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«John R. Graham, Co-Supervisor David T. Robinson, Co-Supervisor Manuel Adelino Alon P. Brav Manju Puri Aaron K. Chatterji Dissertation submitted in ...»

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The rationale of information acquisition for CVC investment is convincing only if CVC parents can use newly gathered information to improve their operations. Several economic frictions could hinder CVCs from gathering and integrating information from startups, challenging the information acquisition rationale. Hellmann (2002) theoretically shows that entrepreneurs could intentionally avoid CVC investment to protect their innovation. Dushnitsky and Lenox (2005b) and Kim, Gopal, and Hoberg (2013) argue that the absorptive ability (Cohen and Levinthal, 1990) of CVC parent firms imposes a limit on the knowledge transferred through the relationship. Gompers and Lerner (2000) suggest that the efficiency of CVC is constrained by the incentive problem embedded in its organizational and compensation structure. In addition, 46 high adjustment costs of R&D investment (Hall, Griliches, and Hausman, 1986; Lach and Schankerman, 1989) can decrease the speed and intensity of the integration of new knowledge acquired through CVC.

Showing how information is incorporated into corporate decisions can be challenging due to the invisible nature of information. In this subsection, I undertake two empirical settings to study how information acquired through CVC influences the parent firm. First, following the literature that uses patent citations as a measure of knowledge spillover (Gomes-Casseres et al., 2006), I study how CVC parent firms internalize acquired information into organic R&D by tracking patent citations made to their portfolio companies. I then switch to another setting where I look at the efficiency of corporate decisions in which the acquired information could be crucial.

5.2.1 Internal Research and Development

I identify the specific information flow from portfolio companies that is further incorporated into parents’ internal innovative activities. Empirically, I follow the economic literature on knowledge spillover (Jaffe and Trajtenberg, 2002),2 and estimate whether CVC parent firm i makes new citations to startup company j’s patents or

knowledge after the CVC invests in the startup, using the following model:

–  –  –

To control for observed characteristics of CVC parents that could influence their behaviors in citing entrepreneurial companies, I construct a tighter control group for those firms. I use a propensity score matching method and match each CVC parent firm i that launches its CVC unit with two non-CVC firms from its CVC launch year and 2-digit SIC industry that has the closest propensity score estimated using firm 2 Alcacer and Gittelman (2006) and Gomes-Casseres et al. (2006), among others, discuss the advantages and potential pitfalls in using this approach.

47 size (the logarithm of total assets), market-to-book ratio, ∆Innovation, and patent stock,3 similar to the sample construction strategy in Bena and Li (2014). The CVC launching year for a CVC parent firm is also the “pseudo-CVC” year for its matched firms.

Observations are at the i-j-t level. The full set of i-j pairs then denotes the potential information flow that could happen between a CVC parent firm (or a matched firm) and a startup, captured by patent citations. IpCV CP arentq is a dummy variable indicating whether firm i is a CVC parent or a matched control firm.

IpP ortf olioq indicates whether company j is in the CVC portfolio of firm i. For each i-j pair, two observations are constructed, one for the five-year window before firm i invests in company j, and one for the five-year window after the investment.4 IpP ostq indicates whether the observation is within the five-year post-investment window.

The dependent variable, Citeijt, indicates whether firm i makes new citations to company j’s innovation knowledge during the corresponding time period.

The key variable of interest, IpCV CP arentq ˆ IpP ostq ˆ IpP ortf olioq, captures the incremental intensity of integrating a portfolio company’s innovation knowledge into organic innovation after a CVC invests in the company. Table 5.2 column (1) shows the regression results. The coefficient of 0.159, means that the citing probability increases by 15.9% after establishing the link through CVC investment.

3 Patent stock is constructed as the total number of patents applied for by the firm up to year t ´ 1.

4 A matched control firm is assumed to have the same investment history as the CVC parent firm to which it is matched to.

48 Table 5.2: Direct Information Acquisition from Portfolio Companies This table studies the direct information acquisition of CVC parent firms from their portfolio companies by investigating how investing in an entrepreneurial company affects the CVC parent firm’s possibility of innovating based on the entrepreneurial company’s innovation. I first identify all the patents applied by a CVC parent firm (or a matched control firm) i, and all the patents cited by those patents. I then identify all the patents applied by an entrepreneurial company j. These data further allow me to determine whether firm i makes a new citation, which it never cited before, to a patent that is possessed by company j. The analysis is performed based on the

following framework:

Citeijt “ α ` β ¨ IpCV CP arentq ˆ IpP ostq ˆ IpP ortf olioq ` ΦrIpCV CP arentq, IpP ostq, IpP ortf olioqs ` εijt.





The sample is at the i-j-t level. The full set of i-j pairs then denotes the potential information flow that could happen between a CVC parent firm (or a matched firm) and a startup, captured by patent citations. IpCV CP arentq is a dummy variable indicating whether firm i is a CVC parent or a matched control firm. IpP ortf olioq indicates whether company j is in the CVC portfolio of firm i. For each i-j pair, two observations are constructed, one for the five-year window before firm i invests in company j, and one for the five-year window after the investment. IpP ostq indicates whether the observation is within the five-year post-investment window. The dependent variable, Citeijt, indicates whether firm i makes new citations to company j’s innovation knowledge during the corresponding time period. The key variable of interest, IpCV CP arentq ˆ IpP ostq ˆ IpP ortf olioq, captures the incremental intensity of integrating a portfolio company’s innovation knowledge into organic innovation after a CVC invests in the company. Column (1) reports the result. Column (3) performs an analysis similar to that in column (1) except that it estimates the probability that a CVC parent firm cites not only patents owned by the startup but also patents previously cited by the startup. In other words, the potential citation now covers a broader technological area that the startup works in. Columns (2) and (4) separately estimate the intensity of citing knowledge possessed by companies that either exit successfully (acquired or publicly listed) or fail at last. All specifications include fixed effects imposing analysis across firms in the same industry and same year of (pseudo-) launching their CVC programs to absorb time-variant industrial technological trends. T-statistics are shown in parentheses and standard errors are clustered by firm. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

–  –  –

Observations 1,406,734 1,406,734 R-squared 0.01 0.02 Yes Yes Industry ˆ CVC Year FE I further explore the depth of information acquisition from portfolio companies.

Specifically, in column (3), I perform an analysis similar to that in column (1) except that I look at the probability that a CVC parent firm cites not only patents owned by the startup but also patents previously cited by the startup. In other words, the potential citation now covers the broader technological area that the startup works in. Column (3) extends the message conveyed in column (1)—CVC parent firms not only cite the portfolio company’s own patents, but also benefit from the knowledge indirectly carried by portfolio companies, reaching to the broader knowledge behind.

Does information acquisition concentrate only on successful investment? I explore this question by modifying model (5.2) and separately estimate the intensity of citing knowledge possessed by companies that either exit successfully (acquired or publicly listed) or fail at last. The result is reported in columns (2) and (4), and it appears that CVC parents acquire knowledge from both successful and failed ventures.

5.2.2 Using Information through External Acquisitions

Here I explore an alternative channel through which firms could benefit from CVCacquired information—acquiring external innovation. Acquiring innovation has become an important component of corporate innovation (Bena and Li, 2014; Seru, 2014), and identifying promising acquisition targets (companies or innovation) requires a valuable information set, such as great understanding of markets and technological trends. Under the information acquisition hypothesis, CVC-acquired information allows parent firms to form more precise expectations of acquisition deals, thereby improving efficiencies when making such decisions.5 I first study how efficiently CVC parent firms conduct acquisitions of companies.

Following the literature, acquisition efficiency is measured using three-day, five-day, 5 Those acquisitions are not necessarily limited to their CVC portfolio companies and can reach a broader domain using the general innovation and industry knowledge they learn from CVC experience.

51 and seven-day cumulative abnormal returns (CAR) of an acquisition deal centered on the acquisition announcement day. The analysis is performed on a cross section of M&A deals conducted by CVCs and their matched control firms between five years before and five years after (pseudo-) CVC initiations, and the unit of observation is an acquisition deal. The key variable of interest is the difference-in-differences variable IpCV CP arentqi ˆ IpP ostqi,t, indicating whether the acquirer i is within five years after launching its CVC division. If firms could conduct more efficient external acquisitions based on the information gathered from CVC investment, one would expect the abnormal announcement returns to be higher for these deals.

Table 5.3: Integration of CVC-Acquired Information through External Acquisitions This table studies the efficiency of acquiring companies or innovation around the start of CVC

investment. The analysis is based on the following standard difference-in-differences (DiD) framework:

yi,t “ αF E ` β ¨ IpCV CP arentqi ˆ IpP ostqi,t ` β 1 ¨ IpCV CP arentqi ` β 2 ¨ IpP ostqi,t ` γ ˆ Xi,t ` εi,t.

The sample consists of acquisition deals (Panel A) and patent purchases (Panel B) conducted by CVCs and their matched control firms during five years before CVC initiations and five years after CVC initiations, and the unit of observation is an acquisition deal (Panel A) and a patent purchase (Panel B). The sample consists of CVCs and their propensity score-matched firms. The dependent variables yi,t are cumulative abnormal returns (CARs) for acquisition of companies (Panel A) and annual citation growth for purchases of patents (Panel B). IpCV CP arentqi is a dummy variable indicating whether firm i is a CVC parent or a matched control firm. IpP ostqi,t indicates whether the firm-year observation is within the rt ` 1, t ` 5s window after (pseudo-) CVC initiations. The model includes industry-by-year fixed effects αindustryˆt. Firm-level control variables include ROA, size (logarithm of total assets), leverage, and R&D ratio (R&D expenditures scaled by total assets).

T-statistics are shown in parentheses and standard errors are clustered by firm. *, **, *** denote statistical significance at the 10%, 5%, and 1% levels, respectively.

–  –  –

Table 5.3 Panel A presents the result.

Columns (1) to (3) examine three-day, five-day, and seven-day CAR (in basis points, bps), respectively. The positive and significant coefficients across all three columns confirm that firms conduct more successful external acquisitions as they internalize the information acquired through their CVC investment. Quantitatively, compared to their industry peers, acquisitions made by CVC parent firms experience a 65 bps improvement in the three-day abnormal return from one day before the announcement to one day after the announcement, and a greater than 130 bps increase in abnormal return during the r´3, 3s window.

To study how CVC-acquired information is capitalized through acquisitions of 53 innovation, I compile a detailed data set on firms’ acquisition of patents (either “company and patents” or “patents only”). The database on patent transactions is based on USPTO patent assignment files, hosted by Google Patents. This database provides useful information for identifying patent transactions: the assignment date;

the participating parties, including the assignee—the “buyer” in a transaction— and the assignor—the “seller” in a transaction; and comments on the reason for the assignment. To gather additional information on the original assignee and patent technology classes, I merge the raw assignment data with the USPTO patent databases, and with the HBS inventor database. I then follow a procedure, based on Serrano (2010) and Akcigit, Celik, and Greenwood (2013), in which I separate patent transactions from all patent reassignment records, that is, I remove reassignments associated with cases such as a patent transfer from the employee inventor to the employer firm, or a patent transfer between different subsidiaries of a firm. A more detailed description of the data and methodology is provided in the Appendix.



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