JOB TALK PAPER

On the Informativeness of Joint Ventures and Strategic Alliances*

Nir Yehuda Columbia University

Graduate School of Business Email: ny119@columbia.edu

January 2005 Preliminary - Comments welcome

___________________________________________ * Based on my dissertation at Columbia University. I would like to thank the members of my committee: Stephen Penman (Co-Chair), Nahum Melumad (Co-Chair), Baruch Lev, Bjorn Jorgensen, Partha Mohanram and Doron Nissim for their helpful guidance and discussions. I would also like to thank Joseph Aharony, Marc Badia, Sid Balachandran, Moshe Bareket, Tomer Berkovitz, Ynon Gablinger, Jaywon Lee, Stephan Siegel and Ira Weiss for their helpful comments and Jeffrey Simonoff for his statistical help with the ZIP model. All errors are mine.

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On the Informativeness of Joint Ventures and Strategic Alliances

Abstract

This study examines the information content of disclosures regarding three widely used types of strategic alliances – marketing, manufacturing and R&D. I find that information concerning both Separate Entity Alliances (SEAs) and Non-Separate Entity Alliances (NSEAs) predicts future growth. However, while SEAs are fully priced contemporaneously by investors, NSEA are only partially reflected in current stock prices. Specifically, I document a significant association with future stock returns for marketing and manufacturing NSEAs, but not for R&D NSEAs. These results support the FASB 1999 special report recommendation for a detailed disclosure of significant involvement in joint ventures and similar arrangements. The results might also be relevant for IASB in its current project to improve IAS 31.

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I. Introduction

In the past two decades, strategic alliances have evolved from being a peripheral business

vehicle, mainly used to reach restricted overseas markets, into a centerpiece of corporate strategy.

Nowadays, the financial press regularly reports the formation of strategic alliances. Companies such as

General Electric, Eli Lilly, IBM, Federal Express, Cisco Systems, Starbucks and Siebel Systems all

engage in one or more strategic alliances. The number of newly established alliances has increased

from several hundred in 1970 to almost 9,000 in 2000 (Duysters and de Man, 2003). The strategy

literature (e.g. Bamford et al., 2003; Doz and Hamel, 1998) argues that today’s alliances are

considerably more flexible and central to the firm strategy than traditional joint ventures. As such, the

organizational structure of alliances has moved from one involving a separate entity into a non-equity

form, where no separate entity is established. The percentage of non-separate entity alliances in the

total number of alliances has increased from 45 percent in 1970 to more than 90 percent in 2000

(Duysters and de Man, 2003). Despite the profound change in both alliances’ structure and in the role

they play in the conduct of business, “the body of generally accepted accounting principles, or GAAP,

hasn't kept pace…In a world where strategic alliances make increasingly powerful value drivers,

financial statements fall short - airline alliances create tremendous value for airlines but don't appear

anywhere in the financial statements1”.

Separate Entity Alliances (hereafter SEAs) and Non-Separate Entity Alliances (hereafter

NSEAs) are subject to different accounting treatments and disclosure requirements, the later of which I

focus on in this paper. SEAs are included in the GAAP definition of joint ventures (Accounting

Principles Board Opinion No. 18, March 1971) and its interpretations. As such, they are usually

reported using the equity method2 and are subject to the same disclosure requirements as other equity

investments, including related parties and guarantees disclosures. In contrast, NSEAs are excluded

1 Gregory J. Millman, “Wake of the flood: the accounting scandals weren't all bad. Yes, they shook investor confidence. But they're also driving overdue changes in financial reporting”, The Chief Executive Jan-Feb, 2003 2 APB 18 requires the equity method for investments in common stock of enterprises and Emerging Issues Task Force (EITF) Issue No. 00-01 from January 2000 expands this treatment to investments in other entities.

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from the definition of joint ventures3. Each participant records the transaction in his own books, and

there are no specific disclosure requirements for those alliances beyond the general standards of fair

disclosure in financial statements. This leads to limited information regarding NSEAs in the financial

statements.

Recently, there has been a growing interest by the standard setters in this issue. In September

1999 the FASB, in conjunction with the G4+14, issued a special report titled “Reporting Interests in

Joint Ventures and Similar Arrangements” (hereafter FASB special report). One of its main

recommendations is a detailed disclosure of significant involvement in joint ventures and similar

arrangements. The IASB has launched a project to improve IAS 31, the international standard that

governs joint venture accounting treatment. It is expected that the FASB will also include this subject

in its agenda as part of the mutual decision of the FASB and the IASB in April 2004 to coordinate

their future technical agendas5.

In this study, I investigate whether alliance-based information is value-relevant for investors. I

focus on three widely used types of operating alliances - manufacturing, marketing and R&D - and

compare SEAs and NSEAs. My analysis is twofold. First, I study the incremental growth associated

with investment in alliances over similar investments carried out exclusively by the firm. Second, I

examine whether this information is reflected in (a) current stock prices and (b) future stock returns.

While the first analysis captures the relationship between alliance-based information and an ex-post

realized measure of growth, the contemporaneous stock prices mirror the ex-ante market expectations

with regard to this information. If investments through alliances do generate incremental growth, then

in a semi-strong efficient market, this information should be fully anticipated by investors and

embedded in current stock prices. However, in an inefficient market, this information might predict

3 This paper focuses on NSEAs, which are defined in IAS 31, as “joint operations”. If the NSEA involves jointly controlled assets, the Proportionate Consolidation Method is used. See section 2.2.2 4 The G4+1 is a group of accounting standard setters that consists of representatives from Australia, Canada, New Zealand, the United Kingdom and the United States, with representatives from the IASB participating as observers. 5 SFAS 141 states that “the Board intends to consider issues related to the accounting for the formation of joint ventures … in another project”.

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subsequent stock returns, as prices gravitate towards intrinsic values. Given that the efficiency of the

market with regards to this information is, to a large degree, a function of the current disclosure of

alliances, examining this efficiency empirically may shed light on the quality of this disclosure for

SEAs and NSEAs.

The results of my first test indicate that both types of alliances generate positive incremental

growth. These results are consistent with the strategy and economics literature, which suggests that

investments in alliances create more value than similar investments carried out exclusively by each of

the partners, as they allow firms to gain competitive advantages that are too slow or costly to achieve

alone. This literature (e.g. Contractor and Lorange, 1998; Spekman et al., 2000; Harrigan, 2003) offers

several explanations for the incremental growth associated with investments in alliances: enabling the

participant firms to focus on activities in which they have a competitive advantage, reducing up front

investments, diversifying risk, blocking competition, reaching new markets and enjoying the benefits

of economies of scale.

The results of my second test indicate that information regarding both types of alliances is

positively related to the enterprise market-to-book ratio, even after controlling for return on assets and

long-term earnings growth, suggesting that investors consider this information in setting stock prices.

However, NSEA information predicts future stock returns, while SEA information does not. This

suggests that information regarding NSEAs is not fully impounded in stock prices. It can be explained

in light of the difference in the disclosure requirements for the different types of alliances. Limited

information is provided to investors about NSEA activities, and most of the information that is being

disclosed is not included in the financial statements. Therefore, it is reasonable to expect that

investors, “having limited attention and processing power (i.e. capacity for handling information)”

(Hirshleifer and Teoh, 2003), will overlook some of the benefits of alliance investments in setting

stock prices.

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The relationship between joining alliances and future growth might raise endogeneity

concerns. If the firm's number of alliances is determined by its growth prospects in the first place, then

this number cannot be considered exogenous. In this case OLS regression might yield inconsistent

estimators. To mitigate this problem, I employ a two-stage least squares model and estimate an

instrument for the number of alliances using a ZIP (Zero Inflated Poisson) regression (Lambert, 1992;

Greene, 1994).

In conducting my analysis, I study the effect of marketing, manufacturing and R&D NSEAs

separately. While all three types are positively associated with future performance and the price-to-

book ratio, I document differences in their ability to predict subsequent returns. I find a significant

association between alliance-based information and subsequent stock returns for marketing and

manufacturing alliances, but not for R&D alliances. This is consistent with the observation that R&D

alliances are more familiar to investors than other types of alliances. In addition, R&D alliances often

result in the development of new patents or drugs, which may assist investors in considering their

effects.

The remainder of the paper proceeds as follows: In the next section I discuss the essence of

strategic alliances. Section III develops the hypotheses. In Section IV, I elaborate the dataset, variable

measurement and descriptive statistics. The tests and findings are summarized in Section V, robustness

checks are detailed in Section VI, and conclusions are offered in Section VII.

II. Accounting Treatment of Strategic Alliances

2.1 What exactly are strategic alliances? – an economic definition

An economic definition of an alliance is provided by Spekman et al. (2000, p. 37) as “a close,

collaborative relationship between two or more firms with the intent of accomplishing mutually

compatible goals that would be difficult for each to accomplish alone.” This definition embraces a

wide range of organizational forms and indeed Spekman et al. (2000) point out that the term alliance

covers a number of relationships that span the continuum between open market transactions and

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vertical integration. At one end are bilateral contracts, which are informal NSEAs, such as enhanced

supplier agreements, contractual research collaborations, marketing affiliations, licenses and multi-

partner consortia. At the other end of the spectrum are the traditional SEAs and partnerships. The

considerations in choosing between SEAs and NSEAs are discussed in appendix A.

2.2 Accounting for alliances

The accounting for joint ventures and strategic alliances involves three main issues: (a) the

definition of a joint venture for accounting purposes; (2) treatment (equity method, proportionate

consolidation or aggregation within each participant account) and (3) disclosure. I will discuss these

issues while contrasting APB 18, the US standard for joint ventures, and IAS 31, the international

standard that governs joint ventures. This comparison will demonstrate the difficulties in the

accounting for alliances.

2.2.1 Definition

As the term alliance or joint venture6 covers a wide range of arrangements, it is difficult to

define for accounting purposes. In the US, the definition of a joint venture includes only SEAs. APB18

addresses investments in common stock of corporations and defines “corporate joint venture”

narrowly as “a corporation owned and operated by a small group of businesses as a separate and

specific business or project for the mutual benefit of the members of the group”. EITF Issue No. 00-1

expands this definition to any other form of entity, as will be discussed in the treatment section.

In contrast, IAS 31 defines “joint venture” broadly to include both SEAs and NSEAs. The

exact definition is “a contractual arrangement whereby two or more parties undertake an economic

activity that is subject to joint control”. Joint control is defined as “the contractually agreed sharing of

control over an economic activity”, and exists only when the strategic financial and operating

decisions relating to the activity require the unanimous consent of the parties sharing control (the

6 The strategy literature usually uses the term “joint venture” with regards to alliances involving a separate entity and the term “alliances” is used in a broader sense to encompass all possible joint agreements. Accounting standards regard all types of joint agreements as “joint ventures”.

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venturers). The standard identifies three broad forms of joint ventures: (1) jointly controlled entities –

this is similar to the APB definition, (2) jointly controlled operations - the use of assets and other

resources of the venturers rather than the establishment of a separate entity. Each venturer uses its own

assets, incurs its own expenses and liabilities, and raises its own finance. For example, when two or

more venturers combine their operations to manufacture and market a product. Finally, there are (3)

jointly controlled assets which involve the joint control, and often the joint ownership, of assets that

are not a separate entity. Examples may include sharing ownership of a mine or an oil pipeline7;

2.2.2. Treatment

SEAs are reported using the equity method. APB 18 requires the use of the equity method for

investments in corporate joint ventures. Investors in unincorporated entities such as partnerships and

other unincorporated joint ventures generally account for their investments using the equity method of

accounting by analogy to Opinion 18 if the investor has the ability to exercise significant influence

over the investee (APB 18 interpretation no. 2). Paragraph 19(c) of APB 18 requires investments

accounted for by the equity method to be displayed as a single amount in the investor's balance sheet,

and the investor's share of the investee's earnings or losses to be displayed as a single amount in the

investor's income statement. EITF Issue No. 00-01 discusses the question whether proportionate gross

presentation is appropriate under the equity method of accounting for an investment in a legal entity.

EITF Issue No. 00-1 states that “the task force reached a consensus that a proportionate gross financial

statement presentation is not appropriate for an investment in an unincorporated legal entity”.

The rationale for using the equity method in the case of SEAs stems from the fact that the idea

of an enterprises sharing control over assets and business activities with other enterprises seems to be

inconsistent with an accounting framework that defines assets and accounting enterprises in terms of

exclusive control relationships. An asset is defined as “a resource controlled by the enterprise”. The

objective of consolidated financial statements is to include the accounts of all the subsidiary 7The “Jointly controlled assets” form of joint ventures is common in the oil and gas and real estate industries and is subject to specific accounting standards in these industries.

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enterprises controlled by the parent enterprise. This framework does not seem to provide an evident

basis for determining how to represent assets and enterprises over which control is shared with other

enterprises.

In contrast to APB 18, IAS 31 recommends as a benchmark practice the proportionate

consolidation for SEAs. And, as an alternative, the equity method is also permitted.

The IAS 31 suggested treatment for NSEAs is as follows: (a) jointly controlled assets - each

investor displays, on a proportionate gross basis, those assets and liabilities in the investor's balance

sheet and the related results of operations in the investor's income statement; (b) Jointly controlled

operations - there is no special accounting treatment and each participant records his share according

to general accounting principles. This treatment is consistent with the one in the US.

2.2.3 Disclosure

The disclosure of SEAs is governed by APB 18. It requires, subject to materiality thresholds,

that the participant will disclose parenthetically, in notes to financial statements, or in separate

statements or schedules: (1) the name of each investee and its percentage of ownership of common

stock; (2) the accounting policies of the investor with respect to investments in common stock; and (3)

the difference, if any, between the amount at which an investment is carried and the amount of

underlying equity in net assets, and as well as the accounting treatment of the difference.

Additional disclosure requirements that apply for equity investments apply also in the case of

SEAs. FASB 57 “related parties disclosure” - requires disclosures of transactions between investors

and equity method investees. FASB interpretation 34 “disclosure of indirect guarantees of

indebtedness of others” requires disclosure of any guarantees of indebtedness of equity method

investees, or any arrangements obligating an investor to transfer funds to an investee upon occurrence

of specified events.

Conversely, NSEAs are not governed by specific disclosure requirements beyond the general

standards of fair disclosure in the financial statements, as they do not fall into the definition of joint

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ventures8. However, as the FASB special reports states, in this case “disclosure of the economic

dependence on the other participants in a joint operation, and of any cross guarantees may be

necessary…”. The report further recommends that a “general standards of fair presentation should be

considered to require sufficient information about other joint arrangements to enable financial

statement users to understand their impact and potential implications for the ability of the enterprise to

generate cash flows in future periods.”

2.3. Related Literature

As discussed above, the accounting for joint ventures and strategic alliances involves three

main issues: (a) definition; (2) treatment (equity method, proportionate consolidation or aggregation

within each participant accounts) and (3) disclosure. Previous accounting research in the area of joint

venture has focused on the different treatments – specifically, equity method vs. proportionate

consolidation.

Graham et al. (2003) examine the predictive ability of the equity method vs. proportionate

consolidation for a sample of 78 Canadian firms over the years during the period from 1995 through to

2000. The joint ventures studied are reported using the proportionate consolidation method. The

authors created pro forma equity method balance sheets and income statements for each of the

companies. This allowed them to compare financial ratios under proportionate consolidation with

financial ratios under the equity method. The findings indicate that the proportionate consolidation

method provides better predictions of future profitability than the equity method. Greater predictive

ability suggests that the proportionate consolidation method may provide investors with more relevant

information than the equity method. Kothavala (2002) shows that joint venture revenue and operating

earnings are relevant for forecasting and valuation and that failure to disclose them separately results

in loss of information that is relevant to investors.

8 For certain R&D arrangements, some disclosure is required by SFAS No. 68

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In Healy and Palepu (2001) disclosure literature review, the authors suggest that "innovations

in organizational forms, such as closely-coordinated supply chains and strategic alliances, also

significantly affect the nature of financial reporting and disclosure… Reflecting these types of

interdependencies in financial statements is challenging for standard setters. Current standards ignore

them, potentially reducing the timeliness of accounting information".

III. Development of hypotheses

According to the strategy literature, when compared with other business investments,

investments via alliances create more value and hence are more productive. Contractor and Lorange

(1998) provide possible explanations for this claim, enabling the participant firms to focus on activities

in which they have a competitive advantage, and on reducing up front investments, diversifying risk,

blocking competition and enjoying the benefits of economies of scale. Harrigan (1987, 2003) asserts

that firms use alliances in order to restructure industries, create new products, keep abreast of rapidly

changing technologies, and ease problems of global excess productive capacity.

In contrast to their advantages, alliances also exhibit numerous drawbacks (Harrigan, 1987),

including antitrust problems, sovereignty conflicts, lack of autonomy and loss of competitive

advantage. Antitrust problems are usually due to a governmental objection to agreements that might

limit competition in certain industries. Sovereignty conflicts might arise in international alliances

between the hosting nation and the multinational company. Lack of autonomy may result from the

joint control feature of alliances and joint ventures, which requires unanimous consent with regard to

all major decisions. As different participants might have different long-term goals, poorly structured

ventures encourage political behavior problems. Finally, spillover and know-how transformation

might also raise additional difficulties in managing the alliance.

Given the advantages and disadvantages of alliances, their net impact on the firm value is an

empirical question. Previous economic research has examined the relationship between investment in

alliances and firm performance. Although alliances may take many forms and types, studies in

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economics have concentrated on SEAs, whose primary purpose is to engage in cooperative research

joint ventures. Branstetter and Sakakibara (1998) examine a large sample of Japanese government-

sponsored research consortia. They find a positive relationship between the number of consortia the

firm is affiliated with and its R&D expenditure and research productivity, measured as the number of

patents granted in the US. Furthermore, the authors find evidence that part of this positive relationship

arises from the increased knowledge spillovers that take place within these consortia.

Benfratello and Sembenelli (2002) test whether participation in European Union sponsored

research joint ventures has a positive impact on participating firms' performance. The authors examine

two different programs: EUREKA and (3rd and 4th) Framework Programs for Science and

Technology (FPST). Their results show a positive association between participation, labor productivity

and price cost margin in the case of EUREKA, while firms participating in FPST research joint

ventures do not reveal any clear pattern. On the other hand, Vonortas (1997) analyses the research

joint ventures undertaken in the 1985-1995 period. He documents a negative relationship between the

ratio of net income to sales and research joint ventures participation at both the firm and the industry

level. However, as he explains, his specification does not control for many variables that affect

profitability.

This leads to my first hypothesis, stated in alternative form:

H1: Alliance-based information predicts incremental future growth.

To test this hypothesis, I use the ex-post realization of long term growth. If H1 holds then this

information should either be reflected in current price or predict future returns. In a (semi-strong)

efficient market the current price mirrors the investors' ex-ante expectations. If investments through

alliances do generate future growth and the market is efficient, this information should be fully

reflected in contemporaneous stock prices and should not predict future stock returns. In an inefficient

market, this information should be partially reflected in contemporaneous stock prices and predict

subsequent stock returns, as prices gravitate towards intrinsic values.

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Limited information is provided to investors about NSEAs and most of the information that is

being disclosed is not included in the financial statements. Therefore, it is reasonable to expect that

some investors will overlook part of this information, leading to market inefficiency. Hirshleifer and

Teoh (2003) show that when the attention of investors is limited, the degree to which accounts are

aggregated in financial statements matters. They show that in the case of segment reporting, excluding

information that is currently available in press releases from the financial statements may lead to

mispricing, because some of the investors will not take this information into account9. On the other

hand, the disclosure of SEAs is more enhanced, at least with regards to material investments, in joint

ventures. As this disclosure is included in the financial statements, it is reasonable to conjecture that it

would be taken into account to a large degree by investors.

Another stream of literature has focused on the stock market reaction to the announcements of

joint ventures. McConnell and Nantell (1985), Koh and Venkatraman (1991), and Mohanram and

Nanda (1996) have all documented positive abnormal returns around the announcement date.

Mohanram and Nanda (1996) show that the market reaction is positively related to joint ventures

(SEAs) involving pooling of complementary resources. Joint ventures that are carried out by firms

with high levels of free cash flow are received negatively, indicating that the stock market penalizes

joint ventures that are susceptible to managerial misalignment. Small firms that enter into joint

ventures with larger firms earn significant positive abnormal returns, because the joint ventures signal

to the market the small firm’s value.

Gleason et al (2003) study the effects of the use of SEAs and NSEAs by a sample set of firms

in the banking, investment services, and insurance industries. They also find positive significant

9 Hirshleifer and Teoh (2003) model the case of a firm that has several segments and with a different growth rate for each segment. In their model the market consists of two classes of investors: attentive investors who understand the firms’ segments and that segmental growth rates may differ and inattentive investors, who extrapolate earnings using the firm’s overall growth rate. According to the assumption made by the authors, using the overall firm growth rates will underweight the bigger (and faster growing segments), and cause the inattentive investors to underestimate the growth in the firm’s earnings. As a result, inattentive investors (and therefore the market as a whole) undervalue all firms with multiple segments. Managers of undervalued firms could eliminate the undervaluation error simply by splitting off their segments, and could reduce it by simply releasing segment data.

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abnormal returns on the announcement date. The abnormal returns documented are significantly

positive across the four strategic motives of domestic, international, horizontal, and diversifying

cooperative activities.

This leads to the following two hypotheses, stated in alternative form:

H2: Alliance-based information is fully reflected in contemporaneous stock price.

H3: Non-separate entity alliance-based information predicts future stock returns.

IV. Dataset, Variables measurement and Descriptive Statistics

The dataset is constructed from five sources: (1) Securities Data Company (SDC) Database on

Joint Ventures and Alliances, (2) Compustat for financial data, (3) IBES for analysts’ growth

forecasts, (4) CRSP for stock returns, (5) the NBER U.S. Patent Citations Data File (for detailed

discussion of this database see Hall et al., 2001). The SDC database contains information on all types

of alliances and is compiled from publicly available sources including SEC filings, industry and trade

journals, news reports and press releases. The SDC dataset is among the most comprehensive sources

of information on alliances and the only source publicly available for large-scale empirical studies on

alliance activity. SDC has collected information on alliances since 1970. However, coverage of

alliances from 1988 on is more comprehensive than that of the pre-1988 period.

The time period covered by the sample is 1988-2002. All NYSE and AMEX listed firms on the

Compustat annual database were included, both survivors and non-survivors, with the exception of

financial firms. For each firm, in any given year, I have summed up the number of alliances reported

in SDC with the exception of partnerships and alliances in which one participant has more than 50

percent control (subsidiaries)10. In estimating regression equations, I trimmed firms with the top and

bottom one percent (in the data pooled over all years) of each variable included in the regressions.

After outliers removal, the sample consists of 21,130 firm years.

10 I examined the accounting treatment for a sample of 20 transactions and found out that companies treat these transactions as an acquisition on their financial statements.

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To determine whether a firm participated in an alliance in a given year I used the effective

beginning date and termination date from SDC. For firms without an effective beginning I used the

announcement date if the firm had already signed the deal at the date of the announcement. For firms

without a termination date I used four years as the alliance lifespan for NSEAs and five years for

SEAs11.

Panel A of Table 1 presents the distribution of the number alliances per firm year12. The

numbers represents the total number of active alliances per firm within each year, and not only the

newly established alliances in that year. Overall, the number of NSEAs per firm-year is larger than the

number of SEAs. When examining the number of alliances from both types (SEAs and NSEAs) per

firm-year, about 25% of the firm year observations indicate a participation in at least one alliance. The

table further classifies each type by business activity - marketing, manufacturing and R&D. The

largest number of NSEAs per firm-year is R&D and the lowest number is manufacturing. The largest

number of SEAs per firm-year is manufacturing and the lowest number is marketing. Note that an

alliance can be classified as more than one type.

Panel B of Table 1 shows the mean investment levels for firms which participated in at least

one alliance during the year and firms that did not. The comparison is made across firm-year

observations. When examining participation in alliances, I find that the total number of firm-year

observations in which the firm has participated in at least one SEA is 3,463, while the number of firm-

year observations in which the firm has participated in at least one NSEA is 4,174. Also the number of

firm years with participation in at least one NSEAs is higher than the equivalent number for SEAs for

marketing (3,249 vs. 2,043 firm years) and R&D (2,978 vs. 1,513 firm years) alliances but not for

manufacturing alliances (2,309 vs. 2,600 firm years). 11 Harrigan (1988) reports a venture duration of 3.5 years for a sample of 895 marketing, manufacturing and R&D joint ventures operating in the years 1924-1985 in 23 industries. Hatfield et al. (1998) examine a sample of 72 manufacturing joint ventures and report an average duration of 4.18 years. Lerner and Malmendier (2003) report a duration of 3.9 years for biotech alliances. Repeating the analysis with a duration of 3 and 4 years for both NSEAs and SEAs yielded similar results. 12Appendix B presents firm year observations with SEAs and NSEAs as a percentage of total firm year observations in the same industry.

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Investment in NSEAs is usually included in the relevant account: investment in R&D NSEA is

included in the R&D expenditure on the firm’s income statement, investments in manufacturing

NSEA are usually reported as part of total capital expenditure - CAPEX13 - on the statement of cash

flows, and investments in marketing NSEA would be included in the SG&AI - the investment portion

of SG&A14 of the firm on its income statement. In contrast, investments in SEAs are usually included

as a one-line item on the statement of cash flows titled “equity and joint ventures investments”. The

table presents the mean level of investment for those firms that are and are not involved in alliances.

The investments are scaled by total operating assets (defined in Nissim and Penman, 2001) at the

beginning of the year. Panel B of Table 1 indicates that the R&D expenditure is significantly larger for

firms which are involved in R&D alliances (both NSEAs and SEAs), but CAPEX is lower for firms

involved in manufacturing alliances. For firms involved in NSEAs, SG&AI is significantly larger.

Finally, firms that are involved in alliances (both NSEAs and SEAs) have significantly higher equity

and joint ventures investments.

V. Tests and results

This section discusses the tests and results of the hypotheses presented in section III.

5.1 The relationship between alliance-based information and future growth

To test H1, I examine whether alliance-based information predicts future growth. I depart from

a constant returns to scale production function (Jorgenson and Stiroh, 1999; Lev and Radhakrishnan,

2004) that relates growth (or change) in gross output to growth in inputs. I decompose growth in

capital inputs into investment and depreciation based on the cash flow statement information.

Specifically, I consider the following cross-sectional equation:

13 Throughout this paper, I define CAPEX as the purchase minus sale of PPE. In this way, CAPEX and DEP accounts for the entire change in PPE. 14 Under current GAAP any investment in marketing capital is included in SG&A and expensed immediately. I use 20% of the SG&A as a proxy for the investment portion. This is consistent with the findings of Kovacs (2004). Another possibility is to include only advertising expenses; however, this measure has two disadvantages – first, it only accounts for a small part of the marketing capital investment and second, this variable is zero for many companies.

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1 2 3 4 5 6 7_ _induGROWTH K INV K INV AF DEP L WC ACQ SIZEα β β β β β β β ε= + + • + + ∆ + ∆ + + + (1)

Where: K_INV=CAPEX+R&D+E_INV+SG&AI

All variables except for SIZE are deflated by total operating assets (defined in Nissim and Penman,

2001) at the beginning of the year to mitigate the effect of heteroscedasticity.

The dependent variable, GROWTH, is average total revenue in the subsequent three years

minus current total revenue. Total revenue is defined as sales plus income in affiliates and joint

ventures. I focus on growth in revenue rather than growth in earnings for the following two reasons:

(a) From a theoretical perspective, the model of a production function relates gross output (revenue) to

inputs rather than net output (earnings); (b) earnings includes write-offs and depreciation of the proxy

for capital investment. Focusing on revenue growth allows me to avoid this mechanical effect15. For

equation (1) only, I also study the effect of alliances on one year ahead growth and two years ahead

growth in order to evaluate whether the effect is short- or long-term.

The independent variables are change in capital and change in labor inputs. The change in

capital is the sum of total capital investment and depreciation. Total capital investment - K_INV - is

the sum of CAPEX (capital expenditure), R&D expenditure, E_INV (“equity and joint venture

investments” - from the cash flow statement) and SG&AI, (the investment portion of SG&A, defined

in the previous section). DEP is depreciation and amortization expense. The change in labor inputs is

captured by ∆L - the change in the number of employees. I also include the following control

variables: ∆WC is the change in working capital, which affects short term growth in revenue; ACQ –

business acquisitions and SIZE - the natural logarithm of total assets. The intercept, αindu is an

industry-fixed effect (two-digit SIC code).

AF is alliances frequency. I define frequency as the natural logarithm of the number of

15See Lev and Sougiannis footnotes 2 and 5 and Aboody et al. p 158 for similar treatment. In the robustness checks section, I run this specification again using growth in earnings before deprecation plus income in affiliates as the dependent variable. The results are qualitatively similar but differ in the level of significance.

- 17 -

alliances plus one16 (the number of alliances is calculated for each firm in any given year). I use log

transformation since the relationship between growth in revenue and the number of alliances is

nonlinear. To test H1, I focus on β2, the coefficient of the product of AF and the capital investments

variable, K_INV. If capital investments through alliances are more productive than similar

investments carried out exclusively by the firm, then this coefficient should be positive.

The relationship between investments in alliances and future revenue growth might raise

endogeneity concerns. If the number of alliances a firm joins is affected by its growth prospects in the

first place, then this number cannot be considered exogenous and an OLS regression might yield

inconsistent estimators17. This calls for estimating equation (1) via a two-stage least square method.

Hence, I regress the number of alliances a firm joins on its exogenous determinant variables and

substitute the median predicted value from this regression into equation (1). This specification is

elaborated in the section V.

Panel A of Table 2 reports the summary statistics from estimating equation (1) using two-stage

least squares. As predicted, investments in alliances generate incremental revenue growth. The

coefficient of the product of total capital investment and alliances frequency is 0.05 for growth in

revenue in the subsequent year, but then it is increased to 0.09 and 0.14 for growth in the subsequent

two and three years. Each coefficient represents the average contribution of alliances of both types

(NSEAs and SEAs). The average incremental impact of investment via one alliance on revenue growth

in the first year is 0.05xlog(2)=0.035 (AF is calculated as the natural logarithm of the number of

alliances plus one). The average incremental impact of one alliance on growth in revenue in the

subsequent three years is 0.14xlog(2)=0.10. As the coefficient of total investment (K_INV) is 0.74,

this represents a 13% increase. Namely, for each $100 of investment, an additional $13 of growth in

revenue can be achieved if this investment is carried out using an alliance.

16 Defining frequency as the square root of the number of alliances produces similar results. 17 See Kennedy (1998), p. 157, for a detailed discussion

- 18 -

For firms with a large number of alliances, the incremental growth is even higher. For

example, for a firm with 5 alliances, the incremental impact is 0.14xlog(6)=0.25 (or one third of the

overall impact of total investment 0.25/0.74=0.34).

The adjusted R2 is 0.22 for growth in the subsequent year but then increases to 0.26 for growth

in revenue three years ahead.

5.1.1. SEAs vs. NSEAs

To examine the different impact of SEAs and NSEAs on revenue growth, I estimate equation

(1) above for each type separately. Panel A of Table 2 presents the incremental effect of alliances from

both types - NSEAs and SEAs. When examining growth three years ahead, the coefficient of NSEAs’

frequency is 0.15, which corresponds to an average effect of 0.15xlog(2)=0.10 for one NSEA or

0.10/0.74=13% of the total effect. The coefficient of the SEAs is very similar (0.14), suggesting that

the two types of alliances result in similar revenue growth.

5.1.2. NSEAs by activity (Manufacturing, Marketing and R&D)

To examine the impact of the different NSEAs on revenue growth, I break down K_INV into

its components - namely, CAPEX, R&D and SG&AI. I estimate the following equation:

1 2 3 4 5 6 7 8 9& &induGROWTH CAPEX SG AI R D AAINV DEP L WC ACQ SIZEα β β β β β β β β β ε= + + + + + + ∆ + ∆ + + + (3) where AAINV - Alliance Investment by Activity - is defined as either CAPEX•MNAF, SG&AI•MAF or

R&D•RDAF. MNAF is manufacturing alliances frequency, RDAF is R&D alliances frequency and

MAF is marketing alliances frequency. β4 is the incremental impact of alliances of a given activity.

GROWTH includes growth in sales and excludes income in affiliates and joint ventures in this case,

because SEAs are not included in the analysis.

Panel B of Table 2 exhibits summary statistics for equation (3) and reveals that each activity

type - manufacturing, marketing and R&D - has a positive and significant effect on the relationship

between investment and revenue growth. The incremental impact of manufacturing, marketing and

R&D alliances is, respectively, 0.61, 1.40 and 0.58. However, when calculated as a percentage of the

- 19 -

coefficient on the respective investment, the percentages are (for one alliance): 0.61xlog(2)/1.30=0.33

for manufacturing alliances (1.30 is the coefficient of CAPEX), 1.40xlog(2)/0.43=2.26 for marketing

alliances (0.43 is the coefficient of SG&AI), and 0.58xlog(2)/0.12=3.35 for R&D alliances (0.12 is the

coefficient of SG&AI). This suggests that R&D alliances have the largest incremental effect on

revenue growth. The findings with regard to marketing alliances can be explained by the fact that

some of these alliances are classified as both marketing and R&D, a common phenomenon in the

biotech industry where a biotech develops a drug that is later on distributed by a pharmaceutical

company. However, caution should be exercised when interpreting the above percentages as the

coefficient on R&D expenditure and SG&AI is insignificant.

5.2 The relationship between alliance-based information and the enterprise market-to-book ratio

In this section, I examine H2 - namely, the extent to which contemporaneous stock prices

reflect the growth information conveyed by alliance-based information. In particular, I study the

following equation (all variables except for SIZE are deflated by total operating assets):

1 2 3 4 5 6 7 8/ _ _induVOA OA ROA LTG K INV K INV AF ACQ OLLEV FALEV SIZEα β β β β β β β β ε= + + + + • + + + + + (4)

VOA/OA is the ratio of the value of operating assets to their book value at the end of the first quarter.

Previous research (e.g., Penman, 1996) indicates that the determinants of the market-to book ratio are

future return on assets and future growth in earnings. It has also been shown that financial and

operating leverage affects this ratio (Nissim and Penman, 2003). This has led me to choose the

following controls in equation (4): current ROA - return on operating assets - is a proxy for future

ROA, LTG - analysts’ forecasts of long term growth in earnings - is a proxy for future growth;

FALEV is the ratio of financial assets to operating assets; OLLEV is the ratio of operating liabilities to

total operating assets; ACQ represents acquisitions; and SIZE is the natural logarithm of total sales. I

also include the capital investment and its interaction with the frequency of alliances in order to

estimate the incremental effect of investments in alliances.

- 20 -

Table 3 reports estimates from equation (4). For each classification of alliances, I estimate (4)

with and without LTG as the forecast is not available for all firms. Panel A indicates that investors do

take into account contemporaneously the information regarding alliances. In setting prices, investors

account for the effect of alliances on future growth (a coefficient of 0.80 and a t-statistic of 3.09, when

controlling for ROA; and a coefficient of 0.46 and a t-statistic of 2.24, when controlling for both ROA

and LTG).

When examining the coefficients of SEAs vs. NSEAs the results are very similar: when

controlling only for ROA the coefficient of SEAs is 0.78 and the coefficient of NSEAs is 0.83; when

controlling for both ROA and LTG, the coefficient of SEAs is 0.41 and the coefficient of NSEAs is

0.47.

Panel B examines the pricing of alliance investments by activities. The findings indicate that

investors use information about investments in alliances in setting prices. The coefficient of

manufacturing, marketing and R&D alliances is respectively 2.82, 3.41 and 0.63. When calculated as a

percentage of the coefficient on the respective investment, the percentages are (for one alliance):

2.82xlog(2)/2.64=0.74 for manufacturing alliances, 3.41xlog(2)/4.72=0.50 for marketing alliances and

0.63xlog(2)/5.87=0.07 for R&D alliances. This suggests that the manufacturing alliances have the

highest incremental effect on the enterprise market-to-book ratio, while R&D alliances have the lowest

effect. The low pricing of R&D alliance information might be the result of the uncertainty surrounding

them, causing investors to be conservative in setting prices.

5.3 The relationship between alliance-based information and future returns

This section tests H3 - the possibility that prices do not fully reflect information on alliances. I

study the association of this information with future returns. Based on prior research (e.g., Fama and

French 1992) I run the following cross sectional specification:

1 2 3 4 5 6 7_ _ induR K INV AF K INV AF SIZE E/P B/P BETA VOLα β β β β β β β ε= + • + • + + + + + + (5)

- 21 -

where R is the one-year-ahead buy-and-hold stock return (including all distributions to shareholders),

measured from the beginning of May of the subsequent year.18 (For securities that delist during the

one-year holding period, proceeds from the issue are invested in the NYSE, AMEX, and NASDAQ

value-weighted indexes until the end of the holding period). SIZE is the logarithm of the market value

of equity at the beginning of May of the subsequent year; BETA - systematic risk - is estimated using

monthly stock returns and the CRSP value-weighted returns (including all distributions) during the

five years that end in April of the subsequent year (at least 30 observations are required). VOL -

idiosyncratic volatility - is the root-mean-squared error from the BETA regression.

Table 4 presents the results of equation (5) for companies with a December fiscal year end. The

coefficient of investment in alliances is positive and significant (0.09 with a t-statistic of 2.06) for all

types of alliances. However, when examining SEAs and NSEAs separately, the coefficient for NSEAs

is larger – 0.15 with a t-statistic of 2.70 – suggesting that the market is inefficient with regard to

information about these alliances. In contrast, the coefficient for SEAs is negative and insignificant,

suggesting that this information is taken into account by investors. Panel B examines this relationship

by activity. I find a positive and significant coefficient for marketing and manufacturing alliances,

indicating the market does not take them fully into account. I do not find significant association for

R&D alliances. A possible explanation might be that investors are more familiar with R&D alliances,

because their results are tangential – new patents or drugs. In addition, the media coverage of R&D

alliances is more extensive19.

There could be two possible explanations for the significant association between NSEA

information and future stock returns. The first one is the limited disclosure in the financial statements

regarding alliances. The second one is the time it takes for investors to fully understand the

18 For the future stock returns analysis, I reassign firms to years based on the calendar year in which the fiscal year ends, rather than use the Compustat year (which includes in the same year all fiscal year-ends between June of that year and May of the following year). I measure stock returns from May of the subsequent year to assure that investors had access to the annual financial report. 19 When replicating the results for the entire sample – including firms with a fiscal year end different from December the results for each type of NSEA are similar.

- 22 -

relationship between alliances and future revenue growth. As was explained before, the phenomenon

of strategic alliances not involving a separate entity is relatively new. Moreover, as documented in

Table 2, it takes about 3 years for alliances to substantially affect future revenue growth. Therefore, it

is reasonable to expect that the information would not be incorporated into the stock price

immediately, but rather gradually.

If the second explanation holds, namely that as time passes investors learn more about the use

of strategic alliances, than it is expected that over time, the market would become more efficient with

regards to this information. To test this explanation, I divide the sample period into two sub-sample

periods, 1988-1994 and 1995-2001, and estimate equation (5) for each period separately. As each sub-

sample period is short (7 years), I estimate industry- and year-fixed effects regressions.

Table 5 presents the regression estimates for each of two sub-sample periods. The coefficient

on NSEAs has slightly increased from 0.17, with a t-statistic of 2.05, for the period 1988-1994, to

0.21, with a t-statistic of 2.60 for the period 1995-2001. This result suggests that investors did not

learn to better use this information over time and it strengthens the conclusion that the market

inefficiency is a result of limited disclosure.

5.4 The determinants of participation in alliances

This section details the regressions of the number of alliances on its exogenous determinants.

The predicted values from these regressions are then substituted into equation (1) above, as discussed

in section 5.1. Section 5.4.1 reviews the theoretical considerations of joining an alliance and section

5.4.2 discusses the empirical estimation.

5.4.1 Theoretical considerations

Extensive theoretical work on alliances and joint ventures has pointed out the main

considerations for a single firm when deciding whether to join an alliance. I will now describe these

considerations.

- 23 -

(a) Cost sharing: An alliance might be a desirable vehicle to carry out large outlay projects or projects

for which the firm lacks required skills. The potential cost reduction should positively influence firms'

willingness to join an alliance.

(b) Market Concentration: Traditionally, joint ventures and alliances have been suspected of

potentially adverse effects on competition (e.g. Martin, 1994) due to the partial vertical or horizontal

integration they provide. An alliance offers additional opportunities for firms to meet and learn how to

coordinate their interests regarding both existing product markets and (future) innovations' markets

and thereby reduce the intensity of competition in the marketplace. Therefore, the lower the market

concentration (or the higher the competition), the more the incentive to form a joint venture.

(c) Knowledge appropriation: Knowledge spillovers reflect the transfer of idiosyncratic knowledge

(i.e., knowledge which is not necessarily embodied in a product or service) from one participant to

another without adequate compensation. Utilization of spillover by the other participants in the

alliance might result not only in a loss of future income but also in strengthening the competitors

(Katz, 1986). A possible remedy to potential utilization of spillovers is offered by patent laws. Having

a patent entails publicly disclosing the technology that is patented in return for perfect appropriability

for a limited time. Therefore, firms in industries with a high number of patents will be more inclined to

participate in an alliance.

(d) Asymmetries across firms: The degree of size asymmetries between firms will affect their

participation decisions. Hernan et al. (2003) find that forming research joint ventures is positively

related to the firm size.

(e) Previous experience: As Harrigan (2003) and Spekman et al. (2000) point out, managing an

alliance is an acquired skill. Therefore, previous experience with cooperative agreements of the same

sort would positively affect the decision to enter an alliance.

- 24 -

5.4.2 Empirical estimation

There has not been much empirical work on the incentives of firms to participate in alliances,

especially from a single firm perspective. This is probably so because of the difficulties in finding

proxies for the different motivations (a)–(e) described above. The only study that examines this

question empirically is Hernan et al. (2003). They analyze research joint ventures formed under the

umbrella of the Eureka and EU Framework Programmers using a Logit specification. I use some of

their proxies in my analysis.

To estimate the number of alliances each firm participates in, I estimate the following Zero-

Inflated Poisson (ZIP) model:

0 1 2 3 4 5 6

7 8

Pr( =n) ( & &

)year

t

AN F ISG AI ICAPEX IR D HHI SIZE EXP

SIZE EXP PATENT

α α β β β β β β

β β ε

= + + + + + + +

+ • + + (6)

Poisson Regression Models are usually applied to count data. However, as many firms do not

participate in alliances at all, the number of alliances a firm participates in exhibits an excess of zeros

relative to the number that would be predicted using a Poisson Regression Model. The ZIP model

overcomes this problem by combining a Poisson Regression with a Logit Regression. The Logit

component is used to estimate the probability of joining an alliance. The Poisson component is used to

estimate the number of alliances. The same independent variables are used in estimating both

components20. I estimate equation (5) separately for different alliance activities and organizational

forms.

The independent variables are proxies for the motivations (a)–(e) described above, and are as

follows:

(a) IINV, the industry average investment outflow, is a proxy for the potential cost sharing. IR&D (the

industry R&D expenditure) in the case of R&D alliances, ICAPEX (the industry CAPEX investment)

20 A discussion of the Zero-Inflated Poisson (ZIP) model can be found in appendix C.

- 25 -

in the case of manufacturing alliances, and ISG&AI (the industry ISG&A) in the case of marketing

alliances. Industry is defined using the two-digit SIC code.

(b) HHI, the Herfindahl-Hirschman Index, is a measure of market concentration. The HHI of a market

is calculated by summing the squares of the percentage market shares held by the respective

firms. This index gives higher weighting to large-share companies. It can go from near zero, in perfect

competition, to 10,000, which indicates a monopoly21. To calculate the market share of each firm, I

divide its total revenue by the total revenue of the industry (grouped according to the two-digit SIC

codes).

(c) I use SIZE, the natural logarithm of the firm's total assets, as a proxy for asymmetries across firms.

(d) PATENT is a proxy used to estimate the industry knowledge appropriation between the partners. I

use the NBER patent citations data file (Hall et al., 2001) to get the following industry patent

information22: (1) the number of patents for firms in a given industry; (2) the cumulative number of

citations of patents, made by the firm when registering its own patents, by the beginning of the fiscal

year. This variable is indicative of knowledge flow between the firm and its competitors; (3) the

cumulative number of citations received on the firm's patents. This variable is indicative of the

importance of the firm's own patents. As the three variables are highly correlated I use principal

component analysis to reduce them into one factor23.

(e) Finally, EXP captures differences in experiences with cooperative agreements. It represents the

cumulative number of any alliances (of all types) of the same organizational form the firm has joined

for the beginning of the year. As the importance of experience varies with size (it is a more important

factor for small firms than for large ones), I include an interaction term for size and experience.

21 The HHI index is also used in practice by the Antitrust Division of the Department of Justice (DOJ) and Federal Trade Commission (FTC) as a guide when considering proposals for horizontal mergers. According the DOJ/FTC guidelines, an HHI index below 1,000 indicates the market is unconcentrated. An index of from 1,000 to 1,800 indicates moderate concentration. Above 1,800 indicates high concentration. 22 This database covers the years 1975-1999. 23 Cronbach's alpha in this case is 0.90.

- 26 -

Table 6 exhibits the results of the year-fixed effect ZIP regressions (equation 6). For each type

of alliance, the first row presents the Poisson component of the regression and the second row presents

the results for the Logit component24. The Logit model estimates the probability of joining an alliance.

The Poisson model estimates the number of alliances formed. Panel A exhibits the results of

estimating the ZIP model for both types of alliances, and then for SEAs and NSEAs separately. It can

be seen that when deciding to enter an alliance, each firm takes into account the magnitude of the

industry investment - the larger the ISG&A and IR&D, the greater the motivation for forming an

alliance. An inverse relationship exists with total capital expenditure: higher ICAPEX indicates a

lower probability of entering an alliance. For NSEAs the magnitude of the IR&D affects more the

number of alliances established than the probability of entering an alliance, while the magnitude of

ISG&A and ICAPEX affects more the decision to enter an alliance than the number of alliances

formed. For SEAs the reverse holds, namely, IR&D affects more the decision to enter rather than the

number of alliances established. Size is also a dominant factor in the decision to enter an alliance, but

mostly affects the number of alliances established (larger firms tend to form more alliances). The

experience of a firm with similar organizational forms in the past is also more important to the

decision to enter an alliance than it is to the number of alliances established. The importance of

experience decreases with size: the larger the firm, the less of a role experience plays. Finally, the

appropriation of returns through patents affects mainly the question whether to enter an alliance and

less the number of alliances established. HHI is an important factor mainly with regard to the decision

concerning the number of alliances to be made, but its coefficient changes. Panel B examines the ZIP

regression for NSEAs by types of business activities and reveals similar patterns.

24In the model specification, the Logit equation estimates the probability of a zero outcome, i.e. the probability of not joining an alliance. To allow for easy interpretation, I have reversed the signs of the Logit regression. It can now be interpreted as the probability of joining an alliance.

- 27 -

VI. Robustness checks

6.1 Omitting equity investment from the measure of capital investment

Section 5.1 estimated a production function specification in which the dependent variable was

future revenue growth, defined as sales plus earnings in affiliates and joint ventures, and one of the

independent variables was total capital investment, including equity and joint venture investments.

This specification may introduce measurement errors in both the independent variable and the

dependent variable.

The possible error in the dependent variable is due to the fact that the revenue generated by the

SEAs is not disclosed. The parent recognizes its share in the total income of the SEAs as a one-line

item on the income statement. The income of the SEAs includes write-offs and depreciation of the

investment made by the parent. Therefore, summing up the revenue of the parent with the income of

the SEAs might also introduce a measurement error.

The possible error in the total capital investment estimate stems from the fact that investments

in SEAs are usually aggregated with other equity investments made by the parent for materiality

reasons. Therefore, my measure of investments in SEAs includes equity investments in affiliates

which are not alliances. In addition, SEAs investments are reported as a single number and there is not

enough information to separate out investments in R&D, marketing and manufacturing alliances.

Therefore the comparison between the impact of SEAs and NSEAs on future growth can only be

studied for the total investment, and not by investment activity.

In order to assess the potential effects of measurement error in the above specification, I rerun

the analysis, excluding earnings in affiliates from total revenue, and measuring capital investment as

the sum of CAPEX, R&D expenditure, and SG&AI., excluding equity and joint venture investments.

This specification omits the effect of SEAs. I find that none of my inferences discussed above is

affected.

- 28 -

6.2 Estimating growth in earnings

As detailed in section 5.1, focusing on growth in revenue rather than growth in earnings was

done because theoretically, the model of a production function defines output in gross rather then net

terms. Also, earnings includes write-offs and depreciation of the proxy for capital investment (my

independent variables). In this section, I replicate the analysis using growth in earnings, defined as

earnings before depreciation plus income in affiliates. The results are qualitatively similar to growth in

revenue, but differ in the level of significance. The coefficient on total capital investment is 0.14 for

all alliances, and the coefficient of the interaction of total capital investment and alliance frequency is

0.03. This coefficient is significant for all alliances (t-statistic of 2.50) and non-separate entity

alliances (t-statistic of 2.81) but not for separate entity alliances (t-statistic of 0.88). A possible

explanation for the insignificant coefficient for the SEAs might be due to the fact that I could not use

earnings before depreciation for investments reported using the equity method. This finding is

consistent with the findings of Graham et al. (2003) of a lower predictive ability of equity method over

proportionate consolidation.

VII. Summary and conclusions

This paper examines the value relevance of alliance-based information, comparing alliances

that involve a separate entity (Separate Entity Alliances or SEAs) and alliances that do not (Non-

Separate Entity Alliances or NSEAs). In particular, I focus on three widely used types of strategic

alliances – marketing, manufacturing and R&D. My analysis is twofold. First, I study the association

between alliance-based information and future growth. I then examine whether this information is

reflected in (a) current stock prices and (b) future stock returns.

The results indicate that both types of alliance are positively associated with future growth.

This is consistent with the strategy and economics literature, which suggests that investments in

alliances create more value than similar investments made exclusively by each of the partners, because

of the synergy they enable. Alliance-based information is also reflected in the stock prices, which

- 29 -

indicate that investors take this information into account. Notwithstanding, I document that NSEA

information is associated with future stock returns, which suggests that it is not fully incorporated into

current stock prices. This is consistent with the limited disclosure of NSEAs in the financial

statements. The ability of NSEAs to predict future stock returns has not changed much over the sample

period.

I further examine NSEAs by business activities - marketing, manufacturing and R&D. All

three types have a positive and significant relationship with future performance. However, I find that

information regarding manufacturing and marketing alliances is positively associated with future stock

returns while information regarding R&D is not. This is consistent with the observation that marketing

and manufacturing alliances are less familiar to investors than R&D alliances.

Taken together, this study supports the FASB recommendation to enhance disclose of joint

ventures and other similar arrangements in its special report with the G4+1 entitled “Reporting

Interests in Joint Ventures and Similar Arrangements”. The fact that strategic alliances play an

important role in the conduct of business adds weight to this disclosure recommendation. Of course,

this recommendation is based on a partial analysis of one aspect of the public disclosure of alliances

(the prediction of future performance). A comprehensive analysis of this issue should consider

possible costs to the disclosing companies.

An interesting question for future research concerns the differences between domestic and

international alliances. This question has become more important in recent years, as the number of

international alliances has increased significantly. A quick observation is that, in some cases,

multinationals establish cross-border alliances just to comply with foreign regulatory restrictions. I

conjecture that such alliances may be more costly and less productive than domestic alliances, which

are driven primarily by economic considerations. While the FASB special technical report (1999) does

not include a recommendation to disclose alliance information by geographical location, it might be

appropriate to do so.

- 30 -

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TABLE 1 Summary Statistics

Panel A: The Number of Alliances per Firm-Year Non-Separate Entity Alliances

Percentile Manufacturing Marketing R&D Total 50% 0 0 0 0 75% 0 0 0 0 90% 1 1 1 2 95% 2 3 4 5 99% 7 14 24 25

Separate Entity Alliances

Percentile Manufacturing Marketing R&D All 50% 0 0 0 0 75% 0 0 0 0 90% 1 0 0 1 95% 2 1 1 2 99% 6 3 4 7

Panel B: Investment Characteristics partitioned on Alliance Participation

Non-Separate Entity Alliances Investment Metric With NSEA Without NSEA Difference t-statistic

E_INV/OA 0.25% 0.10% -0.15% -4.18 4,174 16,956

CAPEX/OA 6.49% 6.96% 0.47% 3.36 2,309 18,821

SG&AI/OA 6.80% 5.35% -1.45% -17.35 3,249 17,881

R&D/OA 5.56% 1.41% -4.15% -60.57 2,978 18,152

Separate Entity Alliances Investment Metric With SEA Without SEA Difference t-statistic

E_INV/OA 0.27% 0.10% -0.17% -4.56 3,463 17,667

CAPEX/OA 6.50% 6.97% 0.47% 3.53 2,600 18,530

SG&AI/OA 5.50% 5.58% 0.08% 0.77 2,043 19,087

R&D/OA 4.80% 1.78% -3.02% -30.84 1,513 19,617

Panel A reports descriptive statistics regarding the number of alliances per firm year, by type. Panel B reports the mean investment level per firm year for firms which participated in at least one alliance and firms that did not. E_INV is equity and joint venture investments (from the statement of cash flows). CAPEX is capital expenditure net of disposals. R&D is research and development expenditure. SG&AI is the investment portion of SG&A. OA is operating assets at the beginning of the period. The t-statistics are two-tailed differences in means tests. The number of observations represents firm-years.

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TABLE 2 Cross-Sectional Regressions of Future Revenue Growth on Investments via Alliances and

Control Variables

Panel A: All Types of Alliances:

1 2 3 4 5 6 7_ _induGROWTH K INV K INV AF DEP L WC ACQ SIZEα β β β β β β β ε= + + • + + ∆ + ∆ + + + All Alliances (both SEAs and NSEAs)

K_INV K_INV·AF DEP ∆L ∆WC ACQ SIZE Adj. R2

Three years ahead 0.74 0.14 0.18 82.12 0.60 0.64 -0.05 0.26 (8.01) (4.65) (0.67) (16.76) (3.11) (3.66) (-5.73)

Two years ahead 0.51 0.09 0.01 65.31 0.44 0.58 -0.03 0.25 (6.01) (3.71) (0.07) (22.19) (3.40) (5.41) (-4.91)

One year ahead 0.31 0.05 -0.06 48.57 0.29 0.48 -0.02 0.22 (4.91) (1.79) (-0.39) (31.41) (4.05) (7.93) (-3.77)

Non-Separate Entity Alliances

K_INV K_INV·AF DEP ∆L ∆WC ACQ SIZE Adj. R2

Three years ahead 0.74 0.15 0.18 82.17 0.60 0.64 -0.05 0.26 (8.11) (4.02) (0.70) (16.62) (3.09) (3.66) (-5.58)

Two years ahead 0.51 0.09 0.02 65.32 0.44 0.58 -0.03 0.25 (6.22) (3.64) (0.08) (22.01) (3.38) (5.40) (-4.80)

One year ahead 0.31 0.05 -0.06 48.60 0.29 0.48 -0.02 0.22 (5.09) (1.94) (-0.37) (31.24) (4.04) (7.91) (-3.76)

Separate Entity Alliances

K_INV K_INV·AF DEP ∆L ∆WC ACQ SIZE Adj. R2

Three years ahead 0.82 0.14 0.19 81.67 0.60 0.63 -0.05 0.26 (8.42) (3.71) (0.75) (16.68) (3.12) (3.64) (-5.75)

Two years ahead 0.56 0.10 0.02 64.96 0.44 0.58 -0.03 0.25 (6.94) (2.75) (0.09) (22.10) (3.41) (5.39) (-4.87)

One year ahead 0.34 0.06 -0.06 48.34 0.29 0.48 -0.02 0.22 (5.96) (1.99) (-0.37) (31.46) (4.02) (7.92) (-3.71)

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Panel B: Non-Separate Entity Alliances by Activity

1 2 3 4 5 6 7

8 9

& &induGROWTH CAPEX SG AI R D AAINV DEP L WCACQ SIZE

α β β β β β β ββ β ε

= + + + + + + ∆ + ∆ ++ +

Where: AAINV (Alliance Investment by Activity) = CAPEX•MNAF, SG&AI•MAF or R&D•RDAF

Type CAPEX SG&AI R&D AAINV DEP ∆L ∆WC ACQ SIZE Adj. R2

Manufacturing 1.30 0.65 0.44 0.61 0.11 79.18 0.60 0.66 -0.06 0.33 (10.16) (1.88) (1.48) (2.94) (0.33) (15.13) (2.99) (3.51) (-6.30)

Marketing 1.36 0.43 0.09 1.40 0.12 79.24 0.61 0.66 -0.06 0.33 (10.18) (1.23) (0.31) (6.52) (0.37) (15.08) (3.05) (3.52) (-6.55)

R&D 1.34 0.66 0.12 0.58 0.13 78.94 0.61 0.66 -0.06 0.33 (10.11) (1.92) (0.53) (4.09) (0.39) (15.12) (3.02) (3.47) (-6.34)

GROWTH is average total revenue in the subsequent three years minus current total revenue. Total revenue is defined as sales plus income in affiliates and joint ventures. In Panel A only, I include measures of subsequent one and two year revenue growth defined in a similar way. αindu is an industry fixed effect (two digit SIC code). K_INV is total capital investment including CAPEX, R&D, SG&AI (the investment portion of SG&A), and E_INV equity and joint venture investments (from the statement of cash flows). DEP is the depreciation and amortization expense. ∆L is the change in the number of employees. ∆WC is the change in working capital. ACQ is business acquisitions. SIZE is the natural logarithm of total assets. All variables except for SIZE are deflated by total operating assets at the beginning of the year.

AF (alliances frequency) is the frequency of alliances. Frequency is defined as the natural logarithm of the predicted number of alliances plus one. The predicted number of alliances results from table 6. The table summarizes cross-sectional regressions for the years 1988 – 2002. Reported coefficients are the means of the estimates. The t-statistics are the ratio of the mean cross-sectional coefficients to their standard errors estimated from the time series of coefficients. MNAF is manufacturing alliances frequency. RDAF is R&D alliances frequency. MAF is marketing alliances frequency.

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TABLE 3 Cross-Sectional Regressions of Enterprise Market-to-Book Ratios

on Investments via Alliances and Control Variables

Panel A: All Types of Alliances:

1 2 3 4 5 6

7 8

/ _ _induVOA OA ROA LTG K INV K INV AF ACQ OLLEVFALEV SIZE

α β β β β β ββ β ε

= + + + + • + ++ + +

All Alliances (both SEAs and NSEAs)

ROA LTG K_INV K_INV·AF ACQ OLLEV FALEV SIZE Adj. R2

6.78 4.11 0.80 2.01 0.20 1.40 -0.03 0.79 (19.15) (12.89) (3.09) (11.63) (1.55) (7.32) (-2.02) 11.32 0.05 3.00 0.46 1.08 0.34 1.02 0.03 0.86

(22.93) (12.71) (8.82) (2.24) (5.74) (3.05) (5.19) (1.64)

Non-Separate Entity Alliances

ROA LTG K_INV K_INV·AF ACQ OLLEV FALEV SIZE Adj. R2

6.75 4.17 0.83 2.01 0.21 1.38 -0.03 0.79 (19.25) (12.92) (2.72) (11.76) (1.70) (7.47) (-1.82) 11.30 0.05 3.04 0.46 1.08 0.34 1.00 0.03 0.86

(22.89) (12.91) (8.86) (2.08) (5.73) (3.20) (5.33) (1.75)

Separate Entity Alliances

ROA LTG K_INV K_INV·AF ACQ OLLEV FALEV SIZE Adj. R2

6.76 4.49 0.78 1.96 0.20 1.52 -0.02 0.78 (19.48) (11.19) (2.93) (10.65) (1.65) (7.10) (-0.75) 11.32 0.05 3.28 0.47 1.05 0.35 1.11 0.04 0.86

(23.36) (12.51) (7.35) (2.12) (5.40) (3.24) (4.99) (1.85)

- 37 -

Panel B: Non-Separate Entity Alliances by Activity (Manufacturing, Marketing and R&D):

1 2 3 4 5 6 7 8/ induVOA OA ROA LTG AINV AAINV ACQ OLLEV FALEV SIZEα β β β β β β β β ε= + + + + + + + + + Where: AINV (Investment by Activity) =CAPEX, SG&AI and R&D

AAINV (Alliance Investment by Activity) = CAPEX•MNAF, SG&AI•MAF or R&D•RDAF

Type ROA LTG AINV AAINV ACQ OLLEV FALEV SIZE Adj. R2

Manufacturing 7.11 4.17 4.82 1.94 0.75 1.39 -0.07 0.74 (17.18) (15.07) (2.50) (13.07) (6.05) (6.66) (-3.59)

Marketing 7.56 6.88 6.27 1.86 0.08 1.84 -0.03 0.74 (19.82) (11.99) (4.30) (9.49) (0.66) (8.62) (-1.63)

R&D 8.20 9.26 1.07 1.94 0.39 1.03 -0.06 0.76 (23.08) (7.23) (1.89) (11.98) (3.60) (5.28) (-3.27)

Manufacturing 12.01 0.05 2.64 2.82 1.05 0.56 1.04 0.01 0.85 (19.25) (14.43) (10.17) (1.95) (6.01) (7.27) (5.95) (0.81)

Marketing 11.76 0.06 4.72 3.41 0.90 0.40 1.09 0.02 0.84 (20.75) (10.71) (10.00) (3.92) (4.27) (3.92) (5.11) (1.26)

R&D 12.52 0.05 5.87 0.63 1.01 0.43 0.71 0.005 0.85 (21.71) (12.75) (4.35) (1.86) (5.72) (4.94) (4.48) (0.21)

VOA/OA is the ratio of the value of operating assets to their book value at the end of the first quarter. ROA is the return on operating assets at the end of the year. LTG is the mean analysts’ long-term earnings growth forecast. OLLEV (operating liabilities leverage) is the ratio of operating liabilities to operating assets. FALEV (financial assets leverage) is the ratio of financial assets to operating assets. The rest of the variables are defined in table 2.

The table summarizes cross-sectional regressions for the years 1988–2002. The reported coefficients are means of the estimates. The t-statistics are the ratio of the mean cross-sectional coefficients to their standard errors, estimated from the time series of coefficients.

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TABLE 4 Cross-sectional Regressions of One Year Ahead Stock Return on Investments via Alliances

Panel A: All Types of Alliances

1 2 3 4 5 6 7_ _ induR K INV K INV AF SIZE E/P B/P BETA VOLα β β β β β β β ε= + + • + + + + + +

Type K_INV K_INV·AF SIZE E/P B/P BETA VOL Adj. R2

All 0.04 0.09 -0.01 -0.13 -0.01 -0.01 0.11 0.22 (1.14) (2.06) (-1.44) (-1.67) (-0.38) (-0.51) (0.36)

NSEA 0.04 0.15 -0.01 -0.13 -0.01 -0.01 0.08 0.22 (1.02) (2.70) (-1.61) (-1.70) (-0.38) (-0.49) (0.27)

SEA 0.08 -0.03 -0.009 -0.13 -0.003 -0.01 0.16 0.22 (1.87) (-0.67) (-0.69) (-1.77) (-0.09) (-0.47) (0.53)

Panel B: Non-Separate Entity Alliances by Activity (Manufacturing, Marketing and R&D):

1 2 3 4 5 6 7induR AINV AAINV SIZE E/P B/P BETA VOLα β β β β β β β ε= + + + + + + + + Where: AINV (Investment by Activity) =CAPEX, SG&AI and R&D

AAINV (Alliance Investment by Activity) = CAPEX•MNAF, SG&AI•MAF or R&D•RDAF

Type AINV AAINV

SIZE

E/P

B/P

BETA

VOL Adj. R2

Manufacturing -0.10 0.30 -0.01 -0.15 0.02 -0.01 0.18 0.22 (-2.18) (2.97) (-1.42) (-1.85) (1.04) (-0.47) (0.67)

Marketing 0.45 0.45 -0.005 -0.09 -0.01 -0.01 0.09 0.23 (2.31) (2.61) (-0.67) (-1.66) (-0.52) (-0.43) (0.32)

R&D 0.59 0.17 -0.01 -0.13 0.02 -0.01 0.10 0.23 (1.56) (1.53) (-1.11) (-1.61) (0.84) (-0.63) (0.42)

The annual return (R) is measured from May 1 of the subsequent year (all sample firms have a December fiscal year end). αindu is an industry fixed effect (two digit SIC code). SIZE (logarithm of market value of equity) is measured at the end of April of the subsequent year. E is earnings (income before extraordinary items). B is book value at fiscal year-end. P is market value of common equity at fiscal year-end. BETA - systematic risk - is estimated using monthly stock returns and the CRSP value-weighted returns (including all distributions) during the five years that end in April of the subsequent year. VOL - idiosyncratic volatility - is the root-mean-squared error from the BETA regression. All other variables are defined in table 2. All investment variables are deflated by P. The table summarizes cross-sectional regressions for the years 1988–2001. Reported coefficients are means of the estimates. The t-statistics are the ratio of the mean cross-sectional coefficients to their standard errors estimated from the time series of coefficients.

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TABLE 5 Fixed Year Effect Regressions of One Year Ahead Stock Return on Investments via Alliances

Panel A: 1988-1994

_ 1 2 3 4 5 6 7_ _ indu yearR K INV K INV AF SIZE E/P B/P BETA VOLα β β β β β β β ε= + + • + + + + + +

Type K_INV K_INV·AF SIZE E/P B/P BETA VOL Adj. R2

All 0.01 0.12 0.00 -0.07 0.04 0.00 0.22 0.15 (0.12) (1.68) (0.49) (-1.30) (1.55) (0.14) (0.70)

NSEA 0.001 0.17 0.00 -0.08 0.04 0.00 0.19 0.15 (0.02) (2.05) (0.69) (-1.37) (1.55) (0.09) (0.62)

SEA 0.08 -0.12 0.00 -0.08 0.04 0.00 0.30 0.15 (1.27) (1.52) (0.78) (-1.44) (1.64) (0.07) (0.93)

Panel B: 1995-2001

_ 1 2 3 4 5 6 7_ _ indu yearR K INV K INV AF SIZE E/P B/P BETA VOLα β β β β β β β ε= + + • + + + + + +

Type K_INV K_INV·AF SIZE E/P B/P BETA VOL Adj. R2

All 0.12 0.15 -0.02 -0.15 -0.02 0.03 -0.35 0.19 (1.46) (2.33) (-3.44) (1.54) (0.65) (1.24) (-1.25)

NSEA 0.11 0.21 -0.02 -0.15 -0.02 0.03 -0.38 0.19 (1.46) (2.60) (-3.59) (1.54) (0.66) (1.24) (-1.38)

SEA 0.17 0.04 -0.02 -0.14 -0.01 0.03 -0.31 0.19 (2.16) (0.55) (-2.90) (1.50) (0.55) (1.30) (-1.10)

The annual return (R) is measured from May 1 of the subsequent year (all sample firms have a December fiscal year end). αindu_year is a year and industry-fixed effect (two digit SIC code). SIZE (logarithm of market value of equity) is measured at the end of April of the subsequent year. E is earnings (income before extraordinary items). B is book value at fiscal year-end. P is market value of common equity at fiscal year-end. BETA (systematic risk) is estimated using monthly stock returns and the CRSP value-weighted returns (including all distributions) during the five years that end in April of the subsequent year. VOL (idiosyncratic volatility) is the root-mean-squared error from the BETA regression. All other variables are defined in Table 2. All investment variables are deflated by P. Robust t statistics are reported in parentheses.

- 40 -

TABLE 6 Fixed Year Effect Zero Inflated Poisson Regressions of the Number of Alliances on its Determinants

Panel A: All Types of Alliances

0 1 2 3 4 5 6 7 8Pr( =n) ( & & )year tAN F ISG AI ICAPEX IR D HHI SIZE EXP SIZE EXP PATENTα α β β β β β β β β ε= + + + + + + + + • + +

Constant ISG&AI ICAPEX IR&D HHI SIZE EXP SIZE·EXP PATENT Pseudo R2 All Poisson -3.51 3.20 -2.16 11.50 0.01 0.40 0.08 -0.01 0.004 0.58

(-26.07) (6.62) (-6.65) (21.62) (2.86) (42.14) (8.49) (-7.68) (0.70) Logit -4.25 7.64 -5.26 4.04 -0.02 0.20 7.41 -0.62 0.21 (-15.11) (6.18) (-6.33) (3.24) (-2.92) (7.06) (23.77) (-18.09) (4.92)

NSEA Poisson -2.90 2.69 -1.79 12.09 0.01 0.33 0.08 -0.01 0.01 0.56 (-18.87) (4.04) (-4.01) (19.02) (2.85) (29.96) (7.84) (-7.00) (1.74) Logit -5.23 6.84 -4.78 3.34 -0.01 0.25 6.76 -0.51 0.15 (-16.21) (6.35) (-6.53) (2.36) (-1.32) (6.69) (14.94) (-7.44) (2.70)

SEA Poisson -3.78 4.23 -2.86 4.92 -0.01 0.36 0.59 -0.05 0.01 0.43 (-20.70) (9.56) (-9.62) (9.88) (-2.82) (26.47) (24.12) (-22.11) (2.32) Logit -3.85 1.50 -1.03 6.42 -0.01 0.11 13.69 -1.18 0.23 (-9.53) (0.80) (-0.82) (3.44) (-1.67) (2.58) (12.26) (-8.74) (5.25)

- 41 -

Panel B: Non-Separate Entity Alliances by Activity (Manufacturing, Marketing and R&D):

0 1 2 3 4 5 5Pr( =n) ( )year tAN F IINV HHI SIZE EXP SIZE EXP PATENTα α β β β β β β ε= + + + + + + • + + Where: IINV (Industry Investment) = ISG&AI, ICAPEX or IR&D.

Constant IINV HHI SIZE EXP SIZE·EXP PATENT Pseudo R2 Marketing Poisson -1.62 0.02 -0.01 0.23 0.08 -0.01 0.04 0.43

(-9.04) (1.80) (-1.46) (19.49) (7.88) (-7.07) (5.13) Logit -4.89 -0.12 -0.004 0.19 6.63 -0.56 0.17 (-15.25) (-5.54) (-0.70) (6.13) (23.02) (-18.60) (3.00)

Manufacturing Poisson -1.66 -0.02 -0.03 0.27 0.06 -0.004 -0.005 0.25 (-7.45) (-1.65) (-7.46) (17.98) (5.19) (-4.33) (-0.44) Logit -5.91 -0.07 0.02 0.24 5.61 -0.49 0.23 (-13.96) (-3.14) (3.15) (5.81) (14.61) (-12.04) (4.82)

R&D Poisson -2.63 14.95 0.01 0.33 0.07 -0.01 0.02 0.55 (-16.59) (21.98) (2.85) (29.62) (6.90) (-6.00) (1.90) Logit -5.68 10.27 -0.004 0.34 5.29 -0.45 0.17 (-19.20) (7.71) (-0.76) (10.89) (17.99) (-15.03) (4.02)

AN (alliances number) is the number of alliances for each firm in a given year. “Poisson” represents the Poisson component of the model (counts) while “Logit” represents the Logit component of the model. For easier interpretation, I have reversed the signs of the coefficients estimated using the Logit regression, so that the Logit regression measures the probability of a positive outcome. αyear is a year-fixed effect. ICAPEX, ISG&AI and IR&D are the average industry capital expenditure, the investment portion of SG&A, and R&D expenditure respectively. HHI is the Herfindahl-Hirschman Index. SIZE is the natural logarithm of total assets. EXP (experience) is measured as the cumulative number of all types of alliances of the same organizational form a firm participated in as of the beginning of the year. FPATENT is a factor that consists of the cumulative industry number of citations of patents, by the beginning of the fiscal year, the cumulative industry number of citations received and the total industry number of patents. The table summarizes fixed year effect regressions for the years 1988-2000. Robust z statistics are reported in parentheses.

- 42 -

APPENDIX A Considerations in Forming SEAs vs. NSEAs:

The choice between SEAs and NSEAs involves several considerations. In this appendix, I will review

these factors:

Cost – SEAs are usually more costly than NSEAs because of the additional legal costs of forming the

entity and the operational costs of running it.

Flexibility and control – NSEAs provide greater flexibility to the sides involved because the corporate

relationship between the two parties may be as basic as a contract. However, this flexibility comes

with a cost of less control. On the one hand, NSEAs allow more flexibility than SEAs in providing an

“escape hatch” if the strategic alliance does not progress as expected. On the other hand, NSEAs might

be less desirable than SEAs because they might encourage more opportunistic behavior by the

participants relative to a more formal “tie up” relationship (see, Lajoux and Nesvold, 2004).

Financial statement presentation – As stated above in section 2.2.2, investments in SEAs are reported

using the equity method and usually included as a one-line item on the statement of cash flows titled

“equity and joint ventures investments”. In contrast, investment in NSEAs is usually reported within

the relevant accounts of the participants: investment in R&D NSEAs in the R&D expenditure on the

firm’s income statement, investments in marketing NSEAs in the SG&A of the firm also on its income

statement, and investments in manufacturing alliances are as part of total capital expenditure - CAPEX

- on the statement of cash flows.

Taxation – There are several advantages to choosing a NSEA form. First, under some circumstances,

the IRS may require the parties to capitalize start-up costs under SEAs (rather than allow the

immediate expensing of such costs in the case of NSEAs). Second, the IRS may tax the parties upon

exiting the entity. Finally, a foreign partner to the alliance could avoid being subject to broader

jurisdiction in the United States in the case of NSEAs. However, the choice of NSEAs does not

necessarily ensure the above advantages. Despite the lack of an entity form, the participants in a

- 43 -

NSEA may still be impacted by entity-related rules if a joint profit motive is found to exist between

them.

Antitrust regulation - The Hart-Scott-Rodino Antitrust Improvements Act requires parties who plan to

form a corporate SEA meeting certain thresholds to notify both the FTC and the U.S. Department of

Justice (DOJ) prior to its incorporation. Generally, the thresholds are $10 million of annual sales or

$100 million in total assets (this law is, however, not applicable to the formation of joint venture

partnerships).

Incomplete contracts and the hold-up problem - Quantifying the relative contribution of each of the

partners is difficult in alliances, especially those involving R&D. Also, to identify the specific nature

of the end-product and which of the two partners may stand to benefit more from it is a complicated

task (Grossman and Hart, 1986). As a result, designing a contract that forestalls all contingencies and

allocates the final output and profits from the alliance is difficult. Therefore, in such alliances

contracting problems likely arise as a contract written at the inception of the alliance would have to be

complete as well as flexible to accommodate the innovations in the alliance. Incomplete contracts may

lead to potential hold-up costs and underinvestment. SEAs mitigate these problems of incomplete

contracting. Aghion and Tirole (1994) show that through equity ownership, the costs and benefits of

innovating, designing, manufacturing, and marketing the product from an alliance are proportionally

shared.

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APPENDIX B Firm Year Observations with SEAs and NSEAs as a Percentage of Total Firm Year

Observations in the Same Industry*

SIC SEAs NSEAs SIC SEAs NSEAs 1 16.4% 29.5% 40 2.6% 7.8%

10 10.7% 3.8% 44 8.6% 7.4% 12 14.0% 7.0% 45 6.0% 10.4% 13 6.9% 2.5% 47 15.4% 0.0% 14 0.0% 6.3% 48 19.9% 28.4% 15 5.0% 1.2% 49 14.3% 10.0% 16 8.9% 7.3% 50 6.1% 13.9% 17 0.0% 14.8% 51 6.7% 9.9% 20 23.5% 22.4% 52 0.0% 27.1% 21 23.8% 19.0% 53 12.6% 16.0% 22 10.3% 7.6% 54 6.8% 6.8% 23 2.9% 30.2% 55 0.0% 8.9% 24 9.1% 8.1% 56 10.0% 16.5% 25 18.8% 19.7% 57 3.4% 20.5% 26 20.0% 15.7% 58 5.0% 7.9% 27 18.2% 30.4% 59 6.6% 9.4% 28 30.9% 40.0% 70 7.5% 10.0% 29 30.5% 16.5% 72 6.5% 0.0% 30 15.9% 21.9% 73 9.8% 24.0% 31 4.2% 33.8% 75 3.0% 7.6% 32 20.3% 14.9% 78 15.8% 18.3% 33 27.0% 19.3% 79 5.0% 10.3% 34 12.3% 7.4% 80 3.4% 15.7% 35 23.3% 34.6% 82 11.3% 7.0% 36 22.2% 32.3% 83 0.0% 10.3% 37 28.6% 24.8% 87 10.1% 20.1% 38 14.5% 34.8% 99 30.5% 23.2% 39 13.2% 23.9%

* Two-digit major SIC classification

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APPENDIX C Zero-Inflated Poisson regressions

The Zero-Inflated Poisson (ZIP) model is used for modeling counts of events. This model is formed by

a mixture of two distributions: a point mass distribution at zero, and a Poisson distribution.

In a 1992 Technometrics paper, Lambert (1992) described Zero-Inflated Poisson (ZIP) Regression, a

class of models for count data with excess zeros. In a ZIP model, a count response variable is assumed

to be distributed as a mixture of a Poisson (µ) Distribution and a distribution with point mass of 1 at

zero, with mixing probability iφ .

Yi = 0 with probability iφ .

Yi ~ Poisson (µi) with probability 1 iφ−

(1 ) ii i e µφ φ −+ − iif y 0=

( / )i i iP Y y x= { (1 ) / !i iyi i ie yµφ µ−− iif y 0≠

Counts are generated from one or two process: the usual Poisson random variable with mean µ with

probability 1 iφ− and zero with probability iφ . The regression model generalizes the univariate ZIP

model by allowing iφ to vary from observation to observation. Both iφ and µ are allowed to depend on

covariates through canonical link generalized linear models.