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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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First, knowledge begets knowledge. Isaac Newton said, “If I have seen further it is by standing on ye sholders of Giants.” Indeed, the knowledge stock of an innovative individual or institution determines the quantity and quality of their innovation and knowledge production. This observation has been formalized and discussed in several strands of literature (see Jones (2009) and the papers cited therein).

Second, knowledge ages. Since the 1950s, several disciplines have developed the concept of the obsolescence of knowledge/skills/technology. The most famous result might be, roughly speaking, that half of our knowledge today will be of little value (or even proven wrong) after a certain amount of time (half-life), and this half-life is 75 becoming shorter and shorter (Machlup, 1962). In economics, people have studied the effect of obsolescence of knowledge and skills on labor, industrial organization as well as the aggregate growth (Rosen, 1975).

Third, predicting knowledge trends is difficult, if not impossible. Even though mathematicians and bibliometricians have been developing mathematical models to fit the half-life dynamics of the overall knowledge stock, predicting the trend for each specific stock has not been successful. Indeed, it is this “impossibility” that creates the possibility of creative destruction and the fading of generations of firms.

Fourth, knowledge absorption can be difficult and slow. For any individual and institution, knowledge can be identified, absorbed, and managed at a limited rate.

Even for firms, which have the option to replace human capital (innovators), the adjustment costs and uncertainty associated with the matching process limits their ability to do so.

Based on these observations, Obsolescence proxies for a shock to the value and usefulness of knowledge possessed by each firm, which in turn affects innovation performance of the firm. Following Newton’s storyline, when a firm is already on the shoulder of a standing Giant, the measure captures a shock to the height of the Giant (to make the Giant sit or jump, for example), and this shock exogenously determines how far the firm can see.

B.2 Variable Construction

The instrument, Obsolescence, attempts to capture an exogenous technological variation that is independent of a firm’s recent operations but influences the firm’s innovation performance. For each firm i in year t, this instrument is constructed in three steps. First, I define firm i’s predetermined knowledge space in year t ´ τ as all the patents cited by firm i (but not belonging to i) up to year t ´ τ. Then, I calculate the number of citations received by this KnowledgeSpacei,t´τ in t ´ τ

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and a more negative Obsolescence means a larger decline of the value of a firm’s


Obsolescenceτ “ ´rlnpCitt pKnowledgeSpacei,t´τ qq´lnpCitt´τ pKnowledgeSpacei,t´τ qqs.

i,t Simply put, this instrument first defines the knowledge space of a firm by incorporating detailed information on the firm’s innovation profile and citation history (“tree”) and then measures the rate of obsolescence using exogenous citation dynamics to this knowledge space.

It is worth discussing the validity of the exclusion restriction for using this instrument in the CVC study. Generally speaking, the validity of this approach rests on the assumption that, controlling for industry-specific technological trends and firm-specific characteristics, the measured obsolescence of a firm’s knowledge space predetermined years ago is orthogonal to its current CVC strategy other than through affecting current innovation performance.

- One might worry that a firm’s knowledge space might be affected by the type and capability of managers, yet this concern should be allayed by using a predetermined knowledge space formed along the corporate history rather than the concurrent one.

- One might also worry that the firm itself could be the main driver of the technological evolution. This concern is addressed first by excluding patents owned by the firm from its own knowledge space and excluding all citations made by the firm itself in the variable construction. It is further mitigated by a robustness check on a subsample of medium and small firms, which are less likely to endogenize technological evolution.

77B.3 A Simple Illustrative Example Using the Instrument

To illustrate how the instrumental variable can correct the estimation bias raising from the endogeneity problem, I describe the following simple example.1 Assume that a firm’s probability of launching a CVC unit is determined by an unconditional probability and a incremental probability determined by ∆Innovation realized in the near past, formulated as PCV C ` q ¨ ∆Innovation. PCV C stands for the unconditional probability of CVC initiations, and ∆Innovation is a dummy indicating whether the firm experienced an innovation increase (∆Innovation “ 1) or an innovation deterioration (∆Innovation “ ´1). I make ∆Innovation a binary dummy to simplify the illustration.

Suppose that the unconditional probability of launching a CVC is heterogeneous and is correlated with ∆Innovation in some endogenous way (e.g., manager type could be driving both at the same time). Specifically, assume that there are three types of firms based on their ability to innovate: High-type firms are on a upward trajectory innovation (∆Innovation “ 1 unconditionally) and have an unconditional probability of launching CVC PCV C “ pH. Sensitive-type firms are sensitive to technological evolution and will have ∆Innovation “ 1 p´1q if the technology trend works in favor of (against) them, and these firms have a type-specific CVC probability of PCV C “ pM.

Low -type firms are in a struggling situation (∆Innovation “ ´1 unconditionally) and PCV C “ pL. For simplicity, assume that the knowledge obsolescence will be either favorable or disruptive to the firm each with probability 50%, and each firm type is with probability 1/3. In the table below, we can summarize the probability of

initiating a CVC unit in the six possible cases:

1 This example is based on Bennedsen, P´rez-Gonz´lez, and Wolfenzon (2010) and Bernstein e a (2015).

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The OLS estimates essentially compare firms that experience an innovation increase (∆Innovation “ 1, the upper left triangle) with the firms that experience an innovation deterioration (∆Innovation “ ´1, the bottom right triangle), and the result reflects both the “treatment effect” and the selection bias (from the

heterogeneity of PCV C ):

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The bias term, p1{3q ˆ ppH ´ pL q, could be either positive or negative based on the assumption on the order of tpH, pM, pL u. On the one hand, if we assume that bad governance could be driving both innovation decline and CVC initiation, then pL ą pH and βOLS is more negative than the true effect q. On the other hand, if we assume that forward-looking managers could be driving both innovation improvements and CVC business, then pL ă pH and βOLS is more positive than q. The true size of the bias is hard to ascertain under this framework.

The IV approach uses the exogenous variation in Obsolescence, which affects ∆Innovation, to help back out the true q. The “first-stage” regression captures the

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To conclude this example, I wish to highlight two points. First, as shown in the derivation, the IV approach essentially uses only the “Sensitive” group to estimate the true q, or, in technical terms, the estimation relies on the “Local Average Treatment Effect (LATE)” based on the “compliers” (the observations that are responsive to the instrument). Second, both ∆Innovation and Obsolescence take binary values in this example for simplicity. Obviously, those two variables both take continuous value in the data—the example’s derivation can be extended to this case by weighting-average the estimates along the support of the instrument.

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Merging VentureXpert with Patent Databases In this section, I describe the process to merge entrepreneurial companies in VentrueXpert database with USPTO patent databases, through matching company names in VentureXpert with assignee names in the USPTO patent database. To minimize potential problems introduced by the minor discrepancy between different versions of the USPTO database, I use both NBER and Harvard Business School (HBS) patent databases to provide patent assignee information. After this step, each company in VentureXpert will have its original name, standardized name and a stem name;

similar for USPTO assignees.

C.1 Name Standardization

I begin by standardizing company names in VentureXpert and assignee names from NBER and HBS patent database, using the name standardization algorithm developed by the NBER Patent Data Project. This algorithm standardizes common company prefixes and suffixes, strips names of punctuation and capitalization; it also isolates a company’s stem name (the main body of the company name) excluding these prefixes

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C.2 The Matching Procedure With these standardized and stem company (assignee) names and demographic information provided by both VentureXpert and USPTO, I merge the databases

following the matching procedures below:

1. Each standardized VentureXpert company name is matched with standardized names from the NBER data and HBS data.

(a) If an exact match is identified, I consider this as a “successful match.” The company is removed from the set of names waiting to be matched on both sides.

(b) Otherwise, next step.

2. Each stem VentreXpert company name is matched with stem names from the NBER data and HBS data.

(a) If an exact match of stem names if identified, and the two companies are located in the same city and state OR the two comapnies are located in the same state and the earliest patenting year in NBER and HBS databases is later than the founding year in VentureXpert, I consider this as a “successful match.” The company is removed from the set of names waiting to be matched on both sides.

(b) If an exact match of stem names is identified, but the two companies do not satisfy the location and chronology criterions above, I consider this as a “potential match.” The company is moved to a pool of firms waiting for manual checks.

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3. For the remaining companies, each stem VentureXpert company name is matched with up to 3 close stem names from the USPTO data using a fuzzymatching method based on the Levenshtein edit distance.1 The criterion is based on the length of the strings and the Levenshtein distance, and the threshold is determined through a random sampling procedure.

(a) If the fuzzy-matched pair is located in the same city and state OR the two comapnies are located in the same state and the earliest patenting year in NBER and HBS databases is later than the founding year in VentureXpert, I consider this as a “potential match.” (b) Otherwise, the companies are categorized as “failed to match.”

4. The “potential matches” set identified in the procedures above are reviewed by hand, incorporating information from both data sources, including full patent abstracts, and company business descriptions.

(a) Pairs confirmed as successful matches through the manual check are moved

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1 The Levenshtein edit distance measures the degree of proximity between two strings, and corresponds to the number of substitutions, deletions or insertions needed to transform one string into the other one (and vice versa).

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This appendix provides a detailed description of the method used to identify patent transactions. I first introduce the raw dataset on patent assignments and then present the methodology used to identify patent transactions. Specifically, patent assignments other than transfers from an inventor to the firm she works at or from a subsidiary to its corporate parent.

D.1 Data Sources

I begin with the raw patent assignment database, downloaded from the United States Patent and Trademark Office (USPTO) patent assignment files, hosted by Google Patents. A patent assignment is the transfer of (part of) an owner’s property right in a given patent or patents, and any applications for such patents. The patent transfer may occur on its own or as part of a larger asset sale or purchase. These files contain all records of assignments made to U.S. patents from the late 1970s. The original files are then parsed and combined to serve as the starting raw dataset, including all patents assigned from an inventor to the firm, from a firm to an inventor, from one 84 inventor (firm) to another inventor (firm).

I make use of the following information for the purpose of identifying patent transactions. First, in regards to patent assignment information, I retrieve information on 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. Some important reasons include assignment of assignor’s interest, security agreement, merger, and change of names. Second, in regards to patent information, I retrieve information on patent application and grant dates, identification numbers (patent number and application number), and patent title. I then merge the raw assignment data with the USPTO patent databases so as to gather additional information on the original assignee and patent technology classes. I also combine the dataset with the inventor level data maintained at Harvard Business School (HBS), which allows me to identify the inventor(s) of any given patent. Since I focus on utility patents, I remove entries regarding design patents.

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