SCHOOL OF MANAGEMENT IN PARTIAL

FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY IN MANAGEMENT

at the

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

MAY 2020

©2020 Massachusetts Institute of Technology. All rights reserved.

Signature of Author:__________________________________________________________

Department of Management April 25, 2020

Certified by: ________________________________________________________________

Eric So Sarofim Family Career Development Professor

Thesis Supervisor

Certified by: ________________________________________________________________ Rodrigo Verdi

Nanyang Technological University Professor Thesis Supervisor

Accepted by: _______________________________________________________________

Catherine Tucker Sloan Distinguished Professor of Management

Professor, Marketing Faculty Chair, MIT Sloan PhD Program

The Effect of Financial Reporting on Strategic Investments: Evidence from Purchase Obligations∗

by Suzie Noh

Submitted to the Sloan School of Management on April 25, 2020 in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Management

Abstract

I examine whether mandating the disclosure of investments influences firms’ strategic interactions. I exploit an SEC regulation requiring firms to report off-balance sheet purchase obligations, such as commitments to inventory purchases, CAPEX, R&D, and advertising. Motivated by theory on strategic investments, I predict and find that firms respond to the regulation by increasing investments if they have substitutive product market strategies with competitors, and decreasing investments if they have complementary strategies. This two-way finding is consistent with firms using investments to influence competitors’ behavior in ways that increase their own profits. I show that changes in investments are concentrated among firms with large market share, which have a greater ability to influence competitors’ actions, and that they have real effects on firms’ sales and profit margins. Collectively, my results illustrate a novel channel through which financial reporting shapes firms’ investments and competition. Thesis Supervisor: Eric So Title: Sarofim Family Career Development Professor Thesis Supervisor: Rodrigo Verdi Title: Nanyang Technological University Professor

∗ I am sincerely grateful to Eric So (co-chair), Rodrigo Verdi (co-chair), and Joe Weber (committee member) for their helpful feedback and insights in developing this idea. I thank Inna Abramova, Matt Bloomfield, Matthias Breuer, Ki-Soon Choi, Jinhwan Kim, Kwang J. Lee, Rebecca Lester, Gabriel Pundrich, Steve Stubben, Dan Taylor, and Rachel Yoon for providing helpful comments and suggestions. I also thank seminar participants at MIT, New York University, Stanford University, London Business School, University of Chicago, University of Pennsylvania, Columbia University, Yale University, University of Michigan, University of Colorado Boulder, and Harvard Business School. I gratefully acknowledge generous financial support from the Deloitte Foundation. All errors are my own. The internet appendix can be found at: http://bit.ly/Noh2020Appendix. Email: snoh3@mit.edu.

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1. Introduction

I examine how mandating the disclosure of investments shapes firms’ strategic interactions.

Specifically, I study the effects of a regulation requiring disclosures of off-balance sheet

investments on firms’ strategic investments. My study is motivated by the idea that investments

are more likely to affect competitors’ behavior when they are observable, and thus that mandated

reporting increases the extent to which firms strategically change their investments to affect

competitors’ behavior. My findings provide support for this idea and, in doing so, suggest that

increased disclosures about investments have real effects on competitive dynamics.

Following the literature on strategic investments, I define investments to be activities that are

(partially) irreversible and time-bound. The first implies that they cannot be cancelled without

incurring some losses or costs, and the latter implies that they need to be executed in a timely

manner before production or sales. Activities with such characteristics signal credible

commitments to future strategies (e.g., von Stackelberg 1934). Therefore, consistent with prior

literature, I consider a wide range of activities including inventory purchases, CAPEX, R&D,

advertising, marketing, etc. (Ellison and Ellison 2011; Bloomfield and Tuijin 2019).

My study exploits a regulation (hereafter “the regulation”) implemented by the U.S. Securities

and Exchange Commission (SEC) in 2003 that requires firms to disclose in their 10-Ks off-balance

sheet purchase obligations. These are minimum or non-cancellable future expenditures, such as

payment obligations for inventory purchases, CAPEX, R&D, and advertising. This regulation is

well suited for my study, because purchase obligations are irreversible and timely, which makes

them effective at signaling commitments to future product market strategies.

Intuitively, the specific investment strategy that firms choose likely depends on how they

interact with other firms. Accordingly, my predictions for how firms respond to the 2003

regulation depend on their mode of competition. To guide my predictions, I rely on classical theory

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on strategic investments developed in the industrial organization literature, which classifies

competition into two types: competition with strategic substitutes and competition with strategic

complements (Fudenberg and Tirole 1984; Bulow et al. 1985).1

Firms are considered to be in competition with strategic substitutes when more aggressive

strategies, such as increasing quantity, induce competitors to adopt less aggressive strategies by

reducing competitors’ marginal profits. For example, suppose Coca-Cola signals that it intends to

flood the market with soft drinks by making large investments in distribution centers. Coca-Cola

would be classified as having strategic substitutes if these investments induced smaller competitors,

such as Shasta, to reduce the quantity of production in anticipation of a reduction in the prices

consumers are willing to pay for their products. Thus, in competition with strategic substitutes,

firms’ choices have negative correlations.

Firms are considered to be in competition with strategic complements when more aggressive

strategies, such as lowering price or increasing quality, induce competitors to similarly adopt more

aggressive strategies by increasing competitors’ marginal profits. For example, suppose Boeing

signals that it intends to increase the energy efficiency of its aircraft. Boeing would be classified

as having strategic complements if these investments induced smaller competitors, such as General

Dynamics, to also improve the quality of their aircraft to avoid losing market share. Hence, in

competition with strategic complements, firms’ choices have positive correlations.2

I develop a two-way prediction that, after the SEC regulation, firms in competition with

strategic substitutes increase investments, and those in competition with strategic complements

reduce investments. In the examples above, Coca-Cola increases investments in distribution

1 See Appendix C for detailed discussions on competition with strategic substitutes and strategic complements. 2 Competition with strategic substitutes is commonly referred to as Cournot competition, and competition with strategic complements is commonly referred to as Bertrand competition. This categorization holds true under general conditions, such as when demand is linear and marginal cost is constant (e.g., Bulow et al. 1985).

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centers, and Boeing reduces investments in energy efficiency. The intuition is that firms desire to

induce less aggressive strategies from competitors, as this helps increase their own profits, and

greater observability of investments increases firms’ ability to use investments as a signal to induce

desired responses from competitors. To induce less aggressive strategies from competitors, firms

with strategic substitutes signal commitments to more aggressive strategies, whereas firms with

strategic complements signal commitments to less aggressive strategies.

The predictions from classical models of strategic investments center on firms with a first-

mover advantage (Fudenberg and Tirole 1984; Bulow et al. 1985). Accordingly, my predictions

center on dominant firms (i.e., those with large market share), which have the capacity to exert a

significant influence on the quantity and price of products in the industry, and hence on the

subsequent actions of other firms (e.g., Gisser 1984, 1986; Lieberman and Montgomery 1988).

To test my predictions, I employ difference-in-differences tests around the regulation on

dominant firms. I examine whether dominant firms with a greater increase in investment

observability (i.e., a greater degree of “treatment”) change investments by a greater amount. To

estimate the degree of “treatment”, I count redacted investment contracts as manifested in 10-K/Q

and 8-K exhibits before the regulation.3 Because the regulation increases disclosure of contractual

investments, firms that redact more investment contracts in the pre-period likely experience a

greater increase in observability of their investments.4 To partition firms into different competition

types, I use a measure developed by Kedia (2006).

3 To count redacted contracts related to investments, I conduct a textual analysis similar to those of Verrecchia and Weber (2006), Boone et al. (2016), Glaeser (2018), and Bourveau et al. (2019). See Section 4.1 for details. 4 I use the unscaled number of redacted contracts, not the ratio of redacted contracts over all investment contracts. This is because the extent of firms’ use of contractual investments has large across-firm variation. Using the ratio imposes an assumption that firms equally rely on contractual investments. I also validate using the unscaled number in Table 7, where I show firms with more unscaled investment contracts are more likely to have a greater amount of purchase obligations.

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My tests examine changes in firms’ investments recognized in financial statements from the

pre- to post-regulation period, because firms’ off-balance sheet purchase obligations are not

observable prior to the regulation. Thus, an assumption of my empirical design is that off-balance

sheet purchase obligations—such as inventory purchases, CAPEX, R&D, and advertising

expenses—are soon reflected in financial statements under corresponding items. This assumption

seems reasonable given that purchase obligations reflect non-cancellable amounts of payments.

Consistent with my predictions, I find that dominant firms with strategic substitutes are more

likely to increase their investments if the 2003 regulation makes their investments more observable.

In terms of economic magnitudes, a one-standard-deviation increase in the exposure to the

regulation leads to an approximately 5% increase in investments for an average firm. In contrast,

dominant firms with strategic complements display a change in investments of similar economic

magnitude, but of opposite sign. Specifically, they are more likely to decrease their investments if

the 2003 regulation makes their investments more observable.

I also document that dominant firms with strategic substitutes primarily increase investments

in capacity (e.g., inventory purchases and CAPEX), whereas those with strategic complements

primarily reduce investments in product differentiation (e.g., R&D and advertising). These

findings are consistent with firms using differing levers depending on their mode of competition

(e.g., Kreps and Scheinkman 1983; Singh and Vives 1984). Specifically, my finding that firms

with strategic substitutes increase investments in capacity is consistent with these firms being more

likely to compete in quantity, and aligns with the example above of Coca-Cola. My finding that

firms with strategic complements reduce investments in product differentiation is consistent with

these firms being more likely to compete in quality, and aligns with the example above of Boeing.

To sharpen my main inferences, I show that the divergence in investments in each type of

competition only emerges after the 2003 regulation, consistent with firms displaying parallel trends

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prior to the regulation. I also run falsification tests showing that firms do not appear to change

expenditures on acquisitions or operating leases, which are informative about future strategies but

whose disclosures are not affected by the regulation.

To understand the consequences of the strategic actions taken by dominant firms, I also study

the behavior of non-dominant firms. I find that they decrease investments after the 2003 regulation

across both types of competition. Continuing with the above examples, the decrease in investments

by non-dominant firms with strategic substitutes is consistent with Shasta rationally reducing its

investments in production when Coca-Cola’s increased investments signal an intention to flood

the market. In contrast, the decrease in investments by non-dominant firms with strategic

complements is consistent with General Dynamics optimally engaging in less aggressive

investments in technology when Boeing’s reduced investments signal reduced commitments to

improving energy efficiency.

I corroborate my findings by analyzing the costs of goods sold (COGS)—which increase with

the quantities sold—in each type of competition. I confirm that firms’ investments foretell their

aggressiveness, proxied by COGS (i.e., investments are not “cheap talk”). Furthermore, to help

substantiate the effect of dominant firms’ strategic investments on competition, I show that, after

the 2003 regulation, dominant firms with strategic substitutes increase sales by capturing larger

market share, and dominant firms with strategic complements increase profit margins through less

intense competition. These findings suggest that dominant firms’ strategic investments have real

effects on product market outcomes.

In the final section of this paper, I perform a series of tests to validate my methodology and

confirm the robustness of my main findings. First, I show firms with more investment contracts—

redacted or non-redacted—before the regulation are more likely to report greater amounts of

purchase obligations after the regulation. This positive relation supports the assumption for my ex

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ante “treatment” measure that firms with more redacted investment contracts experience a greater

increase in their disclosure of contractual investments. Second, I verify that amounts of off-balance

sheet purchase obligations positively predict amounts of investments subsequently reported. This

suggests that purchase obligations are soon reflected in investments, and thus validates my tests

examining changes in investments from the pre- to post-regulation period. Lastly, I confirm that

my results are robust to using alternative proxies for competition type used by Bloomfield (2019),

using a dichotomous “treatment” variable, and using a shorter period excluding the dot-com bubble.

Readers may ask why dominant firms did not engage in strategic investments prior to the

regulation by voluntarily disclosing their future strategies. Disclosing future strategies may be

considered as anti-competitive practices and it increases the risk of antitrust investigations

(Antitrust Guidelines for Collaborations Among Competitors April 2000; Steuer et al. 2011;

Bourveau et al. 2019). The SEC regulation likely provided firms with legitimate channels to

increase their disclosure about future strategies. Therefore, my findings add to growing evidence

on potential conflicts between antitrust and securities regulations (e.g., Bourveau et al. 2019).

Also, while I interpret my results in light of the theory on strategic investments, readers may

be concerned that they are driven by alternative channels through which financial reporting affects

investments, such as an increase in proprietary costs (e.g., Verrecchia 1983; Ali et al. 2014). The

theory I rely on yields a two-way prediction for dominant firms’ investments that specifically

hinges on their competition type. Although alternative explanations can account for some aspects

of my findings, I am not aware of any theory that would explain opposite changes in investments

for firms with substitutive versus complementary strategies, and the concentration of such changes

among dominant firms. Therefore, a plausible alternative story would need to be quite complex.

Nonetheless, my findings are subject to an important caveat that they may reflect changes in

investments net of the effects of the regulation through these alternative channels.

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The central contribution of my study is to show that financial reporting allows firms to make

strategic gains through investments. A growing literature in accounting investigates the

relationship between firms’ strategic disclosures and the dynamics of competition.5 This paper

expands this literature and examines how mandatory disclosures affect firms’ strategic real

decisions. One of the few papers that study this relationship is by Bloomfield (2019), who finds

that large firms with complementary strategies adopt revenue-based CEO pay packages to commit

to aggressive behavior after a mandatory increase in executive pay disclosures. I provide related

evidence on a different commitment mechanism under which financial reporting affects

competition: strategic investments.

This study also contributes to the investment literature by examining a type of investment

disclosure overlooked in the literature: off-balance sheet purchase obligations. Purchase

obligations reflect wide-ranging future strategies, as they include future expenditures not only for

CAPEX and R&D but also for inventory purchases and advertising. This paper highlights the

economic significance of purchase obligations and their strategic uses.

Finally, my findings have important implications for regulators. The primary objective of the

regulation was to provide investors with information about firms’ obligations from off-balance

sheet arrangements. Although they do not speak to the net effect of the regulation, my finding that

firms use the strategic effect of disclosures about their investments to their advantage sheds light

on a potential unintended effect of the regulation and an unexplored role of financial reporting.

The remainder of the paper proceeds as follows: Section 2 discusses the regulatory

background of purchase obligation disclosures. Section 3 develops hypotheses, and Section 4

describes my sample and data. Section 5 reports my empirical results, and Section 6 concludes.

5 e.g., Bernard (2016); Aobdia and Cheng (2018); Bloomfield and Tuijin (2019); Bourveau et al. (2019); Glaeser and Landsman (2019); Kepler (2019); Kim et al. (2019).

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2. Setting: Disclosure of Purchase Obligations

In response to the Sarbanes-Oxley Act of 2002, the SEC adopted in April 2003 amendments

to the Securities Exchange Act of 1934 to require audited disclosure of off-balance sheet

arrangements. The regulation was implemented to primarily provide investors with contextual

information to assess firms’ short- and long-term liquidity and capital resource needs and demands,

after the failures of giant firms such as Enron and Winstar following their accounting scandals.6

This regulation has the following two components, which are enforced sequentially over a six-

month period.

The first is that it requires SEC-registered firms, except for small business owners, to provide

an explanation of their contractual off-balance sheet arrangements in the Management's Discussion

and Analysis (MD&A) section of their 10-Ks for their fiscal years ending on or after June 15,

2003.7, 8 Firms need to disclose the material facts and circumstances that provide investors with a

clear understanding of firms’ off-balance sheet arrangements and their material effects on changes

in financial condition, revenues and expenses, results of operations, liquidity, capital expenditures,

and capital resources.

A second key feature of the regulation is requiring a detailed tabular disclosure of contractual

obligations in the MD&A section of 10-Ks for the fiscal years ending on or after December 15,

2003. Firms need to provide, in a single location in the MD&A section, tabular information about

future payments by specified category of contractual obligations (i.e., long-term debt obligations,

6 The complete text of this regulation “SEC Final Rule: Disclosure in Management’s Discussion and Analysis about Off-Balance Sheet Arrangements and Aggregate Contractual Obligations” (Release No. 33-8182) is available at https://www.sec.gov/rules/final/33-8182.htm 7 “Small business issuer” is defined as any entity that (1) has revenues of less than $25,000,000; (2) is a U.S. or Canadian issuer; (3) is not an investment company; and (4) if a majority-owned subsidiary, has a parent corporation that also is a small business issuer. An entity is not a small business issuer, however, if it has a public float (the aggregate market value of the outstanding equity securities held by non-affiliates) of $25,000,000 or more. 8 The regulation also requires these explanations in 10-Qs if there exist material changes outside their ordinary course of business. Therefore, most firms are expected to not report updates on their off-balance sheet arrangements or simply include a reference to their latest 10-Ks. My own examination of numerous 10-Qs is consistent with this.

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capital lease arrangements, operating lease arrangements, purchase obligations, and other long-

term liabilities reflected on the balance sheet) and by due date (e.g., less than one year, one to three

years, three to five years, and more than five years). The SEC provides a format that a firm’s table

should substantially conform to, which is shown in Appendix B. Appendix B also contains actual

tabular disclosures made by a few sample firms after the regulation.9

Before this regulation, firms were already required to aggregate and disclose their contractual

payment obligations for debt and for capital or operating leases (see FASB SFAS No. 13,

Accounting for Leases (Nov. 1976); SFAS No. 47, Disclosure of Long-Term Obligations (March

1981)). This regulation additionally requires disclosures of purchase obligations as of the latest

fiscal year-end date. Purchase obligations are defined as agreements to purchase goods or services

that are enforceable and legally binding on the firm. These unconditionally binding definitive

agreements, subject only to customary closing conditions, specify all significant terms, including:

fixed or minimum quantities to be purchased; fixed, minimum or variable price provisions; and

the approximate timing of the transaction.10 They include a broad range of arrangements, including

inventory purchases, CAPEX, R&D, royalty/licensing, advertising/marketing, and strategic

alliances.

This forward-looking information related to firms’ inputs for production and sales is not

available in firms' financial statements, because executory contracts—where both parties to the

contract have not yet performed their duties—are not recorded on firms’ balance sheets. Moreover,

9 The regulation allows a firm “to disaggregate the specified categories by using other categories suitable to its business, but the table must include all of the obligations that fall within specified categories. In addition, the table should be accompanied by footnotes necessary to describe material contractual provisions or other material information to the extent necessary for an understanding of the timing and amount of the contractual obligations in the table.” 10 If the purchase obligations are subject to variable price provisions, then the firm must provide estimates of the payments due and include footnotes about payments that are subject to market risk. In addition, the footnotes should discuss any material termination or renewal provisions to the extent necessary for an understanding of the timing and amount of the firm’s payments under its purchase obligations.

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prior to the regulation, if firms requested confidential treatment of their material contracts and

redacted their contracts, this forward-looking information was not available to outsiders.

Although they are off-balance sheet expenses, the amounts of purchase obligations reported

are economically significant. Using disclosures of purchase obligations made in fiscal year 2007

and applying an annual discount rate of 5%, Lee (2010) reports that the average value is 9.3% of

total assets for non-financial firms including those not reporting purchase obligations. Furthermore,

he shows that disclosures of purchase obligations after the regulation provide useful information

to investors, because growth in purchase obligations is associated with higher future sales and

earnings. In my online appendix, I document a greater reduction in analysts’ dispersion for firms

with a greater exposure to the regulation (i.e., firms that redacted more contracts before the

regulation), which suggests that the regulation increased the information set of outsiders about

firms’ future operations.

Firms typically need to enter into purchase obligations well in advance, in time for future

production and/or sales, and these purchase obligations reflect minimum or legally binding (e.g.,

non-cancellable) amounts that are audited. Therefore, disclosures of purchase obligations are likely

to credibly and effectively signal a commitment to future product market strategies. I use this

regulation to study strategic changes in firms’ investments, because it increases the information

about firms’ future strategies that is observable to competitors.

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3. Hypotheses

Due to their irreversible and time-bound nature, investments signal credible commitments to

future strategies that subsequently alter competitors’ decisions (von Stackelberg 1934; Schelling

1960; Spence 1977; Dixit 1980). For example, increased purchases of inventory or R&D signal

more aggressive future strategies (e.g., greater quantity, lower price, or higher quality), and the

opposite signal less aggressive strategies. These signals affect competitors’ decisions on future

strategies, because competitors deem those as credible commitments. Therefore, in a dynamic

setting, investments not only have an internal profit-increasing value, but also have an important

strategic value.

My first set of hypotheses examines whether reporting of purchase obligations increases the

extent to which firms strategically change their investments to affect competitors’ behavior. When

more information about firms’ investments becomes observable to their competitors, firms will

adjust their investments to exploit their increased strategic value. Therefore, reporting of purchase

obligations should increase the extent to which firms’ investment decisions are influenced by

strategic motives. I develop specific predictions on firms’ investment choices based on classical

theory of strategic investments (Fudenberg and Tirole 1984; Bulow et al. 1985).11

Theory on strategic investments classifies firms’ competition into two types—competition

with strategic substitutes and competition with strategic complements. This classification applies

to firms that produce imperfectly or perfectly substitutive products and hence horizontally compete

for profits. If a firm’s more aggressive strategy (e.g., greater quantity, lower price, higher quality)

11 I rely on theory on strategic investments for entry accommodation (equivalent to incumbent competition). There exists related, but distinct, theory on strategic investments for entry deterrence (e.g., Spence 1977, 1979; Dixit 1979; Smiley 1988; Ellison and Ellison 2011; Cookson 2017, 2018). Based on this theory, recent papers by Bloomfield and Tuijin (2019) and Glaeser and Landsman (2019) empirically show that firms facing a threat of entry increase voluntary disclosure of greater investments to deter the entry of competitors. To help the reader understand the underlying theory for entry deterrence versus entry accommodation as well as the notions of overinvestment versus underinvestment, I discuss theory on strategic investments introduced by Tirole (1988) in my online appendix.

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decreases competitors’ marginal profits, then it has competition with strategic substitutes (e.g.,

Cournot competition). If it increases competitors’ marginal profits, then it has competition with

strategic complements (e.g., Bertrand competition).12

According to theory, firms desire to commit to actions that induce their competitors to take

less aggressive product market strategies, because this increases their expected future profits (e.g.,

Bulow et al. 1985; Sundaram et al. 1996). However, the action they need to take to induce such

strategies is contingent on how they expect their moves will affect their competitors’ marginal

profits or, equivalently, on whether they have strategic substitutes or complements with their

competitors. If they face competition with strategic substitutes, then firms will increase their

investments to signal that they will use aggressive product market strategies in the future (e.g.,

greater quantity, lower price, higher quality). This is because doing so reduces competitors’

marginal profits and induces them to adopt less aggressive strategies in response. On the other

hand, if firms face competition with strategic complements, then they will reduce their investments.

This is because doing so reduces competitors’ marginal profits and induces them to match firms’

less aggressive strategies.13

I expect to find a change in investments only among dominant firms with large market share

which have the “first-mover” advantage. They can exert a significant influence on the quantity and

price of products in the industry, and hence the actions of other firms, while small firms cannot

(e.g., Gisser 1984, 1986; Lieberman and Montgomery 1988; Gourio and Rudanko 2014; Aobdia

and Cheng 2018; Bloomfield 2019). For example, dominant firms likely have advantages in capital

(e.g., liquidity, fixed assets, technology) and costs (e.g., economies of scale, bargaining power,

12 By construction, an increase in a firm’s aggressiveness reduces its competitors’ profits. The difference between strategic substitutes and complements is its effect on competitors’ marginal profits with respect to their aggressiveness. See Appendix C for further discussion of competition with strategic substitutes versus complements. 13 This prediction is based on the assumption that an increase in a firm’s investments, on average, raises its competitors’ expected aggressiveness of the firm and a decrease in a firm’s investments lowers it.

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customer loyalty) that give them enough flexibility to substantially increase or decrease

investments. Therefore, I predict that dominant firms whose investment choices are more revealed

by the regulation increase their investments if they have substitutive strategies with competitors,

and reduce them if they have complementary ones. This leads to my first set of hypotheses, H1a

and H1b:

H1a: In competition with substitutive strategies, dominant firms with an increase in

disclosures about investments raise their investments after the regulation.

H1b: In competition with complementary strategies, dominant firms with an increase in

disclosures about investments reduce their investments after the regulation.

My next hypothesis examines the responses of non-dominant firms to their dominant

competitors’ strategic investments. Theory predicts that firms’ commitments signaled by

investments affect competitors’ decisions about their future actions (e.g., von Stackelberg 1934;

Fudenberg and Tirole 1984).14 Therefore, non-dominant firms are expected to choose their optimal

product market strategies conditional on their dominant competitors’ strategies signaled through

their investments.

Specifically, after dominant firms change their investments to signal their future product

market strategies, non-dominant firms will re-optimize their product market decisions based on

their new marginal profitability. The direction of this readjustment depends on whether their new

marginal profitability is decreased or increased. Because dominant firms’ strategies signaled by

their strategic investments reduce their marginal profits in both types of competition, I expect non-

dominant firms to optimally reduce their aggressiveness. Furthermore, because investments

foreshadow product market strategies due to their irreversible and time-bound nature, reductions

14 For example, in industries with quantity competition, competitors may interpret the firm’s purchase of inventory as bad news about their profitability and may reduce their quantity. This is because purchase of large inventory, which is costly to remove if it goes unsold, credibly signals a plan to produce and sell a large quantity.

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in the aggressiveness of non-dominant firms’ strategies will be first manifested as lower

investments. This leads to the following hypothesis, H2:

H2: In both types of competition, non-dominant firms reduce investments after the regulation.

Results consistent with these hypotheses would suggest that reporting of future investments

makes dominant firms engage more in strategic investments and subsequently makes non-

dominant firms adopt less aggressive product market strategies.

4. Sample & Data

To construct my sample, I start with the universe of firms at the intersection of Compustat and

CRSP. I then discard utility (SIC codes 4900–4949) and financial (SIC codes 6000–6999) firms,

which are highly regulated, and drop small business companies, which are exempt from the

regulation. Excluding firm-years that end between the first and second effective dates of the

regulation (i.e., June 15, 2003 and December 15, 2003, respectively), I use 5 years before and after

the regulation—a 10-year window surrounding the regulation—to examine its impact on strategic

investments.15 Furthermore, to mitigate the possibility that changes in sample composition affect

the results, I only keep firms with at least one year of data in each of the pre- and post-regulation

periods.

Due to data availability of text-based SEC filings from the EDGAR website and Hoberg and

Phillips’ similarity score, as well as an additional sample restriction of having at least one

investment contract prior to the regulation (discussed below in section 4.1), my main sample

consists of 14,712 firm-years representing 1,890 firms spanning from 1998 through 2008. Below,

I discuss in detail how I obtain the data used in my analyses.

15 I use 5 years after the regulation to allow for changes in off-balance sheet purchase obligations to manifest in investments recognized in financial statements. I find that the average and median durations of purchase obligations reported are 3.2 and 3.3 years, respectively.

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4.1. Data on Pre-regulation Redaction of Contracts Related to Investments

I collect data on investment contracts by conducting a textual analysis on all material contracts

that are filed as exhibits to 10-K/Q and 8-K required by Item 601 of Regulation S-K. Using Python,

I first extract all material contracts filed during the 5 years prior to the regulation using the string

“<TYPE>EX-10” which the EDGAR system adds to the top of every contract for identification

purposes (Li 2013). I then identify investment contracts by counting the number of words related

to investments. By building a search string based on prior papers, such as Merkley (2014) and

Costello (2013), I ensure that I build a sufficiently comprehensive set of search terms to find

contracts related to investment activity.

The words or portions of words I use to capture firms’ investments are the following: advertis,

aircraft, build, built, buy, bought, capacity, capacities, CAPEX, clinical, collaborat, construct,

consumer, customer, deliver, develop, distribut, drug, engineer, equipment, estate, exclusive,

expand, expansion, expenditure, facility, facilities, factory, factories, fuel, hardware, infrastructure,

innovate, invent, invest, joint venture, land, license, licensing, manufactur, marketing, material,

merchandis, operat, outsource, patent, plant, procure, product, project, property, properties,

purchas, research, R&D, right, royalt, science, scientist, sell, software, sold, sponsor, store, storage,

supply, supplie, technology, transportation, truck, vehicle, and warehouse. 16

I categorize a contract to be related to investments if it contains at least 5 unique words in the

set of search terms for investments. Furthermore, to ensure that I capture the extent of a firm’s

investment outsourcing, instead of investment insourcing, I drop any contract that includes at least

one term from “acquire”, “acquisition”, “merge”, and “M&A” even if it includes at least 5 unique

words related to investments. I also drop contracts that include at least one term related to

16 I do not include words related to operating or capital leases, despite them relating to investments, because disclosures about future lease obligations were required in a footnote before the regulation (FASB SFAS No. 13 and SFAS NO. 47).

16

employee compensation and debt or shareholder contracts even if it includes at least 5 unique

words related to investments.17 These additional steps further increase the accuracy of my ex ante

“treatment” as a proxy for a firm’s exposure to the regulation. Additionally, to address the concern

that firms with contractual investments could be fundamentally different from those without, I

exclude firms from all my analyses that have no investment contracts during the 5-year pre-

regulation period.

Next, among the contracts categorized as investment contracts, I identify those that are

redacted. By Rule 406 and Rule 24b-2, portions of material contracts can be redacted if they are

deemed by the SEC to cause competitive harm to the filing firm. A redacted copy of the material

contract should still be filed as an exhibit to 10-K/Q and 8-K, and I identify redacted contracts by

taking an approach similar to those of Verrecchia and Weber (2006), Boone et al. (2016), Glaeser

(2018), and Bourveau et al. (2019). Specifically, I use Python to search contracts for the following

phrases: “confidential treatment”, “confidential request”, “redact”, “CT Order”, “Freedom of

Information Act”, “FOIA”, “Rule 406”, “Rule 24b-2”, “confidential…redact/omit/delete…”,

“redact/omit/delete…confidential…”, “intention… redact/omit/delete…”, and

“redact/omit/delete…intention...”18 I classify an investment contract as a redacted one, if it contains

any of these phrases.

4.2. Tabular Data on Off-Balance Sheet Purchase Obligations

I use a Python code to collect the purchase obligation data for 5 years after the regulation. In

addition to using the terms like purchase obligation(s), firms use various labels to report their

17 The keywords used are as follows: bonus plan/agreement, compensation plan/agreement, employment plan/agreement, incentive plan/agreement, stock award/incentive/option, severance, pension plan/agreement, retirement benefit/plan/agreement, savings plan, loan (modification) plan/agreement, debenture, promissory note, credit agreement/facility, stock/share (re)purchase, shareholder agreement, shareholders’ agreement, and shareholder agreement. 18 According to Heinle et al. (2018), one could use confidential treatment (CT) order forms to identify redacted contracts. However, this information is only available on EDGAR from 2009.

17

future contractual investments, including “supply contract”, “exclusive license agreement”,

“production-related obligation”, “commercial commitments”, etc. Therefore, I first read

approximately two hundred 10-Ks to create the following list of words or portions of words that

firms use to indicate purchase obligations: advertis, agreement, aircraft, alliance, build, buy,

capacit, capex, capital, clinical, collaborat, commitment, commercial, connectivity, construct,

consult, consumer, customer, deliver, develop, distribut, drug, employment, energy, engineer,

equipment, estate, exclusiv, expand, expansion, expenditure, facility, facilities, factory, factories,

fuel, gas, hardware, infrastructur, innovat, intellectual, invent, invest, joint venture, land, license,

licensing, manufactur, marketing, material, merchandis, methane, obligation, oil, operat, outsourc,

patent, plant, procure, product, program, project, promot, property, properties, purchas, research,

R&D, right, royalt, science, scientist, sell, software, sponsor, storage, store, supplie, supply, take-

or-pay, technology, transmission, transportation, truck, utilities, utility, vehicle, ventures, and

vessel.

Then, I scrape the relevant data related to firms’ purchase obligations from 10-Ks downloaded

from the EDGAR website, including the unit used (e.g., thousands, millions) and the amounts due

each period (e.g., less than one year, one to three years, three to five years, more than five years).

To ensure that I only scrape data on purchase obligations, not other types of long-term liabilities

(e.g., long-term debt, operating/capital leases, employee benefits), I drop the data whenever its

label includes one of the following words: borrowing, benefit, credit, debt, debenture, deposit,

equity, financing, interest, lease, loan, minority, note, pension, and tax.

4.3. Data on Types of Competition

To find out whether a firm faces competition with strategic substitutes or complements, I first

need to identify its competitors that have the same targeted customers. Therefore, I define as

competitors the 5 nearest firms identified by Hoberg and Phillips (2010, 2016)’ firm-by-firm

18

pairwise similarity score in the year prior to the regulation.19 I obtain this data from the Hoberg-

Phillips Data Library. Hoberg and Phillips’ score is based on the similarity of two firms’ final

products, not production processes (which some of the more traditional industry classifications do),

and is purged of vertical relationships. These features make their measure better at identifying a

small set of direct competitors than traditional industry classifications, such as SIC, NAICS, and

Fama-French industry classifications, which tend to be more crude.20

Having identified a firm’s direct competitors, following Bloomfield (2019), I use a measure

developed by Kedia (2006) to classify the firm’s competition type using the 5-year quarterly data

on sales and net income prior to the regulation. The distinction of strategic substitutes versus

complements is determined by whether more aggressive strategies (e.g., greater quantity, lower

price, higher quality) by competitors decrease or increase a firm’s marginal profitability (Bulow

et al. 1985). Kedia’s measure is designed to directly estimate this change in marginal profitability

by empirically measuring the slope of a firm’s reaction function (i.e., cross partial derivative of a

firm’s net income with respect to the firm’s own sales and its competitors’ sales). If the value of

the measure is negative, then the firm faces competition with strategic substitutes. If the value of

the measure is positive, then the firm faces competition with strategic complements.

By using firms’ quarterly data on sales and net income during the 5 years before the regulation,

I assume that firms’ competition types do not vary from the pre- to post-regulation period as is

commonly done in the literature (e.g., Sundaram et al. 1996; Bloomfield 2019). This assumption

is reasonable because firms’ competition types are determined by their demand functions (e.g.,

elasticity) and cost functions (e.g., decreasing marginal cost), which are unlikely to change much

19 My results are robust to using 10 nearest firms, although they become slightly weaker. 20 Hoberg and Phillips (2010, 2016) show that their measure outperforms SIC and NAICS in explaining firm-specific characteristics, such as profitability, Tobin’s Q, and dividends. Their measure is based on web-crawling and text-parsing algorithms that process the text in the business descriptions of 10-K annual filings on the EDGAR website.

19

during a short time window around the 2003 regulation. See Appendix C for underlying theory for

strategic substitutes and complements as well as discussion of Kedia’s empirical proxy.

Although it is considered a state-of-art measure for competition type, Kedia (2006)’s measure

is subject to errors as it uses ex post data on sales and profits to capture firms’ ex ante incentives.

Therefore, in a robustness test, I use the three measures alternative to Kedia (2006) used by

Bloomfield (2019). The results from using the two measures based on production flexibility and

R&D spending, respectively, are in Table 8. The results from using mining firms are tabulated in

the online appendix.

5. Findings

5.1. Descriptive Statistics

Panel A of Table 1 presents descriptive statistics for the key variables used in my main tests.

In the 5-year pre-regulation period, firms on average have 0.7 investment contract per year and

redact 0.12 investment contract per year. I find that 16.6% (=313/1890) of firms have at least one

redacted investment contract during pre-regulation years. These statistics are similar to what prior

literature documents. For example, Heinle et al. (2018) find the average annual redacted

disclosures of 0.13. Also, Verrecchia and Weber (2006) and Glaeser (2018) report that about 16-

17% of firms redact their material contracts. The average amount of total investments reflected on

firms’ financial statements—which include inventory purchases, CAPEX minus sale of PP&E,

R&D, and advertising expenses—is approximately 100% of lagged total assets. As expected, the

majority of these investments are inventory purchases, which constitute about 84% of lagged total

assets on average.

Panel B of Table 1 reports descriptive statistics of purchase obligations required by the

regulation, which are primarily used in my validation tests. In the post-regulation period,

20

approximately 69% of my sample firm-years report purchase obligations in their 10-Ks, and the

average amount of purchase obligations is 25% of total assets.21 If I restrict my sample to those

reporting purchase obligations, the amount of total purchase obligations is large, with an average

of $703 million (or 51% of total assets) and a median of $29 million (or 9% of total assets). These

suggest that firms’ use of purchase obligations is economically meaningful, although right-skewed.

Moreover, off-balance sheet purchase obligations are economically large, even compared to

total investments reflected in firms’ financial statements. The average and median amounts due

within one year are 29-30% and 5-7% of annual total investments, respectively. The average and

median durations are 3.2 and 3.3 years, respectively. Moreover, the average and median durations

weighted by the amount due each period are 2 and 1.7 years, respectively. Figure 1 plots the

distribution of purchase obligations by each due date. The figure indicates that the majority of the

total payment is due within the first two years. For example, on average, 59% of total purchase

obligations are due within one year after the reporting date.22

In the rest of Panel B of Table 1, I show descriptive statistics of purchase obligations by type.

Taking advantage of the labels firms use to report purchase obligations in their 10-Ks, I categorize

purchase obligations into four types: inventory purchases, CAPEX, R&D, and advertising

expenses.23 While all these four types are informative about firms’ future strategies, they represent

investments into different assets (e.g., inventory, PP&E, intangibles). I find that inventory

purchases are the most economically significant, with the average amount of 56% of total assets

21 By summing payment obligations across years, I effectively assume a zero discount rate. 22 To compute the duration of purchase obligations, I assume a duration of 1 year for payments due within 1 year, 2 years for payments due in 1-3 years, 4 years for payments due in 3-5 years, and 5 years for payments due after 5 years. 23 The words used to identify inventory purchases include deliver, inventory, manufacture, merchandise, supplies, etc. Those used to identify CAPEX include capex, capacity, capital expenditure, equipment, facility, plant, etc. Those used to identify R&D include alliance, clinical, collaboration, develop, innovation, license, patent, R&D, research, joint venture, royalty, etc. Those used to identify advertising expenses include advertising, marketing, promotion, sponsor, etc. I categorize a purchase obligation as multiple categories, if its label contains more than one keyword for different categories.

21

(or $730 million) for firms reporting inventory purchases as purchase obligations and 5.7% of total

assets for all reporting and non-reporting firms. This suggests that purchase obligations differ

significantly from traditional investments considered in prior literature, which tends to focus just

on CAPEX or R&D.

Panel A of Figure 2 provides similar information graphically, indicating that inventory

purchases are the most frequent and largest type of purchase obligations, followed by R&D,

CAPEX, and advertising expenses. This order of magnitudes is the same as the order of magnitudes

among the four corresponding financial statement items (see Panel A of Table 1). This is consistent

with purchase obligations foreshadowing future investments to be recognized in financial

statements. Panel B of Figure 2 illustrates economic magnitudes of the four types of purchase

obligations, conditioned on reporting each corresponding type. It shows that all four types have

large magnitudes on average when I restrict my sample to reporting firms. For example, although

only 9% of purchase obligations correspond to advertising expenses (Panel A of Figure 2), the

average and median amounts reported are 65% and 3% of total assets, respectively, for those

reporting advertising expenses as purchase obligations (Panel B of Figure 2).

Altogether, the descriptive statistics provided in Table 1, Figure 1, and Figure 2 suggest that

purchase obligations disclosed in a given year are likely to be an informative signal about firms’

investments (i.e., expenditures for operations, fixed assets, and innovations) and hence about their

product market strategies in the near future. This is consistent with the findings of Lee (2010), who

shows that growth in purchase obligations is associated with higher future sales and earnings.

5.2. Tests on Investments (H1a, H1b, and H2)

My main tests examine whether dominant firms affected by the 2003 regulation strategically

change their investments (H1a and H1b). Because not all firms are affected by the regulation to

the same degree, I investigate whether dominant firms whose investment choices are more

22

revealed by the regulation increase their investments by a greater amount if they have substitutive

strategies with competitors, and reduce by a greater amount if complementary. I run the following

difference-in-differences regression model separately for dominant firms with strategic substitutes

and those with strategic complements:

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡

= 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + �𝛽𝛽𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡

+ �𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛿𝛿𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡(𝑇𝑇𝑃𝑃 𝜏𝜏𝑡𝑡 𝑇𝑇𝑃𝑃 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡) + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡, (1)

where the dependent variable captures the amount of investments recognized in financial

statements (i.e., balance sheet, income statement, and cash flow statement). The key independent

variable PreRegRedaction is the annual average number of redacted investment contracts in pre-

regulation years, which serves as an ex ante “treatment” measure. Post is an indicator variable that

takes the value of one for post-regulation years. 𝜏𝜏𝑡𝑡, 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡, and 𝛾𝛾𝑖𝑖 are year, industry by year,

and firm fixed effects, respectively. For control variables, I follow prior research on investments

(e.g., Durnev and Mangen 2009; Badertscher et al. 2013; Beatty et al. 2013; Kausar et al. 2016). I

include return on assets, book to market (BTM), market value of equity, leverage, losses indicator,

illiquidity, volatility, size-adjusted return, institutional ownership, Tobin’s Q, sales growth, cash

flows from operations (CFO), cash and cash equivalents, and asset tangibility.24

I measure the dependent variable as the sum of inventory purchases, R&D expenditure,

CAPEX, and advertising expenditure less cash receipts from sale of PP&E multiplied by 100 and

scaled by lagged total assets, following Biddle et al. (2009). I do not scale investments by sales,

because strategic investments can lead to changes in sales (see Table 6). Also, I do not include

acquisition costs because their disclosure is not affected by the regulation.

24 See Appendix A for variable definitions.

23

To classify firms into competition with strategic substitutes versus complements, I use a proxy

constructed by Kedia (2006) using pre-regulation quarterly sales and net income data for firms and

their 5 closest competitors identified by Hoberg and Phillips (2010, 2016) (see Section 4.3 for

details). I categorize a firm as a dominant firm if its market share is above the median of its

competition group, consisting of the firm itself and its 5 nearest competitors, and a non-dominant

firm if its market share is equal to or below the median.

Table 2 reports results consistent with my hypotheses (H1a and H1b). In particular, I find that

dominant firms with a greater increase in investment observability (i.e., a greater degree of

“treatment”) increase their investments by a greater amount if they have substitutive strategies

with competitors, and reduce them by a greater amount if they have complementary strategies. The

coefficient of 25.609 in Column (3) of Table 2 suggests that a one-standard-deviation increase in

pre-regulation redacted investment contracts leads to a 5.1% (=25.609⨯0.2/100) increase in

investments for a dominant firm with the average value of investments in competition with

strategic substitutes.25 Similarly, the coefficient of –24.734 in Column (6) suggests that a one-

standard-deviation increase in pre-regulation redacted investment contracts leads to a 4.9% (=–

24.734⨯0.2/100) reduction in investments for an average dominant firm with strategic

complements. According to theory on strategic investments, this two-way finding is consistent

with firms strategically changing investments in directions that reduce the marginal profitability

of competitors and thus induce less aggressive strategies from them.

25 I find results of similar economic magnitudes when I use log-transformed variables. For example, when I use the log of investments and the log of one plus pre-regulation average redacted investment contracts as the dependent variable and the key independent variable, respectively, I find that an increase of approximately 0.2 in pre-regulation average redacted investment contracts (or an 18% increase in one plus pre-regulation average) results in approximately a 6.2% increase in investments for an average firm with strategic substitutes and a 5.1% reduction for an average firm with strategic complements. To facilitate interpretations of results, I report results using variables without log transformations.

24

These results are especially intuitive if we view firms with strategic substitutes as competing

in quantity, and firms with strategic complements as competing in price or quality, which is a

common approach in the literature (e.g., Gal-or 1986; Darrough 1993).26 My findings then suggest

that dominant firms competing in quantity increase their investments to signal a larger quantity, as

it will reduce the market-clearing prices of competitors’ products and induce them to reduce their

quantities. In contrast, dominant firms competing in price or quality reduce their investments to

signal less aggressive pricing or quality strategies, as it will induce competitors to similarly engage

in less aggressive behavior.

In Figure 3, I show that the trend lines for investments between dominant firms affected (i.e.,

“treated” firms) and unaffected (i.e., “control” firms) by the regulation decouple after the

regulation for both types of competition, while showing parallel trends prior to the regulation. The

figure plots the coefficients on Year⨯ PreRegRedaction for years surrounding the regulation date

and their 90% confidence intervals. The notation Year+1 denotes the first firm-year after the

regulation date, Year+2 denotes the second firm-year, and so on. I exclude 4 and 5 years before

the regulation (i.e., Year-5 and Year-4) to find the average difference in investments between

firms affected and unaffected by the regulation in the absence of the regulation.27 Therefore, the

coefficients on the interaction terms measure the change in investments relative to the baseline

years Year-5 and Year-4. The coefficients become significant in the year following the regulation,

suggesting that “treated” firms and “control” firms display strong similarities in investments

leading up to the regulation. After the regulation date, “treated” firms engage in significantly

higher investments in competition with strategic substitutes and lower in competition with strategic

complements.

26 This categorization is always true when demand is linear and marginal cost is constant. 27 In all tests, I exclude firm-years that end between the two effective dates of the regulation (i.e., June 15, 2003 and December 15, 2003, respectively).

25

I supplement my main tests with tests on different components of investments. Specifically, I

estimate the regression model (1), after replacing the dependent variable with investments for

capacity and for product differentiation. These tests are motivated by the idea that competition

with strategic substitutes has greater physical capacity (e.g., Kreps and Scheinkman 1983; Maggi

1996), and competition with strategic complements has a greater degree of product differentiation

or customer loyalty (e.g., Chamberlin 1933; Lancaster 1966; Schmalensee 1982; Singh and Vives

1984). These characteristics suggest that investments in capacity likely have a greater strategic

value in competition with strategic substitutes, and investments in product differentiation have a

greater strategic value in competition with strategic complements.

I use the sum of inventory purchases and CAPEX less cash receipts from sale of PP&E as

investments in capacity, and the sum of R&D and advertising expenses as investments in product

differentiation. The results of tests on these two types of investments are shown in Table 3. Table

3 shows that, after an increase in the observability of investments, dominant firms with strategic

substitutes primarily increase investments in capacity, and those with strategic complements

primarily reduce investments in product differentiation. These results provide additional assurance

that changes in investments are driven by strategic motives. 28 Moreover, these results are

consistent with firms with strategic substitutes primarily competing in quantity and those with

strategic complements primarily competing in quality. If R&D expenses are considered to be

reducing production costs rather than increasing product differentiation, the result for firms with

strategic complements is consistent with them primarily competing in price.

28 Further tests show that results in competition with strategic substitutes concentrate in inventory purchases and results in competition with strategic complements concentrate in R&D (untabulated). This is consistent with the 2003 regulation primarily increasing disclosures of firms’ future investments in inventory purchases and R&D (see Panel B of Table 1).

26

I report in Table 4 the results of falsification tests where I use acquisition costs or off-balance

sheet future operating lease expenses as the dependent variable in model (1). Although these two

investment items are informative about future strategies, their disclosures were required even

before 2003. By SFAS No. 13, disclosures about future operating leases were required in a 10-K

footnote before the regulation. Also, disclosures about acquisitions were required, prior to the

regulation, on Form 8-K, Schedule 14A, S-4, etc. I find that no dominant firms change their

acquisition costs or operating lease expenses after the 2003 regulation. 29 This non-result for

investment items whose disclosures are not affected by the regulation adds further confidence to

my findings.30

Next, I investigate whether and how non-dominant firms respond to dominant firms’ strategic

investments. To test this, I change model (1) such that Post is interacted with the pre-regulation

average number of redacted investment contracts for a firm i’s dominant competitors among the 5

nearest competitors identified by Hoberg and Phillips (2010, 2016) (i.e., average “treatment” of

dominant competitors). This is because non-dominant firms’ response will be correlated with how

much their dominant competitors are affected by the regulation and therefore are engaging in

strategic investments. The model for testing non-dominant firms’ response is as follows:

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡

= 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼−𝑖𝑖 + �𝛽𝛽𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡

+ �𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡. (2)

I predict 𝛼𝛼1<0 for non-dominant firms across both types of competition (H2) for the following

reasons. In competition with strategic substitutes, it is optimal for non-dominant firms to reduce

29 I measure off-balance sheet operating lease expenses as the sum of all future operating expenses. The results are robust to including the current operating lease expense recognized in income statements. 30 The results on operating lease expenses are robust to scaling by lagged PP&E, not lagged total assets.

27

the aggressiveness of strategies when dominant competitors signal more aggressive strategies

through increased investments, because competitors’ aggressive strategies reduce their marginal

profitability. Similarly, in competition with strategic complements, it is optimal to reduce

aggressiveness when dominant competitors signal less aggressive strategies through reduced

investments, because competitors’ less aggressive strategies reduce their marginal profitability.

Non-dominant firms’ less aggressive strategies will manifest as lower investments, because

investments are indicative of firms’ future product market strategies.

The results in Panel A of Table 5 are consistent with my prediction for H2. The coefficients

of –48.397 and –58.036 in Columns (1) and (2), respectively, suggest that a one-standard-deviation

increase in a dominant competitor’s pre-regulation redacted investment contracts reduces an

average non-dominant firm’s investments by 3.2% (=–48.397⨯(0.2/3)/100) in competition with

strategic substitutes and by 3.9% (=–58.036⨯(0.2/3)/100) in competition with strategic

complements. These suggest that non-dominant firms respond optimally to dominant firms’

signaling of future strategies that reduce their marginal profits.

Again, if we view firms with strategic substitutes as competing in quantity, and firms with

strategic complements as competing in price or quality, these results for non-dominant firms are

very intuitive. In quantity competition, when dominant firms increase their investments to signal

a larger quantity, it is optimal for non-dominant firms to reduce their investments in quantity and

avoid a further reduction in the market-clearing prices of their products. In price or quality

competition, when dominant firms reduce their investments to signal less aggressive pricing or

quality strategies, it is optimal for non-dominant firms to also reduce their investments in lowering

price or improving quality and benefit from greater profit margins.

Furthermore, as falsification tests, I show in Panel B of Table 5 that non-dominant firms’

changes in investments are not correlated with increases in the observability of their own

28

investments. This is consistent with non-dominant firms not engaging in strategic investments as

their investments do not have strategic effects (i.e., they do not have a “first-mover” advantage).

In sum, my findings are consistent with H1a, H1b, and H2. I find that dominant firms

strategically change investments after the regulation, which increased the observability of future

investments to competitors and therefore the signaling value of investments. I also find that these

changes in investments induce less aggressive behavior from their non-dominant competitors.

5.3. Implications for Competition

In this section, I test whether dominant firms’ strategic investments change their own and non-

dominant competitors’ product market outcomes. I do so by estimating the following two

regression models for dominant firms and non-dominant firms, respectively:

𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝐼𝐼𝑇𝑇𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡

= 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + �𝛽𝛽𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + �𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡

+ 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡 , (3) 𝑇𝑇𝐼𝐼𝑃𝑃

𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝐼𝐼𝑇𝑇𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡

= 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼−𝑖𝑖 + �𝛽𝛽𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + �𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡

+ 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡 . (4)

In these two models, the dependent variable represents various product market outcomes, such

as COGS, sales, and profit margins. Post is an indicator variable that takes the value of one for

post-regulation years. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively.

𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 in model (3) is the pre-regulation average number of firms’ own redacted

investment contracts in the 5-year pre-regulation period (i.e., “treatment”), and

𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼−𝑖𝑖 in model (4) is the pre-regulation average number of redacted investment

29

contracts of dominant competitors identified by Hoberg and Phillips (2010, 2016) (i.e., average

“treatment” of dominant competitors).

I first investigate whether both dominant and non-dominant firms’ COGS change in the same

direction as their investments. This is to validate that investments are credible commitments. I use

COGS as the dependent variable to capture the aggressiveness of strategies, because a higher value

of COGS is consistent with a larger quantity sold via aggressive product market strategies, and a

lower value of COGS is consistent with a smaller quantity sold. I predict 𝛼𝛼1 > 0 for dominant

firms with strategic substitutes, 𝛼𝛼1 < 0 for dominant firms with strategic complements, and 𝛼𝛼1 <

0 for non-dominant firms across both types of competition.

The results for dominant firms are shown in Columns (1) and (4) of Table 6 Panel A. The

coefficient of 27.533 in Column (1) suggests that a one-standard-deviation increase in pre-

regulation redacted investment contracts results in a 6.6% (=27.533⨯0.2/83) increase in COGS

over lagged total assets for an average dominant firm with substitutive strategies. Similarly, the

coefficient of –17.653 in Column (4) indicates that a one-standard-deviation increase in pre-

regulation redacted investment contracts results in a 4.3% (=–17.653⨯0.2/83) reduction in COGS

over lagged total assets for an average dominant firm with complementary strategies. This suggests

that dominant firms’ signaling of commitments through investments foretells the aggressiveness

of their future strategies (i.e., it is not “cheap talk”), which is expected, as investments are

irreversible and time-bound.

The results for non-dominant firms are shown in Columns (1) and (4) of Table 6 Panel B. The

results suggest that non-dominant firms adopt less aggressive strategies, consistent with their

reduced investments. The coefficients in Columns (1) and (4) suggest that a one-standard-deviation

increase in one of the dominant competitors’ pre-regulation redacted investment contracts leads to

a 3.5% (=–42.956⨯(0.2/3)/83) reduction in COGS over lagged total assets for an average non-

30

dominant firm in competition with strategic substitutes, and a 4.2% (=–52.257⨯(0.2/3)/83)

reduction for an average non-dominant firm in competition with strategic complements. The

results in COGS for both dominant and non-dominant firms confirm that increases and decreases

in investments indicate increases and decreases in the aggressiveness of product market strategies,

respectively.

As a natural next step, I investigate changes in sales and profit margins as a consequence of

changes in the aggressiveness of product market strategies. I estimate model (3) for dominant firms,

using sales or profit margins as the dependent variable. I similarly estimate model (4) for non-

dominant firms.

When the dependent variable is sales, I predict 𝛼𝛼1 > 0 for dominant firms and 𝛼𝛼1 < 0 for

non-dominant firms in competition with strategic substitutes. This is because dominant firms that

adopt more aggressive strategies will take away sales from non-dominant firms that adopt less

aggressive strategies. I do not make predictions about sales for firms with strategic complements,

where both dominant and non-dominant firms adopt less aggressive strategies. This is because an

increase in prices and a reduction in the quantities sold have offsetting effects on sales, making the

net effect ambiguous.

When the dependent variable is profit margins, I predict 𝛼𝛼1 > 0 for both dominant and non-

dominant firms with strategic complements. This is because they charge higher prices or raise

market-clearing prices by reducing the total quantities sold. I do not make predictions for firms

with strategic substitutes, because an increase in the quantities sold by dominant firms and a

decrease in the quantities sold by non-dominant firms will have offsetting effects on the market-

clearing prices of dominant firms’ products.

The rest of Table 6 shows results for sales and profit margins. In Column (2) of Panel A, I

find that a one-standard-deviation increase in pre-regulation redacted investments leads to a 5.2%

31

(=31.468×0.2/120) increase in sales over lagged total assets for an average dominant firm with

strategic substitutes. In Column (2) of Panel B, I find that a one-standard-deviation increase in one

of the dominant competitors’ pre-regulation redacted contracts leads to a 1.7% (=–

30.173×(0.2/3)/120) reduction in sales over lagged total assets for an average non-dominant firm

with strategic substitutes. These results are consistent with my predictions. In Column (5) of Panel

A and Panel B, I find no significant change in sales for firms with strategic complements. I interpret

this as increases in prices being offset by reductions in quantities, or vice versa.

I also find results consistent with my predictions for profit margins. In Column (6) of Panel

A, I find that a one-standard-deviation increase in pre-regulation redacted investment contracts

raises profit margins of a median dominant firm by 3.7% (=6.506×0.2/35) in competition with

strategic complements. Also, in Column (6) of Panel B, I find that a one-standard-deviation

increase in one of the dominant competitors’ redacted contracts raises profit margins of a median

non-dominant firm by 2.6% (=13.547×(0.2/3)/35). I find no significant changes in profit margins

for both dominant and non-dominant firms with strategic substitutes, which suggests that changes

in their sales are primarily driven by changes in their quantities sold, not changes in their selling

prices.

Overall, the results in Table 6 suggest that strategic investments have real effects on firms’

product market strategies and competitive dynamics. After the 2003 regulation, dominant firms

with strategic substitutes take away sales from non-dominant firms by increasing investments and

adopting more aggressive strategies. Also, dominant firms with strategic complements induce an

anti-competitive environment by decreasing investments and adopting less aggressive strategies.

These results are consistent with the 2003 regulation giving advantages to dominant firms by

increasing the strategic role of their investments.

32

5.4. Validation and Robustness Tests

In this section, I run several tests to corroborate my main findings. My first set of tests seeks

to validate my methodology. To estimate the degree of “treatment” by the 2003 regulation, I use

the number of redacted investment contracts before the regulation. My choice of the “treatment”

measure assumes firms that redacted more investment contracts—hence withheld more

information about their contractual investments—in the pre-period experience a greater increase

in observability of their investments after the regulation. Additionally, to estimate changes in

investments from the pre- to post-regulation period, I examine changes in firms’ investments

recognized in financial statements, because firms’ off-balance sheet purchase obligations are not

reported prior to the regulation. This assumes that changes in off-balance sheet purchase

obligations—such as commitments to inventory purchases, CAPEX, R&D, and advertising

expenses—are soon reflected on firms’ balance sheets, income statements, or statements of cash

flows.

These two assumptions are reasonable, as purchase obligations reflect non-cancellable

payments for firms’ future contractual obligations. However, I further run two validation tests to

provide evidence supporting these two assumptions, respectively. First, I run regressions for the

post-regulation period as follows:

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ𝑇𝑇𝐼𝐼𝐼𝐼𝑃𝑃𝑎𝑎𝑇𝑇𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡

= 𝛼𝛼1𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + �𝛽𝛽𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡, (5)

where the dependent variable is either the log of 1 plus the total amount of purchase obligations

scaled by total assets or an indicator that takes the value of 1 if purchase obligations are reported

in a given firm-year, and 0 otherwise. The key independent variable is the annual average number

33

of investment contracts in pre-regulation years, including both redacted and non-redacted ones.

𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 are industry by year fixed effects, and 𝛾𝛾𝑖𝑖 are firm fixed effects.

The results of the regressions are tabulated in Panel A of Table 7. The coefficients 𝛼𝛼1 on Pre-

regulation Average Investment Contracts are positive and significant. This suggests that firms with

more investment contracts—redacted or non-redacted—are more likely to report a greater amount

of purchase obligations after the regulation. The coefficient of 0.266 in Column (1) suggests that,

for an average firm, one additional investment contract in the pre-regulation period (i.e., 0.2

contract per pre-regulation year) increases the reported amount of purchase obligations scaled by

total assets by 7% to from 25% to 32%.31 The coefficient on 0.513 in Column (2) suggests one

additional investment contract in the pre-regulation period increases the probability of reporting

purchase obligations by 10.3% (=0.2×0.513).

This finding confirms that firms with more investment contracts pre-regulation are more likely

to report a greater amount of purchase obligations in their 10-Ks post-regulation, which makes my

assumption more plausible that firms with more redacted investment contracts experience a greater

increase in their disclosure of investments (i.e., provide new disclosures about a larger amount of

investments). These findings are also consistent with those of Moon and Phillips (2019), who use

purchase obligation data to measure the extent of firms’ production outsourcing.

Second, I run the following regressions on a subset of firm-years that report purchase

obligations in the post-regulation period:

31 𝐼𝐼ln(1+0.25)+0.2×0.266 − 1=31.8%, where 0.25 is the average value of purchase obligations scaled by total assets (Panel B of Table 1). I find significant results for both dominant and non-dominant firms.

34

𝑃𝑃𝐼𝐼𝑃𝑃 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡+𝜏𝜏

= 𝛼𝛼1ln (1 + 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ𝑇𝑇𝐼𝐼𝐼𝐼𝑃𝑃𝑎𝑎𝑇𝑇𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼)𝑖𝑖,𝑡𝑡 + �𝛽𝛽𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖

+ 𝜖𝜖𝑖𝑖,𝑡𝑡, (6)

where the dependent variable is the average amount of total investments recognized in financial

statements (i.e., balance sheet, income statement, and cash flow statement) in the subsequent 2, 3,

or 5 years scaled by total assets multiplied by 100, and the key independent variable is the log of

1 plus the total amount of purchase obligations reported in a given year scaled by total assets.

𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 are industry by year fixed effects, and 𝛾𝛾𝑖𝑖 are firm fixed effects.

Panel B of Table 7 reports estimates from the regressions. The coefficients 𝛼𝛼1 across columns

(1)–(3) indicate that, for a firm with the average value of total investments, a 10% increase in 1

plus the amount of purchase obligations scaled by total assets increases the future investments

recognized in financial statements by 2.1%, 1.6%, and 1.0%, respectively, for the subsequent 2, 3,

and 5 years.32 These results are consistent with the fact that purchase obligations reflect non-

cancellable, legally binding amounts of payments. Moreover, this positive relationship supports

my assumption that off-balance sheet purchase obligations are soon reflected in firms’ financial

statements, and therefore validates interpreting changes in investments as stemming from changes

in purchase obligations.33

My second set of tests in this section is aimed at validating the robustness of my main findings

to other research design choices. I show that my results are robust to (i) using alternative measures

not based on Kedia (2006) to classify competition as strategic substitutes and complements, (ii)

using a dichotomous variable to capture firms’ exposure to the 2003 regulation (i.e., ex ante

32 2.1% = 10% × 20.997/100; 1.6% = 10% × 15.698/100; 1.0% = 10% × 10.273/100. 33 I also find significant results when running regressions separately for dominant and non-dominant firms.

35

“treatment”) instead of a count variable, and (iii) using a 4-year window around the 2003 regulation

(i.e., 2 years before and after) instead of a 10-year window.

First, I re-estimate model (1) for dominant firms and model (2) for non-dominant firms

separately for those identified as having strategic substitutes and strategic complements using

alternative measures, and I find that my results do not change much in terms of magnitude and

statistical significance. I use the three alternative measures used by Bloomfield (2019): production

flexibility, R&D spending, and the mining sector. In Table 8, I tabulate results of using the

production flexibility measure and the R&D spending measure as proxies for competition type.

For brevity here, the results of using the last measure that conservatively uses only firms in the

mining sector as firms with strategic substitutes are tabulated in my online appendix, because the

measure only uses 57 dominant firm-years and 42 non-dominant firm-years.

The use of the production flexibility measure is based on the idea that firms with strategic

substitutes have a greater amount of fixed capital (e.g., Kreps and Scheinkman 1983; Maggi 1996).

I classify a firm as facing competition with strategic substitutes if the average gross PP&E over

total assets of its competition group is above the median of all competition groups, where the

competition group consists of the firm itself and its 5 nearest competitors identified by Hoberg and

Phillips (2010, 2016). I classify a firm as facing competition with strategic complements if the

average of its competition group is below the median.

The use of the R&D spending measure is based on the idea that firms with strategic

complements on average have a greater degree of product differentiation (e.g., Chamberlin 1933;

Lancaster 1966; Schmalensee 1982; Singh and Vives 1984). I classify a firm as facing competition

with strategic complements if the average R&D expense over total assets of its competition group

is above the median of all competition groups, and as facing competition with strategic substitutes

if the average is below the median.

36

Panels A and B of Table 8 show results for dominant firms and non-dominant firms,

respectively. I continue to find results consistent with my hypotheses H1a, H1b, and H2. In Panel

A, I find that dominant firms with a greater increase in investment observability increase their

investments by a greater amount if they have substitutive strategies, and reduce them by a greater

amount if they have complementary strategies. Similarly, in Panel B, I find that both non-dominant

firms with substitutive strategies and those with complementary strategies reduce investments in

response.

Second, I show that my results are robust to using a dichotomous ex ante “treatment” measure

and a shorter time window around the 2003 regulation date. I again re-estimate model (1) for

dominant firms and model (2) for non-dominant firms separately for those with strategic

substitutes and strategic complements using these alternative research design choices.

In Panel A of Table 9, I use a dichotomous version of PreRegRedaction in models (1) and (2)

to capture the extent of firms’ exposure to the 2003 regulation, which relaxes the assumption that

a higher frequency of redacted contracts pre-regulation implies a greater exposure to the 2003

regulation. For a dominant firm, the variable takes the value of 1 if the firm has at least one redacted

investment contract prior to the regulation, and 0 otherwise. For a non-dominant firm, the variable

takes the value of 1 if the firm’s dominant competitors have at least one redacted investment

contract, and 0 otherwise. In Panel B of Table 9, I use a 4-year window surrounding the regulation

date—2 years before and after—to address concerns that my results are driven by the dot-com

bubble that burst in 2000. It is unlikely that my redaction-based measure of firms’ exposure to the

2003 regulation is correlated with firms’ exposure to the bubble in opposite ways for firms with

strategic substitutes and for those with strategic complements. However, I further conduct tests

using two firm-years before and after the 2003 regulation date. In both Panels of Table 9, I continue

to find results consistent with my hypotheses H1a, H1b, and H2.

37

I perform additional validation or robustness tests, which are tabulated in my online

appendix.34 There, I show that the disclosures required by the 2003 regulation reduced information

asymmetry measured by analyst dispersion, which suggests the disclosures are likely informative

about firms’ future operations to outsiders (including competing firms). I also show that my results

for H1a, H1b, and H2 are not sensitive to how I identify investment contracts, whether I include

firms with no investment contracts in the control group or not, and whether I use firms in the

mining sector as firms with strategic substitutes.

34 Available at http://bit.ly/Noh2020Appendix.

38

6. Conclusion

I provide novel evidence that financial reporting increases the strategic role of investments by

making investments more visible to competitors. The evidence suggests that, after an increase in

disclosures about future investments, large firms strategically increase or decrease investments to

affect competitors’ behavior. This is because investments serve as an effective commitment

mechanism, and better observability of investments further increases their strategic value. My

findings also suggest that this strategic behavior has significant impacts on product market

outcomes, such as firms’ COGS, sales, and profit margins. These findings expand our limited

understanding of the role of mandatory corporate disclosures in firms’ strategic investments and

their implications (Roychowdhury et al. 2019).

This paper also makes an important contribution to the investment literature by underscoring

the economic significance of off-balance sheet purchase obligations and their strategic uses. My

findings suggest that papers studying investments may benefit from examining purchase

obligations. They include future expenditures for operations (e.g., inventory purchases,

advertising/marketing) as well as for fixed assets and innovations (e.g., CAPEX, R&D), and

therefore reflect wide-ranging future strategies in a timely manner.

Finally, my paper should be of interest to regulators such as the SEC and FASB. The 2003

regulation was intended to provide investors with information about firms’ off-balance sheet

obligations. My finding that firms, especially large firms, exploit it to make a gain sheds light on

an unintended effect of the regulation and an unexplored role of financial reporting in competition.

Furthermore, to the extent that disclosures in financial statements are more credible and

informative about future investments, my finding speaks to the recent debate of whether rights and

obligations from executory contracts, such as operating leases and purchase obligations, should be

recognized in balance sheets.

39

Appendix A: Variable Definitions

Firm Classification

Dominant Firms A firm is categorized as a dominant firm if its market share is above the median of its competition group, consisting of the firm itself and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016).

Non-Dominant Firms A firm is categorized as a non-dominant firm if its market share is equal to or below the median of its competition group, consisting of the firm itself and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016).

Competition Classification based on Kedia (2006)

Strategic Substitutes

A firm is classified as facing competition with strategic substitutes if the value of its Kedia (2006) measure is negative. Kedia (2006)’s measure is computed using the 5-year pre-regulation quarterly data on sales and net income for the firm and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016). See Section 4.3 for details.

Strategic Complements

A firm is classified as facing competition with strategic complements if the value of its Kedia (2006) measure is positive. Kedia (2006)’s measure is computed using the 5-year pre-regulation quarterly data on sales and net income for the firm and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016). See Section 4.3 for details.

Variables for Tests on Investments and Product Market Outcomes

Pre-Regulation Average Investment Contracts (PreRegAvgContractsi)

The annual average number of investment contracts for a firm during 5 years before regulation (see Section 4.1 for details).

Pre-Regulation Average Redacted Investment Contracts (PreRegRedactioni)

The annual average number of redacted investment contracts for a firm during 5 years before regulation (see Section 4.1 for details).

Indicator for Pre-regulation Redaction of Investment Contracts

A dichotomous variable that takes the value of 1 if a firm has at least one redacted investment contract during 5 years before regulation, and 0 otherwise.

Pre-regulation Average Redacted Inv. Contracts of Dominant Competitors (PreRegRedaction-i)

The average number of redacted investment contracts by a non-dominant firm’s 3 dominant competitors during 5 years before regulation. The 3 dominant competitors are firms with above-median market share among the firm itself and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016).

Indicator for Pre-regulation Redaction of Inv. Contracts of Dominant Competitors

A dichotomous variable that takes the value of 1 if a firm’s dominant competitors have at least one redacted investment contract during 5 years before regulation, and 0 otherwise.

Post 1 for firm-years ending after regulation (i.e., December 15, 2003), and 0 otherwise.

Total Investments (Inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense)×100/lagged total assets, where inventory purchase is measured as the change in inventory balance plus the cost of goods sold.

Average Total Investments (The average amount of total investments in subsequent 2, 3, or 5 years)×100/total assets. Total investments are (inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense).

Inventory Purchases Inventory purchase×100/lagged total assets, where inventory purchase is measured as the change in inventory balance plus the cost of goods sold.

CAPEX (CAPEX - sale of PP&E)×100/lagged total assets.

R&D R&D expense×100/lagged total assets.

Advertising Expense Advertising expense×100/lagged total assets.

Capacity (Inventory purchase + CAPEX - sale of PP&E)×100/lagged total assets.

Product Differentiation (R&D expense + advertising expense)×100/lagged total assets.

40

Appendix A (cont’d): Variable Definitions

Variables for Tests on Investments and Product Market Outcomes (cont'd)

Acquisition Cost Acquisition cost×100/lagged total assets.

1 if Acquisition Cost > 0 1 if Acquisition Cost > 0, and 0 otherwise

Future Operating Lease Expense The sum of future operating lease expenses disclosed in 10-K footnote×100/lagged total assets.

COGS Cost of goods sold×100/lagged total assets.

Sales Sales×100/lagged total assets.

Profit Margins (Sales - COGS)×100/sales.

ROA Net income scaled by average total assets.

BTM Book value of equity scaled by market value of equity.

ln(MVE) The natural logarithm of the market value of equity (in millions of USD) measured as the price per share multiplied by the number of shares outstanding.

Leverage Total liabilities scaled by total assets.

Loss Indicator 1 for firm-years with losses, and 0 otherwise.

Illiquidity The annual average of daily bid-ask spreads measured by (ask−bid)×100/[(ask+bid)/2].

Volatility The standard deviation of daily stock returns over a firm-year.

Size-adjusted Stock Return The size-adjusted buy-and-hold abnormal return over a firm-year.

Institutional Ownership The percentage of institutional investors by a firm-year end obtained from Thomson Reuters.

Insider Trading The total insider trades (i.e., sales + purchases) of the CEO and CFO over a firm-year, obtained from Thomson Reuters, scaled by shares outstanding at the beginning of the firm-year.

Tobin Q The market value of equity plus the book value of short- and long-term debt scaled by total assets.

Sale % Change Percentage change in sales.

CFO Cash flows from operations scaled by average total assets.

Cash and Cash Equivalents Total cash and cash equivalents scaled by total assets.

Asset Tangibility Net property, plant and equipment scaled by total assets.

Alternative Competition Classifications

Competition with Strategic Substitutes (Complements)

A firm is classified as facing competition with strategic substitutes (complements) if the average production flexibility, measured as gross PP&E over total assets, of its competition group is above (below) the median value of all competition groups. A firm’s competition group consists of the firm itself and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016). A firm is classified as facing competition with strategic substitutes (complements) if the average R&D spending, measured as R&D over total assets, of its competition group is below (above) the median value of all competition groups. A firm’s competition group consists of the firm itself and its 5 nearest competitors identified by Hoberg and Phillips (2010, 2016).

41

Appendix A (cont’d): Variable Definitions

Additional Variables for Post-Regulation Purchase Obligation Data

1 if Purchase Obligations are reported, 0 otherwise 1 for firm-years in the post-regulation period that disclose purchase obligations in 10-Ks.

Total Amount of Purchase Obligations as % of Total Assets including Non-reporting Firm-years

The sum of all purchase obligations×100/total assets for all firm-years in the post-regulation period.

Total Amount of Purchase Obligations as % of Total Assets

The sum of all purchase obligations×100/total assets for firm-years that disclose purchase obligations in 10-Ks.

ln(1 + Purchase Obligations) The natural logarithm of 1 plus the sum of all purchase obligations scaled by total assets for firm-years that disclose purchase obligations in 10-Ks.

Total Amount of Purchase Obligations (in $ millions) The sum of all purchase obligations (in millions of USD).

Total Amount of Purchase Obligations as % of Total Investments of Reporting Year The sum of all purchase obligations×100/total investments.

Amount of Purchase Obligations Due in 1 year as % of Total Investments of Reporting Year

The amount of purchase obligations due in 1 year×100/total investments of the same year.

Amount of Purchase Obligations Due in 1 year as % of Total Investments 1 Year After

The amount of purchase obligations due in 1 year×100/total investments of the next year.

Duration of Purchase Obligations (in years)

The duration of purchase obligations for a firm-year. I assume a duration of 1 year for payments due within 1 year, 2 years for payments due in 1-3 years, 4 years for payments due in 3-5 years, and 5 years for payments due after 5 years. e.g., if a firm reports $10 million due in 1 year and $20 million due between 1-3 years, then the duration is 2 years.

Amount-weighted Duration of Purchase Obligations (in years)

The average duration of purchase obligations for a firm-year weighted by the total amount due for each time period. I assume a duration of 1 year for payments due within 1 year, 2 years for payments due in 1-3 years, 4 years for payments due in 3-5 years, and 5 years for payments due after 5 years. e.g., if a firm reports $10 million due in 1 year and $20 million due between 1-3 years, then the amount-weighted duration is (1 year×($10/(10+20)+2 years×($20/(10+20)) = 1.67 years.

42

Appendix B: Tabular Disclosure Required by the 2003 Regulation The table below shows a format required by the SEC that a firm’s table should substantially conform to (Release No. 33-8182). The table is followed by tabular disclosures reported by a few sample firms after the regulation.

Contractual Obligations Payments due by period

Total Less than 1 year

1-3 years

3-5 years

More than 5 years

Long-term debt Capital Lease Obligations Operating Leases Purchase Obligations Other Long-term Liabilities Reflected on Balance Sheet under GAAP

Total (1) Coca-Cola 10-K for the fiscal year ending on December 31, 2003 (in millions):

43

Appendix B (cont’d): Tabular Disclosure Required by the 2003 Regulation

(2) Costco Wholesale Corporation 10-K for the fiscal year ending on August 29, 2004 (in thousands):

(3) Kellogg Co 10-K for the fiscal year ending on December 27, 2003 (in millions):

44

Appendix B (cont’d): Tabular Disclosure Required by the 2003 Regulation (4) E. I. Du Pont De Nemours and Company 10-K for the fiscal year ending on December 31,

2003 (in millions):

(5) Boeing Company 10-K for the fiscal year ending on December 31, 2003 (in millions):

45

Appendix C: Strategic Substitutes and Complements and Their Empirical Proxies

Bulow et al. (1985) theoretically derives notions of strategic substitutes and strategic

complements, which fundamentally affect the way firms interact with their competitors. They first

construct firm i’s strategic interaction variable Π𝑖𝑖𝑖𝑖𝑖𝑖 = ∂2Π𝑖𝑖

∂xi𝑥𝑥𝑗𝑗, which is the cross-partial of firm i’s

profit Π𝑖𝑖 with respect to both the aggressiveness of firm i’ own strategy 𝑥𝑥𝑖𝑖 and the aggressiveness

of competitor j’s strategy 𝑥𝑥𝑖𝑖. The greater 𝑥𝑥𝑖𝑖 𝑜𝑜𝑜𝑜 𝑖𝑖 is, the more aggressive firm i or j is. They show

that, if Π𝑖𝑖𝑖𝑖𝑖𝑖 is less than zero, then firms i and j have strategic substitutes, and if Π𝑖𝑖𝑖𝑖𝑖𝑖 is greater than

zero, then firms i and j have strategic complements.

The intuition is that Π𝑖𝑖𝑖𝑖𝑖𝑖 is equal to ∂∂xj

(𝜕𝜕Πi

𝜕𝜕𝑥𝑥𝑖𝑖), which can be interpreted as firm i's marginal

profitability with respect to 𝑥𝑥𝑖𝑖 when competitor j’s strategy 𝑥𝑥𝑖𝑖 becomes more aggressive.

Equivalently, Π𝑖𝑖𝑖𝑖𝑖𝑖 captures firm i’s optimal response to changes in competitor j’s strategy 𝑥𝑥𝑖𝑖, or the

“slope” of firm i’s best response function with respect to competitor j’s strategy 𝑥𝑥𝑖𝑖. The sign of

Π𝑖𝑖𝑖𝑖𝑖𝑖 is determined by firms i and j’s demand functions (e.g., elasticity) and cost functions (e.g.,

decreasing marginal cost). A commonly accepted example of competition with strategic substitutes

is Cournot competition, and of competition with strategic complements is Bertrand competition

(Bulow et al. 1985). This categorization is true under general conditions, such as when demand is

linear and marginal cost is constant.

In Cournot competition, firms compete in quantity. The greater competitor j’s quantity 𝑥𝑥𝑖𝑖 is,

the more aggressive competitor j is. If competitor j increases its aggressiveness by increasing its

quantity, then the marginal profitability of firm i is affected. Firm i re-optimizes such that its

marginal profitability is zero, or its marginal revenue is equal to marginal cost.

46

Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies

When marginal cost is assumed to be constant, firm i’s reaction solely depends on whether an

increase in competitor j’s quantity increases or decreases firm i’s marginal revenue (with respect

to quantity), which can be expressed as follows:

𝑃𝑃𝑃𝑃𝑖𝑖 = 𝑝𝑝𝑖𝑖�𝑞𝑞𝑖𝑖 , 𝑞𝑞𝑖𝑖� �1 +1Ε𝑖𝑖� , where Ε𝑖𝑖 =

𝜕𝜕𝑞𝑞𝑖𝑖/𝑞𝑞𝑖𝑖𝜕𝜕𝑝𝑝𝑖𝑖(𝑞𝑞𝑖𝑖, 𝑞𝑞𝑖𝑖)/𝑝𝑝𝑖𝑖(𝑞𝑞𝑖𝑖, 𝑞𝑞𝑖𝑖)

.

If competitor j increases its aggressiveness by increasing its quantity, then the market-clearing

price of firm i’s product 𝑝𝑝𝑖𝑖�𝑞𝑞𝑖𝑖, 𝑞𝑞𝑖𝑖� goes down by the law of demand, as the two firms’ products

are (imperfect) substitutes. As long as the elasticity Ε𝑖𝑖 does not change much around the

equilibrium point, the reduction in 𝑝𝑝𝑖𝑖�𝑞𝑞𝑖𝑖, 𝑞𝑞𝑖𝑖� decreases marginal revenue and, hence, marginal

profitability of firm i. Therefore, the optimal reaction of firm i is to reduce its quantity 𝑥𝑥𝑖𝑖 (i.e.,

reduce its aggressiveness) and bring up its marginal revenue to its marginal cost. This negative

relationship between competitor j’s aggressiveness and firm i’s marginal profitability—Π𝑖𝑖𝑖𝑖𝑖𝑖 =

∂∂xj�𝜕𝜕Π

i

𝜕𝜕𝑥𝑥𝑖𝑖� < 0—makes them to be in competition with strategic substitutes.

In Bertrand competition, firms compete in quality or the inverse of price. The higher

competitor j’s quality 𝑥𝑥𝑖𝑖 is, the more aggressive competitor j is, or the higher competitor j’s inverse

of price 𝑥𝑥𝑖𝑖 is, the more aggressive competitor j is. For the sake of a simpler illustration, suppose

the unit of aggressiveness 𝑥𝑥 is price. If competitor j increases its aggressiveness by reducing its

price, the marginal profitability of firm i is affected. Firm i adjusts its aggressiveness such that its

marginal profitability is zero, or its marginal revenue is equal to marginal cost.

47

Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies

When marginal cost is assumed to be constant, firm i’s reaction solely depends on whether a

reduction in competitor j’s price increases or decreases firm i’s marginal revenue (with respect to

price), which can be expressed as follows:

MRi = 𝑝𝑝𝑖𝑖 �1 +1Ε𝑖𝑖� , where Ε𝑖𝑖 =

𝜕𝜕𝑞𝑞𝑖𝑖(𝑝𝑝𝑖𝑖,𝑝𝑝𝑖𝑖)/qi(𝑝𝑝𝑖𝑖,𝑝𝑝𝑖𝑖)𝜕𝜕𝑝𝑝𝑖𝑖/𝑝𝑝𝑖𝑖

.

If competitor j increases its aggressiveness by reducing its price, then the demand for firm i’s

product goes up, and, when the demand is linear, the demand for firm i’s product becomes more

elastic (i.e., more sensitive to prices). This increases the value of �1 + 1Ε𝑖𝑖�. This in turn increases

marginal revenue and, hence, marginal profitability of firm i. Therefore, the optimal reaction of

firm i is to reduce its price 𝑥𝑥𝑖𝑖 (i.e., increase its aggressiveness) and bring down its marginal revenue

to its marginal cost. This positive relationship between competitor j’s aggressiveness and firm i’s

marginal profitability—that is, Π𝑖𝑖𝑖𝑖𝑖𝑖 = ∂∂xj�𝜕𝜕Π

i

𝜕𝜕𝑥𝑥𝑖𝑖� > 0 —makes them to be in competition with

strategic complements. It is straightforward that the same result is obtained if we define the unit

of aggressiveness 𝑥𝑥 to be quality 𝑞𝑞, and define price 𝑝𝑝(𝑞𝑞) as a decreasing function of quality (i.e.,

increasing quality is analogous to reducing price).

However, Cournot competition may have strategic complements, and Bertrand competition

may have strategic substitutes, if we allow for some variations in the local curvature of firms’

demand or cost functions. Moreover, not all firms can be categorized as having quantity, price, or

quality competition, as most have discretion over all of these factors. Therefore, as suggested by

Bulow et al. (1985), I rely on an empirical proxy to identify competition with strategic substitutes

48

Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies

and complements, instead of relying on theory to identify whether competition is in quantity, price,

or quality.

In order to empirically estimate the relationship between ∂Πi

𝜕𝜕𝑥𝑥𝑖𝑖 and 𝜕𝜕𝑥𝑥𝑖𝑖 (i.e., which determines

the sign of Π𝑖𝑖𝑖𝑖𝑖𝑖 = ∂2Πi

∂xi𝑥𝑥𝑗𝑗= ∂

∂xj(𝜕𝜕Π

i

𝜕𝜕𝑥𝑥𝑖𝑖) ), Sundaram et al. (1996) compute correlation coefficients

between ΔΠi

Δ𝑥𝑥𝑖𝑖 and Δxj. They use quarterly changes in profits and sales of firm i as proxies for ΔΠi

and Δxi, respectively, and a quarterly change in the average sales of all other firms in the same

four-digit SIC industry group as a proxy for Δxj . To address the concern that correlation

coefficients can be confounded by common supply or demand shocks (e.g., reduction in the cost

of raw materials), Kedia (2006) introduces a regression-based measure that captures the

relationship between ΔΠi

Δ𝑥𝑥𝑖𝑖 and Δxj while controlling for changes in firm i’s strategy Δxi. Below, I

provide details on how Kedia (2006) constructs her measure for firm i’ strategic interaction Π𝑖𝑖𝑖𝑖𝑖𝑖 .

Kedia defines firm i's profit as:

Π𝑖𝑖 = 𝐷𝐷𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖�𝑥𝑥𝑖𝑖 − 𝐶𝐶𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖�,

where 𝐷𝐷𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖� is the demand function, and 𝐶𝐶𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖� is the total cost function for firm i. The

demand function reflects that firms i and j are direct competitors facing the same targeted

customers. To derive an empirical estimation of the cross-partial Π𝑖𝑖𝑖𝑖𝑖𝑖 = ∂2Πi

∂xi𝑥𝑥𝑗𝑗= ∂

∂xj(𝜕𝜕Π

i

𝜕𝜕𝑥𝑥𝑖𝑖), Kedia

first takes the total differential of firm i’s marginal profit 𝜕𝜕Πi

𝜕𝜕𝑥𝑥𝑖𝑖 with respect to 𝑥𝑥𝑖𝑖 and 𝑥𝑥𝑖𝑖:

49

Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies

𝑃𝑃𝜕𝜕Π𝑖𝑖

𝜕𝜕𝑥𝑥𝑖𝑖= Π𝑖𝑖𝑖𝑖𝑖𝑖 𝑃𝑃𝑥𝑥𝑖𝑖 + Π𝑖𝑖𝑖𝑖𝑖𝑖 𝑃𝑃𝑥𝑥𝑖𝑖 .

Because the second derivatives are Π𝑖𝑖𝑖𝑖𝑖𝑖 = 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖�𝑥𝑥𝑖𝑖 + 2𝐷𝐷𝑖𝑖𝑖𝑖�𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖� − 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 (𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖) and Π𝑖𝑖𝑖𝑖𝑖𝑖 =

𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖�𝑥𝑥𝑖𝑖 + 𝐷𝐷𝑖𝑖𝑖𝑖�𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖� − 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 (𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖), we have:

𝑃𝑃𝜕𝜕Π𝑖𝑖

𝜕𝜕𝑥𝑥𝑖𝑖= [𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖�𝑥𝑥𝑖𝑖 + 2𝐷𝐷𝑖𝑖𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖� − 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 (𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖)] 𝑃𝑃𝑥𝑥𝑖𝑖 + [𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖�𝑥𝑥𝑖𝑖 + 𝐷𝐷𝑖𝑖𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖�

− 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 (𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖)]𝑃𝑃𝑥𝑥𝑖𝑖 .

This can be re-written as:

𝑃𝑃𝜕𝜕Π𝑖𝑖

𝜕𝜕𝑥𝑥𝑖𝑖= [𝛽𝛽1𝑥𝑥𝑖𝑖 + 𝛽𝛽2] 𝑃𝑃𝑥𝑥𝑖𝑖 + [𝛽𝛽3𝑥𝑥𝑖𝑖 + 𝛽𝛽4]𝑃𝑃𝑥𝑥𝑖𝑖

= 𝛽𝛽1𝑥𝑥𝑖𝑖𝑃𝑃𝑥𝑥𝑖𝑖 + 𝛽𝛽2𝑃𝑃𝑥𝑥𝑖𝑖 + 𝛽𝛽3𝑥𝑥𝑖𝑖𝑃𝑃𝑥𝑥𝑖𝑖 + 𝛽𝛽4𝑃𝑃𝑥𝑥𝑖𝑖 ,

where 𝛽𝛽1 = 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖�, 𝛽𝛽2 = 2𝐷𝐷𝑖𝑖𝑖𝑖�𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖� − 𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖�, 𝛽𝛽3 = 𝐷𝐷𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖�, and 𝛽𝛽4 = 𝐷𝐷𝑖𝑖𝑖𝑖�𝑥𝑥𝑖𝑖, 𝑥𝑥𝑖𝑖� −

𝐶𝐶𝑖𝑖𝑖𝑖𝑖𝑖 �𝑥𝑥𝑖𝑖 , 𝑥𝑥𝑖𝑖�. So, the strategic interaction Π𝑖𝑖𝑖𝑖𝑖𝑖 is given by 𝛽𝛽3𝑥𝑥𝑖𝑖 + 𝛽𝛽4 . Similar to Sundaram et al.

(1996), Kedia (2006) uses quarterly sales and profits of firm i to proxy for 𝑥𝑥𝑖𝑖 and Π𝑖𝑖, respectively,

and defines as competitor j all of the rest of the firms in the same four-digit SIC industry group.35

The following OLS regression model can be used to estimate 𝛽𝛽3� and 𝛽𝛽4�:

ΔΔ𝑝𝑝𝑃𝑃𝑇𝑇𝑝𝑝𝑃𝑃𝑇𝑇𝑖𝑖,𝑡𝑡Δ𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡

= 𝛽𝛽1𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡Δ𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛽𝛽2Δ𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡Δ𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛽𝛽4Δ𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡,

35 According to Kedia (2006), with linear demand functions and constant marginal cost, using sales as a proxy for a firm’s aggressiveness (i.e., firm-level price and output) yields the same sign as the true strategic interaction though it differs in magnitude. Therefore, I exploit the sign of the interaction to determine the nature of competition and do not consider its magnitude.

50

Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies

where 𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖 is the average contemporaneous quarterly sales of all other firms in the same four-

digit SIC group. The sign of the estimator 𝛽𝛽3� 𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼�������𝑖𝑖 + 𝛽𝛽4�, where 𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼�������𝑖𝑖 is the average quarterly

sales of firm i, reflects the nature of the competition that firm i faces. If 𝛽𝛽3� 𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼�������𝑖𝑖 + 𝛽𝛽4� < 0, it has

strategic substitutes with its competitors. If 𝛽𝛽3� 𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼�������𝑖𝑖 + 𝛽𝛽4� > 0, firm i has strategic complements.

If 𝛽𝛽3� 𝐼𝐼𝑇𝑇𝑇𝑇𝐼𝐼𝐼𝐼�������𝑖𝑖 + 𝛽𝛽4� is not significantly different from zero, firm i does not face any strategic

interactions.

51

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Figure 1: Purchase Obligations by Due Date The figure plots mean, median, 1st percentile and 99th percentile values of purchase obligations by each due date: 1 year, 1 to 3 years, 3 to 5 years, and more than 5 years.

55

Figure 2: Values of Purchase Obligations by Type Panel A plots the average amount of purchase obligations for each type as a percentage of total assets, as well as the relative frequency of each type. These statistics are based on all firm-years in the post-regulation period regardless of whether they report purchase obligations. Panel B plots the average and median total amounts of purchase obligations for each type for firm-years reporting the specific type of purchase obligations in the post-regulation period. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. Panel A: All Firm-Years in Post-regulation Period

Panel B: Conditioning on Reporting a Specific Type of Purchase Obligations

56

Figure 3: Trends Surrounding the 2003 Regulation This figure plots coefficients 𝛼𝛼 and their 90% confidence intervals estimated from the following regression on dominant firms: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 = ∑ 𝛼𝛼5

𝑘𝑘=−3 𝑌𝑌𝐼𝐼𝑇𝑇𝑃𝑃[𝑃𝑃]𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 ×𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡 , where the dependent variable Total Investments captures the total amount of investments recognized in financial statements measured as (inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense)×100/lagged total assets. The key independent variable PreRegRedaction is the annual average number of redacted investment contracts in pre-regulation years, which serves as an ex ante “treatment” measure. Year[k]t is equal to 1 for k-th firm-year relative to the regulation date, and 0 otherwise. I exclude firm-years that end between the two effective dates (i.e., June 15, 2003 and December 15, 2003, respectively) of the regulation. Dominant firms are firms whose market shares are above the median of their respective competition groups. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively.

57

Table 1: Descriptive Statistics The table below reports summary statistics of various variables. Refer to Appendix A for variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. Panel A: Firm-Level Data for Pre- and Post-Regulation Periods

mean p1 median p99 sd N Pre-Regulation Average Investment Contracts Per Year 0.7 0.2 0.42 2.7 0.56 1890 Pre-Regulation Average Redacted Investment Contracts Per Year: - For Firms with At Least One Inv. Contracts Pre-Regulation 0.12 0.0 0.0 1.1 0.20 1890 - For Firms with At Least One Redacted Inv. Contracts Pre-Regulation 0.44 0.2 0.3 1.3 0.28 313 Total Assets (in $ millions) 2183 11 280 27858 15292 1890 Total Investments (in $ millions) 1345 7.1 199 23348 3699 1890 Total Investments as % of Lagged Total Assets 100% 17% 82% 370% 0.70 1890 Inventory Purchases as % of Lagged Total Assets 84% 4.9% 64% 370% 0.72 1890 CAPEX-Sale of PP&E as % of Lagged Total Assets 5.6% 0.4% 4.1% 27% 0.05 1890 R&D as % of Lagged Total Assets 8.4% 0.0% 2.0% 59% 0.13 1890 Advertising Expense as % of Lagged Total Assets 1.3% 0.0% 0.0% 17% 0.03 1890 Acquisition Cost as % of Lagged Total Assets 3.6% -0.2% 1.7% 22% 0.05 1890 Future Operating Lease Expense as % of Lagged Total Assets 16% 0.0% 7.4% 130% 0.25 1890 Cost of Goods Sold as % of Lagged Total Assets 83% 4.6% 63.0% 370% 0.71 1890 Sales as % of Lagged Total Assets 120% 4.8% 110.0% 420% 0.83 1890 Profit Margins as % of Sales -3.4% -13% 35% 88% 2.20 1890

Panel B: Firm-Year-Level Purchase Obligation Data for Post-Regulation Period

All Types mean p1 median p99 sd N 1 if Purchase Obligations are reported, 0 otherwise 0.69 0 1 1 0.5 7002 Total Amount as % of Total Assets including Non-reporting Firm-years 25% 0.0% 2% 420% 0.8 7002 Total Amount as % of Total Assets 51% 0.1% 9% 840% 2.8 4814 Total Amount (in $ millions) 703 0.13 29 19689 2809 4814 Amount Due in 1 year as % of Total Investments of Reporting Year 29% 0.0% 5% 130% 0.3 4814 Amount Due in 1 year as % of Total Investments 1 Year After 30% 0.0% 7% 130% 0.3 4814 Duration (in years) 3.2 1 3.3 5 1.9 4814 Amount-weighted Duration (in years) 2.0 1 1.7 5 1.1 4814

Inventory Purchases mean p1 median p99 sd N Total Amount as % of Total Assets including Non-reporting Firm-years 5.7% 0.0% 0.0% 92% 0.2 7002 Total Amount as % of Total Assets 56% 0.1% 5.3% 1400% 4.5 2851 Total Amount (in $ millions) 730 0.15 28 14378 3534 2851

CAPEX mean p1 median p99 sd N Total Amount as % of Total Assets including Non-reporting Firm-years 1.6% 0.0% 0.0% 40% 0.06 7002 Total Amount as % of Total Assets 25% 0.1% 6.0% 400% 0.90 760 Total Amount (in $ millions) 582 0.10 38 15134 2424 760

R&D mean p1 median p99 sd N Total Amount as % of Total Assets including Non-reporting Firm-years 1.6% 0.0% 0.0% 38% 0.06 7002 Total Amount as % of Total Assets 18% 0.1% 4.1% 380% 0.97 996 Total Amount (in $ millions) 260 0.09 22 5868 1190 996

Advertising mean p1 median p99 sd N

Total Amount as % of Total Assets including Non-reporting Firm-years 0.2% 0.0% 0.0% 15.0% 0.03 7002

Total Amount as % of Total Assets 65% 0.1% 3.1% 810% 5.6 533 Total Amount (in $ millions) 1290 0.08 20 43071 7033 533

58

Table 2: Effects of Purchase Obligation Disclosures on Dominant Firms’ Investments

The table below reports estimates from the following regression for dominant firms: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛿𝛿𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡(𝑇𝑇𝑃𝑃 𝜏𝜏𝑡𝑡 𝑇𝑇𝑃𝑃 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡) + 𝛾𝛾𝑖𝑖 +𝜖𝜖𝑖𝑖,𝑡𝑡, where the dependent variable Total Investments captures the total amount of investments recognized in financial statements measured as (inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense)×100/lagged total assets. The key independent variable PreRegRedaction is the annual average number of redacted investment contracts in the 5-year pre-regulation period, which serves as an ex ante “treatment” measure. Post is an indicator variable that takes the value of one for post-regulation years. 𝜏𝜏𝑡𝑡, 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡, and 𝛾𝛾𝑖𝑖 are year, industry by year, and firm fixed effects, respectively. Dominant firms are firms whose market shares are above the median of their respective competition groups. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively.

(1) (2) (3) (4) (5) (6) Pr. Strategic Substitutes Pr. Strategic Complements

Y: Total Investments Post×Pre-regulation Average + 19.346** 18.892** 25.609*** - -28.077** -28.471** -24.734** Redacted Investment Contracts (2.12) (2.08) (2.63) (-2.44) (-2.48) (-2.35) Post -10.326 0.208

(-0.70) (0.01) lag ROA -5.896 -10.506 -10.932 -12.647 -16.755 -22.805*

(-0.34) (-0.60) (-0.55) (-1.14) (-1.53) (-1.89) lag BTM -36.566*** -32.960*** -32.934*** -34.486*** -29.695*** -28.568***

(-9.09) (-8.31) (-8.55) (-7.20) (-6.03) (-5.41) lag ln(MVE) -29.493*** -28.624*** -29.598*** -23.769*** -23.250*** -25.282***

(-10.89) (-10.18) (-8.70) (-8.06) (-8.37) (-8.13) lag Leverage -12.564 -10.540 -12.426 -38.292*** -32.947*** -27.038*

(-1.30) (-1.11) (-1.25) (-3.25) (-2.79) (-1.88) lag Loss Indicator -7.996 -7.244 -7.875 -20.637*** -18.388*** -19.759***

(-1.47) (-1.28) (-1.40) (-3.53) (-3.19) (-3.17) Illiquidity -1.871* -3.044** -3.267** -0.870 -2.110* -2.275**

(-1.72) (-2.60) (-2.14) (-0.77) (-1.80) (-2.24) Volatility -172.754 -168.281 -232.616 145.285 160.831 70.768

(-1.36) (-1.22) (-1.16) (0.89) (0.96) (0.42) Size-adjusted Stock Return -0.129 0.491 -0.377 -3.014 -2.682 -4.571*

(-0.05) (0.21) (-0.18) (-1.09) (-0.98) (-1.72) Institutional Ownership 5.400 8.972 10.984 1.683 4.194 7.650

(0.73) (1.23) (1.35) (0.29) (0.71) (1.20) Insider Trading 0.351 0.369* 0.275 0.450** 0.434** 0.457*

(1.52) (1.75) (1.15) (2.40) (2.27) (1.91) lag Tobin Q 11.039*** 10.763*** 11.082*** 8.832*** 8.666*** 8.553***

(4.92) (4.64) (4.46) (6.20) (6.50) (6.76) lag Sale % Change 4.985 5.026 4.645 -2.357 -2.662* -4.554***

(1.59) (1.50) (1.14) (-1.53) (-1.85) (-2.87) lag CFO -56.086* -53.472* -57.897 -44.552* -37.464 -39.342*

(-1.91) (-1.79) (-1.67) (-1.83) (-1.56) (-1.73) lag Cash and Cash Equivalents -28.871** -25.569* -23.849 -17.299 -16.252 -9.499

(-2.18) (-1.86) (-1.54) (-1.40) (-1.44) (-0.66) lag Asset Tangibility -10.358 -12.378 -9.092 27.414 28.190 39.860 (-0.50) (-0.63) (-0.38) (1.09) (1.19) (1.46) Post × Controls Y Y Y Y Y Y Firm FE Y Y Y Y Y Y Year FE N Y N N Y N Year × Industry FE N N Y N N Y s.e. clustering by industry by industry by industry by industry by industry by industry N 3451 3451 3451 3577 3577 3577 Adj. R-sq 85.8% 86.0% 86.1% 81.3% 81.6% 82.3%

59

Table 3: Effects of Purchase Obligation Disclosures on Dominant Firms’ Investments by Type The table below reports estimates from the following regression for dominant firms: 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇 𝑇𝑇𝑇𝑇𝑝𝑝𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡, where the dependent variable is Capacity for Columns (1) and (3) and Product Differentiation for Columns (2) and (4). Capacity is measured as (inventory purchase + CAPEX - sale of PP&E)×100/lagged total assets, and Product differentiation is measured as (R&D expense + advertising expense)×100/lagged total assets. The key independent variable PreRegRedaction is the annual average number of redacted investment contracts in the 5-year pre-regulation period, which serves as an ex ante “treatment” measure. Post is an indicator variable that takes the value of one for post-regulation years. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively. Dominant firms are firms whose market shares are above the median of their respective competition groups. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. (1) (2) (3) (4) Strategic Substitutes Strategic Complements

Y: Pr. Capacity Product Differentiation Pr. Capacity Product

Differentiation Post × Pre-regulation Average +, 0 28.271*** 2.897 0, - -15.456 -6.425*** Redacted Investment Contracts (2.73) (1.20) (-1.52) (-2.61) Controls Y Y Y Y Post × Controls Y Y Y Y Firm FE Y Y Y Y Year × Industry FE Y Y Y Y s.e. clustering by industry by industry by industry by industry N 3451 3451 3577 3577 Adj. R-sq 88.3% 77.5% 85.5% 76.5%

60

Table 4: Falsification Tests on Dominant Firms’ Investments Not Affected by the Regulation The table below reports estimates from the following regression for dominant firms: 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇 𝑇𝑇𝑇𝑇𝑝𝑝𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡, where the dependent variable is Acquisition Cost for Columns (1) and (4), Indicator for Acquisition Cost for Columns (2) and (5), and Future Operating Lease Expense for Columns (3) and (6). Acquisition Cost is Acquisition Cost×100/lagged total assets, and Indicator for Acquisition Cost takes the value of 1 if Acquisition Cost<0, and 0 otherwise. Future Operating Lease Expense is the sum of future operating lease expenses×100/lagged total assets available in 10-K footnotes. The key independent variable PreRegRedaction is the annual average number of redacted investment contracts in the 5-year pre-regulation period, which serves as an ex ante “treatment” measure. Post is an indicator variable that takes the value of one for post-regulation years. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively. Dominant firms are firms whose market shares are above the median of their respective competition groups. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. (1) (2) (3) (4) (5) (6) Y Strategic Substitutes Strategic Complements

Pr. Acquisition Cost

1 if Acq. Cost > 0

Future Operating

Lease Expense Pr. Acquisition

Cost 1 if Acq. Cost > 0

Future Operating

Lease Expense

Post×Pre-regulation Avg. 0 1.099 0.038 3.178 0 0.104 0.008 -0.112 Redacted Inv. Contracts (0.68) (0.43) (1.29) (0.04) (0.11) (-0.04) Controls Y Y Y Y Y Y Post*Controls Y Y Y Y Y Y Firm FE Y Y Y Y Y Y Year × Industry FE Y Y Y Y Y Y s.e. clustering by industry by industry by industry by industry by industry by industry N 3451 3451 3451 3577 3577 3577 adj. R-sq 15.8% 39.6% 88.4% 16.8% 41.2% 85.3%

61

Table 5: Effects of Purchase Obligation Disclosures on Non-Dominant Firms’ Investments The table below reports estimates from the following regression for non-dominant firms: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼−𝑖𝑖 𝑜𝑜𝑜𝑜 𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡 , where the dependent variable Total Investments captures the total amount of investments recognized in financial statements measured as (inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense)×100/lagged total assets. Post is an indicator variable that takes the value of one for post-regulation years. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively. In Panel A, the key independent variable is PreRegRedaction-i, which is defined as the annual average number of redacted investment contracts for dominant competitors in the 5-year pre-regulation period. In Panel B, the key independent variable is PreRegRedactioni, which is defined as a firm’s own annual average number of redacted investment contracts in the 5-year pre-regulation period. Non-dominant firms are firms whose market shares are equal to or below the median of their respective competition groups. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. Panel A: Changes in Investments relating to Changes in Observability of Investments by Dominant Competitors (1) (2)

Strategic Substitutes Strategic Complements Y: Pr. Total Investments Post × Pre-regulation Average Redacted -, - -48.397** -58.036** Investment Contracts of Dominant Competitors (-2.16) (-2.19) Controls Y Y Post × Controls Y Y Firm FE Y Y Year × Industry FE Y Y s.e. clustering by industry by industry N 3739 3945 Adj. R-sq 77.4% 74.1% Panel B: Changes in Investments relating to Changes in Observability of Own Investments (1) (2)

Strategic Substitutes Strategic Complements

Y: Pr. Total Investments Post × Pre-regulation Average Redacted 0, 0 -13.782 -3.011 Investment Contracts (-1.00) (-0.18) Controls Y Y Post × Controls Y Y Firm FE Y Y Year × Industry FE Y Y s.e. clustering by industry by industry N 3739 3945 Adj. R-sq 77.4% 74.0%

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Table 6: Effects of Strategic Investments on Product Market Outcomes Panel A reports estimates from the following regression for dominant firms: 𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇 𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝐼𝐼𝑇𝑇 𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡. Panel B reports estimates from the following regression for non-dominant firms: 𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇 𝑃𝑃𝑇𝑇𝑃𝑃𝑃𝑃𝐼𝐼𝑇𝑇 𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 ×𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼 𝑖𝑖 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡 . In Panel A, the key independent variable is PreRegRedactioni, which is defined as a firm’s own annual average number of redacted investment contracts in the 5-year pre-regulation period. In Panel B, the key independent variable is PreRegRedaction-i, which is defined as the average redacted investment contracts for dominant competitors in the 5-year pre-regulation period. For both Panel A and Panel B, the dependent variable is COGS as % of lagged total assets for Columns (1) and (4), sales as % of lagged total assets for Columns (2) and (5), and profit margins as % of sales for Columns (3) and (6). Post is an indicator variable that takes the value of one for post-regulation years. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively. Dominant firms are firms whose market shares are above the median of their respective competition groups. Non-dominant firms are firms whose market shares are equal to or below the median of their respective competition groups. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. Panel A: Dominant Firms

(1) (2) (3) (4) (5) (6) Strategic Substitutes Strategic Complements

Y: Pr. COGS Sales Profit Margins Pr. COGS Sales Profit

Margins Post×Pre-regulation Average +, +, ? 27.533** 31.468** -9.560 -, ?, + -17.653* -6.641 6.506** Redacted Investment Contracts (2.32) (2.36) (-1.11) (-1.89) (-0.49) (2.11) Controls Y Y Y Y Y Y Post × Controls Y Y Y Y Y Y Firm FE Y Y Y Y Y Y Year × Industry FE Y Y Y Y Y Y

s.e. clustering by industry

by industry

by industry by

industry by

industry by

industry N 3451 3451 3451 3577 3577 3577 Adj. R-sq 90.1% 88.5% 56.6% 87.2% 84.9% 57.2%

Panel B: Non-Dominant Firms

(1) (2) (3) (4) (5) (6) Strategic Substitutes Strategic Complements

Y: Pr. COGS Sales Profit Margins Pr. COGS Sales Profit

Margins Post×Pre-regulation Average Redacted -, -, ? -42.956** -30.173* 2.269 -, ?, + -52.257** -27.175 13.547** Inv. Contracts of Dominant Competitors (-2.52) (-1.83) (0.36) (-2.42) (-0.99) (2.24)

Controls Y Y Y Y Y Y Post × Controls Y Y Y Y Y Y Firm FE Y Y Y Y Y Y Year × Industry FE Y Y Y Y Y Y

s.e. clustering by industry

by industry

by industry by

industry by

industry by

industry N 3739 3739 3739 3945 3945 3945 Adj. R-sq 82.2% 83.1% 64.0% 80.1% 80.6% 63.9%

63

Table 7: Validation Tests on Empirical Measures Panel A reports estimates from the following regression: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ𝑇𝑇𝐼𝐼𝐼𝐼𝑃𝑃𝑎𝑎𝑇𝑇𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 +∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡, where the dependent variable is the log of 1 plus the total amount of purchase obligations scaled by total assets in Column (1), and an indicator that takes the value of 1 if purchase obligations are reported in a given firm-year, and 0 otherwise in Column (2). The key independent variable is the annual average number of investment contracts in pre-regulation years, including both redacted and non-redacted ones. Panel B reports estimates from the following regression on a subsample of firm-years that report purchase obligations: 𝑃𝑃𝐼𝐼𝑃𝑃 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡+𝜏𝜏 = 𝛼𝛼1 ln(1 + 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃ℎ𝑇𝑇𝐼𝐼𝐼𝐼𝑃𝑃𝑎𝑎𝑇𝑇𝑃𝑃𝑃𝑃𝑇𝑇𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝐼𝐼)𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡, where the dependent variable is the average amount of total investments recognized in financial statements in the subsequent 2, 3, or 5 years scaled by total assets multiplied by 100. The key independent variable is the log of 1 plus the total amount of purchase obligations scaled by total assets reported in a given year. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 are industry by year fixed effects, and 𝛾𝛾𝑖𝑖 are firm fixed effects. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. Panel A: Relation between Pre-Regulation Investment Contracting and Post-Regulation Purchase Obligations (1) (2)

Y: Pr. ln(1+Purchase Obligations) 1 if Purchase Obligations Reported

Pre-regulation Average Investment Contracts +, + 0.266** 0.513* (2.568) (1.953) Controls Y Y Firm FE Y Y Year × Industry FE Y Y s.e. clustering by industry by industry N 7002 7002 Adj. R-sq 52.7% 50.8%

Panel B: Post-Regulation Relation between Off-Balance Sheet Purchase Obligations and Total Investments (1) (2) (3) Y: Average Total Investments Time Window for Y: Pr. 2 Subsequent Years 3 Subsequent Years 5 Subsequent Years ln(1+Purchase Obligations) +, +, + 20.997** 15.698** 10.273** (2.085) (1.980) (1.997) Controls Y Y Y Firm FE Y Y Y Year × Industry FE Y Y Y s.e. clustering by industry by industry by industry N 4814 4814 4814 Adj. R-sq 88.8% 88.4% 88.7%

64

Table 8: Alternative Measures for Identifying Strategic Substitutes versus Complements The table below reports results from the following regression separately estimated for dominant firms and non-dominant firms with strategic substitutes versus complements: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝑃𝑃𝑇𝑇𝐼𝐼𝑖𝑖 𝑜𝑜𝑜𝑜−𝑖𝑖 +∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡. In Panel A, the key independent variable is PreRegRedactioni, which is defined as a firm’s own annual average number of redacted investment contracts in 5-year pre-regulation period. In Panel B, the key independent variable is PreRegRedaction-i, which is defined as the annual average redacted investment contracts for dominant competitors in the 5-year pre-regulation period. Across the two panels, production flexibility and R&D spending are used to determine strategic substitutes versus complements. The dependent variable Total Investments captures the total amount of investments recognized in financial statements measured as (inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense)×100/lagged total assets. Post is an indicator variable that takes the value of one for post-regulation years. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively. Dominant (Non-Dominant) firms are firms whose market shares are above (equal to or below) the median of their respective competition groups. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. Panel A: Effects of Purchase Obligation Disclosures on Dominant Firms’ Investments (1) (2) (3) (4)

Strategic Substitutes Strategic Complements Competition Type Measure: Production Flex. R&D Spending Production Flex. R&D Spending Y: Pr. Total Investments Pr. Total Investments Post × Pre-regulation Average + 21.931* 26.722* - -16.490** -21.146** Redacted Investment Contracts (1.80) (1.87) (-2.35) (-1.99) Controls Y Y Y Y Post × Controls Y Y Y Y Firm FE Y Y Y Y Year × Industry FE Y Y Y Y s.e. clustering by industry by industry by industry by industry N 3207 3172 3821 3856 Adj. R-sq 82.4% 86.8% 85.4% 78.2% Panel B: Effects of Purchase Obligation Disclosures on Non-Dominant Firms’ Investments (1) (2) (3) (4)

Strategic Substitutes Strategic Complements Competition Type Measure: Production Flex. R&D Spending Production Flex. R&D Spending Y: Pr. Total Investments Pr. Total Investments Post × Pre-regulation Average Redacted - -38.597** -32.731* - -42.133** -36.096** Inv. Contracts of Dominant Competitors (-2.47) (-1.86) (-2.52) (-2.20) Controls Y Y Y Y Post × Controls Y Y Y Y Firm FE Y Y Y Y Year × Industry FE Y Y Y Y s.e. clustering by industry by industry by industry by industry N 3864 4085 3820 3599 Adj. R-sq 77.6% 72.0% 74.6% 76.6%

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Table 9: Alternative Treatment Measure and Time Period The table below reports results from the following regression separately estimated for dominant firms and non-dominant firms with strategic substitutes versus complements: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝐼𝐼𝐼𝐼𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 = 𝛼𝛼1𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝑃𝑃𝑃𝑃𝐼𝐼𝑃𝑃𝐼𝐼𝑃𝑃𝑃𝑃𝑇𝑇𝑃𝑃𝑖𝑖 𝑜𝑜𝑜𝑜−𝑖𝑖 +∑𝛽𝛽 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + ∑𝛽𝛽 𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 𝐶𝐶𝑇𝑇𝐼𝐼𝑇𝑇𝑃𝑃𝑇𝑇𝑇𝑇𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 + 𝛾𝛾𝑖𝑖 + 𝜖𝜖𝑖𝑖,𝑡𝑡. In Columns (1)-(2) of Panel A, PreRegVar is an indicator variable that takes the value of 1 if a firm has at least one redacted investment contracts during 5 years before the regulation, and 0 otherwise. In Columns (3)-(4) of Panel A, PreRegVar is an indicator variable that takes the value of 1 if a firm’s dominant competitors have at least one redacted investment contracts during 5 years before the regulation, and 0 otherwise. In Columns (1)-(2) of Panel B, PreRegVar is a firm’s own annual average number of redacted investment contracts during 5 years before the regulation. In Columns (3)-(4) of Panel B, PreRegVar is the annual average redacted investment contracts for dominant competitors during 5 years before the regulation. In both Panels, the dependent variable Total Investments captures the total amount of investments recognized in financial statements measured as (inventory purchase + CAPEX - sale of PP&E + R&D expense + advertising expense)×100/lagged total assets. 𝜌𝜌𝑖𝑖𝑖𝑖𝑖𝑖 × 𝜏𝜏𝑡𝑡 and 𝛾𝛾𝑖𝑖 are industry by year and firm fixed effects, respectively. Dominant (Non-Dominant) firms are firms whose market shares are above (equal to or below) the median of their respective competition groups. For brevity, estimated coefficients on control variables are not tabulated. Control variables are listed in Table 2. Refer to Appendix A for other variable definitions. All continuous variables are winsorized at the 1% and 99% levels to limit the influence of outliers. *, **, *** indicate statistical significance at less than 10%, 5%, and 1%, respectively. Panel A: Using a Dichotomous Measure to Capture Firms’ Exposure to the Regulation

(1) (2) (3) (4) Dominant Firms Non-Dominant Firms Strat. Sub. Strat. Comp. Strat. Subs. Strat. Comp.

Y: Pr. Total Investments Pr. Total Investments Post×Indicator for Pre-reg. +, - 23.316** -17.104** Post×Indicator for Pre-reg. Redaction -, - -20.346* -31.223* Redaction of Inv. Contracts (2.50) (-2.42) Of Inv. Cont. of Dominant Comp. (-1.86) (-1.74) Controls Y Y Y Y Post × Controls Y Y Y Y Firm FE Y Y Y Y Year × Industry FE Y Y Y Y s.e. clustering by industry by industry by industry by industry

N 3451 3577 3739 3945 Adj. R-sq 86.2% 82.3% 74.3% 73.1%

Panel B: Using 2 Years Before and After the Regulation (i.e., 4-year Window)

(1) (2) (3) (4) Dominant Firms Non-Dominant Firms Strat. Sub. Strat. Comp. Strat. Subs. Strat. Comp.

Y: Pr. Total Investments Pr. Total Investments Post×Pre-regulation Average +, - 22.980** -20.668** Post×Pre-regulation Avg. Red. -, - -26.066* -35.958** Redacted Investment Contracts (2.53) (-2.47) Inv. Cont. of Dominant Comp. (-1.79) (-1.97) Controls Y Y Y Y Post × Controls Y Y Y Y Firm FE Y Y Y Y Year × Industry FE Y Y Y Y

s.e. clustering by industry by industry by industry by industry

N 1546 1587 1760 1697

Adj. R-sq 91.3% 88.1% 83.5% 85.5%