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The Effect of Financial Reporting on Strategic Investments: Evidence from Purchase Obligations∗
Suzie Noh MIT Sloan School of Management
January 2020
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.
∗ I sincerely thank Eric So (co-chair), Rodrigo Verdi (co-chair), and Joe Weber (committee member) for their helpful feedback and insights in developing this idea. I also thank Ki-Soon Choi, John Core, Jacquelyn Gillette, Michelle Hanlon, Jinhwan Kim, Kwang J. Lee, Chris Noe, Gabriel Pundrich, Delphine Samuels, Nemit Shroff, Steve Stubben, Andrew Sutherland, and Dan Taylor for providing helpful comments and suggestions. 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: [email protected]. Personal Website: http://bit.ly/SuzieNoh.
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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.
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 (e.g., von Stackelberg 1934).
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 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, 2
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
1 See Appendix C for detailed discussions on competition with strategic substitutes and strategic complements. 2 See Appendix D for detailed discussions on underlying theory for strategic investments.
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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 quality of its aircraft by investing in energy efficiency. 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.3
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 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.
3 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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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.4 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. For instance, firms that outsource R&D but redact related contracts are more
affected by the regulation than those that do not redact or that rely on in-house departments. To
partition firms into different competition types, I use a measure developed by Kedia (2006).
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, and that the amounts due
within the next year on average account for roughly 60% of the total reported amounts.
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.
4 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.
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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. Additionally, 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
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
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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) of dominant and non-
dominant firms in each type of competition. I confirm that firms’ investments foretell their quantities
sold or 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 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 investments. While some dominant firms did so
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before the regulation, the regulation likely made more dominant firms engage in strategic investments
by facilitating their coordination and helping them take similar investment decisions (e.g., Fried 1984;
Cooper et al. 1992; Arya and Mittendorf 2016). Some dominant firms may have been reluctant to
communicate investments through voluntary disclosures, because such disclosures may reveal
proprietary information or increase the risk perceived by investors who view future payment
obligations as liabilities. The regulation likely increased the expected benefit of strategic investments
by making all firms disclose future investments and thereby allowing dominant firms to better
coordinate their strategic investments targeted at their common non-dominant competitors.
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 a reduction in adverse selection or moral hazard costs (e.g., Bushee 1998; Biddle
et al. 2009) or 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. This identification
approach is akin to “identification by functional form” (Lewbel 2019; Samuels et al. 2019).
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. To the extent these
effects are similar across firms with strategic substitutes and complements, my inference is unchanged,
as it is based on the difference in changes in investments between the two types of firms. I also devise
further tests to mitigate concerns about these alternative channels driving my results.5
5 In untabulated tests, I confirm that the regulation had similar effects on firms’ stock liquidity and proprietary costs—proxied by firms’ redaction of contracts—across firms with strategic substitutes and those with strategic complements.
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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.6 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. My finding that firms use the strategic effect of disclosures about their investments to
their advantage sheds light on a potential unintended consequence 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.
6 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.7 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, 2003.8, 9
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, 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,
7 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 8 “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. 9 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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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.10
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 and that 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.11 They include a broad range of arrangements, including
inventory purchases, CAPEX, R&D, royalty/licensing, and advertising/marketing.
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, 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
10 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.” 11 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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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.
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
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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).12
Theory on strategic investments classifies firms’ competition into two types—competition with
strategic substitutes and competition with strategic complements—depending on whether more
aggressive strategies (e.g., greater quantity, lower price, higher quality) by firms decrease or increase
competitors’ marginal profitability. The first case, where more aggressive strategies decrease
competitors’ marginal profits, is competition with strategic substitutes (e.g., Cournot competition).
The latter case, where more aggressive strategies increase competitors’ marginal profits, is
competition with strategic complements (e.g., Bertrand competition).13
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
12 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 briefly discuss a simple model introduced by Tirole (1988) on strategic investments in Appendix D. 13 See Appendix C for further discussion of competition with strategic substitutes versus complements.
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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.
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, 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
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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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 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.
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.
14
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.
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
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).
15
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 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.
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.
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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 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.
17
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 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. 21 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. See Appendix C for underlying theory for
strategic substitutes and complements as well as discussion of Kedia’s empirical proxy.
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. 21 I also 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.
18
Strictly speaking, because my definition of strategic substitutes and complements is based on a
firm’s expectations of how its competitors will respond to its move, I need to compute Kedia’s
measure for an average competitor of the firm. However, it is not possible to compute it due to the
difficulty of empirically estimating marginal profits of an average competitor.22 Therefore, following
Fudenberg and Tirole (1984) and Sundaram et al. (1996), I assume that competing firms face the same
type of competition (i.e., adopt symmetric strategies) and use Kedia’s measure of a firm as that of its
average competitor.
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, approximately 69% of
22 For example, using profits and sales summed or averaged over all competitors will not produce a good proxy for the marginal profits of an average competitor.
19
my sample firm-years report purchase obligations in their 10-Ks, and the average amount of purchase
obligations is 25% of total assets.23 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.24
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.25
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 (or $730 million) for firms
reporting inventory purchases as purchase obligations and 5.7% of total assets for all reporting and
23 By summing payment obligations across years, I effectively assume a zero discount rate. 24 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. 25 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.
20
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 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-
21
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.26
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.
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
26 See Appendix A for variable definitions.
22
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.27
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.
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). 28 My findings then suggest that
27 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. 28 This categorization is always true when demand is linear and marginal cost is constant.
23
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.29 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.
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
29 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).
24
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.30 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.
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
30 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).
25
operating lease expenses after the 2003 regulation.31 This non-result for investment items whose
disclosures are not affected by the regulation adds further confidence to my findings.32
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) 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 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
31 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. 32 The results on operating lease expenses are robust to scaling by lagged PP&E, not lagged total assets.
26
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 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.
27
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 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
28
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-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.
29
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%
(=31.468×0.2/120) 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
30
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.
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
31
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 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%.33 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
33 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.
32
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:
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.34
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-
34 2.1% = 10% × 20.997/100; 1.6% = 10% × 15.698/100; 1.0% = 10% × 10.273/100.
33
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.35
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 “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,
35 I also find significant results when running regressions separately for dominant and non-dominant firms.
34
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.
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,
35
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.
I perform additional validation or robustness tests, which are tabulated in my online appendix.36
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.
36 Available at http://bit.ly/Noh2020Appendix.
36
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 consequence 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.
37
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.
38
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).
39
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.
40
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):
41
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):
42
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):
43
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.
44
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.
45
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. Therefore, as suggested by Bulow et al. (1985), I rely on an empirical proxy to identify
competition with strategic substitutes and complements, instead of relying on theory to identify
whether competition is in quantity, price, or quality.
46
Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies
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 ΔΠ
i
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:
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:
47
Appendix C (cont’d): Strategic Substitutes and Complements and Their Empirical Proxies
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.37 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,
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.
37 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.
48
Appendix D: Simple Model for Strategic Investments from Tirole (1988)
In this Appendix, I briefly discuss a simple model presented by Tirole (1988) on strategic
investments for two games: (1) entry deterrence (equivalent to exit inducement), and (2) entry
accommodation (equivalent to incumbent competition). In doing so, I introduce the notions of
overinvestment and underinvestment. The model is based on Fudenberg and Tirole (1984) and Bulow
et al. (1985).
This simple model is a two-period dynamic game, as the commitment value of investment is a
multi-period phenomenon. In other words, the two-period game is meant to convey the idea that
product market competition is the final stage of competition, and that investment decisions are made
before product market competition begins. The figure below illustrates the timeline of the model.
There exist two firms in the game: firm 1 with a first-mover advantage, and firm 2 without it. In
period 1, firm 1, which has a first-mover advantage, chooses some investment amount of 𝐾𝐾1, and firm
2 observes it. Investment 𝐾𝐾1 in period 1 sends a signal about 𝑥𝑥1, because it represents time-bound and
(partially) irreversible activity that is needed to increase capacity, reduces marginal production costs,
and/or increases consumer demand in period 2.
In period 2, both firm 1 and firm 2 simultaneously choose their product market strategies, 𝑥𝑥1 and
𝑥𝑥2, respectively. Their profits in period 2 are ∏ (𝐾𝐾11 , 𝑥𝑥1, 𝑥𝑥2) for firm 1 and ∏ (𝐾𝐾12 , 𝑥𝑥1, 𝑥𝑥2) for firm 2.
Second-period choices, 𝑥𝑥1 and 𝑥𝑥2, are determined by a Nash equilibrium {𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)}. 38
38 Both ∏ (𝐾𝐾11 , 𝑥𝑥1, 𝑥𝑥2) and ∏ (𝐾𝐾12 , 𝑥𝑥1, 𝑥𝑥2) are assumed to be differentiable and strictly concave in 𝐾𝐾1. 𝑥𝑥1∗(𝐾𝐾1) and 𝑥𝑥2∗(𝐾𝐾1) are also assumed to be differentiable.
49
Appendix D (cont’d): Simple Model for Strategic Investments from Tirole (1988)
• Entry Deterrence or Exit Inducement Game
In the model above, firm 1 can be considered an incumbent firm, and firm 2 can be considered a
would-be entrant in period 1. Firm 1 may choose a level of 𝐾𝐾1 in period 1 so as to deter firm 2 from
entering the market in period 2. That is, firm 1 may choose 𝐾𝐾1 such that ∏ (𝐾𝐾12 , 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)) ≤ 0.
This game is called an entry deterrence game. If the model is analogously taken to have two
incumbent firms with only one having a first-mover advantage, instead of one incumbent firm and
one would-be entrant, this game is instead called an exit inducement game. In an entry deterrence or
exit inducement game, 𝐾𝐾1 should satisfy:
(1) ∏ (𝐾𝐾12 , 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)) ≤ 0, and
(2) ∏ (𝐾𝐾11 , 𝑥𝑥1(𝐾𝐾1) ) ≥ ∏ (𝐾𝐾11 , 𝑥𝑥1(𝐾𝐾1),𝑥𝑥2(𝐾𝐾1)).
If such 𝐾𝐾1 does not exist, firm 1 plays an entry accommodation game, which is explained below, and
maximizes ∏ (𝐾𝐾11 , 𝑥𝑥1(𝐾𝐾1),𝑥𝑥2(𝐾𝐾1)) as firm 1 competes with firm 2 for profits.
In an entry deterrence or exit inducement game, by concavity and continuity of Π2, firm 1
chooses 𝐾𝐾1 such that ∏ (𝐾𝐾12 , 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)) = 0. Note that 𝜕𝜕Π2
𝜕𝜕𝑥𝑥2∗�𝐾𝐾1, 𝑥𝑥1∗(𝐾𝐾1), 𝑥𝑥2∗(𝐾𝐾1)� = 0. By the
envelope theorem, the total derivative of ∏ (𝐾𝐾12 , 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)) with respect to 𝐾𝐾1 is:
𝑃𝑃Π2
𝑃𝑃𝐾𝐾1=𝜕𝜕Π2
𝜕𝜕𝐾𝐾1�+𝜕𝜕Π2
𝜕𝜕𝑥𝑥1∗𝑃𝑃𝑥𝑥1∗
𝑃𝑃𝐾𝐾1�����.
Direct effect Strategic effect
The first term on the right-hand side 𝜕𝜕Π2
𝜕𝜕𝐾𝐾1 captures the “direct” effect of firm 1’s investment on
firm 2’s profit. This would be the only effect of investment 𝐾𝐾1 if 𝐾𝐾1 was not observed by firm 2 before
its choice of 𝑥𝑥2. The equilibrium 𝐾𝐾1 when 𝐾𝐾1 is not observed by firm 2 is called an “open-loop”
50
Appendix D (cont’d): Simple Model for Strategic Investments from Tirole (1988)
solution, because firm 2’s strategy 𝑥𝑥2 cannot be contingent on 𝐾𝐾1. The open-loop equilibrium is used
as a benchmark against which to compare the effect of a change in 𝐾𝐾1 observability. Hence, this direct
effect is not considered when classifying overinvestment and underinvestment, which are defined
relative to the “open-loop” solution.39
The second term on the right-hand side 𝜕𝜕Π2
𝜕𝜕𝑥𝑥1∗ 𝑖𝑖𝑥𝑥1∗
𝑖𝑖𝐾𝐾1 captures the “strategic” effect of firm 1’s
investment, which is the effect of firm 1’s investment 𝐾𝐾1 on firm 2’s profit Π2 that channels through
firm 1’s second-period choice 𝑥𝑥1∗. This strategic effect exists because 𝐾𝐾1 changes firm 1’s second-
period action 𝑥𝑥1∗ by affecting its marginal cost, capacity, or consumer demand, which then affects
firm 2’s own profit. Given the concavity of Π2, the optimal 𝐾𝐾1 exceeds the “open-loop” solution if
and only if 𝜕𝜕Π2
𝜕𝜕𝑥𝑥1∗ 𝑖𝑖𝑥𝑥1∗
𝑖𝑖𝐾𝐾1 < 0. Because firms are competing for profits, we assume that 𝜕𝜕Π
2
𝜕𝜕𝑥𝑥1∗ 𝑖𝑖𝑥𝑥1∗
𝑖𝑖𝐾𝐾1< 0.40
Therefore, relative to the “open-loop” solution, firm 1’s optimal strategy is to overinvest.
The entry-deterring or exit-inducing level of 𝐾𝐾1 satisfying condition (1) above is typically very
costly. There are rarely cases for 𝐾𝐾1 that satisfy both conditions (1) and (2) simultaneously. Unless
there is a shock that makes condition (1) or (2) slack, such as an impending threat of entry or a sharp
increase in production costs, firms are unlikely to play an entry deterrence or exit inducement game.
Therefore, I reasonably assume that firms, during my sample period, on average play an entry
accommodation or incumbent competition game, which I discuss below.
39 𝜕𝜕Π
2
𝜕𝜕𝐾𝐾1 is often assumed to be zero. However, this effect can be non-zero if, for example, 𝐾𝐾1 is on accumulating firm 1’s
clientele, which effectively reduces the size of the market available to firm 2. The assumption on the value side 𝜕𝜕Π2
𝜕𝜕𝐾𝐾1 does
not affect the taxonomy of overinvestment and underinvestment. 40 For example, firm 1’s investment reduces firm 2’s profit by increasing firm 1’s quantity in quantity competition or reducing firm 1’s price in price competition.
51
Appendix D (cont’d): Simple Model for Strategic Investments from Tirole (1988)
• Entry Accommodation or Incumbent Competition Game
The terminology “entry accommodation” is used because the model assumes that incumbent firm
1 accommodates or allows the entry of firm 2, and chooses an investment strategy that maximizes its
profit given the presence of firm 2. In other words, the incumbent firm finds it more profitable to let
the entrant enter than to erect costly barriers to entry. This is equivalent to an incumbent competition
game, where there are two competing firms and only one has a first-mover advantage to choose an
investment strategy (e.g., von Stackelberg 1934).
In an entry accommodation or incumbent competition game, firm 1’s behavior is dictated by firm
1’s own profit. This is in sharp contrast to an entry deterrence or exit inducement game, where firm
1’s behavior is dictated by firm 2’s profit ∏ (𝐾𝐾12 , 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)), which has to be driven down to
zero (see condition (1) above). Note that 𝜕𝜕Π2
𝜕𝜕𝑥𝑥1∗�𝐾𝐾1, 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)� = 0. By the envelope theorem, the
total derivative of ∏ (𝐾𝐾11 , 𝑥𝑥1∗(𝐾𝐾1),𝑥𝑥2∗(𝐾𝐾1)) with respect to 𝐾𝐾1 is:
𝑃𝑃Π1
𝑃𝑃𝐾𝐾1=𝜕𝜕Π1
𝜕𝜕𝐾𝐾1�+𝜕𝜕Π1
𝜕𝜕𝑥𝑥2∗𝑃𝑃𝑥𝑥2∗
𝑃𝑃𝐾𝐾1�����.
Direct effect Strategic effect
The first term on the right-hand side 𝜕𝜕Π1
𝜕𝜕𝐾𝐾1 captures the “direct” effect of firm 1’s own investment.
This would be the only effect of investment 𝐾𝐾1 if 𝐾𝐾1 was not observed by firm 2 before its choice of
𝑥𝑥2. This case is called the “open-loop” case, which is the same game except 𝐾𝐾1 is not observable by
firm 2 prior to its decision and therefore cannot affect 𝑥𝑥2. The open-loop equilibrium is a benchmark
against which to compare the effect of a change in 𝐾𝐾1 observability. Hence, this “direct” effect is not
considered when classifying overinvestment and underinvestment, which are defined relative to the
“open-loop” solution.
52
Appendix D (cont’d): Simple Model for Strategic Investments from Tirole (1988)
The second term on the right-hand side 𝜕𝜕Π1
𝜕𝜕𝑥𝑥2∗ 𝑖𝑖𝑥𝑥2∗
𝑖𝑖𝐾𝐾1 captures the “strategic” effect of firm 1’s own
investment, which is the effect of investment 𝐾𝐾1 on Π1 that channels through firm 2’s second-period
choice 𝑥𝑥2∗. Given the concavity of Π1, the optimal 𝐾𝐾1 exceeds the “open-loop” solution if and only if
𝜕𝜕Π1
𝜕𝜕𝑥𝑥2∗ 𝑖𝑖𝑥𝑥2∗
𝑖𝑖𝐾𝐾1 > 0. In other words, relative to the “open-loop” solution, firm 1’s optimal strategy is to
overinvest if the sign of strategic effect is positive and to underinvest if it is negative. Therefore, the
prediction of the model for firm 1’s optimal investment strategy depends on the type of competition,
which determines whether the sign of the strategic effect 𝜕𝜕Π1
𝜕𝜕𝑥𝑥2∗ 𝑖𝑖𝑥𝑥2∗
𝑖𝑖𝐾𝐾1 is positive or negative.
The figure below illustrates a Nash equilibrium {𝑥𝑥1∗(𝐾𝐾1), 𝑥𝑥2∗(𝐾𝐾1)} for the two types of
competition: strategic substitutes and strategic complements. This Nash equilibrium is a point where,
for a given level of 𝐾𝐾1 , firm 1’s best response function with respect to 𝑥𝑥2 (i.e., 𝑃𝑃1(𝑥𝑥2) = 𝑥𝑥1
maximizing ∏ (𝐾𝐾11 , 𝑥𝑥1, 𝑥𝑥2)) and firm 2’s best response function with respect to 𝑥𝑥1 (i.e., 𝑃𝑃2(𝑥𝑥1) = 𝑥𝑥2
maximizing ∏ (𝐾𝐾12 , 𝑥𝑥1, 𝑥𝑥2) ) intersect. In competition with strategic substitutes, 𝑃𝑃𝑖𝑖′(𝑥𝑥𝑖𝑖) < 0. In
competition with strategic complements, 𝑃𝑃𝑖𝑖′(𝑥𝑥𝑖𝑖) > 0.
The sign of strategic effect of investment can be decomposed as:
sign(𝜕𝜕Π1
𝜕𝜕𝑥𝑥2∗𝑖𝑖𝑥𝑥2∗
𝑖𝑖𝐾𝐾1) = sign(𝜕𝜕Π
2
𝜕𝜕𝑥𝑥1∗𝑖𝑖𝑥𝑥1∗
𝑖𝑖𝐾𝐾1) × sign(𝑖𝑖𝑥𝑥2
∗
𝑖𝑖𝑥𝑥1∗) = sign(𝜕𝜕Π
2
𝜕𝜕𝑥𝑥1∗𝑖𝑖𝑥𝑥1∗
𝑖𝑖𝐾𝐾1) × sign(𝑃𝑃2′ (𝑥𝑥1∗)).
53
Appendix D (cont’d): Simple Model for Strategic Investments from Tirole (1988)
This follows from the fact that 𝑖𝑖𝑥𝑥2∗
𝑖𝑖𝐾𝐾1= �𝑖𝑖𝑥𝑥2
∗
𝑖𝑖𝑥𝑥1∗� �𝑖𝑖𝑥𝑥1
∗
𝑖𝑖𝐾𝐾1� and sign(𝜕𝜕Π
1
𝜕𝜕𝑥𝑥2) = sign(𝜕𝜕Π
2
𝜕𝜕𝑥𝑥1). Because firms are direct
competitors battling for profits, sign(𝜕𝜕Π2
𝜕𝜕𝑥𝑥1∗𝑖𝑖𝑥𝑥1∗
𝑖𝑖𝐾𝐾1) is negative.41 Therefore, sign(𝑃𝑃2′ (𝑥𝑥1∗)) is the opposite
of sign(𝜕𝜕Π1
𝜕𝜕𝑥𝑥2∗𝑖𝑖𝑥𝑥2∗
𝑖𝑖𝐾𝐾1), which determines whether firm 1 overinvests or underinvests relative to the “open-
loop” equilibrium.
It follows that, when firm 1’s investment 𝐾𝐾1 becomes observable to firm 2, firm 1’s optimal
strategy is to increase its investment if sign(𝑃𝑃2′ (𝑥𝑥1∗)) is negative, and to decrease if sign(𝑃𝑃2′ (𝑥𝑥1∗)) is
positive. The case where sign(𝑃𝑃2′ (𝑥𝑥1∗)) is negative (i.e., firm 2's best response curve is sloping
downward) is competition with strategic substitutes, and the case where sign(𝑃𝑃2′ (𝑥𝑥1∗)) is positive (i.e.,
firm 2’s best response curve is sloping upward) is competition with strategic complements. Bulow et
al. (1985) show that the slope of firm 2’s best response function with respect to firm 1’s strategy
𝑃𝑃2′ (𝑥𝑥1∗) can be shown as ∂2π2
∂x1𝑥𝑥2 or ∂
∂x1(𝜕𝜕𝜋𝜋
2
𝜕𝜕𝑥𝑥2), which can be interpreted as the change in firm 2’s
profitability for firm 2 being more aggressive when firm 1 becomes more aggressive.
• Comparison of Predictions for Two Games
Whereas the theory on entry deterrence or exit inducement makes the same prediction for
strategic substitutes and strategic complements, the theory on entry accommodation or incumbent
competition makes opposite predictions for the two types. In the table below, I compare the
predictions of the two theories. In particular, in competition with strategic complements, the firm with
a first-mover advantage should overinvest in an entry deterrence or exit inducement game, but
underinvest in an entry accommodation or incumbent competition game. This difference in
41 For example, firm 1’s investment reduces firm 2’s profit by increasing firm 1’s quantity in quantity competition or reducing firm 1’s price in price competition.
54
Appendix D (cont’d): Simple Model for Strategic Investments from Tirole (1988)
predictions arises from different objective functions. In an entry deterrence or exit inducement game,
the firm with a first-mover advantage minimizes the expected profits of the other firm because it tries
to prevent the entry or induce the exit of the other firm. In an entry accommodation or incumbent
competition game, the firm with a first-mover advantage maximizes the expected profits of its own,
conditional on the existence of the other firm, because it is more costly to deter entry or induce exit
of the other firm than to compete with it.
Optimal Investment for First-Movers
Strategic Substitutes Strategic Complements
Entry Deterrence (= Exit Inducement) overinvest overinvest
Entry Accommodation (= Incumbent Competition) overinvest underinvest
I assume that, during my sample period, firms on average play an entry accommodation or
incumbent competition game. This is because making large enough investments to deter entry or
induce exit is very costly and not worthwhile, unless there is an impending threat of entry, a sharp
increase in production costs, a reduction in demand, etc. For example, if erecting barriers to entry is
too costly, the incumbent firm in competition with strategic complements likely has a stronger
incentive to engage in underinvestment to avoid a price war with the new competitor or other
incumbent competitors, rather than to engage in costly overinvestments to deter its entry. Also,
without an impending threat of entry, firms are more likely to make strategic investment decisions to
effectively compete with existing competitors rather than to block unidentified potential entrants.
55
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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.
59
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
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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.
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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
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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 + ∑𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 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%
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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 + ∑𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 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%
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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 + ∑𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 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%
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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 + ∑𝛽𝛽𝑃𝑃𝑇𝑇𝐼𝐼𝑇𝑇𝑡𝑡 × 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 + ∑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 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%
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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%
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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%