Patents as Loan Collateral in Sweden1187601/FULLTEXT01.pdf · Patents as Loan Collateral in Sweden...

IN DEGREE PROJECT INDUSTRIAL MANAGEMENT,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2017

Patents as Loan Collateral in SwedenAn empirical analysis of what patent characteristics matter for collateralization

FELIX BRACHT

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

Master of Science Thesis INDEK 2017:144

Patents as Loan Collateral in Sweden

An empirical analysis of what patent characteristics matter for collateralization

Felix Bracht

Approved

2017-10-09

Examiner

Kristina Nyström

Supervisor

Ali Mohammadi

Patents as Loan Collateral in SwedenAn empirical analysis of what patent characteristics

matter for collateralization

Felix Bracht∗

October 7, 2017

Abstract

This study analyses empirically what patent characteristics matter forcollateralization. In accordance with the finance literature, loan collateralis determined by the liquidation value of the asset which in turn dependson the three factors "physical attributes of the asset", "number of alter-native users" and "financial strength of alternative users". Hence, thestudy is focusing on patent characteristics influencing the three factorsof the liquidation value. To control for firm effects of the patent pledg-ing firms, a treatment group of pledged patents and a comparison groupof unpledged patents have been matched based on firm characteristics ofthe patent owner. The subsequent empirical analysis revealed that patentcharacteristics related to the physical attributes of patents enhancing theirredeployability matter for collateralization. Patent characteristics relatedto the market liquidity measuring the financial strength of alternativeusers, are insignificant. Furthermore, the study confirms the additionalfunction of patents as source of finance by offering them for loan collateral.Especially small and young firms, scare of tangible assets pledge patentsfor receiving debt finance.

Key Words: Patents, Loan Collateral, Coarsened Exact Matching

∗[email protected]

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mailto:[email protected]

Acknowledgments

I would like to express my special gratitude to my advisor Ali Mohammadi forhis enthusiastic encouragement, valuable comments and great guidance throughthis thesis project.

I would also like to thank the employees from the Economics unit of theDepartment of Industrial Economics and Management at the Royal Instituteof Technology under the direction of Prof. Hans Lööf, who gave me a place towork and furthermore, were always willing to give me useful advice.

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Contents

1 Introduction 1

2 Theory 32.1 Patents as Loan Collateral . . . . . . . . . . . . . . . . . . . . . . 32.2 Patent Characteristics matter for Collateralization . . . . . . . . 3

2.2.1 Redeployability . . . . . . . . . . . . . . . . . . . . . . . . 42.2.2 Patent Market Liquidity . . . . . . . . . . . . . . . . . . . 5

3 Data 73.1 Data from the Swedish Patent and Registration Office (PRV) . . 73.2 Data from EPO’s patent statistical database (PATSTAT) . . . . 83.3 Serrano Database . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

4 Descriptive Findings 104.1 Patent Level Analysis . . . . . . . . . . . . . . . . . . . . . . . . 104.2 Firm Level Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 13

5 Sample Construction 165.1 Matching Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 165.2 Matching Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

6 Empirical Model 196.1 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196.2 Controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

7 Results 257.1 Descriptive Statistics . . . . . . . . . . . . . . . . . . . . . . . . . 257.2 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277.3 Robustness Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

8 Discussion 30

9 Conclusion 329.1 Limitations and Further Research . . . . . . . . . . . . . . . . . . 32

Appendices 38

A Patent Level Analysis 38

B Robustness Tests 41

C Main Variables and Data Sources 44

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List of Figures

1 Theoretical model on patent characteristics matter for collateral-ization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2 Yearly number of patent pledges in Sweden . . . . . . . . . . . . 103 Top holders of pledged patents in Sweden . . . . . . . . . . . . . 124 Patents pledged per firm . . . . . . . . . . . . . . . . . . . . . . . 135 Industries of patent pledging firms . . . . . . . . . . . . . . . . . 146 Cumulative distribution of firm size at the pledge date . . . . . . 157 Cumulative distribution of firm’s age at the pledge date . . . . . 158 Technical fields of pledged patents in Sweden . . . . . . . . . . . 389 Technical fields of all Swedish patents filed by firms . . . . . . . . 3810 Years between patent filing and patent pledging . . . . . . . . . . 3911 Pledged patent portfolio size . . . . . . . . . . . . . . . . . . . . 3912 Number of patent pledges per firm in Sweden . . . . . . . . . . . 4013 Patent stock of firms one year before patents have been pledged . 40

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List of Tables

1 Matching statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 182 Summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Correlation matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 264 Main results with the logit model . . . . . . . . . . . . . . . . . . 275 Main results with the probit model . . . . . . . . . . . . . . . . . 416 Regression excluding patents pledged to Almi AB . . . . . . . . . 427 Regression excluding years of the financial crisis . . . . . . . . . . 438 Main variables and data sources . . . . . . . . . . . . . . . . . . . 44

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Abbreviations

CEM Coarsened Exact Matching

EP European patent

EPO European Patent Office

INPADOC EPO’s worldwide legal status database

NACE Statistical classification of economic activities

PRV Swedish Patent and Registration Office

USPTO United States Patent and Trademark Office

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1 Introduction

Innovation is the fundamental source of economic wealth, especially for de-veloped countries (Schumpeter 1942). However, it is commonly known thatinnovators suffer under capital restrictions. The uncertain innovation processincreases agency conflicts with investors due to information asymmetries (Hall& Lerner 2010). Equity finance can solve information asymmetries but comesat the cost of dilution in equity per share. In contrast, debt finance prevents thedilution and further allows a leverage effect on the return on equity (Modigliani& Miller 1958). The information asymmetries related to debt finance are usu-ally solved by offering collateral. Since young and innovative firms have veryfew tangible assets (Hall & Lerner 2010), they can revert to intangible assets,namely patents offered for collateral. Hence, patents as loan collateral can helpto overcome capital restrictions and provide a suitable source of finance forinnovative firms.

There is little research on the use of patents as loan collateral. Fischer &Ringler (2014) analyze characteristics of pledged US patents. They find thattechnology-related patent characteristics matter for patent pledging in the USmarket. Similarly, Mann (2016) provides an overview of how patent character-istics facilitate their pledgeability. However, both focus on US patents and havelittle information about firms pledging the patents. Furthermore, the studiesinvestigate patent characteristics independent from important findings in theresearch on loan collateral. Additionally, firm characteristics on the decision topledge patents have generally been overlooked.

This paper aims to provide a more complete picture on what patent charac-teristics matter for collateralization. In accordance with the finance literature,the liquidation value of the pledged asset is the main driver for loan collateral(Benmelech 2008, Shleifer & Vishny 1992). Hence, I assign patent characteris-tics to factors influencing the liquidation value of patents. To the best of myknowledge, it will be the first theoretical model on what patent characteristicsfacilitate pledging, that incorporates findings from the theory on collateral. In asecond step, I will analyze the patent characteristics empirically for the Swedishpatent market. For that, I will match a treatment group of pledged patents anda comparison group of unpledged patents based on firm characteristics of theowner. In contrast to prior studies, any firm and time effects on the decisionto pledge patents will be ruled out. This is important since firm characteristicshighly influence the decision to secure loans by collateral (Berger & Udell 1995,Voordeckers & Steijvers 2006). Thus, treatment effects between pledged andunpledged patents are based on aimed patent characteristics.

The empirical analysis reveals that characteristics related to the physical

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attributes of patents increase the likelihood of patents being pledged. Thetechnical- and commercial quality of the underlying invention as well as theage and the number of prior trades of patents have a positive impact on collat-eralization. Hence, patent pledging in Sweden is driven by physical attributesof patents. Patent characteristics related to the market liquidity, as a seconddeterminant of the liquidation value, are insignificant. Furthermore, the studyconfirms the additional function of patents serving as a source of finance. Es-pecially young and small firms, scare of tangible assets, offer patents as loancollateral to overcome financial constraints.

Among them, also firms developing sustainable technologies suffer undercapital restrictions (Popp et al. 2009). Hence, patents as a source of finance canfurther help to promote sustainable businesses.

The rest of the paper is structured as follows; section 2 describes the theoryof pledging and derives a model on patent characteristics that matter for collat-eralization; section 3 and section 4 describe the data and provide an overviewabout the patent pledging activity in Sweden; Subsquently, section 6 and section5 explain the empirical strategy and the important matching process, to purgeout firm effects on the pledging decision; section 7 presents the results on whatpatent characteristics facilitate pledging; Finally, section 8 discusses the results.

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2 Theory

2.1 Patents as Loan Collateral

In case of debt financed innovation, the relationship between creditor and debtoris shaped by agency conflicts, due to the presence of information asymmetries.Creditors bear the risk of selecting unprofitable projects known as adverse selec-tion. Furthermore, the behavior of debtors is not completely observable leadingto the moral hazard effect, in which debtors choose undesirable high risk. Bothconflicts can result in credit rationing, were profitable innovation projects do notreceive debt finance (Steijvers & Voordeckers 2009). Patents offered as collateralcan mitigate credit rationing problems. First, the signaling value of patents re-duce the adverse selection problem. Patents providing information for investorsabout the quality of the invention, as well as the involved team (Jimenez et al.2006). Second, the ownership-right of the pledged patent will be transferred tothe investor in case of a credit default. The threat of losing the pledged assetdisciplines debtors and hence, mitigates the moral hazard problem (Berger &Udell 1995).

However, the evaluation of patents is problematic, since future profits fromthe underlying invention are highly uncertain. Moreover, the value of the patentportfolio is highly skewed, with just a very few patents protecting profitableinventions (Harhoff 2011). Hence, only a minority of patents provide enoughvalue to be used as collateral. In accordance with the finance literature, creditorsdemand a high liquidation value for pledged assets (Benmelech 2008, Shleifer &Vishny 1992). In case of credit default, creditors receive the ownership-rights onthe pledged asset, that can be liquidated to offset losses. Consequently, patentcharacteristics influencing its liquidation value matter for collateralization.

Following, I will first explain factors of the liquidation value given in thefinance literature. I will then determine relevant patent characteristics influenc-ing the factors. The specified patent characteristics will be operationalized laterin section 6.1 and analyzed empirically.

2.2 Patent Characteristics matter for Collateralization

The finance literature defines the liquidation value of the pledged asset as themain determinant for the decision to secure loans by collateral (Benmelech 2008,Shleifer & Vishny 1992). Thereby, the liquidation value is determined along thetwo dimensions redeployability and liquidity.

Redeployability describes the alternative uses of pledged assets, which de-pend on its physical attributes together with the number of alternative users(Benmelech 2008). Assets having vast number of alternative uses will attract

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more customer and reach a higher liquidation value. However, even though anasset is highly redeployable, alternative users need to have the financial strengthto purchase. Hence, liquidity describing the ability of alternative user to buythe asset is crucial for reaching high liquidation values as well (Shleifer & Vishny1992).

Summarized the three factors "physical attributes of the asset", "numberof alternative users" and "financial strength of alternative users" determinethe liquidation value, which in turn, is the main driver for loan collateral. Itcan therefore be assumed, that especially patent characteristics influencing thethree factors of the liquidation value matter for collateralization. Following, Idetermine patent characteristics which have an impact on the three discussedfactors.

Financial strength of usersNumber of usersPhysical attributes

Determinant for loan collateral: Liquidation value of the asset

Redeployability Liquidity

Firm-specificity

Technical quality Patent market liquidity

Commercial quality

Sector density

Patent age

Prior trades

Determinants of the liquidation value Patent characteristics

Figure 1: Theoretical model on patent characteristics matter for collateraliza-tion

2.2.1 Redeployability

For physical attributes of patents, especially those attributes are of interestthat increase their value and by that, attract alternative users on the secondarymarket. Determinants of the patent value and suitable measurements have beenanalyzed in the previous literature (Harhoff et al. 2003, Reitzig 2003). First, thetechnological quality in sense of the technical importance of a patent. Highlyimportant technologies increase the value of the patent for alternative users re-deploying the underlying invention (Fischer & Ringler 2014). Patents protectinghighly important technologies are those on which most following patents build

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upon and hence, receive many citations (Trajtenberg 1990). Second, the com-mercial quality in the sense of the markets a patent sought protection. The sameinvention can be patented in several jurisdictions, so-called patent authorities.Patents from different jurisdictions protecting the same technology are definedas a patent family. Hence, technologies patented within big patent families canbe exploited in larger markets and increase their value for alternative users (Put-nam 1997). Third, the age of a patent. Patents grant the monopoly right onthe returns of the invention for a specified time. The closer the patent is to itsexpiration date, the less time for buyer to offset losses occurred from the trans-action (Serrano 2005). Hence, younger patents close to their filing date providemore time to exploit monopoly profits for alternative users. Fourth, the numberof prior trades. Serrano (2005) shows that previously traded patents are morelikely to be re-traded. Prior traded patents have proved their redeployabilityand hence, made transactions to alternative users easy.

Besides the mentioned physical attributes, the number of alternative usersas the market size also influences the liquidation value. In thicker secondarymarkets the matching between seller and buyer arises more effectively, enablinghigher liquidation values (Gavazza 2011). Two characteristics are relevant forthe number of potential users buying pledged patents. First, firms patentingin the same technological fields are potential users of the underlying technol-ogy (Mann 2016). The more firms patenting in the same field, the higher theprobability to selling the patent. Sector density describes the number of patent-ing firms in the same technical field. Second, to address a wide range of cus-tomers the assets firm-specificity is essential. Highly firm specific assets havelimited customers by definition (Williamson 1988). Hence, highly firm-specificpatents having limited number of alternative users. Especially, patents citingown previous patents build upon the own knowledge base and can be identifiedas firm-specific (Wang et al. 2009).

To summarize, the following patent characteristics influence an asset’s re-deployability: Technical quality, commercial quality, patents age, prior patenttrades, sector density and patent firm-specificity.

2.2.2 Patent Market Liquidity

In accordance with Shleifer & Vishny (1992), despite the redeployability of apledged patent, potential buyers need to have the ability to buy the collateral atthe event of liquidation. Even though a patent is highly redeployable throughits underlying technology having vast of alternative uses, potential buyers mightnot have the financial strength to purchase. It becomes more clear during anindustry-wide recession. In this situation credit default is likely to occur and the

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pledge holder might be forced to liquidate the patent. The highest valuationbuyers are those closely related to the industry, due to the fact of exploringthe benefits of redeployablity most. However, in recession it is most likely thatbuyers from similar technological fields will suffer under same financial distressand will not be able to buy the offered patent. In consequence, the patentwill reach a price below value in best use. Hence, patent market liquidity isan important determinant of the liquidation value. Contribute to the patentmarket liquidity, I will follow Hochberg et al. (2014) and calculate the annuallikelihood of patents being traded.

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3 Data

The empirical analysis of what patent characteristics matter for collateraliza-tion, is focusing on the Swedish patent market. Thus, the data is restrictedto patents filed at the Swedish patent office during the period 1980 to 2015.Furthermore, I will match a treatment group of pledged patents and a compar-ison of unpledged patents based on firm characteristics of the patent owners,described in section 5. The matching process is necessary to control for firmcharacteristics, which highly influence the decision to secure loans by collateral.The matching process requires detailed firm level data, which are just avail-able for Swedish firms. Hence, the sample will be further restricted for patentsowned by Swedish firms. After, matched pledged and unpledged patents will beanalyzed empirically on prior discussed patent characteristics.

Patent and firm level information have been extracted from several datasources. The data have been aggregated into two databases separate for patentlevel information and firm level information.

3.1 Data from the Swedish Patent and Registration Office(PRV)

The Swedish Patent and Registration Office (PRV) provides detailed informa-tion about all pledged Swedish patents between 1980 and 2015. The data con-tains the patent number serving as unique-identifier for each patent, the date thepledge was granted1 and the pledge holder. Additionally, PRV delivered dataon change of ownership or name for all Swedish patents, to derive the applicantat the time the pledge was granted, as well as the patent trading activity inSweden. Based on the dates for the change of ownership, the patent applicantsat the pledged grant date have been added for each patent. Since, the empiricalanalysis is just focusing on patents owned by Swedish firms, pledged patentsowned by individuals or foreigner firms have been dropped. The ownership of apatent can be allocated to several applicants. However, the resulting data doesnot include pledged patents owned by more than one firm. This is expectable,since firms sharing the ownership of a patent do not have full access to pledgethem. Furthermore, Swedish organisationnumbers have been matched to theremaining patent owner manually, based on firm names and addresses. Theorganisationnumber is a unique identifier for Swedish firms and is determinedby the Swedish Tax Office.

1Patent pledges are executed at the pledge grant date.

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3.2 Data from EPO’s patent statistical database (PAT-STAT)

The data provided by PRV is accomplished by patent data from the EPO’spatent statistical database (PATSTAT).2 PATSTAT has a worldwide coverageof bibliographical and legal status patent data hence, listing all Swedish patentsas well. However, it does not contain information on patent pledges, nor does itrecord the change of ownership for Swedish patents making it necessary to usethe data from PRV.3 It can be assumed that Swedish patents that are not listedin the pledged data provided by PRV have not been pledged. This is due to thefact, that first, in accordance with the Swedish patent act pledge agreementsshall be register at PRV (Ministry of Justice Stockholm 1967) and second, cred-itors have a high interest in the registration to ensure legal certainty.4 Hence,unlisted Swedish patents initially filed by Swedish firms during the examinationperiod, are part of the control group. In a prior project, Swedish organisation-numbers have been added to all Swedish firms listed in the PATSTAT database.5

However, the project did not consider subsequent changes of ownership or namesafter the patent had been filed. Due to that, unpledged Swedish patents servingas comparison group list their initial applicants, that have filed the patent tothe Swedish patent office.6

3.3 Serrano Database

The Serrano database is used to gain information on the financial history ofSwedish firms having pledged or unpledged patents. The database accumulatesyearly information of financial statements and general company data, collectedby Swedish authorities. The information given in Serrano can be matched topatents based on the Swedish organisationnumber that are part of the Ser-rano database as well. Since the Serrano database only contains data for yearsafter 1997, the financial information does not cover the entire patent filing pe-

2PATSTAT Biblio and PATSTAT Legal Status in the 2016 Autumn Edition.3PATSTAT lists the initial patent applicants at the time the patent was filed. Since patents

can be sold to new applicants at any time, the patent holder at the time the pledge was grantedis not necessarily the initial patent applicant. Hence, data on change of ownership or name isused to derive the patent applicant, in form of a company, responsible for the collateralization.

4The legal certainty results in particular from the protection of the pledged patent againstsubsequent collateralization.

5In the project Swedish organisationnumbers have been matched to the correspondingpatent applicants by using a comparison algorithm. The algorithm matched Swedish organi-sationnumber based on similarities in the addresses and names of patent applicants given inPATSTAT. Overall 92% of all Swedish firm applicants listed in PATSTAT have been matchedwith their organisationnumbers.

6PRV provided data on change of ownership that have been used to analyze the patenttrading activity in Sweden. However, since new owners have not been matched with thecorresponding Swedish orgnaisationnumbers, financial data of the initial patent applicanthave been used for unpledged patents part of the comparison group.

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riod. Thus, I have constructed two databases of pledged and unpledged Swedishpatents, filed by Swedish firms. The first database contains all patent level in-formation of the pledged and unpledged patents for the entire sample period1980 until 2015. The second database contains firm level information startingin 1998 for applicants that have either pledged a patent after 1997, or have filedan unpledged patent in the entire sample period, serving as comparison group.

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4 Descriptive Findings

This section provides an overview of the patent pledging activity in Sweden.First, I will describe patterns on the patent level by analyzing patent data ofpledged patents. The data consists of information from PRV that have beenaccomplished by data from PATSTAT. Information on patent level covers theentire time period from 1980 until 2015. Second, I will describe patterns onthe firm level of patent applicants that have pledged patents. The firm levelinformation extracted from the Serrano database is restricted to the periodfrom 1998 until 2015.

4.1 Patent Level Analysis

020

40

60

80

nu

mb

er

of

ple

dg

ed

pa

ten

ts

1990 1995 2000 2005 2010 2015

execution year

Without THULE AB

Figure 2: Yearly number of patent pledges in Sweden

The number of pledged patents has been growing till 2007 in absolute terms.Overall 725 patents owned by Swedish firms have been pledged in the period1980 until 2015 in Sweden. Figure 2 shows the total number of pledged patentsper year.7 The low number of pledged patents in 2008, as well as sharp in-crease immediately after, can be explained by the impact of the financial crisis.Ivashina & Scharfstein (2010) claim that during this period new loans to bor-rowers fell dramatically, making the demand for collateral needless. This inturn lead to put-up demand for loans, which have been served in the year af-

7THULE AB pledged more than 15% of all patents has been excluded, since it will beoverrepresented in the sample.

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ter and increased the demand for collateral as well. However, between 2009and 2014 patent pledges have been decreasing constantly. This is in contrastto the US patent market. In their publications Mann (2016) and Marco et al.(2015) present a constant increase of patents recorded as collateral over thepast three decades in the USPTO patent assignment data. The further rise ofpledged patents in Sweden, in 2015, might break the decreasing trend, howeverthe evidence remains a concern for further research.

Most of the patents that have been pledged in the period 1980 until 2015protect technologies in the field of civil engineering, medical technology andhandling (Appendix A Figure 8). Interestingly, a similar distribution of techno-logical fields account for all Swedish patents filed in that time, indicating thatmostly patents of technological fields with strong patenting activities have beenpledged. For instance, civil engineering, handling and mechanical elements arepart of the top five technological fields accounting for similar amount of pledgedand overall patents, filed in Sweden by firms8 (Appendix A Figure 8 and Figure9).

Since patents can be pledged at any point during their lifetime, it is of highinterest when firms do pledge their patents. More precisely, how many yearsafter the date of filing patents are used as loan collateral. Half of the patentshave been pledged within a period of five years after the date of filing and80% within ten years (Appendix A Figure 10). Hence, mainly young patentsproviding more time to exploit monopoly profits from the underlying technologyhave been pledged. Serrano (2005) showed similar pattern for the tradeability ofpatents, which influences their redeployablity and by that the decision to pledgethe patent.

Before patents have been granted, they have limited legal protection andhence, limited value as a collateral (Van Zeebroeck 2011). Indeed, more thantwo-third of pledged patents have been granted before collateralization tookplace. The amount increases up to 76% by accounting for granted patents thatbelong to the same patent family. As previously mentioned, patent familiesrepresent patents in different countries protecting the same invention. As soonas the first patent of a patent family have been granted, the probability of beinggranted in all remaining countries highly increases (Harhoff et al. 2003).

Besides the analysis of the patent life on patent collateralization, the pledgingbehavior of applicants are of high interest. First, applicants can pledge severalpatents to the same pledge holder at the same time, building a pledged patentportfolio. Second, they might pledge patents several times, allowing conclusionsabout the frequency of using patents as a source of finance. Three quartersof the applicants do not pledge more than two patents at the same time, to

8Patent filings of Swedish and foreigner firms.

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the same pledge holder, indicating mainly small patent portfolios have beenpledged. The maximum size of pledged portfolios are 43 patents, that have beenpledged by THULE AB, which is responsible for all pledged portfolios with morethan 40 patents (Appendix A Figure 11). In addition, the vast majority, 84%of applicants pledged patents once (Appendix A Figure 12). Hence, showingthat patents are a rare source of finance for firms. Moreover, the small size ofpledged patent portfolios together with rare usage of patent as loan collateralper applicant, indicating that mostly small and young firms will use such aninstrument. This assumption will be undermined by looking at the patent stockof the applicants one year before they have pledged a patent. 30% do not havepatents in the prior year of pledging (Appendix A Figure 13), due to the factthat those firms did not exist or have not been involved in patenting activities.Again, the outlier accounting for patent stocks with more than 1000 patents isTHULE AB.

7.0

6.8

5.0

4.4

3.7

3.1

2.9

2.9

2.8

2.8

2.5

2.4

2.4

2.3

1.6

1.5

1.5

0 2 4 6 8

percent of pledged patents

Swedbank AB

Skandinaviska Enskilda Banken AB

Svenska Handelsbanken AB

Citicorp Trustee Company Limited

Nordea Bank AB (publ)

Almi Företagspartner AB

Livförsäkrings AB Skandia

Danske Bank A/S

J P Morgan Europe Limited

Citibank

Almi Företagspartner Stockholm AB

The Royal Bank of Scotland plc.

Norrlandsfonden

Commerzbank Aktiengesellschaft

Xelerated Holdings Inc.

PlayPower Inc.

Kreos Capital III (Luxembourg) S.A.R.L

Pledge holder with more than 1.4% of pledged patents and without THULE AB

Figure 3: Top holders of pledged patents in Sweden

Last, Figure 3 presents the top ten pledge holders of Swedish patents pledgedby Swedish firms in the period 1980 till 2015. The four major banking groups(Handelsbanken AB, Nordea Bank AB, Skandinaviska Enskilda Banken ABand Swedbank AB) are under the top five pledge holders. These banks arealso dominating the Swedish financial market for years (Ekman et al. 2014).Furthermore, many patents have been pledged to regional subsidiaries of thestate-owned development bank ALMI AB. They are focusing on financing smalland medium size companies by providing loans where nobody else does. Hence,

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making ALMI AB a suitable provider of debt secured by pledged patents (AlmiFöretagspartner 2017).

4.2 Firm Level Analysis

122

36

25

22

21

20

20

19

18

13

12

11

11

0 50 100 150

number of pledged patents

THULE SWEDEN AB

GAMBRO LUNDIA AB

FLÄKT WOODS AB

PRINT DREAMS EUROPE AB

SMARTEQ WIRELESS AKTIEBOLAG

GGP SWEDEN AB

ABA OF SWEDEN AKTIEBOLAG

ISABERG RAPID AKTIEBOLAG

HAGS ANEBY AB

NORDISKA BALCO AKTIEBOLAG

VSM GROUP AKTIEBOLAG

JELD−WEN SVERIGE AB

ALFA LAVAL LUND AB

Applicants with more than 10 pledged patents

Figure 4: Patents pledged per firm

Overall 217 individual firms have pledged 597 patents in Sweden between1998 and 2015. Most patents in absolute numbers have been pledged by THULEAB. Together with GAMBRO LUNDIA AB having pledged the second mostpatents, they are also responsible for the biggest pledged patent portfolios, asseen in the previous patent level analysis (Appendix A Figure 11).

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44.0

17.7

14.8

9.5

8.6

5.3

0 10 20 30 40

percent of patent pledging firm’s

Knowledge−intensive services (KIS)

Less knowledge−intensive services (LKIS)

Medium−high−technology

Low technology

Medium−low−technology

High−technology

Figure 5: Industries of patent pledging firms

More than half of the Swedish firms, that have pledged patents in the exam-ination period, belong to the sector of industrial goods and corporate services.In the more detailed analysis of the industrial classification, 44% of the appli-cants are active in knowledge-insentive services. Figure 5 shows the aggregationinto high-tech industry and knowledge-intensive services based on the NACERev. 2 classification at 2-digit level of patent pledging firms.9 Especially firmsinvolved in knowledge-insentive services having few tangible assets and rely onthe output of the innovation process (Amara et al. 2008). Moreover, most firmsare part of service based industries, rather than manufacturing based industriesundermining the importance of scarce tangible assets for the decision to pledgepatents.

9In accordance with the high-tech aggregation of NACE Rev. 2 at 2-digit level providedby EUROSTAT (Eurostat 2017).

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0.2

.4.6

.81

EC

DF

of

siz

e c

ate

go

ry

0 1−4 5−9 50−9910−19 20−49 100−199 200−... Missing

size category in number of employees

Figure 6: Cumulative distribution offirm size at the pledge date

0.2

.4.6

.81

EC

DF

of

firm

’s a

ge

0 20 40 60 805 10 30 50 70

firm’s age

Figure 7: Cumulative distribution offirm’s age at the pledge date

Figure 6 and Figure 7 reveal the size category as well as the age structure ofpatent pledging firms, at the time they have pledged patents. First, more than80% of the firms do not have more than 50 employees and hence, classified assmall firms in accordance with the European Commission. In addition, turnoverand total assets of all patent pledging firms are below that definition of smallfirms as well (European Commission 2017). Second, 40% of the firms are fiveyears old or younger, at the time they have pledged patents. The fact thatpatent applicants pledge patents in recent years after they have been founded,indicates that this source of finance is mostly used at early stages.

In summary, most of the firms pledging patents in Sweden are young andsmall firms active in the in knowledge-insentive services, that is scarce of tan-gible assets. Indeed, especially this group of firms is affected most by capitalrestrictions and at the same time driving the most radical innovations (Kerr &Nanda 2015, Brown et al. 2009, Hall & Lerner 2010). Hence, it is not surprisingthat the same offer patents as loan collateral to overcome financial constraints.

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5 Sample Construction

To investigate patent characteristics that matter for collateralization, I will usea binary choice model to estimate the likelihood of patents being pledged. Tobegin, I will use the Coarsened Exact Matching (CEM) algorithm describedby Iacus et al. (2011) to match a treatment group of pledged patents and thecomparison group of unpledged patents. In contrast to the commonly usedpropensity score matching, CEM generates better balanced matching solutionsthat are less model depended and as result, reduce bias (Iacus et al. 2011).The matching is based on firm characteristics of the patent applicants. Hence,any firm effects influencing the decision to pledge patents can be ruled out.This is necessary since prior research has shown, that firm characteristics highlyinfluence the decision to secure a loan by collateral (Berger & Udell 1995, Vo-ordeckers & Steijvers 2006, Klapper et al. 2001). The subsequent logit andprobit regression is then estimating the treatment effects of the prior definedpatent characteristics for the matched data.

Following, I will first define and operationalize firm characteristics used asmatching variables for the coarsened exact match. After, I will describe thematching data and CEM-process in more detail.

5.1 Matching Variables

Firm characteristics, namely the credit quality of the borrower, highly influencethe decision to secure loans by collateral. Like in the previous finance liter-ature revealed, riskier firms need to provide more collateral (Berger & Udell1995). Moreover, borrower’s characteristics are more important determinantsof secured loans than loan and lender characteristics (Voordeckers & Steijvers2006). Due to that, it is necessary to control especially for borrower’s charac-teristics when investigating the single effect of patent characteristics on theirpledgeability.

Klapper et al. (2001) specified and tested firm characteristics influencing thedecision to secure loans. Those firm characteristics restricted by the existingdata are used as matching variables in the coarsened exact match.10 First, thefirm size measured by total asset, net sales and number of employees. Small firmsare more credit rationalized and have less access to the credit market, than theirbigger counterparts (Lehmann & Neuberger 2001). Consequently, more credit-constraint firms need to pledge more collateral. Second, firm’s profitabilitymeasured by the return on assets. Unprofitable firms are more likely to default

10Klapper at al. defined several firm characteristics influencing the decision to secure loanby collateral. However, not all of them can be used as controls, since the Serrano databasedoes not cover all required information.

16

and hence need to provide more collateral (Chakraborty & Hu 2006). Third, thedebt level of firms measured by debt to equity ratio. Similar to the profitabilityhighly leveraged firms have increased risk of default increasing the demand forcollateral. Last, firm’s intangible assets measured by the ratio intangible assetsto total assets. As previously discussed, intangible assets are difficult to evaluateand hence, firms need to provide increased collateral to compensate for theevaluation risk (Loumioti 2012).

5.2 Matching Data

To match patent applicants based on the revealed firm characteristics, a databasecontaining yearly financial and general company data has been created. Thedatabase contains patent numbers of patents that have been pledged after 1998and all unpledged patents together with firm level data of the patent applicants.The restriction to patent pledges after 1998 is necessary, since firm level datahave been extracted from the Serrano database, that does not contain data forprior years.

For pledged patents, the firm level data are measured with a lag of oneyear before the pledge grant date to consider the processing time until thepatent pledge is executed. In case that one applicant pledged patents severaltimes, just the first patent that have been pledged will be used for the matchingprocess and following analysis. Similarly, in case that one patent has beenpledged several times, just the first collateralization will be considered. Theexclusion of multiple pledges per firm, as well as multiple pledges per patentis necessary, since once a pledge is done subsquent decisions to pledge patentsare endogenous to prior once. However, pledged patent portfolios consisting ofpatents that have been pledged by one firm, at the same time, to the same pledgeholder, will not be restricted. In that case, patents that have been pledged in aportfolio will list the same firm level data of the pledging firm. To control for bigpledged patent portfolios, THULE AB and GAMBRO LUNDIA AB have beenexcluded. Sections 4.1 revealed, that both companies account for the biggestpledged patent portfolios and hence, would be overrepresented in the treatmentgroup.11 Overall, the matching data contains, first 430 pledged patents togetherwith the corresponding firm level data, one year before the pledge have beenexecuted (Table 1).

For unpledged patents, it will be assumed that those could have been pledgedat any time from the date of filing, however earliest one year after 1998 aspreviously explained. Hence, for the 43,801 unpledged patents firm level datafor all present years have been matched, summing up to 553,638 observation

11In the remaining sample no company to account for more than 6% of the pledged patents.

17

for the comparison group (Table 1). Since, any date won’t be influenced byprior pledging decisions, it is not necessary to control for multiple firm leveldata in the comparison group. Instead, the CEM algorithm will select the bestfitting unpledged patent based on the firm level data of the patent applicant,that matches most closely to the firm level data of applicants having pledgedpatents. The subsquent year of the matched firm level data then represents thehypothetical pledge date of an unpledged patent, which serve as a comparisongroup. In contrast to pledged patents, there are several unpledged patentsowned by more than one firm. However, it will be simply assumed that any ofthe patent filing firms can pledge the patent by having full ownership rights.

Both the pledged patents with uniform firm level data and the unpledgedpatents with multiple firm level data forming the database for the coarsened ex-act match. The algorithm is selecting the matching data by temporally coarseneach variable into groups and matches on these coarsened data. However, CEMcan also be specified to run the matching on uncoarsened variables (Blackwellet al. 2009). I will use such an exact match on the applicant’s branch and theyear of the firm level data, in order to control for sector effects of the firms aswell as for the year the collateralization was decided. The remaining variablestotal assets, net sales, number of employees, return on assets, debt to equityratio and intangibles per total assets are coarsen to match the groups.

Table 1: Matching statistics

unpledged patents pledged patents

All 553,638 430Matched 419 419

Unmatched 553,219 11

As a result, the coarsened exact matching is generating a treatment group of419 pledged patents and comparison group of 419 unpledged patents, both withsimilar applicant characteristics, one year before patents have been pledged.Since, unpledged patents have not been restricted to multiple firm level data,six patents in the comparison group are duplicates. This is due to the fact,that those patents have been matched twice on firm level data from two differ-ent years. Furthermore, no firm accounts for more than 4% of the patents inthe matched group, meaning that the patents are equally distributed over allapplicants.

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6 Empirical Model

The estimation of the likelihood that patents are used as loan collateral is donefor patents selected in the prior coarsened exact match. The dependent variableis a dummy variable capturing if a patent has been pledged or not. Independentvariables are the operationalized patent characteristics influencing the liquida-tion value, as the main determinant of loan collateral. The relevant character-istics have been derived and discussed in section 2 and will be operationalizedin the following subsection 6.1. Additionally, the model contains controls forthe temporally coarsen variables applied in the prior matching procedure, thepatent stock of the patent applicants, received venture capital funds as well asfixed effects for the filing year and technical field of patents.

6.1 Variables

Like previously discussed, the three factors of the liquidation value are "physicalattributes of the asset", "number of alternative users" and "financial strengthof alternative users". The factors have been assigned to relevant patent char-acteristics in section 2.2. Defined patent characteristics are operationalized asfollowing:

Technical qualitynb_forward_citesit

total_citesst0t(1)

Technical quality of a patent i, measured by the number of forward citationsreceived from other patents, nb_forward_citesit, normalized by the total num-ber of citations of patents, total_citesst0t, in the same technical field s and filedin the same year t0. The more subsquent patents citing the pledged patent, thehigher its technological relevance and hence, its quality. However, since olderpatents had more time to receive citations, as well as different technical fieldshighly differ in the number of citations, it is necessary to normalize the variable.The number of total citations is given in PATSTAT. To normalize the variablethe sum of all citations of patents filed in the same year and same technical fieldhas been calculated additionally for the subsequent division.12

12PATSTAT classify patents into 35 technology fields based on the much more detailed IPCclassification. The technology classification is based on the common ISI-OST-INPI classifi-cation introduced by the World Intellectual Property Organisation (WIPO)(Schmoch 2008).Further, since patents can belong to several technical fields, PATSTAT provides weights towhich degree an application belongs to one or more technical fields. The weights are used toweight sector specific variables on the patent level.

19

Commercial qualitynb_family_membersit (2)

Commercial quality of a patent i, measured by the number of patent familymembers, nb_family_membersit, of given application. The bigger the num-ber of designated states a patent sought protection, the bigger the market forredeploying the underlying technology. Similar to forward citations, PATSTATprovides the variable without requiring further calculations.

Patent age(match_year − filing_year)i (3)

The age of a patent i, measured as number of years from the date of filing,filing_yeari, till one year prior the pledge has been executed, match_yeari.The older a pledged patent is, the less time for alternative users benefitingfrom its monopoly rights. Again, the prior year of pledging is used to considerprocessing time between the decision and execution of the patent pledge.

Prior patent tradesnb_tradesit′ (4)

Number of prior trades of a patent i, measured by the number of changeof ownership’s, nb_tradesit, in the period t′ between filing year and one yearprior the patent has been used for loan collateral. The Swedish Patent andRegistration Office (PRV) also provided a database on changes of ownership forall Swedish patents in the period 1980 until 2015. The data was cleaned foradministrative events like change of names or addresses by deleting transfers,in which rather the address or the name for the new owner has not changed.To improve the cleaning, entries in which the name of the new owner is similarto the prior once, have been deleted as well.13 Additionally, in accordance withSerrano (2010) just transactions between firm boundaries have been considered.This is necessary since many transactions between individuals and firms repre-senting employees - employer’s transactions, that are not based on patent sales.In contrast to Serrano (2005) trades between large companies have not beenexcluded, since even such trades contribute to the redeployability of a patent.

Besides the physical attributes of patents, the number of alternative userssignificant influencing the redeployabilty of the pledged asset. The previouslyderived patent characteristics, relevant for the number of alternative users areoperationalized as following:

13Entries on the change of ownership have been deleted manually in case that the name ofthe new owner enclosed elements from the name of the previous owner.

20

Sector densitynb_patenting_firmsit0 (5)

Sector density of a patent i, measured by the number of firms,nb_patenting_firmsst0 , filing patents in the same year t0 and same technolog-ical field s. Firms that file patents in the same technological field are the mostlikely buyers of the pledged patent, since those exploring the benefits from theredeployabilty most. The number of firms filing patents in the same technolog-ical field are calculated based on the sector-classification for patent applicantsgiven in PATSTAT.14

Firm-specificity

firm_specificityit =weighted_nb_self_citesit

total_nb_citesit(6)

weighted_nb_self_citesit =

N∑n=1

self_citeitntotal_nb_cites_receivedtn

(7)

Firm-specificity of a patent i, measured by the share of self-citations normal-ized by the number of total citations a self-cited patent n received overall. Themore firm-specific the underlying technology is, the less alternative user can re-deploy the technology. Self-citations of own previous patents reveal firm specifictechnologies, since the accumulated knowledge build up on their own knowledgebase. However, in case the self-cited patent n is widely cited by other patentsas well, the underlying technology is more general and hence, contributes lessto firm-specificity. Thus, the measurement becomes more accurate by weightingthe number of self-citations, self_citesitn, with the total number of citations,total_nb_cites_receivedtn , each self-cited patent n received. PATSTAT pro-vides information about citations between patent families. The variables aretherefore calculated on the family-level, where one member of family cites atleast one member of another family. Self-citations in that respect are those, onwhich both patent families belong to the same applicant.

Section 2.2 revealed patent market liquidity as a further factor of the liqui-dation value for pledged patents. Patent market liquidity will be measured bythe annual likelihood that the pledged patent will be traded similar to Hochberget al. (2014).

14Applicants classified as COMPANY have been counted per filing year and technical field.

21

Patent market liquidity

prob_patent_liquidationstp =nb_traded_patentsstp

nb_active_granted_patentstp(8)

The probability of patent liquidation, measured by the number of patenttrades, nb_traded_patentsstp , one year before the collateralization tp and pertechnical field s, divided by the total number of valid granted patents,nb_active_granted_patentsstp in the same year and same technical field. Therebythe patent trades are considered as the demand for patents and vice versa, the to-tal number of valid patents as the supply of patents, available for trade. Hence,the more patents have been traded relative to the total number of availablepatents, the higher the probability to sell pledged patents in that market, lo-calized by the technical field. Since the number of Swedish patents applied atthe Swedish patent office is relatively low and decreasing over time (Granstrand& Holgersson 2012), I will calculate the variable for European patents in orderto get a more accurate measurement.15 Additionally, it has the advantage ofdepicting a larger market, on which pledged patent can be sold at the eventof liquidation. First, the number of patent trades per technical field and yearis approximated by the number of changes of ownership for all EP patents,filed between 1980 and 2015. Data on change of ownership is available in thePATSTAT extension covering the worldwide legal status database (INPADOC).Similar to equation 4, change of ownership involving individuals will not be con-sidered. Since PATSTAT does not provide a sector classification for new patentowners, an algorithm searching for common company entities were used to ex-clude individuals in the data on change of ownership.16 Furthermore, to controlfor patent sales in large blocks any transaction of more than five patents tothe same applicant, at the same date, have been dropped. Second, the totalnumber of granted patents in force per technical field and year is determinedby considering the lifetime of EP patents, filed between 1980 and 2015. Non-granted patents having limited legal protection will not be considered.17 Thelifetime of an EP patent is 20 years18 from the date of filing (European Patent

15The decreasing number of Swedish patent applications is due to the increasing use ofEuropean patents (EP) instead. EP patents can be validated in Sweden and are more easy toextended to any other member state.

16The algorithm does not distinguish between firms, universities or governmental organiza-tions. However, it will be assumed that any applicant of this sectors is a potential buyer forpledged patents. Overall 3,547,433 observations have been processed by the algorithm. In amanually check of randomly selected 500 observations, 99.4% of the observations have beenidentified correctly as non-individuals.

17Further, INPDOC does not provide legal data for non-granted patents, making the deter-mination of their lifetime inaccurate.

18Supplementary protection certificate (SPC) as the right to extend the patent lifetime willnot be considered, since it can just be applied for pharmaceutical patents (European PatentOffice 2017c)

22

Office 2017a). However, a patent might become invalid before, due to not be-ing granted, successfully filed opposition against the invention or non-paymentof renewable fees (van Zeebroeck et al. 2007). INPADOC lists relevant legalevents occurring during the patent lifetime and leading to unnatural death ofthe patent. These including the date of the last renewable fee, that should bepayed to keep the patent in force. The renewable fees of granted patents haveto be payed to the national patent authority the EP patent sought protection(European Patent Office 2017b). Most of the member states require an yearlypayment of the renewable fee. Hence, any patent for which the renewable fee hasnot been payed within a year, to at least one member state, will become invalid.To account for grace time, the unnatural patent death has been calculated 18month after the last renewable fee payment.19 Besides the absence of renewablefee payment, successfully filed opposition leading to the revocation of patentscontribute to unnatural patent death as well. Any legal event indicating suchan incident has been considered as well. Residual granted patents that havepaid all renewable fees and have not been revoked, considered to become invalidmaximum 20 years from the date of filing.

6.2 Controls

The empirical analysis incorporates several control variables. First, in the pre-vious coarsened exact match, the treatment and control group is matched ontemporally coarsened financial data of the patent applicants. Hence, not all firmeffects can be excluded in the matched data, making it necessary to include thetemporally coarsened matching variables in the subsequent regression.

Second, a dummy variable for venture backing, vc_backingi, is includedindicating that the patent applicant received venture backing one year priorthe pledge has been executed. Hochberg et al. (2014) showed that venturebacking positively influences subsequent lending. This is due to the signalingvalue of venture backing that serves as a quality sign of borrowers. The dataon venture backed Swedish firms has been extracted from several data sources20

and matched to the patent applicants. However, the data is restricted to periodsafter 1997, meaning the VC dummy indicating weather a patent applicant hasever received venture capital after 1997 till the prior year of the patent pledge.

Third, the variable patent_stocki controls for the number of patents filedby the pledging firm till the prior year of the patent pledge.

19The period of 18 month has been suggested by an EPO patent expert.20The data has been collected manually from several websites.

23

Fourth, filing year dummies control for the year the matched patents havebeen filed. Additional, dummies for technical fields of patents control for tech-nology based effects on the decision to pledge them.

24

7 Results

7.1 Descriptive Statistics

Table 2: Summary statistics

Treatment Group Comparison Groupcount mean sd count mean sd

nb_forward_cites 419 0.011 0.031 419 0.004 0.011nb_family_members 419 6.606 5.108 419 4.745 4.075nb_trades 419 0.074 0.280 419 0.017 0.128patent_age 419 4.826 4.049 419 8.819 6.719nb_patenting_firms 419 110.026 69.501 419 129.413 91.697firm_specificity 419 0.008 0.060 419 0.005 0.022patent_market_liquditidy 419 0.784 0.197 419 0.812 0.221patent_stock 419 10.993 12.792 419 26.969 112.865vc_backed 419 0.110 0.313 419 0.072 0.258total_assets 416 0.555 2.655 416 0.880 3.585net_sales 416 0.345 0.703 416 0.296 0.777employees 416 171.197 333.459 416 114.663 243.272return_on_assets 416 -0.230 0.687 416 -0.071 0.516debt_equity_ratio 393 5.353 10.014 393 3.720 10.521intangibles_per_assets 416 0.218 0.302 416 0.213 0.306

The prior coarsened exact match is matching a treatment group of 419 pledgedpatents and a comparison group of 419 unpledged patents. Table 2 presents thesummary statistics, separated for pledged and unpledged patents. The num-ber of observations for the matching variables used in the prior coarsened exactmatching are lower than the total number of observations, due to missing valuesin the Serrano database. The mean and standard deviation for the matchingvariables do not differ significant in the treatment and comparison group, proof-ing successful matching results.

Table 3 presents the correlation matrix containing all variables despite thematching variables, used in the coarsened exact match. The table does notshow significant correlation between the independent variables, meaning thatmulticollinearity is not expected. In a more formal test, the variance inflationfactors have been calculated for all predictors, including the matching variablesas well. The test proofs the assumption that multicollinearity can be excluded.

25

Tab

le3:

Correlation

matrix

pledged

nb_forw

ard

_cites

nb_family

_mem

bers

nb_trad

espa

tent_ag

enb

_pa

tenting

_firms

firm_specificity

patent_market

_liq

uidity

patent_stock

vc_ba

cked

pledged

1nb

_forw

ard_

cites

0.14

51

nb_family

_mem

bers

0.19

80.23

11

nb_trad

es0.13

10.01

040.12

31

patent_ag

e-0.339

-0.130

0.0101

0.06

541

nb_pa

tenting_

firms

-0.118

-0.111

0.08

170.10

20.29

01

firm_specificity

0.03

00-0.020

50.00

630

-0.004

11-0.009

04-0.048

91

patent_market_

liquidity

-0.067

8-0.039

50.04

910.02

850.0045

40.20

2-0.023

61

patent_stock

-0.099

1-0.022

7-0.019

8-0.030

60.06

790.04

370.01

77-0.027

11

vc_ba

cked

0.06

650.10

9-0.018

8-0.027

4-0.162

-0.041

7-0.026

5-0.067

9-0.025

91

26

7.2 Main Results

Table 4: Main results with the logit model

Dependent variable: Logit model Average marginal effectspatent has been pledged b/se b/se

nb_forward_cites 60.81∗∗∗ 9.33∗∗∗

(16.81) (2.46)

nb_family_members 0.09∗∗∗ 0.01∗∗∗

(0.02) (0.00)

nb_trades 1.99∗∗∗ 0.30∗∗∗

(0.55) (0.08)

patent_age −0.21∗∗∗ −0.03∗∗∗

(0.04) (0.01)

nb_patenting_firms 0.00 0.00(0.00) (0.00)

firm_specificity 0.78 0.12(1.35) (0.21)

patent_market_liquidity −1.04 −0.16(0.72) (0.11)

patent_stock −0.02∗∗ −0.00∗∗

(0.01) (0.00)

vc_backed 0.86 0.13(0.51) (0.08)

total_assets −0.03 −0.00(0.04) (0.01)

net_sales −0.01 −0.00(0.29) (0.04)

employees 0.00∗∗∗ 0.00∗∗∗

(0.00) (0.00)

return_on_assets −0.94∗ −0.14∗

(0.43) (0.07)

debt_equity_ratio 0.03 0.00(0.02) (0.00)

intangibles_per_assets −0.78∗ −0.12∗

(0.37) (0.06)

Constant 1.11(0.99)

Filing Year Fixed Effects Included Included

Technical Field Fixed Effects Included Included

Observations 736 736

Standard errors in parenthesesRobust standard errors∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

27

Table 4 presents the coefficients and the average marginal effects for thelogit model, estimating the likelihood of a patent being pledged. First, thenumber of forward citations per patent positively influence the pledgeability ofpatents. As expected, better technical quality of the underlying invention in-crease the likelihood that patents are used as loan collateral. Patents protectinghighly important technologies having increased value for alternative users andhence, are more likely to be redeployed. Similar, the number of family memberscounting the jurisdiction, for which the invention sought protection, increasesthe likelihood of patents being pledged. Inventions with high commercial qual-ity are patented in big families and hence, can be exploited in larger markets.Third, the number of trades before the patent has been pledged strongly posi-tive influences the pledgeability of patents. In accordance with the theory, prioroccurred technology transfers to new patent owner indicating strong redeploya-bility of the underlying technology, required for loan collateral. Fourth, patentage negatively influences the likelihood of patents being pledged. As previ-ously explained, older patents provide less time to benefit from the monopolyrights makes them unattractive for alternative users able to redeploy the un-derlying technology. Furthermore, all four variables characterizing the physicalattributes of the asset are highly significant at the one percent level. Hence, inaccordance with the theory on collateralization, physical attributes of patentsenhancing their redeployability playing a major role in the decision to secureloans by pledged patents.

The number of patenting firms, as well as the firm-specificity are both in-significant. The variables influence the number of alternative users which is im-portant for the redeployability of assets as well. However, there is no empiricalsupport for the number of alternative users of patents matter for collateraliza-tion.

Similar, the coefficient for the variable patent market liquidity is insignifi-cant. The liquidity of the patent market approximated by the probability that apatent will be traded, does not influence the likelihood of patents being pledged.

The control variable patent stock is weakly negative but significant at theone percent level. This is in accordance with the descriptive findings in sec-tion 4.2, which revealed that most applicants pledging patents are small andyoung firms. Small and young firms have less patents and hence, smaller patentstocks. The control variable for received venture capital prior the patent pledgeis insignificant. This might be due to the incomplete data on venture backingfor Swedish firms available earliest in 1998. Similar most of the variables thathave been used in the coarsened matching are insignificant. However, the in-significance showing that their effects on the decision to pledge patents couldbe purged out by applying the matching process before.

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7.3 Robustness Tests

Several adoptions in the regression model have been done to control for robustresults. First, the regression has been repeated by applying the alternativeprobit model. The results for the coefficients as well as the average marginaleffects are similar to those under the logit model. The results can be found inappendix B table 5.

In a second model, any patent pledges to ALMI AB have been excluded. Asstate-owned development bank ALMI AB aims to promote small and medium-size companies as well as new entrepreneurs. This results in less strict creditselection criteria’s to provide loans for riskier creditors, than offered by commer-cial banks (Almi Företagspartner 2017). Hence, ALMI AB’s demand for col-lateral will be based on less strict criteria’s as well. Excluding patents pledgedto ALMI AB yield to similar results without minor changes in the coefficients(Appendix B Table 6).

In a third model, patent pledges occurred in the period 2006 until 2010 havebeen excluded to control for the global financial crisis, happened from 2007 till2008. The number of patent pledges during the crisis decreased substantially,revealed in section 4.1. Matching years for the firm level data in the priorcoarsened exact match are then restricted to years before 2006 and after 2008.Hence, prior and subsequent years of the financial crises will be excluded aswell. The results in the subsequent logit regression do not change significantly,undermining the robust results (Appendix B Table 7).

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8 Discussion

The theoretical model in section 2 defined patent characteristics influencing theliquidation value as the main determinant for loan collateral, in accordance withthe finance literature. Thereby the characteristics have been assigned to thethree factors of the liquidation value, namely "physical attributes of the asset","number of alternative users" and "financial strength of alternative users". Sub-sequent empirical analysis found evidence that physical attributes enhancing theredeployability of patents matter for collateralization. The technical quality ofpatents, measured by the number of forward citations, and commercial qualityof patent, measured by the number of family members, positively influence thelikelihood of patents being pledged. Similar, the number of prior trades occurredbefore the pledge execution, has a positive impact on the pledging of patents.Furthermore, in accordance with the theory, the age of patents is negative asso-ciated with its pledgeability. Moreover, all variables characterizing the physicalattributes of the patent are highly significant and robust. However, variablesinfluence the number of alternative users that might buy pledged patents incase of credit default, do not have an impact on the likelihood of patents beingpledged. The number of patenting firms, as the most likely buyers of pledgedpatents, as well as the measurement for firm-specificity of patents, are both in-significant. Moreover, the variable patent market liquidity approximated by theprobability of patents being traded is insignificant as well.

The findings have several implications. First, the patent pledging activityin Sweden is mainly driven by the physical attributes of patents. This is inaccordance with previous studies found similar results. Fischer & Ringler (2014)found evidence that the technical and commercial quality of patents matter forcollateralization in the US market. Second, there is no empirical evidence for thenumber of alternative users and the liquidity of the patent market influencingthe decision to pledge patents. However, this contrasts with findings for the USpatent market. Hochberg et al. (2014) show a positive impact of the US patentmarket liquidity on the patent collateralization in their empirical analysis.

Present research attributes two functions to patents. First, the monopolyright resulting from the legal protection against competitors imitating the un-derlying technology (Barney & Arikan 2001). Hence, patents isolate firms fromcompetitors and allowing them to exploit monopoly profits (Hall & Harhoff2012). Second, patents serve as quality signal to external resource providerby convoying observable characteristics. Such characteristics not just cover thequality of the invention, they also cover the quality of the involved team (Czar-nitzki et al. 2014). The empirical findings confirms an additional function ofpatents serving as a source of finance. Creditors have the ability to assess the

30

physical attributes of patents matter for collateralization. Thus, patents canbe offered as loan collateral to overcome financial constraints. This especiallyconcerns young and small firms, scarce of tangible assets (Kerr & Nanda 2015,Hall & Lerner 2010). Descriptive findings of firms pledging patents in Section4.2 support this assumption.

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9 Conclusion

This study provides a first overview of the patent pledging activity in Swe-den. The descriptive findings of patents offered as loan collateral revealed thatmost of them have been granted, but still provide enough time to benefit frommonopoly rights. Furthermore, pledged patent portfolios are rather small andmost firms pledge patents once. Moreover, applicants that have pledged patentsare young and small firms, which relay heavily on intangible assets. Those find-ings are in light with the theory showing that young and innovative firms sufferunder capital restrictions are most likely to pledge patents, in order to overcomefinancial constraints (Kerr & Nanda 2015, Hall & Lerner 2010).

In a second step, patent characteristics matter for collateralization have beenanalyzed empirically. For that, patent characteristics have been assigned tofactors of the liquidation value as the main determinant for loan collateral, inaccordance with the finance literature. The developed link between financeliterature and theory of patents provides a more complete model about the useof patents as loan collateral. Subsequent empirical analysis revealed that thepatent pledging in Sweden is mainly driven by the physical attributes of patents,enhancing their redeployability.

Furthermore, the study confirms an additional function of patents, whosevalue has been reduced to their monopoly rights and their function as a qualitysignal (Hsu & Ziedonis 2013). However, patents can also serve as a source offinance by offering them for loan collateral.

9.1 Limitations and Further Research

There are several limitations for the study on patent collateralization in Swe-den. First, firms pledging patents do not provide security agreements to theSwedish Patent and Registration Office (PRV). The data on pledged patents pro-vided by PRV only covers the pledging date and the name of the pledge holder.Hence, firms pledging patents have been derived on additional information onthe change of ownership, which comes with the cost of impreciseness. Secu-rity agreements include additional information on the names of patent pledgingfirms, the amount of the loan secured by collateral and the legal framework ofthe contract (Persson 2008). Especially the amount of the loan is of high inter-est to draw conclusions about the evaluation of patents, that have been used ascollateral. Moreover, the amount of the secured loan can help to determine theimpact of patent characteristics on the value of the collateral. The value basedanalysis of patents used as collateral remains a question for further research.In addition, the legal framework reveals possible restrictions for patent ownersusing the underlying technology as well as other assets part of the collateral.

32

Potential trade-offs in using patents as loan collateral could be analyzed in moredetailed.

Second, for the comparison group firm characteristics of the initial patentapplicants, filed the patents, have been used. Hence, subsequent changes ofownership have not been considered. Exception is the variable on prior trades,calculated for all patents by adding up the number of patent transfers to newowners. However, the prior coarsen exact match is based on firm characteristicsof the initial applicants, due to restrictions on the available data. Since patenttransfers happened rarely, potential biases by using patent characteristics of oldpatent owners are small.

Last, there is no guarantee that all firms have announced patent pledges toPRV. There might be cases in which security agreements between creditor anddebtor will be kept secret. However, in accordance with the Swedish patent actsecurity agreements shall be registered and further there is a high interest forregistration to avoid subsequent collateralization of pledged patents. Hence, thenumber of pledged patents not covered by the data from PRV is likely to besmall.

33

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37

Appendices

A Patent Level Analysis

4.04.5

4.05.1

2.10.3

1.06.7

1.30.3

0.90.1

7.16.5

3.91.0

0.25.2

0.36.7

3.70.5

2.83.0

2.82.0

3.05.2

7.24.5

0.31.4

0.21.3

0.9

0 2 4 6 8

percent

TransportThermal processes and apparatus

Textile and paper machinesTelecommunications

Surface technology, coatingSemiconductorsPharmaceuticals

Other special machinesOther consumer goodsOrganic fine chemistry

OpticsMicro−structural and nano−technology

Medical technologyMechanical elements

MeasurementMaterials, metallurgy

Macromolecular chemistry, polymersMachine tools

IT methods for managementHandling

Furniture, gamesFood chemistry

Environmental technologyEngines, pumps, turbines

Electrical machinery, apparatus, energyDigital communication

ControlComputer technology

Civil engineeringChemical engineering

BiotechnologyBasic materials chemistry

Basic communication processesAudio−visual technology

Analysis of biological materials

Without THULE AB

Figure 8: Technical fields of pledged patents in Sweden

6.73.0

4.03.2

2.00.7

2.94.5

1.83.1

1.30.1

4.26.2

4.22.9

1.05.5

0.35.3

2.50.7

2.34.3

4.82.12.2

2.05.5

4.41.2

2.00.9

1.80.7

0 2 4 6 8

percent

TransportThermal processes and apparatus

Textile and paper machinesTelecommunications

Surface technology, coatingSemiconductorsPharmaceuticals

Other special machinesOther consumer goodsOrganic fine chemistry

OpticsMicro−structural and nano−technology

Medical technologyMechanical elements

MeasurementMaterials, metallurgy

Macromolecular chemistry, polymersMachine tools

IT methods for managementHandling

Furniture, gamesFood chemistry

Environmental technologyEngines, pumps, turbines

Electrical machinery, apparatus, energyDigital communication

ControlComputer technology

Civil engineeringChemical engineering

BiotechnologyBasic materials chemistry

Basic communication processesAudio−visual technology

Analysis of biological materials

Figure 9: Technical fields of all Swedish patents filed by firms

38

0.1

0.2

0.7

0.4

1.1

1.2

1.9

1.5

3.7

3.0

5.1

5.2

4.7

7.3

7.6

5.8

8.1

9.3

11.1

11.2

7.7

2.8

0 5 10

percent

22

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

0years

betw

een p

ate

nt−

filin

g a

nd −

ple

dgin

g

Figure 10: Years between patent filing and patent pledging

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.3

0.8

1.7

0.8

0.3

0.6

0.8

2.8

5.1

6.5

17.2

60.8

0 20 40 60

percent

43

40

39

33

25

21

19

13

12

11

10

9

8

7

6

5

4

3

2

1

num

ber

of pate

nts

per

colla

tera

l

Figure 11: Pledged patent portfolio size

39

0.3

0.3

2.0

13.2

84.1

0 20 40 60 80

percent

6

4

3

2

1

pate

nt ple

dges p

er

firm

Figure 12: Number of patent pledges per firm in Sweden

0.30.60.6

0.30.30.6

0.30.9

1.50.90.9

1.50.6

1.50.30.3

1.81.51.8

1.21.21.2

2.53.1

3.74.3

5.25.5

5.235.1

0 10 20 30 40

percent

30282726252423222120191817161514131211109876543210

pate

nt sto

ck p

er

firm

Patent stocks with less than 30 patents

Figure 13: Patent stock of firms one year before patents have been pledged

40

B Robustness Tests

Table 5: Main results with the probit model

Dependent variable: Probit model Average marginal effectspatent has been pledged b/se b/se

nb_forward_cites 33.47∗∗∗ 8.85∗∗∗

(8.01) (2.03)


(0.01) (0.00)

nb_trades 1.13∗∗∗ 0.30∗∗∗

(0.31) (0.08)

patent_age −0.12∗∗∗ −0.03∗∗∗

(0.02) (0.01)




patent_stock −0.01∗∗ −0.00∗∗

(0.00) (0.00)

vc_backed 0.44 0.12(0.26) (0.07)

total_assets −0.02 −0.00(0.02) (0.01)

net_sales −0.04 −0.01(0.15) (0.04)

employees 0.00∗∗∗ 0.00∗∗∗

(0.00) (0.00)

return_on_assets −0.51∗∗ −0.14∗∗

(0.19) (0.05)


intangibles_per_assets −0.45∗ −0.12∗

(0.21) (0.05)

Constant 0.71(0.60)





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Table 6: Regression excluding patents pledged to Almi AB

Dependent Variable: Logit model Average marginal effectspatent has been pledged b/se b/se

nb_forward_cites 21.86∗ 3.51∗

(10.80) (1.73)


(0.03) (0.00)

nb_trades 3.12∗ 0.50∗

(1.42) (0.22)

patent_age −0.19∗∗∗ −0.03∗∗∗

(0.04) (0.01)




patent_stock −0.02∗∗∗ −0.00∗∗∗

(0.00) (0.00)

vc_backed 0.29 0.05(0.40) (0.06)

total_assets −0.00 −0.00(0.00) (0.00)

net_sales 0.00 0.00(0.00) (0.00)

employees 0.00∗∗ 0.00∗∗

(0.00) (0.00)

return_on_assets −0.16 −0.03(0.18) (0.03)


intangibles_per_assets −0.55 −0.09(0.41) (0.07)

Constant 1.41(1.19)





42

Table 7: Regression excluding years of the financial crisis

Dependent variable: Logit model Average marginal effectspatent has been pledged b/se b/se

nb_forward_cites 74.53∗∗ 11.90∗∗∗

(22.75) (3.41)

nb_family_members 0.09∗∗ 0.02∗∗

(0.03) (0.01)

nb_trades 1.94∗∗ 0.31∗∗∗

(0.60) (0.09)

patent_age −0.20∗∗∗ −0.03∗∗∗

(0.04) (0.01)

nb_patenting_firms −0.00 −0.00(0.00) (0.00)



patent_stock −0.02∗∗∗ −0.00∗∗∗

(0.00) (0.00)

vc_backed 0.48 0.08(0.51) (0.08)

total_assets −0.00 −0.00(0.00) (0.00)

net_sales −0.00 −0.00(0.00) (0.00)

employees 0.00∗∗ 0.00∗∗

(0.00) (0.00)

return_on_assets −0.19 −0.03(0.22) (0.04)


intangibles_per_assets −0.63 −0.10(0.39) (0.06)

Constant 0.86(1.01)




Standard errors in parenthesesRobust standard errorsForward citation winsorized at 5th percentile∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

43

C Main Variables and Data Sources

Table 8: Main variables and data sources

Variables Description Data SourcesDependent Variablepledged Indicator set 1 if patent has been pledged. Data from PRVMain Independent Variablesnb_forward_cites Number of forward citations received from other

patents, normalized by the total number of cita-tions of patents, in the same technical field andfiled in the same year.

PATSTAT

nb_family_members Number of patent family members of given appli-cation.

PATSTAT

patent_age Age of a patent, measured as number of yearsfrom the date of filing till one year prior the pledgehas been executed.

PATSTAT

nb_trades Number of prior trades of a patent, measured bythe number of ownership changes in the periodbetween filing year and one year prior the patenthas been used as collateral.

Data from PRV,PATSTAT

nb_patenting_firms Number of firms filing patents in the same yearand same technological field.

PATSTAT

firm_specificity Firm-specificity of a patent, measured by theshare of self-citations, normalized by the num-ber of total citation a self-cited patent receivedoverall.

PATSTAT

patent_market_liquidity The probability of patent liquidation, measuredby the number of patent trades one year beforethe collateralization and per technical field, di-vided by the total number of valid granted patentsin the same year and same technical field.

INPADOC,PATSTAT

patent_stock Number of patents owned by firms one year beforepatents have been pledged.

PATSTAT

vc_backed Dummy for received venture capital funds beforepatents have been pledged.

Internet Databases

Matching Variablestotal_assets Patent applicants amount of total assets one year

before patents have been pledged.Serrano Database

net_sales Patent applicants amount of net sales one yearbefore patents have been pledged.

Serrano Database

employees Patent applicants number of employees one yearbefore patents have been pledged.

Serrano Database

return_on_assets Patent applicants amount of return on assets oneyear before patents have been pledged.

Serrano Database

debt_equity_ratio Patent applicants debt to equity ratio one yearbefore patents have been pledged.

Serrano Database

intangibles_per_assets Patent applicants amount of intangibles per totalassets one year before patents have been pledged.

Serrano Database

44

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