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Essays on Technological Change


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ESSAYS ON TECHNOLOGICAL CHANGE By KEVIN W. CHRISTENSEN A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Kevin W. Christensen

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To my family. Without their love and suppor t none of this would have been possible.

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iv ACKNOWLEDGMENTS I would like to thank my advisors, El ias Dinopoulos and Chunrong Ai, for their help with my research. James Seale, Jr. a nd Doug Waldo were also helpful in developing this finished product. David Figlio, Sarah Hamersma, Jonathan Hamilton, Larry Kenny, and other professors in the Economics Depart ment were generous with their time and input. Special thanks go to committee member and graduate coordinator Steven Slutsky. Professor Slutsky’s insights, guidance, and a dvice were valuable at all stages of my graduate career. The successful completion of my dissertati on would not have been possible without my family and friends. My parents, Homer and Charlene Christensen, my sister Michele Bach-Hansen, and her husband Scott, were unw avering in their love and support. My nieces, Madison and Kayla, provided me with much needed distraction, entertainment, and joy. Friends in Virginia, Florida, a nd elsewhere were always available when I needed them. Finally, I am very grateful to Burin nel. She believed in me when I was sure no one else did.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES...........................................................................................................ix ABSTRACT....................................................................................................................... ..x CHAPTER 1 INTRODUCTION........................................................................................................1 2 A MODEL OF ENTREPRENEURSHIP AND SCALE-INVARIANT GROWTH....4 2.1 Introduction........................................................................................................4 2.2 Previous Literature.............................................................................................5 2.2.1 Endogenous Growth Literature................................................................5 2.2.2 Finance and Growth Literature: Theory.................................................6 2.3 The Model..........................................................................................................9 2.3.1 Consumer Utility......................................................................................9 2.3.2 Competition, Prices, and Profits............................................................11 2.3.3 Innovation..............................................................................................13 2.3.4 Finance Sector........................................................................................15 2.3.5 Financial Intermediation........................................................................16 2.3.6 Stock Market..........................................................................................17 2.3.7 Financial Sector Equilibrium.................................................................18 2.3.8 Labor Market.........................................................................................18 2.4 Balanced Growth Equilibrium.........................................................................19 2.4.1 Transitional Dynamics...........................................................................20 2.4.2 Economic Growth..................................................................................22 2.4.3 Comparative Statics...............................................................................23 2.5 Conclusions and Extensions............................................................................25 3 THE EFFECT OF PRUDENT INVE STOR LAWS ON INNOVATION.................30 3.1 Introduction......................................................................................................30 3.2 Background......................................................................................................32 3.2.1 Prudence of Investment..........................................................................32

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vi 3.2.2 Previous Literature.................................................................................34 3.3 Data and Empirical Methodology....................................................................36 3.3.1 Data........................................................................................................37 3.3.2 Empirical Methodology.........................................................................39 3.4 Tests of Exogeneity & Benchmark Regressions..............................................42 3.4.1 State Innovative Output and the Timing of Adoption...........................42 3.4.2 Evidence from a Long Difference..........................................................44 3.5 Prudent Investor Laws & Innovation...............................................................45 3.5.1 Indirect Investments: Venture Capital..................................................45 3.5.2 Direct Investments: R&D Expenditures...............................................46 3.5.3 Alternative Mechanisms........................................................................48 3.6 Conclusion.......................................................................................................49 4 DO PATENT ATTORNEYS MATTER?..................................................................64 4.1 Introduction......................................................................................................64 4.2 Literature Review.............................................................................................65 4.2.1 Theoretical Models....................................................................................65 4.2.2 Previous Empirical Analyses.................................................................67 4.3 Sources and Descriptive Statistics...................................................................69 4.3.1 Data Sources..........................................................................................69 4.3.2 Description of Variables........................................................................71 4.4 Empirical Methodology...................................................................................77 4.4.1 Regression Specifications......................................................................77 4.4.2 Endogeneity of Lawyer Choice.............................................................78 4.5 Results..............................................................................................................81 4.5.1 Estimated Impact of Lawyers................................................................81 4.5.2 Examiner Experience and Generality....................................................83 4.5.3 Examiner Effects....................................................................................84 4.5.4 Experience as a Proxy for Quality.........................................................85 4.6 Conclusion.......................................................................................................86 5 CONCLUSION.........................................................................................................123 APPENDIX A PROOFS OF PROPOSITIONS................................................................................125 B THE BLUNDELL-BOND ESTIMATOR................................................................128 C INSTRUMENTAL VARIABLES AND THE ENDOGENEITY OF LAWYER CHARACTERISTICS..............................................................................................132 REFERENCES................................................................................................................137 BIOGRAPHICAL SKETCH...........................................................................................143

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vii LIST OF TABLES Table page 2-1. Comparative Statics...................................................................................................29 3-1. Correlation Matrix.....................................................................................................51 3-2. Descriptive Statistics.................................................................................................52 3-3. Descriptive Statistics by Year....................................................................................53 3-4. Year of Adoption of UPIA (or equivalent)................................................................54 3-5. Comparison of Adopters and Non Adopters.............................................................55 3-6. The Timing of Adoption............................................................................................57 3-7. Impact of Prudent Invest or Laws over Long Difference...........................................58 3-8. Estimates of the Impact on Venture Capital Investments in a State..........................59 3-9. Estimates of the Impact on R&D Expenditures in a State.........................................60 3-10. Estimates of the Impact on Citati on Weighted Patent Counts in a State.................62 4-1. Variable Descriptions................................................................................................88 4-2. Technology Subcategories Descriptions...................................................................89 4-3. Count of Unique Occurrences, by Sub category, When Identification of First or Most Experienced Lawyer is Known.......................................................................90 4-4. Averages by Subcategory, When Identi fication of First or Most Experienced Lawyer is Known.....................................................................................................91 4-5. Number of Patents by Country and Sub category, When Identifi cation of First or Most Experienced Lawyer is known........................................................................93 4-6. Correlation between Grant Lag and Independent Variables, by Technology Subcategory..............................................................................................................94

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viii 4-7. Impact of Representation by Patent Attorney or Agent............................................96 4-8. Estimating Grant Lag, Without Lawyer (Patents Where First Lawyer is Known).103 4-9. Impact of Lawyer Experience, Using First Lawyer Listed.....................................106 4-10. Estimating Grant Lag, Without Lawy er (Patents Where Most Experienced Lawyer is Known)..................................................................................................109 4-11. Impact of Lawyer Experience Most Experienced Lawyer Listed........................112 4-12. Impact of Examiner Experience, Controlling for First Listed Lawyer..................115 4-14. Predicted Impact of La wyer Quality, First Lawyer...............................................121 4-15. Predicted Impact of Lawyer Quality, Most Experienced Lawyer.........................122 C-1. OED Examination Dates and Passing Rates...........................................................136

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ix LIST OF FIGURES Figure page 2-1. Equilibrium Conditions for Model............................................................................27 2-2. Stability of Balanced-Growth Equilibrium................................................................28 C-1. Number of Utility Patent s Granted, Annually, by the USPTO...............................135

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x Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ESSAYS ON TECHNOLOGICAL CHANGE By Kevin W. Christensen August 2006 Chair: Elias Dinopoulos Cochair: Chunrong Ai Major Department: Economics My dissertation consists of three essays on the economics of technological change. The first essay develops a theoretical model that describes how financial intermediaries may influence economic growth. Previous th eoretical models on the topic predict that economies with larger populati ons will grow at faster rate s, something which has not been empirically supported. This paper corrects this “scale effects” issue by extending an existing model of economic growth, without scal e effects, to include a finance sector. The financial intermediary evaluates potential entrepreneurs and their ex ante potential for being an entrepreneur. Upon receiving a positive rating from the intermediary, the entrepreneur receives money for R&D which, in turn, may lead to successful innovation. Changes in the steady-state growth rate are explained by shifts in parameter values. In a spirit similar to the first essay, the second essay considers the impact the adoption of prudent investor laws had on innovation. These laws were primarily adopted by states in the late 1990’s and expanded th e scope of investment options available to

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xi financial intermediaries to include new a nd untried enterprises and venture capital. Various specifications using state-by -industry patent counts, venture capital disbursements by state, and R&D expenditures were used to test whether these laws affected technological change. The empirical results show that, contrary to previous evidence, prudent investor laws had only a small effect on technological change. This suggests that the impact of financial inte rmediaries on economic growth may be bounded. The final essay explores the role that another intermediary has on technological change. As active participants in the patent ing process, patent attorneys are involved in writing and defending the claims on an application (among ot her things) and thus can help to establish the scope of patent protection. This chapte r explores the value added of patent attorneys by looking at how more expe rienced lawyers affect the time between filing of an application and th e date a patent is granted. It has been found that more experienced attorneys can reduce the gr ant lag, but the reduction depends on the invention’s technology. This rese arch is the first that consid ers the role of attorneys in the patent process.

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1 CHAPTER 1 INTRODUCTION Technological change affects every s ub-discipline of economics. Microeconomists may explore the role of research and development in competition. They might also explore the role that technol ogy plays in determining industry composition. Labor economists may be concerned with in creased worker productiv ity as a result of new machinery and equipment. Public economis ts may consider the ro le of the Internet in increasing test scores among minorities a nd the poor. Research on international trade may estimate the impact and flow of international technology spillovers. The overarching theme of these scenarios is that technological change is generally good for an economy.1 Nowhere in economics is that made cl earer than in the literature on economic growth. As Schumpeter said, The fundamental impulse that sets and k eeps the capitalist engine in motion comes from the new consumers’ goods, the new methods of production or transportation, the new markets, the new forms of industr ial organization that capitalist enterprise creates….This kind of competition is…the powerful lever that in the long run expands output and brings dow n prices…. (1950, pp. 83-85) This idea is found in theoretical models, su ch as Solow (1956) and Romer (1986), which provide a framework for understanding how advancements in technology can positively affect economic growth. However, theoretica l models require assump tions that abstract from the real world and can assume away so me features to facil itate understanding or 1 Political economists, such as Karl Ma rx, may disagree with this assertion.

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2 computation. One oft-ignored element is th e role of intermediaries in facilitating innovation. Economic theory provides two primary reasons for the existence of intermediaries: cost and information. Intermediaries speciali ze in a particular field and therefore have capabilities and knowledge that more generali zed firms (e.g. a manufacturing company) may not have. It is not that the firms c ould not acquire these cap abilities but that, by specializing, the intermediary is better info rmed and may provide the services cheaper than a general firm could achieve alone. Gi ven this, use of an intermediary causes efficiencies that may, in turn, lead to an increased rate of innovation. This dissertation explores the intersec tion between technolog ical change and intermediaries. Specifically it considers the role that of financial and legal intermediaries have on facilitating technologica l change. The second chapter of the dissertation presents a theoretical model that outlines the role of financial intermediaries in fostering economic growth through technological change. The third chapter empirically tests the effect a law change affecting the types of investments financial intermediaries could be made. Combined, these essays show that financial intermediaries can positively affect technological change but are not necessari ly guaranteed to do so since financial intermediaries are complements to, not substitu tes for, other processes such as research and development or entrepreneurial initiatives. The fourth chapter considers the role of patent attorneys in the pate nt approval process. Using a unique dataset on patent attorneys it is shown that patent attorney s can affect the time in which a patent is approved. This is in stark contrast to previ ous empirical and theoretical literature on the

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3 topic which holds the view that a patent exam iner works independent of other factors. The final chapter summarizes the findings from each the previous three chapters.

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4 CHAPTER 2 A MODEL OF ENTREPRENEURSHIP AND SCALE-INVARIANT GROWTH 2.1 Introduction As early as Schumpeter ( 1934), the finance sector was proposed as an important component in the growth process. Be ncivenga and Smith (1991), Greenwood and Javanovic (1990), and King and Levine (1993b) la ter formalized that proposition within the context of endogenous growth theory.2,3 Beck and Levine (2004), Benhabib and Spiegel (2000), and Rajan and Zingales ( 1998), among others, have empirically shown that the finance sector plays an important role in fostering ec onomic growth. They continue a long line of empirical papers evalua ting the relationship (see Levine (2004) for a review of empirical and th eoretical papers). Compar ed to empirical research, theoretical work on the finance-growth hypot hesis has slowed. As a result, some innovations in endogenous growth theory remain outside the financegrowth literature. One of the most significant omissions is th e treatment of populat ion as a variable changing over time rather than as a parameter. Earlier models of endogenous growth in corporated the undesirable property of scale effects. These models predict that as population incr eases, the long-run rate of growth also increases, implyi ng, ceterus paribus, larger economi es grow at faster rates. For some time, this was considered to be a st rength to the theory as growth in population 2 Other, more classical references in the literature are Goldsmith (1969), McKinnon (1973) and Shaw (1973). 3 Romer (1986), Grossman and Helpman (1991), and Ag hion and Howitt (1992) are major contributors to the endogenous growth literature.

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5 was deemed analogous to globalization. Ho wever, time series tests by Jones (1995a, 1995b) showed that growth had remained r oughly constant regardless of scale of population, contradicting these models. As a result, theorists began to develop secondgeneration models of endogenous growth that had growing population. In spite of this innovation, no previous paper modeling the rela tionship between finance and growth has been updated to account for the scale effects issue and a disconnect remains between the finance-growth theories and the most stat e of the art endogenous growth models. This chapter attempts to bridge that gap by combining a second-generation model of endogenous growth with the King and Levi ne (1993b) finance sector. The general equilibrium model presented here includes growing population and is shown to have a balanced growth equilibrium that is saddlepat h stable. Fluctuations in parameter values explain changes in the growth rate of the economy. The rest of the chapter proceeds as follows. The next section of the chapter review s the relevant literature. The third section introduces the equilibrium conditions for consumers, producers, and financial intermediaries. These conditions are used to specify the ba lanced growth, transitional dynamics, and comparative statics presented in the fourth section. The final section offers conclusions, limitations, and proposes extensions for the model. 2.2 Previous Literature 2.2.1 Endogenous Growth Literature In the late 1990’s three papers, You ng (1998), Howitt (1999), and Segerstrom (1998) were published as the core of the second generation endogenous growth models, each with a distinct answer to scale e ffects. Young introduced the idea that both horizontal and vertical product competition offs ets the scale effects problem. More firms producing at the same level of quality but with different varieties will reduce the spoils

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6 available to any one producer. As a result, growth does not reach the high levels it did in first-generation models. Howitt translated Y oung’s original idea into a more traditional Schumpetarian growth model. In doing so, he reintroduced the result that R&D subsidies provide a positive impact on growth which was lacking in Young’s original model. Segerstrom’s model used a quality ladder appr oach and an R&D difficu lty index to offset the impact of larger population size. The difficulty index remove s the population scale effects while at the same time explaining w hy R&D employment has increased without a commensurate increase in innovation. Neither of the other two mode ls explains this phenomenon. Segerstrom’s model also allows for a positive impact of R&D subsidies on growth. 2.2.2 Finance and Growth Literature: Theory Theoretical models on the finance-growth re lationship are varied in their scope and use of endogenous growth fundamentals. Bencivenga and Smith (1991) consider how a developing finance sector alters the compositi on of consumer savings using a three period overlapping generations model. As in other models, the introduction of a finance sector increases the accumulation of capital. It is shown that these changes do not occur as a result of changes in savings behavior but inst ead are a direct result of the intermediary efficiently allocating consumer savings. Gr eenwood and Javanovic (1990) also consider an evolving finance sector with endogenous improvement in production inputs. In their model, the finance sector matures as the in come of the population increases. Higher rates of savings drive the finance sector (and econom y) forward in the development process. As the finance sector evolves, the rate of return on capital increases. This increased return is what drives growth in the econom y. Therefore a country with a more mature finance sector would have a higher level of growth than a relatively less-mature

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7 economy. Each of these models utilizes an AK endogenous growth model as a starting point where capital accumulation is determined endogenously. Unlike the previous two papers, King a nd Levine (1993b) use Aghion and Howitt’s (1992) endogenous growth model as its founda tion and does not consider an evolving finance sector. Instead, the maturity of the finance sector is treated as given and its impact on the introduction of intermediate products is evaluated. The financial intermediary acts as a filter of prospective entrepreneurs that seek financing. Only projects presented by skillful entrepreneurs will be able to obtain funding. Those individuals without a positive rating cannot compete for the next innovation and instead become production workers. Another change from the previously mentioned models is the introduction of a stock market which accumulates consumer savings and provides revenue to fund entrepreneur ial ventures through the in itial offering of stocks. A paper by Morales (2003) is the most recent known paper modeling the financegrowth relationship.4 It is based on Howitt a nd Aghion’s study (1998) which incorporates a “leading-edge technology parameter” that pr ovides a similar function to Segerstrom’s (1998) R&D difficulty index and uses both capital and labor as factors of production. Two elements make their paper un ique from other finance-growth models. First, the model introduces capital as an i nput in production. By in cluding capital, both capital accumulation and tec hnological change lead to economic growth. The second element is the inclusion of moral hazard between the financial intermediary and the researchers. In spite of the presence of moral hazard, her results show a positive 4 Aghion, Angeletos, Banerjee, and Manova (2004) considers a related (but not identical) issue of how volatility affects technological change. The model developed considers the investments of finitely lived entrepreneurs but assumes the number of entrepreneurs is constant over time.

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8 relationship between the finance sector and the success rate of projects and research. While the addition of capital and moral haza rd are significant contributions, the scale effects issue remains. Therefore the steady-st ate analysis presented in her paper is only stable for a set population not for one growing over time. This chapter presents a model of grow th without scale effects by combining King and Levine’s (1993b) finance sector with Se gerstrom’s (1998) model to obtain a general equilibrium model that has a balanced growth equilibrium and is saddlepath stable. Similar to Morales (2003), the model shows a positive relationship between finance and economic growth spurred through technological ch ange. Unlike Morales’ work, labor is the only input and any potential moral hazard is assumed away. To combine the King and Levine and Segerstrom models, several changes have been made. In the models mentioned above, technological advancement improves intermediate goods that are used to produce a single consumable product. Th e model presented in here utilizes product, rather than process innovations. That is, ra ther than multiple inputs for one final product there are multiple final goods. Entrepreneurs compete to innovate to the next quality level of the final good in a specific industry.5 Economic growth is observed through increases in consumer utility which is aff ected by the quality and the quantity of the goods consumed. The second important change is the endogenous treatment of entrepreneurial competitors. This is done to reflect how the growing population (and increased consumer demand) affects the numbe r of entrepreneurs competing for the next 5 One advantage of this structure is that it fits well with the empirical observations of Hellman and Puri (2000) where a venture capitalist is likely to invest in a technology that is pushing out the technological frontier as opposed to one creating horizontal product innovation. The predicted direction of this model (but not necessarily the magnitude) can help in understanding the role venture capital plays in economic growth.

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9 innovation. Finally, only a por tion of R&D employment dire ctly affects the innovation rate whereas Segerstrom’s model attributes all non-production wo rkers as R&D labor. Since the model introduces a fina ncial sector, labor allocated to the financial intermediary is not growth promoting and does not per se impact the innovation probability. However their importance to the growth process will be highlighted later in the chapter. 2.3 The Model The description of the model begins with a discussion of consumer preferences. Once the consumer equilibrium condition is established the producer side, innovation process, and the role of financial intermedia ries are developed. This section concludes by elaborating on the labor market. These market equilibrium conditions will be used in the fourth section to estimate the balanced growth equilibrium values of per capita consumption and per capita R&D difficulty. 2.3.1 Consumer Utility The model uses dynastic families as ou tlined by Barro and Sala-i-Martin (2001) and used by Segerstrom (1998). Dynastic fam ilies choose to maximize the utility of all family members over an infinite horizon. That is, current fa mily members are altruistic towards their current and future relatives a nd make consumption choices with them in mind. By using the dynastic family assu mption the model bypasses the problems of finitely lived people and allows for a single a nd unified utility function to be maximized. Assuming that each individual has an identical utility function, the ut ility equation is the product of the individual disc ounted utility and the populat ion for the entire economy summed over an infinite horizon.

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10 Each individual in the economy has a discounted utility function equal to 0)] ( ln[ dt t u et where is the discount factor and u(t) is the subutility. Population at time t is nte t N ) ( when initial population is norma lized to 1 and the exogenous growth rate of population is n (births minus deaths). For optimization purposes is assumed to be greater than n .6 The simplified product of these com ponents over an infinite horizon is dt t u e Ut n)] ( ln[0 ) ( (2-1) Product quality and consumer demand are introduced in the su butility. Quality levels are sequential so an industry cannot produce the j+1 quality product without the j quality product already having been discove red. Each industry can produce goods of different qualities at the sa me time. Once price accounts for quality differences, each product within the same industry substitutes pe rfectly. The subutility function is defined as d t j d t uj j 1 0) , ( ln )] ( ln[. (2-2) The quality of product j is denoted by j where the parameter represents the step size of innovation. As increases, the difference betw een the quality of the new good and the old good increases. Since produc t quality improves with each innovation, must be strictly greater than 1. Quantity demanded by an individual consumer is denoted by d(j, t) for a particular quality ( j ) and industry () at a point in time ( t ). The total affect 6 See Barro and Sala-i-Martin (2001, p. 67) for a thorough explanation of this restriction and the transversality condition.

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11 of consumption on utility is simply the pr oduct of the quality and demand summed across all industries, which are indexed along a continuum from 0 to 1. At every point in time consumers choose the amount to spend on an industry’s product. Given a unitary elasticity of subs titution between goods of differing qualities, per capita demands at a point in time are p c d per capita consumption divided by the price of the good.7 To break ties, it is assumed that the consumer purchases the more advanced quality product. Consumers only choose c(t) and treat prices and qualities as given so over time, per capita consumption may vary. Taking this into account and substituting the demands as noted above into the subutility function, maximizing (2-3) is equivalent to maximizing (2-1). 0 ) () ( ln dt t c et n. (2-3) The family’s optimal consumption is bounded by the growth of per capita assets, ) ( t a Consumer assets change due to wages w(t) stock market dividends r(t)a(t) consumption c(t) and division of assets among new family members na(t) Therefore, the constraint for the maximization of utility above is ) ( ) ( ) ( ) ( ) ( t na t c t a t r w t a Solving the dynamic constrained maximization problem yields ) ( t r c c (2-4) 2.3.2 Competition, Prices, and Profits Consider the only possible competitive case where there are two firms in an industry each producing di fferent qualities, j and j+1 The producer of the cutting edge 7 Li (2003) extends Segerstrom’s (1998) model to acc ount for non-unitary elasticities of substitution.

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12 technology, j+1 is called the quality leader and the other firm is referred to as the quality follower. Consumers are indifferent between the qualities if the effect on utility is the same for either good. That is if, ) , 1 ( ) , (1t j d t j dj j Recall that demands are equal to per capita consumption divided by pr ice and that consumers allocate the amount of consumption to an industry, not a specifi c quality. Given this, the equivalent price indifference equation is j jp p 1. Assuming Bertrand competition prevails in all industries, the quality follower se ts its price at the lowest possible level, the marginal cost of production. Since labor is the only input and one unit of labor is required to produce one unit of output the price of the quality leader is w pj 1. This is the case for all quality leaders regardless of industry. A ssuming consumers prefer the quality leader’s product when formally indifferent, the quality leader is the sole pr oducer in equilibrium given the contestable market. Since this will be true for all industries and qualities, the prevailing market price for the economy is w p (2-5) The profits of the quality leader are equa l to the price-cost margin of each product times the number of products sold. Since consumers are assumed to have identical utilities the demands for each individual are also the same, implying that market demand for a specific time, quality and industry is D(j, t)=N(t)d(j, t). The profit equation for the sole producer may therefore be simplified to 1 ) ( ) ( ) ( t c t N t (2-6) The profits earned by the quality leader are greater than zero by definition of N(t) c(t) w and It is the desire for these profits that lead s to innovation.

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13 2.3.3 Innovation Each innovation attempt may advance only one step beyond the current quality level and successful innovati on is far from certain. Each attempt is governed by a Poisson process where the probability of i nnovation increases with the amount of labor used in R&D. In this model there are tw o components of R&D labor: the researchers and the financial interm ediary’s employees. Unlike in Se gerstrom’s (1998) model, not all R&D employees affect the rate of growth of innovation. The labor used by the financial intermediary does not dir ectly advance research so only researcher’s labor, e increases the probability of success. It is possible that multiple entrepreneurs in the same industry may be positively rated by the intermediary so that there is more than one competitor for the next quality step. The endogenous variable H(,t) represents the number of entrepreneurs that attempt to innovate in that industry at each point in time. Even though financial intermediary employees and R&D workers are represented by parameters, the number of competitors and therefore the total number of employees will grow over time. It is plausible to think that early stag e advancements are easier than later stage advancements. That is, simpler innovati ons take no time whereas more complex innovations require extensive te sting, or perhaps even a lengthier review process by government agencies. As time passes and th e industry moves up the quality ladder the probability of successfully innovating decrea ses. To account for this, the innovation probability uses an industry specific R&D difficulty index, X(,t) which increases over time but affects the innovation probability ne gatively. In spite of the growth in competitors it is possible that innovation may st ay constant or decrease depending on if it

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14 is dominated by the R&D difficulty index. Th e probability that an industry innovates to the next quality level is t H t X Ae t ) ( ) ( (2-7) where A is a productivity parameter. Assumption 2-1: The R&D difficulty index increases at a rate equal to ) ( ) ( ) ( t t X t X where the parameter ] 1 0 ( This implies that complexity of products rises as firms become more innovative. There are no spillovers across quality le vels. A veteran participant in the jth patent race now competing in the j+1st race has no advantage over a relative newcomer. Any participants in the patent race for the jth quality must start from the beginning of the process to reach the j+1st quality level so there are no sp illovers between previous and current research nor is there any spillover be tween researchers in the same patent race. Thus, the industry innovation rate is the pr oduct of individual competitor probabilities, t, multiplied by the number of competitors so that, ) ( ) ( ) ( t H t t Each attempt requires a different request for startup capital by entrepreneurs from investors. The return on investment to thes e investors is the exp ected profits from the sale of the product. Given the competitive ma keup of all industries, the profits associated with one quality level disappear when the next innovation occurs. If the current industry quality leader attempts to i nnovate twice to advance two st eps up the quality ladder, the leader becomes indebted to two cohorts of investors. Further, by innovating to the j+1 quality, the firm eliminates the demand for its j quality product and thus cuts off revenues from that product and dilutes the shares contra ry to the interest of its original set of

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15 investors (a similar concept to Myers and Ma jluf (1984)). This business stealing outcome and the inability to repay two cohorts of investors are the reasons each entrepreneur will only choose to advance one quality rung at a ti me. Therefore, while possible, it is not desirable for an entrepreneur to attempt tw o successive levels of innovation. Further advances in quality must come from outside the firm. 2.3.4 Finance Sector As mentioned previously, profits are the incentive fo r innovation, but there are steps that must be taken before these profits are realized. The finance sector is composed of two related areas, a financial intermediary and a stock market. The symbiotic relationship between the two areas is critical to technological change and thus economic growth. In the model, the intermediary pr ovides a means of assurance to investors by rating each entrepreneur and entrepreneuria l venture as either good or bad. Upon a positive rating, the startup capit al necessary to participate in a patent race is provided through the stock market. Only positively rate d firms will receive startup capital and be able to attempt to innovate. In the real world, an interm ediary provides more than just a rating. Startup capital to an entrepreneurial project is invested with the expectation that th ere will be a return on that investment. During the time between i nvestment and realization, the intermediary firm may provide strategic advice, monito ring, or lower the learning curve for new entrepreneurs (in the context of venture capital, see Hellma nn and Puri (2000, p. 960). Finally, by investing in a company, a financial intermediary firm sends a signal to future investors that the project, while risky, has potential. The model presented here eliminates the financial and mentoring responsibilities of the intermediary and focuses solely on the signaling aspect. However, the productiv ity parameter in the innovation probability

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16 could be interpreted as the value added imp act from mentoring. The sole proactive responsibility of the in termediary in the model is to pr ovide assurance to stock market investors. The rating guarantees that the project can succeed but does not guarantee that it will be the first to succeed. 2.3.5 Financial Intermediation It is assumed that individuals posses tra its that will make them successful with a probability The intermediary can reveal a potential entrepreneur’s ability, with certainty, by investigating th e individual at a cost of f units of labor. In equilibrium, the maximum value an intermediary is willing to invest on a rating for an individual project is the expected value of the proposed entrepreneurial project. The structure of the model is such that each equivalent quality step resu lts in the same amount of profit regardless of industry. It is possible that multiple entrep reneurs in the same industry may be positively rated by the intermediary so that there are mu ltiple competitors for the next quality step; however, each potential entrepreneur is considered on a case by case basis. With q representing the expected disc ounted value of the entrepreneur ial venture, th e equilibrium conditions for a financial intermediary are wf q (2-8) we t v t q ) ( ) ( (2-9) With the perfectly competitive labor market w is the same wage as in the production side of the model. The stock market value of a firm is represented by v(t) Proposition 2-1: The expression for equilibrium of one firm, q, is equivalent to the industry equilibrium conditi on. See Appendix A for proof.

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17 Combining these equations and solving for v(t) yields the financial intermediary equilibrium condition. ) ( 1 ) ( t e f w t v (2-10) The structure of the model is such that each equivalent quality step results in the same amount of profit during the same time peri od, regardless of industry. The financial intermediary has no incentive to prefer some industries to others since profits are the same across industries. Due to the symmet ric nature of the financial intermediary, profits, and price equilibria, th e rest of the model focuses on the general case where the innovation rate and entrepreneurial competition is the same across all industries. As a result, the industry component of all f unctions from this point on is dropped. 2.3.6 Stock Market After being rated, an entrepreneur may s eek funding via the stock market to start a new business. This funding is used to pa y for R&D that will hopefully lead to an innovation. The securities issued for new fi rms compete with those from other industries and with stocks from already established qua lity leaders. When making her investment choices, a rational consumer will make comparis ons to a perfectly riskless asset with a rate of return r(t)dt for a time segment dt In equilibrium, the expected rate of return for new stock must be equal to the rate of retu rn on the riskless asset. The expected stock value of the new firm is equal to the realized dividends plus the expected capital gains for the time segment dt The expected value is adjusted downward since the future value disappears when the next product innovation oc curs. The equilibrium condition for the

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18 time segment dt is therefore dt t r dt t t v t v dt dt t t v t v dt t v t ) ( ) ( ) ( 0 ) ( ) ( 1 ) ( ) ( ) ( ) ( Taking the limit as dt approaches zero it follows that ) ( ) ( ) ( ) ( ) ( ) ( t v t v t t r t t v (2-11) As in Segerstrom (1998), the growth rate of the stock market value of monopoly profits must be equal to the growth rate of the R&D difficulty index, ) ( ) ( ) ( ) ( t X t X t v t v .8 One implication of the stock market e quilibrium is that as R&D difficulty increases, the stock market value increases which corresponds with more investment. This model has a decreasing per dollar impact of financial capital on innovation as time progresses so that more capital is needed over time to keep innovation probability the same. 2.3.7 Financial Sector Equilibrium When both the stock market and intermediary are in equilibrium the entire finance sector is in equilibrium. Recalling Assump tion 2-1, the R&D equilibrium condition may now be solved. Where x(t) which equals, X(t)/N(t), is per capita R&D difficulty. 1 ) ( ) ( ) ( ) ( t t r w w w t c e f Ae t wx (2-12) 2.3.8 Labor Market Employees have two choices of employm ent; they may work either in the manufacturing or R&D sectors. Since the wages in these two sectors are the same, 8 See Appendix A for proof.

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19 workers are indifferent between thes e two jobs. Given full employment, N(t) is the sum of manufacturing labor ( NM(t) ) and R&D labor ( NRD(t) which includes financial intermediary labor). The ma nufacturing labor is equal to the market demand summed across the total number of industries since it was assumed that each unit of labor supplies one unit of output. w t N t c d w t N t c t NM ) ( ) ( ) ( ) ( ) (1 0 (2-13) On a per project basis, entrepreneurial employment can be found in the financial intermediary condition. Multiplying this by the number of competing entrepreneurial firms in each industry and summi ng across all industries yields ) ( ) ( ) (1 0t H e f d t H e f t NRD (2-14) Given full employment, the re source constraint for the ec onomy is equivalent to Ae t e f t x w t c ) ( ) ( ) ( 1 (2-15) 2.4 Balanced Growth Equilibrium Now consider the balanced growth e quilibrium where all endogenous variables grow at a constant but not necessarily identi cal rate. Using (2-7) the balanced growth innovation rate is ) ( ) ( t H t H (2-16) Proposition 2-2: In the balanced growth equilibrium n t H t H ) ( ) ( See Appendix A for proof.

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20 It is intuitive that the number of compet itors should grow at the same rate of population since as population grows the se t of potential entrepreneurs grows proportionally (due to being a parameter). Given Propos ition 2-2 and equation 2-16 the balanced growth rate of innovation is n Using this, 0 ) ( ) ( t c t c the wage as numeraire, and both the resour ce (2-15) and R&D (2-12) cond itions, the balanced growth values of x and c may be solved for explicitly. Th e results are graphically represented in Figure 2-1. Only the positive quadrant is considered since per capita consumption and R&D difficulty only have values greater than or equal to zero. The R&D constraint is upward sloping because increased R&D increa ses quality of goods. The increase in quality is translated to increased per capit a consumption due to decreases in quality adjusted prices. The vertical intercept of the resource condition is As the per capita R&D difficulty increases, this signals an increas e in required assets needed to innovate to maintain the same level of industry innovation. As a result, labor resources are shifted away from manufacturing jobs. With fewer products manufactured, per capita consumption must decrease, implying a downw ard sloping resource condition. The two lines intersect at a unique point identifying equilibrium values, x and c 1 1 1 ˆ n e f Ae x (2-17) 1 1 ˆ n n n c (2-18) 2.4.1 Transitional Dynamics Since this is a dynamic model, it must be shown that over time the economy can converge to the equilibrium valu es stated above when out of equilibrium. To formulate

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21 the first differential equation recall that x(t) = X(t)/N(t) Using this and Assumption 2-1, n t t x t x ) ( ) ( ) ( is obtained. The industry innovati on probability is solved by using the resource equilibrium condition (2-15). The resulting differential equation for per capita R&D difficulty is ) ( ) ( 1 t nx t c e f Ae x (2-19) In balanced growth it is assumed that 0 x Transforming the above equation by solving for c(t) yields one that is identical to the equilibrium resource condition. It has already been shown that it is downward sloping with a vertical intercept of The per capita consumption differential eq uation is derived using the maximization of consumer utility, (2-4). The riskless rate of return is substituted by using the R&D equilibrium condition. Th e result of this is ) ( ) 1 ( ) ( 1 ) ( ) ( ) ( ) ( t c t c t c e f t x t Aec t c (2-20) By following the balanced growth assump tions the above equation is found to be upward sloping with a vertical intercept is / 1. This is strictly below the vertical intercept of (2-19) since 1 and ] 1 0 ( Therefore the two equations intersect at point E in Figure 2-2. Increases in x(t) affect (2-19) positively so posi tive changes will lead to larger x and negative changes will lead to a reduced x This affect is identified by the horizontal arrows in Figure 2-2. Likewise, changes in c(t) will have an effect on (2-20). In this case increases in c(t) will result in a decrease of c with the opposite being true for decreases in c(t) Figure 2-2 shows this effect with the ve rtical path arrows. As the figure shows,

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22 there exists a saddlepath where the model will transition from out of equilibrium to the balanced-growth values as descri bed in (2-17) and (2-18). 2.4.2 Economic Growth The final term of balanced growth to be concerned with is the overall rate of growth in the economy. This is defined as the rate of growth in consumer utility which is calculated by using the log of the subutilty function. Substituting in consumer demands for the highest quality product leaves d t j t c t u 1 0) ( log ) ( log ) ( log. (221) The last term of the above equation repres ents the sum of all quality levels across all industries multiplied by log The sum of quality levels is analogous to the sum of innovations that have occurred to date, td t0) ( ) ( Summing this term across the number of industries results in the total number of innovations in the economy. Finally, remember that balanced growth implies that c(t) is constant over time. Differentiating (221) with respect to time yields the growth rate of consumer utility, g Substituting n t ) ( into the equation to get the balanced equilibrium growth rate of the economy, log n u u g (2-22) While it appears that this is the same balan ced growth equilibrium growth rate as in Segerstrom (1998), there are three main diff erences. First the productivity parameter has been constrained to be less than or equal to one. Second, the growth rate n refers to the growth in entrepreneurs, not the overall population. Fina lly, the elimination of the financial intermediary from the economy will result in no innovation. Without

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23 innovation, it is impossible for the economy to grow which highlights the pivotal role of the financial sector in long term growth. 2.4.3 Comparative Statics Changes in the parameters will have different affects on the equilibrium values of x and c A summary of all the first order conditions for the equilibrium values of x ˆ and c ˆ appears in Table 2-1. This section focuses on some of the more important results of the model. Proposition 2-3: Increases in the probability of being a successful entrepreneur will increase the amount of per capita innovation. Changes in the probability can be a result of advanced educational attainment or worker training. A better trained work fo rce will increase the likelihood that any potential entrepreneur will have the skills n ecessary to innovate. While the model does not include R&D subsidies li ke other Schumpetarian models, other government actions such as student loans, government grants, and increased funding to higher education will induce a higher and thus higher growth. Likewi se more flexible standards on evaluating a potential entreprene ur will result in more innovati on. The clarification of the “prudent man” clause can be seen as a loos ening of regulations wh ich then allowed more potential projects to be viewed as good invest ments (see Kortum and Lerner (2000)). Proposition 2-4: Increases in required financial intermediary employment will decrease per capita R&D difficulty while increa sed use of researchers will increase per capita R&D difficulty. Neither researcher nor financial intermediary employment levels affect per capita consumption.

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24 An increase in the number of financial intermediary employees signals an increased cost of evaluation of each entrepreneurial project. The number of positively rated projects will decrease since a higher thres hold of earnings is re quired to offset the increased rating cost. Since research labor in creases the innovation pr obability it is clear that an increase in the number of researcher s will lead to an increase in innovation and therefore R&D difficulty, ceterus paribus Consumers allocate their per capita consumption independently of prices and qual ity. Therefore any changes in parameters that solely affect production will have no effect on per cap ita consumption. A similar argument can be made for increased costs. Corollary 2-1: Increased costs of evaluating an entrepreneur will reduce the amount of capital pr ovided by investors. From the model, it is clear that entrepreneurial projects require startup capital. Fewer acceptable entrepreneurial projects lead s to less startup capital provided. In addition, increased investor sk epticism may result in increase d costs of evaluation. The recent accounting scandals and dot-com “shake-o ut” can be put forth as examples that would increase investor skepticism. Thes e events also corres ponded with decreased levels of investment in new projects. Of c ourse, more research must be done to firmly establish a causal relationship. Proposition 2-5: Positive productivity shoc ks through the parameters and A will positively impact R&D difficulty. The affect has on R&D difficulty comes directly from Assumption 2-1. Further, increases in A will increase the probability of inn ovation. As innovation becomes faster, the R&D difficulty increases. Illustrations of these can be found th rough the Internet and

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25 increased diffusion of computers. The transm ission of information across the Internet has increased the productivity by decreased time la gs and costs. The automation of various processes through computers is refl ected as changes the parameter 2.5 Conclusions and Extensions This chapter develops a general equilibri um model to explain the relationship between the finance sector and economic grow th. It makes several improvements on the previous literature. First, the King and Levi ne (1993b) model of fina ncial intermediaries has been updated to adjust for population scal e effects. Second, the model uses product rather than process innovations. Third, unlike Segerstrom (1998), not all R&D labor makes direct contributions to innovation since some must be alloca ted to the financial intermediary. As a result of these changes, th e model is able to evaluate the impact of the financial intermediary on economic growth without relying on the level of population— something that has been empirica lly shown to lead to inaccura te growth rate predictions. By removing the scale effects property, a barrier inhibiting the understanding of the finance-growth relationship is likewise removed. To highlight the model’s intuitive appeal it has been shown that steady-state growth is affected by changes in the growth rate of entrepreneurs—a direct consequence of how the finance sector is include d in the model. The significance of the finance sector on economic growth is highlighted in the way all innovations are funded. Without funding from an intermediary, there could be no i nnovation. The model also underscores the importance of education and other factors si nce they increase potential entrepreneurial success and thus, innovation. Through parameter shifts, the model is al so able to explain several recent events. Increas es in investor doubt, represen ted by increases in evaluation

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26 cost, will lead to a decrease in the level of investment. Increases in monitoring (or the effectiveness of monitoring) increase the amount of innovation taking place in the economy. In each case the predicted outcome matches the actual outcome as experienced in the United States during the early part of the decade. Accounting scandals were followed by a decrease in investment a nd economic growth. Increased concern by financial intermediaries over their investments excesses led to increased success. In addition to the above results, the model provides the foundation for several extensions that would increase awareness of the contribution a finance sector makes to economic growth. Currently the model assume s perfect information by the intermediary after the period of evaluation. Incorporating asymmetric in formation would prove to be valuable addition to this line of research. The contribution of vent ure capital to economic growth has been considered in recent papers by Kortum an d Lerner (2000) and Hellman and Puri (2000). Although King and Levine ( 1993b) cite the venture capital process as a motivation for their model, to directly translat e the results of their model and this one as the impact of venture capital would be an exaggeration of th e impact of venture capital investments. Adding multiple financial intermediaries to the existing framework would advance the understanding of venture capital’s role in economic growth.

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27 Figure 2-1. Equilibrium Conditions for Model c x R&D Condition Resource Condition x ˆ c ˆ 0

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28 Figure 2-2. Stability of Balanced-Growth Equilibrium c x E 0 c 0 x c ˆ Saddlepath ) 1 ( ) 1 ( ) 1 ( x ˆ

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29 Table 2-1. Comparative Statics x ˆ c ˆ + f 0 + 0 e + 0 A + 0 n + + Comparative statics are the partial derivative of the equilibrium values (Equations 2-17 and 2-18) with respect to the paramete rs stated above. By assumption, ] 1 0 ( If =1, then c x ˆ and ˆboth equal zero but all ot her effects are the same.

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30 CHAPTER 3 THE EFFECT OF PRUDENT INVE STOR LAWS ON INNOVATION 3.1 Introduction As early as 1985, but predominately in the mid-1990s, states began to adopt new laws that govern investments made by fiduciaries. The laws were direct enactments of the Uniform Prudent Investor Act (University of Pennsylvania) as drafted by the National Conference of Commissioners on Uniform State Laws or only marginally different. The primary goals of the laws were to introduce mo dern portfolio theory as it applies to the prudence of investments, remove restrictions on specific financial instruments, and to allow delegation of management. Previous empirical investigations on related subjects have shown that laws regulati ng fiduciaries affect their portf olio holdings, that previous reductions of prudence standards for pensions led to increased investment in venture capital, and that the finance sector can positively affect tech nological change and economic growth. Many theoretical models have also shown the positive affect of financial intermediaries on technological change. These previous theoretical and empirical findings suggest that the changes in prudent investor la ws should alter the composition of investments and lead to incr eased technological pr ogress. Few papers have considered the impact of prudent man or prudent investor laws. Of those that do, none consider the impact of pr udent investor laws on somethi ng other than equities. This chapter considers the impact of the law on innovation by considering its impact on venture capital, R&D, and pate nting and therefore provides a significant expansion to the existing literature.

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31 A variety of regression specifi cations and models are used to determine empirically the effect of the new prudence regime. The basic specification considers how R&D, venture capital investment, and patenting are affected after a state adopts the law. These regressions ignore the complexity of spillove rs across states and how timing of adoption affects the outcomes. Other regressions in cluded variab les that identify neighboring states that had adopted prude nt investor laws in order to account for any interstate spillovers. Regressions were run on state quar tiles since the effect of the law change should be different for states with different sh ares of venture capita l, R&D, or patenting or for those states that adopted the law late r. The majority of evidence shows venture capital, R&D expenditures, and innovation (as proxied by citation weighted patent counts) were unaffected by the new prudence sta ndards. In regressions for states in the bottom quartiles of R&D and patenting there is a slight positive effect, but nothing is found for venture capital. While previous research has shown changes to the prudence standards of pension funds had a significant impact on innovati on, given the small size of the impact, the same cannot be said for funds held it trust by banks. The results presented here provide a significant contribution to the literature on the finance-growth nexus by showing the effectiveness of financial intermediaries in fostering innovation is bounded and that not all policy reforms in the United St ates favorable to financial intermediaries are equally capable of fostering innovation. In section two of this chapter, the background of prudent investor laws are reviewed to underscore the consequences of adopting the new prudence standards. It continues by summarizing relevant literature studying prudent man, prudent investor, and the relationship between finan ce and technological change. The third section reviews the

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32 data sources and empirical methodology. Sec tion four presents benchmark analyses to provide the intuitive foundation for the main empi rical results. It al so considers tests the potential endogeneity of the pr udent investor law. Secti on five presents the main empirical results of the impact on venture capital, R&D expe nditures, and patenting using detailed data and more advanced techniques. The final section offers conclusions and implications of the findings. 3.2 Background 3.2.1 Prudence of Investment1 Laws governing the investments of fiduciari es have long been based on the abstract notion of prudence. The origins of this yardstick date back to 1830 in Harvard College v. Amory The opinion from the Massachusetts Supr eme Judicial Court for that case stated “[a trustee] is to observe how men of prude nce, discretion and intelligence manage their own affairs” and then act similarly (Longstret h (1986, p. 12)). In spite of being around for nearly a century, the prudent man rule was no t the rule of law for a majority of states until the mid-1940’s. Prior to that, many states chose a more strict approach by specifying lists of acceptable investments for trusts.2 In the middle of the 20th century, the Model Pruden t Investment Act (MPIA), backed by the American Banking Association was adopted by many states. The MPIA contained language, nearly verbatim, from the original Harvard College v. Amory ruling and made ‘prudence’ the standa rd in evaluating investments. Rather than specifying a list of investments that were acceptable, the prude nce of an investment was to be decided on 1 This sub-section draws from Longstreth (1986), Shattuck (1951), and Langbein (1995). 2 See Shattuck (1951, p. 502-504) for a detailed cla ssification of state statut es preand post-1940.

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33 a case by case basis. The MPIA did, however, explicitly forbid the use of investments such as those in “new and untried enterprise” which were deemed speculative and therefore imprudent (Langbein 1995 ). In spite of these apparently more liberal rules, the interpretation by courts and subsequent cases of precedence led to rules just as restrictive as legal lists (see Restat ement of the Law (1992)). The relative impotence of some fiduciaries caused by prudent man laws and their interpretation led to the creation of the Un iform Prudent Investor Act (UPIA). The law made five substantive changes to the eval uation of “prudence” and management of investments. One of the more significant ch anges was the inclusion of modern portfolio theory in gauging prudence: A trustee’s investment and management deci sions respecting individual assets must be evaluated not in isolation but in the c ontext of the trust port folio as a whole and as a part of an overall investment st rategy having risk and return objectives reasonably suited to the trust. (U niversity of Pennsylvania § 2(b)) The UPIA further stated that no type of investment can be categorically deemed as imprudent. If the predictions hold true, the adoption of prudent investor laws will have a large impact on venture capital investment, R&D e xpenditures, and innovation. According to Flow of Funds data from the Federal Reserve,3 commercial banks in the U.S. held an average of $1,358.64 billion in treasury s ecurities, municipal s ecurities, corporate equities, or mutual fund shares between 1995 and 2004. Much of the investment was made using money held in trust. It is unlik ely that the adoption of prudent investor laws will cause a complete shift away from these a sset types to just venture capital or new and untried enterprises. However, even a shift of 5% would re sult in an additional $67 billion 3 Board of Governors of the Federal Reserve System (p. 61), line 5 minus line 10.

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34 investment in R&D—about $12 billion more than was spent on R&D in California in 2000. While banks are directly under the jurisdiction of UPIA it is possible that the law’s impact is broader. Survey evidence from Longstreth (1986) sugge sts that pension fund managers were also constrained in their i nvestment choices by prude nt man regulations. This is in spite of the fact that pension f unds have had more liberal prudence guidelines in place since 1979. One explanation for this is that case law and precedents based on Restatement of the Law (1959) were common wh ile the same could not be said for more recent laws governing other fiduciaries. Truste es were thus limiting their investments to some degree in order to protect themselv es from uncertain litigation outcomes (Longstreth, 1986). With prudent investor guidelines, this uncertainty may fade and other fiduciaries may use a different investment strategy. 3.2.2 Previous Literature Previous studies by Del Guercio (1996) and Gompers and Metrick (2001) explore the impact of the restrictive prudent man gui delines. Del Guercio examines the impact prudent man laws have on the management of equities by institutional investors. She observes different reactions by bank and mutu al fund managers. While bank managers are more likely to shift their portfolios toward stocks “viewed by the courts as prudent,” mutual fund managers do not. It concludes that by forcing intermediaries to protect their liability, these laws alter incentives and lead ma nagers to act in ways contrary to the best interest of clients. Gompers and Metrick c onsider prudent man’s e ffect on institutional investor behavior and equ ity prices. They find some evidence that prudent man regulations affect the ownership of stocks in favor of older and larger firms. In other words, those investments that are less ris ky or more prudent. More recently, Hankins, Flannery, and Nimalendran (2005) investig ate the impact of lessening the prudence

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35 standard on equity holdings. They find that the adoption of prudent investor laws affects institutional portfolios in the predicted directi on. Since prudent inve stor laws weaken the duty of fiduciaries, there is a shifting away from dividend paying st ocks to more risky assets. This finding is a l ogical corollary to Del Guerci o’s and Gompers and Metrick’s analyses. Gompers and Lerner (1998) i nvestigate the changes in the levels of investments made by venture capital firms fr om 1972 to 1994. Their approach considers fluctuations in investment leve ls as a result of shifts in the supply and demand for venture capital. One of the more signi ficant shifts in the supply of venture capital resulted from the 1979 ERISA clarification which allowed pens ion funds to invest in venture capital. The Department of Labor’s clar ification and the UPIA redefi ne prudence similarly so the enactment of prudent investor laws may also be considered a positive shift in the supply of venture capital. Finally, Kortum and Lerner (2000) investigat e the contribution of venture capital investment to innovation. They find that ve nture capital dolla rs are about three times more likely to lead to innovations than other forms of R&D financing. Thus, even a small shift in the supply of venture ca pital investment resulting from UPIA should significantly increase innovation. The estima te is based on the results of reduced form and structural regressions, as well as regressions that use the 1979 ERISA cl arification as an instrumental variable to account for the potential endog eneity of venture capital investment. The question posed by Kortum and Lerner (200 0) is slightly different than the one posed here. Rather than considering the role of venture capita l dollars, this essay considers the impact of a law that should have affected th e type of investments banks could make with funds held in trust. Thus, this paper tests the imp act of the law change

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36 on venture capital, R&D, and i nnovation rather than testing the contribution of the entire venture capital industry on innova tion. The different tack al so implies that the same concern over the endogeneity of venture capital found in Kortum and Lerner do not necessarily apply here. In spite these diffe rences, the theoretical model developed in their paper can be used to illustrate how pr udent investor laws should increase the amount of venture capital as well as the total innovative effort. Kortum and Lerner (2000) assume that there are costs associated with venture capital funds (i.e. screening opportunities, recruiting managers etc.) that may affect the type of projects funded by vent ure capitalists. As these cost s are reduced, more and more projects are capable of being funded by the venture capitalist rather than by traditional corporate R&D. Kortum and Lerner motivat e their use of the ERISA clarification by stating it “lowered the cost of funds to vent ure capitalists” (p. 685) wh ich, in their model, was shown to increase both the total innovative effort and the ratio of venture capital to corporate R&D. Since the ERISA clarification and prudent investor la ws are similar, the adoption of the new prudence standard is pred icted to reduce the cost of funds and thus lead to an increased share of ve nture capital and more patenting. 3.3 Data and Empirical Methodology When taking the previous literature as a whole, the predicted effect of prudent investor laws on innovation is clear. Less stringent guidelin es should cause a shift away from established assets in favor of othe r investments, includi ng those in new and innovative companies. The possibility of su ch a shift was underscored in Restatement of the Law (1992) which specifically mentions venture capital funds and new and untried enterprises as newly acceptable investment s under the updated prudence regime. It is through these two mechanisms th at the adoption of prudent investor laws should cause

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37 innovation. The aforementioned theories s uggest that money will be used for R&D purposes which will then lead to innovation. To test the imp act of prudent investor laws on innovation it is necessary to consider th e adoption’s impact on venture capital, R&D, and patenting. By doing so the empirical analys is may also test the viability of the above theories. If patenting were affected, but not R&D or ventur e capital, then an alternative and unknown mechanism is causing a change in innovation. 3.3.1 Data Data on R&D expenditures in a state fo r the years 1993-2002 is taken from a survey from the National Science Foundati on (National Science Foundation). The timeperiod used is limited due to inconsistenc ies in survey methodology and missing data. Prior to 1993, new sampling was not done for each survey year. For these non-sampling years, the National Science Foundation interpolat ed data from the previous sample. After 1993, sampling was done every year the survey was given. This methodological break causes preand post-1993 survey data to be inconsistent. To bypass this problem, only data from 1993 forward was used. Another ca use for concern is that surveys were not given annually until 1997, so data for 1994 and 1996 are not available. Further, some state R&D information was not made publicly av ailable due to privacy restrictions. This is more likely to affect smaller states sin ce there is anonymity in numbers. Instead of interpolating for missing values (and perhaps in troducing errors in the dataset) data was taken as-is and missing data cause d the exclusion of that observation in regressions. The dollar values are in billions of 2000 dollars They were deflated by the implicit GDP deflator as provided by the Bur eau of Economic Analysis. Venture capital data is from the MoneyTreeTM (PriceWaterhouseCoopers, et al.) survey jointly sponsored by PriceWaterhous eCoopers, Thomson Venture Economics, and

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38 the National Venture Capital Association. The survey collects information on venture capital investments made within a state fo r each quarter starting in 1995. For this analysis, total annual venture capital dis bursements in each state for 1995-2001 were used. The investment amounts were deflated similarly to R&D expenditures and are in millions of dollars. There is no indication that the venture capital survey suffers from the same methodological inconsistencies as the R&D survey, however, the original dataset does not distinguish between zero investment and unknown investment in a state. If the survey does not report a dollar va lue for investment for a partic ular state in a particular year then that state is excluded from regressions. Patent statistics are used as a proxy fo r innovation. In su mmarizing previous research on patents, Griliches (1990) describes how patents we ighted by citations may be a better proxy for innovation than raw patent co unts. Patent data from the NBER patent database was updated by Bronwyn Hall (Hall) Her updated dataset contains detailed information on patents granted from 1963 to 2002. Due to data limitations on application year and suspect patent information for 20012002, only successful patents applied for from 1990-2000 are considered.4 The citation weighted patent counts were then summed to get patent counts by state (including Wash ington, D.C.) and year of application. Patents were assigned to a stat e based on the location of the first inventor listed on the patent application.5 Patent applications from fore ign individuals were dropped and 4 Patent information for later years in the database is unreliable due to the time lag between applying for and being granted a patent. Since the database is based on information on granted patents it is likely that 2001 and 2002 patent information is understated as some applications that are patent-worthy have yet to be awarded a patent. 5 It is common to assign patents to states in this manner. Other approaches to identifying geographic origin exist; most coming from the literature on geographic knowledge spillovers (see Thompson and Fox-Kean (2005)).

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39 records with no state information or with values of ‘US’, ‘APO’, or US territories were eliminated. Tables 3-1 th rough 3-3 provide summary statistics for the data used. Adoption of prudent investor laws are represented by an indicator variable which takes on the value of one if a pr udent investor law is active in that state during that year. As shown in Table 3-4, the adoption of prude nt investor laws occurred, by and large, after 1995. By the end of 2000, forty states including Washington, D.C. adopted the UPIA. It is possible that i nvestment originating in one st ate may spillover to neighboring states. To account for this po ssibility some regressions incl uded a variable representing the percent of neighboring states that adopted pr udent investor laws. It is plausible that the neighborhood effects are not linear, i.e., over time the spillovers from states may increase or decrease exponentially. Select regressions include th e squared percent of neighboring states to account for this possi bility. Interstate spillovers were also accounted for by limiting the regressions to a subset of states. 3.3.2 Empirical Methodology The empirical analysis uses several different specifications. Th e fourth section of this chapter presents the results of a hazard model used to explore the determinants for the timing of adoption. It also presents es timates on the impact of adoption using a long difference. The hazard model specification (Equation 3-1) considers how patenting, among other things, affects the adoption of prude nt investor laws a nd can provide a check on whether the adoption of these laws is endoge nous to the innovativen ess of a state. i i X t i LAGADOPT ) ( ln (3-1) The dependent variable is the differen ce between the year of adoption and 1980 (the start of the sample). The vector of indepe ndent variables, Xi, includes average

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40 number of patents for each state from 1980-1989, average Americans for Democratic Action (ADA) scores for U.S. senators from 1979-1997 (ADA), state average median income from 1984-1989, and average size of the state’s finance sector as a percent of gross state product. More detail on the s ources and predicted signs are provided in section four of the chapter. Long differences are also estimated and the results are presented in section four. These regressions are included to provide a basic understanding of how prudent investor regimes affected innovative inputs and outputs without having to account for yearly fluctuations. Each regre ssion uses the specification: i t i s t i t i s t i k t i k s t i t i s tS PI PI Y Y Y Y, 2 , 2 , 1 0 , (3-2) where Y may be R&D, venture cap ital, or raw patent counts. The lag for venture capital and patenting was one year ( k=1 ) but since R&D data is ava ilable every other year the lag was two years ( k=2 ). The difference in prudent inve stor variables may be zero or 1 but never -1 since no state changed back to a prudent man regime. Venture capital information was not available for all states for the same years. For example, the level of venture capital investment in Texa s was known for 1995 through 2004 but was only known for North Dakota for 1997 through 2004, therefore the numb er of years the difference encompasses may be different by stat e. A vector of indicator variables, S, were included to account for these issues. The vector includes indicator variables for differences of 7 years, 8, years, and 9 years. Since North Dakota data uses a seven year difference, the indicator variable representi ng seven years was equal to 1 and all others were equal to zero. For Texas, the indicator variable representing nine years was equal to 1 and all others were equal to zero. Other states were similarly recorded.

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41 The main analysis in section five uses the specification: it it it o itL PI Y Y 1 1 1. (3-3) As before, the dependent variable Y may be venture capital, R&D expenditures, or citation weighted patent counts. A vector of variables on the adopti on characteristics of neighboring states is represented by L These include year indicator variables, the percentage of neighboring states that adopted prudent investor laws, and that percentage squared, although not all regres sions included all variables. As was true for the long difference, R&D data requires that the dependent variable is lagged two years while the other data uses only a one y ear lag. The regressions us e the Blundell-Bond estimator as implemented in the xtabond2 module for STATA.6 This estimation procedure was necessary due to the lagged dependent variab le on the right-hand side of Equation 3-3 and the potential for time-invari ant characteristics The Blundell-Bond estimator (known elsewhere as GMMBB or B-B) uses the first difference of the empirical specification to remove time-invariant characteristics. It th en uses the lagged levels of the dependent variable as an instrumental variable for th e lagged difference of the dependent variable. Since these instruments alone may not be e ffective, the estimator also uses lagged differences as instrumental va riables for lagged levels. Fo r this research the endogenous variables are the prudent inve stor indicator, neighbor st ate effects, and the lagged dependent variable. Year e ffects are also included in bot h the dynamic panel regressions and as exogenous instruments. Tables 3-7 through 3-9 report the results for these regressions. In addition to coefficient esti mates, the tables include p-values for the Arellano-Bond test for autocorrel ation and the Sargan/Hansen te st for overidentification. 6 More information on the xtabond2 module may be found in Roodman (2005). Appendix B provides a more thorough treatment of the Blundell-Bond estimat or and the reasons why it was preferred over OLS.

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42 For the Blundell-Bond estimates to be consistent, second order serial correlation must not exist (Arellano-Bond statistic rejects the null) and the mode l must not be overidentified (Sargan/Hansen test statistic must reject th e null of overidentification). Regressions are one-step GMM unless otherwise noted. The sample was divided into quartiles and regressions were run separately for quartiles of venture capital, R& D, and citation-weighted pate nt counts. By dividing the sample into quartiles of the respective depende nt variable, it is possible to see how the law affected states with diffe rent sized venture capital, R& D, or innovative output. Only the top quartile and bottom quar tile are presented. The sample was also broken into quartiles based on the adoption of the law. It is likely that the first st ates to adopt the law should be those where the impact, if any, is the greatest. Regressions for the bottom quartile of adopters were also included to estimate the pot ential differences prudent investor laws had across states. 3.4 Tests of Exogeneity & Benchmark Regressions 3.4.1 State Innovative Output a nd the Timing of Adoption The UPIA was available for adoption by all states at the same time yet the adoption of the law was not uniform across states. That some states adopted the law earlier than others may be exploited as a means of unders tanding who considers the law important. It also allows a check of whether or not highly pate nting states adopted the law first. If they did, then there is an endogeneity problem. The specification in Equation 3-1 was used for all survival analyses. Estimating survival functions was compli cated due to right-censoring and ties. Data was right censored after 2000 since data on patents is incomplete after that year. Several ties occurred in the data since several states also adopted the law in the same year

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43 which may affect estimation. Proportional hazards models were used for all estimates to account for these problems. Due to the sma ll sample size and low computational cost, exact estimation methods for ties were used (f or more information see Allison (1995, p. 127)). The (t) term is eliminated by using the proportional hazards model. Americans for Democratic Action scores were used as a proxy for a state’s political leanings. They were taken from Francis and Kenny (1999, pp. 88-89). If a state had a senator from both parties, the average of the two scores was taken. For states that only elected members of one party, the ADA score used was the score of that party. The predicted sign of the ADA coefficient is not ob vious, ex ante. On one hand, more liberal states may feel that prudent investor adoption may only benef it the wealthy. Therefore a higher ADA score will lead to a longer lag in adoption. On the other hand, more liberal states are also those with higher levels of income so they may be more likely to adopt prudent investor laws. To ma ke this relationship more clear, median income is also included in some of the regressi ons. Finally, if the finance sect or is a large part of gross state product it is more important to adopt favorable legislation and do so early. Therefore the coefficient for this variable is expected to be positive. Averages of patents across the 1980’s were used for several reasons. With the exception of Delaware, no state adopted prudent investor laws prior to 1992. Therefore, including information from the 1990’s may lead to false precision since adoption of the laws may have affected rates of innovation.7 Adoption of the law is unlikely to be a reaction to one year’s worth of patent statis tics. If patenting di d play a role in the decision to adopt, it is more like ly that historical patent rates were considered. Finally, 7 An indicator variable for Delaware was also considered due to its unique nature. It was not, however, statistically significant.

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44 checks of the data confirmed that despite fl uctuations in the amount of patenting, the ranking of states patenting outcomes did not ch ange much. Since survival analysis uses rank order, the average is an acceptable method of approximation. Table 3-6 shows the results of the regressions. Regardless of specification, the predictive power of a state’s average paten ting on adoption is indi stinguishable from zero. The coefficient with the most explanat ory power belongs to the relative size of the finance sector in a state, which is not su rprising. An industry with large economic significance is likely to have the political capital necessary to influen ce legislative action. The ADA score coefficient is statistically signi ficant when median income or the size of the finance sector is excluded. Like average patents, median income is not statistically significant in any regression. That patenting is not significant suggest s that the potential innovation effects were not a consideration in adopting the law. Thus, the prudent investor law is assumed to be exogenous in the regressions presen ted in section five. 3.4.2 Evidence from a Long Difference Before considering the affect on annual data, it is instructive to see if the change of the dependent variable over a long period is in fluenced by the prudent investor law. If the regressions on the long difference indicate a statistically signifi cant impact then the impact of the law change seen in more deta iled regressions should likewise be large. However, if the coefficient estimates are not significant, then any impact of the prudence regime change should be small or nonexistent. The regression results are presented in Table 3-7. The change of prudence regimes appear s to have no impact on the change in venture capital, research expend itures, or patenting. Based on the R-squared, the venture capital and patenting regressions appear to fit the data well. The regression on R&D

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45 expenditures is not as well estimated, this is undoubtedly a result of the every-other-year nature of the data. These basic regressions are the first (of much) evidence to suggest that prudent investor laws have not had the predicted affect on innovation. 3.5 Prudent Investor Laws & Innovation 3.5.1 Indirect Investments: Venture Capital The most obvious mechanism by which pr udent investor la ws should affect innovation is through venture capita l. Banks invest in venture capital funds which in turn invest in innovative companies. Therefore changes in investments by banks will have an indirect affect on innovation. Investment in venture capital causes se veral difficulties for the analysis. Unlike R&D, there is no intuitive or legal reason that money originating in one state must stay within that state’s borders. In fact, Florida and Smith (1993) finds that venture capital investme nt gravitates towards regions with an established venture capital sector. If this is the case, then Minne sota’s adoption of a prude nt investor law, for example, may not impact the amount of venture capital invested in that state. Instead there may be a flow of money to California. Fortunately, Florida and Smith’s findings also suggest a method on how to deal with th e problem of interstate flows. Just as investments in a state with a small venture capi tal sector will flow outside of the borders, the strong pull of a large ventur e capital sector in a state shou ld keep investments inside its borders. That is, Minnesota may have tr ouble keeping its ventur e capital investment within its borders but California likely doe s not. A state with a small venture capital sector may likewise find a negative or zero effect of the prudent investor law. To consider this issue, states were divide d into quartiles based on the size of their venture capital sectors. The first quartile (states with large venture capital sector) and the fourth quartile (states with the smallest venture capital sector) were considered

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46 separately. The results are presented in Ta ble 3-8. As predicted, the bottom states (fourth quartile) observed no impact. Surpri singly however, neither did the top venture capital states. Another way of dividing the sample is to l ook at when they adopted the prudent investor law. That is, whether the state was among the earliest or among the latest adopters of the new pr udence regime. It was not possi ble to test the impact on the early adopters, since the data on venture capital begins seve ral years after they adopted the law implying no variation in the prudent investor indicato r variable. It was possible to consider the late adopters. As predic ted (regressions 7 through 9 of Table 3-8), the coefficient on the prudent inve stor indicator has a negative sign. Without complementary evidence from the set of early adopting states it is difficult to interpret the results on the late adopters. It might be that, as late adopters, these states were crowded out of collecting venture capital investment. This ma y have less to do with the prudent investor law than to do with the timing of adopti on; a causal versus correlation issue. Confirmation from regressions on R&D or cita tion weighted patent counts are necessary before drawing any firm conclusions. 3.5.2 Direct Investments: R&D Expenditures In addition to venture capital investment the UPIA allows direct investment by banks to new and untried enterprises. This would represent a direct investment of funds held in trust by banks that may encourage innovat ion. In this regard, banks are likely to act as a venture capita list would. A large body of rese arch on venture capital (Lerner (1995), Sorenson & Stuart (2001), and Zook (2002), among others) shows that venture capital investment is local. That is, venture capital funds invest in firms that are close to the offices of the venture capitalist in order to provide better guidance to the (perhaps) uninitiated entrepreneur and pr otect their investment. If ba nks are directly investing in

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47 new and untried enterprises then it is logical to assume they follow the same strategy for similar reasons, therefore the spillover problem seen with venture capital likely does not affect direct investment by banks. Of course “local” does not imply “intra state” since many investments may cross a state’s borders, although, they wi ll be confined to neighbori ng states. Most regressions include the percentage of nei ghboring states that have adopted prudent investor laws to capture these effects. Percentage terms are n eeded since not all states are bordered by the same number of states. Inclusion of the neighbor state impacts cau ses the exclusion of Hawaii and Alaska from regressions. Estimates of the prudent investor law’s impact on direct investment may be found in Table 3-9. Ignoring regressions (1), (4), and (10) due to the low p-value of the Hansen statistic, the measured impact of the prudent investor law on R&D is mixed. As with venture capital, the larger level impact s hould be found in the T op R&D states, states with the highest amount of per capita expenditures. In percentage terms, those states with the least amount of R&D expenditures should s ee gains. The latter is confirmed in regression (6) of Table 3-9. When accounting for interstate spillovers, there is a positive and statistically significant e ffect of prudent inve stor laws for the bottom R&D states. However, their appears to be no impact of the prudent investor law on the top R&D states. As before, the data was divided into qua rtiles based on when each state adopted the UPIA. The evidence for the early adopters is ambiguous since, each specification leads to a different result: positive, negative, or zero. For late the estimates are based on the third quartile since, for the fourth quart ile, the prudent invest or indicator remains

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48 constant. Again, ignoring re gression (10) due to the poor Ha nsen statistic, there appears to be no impact of the prudent investor law on the R&D of late adopters. 3.5.3 Alternative Mechanisms The theories cited in section two indicate that prudent investor laws will impact innovation through the mechanisms of venture capital and R&D. The preceding analysis tested these mechanisms for any change that could be associated with the adoption of prudent investor laws and found some weak supporting evid ence, but only for the late adopters of the law for venture capital or t hose states with the lowest R&D output. Neither of these findings shows a strong positive impact on innovation that previous theoretical and empirical work predict. This final sect ion considers the impact on innovation through any mechanism, not just venture capital or R&D. Citation weighted patent counts for each stat e are used to estimate the impact of the prudent investor law on innovati on through any means. As befo re, states were analyzed, by quartiles, based on the amount of innovation that takes pl ace within that state’s borders and the timing of adoption. The analys is for late adopters is based on the third quartile due to no variation in the prudent inve stor indicator for the fourth quartile. For all but one regression, the re sults indicate the prudent inve stor laws had no effect on innovation. The sole excepti on, regression (5) of Table 310, shows that the prudent investor law had a small, positive impact on innovation for the least patenting states. This is treated as confirmation of the R&D results in the previous section. If the bottom R&D states were positively affected, then the likely consequence of this is that patenting in bottom states would also increase. This is the only evidence that suggests that prudent investor laws had any effect on innovation. Th at the impact is weak and not in the states

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49 expected suggests that previous results c onsidering the impact of venture capital on innovation are likely overstated. 3.6 Conclusion Prior empirical and theoretical research suggests that the adoption of prudent investor laws should have an unambiguously positive effect on innovation. The evidence presented here, by and large, shows otherwise. While some regressions suggest a small positive impact, most estimates indicate that prudent investor laws have had no impact on venture capital, R&D expenditure s, or innovation. The latter results can be interpreted either optimistically or pessimistically. The optimistic scenario is that fiduciaries have yet to fully extend their investments to the poi nt they are legally allowed. This view suggests that states that have adopted prudent invest or laws should experience technological change and economic growth some time in the future. The pessimistic view interprets the results as evid ence that prudent investor la ws do not affect technological change. That is, prudence guidelines for fiduc iaries were already sufficient to promote growth before prudent invest or laws were enacted. It is likely that the latter scenario is th e correct description of events. Under the optimistic view, investment is constrained while case law is established. If this were true, investments, including those in equities, would not be impacted until well after the adoption of these laws. This is contradict ed by Hankins et al. (2005) who show equity investments have already been affected by prudent investor la ws. The pessimistic view is consistent with previous empirical findings on prudent investor laws but runs counter to prior theoretical and empirical work. The resu lts presented here sugge st that not all laws affecting investment decisions will lead to changes in technological change, for good or ill. Further, not all fiduciaries will be able to positively impact innovation.

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50 This research, as with most, does not clos e the door on further investigations on the topic. Estimates on the imp act of prudent investor laws would greatly benefit from micro-level data on financial intermediary i nvestments. The detailed data could expand the analysis to include systems of equations rather than change s in the equilibria.

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51Table 3-1. Correlation Matrix WPCtWPCt-1WPCt-2VCtVCt-1VCt-2R&DtR&Dt-1R&Dt-2PItPIt-1PIt-2Citation Weighted Patent Counts (WPC)1.0000 WPC, lagged 1 year0.97451.0000 WPC, lagged 2 years0.95670.99641.0000 Log of deflated VC disburmsents (VC)0.39220.47950.50501.0000 VC, lagged one year0.37950.46780.49240.90991.0000 VC, lagged two years0.40690.48710.50910.89070.89071.0000 Log of deflated R&D expenditures (R&D)0.49450.56190.57020.74840.74030.83361.0000 R&D, lagged one year0.47410.54110.55070.75320.75660.83570.98341.0000 R&D, lagged two years0.48090.54720.55610.76330.76030.82380.96510.97671.0000 Prudent Investor Indicator (PI)0.06410.09500.09960.08280.04450.00890.09630.08760.10181.0000 PI, lagged 1 year0.07820.10770.11620.02400.0054 -0.00970.0005-0.0337-0.01840.76711.0000 PI, lagged 2 years0.11710.13710.1427-0.0404-0. 0450-0.0533-0.0090-0.0584-0.06560.68520.89321.0000 Due to variation in the time series available for data, correlations are based on 1990-2000 values.

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52 Table 3-2. Descriptive Statistics Variable ObsMean Std. Dev. Min Max Prudent Investor Indicator 5610.2990.458 0 1 Log, Deflated VC disbursments 2804.4272.304 -3.080 10.667 Log, Deflated R&D expenditures 343-0.1471.919 -6.178 3.8236 Citation Weighted Patent Counts 5616395.1813090 3 106580 Descriptive statistics are based on 1990-2000 data.

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53Table 3-3. Descriptiv e Statistics by Year Citation Weighted Patent Counts Log, Deflated VC disbursments Log, Deflated R&D Expenditures Year Mean Median Standard Deviation Mean Median Standard Deviation Mean Median Standard Deviation 1990 10,047 4,283 15,798 1991 9,734 4,111 15,600 1992 9,809 4,098 16,496 1993 9,281 3,760 16,362 -0.38-0.111.91 1994 9,044 3,371 16,122 1995 8,425 3,170 15,492 3.473.532.09 -0.210.081.88 1996 6,223 2,298 11,954 3.783.862.13 1997 4,555 1,536 8,713 3.963.882.14 -0.090.141.78 1998 2,152 786 4,027 4.444.662.16 -0.080.312.06 1999 890 308 1,648 5.165.442.18 0.150.321.76 2000 185 72 324 5.615.912.41 -0.060.191.96 2001 4.845.122.21 Descriptive statistics are based on 1990-2000 data.

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54 Table 3-4. Year of Adopti on of UPIA (or equivalent) State Year of Adoption State Year of Adoption Delaware 1985 Alaska 1998 Illinois 1992 Vermont 1998 Florida 1993 Washington, D.C. 1999 Maryland 1994 Massachusetts 1999 New York 1995 New Hampshire 1999 South Dakota 1995 Ohio 1999 Colorado 1995 Indiana 1999 New Mexico 1995 Wyoming 1999 Oregon 1995 Pennsylvania 2000 Utah 1995 North Carolina 2000 Washington 1995 Virginia 2000 Oklahoma 1995 Michigan 2000 California 1996 Iowa 2000 Arizona 1996 Kansas 2000 Montana 1996 South Carolina 2001 Rhode Island 1996 Tennessee 2002 West Virginia 1996 Nevada 2003 Nebraska 1997 Texas 2004 Connecticut 1997 Arkansas 1997 Minnesota 1997 Hawaii 1997 New Jersey 1997 Idaho 1997 Maine 1997 North Dakota 1997 Source: Hankins et al. (2005)

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55Table 3-5. Comparison of Adopters and Non Adopters Adopters Adoption Year No. of States Adopting Cumulative Adopters Avg. Med. Household Income Avg. Population Avg. GSP Avg. Size of Finance Sector Avg. Industrial R&D Expenditures 1985 1 1$22,980618,280$13,222 $2,893$1,077 1992 1 233,6156,162,678163,510 34,7503,427 1993 1 332,4908,713,017214,290 44,9783,651 1994 1 434,8627,865,153205,654 42,6123,007 1995 8 1233,9855,827,223164,516 36,7702,513 1996 5 1734,2636,498,299191,395 42,1933,588 1997 9 2636,7825,237,148165,383 13,9662,970 1998 2 2839,3304,953,199163,523 13,8902,767 1999 6 3440,9664,870,142170,184 14,0932,729 2000 6 4042,6275,361,696189,869 15,5312,897 2001 1 4143,1455,381,061193,815 16,1282,844 2002 1 4242,9695,439,859200,525 16,6792,801 2003 1 4344,2495,414,360207,290 17,6862,743 2004 1 44 .5,851,535236,413 20,4062,822

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56Table 3-5. Continued Non Adopters Adoption Year Avg. Med. Household Income Avg. Population Avg. GSP Avg. Size of Finance Sector Avg. Industrial R&D Expenditures 1985 $23,177 4,746,110$82,836$14,034$2,585 1992 30,463 4,953,150119,33421,5402,520 1993 31,147 4,825,907121,05421,8142,487 1994 32,213 4,869,498128,57222,6112,517 1995 33,761 4,945,554134,83422,6062,569 1996 35,551 4,551,691129,58620,4192,039 1997 36,419 5,264,710157,5229,2972,125 1998 37,603 5,719,932178,30510,9662,298 1999 38,822 6,300,352200,87511,8052,208 2000 38,620 6,156,757195,84910,8421,314 2001 38,839 6,447,856211,17612,1391,371 2002 39,553 6,607,460221,13313,0731,412 2003 39,021 7,246,438251,29615,3001,548 2004 5,169,695180,49010,648882 Industrial R&D expeditures is for 1995 data onl y since annual data is not always availa ble. They are repor ted in thousands of current dollars. GSP and Finance sector data preand post-1997 are inconsistent due to swit ch to NAICS classifications. GSP & Size o f Finance Sector are in millions of current dollars. Median Income is in current dollars.

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57Table 3-6. The Timing of Adoption Proportional Hazard (1) (2) (3) (4) (5) (6) (7) Avg. Patenting (1980-1989) 0.00016 0.0001 0.0000419 -0.0000842 -0.0000748 -0.0000844 -0.0000745 (0.0001) (0.0002) (0.0002) (0.0002) (0.0002) (0.0017) (0.0002) ADA score a 0.01864 0.01306 0.00159 0.00746 (0.0082) (0.0089) (0.0097) (0.0098) Avg. Median Income (1984-1989) 0.0000391 -0.000003426 -5.4477E-06 (0.0000) (0.0000) (0.0000) Finance Sector as a Percent of GSP 20.74362 19.92661 21.26685 20.68383 (5.6103) (6.9538) (7.8835) (8.6529) Obs. 51 50 50 51 50 51 50 Censored obs. 7 7 7 7 7 7 7 Likelihood Ratio 1.062 6.102 7.9365 14.4335 14.2455 14.4424 14.2975 Score 1.187 6.347 8.4676 15.1175 15.0002 15.3021 15.1154 Wald 1.179 6.266 8.2254 14.6295 14.4011 14.6167 14.3869 LAGADOPT is dependent vari able. Standard errors are reported in parentheses. a Inclusion of ADA scores decreases the num ber of observations by 1 since the District of Columbia does not have Congressmen.

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58Table 3-7. Impact of Prudent Investor Laws over Long Difference Venture Capital R&D Raw Patent Counts (1) (2) (3) Constant -0.4956 -0.4202 4.8 [0.3743] [0.3090] [24.4875] Lagged Difference Yt+s-1-Yt-1 0.9338*** 1.5838*** 0.7554*** [0.0165] [0.3603] [0.0254] Lagged Prudent Investor Difference PIt+s-1-PIt-1 -39.0688 0.3647 -45.8805 [3 6342] [0.3393] [40.3746] Indicator Variables: Sizes of s s=7 -5.3980** [2.0989] s=8 126.5767 [82.3239] s=9 13.4023 [25.3304] Observations 50 51 51 R-squared 0.98 0.71 0.98 The dependent variable is Yt+s-Yt, where Y is Venture Capital Investments, R&D Expe nditures, or Raw Patent Counts for each state. R&D expenditures and Raw Pate nt Counts were available for all st ates for the same period of time. Sizes of s were included in the V.C. regression due to differences in the availability of data across states.Robust standard erro rs in brackets. p<0.10, ** p<0.05, *** p<0.01

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59Table 3-8. Estimates of the Impact on Venture Capital Investments in a State (1)(2)(3)(4)(5)(6)(7)(8)(9) Log of deflated VC investment (lagged one year)0.9664***0.9695***0.9693***0.4118***0.5411***0.5414***0.8797***0.9362***0.9357** [0.0263][0.0249][0.0264][0.1399][0.1412][0.1418][0.0662][0.0522][0.0543] Prudent Investor Indicator (lagged one year)-0.023-0.0393-0.03890.38890.15480.1558-0.4352-0.4561*-0.4563* [0.0364][0.0261][0.0325][0.5042][0.4768][0.4729][0.2747][0.2534][0.2594] Percent of neighboring states adopted (lagged one year)0.01510.02030.0895-0.28750.19830.3048 [0.0555][0.1913][0.5840][2.0950][0.1322][0.6160] Percent of neighboring states adopted, sq uared (lagged one year)-0.00530.3192-0.1293 [0.1767][1.9806][0.6409] Year Indicator VariablesYYYYYYYYY Observations727272484242606060 Number of states121212121010101010 Hansen statistic (P-value)1.0001. 0001.0001.0001.0001.0001.0001.0001.000 Arellano-Bond, AR(1) (P-value)0.0490.0490.0500.3590.2220.2210.0760.0840.084 Arellano-Bond, AR(2) (P-value)0.2400.2330.2360.3830.1070.1020.8090.8350.837 Top VC States (1st Quartile)Bottom VC States (4th Quartile)Late Adopters (4th Quartile) p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are in brackets. Regressions were r un using the Blundell-Bond dynamic panel estimator. The symbol '†' indicates a two-step robust GMM estimati on. All other regressions are robust one-step regress ions. Alaska and Hawaii are dropped when including data on neighboring states. T op VC States (1st Quartile): Illinois, Florida, New York, Colorado, Washington, California, New Jersey, Massachusetts, Pennsylvania, Virginia, and Texas. Bottom VC States (4th Quartile ): South Dakota, New Mexico, Montana, West Vi rginia, Arkansas, Hawaii, Idaho, North Dakot a, Alaska, Vermont, Wyoming, Iowa, and Nevada. Late Adopters (4th Quartile): Delaware, Illinois, Florida, Maryland, New York, South Dakota, Colorado, New Mexico, Oregon, Utah, Washington, and Oklahoma. Late Adopters (4th Quar tile): Pennsylvania, North Caro lina, Virginia, Michigan, Iowa, Kansas, South Carolina, Te nnessee, Nevada, and Texas.

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60Table 3-9. Estimates of the Imp act on R&D Expenditures in a State (1)(2) (3) † (4) † (5) † (6) † Log of deflated R&D expenditures (lagged two years)1.1801***0.9248***1.0988***0.7670***0.6148***0.8146*** [0.1404][0.0942][0.1056][0.1878][0.1820][0.2332] Prudent Investor Indicator (lagged one year)-0.4140.0597-0.17081.1097*0.34020.9719** [0.2719][0.1160][0.3157][0.6489][0.3061][0.4326] Percent of neighboring states adopted (l agged one year)0.00730.1723-0.0090.9747 [0.1404][0.7885][0.4505][2.8315] Percent of neighboring states adopted, squared (lagged one year)-0.3338-0.6081 [0.7441][3.1410] Year Indicator VariablesYYYYYY Observations484848494343 Number of states121212131111 Hansen statistic (P-value)0.5030.9980.9990.530.921 Arellano-Bond, AR(1) (P-value) . . . Arellano-Bond, AR(2) (P-value) . . . Top R&D States (1st Quartile)Bottom R&D States (4th Quartile)

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61 Table 3-9. Continued (7) † (8)(9) (10) † (11) † (12) Log of deflated R&D expenditures (lagge d two years)0.7061***0.8472***0.8594***0.8317***0.8053***0.9209*** [0.0714][0.0343][0.0346][0.0641][0.0691][0.0647] Prudent Investor Indicator (lagged one year)0.2822**-0.6323**-0.4427-0.2947**-0.00680.2988 [0.1114][0.3033][0.7028][0.1168][0.2409][0.3620] Percent of neighboring states adopted (l agged one year)0.0924-0.6519-0.7924-1.7842* [0.3670][2.4100][1.7390][1.0233] Percent of neighboring states adopted squared (lagged one year)0.42231.7835 [2.9749][1.2786] Year Indicator VariablesYYYYYY Observations484848453939 Number of states121212121010 Hansen statistic (P-value)0.8420.9981.0000.4770.9981.000 Arellano-Bond, AR(1) (P-value) . . . Arellano-Bond, AR(2) (P-value) . . . Early Adopters (1st Quartile)L ate Adopters (3rd Quartile) p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are in brackets. Regressions were r un using the Blundell-Bond dynamic panel estimator. The symbol '†' indicates a two-step robust GMM estimati on. All other regressions are robust one-step regress ions. Alaska and Hawaii are dropped when including data on neighboring states. The regressions for the Late Adopters (4th Quartile) are not reported because there is no change in the prudent investor indicator variable. Late Adopters (3rd Quartile) were included instead. Top R&D States (1st Quartile): Illinois New York, Washington, California, Connectic ut, Minnesota, New Jersey, Massachusetts, Ohio, Pennsylvania, Michigan, and Texas. Bottom R&D States (4th Quartile): South Da kota, Montana, West Virginia, Nebraska, Arkansas, Hawaii, Maine, North Da kota, Alaska, Vermont, Washington,

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62Table 3-10. Estimates of the Impact on C itation Weighted Patent Counts in a State (1) † (2) (3) † (4) † (5) † (6) † Citation Weighted Patent Counts (lagge d one year)0.8221***0.8847***-0.27080.6544*0.08590.0523 [0.0518][0.0059][0.9829][0.3778][0.3800][0.4678] Prudent Investor Indicator (lagged one year)-1960.1743-1981.2384-146977.1501127.4741260.3926*298.8881 [1,985.7316][1,572.3731][124,809. 3084][746.4490][155.6210][206.0809] Percent of neighboring states adopted (lagged one year)-3101.5074112319.2625232.87-254.71 [2,345.8993][92,315.6510][226.8410][1,303.2966] Percent of neighboring states adopted, squared (lagged one year)-391398.7295512.31 [324,730.7970][1,449.3961] Year Indicator VariablesYYYYYY Observations120120120130110110 Number of states121212131111 Hansen statistic (P-value) 1.0001.0001.0001.0001.0001.000 Arellano-Bond, AR(1) (P-val ue)0.0740.1340.5390.0610.0790.163 Arellano-Bond, AR(2) (P-val ue)0.120.2010.7940.8580.0970.292 Top Patenting States (1st Quartile)Bottom Patenting States (4th Quartile)

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63Table 3-10. Continued (7)(8)(9)(10)(11)(12) Citation Weighted Patent Counts (lagged one year)0.8162***0.8236***0.8260***0.8535***0.8636***0.8605*** [0.0146][0.0159][0.0189][0.0047][0.0137][0.0084] Prudent Investor Indicator (lagged one year)-5 88.0025-625.1823-645.9265-486.5116-925.3682-963.4427 [661.2821][628.0003][613.8107][759.1179][860.0614][894.0843] Percent of neighboring states adopted (lag ged one year)1,194.2446***2366.092954.4451042.0041 [400.3876][1,448.2099][790.8376][1,514.9252] Percent of neighboring states adopted, squared (lagged one year)-1458.6684-1,076.40 [1,742.1348][1,653.2968] Year Indicator VariablesYYYYYY Observations120120120120100100 Number of states121212121010 Hansen statistic (P-value)1 .0001.0001.0001.0001.0001.000 Arellano-Bond, AR(1) (P-value )0.1760.1810.1730.1180.1230.127 Arellano-Bond, AR(2) (P-value )0.2330.2460.2410.2250.2130.213 Early Adopters (1st Quartile)Late Adopters (3th Quartile) p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are in brackets. Regressions were r un using the Blundell-Bond dynamic panel estimator. The symbol '†' indicates a two-step robust GMM estimati on. All other regressions are robust one-step regress ions. The regressions for the Late Adopters (4th Quartile) are not reported beca use there is no change in the prudent investor indica tor variable. Late Adopters (3rd Quartile) were included instead. Alaska and Hawaii are dropped when including data on neighborin g states. Top Patenting States (1st Quart ile): Illinois, Florida, New York, Calif ornia, Connecticut, Mi nnesota, New Jersey, Massachusetts, Ohio, Pennsylvania, Michigan, and Texas. Bottom Patenting States (4 th Quartile): South Dakota, Montana, West Virginia, Nebraska, Arkansas, Hawaii, Maine, North Dakota, Alaska, Washi ngton, D.C., Wyoming, and Nevada. Early Adopters (1st Quartile): Delaware, Illinois, Florida, Maryland, New York, South Dakota, Colorado, New Mexico, Oregon, Utah, Washington, and Oklahoma. Late Adopters (3rd Quartile) : Arkansas, Minnesota, Hawaii, New Jers ey, Alaska, Vermont, Washington, D.C., Massachusetts, New Hampshire, Ohio, Indiana, and Wyoming.

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64 CHAPTER 4 DO PATENT ATTORNEYS MATTER? 4.1 Introduction The recent patent infringement lawsuit between Research In Motion and NTP has increased public awareness of problems at th e United States Patent and Trademark Office (USPTO) and the various issues that currently burden pate nt policy in this country. While the general public has only recently become aware of the problems, economists have studied these issues for several years, with research on the topic generally falling under two categories. The first group of res earch analyzes the impact of legal changes that occurred during the 1980’ s and 1990’s (see Jaffe (2000) for a good overview). The second group examines institutional problem s within the USPTO, specifically the incentives for and effectiveness of the corps of examiners. One common element ignored in the literature is the role of the patent at torney in the patent approval process. While economists ignore lawyers, inventor s tend to rely upon them during the application and examination stages (to say nothi ng of during litigation). In assembling an application, lawyers may search for prior art, make citations, and write claims to assure that applications comply with USPTO regulat ions and to increase the odds a patent will be granted. They are also the primary c ontact during the appli cation process and may meet with examiners, in person, to discuss an application. If i nventors are right, and lawyers provide an invaluable service, th en economists have i gnored an important component of the patenting process. If lawyers are as unimportant as implied by previous research, then invent ors are wasting thousands of dollars by hiring one. Thus, to

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65 fully understand the economics of patents it is important to be able to answer the question, “Do patent attorneys matter?” This chapter is the first to address th e question by investigating how lawyer characteristics affect a patent application’s gran t lag--the number of days between the date an application is filed a nd the date a patent is grante d. To do so, a unique dataset that contains the names of lawyers represen ting the patent application was collected. Additional lawyer characteristics, such as experience and busyness, were calculated to help quantify and isolate the impact. The empi rical analysis shows that lawyers do affect a patent’s grant lag, but the magnitude of the impact differs by an invention’s technology. Another finding is that the estimated impact of examiner experience on the grant lag is different when lawyer characteristics are incl uded than when they are excluded. This is an important discovery since previous studies have sugge sted that differences in examiner experience affect the quality of patents granted. The rest of the chapter is as follows. The next section motivates the study with a review of the relevant literature. In the third section, the data and variables are described. The fourth section presents the empirical methodology and the fifth section discusses the findings of the research. Th e final section concludes. 4.2 Literature Review 4.2.1 Theoretical Models Theoretical models such as Segerstrom (1998), Denicolo (1996) underscore that the timing of an innovation or pate nt is important. Firms co mpete in R&D in order to develop the next best qualit y of a product and are compensated with monopoly profits until the next quality innovation is discovered. In patent races, such as these, it is imperative that researchers be the first to discover a new quality, or else they cannot

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66 recoup their investment in R&D. Denicolo’s model is partic ularly intriguing in that it estimates the optimal breadth a nd length of patent protection. While the length of patent protection in the United States is statutory, the breadth can po tentially be influenced by a lawyer representing the patent application. To extend De nicolo’s thinking by including legal intermediaries is, in truth, stepping beyond the or iginal model, since having representation implies there is some sort of negotiation and asymmetric information. These elements are not present in the current literature on patent races or even some criticisms of the USPTO (e.g. Griliches (1989) and Merges (1999)). They are, however, included in models of bargaining. Bargaining games of alternating offers, su ch as those in Osborne and Rubinstein (1990), also motivate the analys is and the importance of timing. In these models, two players bargain over the allocation of a pie of finite size and negotia tions continue until an agreement is reached. Each player prefer s to possess a larger piece of the pie, which has adverse effects on the si ze of the pie possessed by hi s/her opponent. The “driving force of the model” is that each player ha s strong preferences over how long it takes to find a solution. In the full information cas e an agreement between the two players is immediately reached. When one player is uninformed, the equilibrium may not occur immediately since “each player may try to deduce from his opponent’s moves the private information that the opponent possesses.” (p. 91) Regardless of when the agreement is reached, the uninformed player receives a sma ller allocation of the pie compared to the fully informed player. With this understanding, it is possible to draw a complete analogy to patent negotiation; where breadth of pr otection is the pie to be divi ded (breadth of zero implies a

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67 rejected patent) and the lawyer and examiner are the players. It is clear why the owner of an invention (and therefore lawyer) may prefer a broader patent since they may be used to deter competition (see Scotchmer (2004 )), increase revenue from licensing (Kamien (1992) and Scotchmer), or facilitate cro ss-licensing agreements (in the context of semiconductors, see Hall and Ziedonis (2001)). What is not necessa rily clear is why examiners may care about the size of the pie, si nce they do not directly benefit from their allocation. Examiners are agents for society and future inventors. They are therefore obligated to withhold patent breadth from the current inventor for use by future inventors.1 If the lawyer is the fully informed party (or, by extension, the one with the most skill) the pie will be di vided in his or her favor and potentially done so quicker than under the opposite scenario. This chapter tests the accuracy of both predictions. 4.2.2 Previous Empirical Analyses Much of the existing empirical literatur e on U.S. patent policy deals with legal issues and the various causes and consequen ces of increased patent litigation. Even though this research considers th e role of lawyers, literature on litigation is not helpful since these studies consider events after a patent is granted rather than during the examination process. Papers that consider institutional issues at the USPTO provide significant guidance on the topic. Three of th e most significant papers in this area are Cockburn, Kortum, and Stern (2003, CKS), King (2003), and Popp, Juhl, and Johnson (2004, PJJ).2 1 Merges (1999) argues that the incentives provided to examiners are not necessarily in line with the demands of society. 2 Merges (1999) criticizes the experience of ex aminers and suggests reforms but does not provide supporting empirical evidence.

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68 CKS (2003) explore how differences between patent examiners affect the quality of granted patents. Their data is based on 182 patents that were exam ined by the Court of Appeals for the Federal Circuit. In addition to patent data they collected information on the examiners that processed the patent appli cation. The examiner data includes tenure with the USPTO, actual time spent evaluating a patent, size of examiner’s docket, and total number of patents the examiner reviewed They cautiously conclude that examiner discretion, but not workload or examiner ex perience, may affect the court’s decision to rule it invalid. King (2003) uses USPTO Time and Activit y Reports to explore the examination process and patent quality. He finds that ex aminer hours and examiner actions have been relatively constant over time, in spite of the increased number of patent applications submitted to the USPTO. He also shows that other measures of examiner quality, such as pendency, have declined. These findings rein force the notion that patent examiners are extremely important to the patenting process. Of particular interest for this essay is King’s finding that as the number of examiner actions per patent increase, so does the probability of approval. As King puts it, “thi s is consistent with examiners interacting with inventors to improve the application a nd resulting patent award, a manifestation of examination quality.” (p. 65) While this ta citly supports this essa y’s hypothesis, he is cautious since poor inventions may also re quire additional help from examiners. PJJ (2004) explore who is most affected by long grant lags, not the causes of the grant lag. Controlling for ch aracteristics of the application, assignee, country of origin, and industry (among others) they find that the primary driver of grant lag is the technological field of the i nvention. They find that pa tent applications from the

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69 biotechnology and computer sectors are most likely to have a longer grant lag. Given their interests on effect rather than causalit y, examiner characteristics are omitted from the analysis. These three empirical papers help to motivate the empirical methodology for this research. Following PJJ’s (2004) example, grant lag is included as the dependent variable.3 In addition, some of the variables (e .g. country indicators, number of figures, and number of drawing pages) used by PJJ are included in the an alysis. Based on the findings of CKS (2003) and King (2003), this analysis includ es measures of examiner experience and busyness. These data and their sources are discussed in more detail in the following section. 4.3 Sources and Descriptive Statistics 4.3.1 Data Sources The primary source for patent data is the NBER patent citations data-file (Hall, Jaffe, Trajtenberg, 2001) which has recently been updated by Bronwyn Hall (Hall). The dataset was expanded to include information from the public-use f iles made available by the USPTO (U.S. Department of Commerce). The new information included the full application date, number of drawing pages, number of figures, the name of the primary examiner and assistant examiner (if present), and the names of lawyer(s) representing the patent.4 The names of examiners and lawyers ex tracted from the USPTO files were not consistently formatted. On some patents, the individual is identified by surname and first 3 Due to data limitations, the other dependent variables used in King (2003) and CKS (2003) could not be considered here. 4 In some cases the name of the law firm was listed on the patent, not the name of the lawyer(s). Unfortunately, due to mergers between firms and inco nsistencies in how the law firm name was entered, patents that list the law firm name are of limited usefulness.

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70 and middle initials. Other patents may exclude the middle initial and print out the full first name. Still others may include “Jr.,” “III,” “Esq.,” or a maiden name. These irregularities had to be correct ed in order to properly estim ate the contribution of lawyers and examiners. The USPTO requires that al l attorneys and agents register with the Office of Enrollment and Discipline (OED) before being al lowed to prosecute a patent application. Upon registration, lawyers are issued a registration number that is to be used on all official correspondence with the USPTO. Si nce registration number s are awarded only once, it is possible to match the names that a ppear on a patent with those that appear on the OED’s register, regardless of format or typographical errors. A list of lawyer names and registration numbers were compiled through various search es on the USPTO’s website.5 Names on a patent that could not be confidently matched with a registration number were marked and not included in the analysis. Compared to lawyers, examiner identifica tion was slightly more difficult since the USPTO does not have a list of registration num bers or other unique identifier publicly available. As a result, identification of examiners can solely be based on the names printed on the patent. To identify examiners, each unique primary examiner name was assigned an identification number A slight misspelling, e.g. inclusion of suffix or middle 5 A list of registered lawyers currently active is ava ilable for download from the OED’s webpage. The list of active registrants was expanded by searching the USPTO’s Patent Application Information Retrieval website (PAIR) and collecting registration numbers and names that do not appear in the active roster. Registration numbers and names were also found via a document identifying attorneys and agents that did not respond to an OED survey. The lowest known registration number-name pairing in the 13,388 and the highest known is 56,991. The compiled roster includes 31,673 names and registration numbers. This set of names and registration numbers were then matched to those names listed in the expanded patent dataset. Care was taken to assure that matching was as accurate as possible. If the accuracy of a match was in question due to misspellings, an additional search of PA IR was made. If a match could not be verified the lawyer was not assigned a registration number. Lawyers with known duplicate names were not included in regressions.

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71 initial, may cause several different identification numbers for the same lawyer. To account for these issues, several different sets of examiner identification numbers were created and each set differed by its sensitivity to typographical errors, use or ignorance of “Jr.” or “Sr.,” or inclusion of middle initial. Regressions were run using both sets of IDs and the results did not signifi cantly change. The results presented here use the least tolerant IDs. Assistant examiners were not similarly identified since not all patents have an assistant examiner and, if they do, wo rk done by assistant examiners is subject to approval by the primary examiner. An indicator variable was used to denote the presence of an assistant examiner working on the patent application. 4.3.2 Description of Variables This research uses data on patents gran ted in either 1999 or 2000 to quantify the impact that legal intermediaries have on the grant lag—the number of days between when an application was filed and a patent was approved.6 Many other factors besides lawyer characteristics may affect a patent’s grant la g. These include application, invention, country, examiner, and other ch aracteristics as de scribed in Table 4-1. Of these, application, invention, and country characte ristics are those most commonly included in previous research. The effect of the number of figures on the grant lag is ambiguous. In some cases, more figures may imply more cl arity, making the examination of a patent quicker and easier. At the same time, more figures may increase the amount of work an examiner has to do since he or she must assure that what is claimed in the patent matches the figures and that the figures are clear. Similarly, th e number of drawing pages may 6 In 1995, the United States changed the length of a patent from seventeen years after the grant of a patent to a length of twenty years after the application date. Successful applications filed after June 8, 1995 were awarded the twenty year term. Only patents that were applied after that date were included in the dataset to avoid issues caused by this change.

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72 increase or decrease the grant lag. More pages may mean more clarity, but they might also require more work by the examiner. Th e number of claims should have a positive impact on the grant lag. Each claim must be evaluated by the examiner in the context of the invention, but also based on prior art. As the number of claims increases, so should the amount of time the examiner has to sp end on the patent. An added benefit to including claims is that it may capture the complexity of an invention. A more complex invention will likely have more claims, and thus lead to an increase in the grant lag. The number of citations will likely also have a positive impact on the grant lag, since each must be checked by the examiner. The contribution of an inven tion to society or an industr y may also play a role in the time a patent is reviewed, however importa nce is difficult to quantify. Two measures, a generality index and citations received, are included in th e regressions to attempt to capture variation in grant lag associated with importance. Each was included in Hall’s updated patent citations data set (Hall). The generality index is calculated as a Herfindahl-type index and evalua tes how important the patent was to inventions of other technologies. A high index implies that the patent was cited by patents from a broad range of technologies. It is expected that pa tents with a higher gene rality index will have a reduced grant lag since important inventi ons likely have little prior art to make comparisons. An important invention may also be cited more by other patents. To also account for an invention’s importance the number of citations received from other patents is also included in regressions. Who owns a patent may also affect the grant lag since some firms may prefer shorter lags than others; how ever, computational constraint s prohibited the inclusion of

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73 assignee indicator variables to fully capt ure the assignee effects. Another, less burdensome measure included in regressions is the type of the assignee, i.e. company, government, individual, or unassigned. Si nce companies are subject to competitive pressure they may strongly prefer a shorte r lag whereas a government may solely be interested in receiving a pate nt and not necessarily the timi ng of a patent. Assignees may also differ in their ability to pay a lawyer and it is likely that larger companies have more money available to hire patent attorneys. Financial data for assignees was not available but it is possible to account for assignee size by looking at the number of patents the assignee has owned over time, and how large that number is compared to other firms. Regressions include two indi cator variables, Assignee Pe rcentile (75) and Assignee Percentile (90), to indicate if the assignee was in the 75th or the 90th percentile of all assignees in terms of patent owne rship, respectively. It is ex pected that the signs of these variables will be negative. Since not all pate nts are assigned, an i ndicator variable is included that equals one if th e patent was assigned and zero ot herwise. Another variable included is an indicator of the grant year. This is included to capture general USPTO effects as well as trends in patenting and innovation. Many interna tional inventors file through the World Intell ectual Property Organization (WIPO) and applications are later transferred to the USPTO. The date of app lication extracted from the patent files may reflect the date it was filed with WIPO and not with the USPTO. Therefore, international patents may have a longer grant lag than domes tic patents. To account for these issues, a set of indicator variables deno ting the country of origin was included in regressions. In addition to capturing measurement error th ey may also capture communication problems between an examiner and the inventor.

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74 Like CKS (2003) and King (2003), this essa y includes examiner characteristics, but there is an important difference between th eir measure of experience and the one used here. In CKS and King, examiner experi ence is the actual number of patents each examiner reviewed during his or her tenure at the USPTO. Here, examiner experience is based on the number of patents the primary examiner reviewed since mid-1996. Since data prior to 1996 is not availa ble, there is imprecision in the calculations of examiner experience. For this reason only patent s granted in 1999 or 2000 were included in regressions.7 It is assumed that, over time, experience estimates will reach a level proportional to their true levels. Even if this is not true, experience prior to mid-1996 is essentially fixed and should be captured in the set of examiner indicator variables included in all regressions. The impact of experience on the grant la g is likely nonlinear. Early in an examiner’s career there is significant learning by doing and they become more efficient at processing patenting applications over time. However it is likely that, beyond a certain point, experien ce of the examiner does not improve performance. To account for this nonlinearity, an experience squared term wa s included in regressions. It is possible that a large examiner wo rkload will cause delays in the patent approval process. In addition to examiner experience and examiner indicator variables, regressions also include a measure of exam iner busyness. Examiner busyness is a count of the number of patent applications that are currently part of an ex aminer’s docket. The count is for all patents granted from mid1996 to 2002 that have overlapping application dates and/or overlappi ng grant dates. 7 Patents granted in 2001 and 2002 are not included because generality measures are biased. See Hall, Jaffe, and Trajtenberg (2001, p. 21-23) for more details.

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75 To analyze the impact of lawyers on patent applications this essay considers two separate measures of lawyer influence. The fi rst measure is an indicator variable that is equal to one if there was a lawyer listed on the patent application. Here the presence, but not the skill, of a lawyer is measured. This general measure of lawyer impact also allows patents that list a law firm (as opposed to a just a lawyer) to be included in the regressions. The cost associated with inclus iveness is that an indicator variable cannot capture the effect of an individual lawyer’s skill. This shortcoming leads to the second measure of lawyer influence— individual lawyer attributes. Skill at patent prosecution can be both dyna mic and static. Dynamic skill implies acquired knowledge or learning by doing. Stat ic skill can be the result of intelligence, education, personality, or other characteristic s that are time invariant and inherent to the lawyer, or at least did not ch ange after passing the OED exam ination. The second set of regressions account for both sta tic and dynamic lawyer attrib utes by including a set of lawyer indicator variables, lawyer experien ce, and lawyer busyness. As with examiner experience, lawyer experience may be nonlin ear. Lawyer busyness and experience were calculated in the same manne r as examiner busyness and experience. Beyond a certain point, additional experience may not make a la wyer more efficient at getting a patent approved. Therefore regressions also include lawyer experience squared. Since these characteristics are individual sp ecific effects, patents that list the law firm were not included in the analysis. Patent applications often have more than one lawyer working on them. As such, it would be difficult to completely disaggregate the impact of each individual lawyer on the patent application. It is assu med that one of the lawyers is the lead lawyer who directs or

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76 guides the work of the other lawyers listed on the patent. For the pur poses of this study the lead lawyer is either the first lawyer li sted on the patent or the most experienced lawyer out of the entire set of lawyers.8 Each is considered separately. One factor that may be an important dete rminant of grant lag, but not yet been discussed is the technology of an invention. As Cohen et al. (2000) and Cohen et al. (2002) point out, different i ndustries prefer different fo rms of intellectual property protection.9 Technology type may also affect th e impact of legal representation and lawyer experience. To account for both gran t lag and lawyer issues separate regressions are run for fourteen different technology subcategories. The subcategories are based on the Patent Classification System as of D ecember 31, 1999 as presented in Appendix 1 of Hall, Jaffe, and Trajtenberg (2001). Table 4-2 lists the fourteen se lected subcategories and the top three assignees for each subcat egory for patents granted during the 1999-2000 window. The chosen technology subcategorie s intentionally cove r a wide range of products including golf equipment, semiconduc tors, and agricultural products. This facilitates comparisons to the entire set of technologies. Tables 4-3 and 4-4 provide summary statisti cs of selected independent variables. The statistics are separated by technology subcategory to underscore the potential differences across technologies. The informa tion is based on patents where the identity of either the first lawyer or most experienced lawyer is known. Note that there is considerable variance in the number of la wyers, examiners, and patents for each 8 The assertion that the first lawyer is the lead lawyer is dubious if lawyer names are listed alphabetically on patents. Visual inspection of the data does not suggest this pattern is consistently used across patents. It makes sense that the most experienced lawyer be the l ead lawyer since, as with most other positions, more success typically leads to promotions. 9 Another example is Levin et al. (1 987), however the survey occurred be fore significant reforms of U.S. patent policy.

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77 subcategory. Significant diffe rences also exist between t echnology types for the average number of figures and lawyer experience. In spite of these differences, there is remarkable similarity in the average number of claims, average grant lag, and citations received across technologies. The number of international patent applic ations has increased recently and it is necessary to control for impacts unique to th e country of origin. Table 4-5 shows the number of patents originating in each c ountry for each technology subcategory. Not surprisingly, Japan and Germany inventors ap pear most often in Semiconductor Devices (Subcategory 46), Motors, Engines & Parts (53) and Optics (54). Table 4-6 presents the correlations between independe nt variables and the dependent variable, grant lag, by technology subcategory. 4.4 Empirical Methodology 4.4.1 Regression Specifications Several different measures of lawyer impact are considered, each requiring a different specification. The first measure of lawyer impact is simply an indicator of having a patent lawyer listed on the patent. These regressions use the specification: i i i i iX grantlag ˆ Index y Genderalit Represent Represent2 1 0 (4-1) where X is a vector of independent variable s (see Table 4-1 for descriptions) and ˆ is the vector of coefficients for those regr essors, for patents in technology subcategory i This simple specification establishes a baseline understanding of lawyer impact without being concerned with lawyer heterogene ity caused by differences in experience, education, or skill. As a consequence of this, all patents for each technology type are

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78 included in regressions, even if those pate nts list a law firm name as opposed to individual lawyer names. The baseline specification assumes all lawyers are on equal footing. In reality lawyers may differ in a variety of ways in cluding education, experience, personality, nationality, and gender. All of these differences may, in some way, affect the ability of the lawyer in representing a patent. To estimat e the impact of these factors another set of regressions are run for each of the fourteen industries. These regressions have the specification: i i i i i i iY grantlag ˆ Index Generality Experience yness Lawyer Bus Experience Experience4 3 2 2 1 0 (4-2) where Experience is the number of patents a lawyer previously represented, Lawyer Busyness is the number of other patents the la wyer is representing at the same time, and Y is the same vector of regressors as in X of Equation 4-1 plus a set of lawyer indicator variables. iˆ is the vector of coefficient estimates for Y Since lawyer specific variables are used, those patents that did not hire a lawy er and those that listed a law firm were not included. To account for the two different defini tions of lead lawyer, two separate sets of regressions were run. The first set defined the lead lawyer as the first lawyer listed on the patent. The second considered the most e xperienced lawyer as the lead lawyer. 4.4.2 Endogeneity of Lawyer Choice Before moving forward, it is necessary to acknowledge the potential endogeneity of lawyer characteristics. Lawyers are chosen by the applicant and are expected to achieve an outcome consistent with the applicant’s de sires. Thus the application’s treatment by lawyer may depend on the applicant’s expectatio ns of the grant lag. If the applicant

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79 expects a patent to take too l ong, he or she may choose a lawyer to reduce the grant lag. Similarly, if the grant lag is too short (due to limited breadth or potential rejection of the application) then an experienced lawyer will be hired to extend the grant lag. Under either case, there may be an unknown variable guiding the choice of the lawyer and that omitted variable may be biasing the coefficient estimates.10 One possible unobserved variable that could affect lawyer selection is the value of the invention to the applicant. An invention may be of high value if it is of significant importance to a product the invent or or assignee wishes to sell It may also be of high value if the patent would be an important piece of a patent portfolio or patent wall. If the applicant values the patent hi ghly, then the expected gran t lag (regardless of how many days it actually is) is too long, and they w ill hire an experienced lawyer to reduce the grant lag. Under this scenar io, the unobserved variable causes coefficient estimates to be biased upwards. Another unobserved variable that may affect lawyer selection is the invention’s merit to be patented. The USPT O requires that inventions must be unique, novel, and non-obvious for them to be patent able, all of which are unobservable to the researcher. However, recent criticisms of the USPTO have revolved around the issuance of patents of questionable validity. Most notably the NTP patents on wireless email.11 If an invention is of questionable merit, then it likely requires more time and a better lawyer to convince the examiner that it is a worthy i nvention. From this perspective, a shorter 10 An alternative argument that is less problematic for the analysis is that lawyer choice is endogenous but lawyer characteristics, such as experience, are pred etermined. The subtle distinction implies that endogeneity bias does no t exist and that the coefficient es timates presented here are correct. 11 Other popular examples include the peanut butter and jelly sandwich (Patent No. 6,004,596; represented by Vickers, Daniels, and Young), Method of Exercisi ng a Cat (5,443,036; represented by Epstein, Edell & Retzer), and Method of Swinging on a Swing (6,368,227; represented by Peter L. Olson, the 5 year old inventor’s father).

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80 expected grant lag would be associated with the choice of an e xperienced lawyer and coefficient estimates would be biased downwards. The common approach to dealing with endoge neity is to find inst rumental variables for all endogenous regressors (i.e. lawyer experience, experience squared, and the complete set of lawyer indicator variables) a nd exploit the variation of the instrument to get at the true contribution of the endogenous variables. While the data on patent characteristics is rich, the data on lawyers is limited. Aside from name, experience, and registra tion number, very little is known about a particular lawyer. This paucity of data limits the set of potential instrumental variables. In regressions not included in this chapter, assignee experience, dates for the OED examination and the number of patents gr anted by the USPTO since a lawyer first appeared on a patent were tested as potential instruments.12 Without exception, the available instruments were invalid or weak. Since weak instruments may be just as bad (or worse) than the problems of endogene ity, only OLS estimates are presented. Even though OLS estimates may be biased knowing the direction of the bias may facilitate interpretation. In the preceding discussion two potential omitted variables, value and merit, showed the bias may be posit ive or negative, respectively. However it is likely that invention value is more powerful th an an invention’s merit. Hiring a lawyer imposes an additional cost (about $10,000 according to Barton (2000)) on the inventor or assignee. If these costs could not be offset by additional gains, no lawyer would be hired. Arguably, high value inventions provide a better means to offset the cost of a lawyer than meritless inventions. Therefore, it is assume d that the endogeneity bias, if it exists, is 12 See Appendix C for a more thorough discussion of the instrumental variables considered.

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81 positive and that the coefficients are conservati ve estimates of the true contribution of a lawyer. 4.5 Results 4.5.1 Estimated Impact of Lawyers Table 4-7 presents the results of regressions using the specificati on in Equation 4-1. Regressions that exclude the representation i ndicator are also presen ted in the table to highlight the contribution of representation. All regressions include application, invention, country, examiner, and other variables as de scribed in Table 4-1. Note that the first and second regressions in the table are for patent s in all fourteen technology subcategories. When considering all subcategor ies, the impact of representation is fairly small; having a lawyer will decrease grant la g by approximately ten days. While the impact is small, the estimate is highly statistically significant. Based on the technology specific regressions, the driving force behind the result for all technologies appears to be Nuclear and X-ray inventions. The presence of a legal intermediary for patents of this technology apparently reduces the grant lag by a bout one month. Lawyers also appear to have a statistically signification impact in Op tics and Apparel & Text iles. Interestingly, including the representation indicator does not significantly affect the coefficients on any of the examiner relevant variables (examine r experience, examiner experiences squared, and examiner busyness). Regressions with the representation indicator variable also included an interaction term between represen tation and the generality index to test the hypothesis that representation may increase th e scope of patent protection. If the hypothesis is true, the coefficient for this interaction term should be statistically significant and positive. None of the selected industries confirm this relationship. The

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82 signs and magnitudes of examiner relevant coe fficients are consistent with findings from CKS (2003) and King (2003). Table 4-8 presents the regression results fo r patents where the identity of the first lawyer is known but the regres sions do not include lawyer eff ects. Table 4-9 presents the results of regressions on the same set of patents but incl udes the experience, experience squared, and busyness of the first lawyer listed on the application. Again, inclusion of lawyer characteristics causes the exclusion of patents with no representation or those that only list a law firm. When acc ounting for lawyer specific char acteristics, the impact of the lawyer is not the same across technology s ubcategories. The grant lag for patents in biotechnology (subcategory 33) Miscellaneous Drug & Medi cal (39), Nuclear and Xrays (44), Semiconductors (46), and Optics (54) appear to be affected by lawyer experience. In these technologies, increases in lawyer experience will reduce the time it takes for a patent to be approved. Since th e estimates for lawyer experience squared are generally positive and significant, there appears to be decreasing returns to lawyer experience. Regressions included an interaction term for lawyer experience and generality to again test the hypothesis that experienced la wyers are fighting for more breadth. As before, the coefficient for this term is predicted to be statistically significant and positive. In Nuclear and X-ray, the only technol ogy subcategory that the coefficient was statistically significant, the sign is negative. This appears to reject one of the predictions of bargaining theory as outlined in sub-sect ion 4.2.1, however caution is merited in its interpretation. The generality index may i ndicate the importance of an invention to an industry and therefore may be an imperfect m easure of breadth. The importance of an

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83 invention is unaffected by the lawyer as it is determined during the research and development stage and not during the prosecuti on of a patent application. A better measure of breadth is needed to fully (and accu rately) test the predictions of bargaining theory as they pertain to the patent approval process. Another findi ng is that, as lawyers become more busy, the grant lag increases. Th is is true in most industries, even those that are not impacted by differe nces in lawyer experience. The same specification was used on patent s where the most experienced lawyer was identified (see Tables 4-10 and 4-11). Th e results are very similar to those based on first lawyer characteristics. A likely cause of the similarity is the large number of patents with only one lawyer. In these cases, the char acteristics of the first and most experienced lawyers are the same. Be that as it may, th e regressions using most experienced lawyers further emphasizes that patent attorneys are si gnificant in the patent approval process. 4.5.2 Examiner Experience and Generality Regressions in the previous sub-section included an interaction between lawyer experience and a patent’s genera lity index to test if, as ba rgaining theory would suggest, more experienced patent attorneys attempt to expand the scope of patent protection. The results indicate weak evidence against the pred iction. Another, prediction of bargaining theory is that experienced examiners would d ecrease the breadth of a patent. Thus, the coefficient of an interaction term between examiner experience and the generality index is predicted to have a negative sign. Recognizing the caveat that the generality index is a flawed measure of patent bread th there still may be some in formation gained from testing this hypothesis. Regressions in this sub-s ection use the specification

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84 i i i i i i iY grantlag ˆ Index Generality LowExamExp LowExamExp LowLawExp Busyness Examiner LowExamExp LowLawExpi5 4 3 2 1 0 (4-3) The main difference between equations (4-3) an d (4-2) is the treatment of experience. An indicator of low experience is used in these regressions rather than a count of the number of previous patents represented or re viewed. Lawyers (examiners) that appear on less than 10 (104) patents—the median expe rience for all lawyers (examiners)—are considered to be low experience. The median was based on the highest amount of experience gained for each lawyer (exami ner), through 2002, regardless of technology type. In addition, to the examiner-generality interaction, lawyer and examiner experience indicators are lik ewise interacted. Tables 4-12 and 4-13 present the results of these regressions using the experience of the first lawyer or most experienced la wyer, respectively. As before, low lawyer experience increases the grant lag in most cas es. The impact of examiner experience is less clear. In some cases low experience increases the grant lag, while in other technology subcategories, the grant lag is decreased. This is in contrast to the evidence in the previous subsection where examiner experi ence clearly decreased the grant lag. The coefficient of the interaction term between examiner experience and the generality index is rarely statistically significant. In th e one case that it is statistically significant (biotechnology), the sign is the opposite of the prediction. This again provides some indication that bargaining may not be taking place, however fu rther investigation with a better measure of patent breadth is warranted. 4.5.3 Examiner Effects As noted earlier, several papers have been extremely critical of examiners and their incentives. The criticisms have been motivat ed, in part, by showing that differences in

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85 examiner experience are a factor in the pate nt approval process. The results of this inquiry confirm these findings. However, without exception, the coefficients for examiner experience are lower (more nega tive) when not controlling for lawyer characteristics than when they are included in regressions. Fo r nuclear, x-ray, and semiconductor patents, the coefficient for exam iner experience is ne arly half as large when lawyers are included. This further sugge sts that examiner diffe rences are important but that these differences are overstated when not accounting for lawyer effects. In other words, previous research that does not include lawyer effects has an omitted variable problem. 4.5.4 Experience as a Proxy for Quality It might be reasonable to interpret lawy er experience as a proxy for quality since low quality lawyers are unlikely to continue to attract business. On the other hand, high quality lawyers will likely at tract a large amount of business and have higher levels of busyness. Tables 4-14 and 4-15 summarize the impact of the median lawyer based on coefficient estimates from subsection 4.5.2. The impact of lawyer quality, as proxied by experience, is significantly different across technology types. Semiconductor technologies experience the larges t reduction in the grant lag. There, lawyer quality is associated with reductions in the grant lag by approximately five months. In Pipes & Joints and Gas technologies, lawyer quality (experience) appears to increase the grant lag. Based on the descriptive information provide d in Tables 4-3 through 4-6, there is no obvious explanation for the result. It is therefore likely that these technologies are intrinsically different than the others consider ed in this analysis and may be interesting case studies for future research.

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86 The evidence in Tables 4-14 and 4-15 conf irms the findings of Cohen et al. (2000) and Cohen et al. (2002). In those papers, su rvey evidence shows th at product innovations are more likely to use patent s as a means of protection th an are process innovations. They also find that some industries (e.g. m achine tools, medical equipment, and drugs) place more of an emphasis on patents than others (e.g. food, textiles, and printing/publishing). Cohen et al. (2000) also examine th e possibility that several different mechanisms are combined to appr opriate intellectual property. Respondents that indicated a high regard for patents also stressed the importance of lead time; further evidence that timing and patents are important and that lawyers should be an important input in the patent approval process. 4.6 Conclusion This research is the first to consider the impact that patent attorneys and agents have on patents. Using an original datase t, it was shown that lawyers do affect the pendency of a patent application, alth ough the impact depends on an invention’s technology. In addition to the results on la wyer impacts, the essay reconfirms and extends previous work on patents. Specifi cally, results provide further evidence that different industries value patents different ly and examiner experience does affect the patent process. Importantly, including lawyer characteristics in regressions decreases the importance of examiner experience. This s uggests that previous research that do not account for lawyer effects may overstate th e problems associated with inexperienced examiners. While the results are promising, the endogeneity of lawyers tempers their applicability. A structural model on the role of lawyers could account for this problem and lead to further understanding of the re lationship between lawyers and examiners.

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87 Bargaining games, such as those presented in Osborne and Rubinstein (1990) would be a good starting point for such a model. More de tailed data on lawyer characteristics may yield stronger instrumental va riables that also may account for endogeneity. Better data on experience could allow mo re years of data to be included in the analysis. Finally, this research does not explore repeated interac tions between lawyers and examiners nor does it estimate any differences between attorneys and agents (i.e. those with or without legal training). These would be in teresting extensions to rese arch on patent attorneys.

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88 Table 4-1. Variable Descriptions VariableDescription Application ClaimsNumber of Claims by Paten t Drawing PagesNumber of Pages for Figures included in file FiguresNumber of Figures included in file Citations MadeNumber of citations made to other patents Invention Citations Received Number of citations received by other patents Generalit y Index Herfindahl-like index of generalit y Countr y Ja p an Patents from Japanese inventors German y Patents from German inventors France Patents from French inventors Great Britain Patents from British inventors Canada Patents from Canadian inventors Other EPO Countries Patents from other European Patent Office countries Other Countries Patents from other countries (not U.S.) Examine r Examiner Experience Number of patents reviewed by primary examiner since mid-1996 Examiner Ex p erience, s q uared Square of Examiner experience Examiner Busyness Number of other patents primary examiner is working on at the same time Assistant Examiner Indicator variable if assistant examiner is used (1 if true, else 0) Lawye r Represented Indicator equal to one if lawyer or law firm was listed on application Lawyer Experience Number of patents represented by lawyer (either first or most experienced) since mid-1996 Law y er Ex p erience, s q uared Square of Lawyer Experience Lawyer Busyness Number of other patents the lawyer (first or most experienced) is working on at the same time Other Variables Examiner binaries Set of indicator variables for Primary examine r Lawyer binaries Set of indicator variables for lawyer (either first or most experienced) Year binaries Indicator for year patent was granted (2000=1) Unassin g ed Whether or not the patent was assigned Assi g nee T yp e Type of Assignee (individuals, corporations, etc.) Assignee Percentile (75)aIndicator if assignee is in the 75th percentile of all assignees, by patent ownership Assignee Percentile (90)aIndicator if assignee is in the 90th percentile of all assignees, by patent ownership a Calculation based on all patents granted from 1976-2000

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89Table 4-2. Technology Sub categories Descriptions Subcategory IDSubcateogory Description Largest Patent Holders by Categor y 11Agriculture, Food, TextilesAmerican Cyanamid Company, Procter & Gamble Company, and Ciba Specialty Chemicals Cor p oration 13GasAir Products And Chemicals, Inc., Boc Grou p Inc., and Praxair Technolo gy Inc. 33BiotechnologyIncyte Pharmaceuticals, Inc., Novo Nordisk A/S, and Smithkline Beecham Corporation 39Miscellaneous-Drug & MedSt. Jude Medical, Inc., Sulzer Orthopedics Inc., and 3M Innovative Properties Company 42Electrical LightingMotorola, Inc., U.S. Philips Corp oration, and Philips Electronics North America Corp. 44Nuclear & X-raysGeneral Electric Com p an y Siemens Aktien g esellschaft, and U.S. Phili p s Cor p oration 46Semiconductor DevicesAdvanced Micro Devices, Inc., Inte rnational Business Machines Corporation, & Taiwan Semiconductor Manufacturing Co., Ltd. 53Motors, Engines & PartsCater p illar Inc., Robert Bosch Gmbh, and Ford Global Technolo g ies, Inc. 54OpticsEastman Kodak Com p an y Fu j i Photo O p tical Co. Ltd., and Xerox Cor p oration 62Amusement DevicesCallawa y Golf Com p an y Mattel Inc., and Walker Di g ital, LLC 63Apparel & TextileLindauer Dornier Gesellschaft Mbh, Sipra PatententwicklungsUnd Beteili g un g s g esellschaft Mbh and Zinser Textilmaschinen Gmbh 64Earth Working & WellsSchlumberger Technology Corpor ation, Weatherford/Lamb, Inc., and Halliburton Energy Sevices Inc. 66HeatingBabcock & Wilcox Com p an y Eastman Kodak Com p an y and IBM Cor p oration 67Pipes & JointsCater p illar Inc., Da y co Products, Inc., and General Electric Com p an y Source: Hall, Jaffe, and Trajtenberg (2001)

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90Table 4-3. Count of Unique O ccurrences, by Subcategory, When Identification of First or Most Experi enced Lawyer is Known SubcategoryArt UnitsLawyersExaminersPatents Use of Assistant Examiners on Patent Assignees Ratio of Examiners to Lawyers Ratio of Lawyers to Patents Agriculture, Food, Textiles26369975311541500.2630.695 Gas1029233363891450.1130.804 Biotechnology2576915115317133790.1960.502 Miscellaneous-Drug & Med23523597682012460.1130.681 Electrical Lighting287908616627373210.1090.475 Nuclear & X-rays15604548282972680.0890.729 Semiconductor Devices42971201451125793590.2070.215 Motors, Engines & Parts37903134242111084660.1480.373 Optics247709319327723070.1210.399 Amusement Devices18605479082671790.0780.666 Apparel & Textile267156410892872650.0900.657 Earth Working & Wells205585711163542410.1020.500 Heating20544536713222680.0970.811 Pipes & Joints24495495982952580.0990.828 The count of unique occurrences (e.g. the number of lawyers that appear at least once in the da ta) are based on a set of patent s where either the first lawyer listed on a patent or the most highly experienced lawyer on the patent were successfully matched to a registration number.

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91Table 4-4. Averages by Subcategory, When Identific ation of First or Most E xperienced Lawyer is Known SubcategoryGrant Lag Generality IndexClaims Number of Drawing Pages Number o f Figures Citations Made Citations Received Agriculture, Food, Textiles721. 250.2117.335.188.3711.051.13 Gas695.880.3017.514.526.6317.552.07 Biotechnology995.060.1417.5311.0113.084.511.17 Miscellaneous-Drug & Med689.530.1917.255.3211.1215.742.11 Electrical Lighting776.950.2515.815.089.418.631.83 Nuclear & X-rays764.630.2117.616.4710.028.061.50 Semiconductor Devices789.910.3516.395.8612.2010.574.00 Motors, Engines & Parts688.160.2213.324.827.6810.331.68 Optics709.000.2415.677.5611.8910.711.88 Amusement Devices701.320.1514.905.419.7111.781.60 Apparel & Textile614.080.1713.984.388.7010.941.20 Earth Working & Wells735.080.1919.226.5211.2816.511.80 Heating693.740.1814.034.978.0011.231.11 Pipes & Joints782.870.2113.194.228.7212.511.25

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92Table 4-4. Continued Subcategory Examiner Experience Examiner Busyness Experience of First Lawyer Experience of Most Exp. Lawyer Busyness, First Lawyer Busyness, Most Exp. Lawyer Agriculture, Food, Textiles299.52401.32129.74153.23154.60176.32 Gas360.44448.41127.08140.20129.61148.32 Biotechnology244.14384.7276.4591.40148.26172.51 Miscellaneous-Drug & Med321.24440.7690.8895.95108.41116.79 Electrical Lighting307.50601.25154.94170.12218.17239.95 Nuclear & X-rays375.60523.53151.40195.61219.71279.54 Semiconductor Devices373.69775.64356.22449.09524.45635.78 Motors, Engines & Parts428.33587.25165.32172.14209.14219.31 Optics360.58610.40161.89183.44204.42223.44 Amusement Devices316.52362.0972.5374.4390.2392.38 Apparel & Textile324.30 399.63170.08174.03184.35188.75 Earth Working & Wells210.25391.0565.3268.2386.8790.46 Heating376.65529.64109.35120.75144.38159.13 Pipes & Joints392.75650.63120.60131.41161.47175.15 Experience is a count of the number of patents each examiner or lawyer appears on since Se ptember 1996. Average lawyer experience is presented for both the first lawyer list on the pate nt and the most experienced la wyer listed on the patent. Bus yness is a count of the number of patents where the la wyer or examiner were listed that have overlapping application dates and/or grant da tes.

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93Table 4-5. Number of Patents by Country and Subcategory, When Identification of First or Most Experi enced Lawyer is known SubcategoryJapanGermanyFrance Great Britain Canada United States Other E.P.O. Other Country Agriculture, Food, Textiles1577191943077713 Gas821214829992 Biotechnology353344532124410236 Miscellaneous-Drug & Med123995126054640 Electrical Lighting94138262826110915388 Nuclear & X-rays3169720106263728 Semiconductor Devices33616048221528001291001 Motors, Engines & Parts18446633575214947956 Optics3174314393313345696 Amusement Devices9111836800835 Apparel & Textile25132581875110149 Earth Working & Wells821656579221630 Heating1548717264882644 Pipes & Joints3077513194122517

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94Table 4-6. Correlation between Grant Lag and I ndependent Variables, by Technology Subcategory Subcategory Generality IndexClaims Drawing PagesFigures Citations Made Citations Received Examiner Experience Agriculture, Food, Textiles 0.02990.06960.02650.02700.11720.0262-0.3075 Gas -0.0397-0.01770.0442-0.0194-0.0967-0.1116-0.2578 Biotechnology 0.03350.02900.04990.04360.00470.0467-0.4281 Miscellaneous-Drug & Med 0.07740.02680.14380.15730.15280.1025-0.3231 Electrical Lighting 0.06640.11370.04130.06830.0888-0.0342-0.3386 Nuclear & X-rays -0.00160.06950.06130.07470.0927-0.0630-0.1758 Semiconductor Devices 0.02790.07390.04350.07740.1043-0.0554-0.4290 Motors, Engines & Parts 0.07400.11100.11690.12130.0624-0.0021-0.2958 Optics 0.07670.07090.02940.04410.05000.0247-0.3577 Amusement Devices 0.02250.20420.15190.11570.11200.0905-0.4613 Apparel & Textile -0.02230.10320.09200.12980.1336-0.0022-0.3421 Earth Working & Wells 0.03750.1405-0.01140.01550.0661-0.0463-0.2901 Heating 0.06460.12560.08270.12180.14730.0548-0.3639 Pipes & Joints -0.04950.10920.09180.07180.17470.0334-0.1985

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95Table 4-6. Continued Subcategory Examiner Experience Examiner Busyness Lawyer Experience (First Lawyer) Lawyer Experience (Most Exp. Lawyer) Lawyer Busyness (First Lawyer) Lawyer Busyness (Most Exp. Lawyer) Agriculture, Food, Textiles -0.30750.1806-0.2051-0.2180-0.0624-0.0509 Gas -0.25780.1714-0.1433-0.1480-0.0794-0.0736 Biotechnology -0.42810.0123-0.2415-0.2596-0.0078-0.0051 Miscellaneous-Drug & Med -0.32310.1032-0.1384-0.13500.04140.0677 Electrical Lighting -0.3386-0.0173-0.1440-0.12430.04170.0682 Nuclear & X-rays -0.17580.0801-0.1694-0.1102-0.04190.0239 Semiconductor Devices -0.4290-0.0332-0.3129-0.2908-0.0932-0.0343 Motors, Engines & Parts -0.29580.2566-0.1337-0.13320.03570.0392 Optics -0.35770.1222-0.1546-0.15810.06020.0563 Amusement Devices -0.46130.2184-0.1881-0.18970.03960.0460 Apparel & Textile -0.34210.1688-0.1016-0.10380.06030.0631 Earth Working & Wells -0.29010.1212-0.1203-0.12270.02410.0216 Heating -0.36390.1147-0.1293-0.12100.04380.0613 Pipes & Joints -0.19850.1453-0.1572-0.13730.03730.0495

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96 Table 4-7. Impact of Representati on by Patent Attorney or Agent COEFFICIENT(1)(2)(3)(4) Represented-11.09***-12.55 [3.811][27.33] Represented Generality Index4.09919.9 [7.844][67.25] Claims0.381***0.388***0.5440.569 [0.0678][0.0679][0.484][0.494] Drawing Pages0.426**0.427**3.993.954 [0.196][0.196][3.622][3.632] Figures0.03750.0411-0.875-0.824 [0.109][0.109][2.239][2.244] Citations Made0.393***0.401***-0.522-0.54 [0.0680][0.0683][0.410][0.414] Citations Received-2.093***-2.104***-7.914***-7.903*** [0.268][0.268][2.927][2.943] Generality Index-8.058***-11.86-0.498-19.03 [2.252][7.630][17.43][66.93] Examiner Experience-2.476***-2.476***-4.355***-4.356*** [0.0232][0.0232][0.256][0.256] Examiner Experience, squared0.00103***0.00103***0.00314***0.00314*** [0.0000182][0.0000182][0.000253][0.000254] Examiner Busyness0.880***0.879***0.883***0.880*** [0.00911][0.00911][0.0938][0.0945] Assistant Examiner-13.89***-13.96***-18.85-18.88 [2.859][2.859][34.74][34.76] Japan-6.174-5.733-21.2-20.56 [3.882][3.888][33.61][33.67] Germany-2.63-2.20414.8115.44 [3.985][3.989][28.68][28.71] France7.2637.8561.3631.9 [7.058][7.065][32.37][32.57] Great Britain7.587.7184.7485.402 [5.569][5.570][26.05][26.08] Canada4.6483.874-22.24-23.89 [6.809][6.808][67.46][67.03] Other EPO11.48**11.92**0.2471.239 [4.796][4.799][25.67][25.61] Other Country-14.53***-14.31***-7.821-7.305 [3.629][3.631][32.81][32.78] Assignee 75th Percentile-4.327-4.2320.0529-0.0222 [3.998][3.998][19.47][19.69] Assignee 90th Percentile6.392**6.143**9.3518.863 [3.120][3.121][14.70][14.90] No Assignee-25.23-26.71.146-0.332 [44.15][44.17][36.20][36.23] Examiner indicatorsYYYY Year indicatorYYYY Assignee type indicatorYYYY Observations4419644196423423 Adjusted R-squared0.740.740.90.9 R-squared0.7480.7480.9220.922 Agriculture, Food, Textiles All Technology Subcategories

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97 Table 4-7. Continued COEFFICIENT(5)(6)(7)(8) Represented-24.93-19.21 [22.19][32.20] Represented Generality Index36.53-68.07 [28.80][75.32] Claims0.2680.2630.04140.0448 [0.399][0.398][0.269][0.270] Drawing Pages3.351*3.241*1.649**1.635** [1.865][1.863][0.674][0.674] Figures-0.534-0.447-1.990***-1.967*** [1.034][1.029][0.588][0.588] Citations Made-0.56-0.5450.2120.223 [0.374][0.375][0.212][0.212] Citations Received-5.766***-5.820***0.2040.226 [1.853][1.862][1.613][1.613] Generality Index-11.26-44.61-22.7543.5 [12.85][27.94][14.48][74.74] Examiner Experience-2.896***-2.886***-5.535***-5.533*** [0.327][0.331][0.114][0.114] Examiner Experience, squared0.00147***0.00146***0.00405***0.00404*** [0.000198][0.000199][0.000123][0.000123] Examiner Busyness0.808***0.814***2.653***2.656*** [0.0988][0.0998][0.119][0.119] Assistant Examiner-183.2-186.9-46.47***-46.68*** [127.2][127.0][10.73][10.75] Japan-27.78-27.65-15.97-15.79 [26.31][26.25][26.07][26.10] Germany-16.78-16.82-5.597-4.772 [29.07][29.04][26.68][26.72] France-32.39-32.2620.5320.73 [24.89][24.85][50.15][50.20] Great Britain-10.24-11.7-24.88-24.71 [33.55][32.68][27.04][27.05] Canada1.111-1.05950.0948.9 [30.32][29.94][36.30][36.27] Other EPO-5.972-5.96827.5128.06 [20.23][20.09][28.53][28.53] Other Country1.6580.3227.7028.293 [20.29][20.46][25.26][25.28] Assignee 75th Percentile-17.96-18.9714.0313.98 [20.04][20.08][25.33][25.34] Assignee 90th Percentile-9.553-9.113-27.16-27.1 [14.73][14.73][17.25][17.25] No Assignee-4.35-9.29-47.24-50.73 [38.55][38.79][40.55][41.14] Examiner indicatorsYYYY Year indicatorYYYY Assignee type indicatorYYYY Observations75575528532853 Adjusted R-squared0.780.780.790.79 R-squared0.7970.7980.8040.804 Amusement DevicesApparel & Textile

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98 Table 4-7. Continued COEFFICIENT(9)(10)(11)(12) Represented-11.92-9.544 [13.54][15.49] Represented Generality Index-5.02622.16 [34.23][24.17] Claims0.01350.02350.403**0.406* [0.205][0.206][0.204][0.208] Drawing Pages-0.0843-0.09451.1431.147 [0.956][0.957][0.721][0.725] Figures0.1670.180.4280.429 [0.393][0.392][0.362][0.363] Citations Made0.2810.2860.399**0.402** [0.202][0.202][0.190][0.192] Citations Received-3.167***-3.158***-4.454***-4.448*** [1.114][1.117][0.789][0.790] Generality Index0.8965.637.965-12.81 [10.97][33.05][6.882][23.35] Examiner Experience-2.478***-2.477***-2.476***-2.476*** [0.0823][0.0825][0.0519][0.0519] Examiner Experience, squared0.000889***0.000888***0.000981***0.000982*** [0.0000351][0.0000351][0.0000339][0.0000339] Examiner Busyness1.448***1.446***0.844***0.844*** [0.0598][0.0599][0.0260][0.0260] Assistant Examiner15.5515.37-9.504-9.371 [11.13][11.14][12.44][12.45] Japan-28.70**-28.14**-13.44-12.98 [14.29][14.33][12.13][12.07] Germany-15.14-15.03-7.402-6.771 [16.82][16.85][12.65][12.57] France-24.76-24.1513.5514.27 [20.20][20.26][31.96][31.92] Great Britain-35.21*-35.40*-9.127-8.573 [20.84][20.87][20.71][20.60] Canada-5.427-6.34312.1412.82 [14.04][14.19][24.79][24.84] Other EPO-6.485-6.266-6.156-5.635 [11.77][11.78][12.45][12.38] Other Country-11.18-11.39-30.00***-29.35*** [13.28][13.24][10.31][10.17] Assignee 75th Percentile-6.367-6.262-10.9-10.7 [12.74][12.76][12.96][12.96] Assignee 90th Percentile10.259.90412.6812.53 [8.474][8.501][9.050][9.057] No Assignee001.7980.538 [0][0][27.81][27.33] Examiner indicatorsYYYY Year indicatorYYYY Assignee type indicatorYYYY Observations1819181935683568 Adjusted R-squared0.80.80.720.72 R-squared0.8110.8110.7350.735 BiotechnologyEarth Working & Wells

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99 Table 4-7. Continued COEFFICIENT(13)(14)(15)(16) Represented-35.41**-6.39 [14.24][7.794] Represented Generality Index13.46-0.653 [31.69][14.34] Claims0.693***0.714***0.292**0.296** [0.222][0.223][0.146][0.147] Drawing Pages-0.246-0.239-0.0499-0.0449 [0.862][0.858][0.346][0.346] Figures0.02370.03560.2020.204 [0.562][0.561][0.178][0.178] Citations Made0.1140.1110.582***0.591*** [0.267][0.264][0.161][0.162] Citations Received-3.989***-3.992***-2.217***-2.227*** [1.226][1.219][0.316][0.316] Generality Index-11.78-24.2-4.173-3.496 [7.822][31.40][4.743][13.65] Examiner Experience-2.278***-2.275***-2.098***-2.098*** [0.112][0.112][0.0286][0.0286] Examiner Experience, squared0.00102***0.00102***0.000805***0.000805*** [0.0000879][0.0000878][0.0000178][0.0000178] Examiner Busyness0.737***0.737***0.786***0.786*** [0.0541][0.0542][0.0137][0.0137] Assistant Examiner17.03**17.05**-16.85***-16.95*** [7.120][7.086][5.516][5.515] Japan32.34*34.36*-7.591-7.472 [17.84][17.90][10.10][10.11] Germany11.1510.18-18.36-18.11 [16.04][15.93][13.87][13.88] France21.7323.429.189.412 [22.54][22.60][16.55][16.55] Great Britain10.6811.432.2462.315 [20.12][19.98][23.18][23.21] Canada20.9122.3824.2824.4 [40.17][39.95][37.78][37.80] Other EPO-6.325-5.32125.75*25.93* [25.17][25.19][14.64][14.64] Other Country17.6217.5-31.03***-31.04*** [23.96][23.66][9.485][9.489] Assignee 75th Percentile2.9733.7958.2988.465 [16.50][16.48][18.78][18.80] Assignee 90th Percentile-17.86-19.3214.2614.2 [13.29][13.34][13.77][13.79] No Assignee-67.74-71.8713.7712.12 [50.89][50.73][42.94][43.01] Examiner indicatorsYYYY Year indicatorYYYY Assignee type indicatorYYYY Observations215521551101211012 Adjusted R-squared0.720.720.70.7 R-squared0.7360.7370.7120.712 Electrical LightingGas

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100 Table 4-7. Continued COEFFICIENT(17)(18)(19)(20) Represented2.76-15.23 [6.338][9.961] Represented Generality Index-22.38-6.837 [15.52][21.80] Claims0.1770.1780.510***0.519*** [0.115][0.115][0.180][0.180] Drawing Pages-0.529-0.515-0.225-0.219 [0.422][0.414][0.373][0.373] Figures0.557**0.547**0.1510.152 [0.273][0.264][0.206][0.206] Citations Made0.482***0.483***0.562**0.585** [0.108][0.108][0.251][0.254] Citations Received-3.157***-3.153***-2.253***-2.289*** [0.454][0.454][0.733][0.731] Generality Index-3.14218.07-4.1412.305 [3.588][15.29][6.264][20.69] Examiner Experience-2.564***-2.564***-2.508***-2.507*** [0.0459][0.0459][0.0686][0.0686] Examiner Experience, squared0.00132***0.00132***0.00123***0.00123*** [0.0000372][0.0000373][0.0000614][0.0000614] Examiner Busyness1.080***1.081***1.052***1.051*** [0.0167][0.0168][0.0219][0.0219] Assistant Examiner2.3492.3519.8289.485 [3.460][3.459][7.192][7.213] Japan-6.762-6.449-13.63-13.1 [7.088][7.115][11.91][11.89] Germany-3.159-2.823-19.52-19.7 [6.627][6.652][14.63][14.55] France8.0068.258-27.35-25.96 [10.10][10.13][24.47][24.51] Great Britain-0.3320.059818.3118.52 [10.19][10.18][14.32][14.25] Canada3.6912.921-30.64*-30.25* [8.281][8.416][16.85][16.88] Other EPO1.3671.584-24.52**-23.96** [7.137][7.159][12.21][12.20] Other Country-7.943-7.964-28.96**-28.47** [6.871][6.870][14.13][14.12] Assignee 75th Percentile-8.277-8.225-19.73*-19.06 [6.147][6.147][11.86][11.89] Assignee 90th Percentile7.829*7.702*16.26*15.49* [4.501][4.503][8.945][8.985] No Assignee00-174.2*-177.7* [0][0][105.9][105.8] Examiner indicatorsYYYY Year indicatorYYYY Assignee type indicatorYYYY Observations6519651950555055 Adjusted R-squared0.860.860.780.78 R-squared0.8640.8640.7920.792 Heating M iscellaneous-Drug & Me d

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101 Table 4-7. Continued COEFFICIENT(21)(22)(23)(24) Represented3.311-12.14 [7.332][14.91] Represented Generality Index-16.8-10.36 [20.40][46.01] Claims0.08830.07760.1440.167 [0.236][0.237][0.238][0.241] Drawing Pages-0.0397-0.0251-0.431-0.465 [0.727][0.731][0.835][0.840] Figures0.2740.2740.4750.489 [0.396][0.397][0.549][0.549] Citations Made0.2670.2640.545**0.560** [0.214][0.215][0.263][0.264] Citations Received-0.0804-0.0811-3.986**-3.997** [2.023][2.026][1.603][1.597] Generality Index-14.380.704-16.48*-6.318 [9.797][19.97][9.496][45.71] Examiner Experience-3.752***-3.752***-2.995***-2.997*** [0.0931][0.0934][0.218][0.218] Examiner Experience, squared0.00339***0.00339***0.00147***0.00147*** [0.000141][0.000141][0.000211][0.000211] Examiner Busyness1.885***1.886***0.998***0.999*** [0.0741][0.0741][0.0467][0.0466] Assistant Examiner-33.14***-33.09***-40.67***-40.41*** [9.136][9.144][9.095][9.116] Japan1.4441.5795.5566.137 [13.36][13.39][11.06][11.13] Germany-14.95-14.9720.03*20.77** [16.96][17.01][10.45][10.47] France82.11***82.00***5.135.455 [31.81][31.78][11.42][11.44] Great Britain-17.9-17.641.8171.452 [24.69][24.72][14.76][14.97] Canada-6.124-5.81113.1511.66 [13.26][13.27][16.54][16.48] Other EPO2.8242.7413.2214.24 [13.60][13.62][9.883][9.943] Other Country-17.52*-17.64*-3.098-2.326 [9.015][9.025][8.468][8.514] Assignee 75th Percentile-3.575-3.365-13.02-12.93 [11.58][11.60][9.278][9.276] Assignee 90th Percentile-1.583-1.80615.72**15.42** [11.28][11.33][7.860][7.860] No Assignee9.5229.291-30.18-32.7 [21.38][21.47][31.79][31.94] Examiner indicatorsYYYY Year indicatorYYYY Assignee type indicatorYYYY Observations2121212128222822 Adjusted R-squared0.830.830.760.76 R-squared0.840.840.7690.77 Motors, Engines & PartsNuclear & X-rays

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102 Table 4-7. Continued COEFFICIENT(25)(26)(27)(28)(29)(30) Represented5.702-25.1314.52 [11.02][17.59][25.83] Represented Generality Index-3.61361.818.339 [24.79][46.60][52.13] Claims0.560***0.553***1.032***1.090***1.422***1.407*** [0.207][0.208][0.375][0.378][0.420][0.420] Drawing Pages-0.869-0.8772.2462.231-1.073-1.036 [0.809][0.810][1.501][1.499][1.569][1.568] Figures0.5130.513-0.149-0.146-0.397-0.429 [0.433][0.433][0.925][0.924][0.796][0.794] Citations Made0.05450.05291.331***1.337***0.04810.0437 [0.132][0.132][0.352][0.352][0.244][0.244] Citations Received-3.504***-3.481***-4.659**-4.673**-2.904-2.926 [1.058][1.054][1.821][1.824][2.608][2.611] Generality Index7.13810.58-2.293-60.56-16.21-24.1 [9.115][23.51][13.18][46.16][11.28][51.51] Examiner Experience-3.187***-3.188***-3.226***-3.224***-2.310***-2.311*** [0.103][0.104][0.110][0.109][0.0839][0.0840] Examiner Experience, squared0.00234***0.00235***0.00184***0.00184***0.00103***0.00103*** [0.000192][0.000194][0.0000766][0.0000763][0.0000595][0.0000597] Examiner Busyness1.299***1.299***1.193***1.190***1.029***1.028*** [0.0503][0.0503][0.0539][0.0538][0.0415][0.0415] Assistant Examiner5.7195.9351.8493.51614.0614.45 [11.05][11.09][17.48][17.66][16.61][16.71] Japan7.3887.44141.98**42.21**1.7652.223 [17.78][17.80][19.76][19.78][19.28][19.38] Germany-16.41-16.1828.80*30.54*-3.396-3.255 [12.83][12.85][16.99][17.30][17.83][17.83] France3.2243.05272.33***74.18***11.8511.89 [19.17][19.20][22.39][22.65][22.53][22.51] Great Britain13.9313.7729.8330.5811.0911.5 [10.94][10.95][26.12][25.56][20.84][20.96] Canada-7.048-6.9327.82*29.98**16.617.2 [11.58][11.56][14.83][14.86][25.07][25.31] Other EPO21.5521.7546.38**48.30**47.83**49.38** [13.81][13.78][22.02][22.29][24.34][24.69] Other Country12.2412.180.5141.16739.03*39.03* [11.50][11.50][15.13][15.30][21.63][21.64] Assignee 75th Percentile-2.382-2.366-3.005-3.219-18.65-18.84 [12.46][12.45][14.31][14.31][15.97][16.00] Assignee 90th Percentile0.3230.4464.074.71820.98*21.16* [10.12][10.09][12.75][12.79][12.16][12.18] No Assignee-57.56-56.890000 [42.16][42.14][0][0][0][0] Examiner indicatorsYYYYYY Year indicatorYYYYYY Assignee type indicatorYYYYYY Observations190019001802180213921392 Adjusted R-squared0.760.760.770.770.740.74 R-squared0.7690.7690.7860.7860.760.76 OpticsPipes & JointsSemiconductor Devices Robust standard errors in brackets p<0. 10, ** p<0.05, *** p<0.01. All estimates are OLS. Represented is an indica tor variable that takes a value of 1 if a lawyer or law firm was listed on the patent. The interaction term with Represented and the generality index is meant to capture whether or not repres entation lead to broader patent scope.

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103 Table 4-8. Estimating Grant Lag, Without Lawyer (Patents Where First Lawyer is Known) Agriculture, Food, TextilesGasBiotechnology MiscellaneousDrug & Med COEFFICIENT (1)(2)(3)(4) Claims0.421-0.2630.2170.0651 [1.535][0.369][0.538][0.471] Drawing Pages21.90**3.3551.783-1.336 [10.60][3.054][1.285][2.295] Figures-11.54*-1.833-3.017***1.497 [6.067][2.037][1.058][0.995] Citations Made-1.889*0.7790.1170.443 [1.128][0.638][0.309][0.344] Citations Received-5.552-7.624***3.962-3.011 [6.614][2.376][3.216][2.236] Generality Index-39.2828.06*-45.22-2.753 [54.86][15.39][30.63][19.38] Examiner Experience-4.284***-2.539***-5.761***-2.566*** [0.514][0.259][0.235][0.158] Examiner Experience, squared0.00302***0.00134***0.00406***0.000874*** [0.000539][0.000171][0.000249][0.0000573] Examiner Busyness0.921***0.817***2.430***1.343*** [0.255][0.129][0.245][0.127] Assistant Examiner-206.1***56.08-63.26***30.71* [77.22][51.30][23.76][18.25] Japan-267.3-1.511-44.83-59.86 [172.7][43.45][62.03][37.88] Germany-118-68.5-27.49-10.15 [123.3][59.33][59.58][23.86] France-7.653-68.65650.4-67.44 [62.54][45.94][518.6][95.58] Great Britain-176.3-86.82-0.322-32.83 [146.5][55.49][47.85][47.47] Canada-668.9***-48.568246.74* [52.69][40.67][89.97][28.14] Other EPO-219.846.13-16.8315.8 [157.4][55.39][43.43][23.16] Other Country-109.3-60.53**-30.04-29.18 [112.9][30.52][56.44][27.27] Assignee 75th Percentile-8.14219.1160.07-16.1 [45.91][25.73][68.86][18.42] Assignee 90th Percentile-21.63-9.63-115.6**13.73 [41.02][24.36][50.75][15.56] No Assignee000-55.75 [0][0][0][64.63] Examiner binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations133260882546 Adjusted R-squared0.880.890.760.81 R-squared0.9460.910.8020.835

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104 Table 4-8. Continued Electrical Lighting Nuclear & Xrays Semiconductor Devices Motors, Engines & Parts COEFFICIENT (5)(6)(7)(8) Claims0.6291.272***0.02680.235 [0.393][0.471][0.281][0.248] Drawing Pages-0.159-1.662-0.174-0.609 [1.564][2.066][0.892][1.018] Figures0.2520.812-0.171.226** [0.480][1.212][0.494][0.530] Citations Made0.1310.230.3920.399* [0.236][0.386][0.241][0.231] Citations Received-4.031***-3.053-2.557***-2.773*** [1.261][2.380][0.516][0.908] Generality Index1.708-10.59-14.475.529 [12.53][13.57][9.101][7.303] Examiner Experience-2.505***-2.380***-2.111***-2.514*** [0.118][0.356][0.0536][0.112] Examiner Experience, squared0.000996***0.00117***0.000800***0.00126*** [0.0000809][0.000304][0.0000293][0.0000839] Examiner Busyness0.793***0.910***0.782***1.088*** [0.0490][0.168][0.0253][0.0377] Assistant Examiner-45.7510.78-33.66***0.163 [28.10][13.02][10.91][6.913] Japan-23.9642.86-1.433-10.5 [27.64][31.59][20.42][13.66] Germany-3.9155.746-35.38*-2.846 [26.26][23.99][19.33][10.74] France93.7486.29***13.930.778 [91.63][30.38][22.84][20.40] Great Britain-29.94-20.06-41.4714.51 [36.35][28.85][42.61][18.66] Canada-3.552-31.386.6524.179 [27.17][33.33][41.74][16.10] Other EPO-14.8611.9832.67*3.667 [17.65][31.98][19.61][12.07] Other Country-30.6829.61-40.82***-24.77 [21.65][31.11][13.09][16.00] Assignee 75th Percentile-25.47-11.3528.83-10.84 [21.49][25.60][38.80][11.96] Assignee 90th Percentile33.42**-5.7795.64611.94 [13.91][20.33][28.86][10.09] No Assignee0000 [0][0][0][0] Examiner binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations118557035241758 Adjusted R-squared0.720.660.70.85 R-squared0.740.7050.720.867

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105 Table 4-8. Continued Optics Amusement Devices Apparel & Textile Earth Working & WellsHeating Pipes & Joints COEFFICIENT (9)(10)(11)(12)(13)(14) Claims0.5290.4890.4230.2981.510**2.128** [0.345][0.345][0.481][0.329][0.680][0.840] Drawing Pages-0.346-1.493-4.173*-2.1611.515-0.505 [0.868][1.159][2.204][1.474][3.724][3.907] Figures0.4270.6781.2330.8790.01750.651 [0.538][0.527][1.170][0.848][2.049][1.543] Citations Made0.3480.949**0.672-0.08181.391-0.347 [0.499][0.480][0.605][0.210][0.865][0.917] Citations Received-7.027***-2.325-4.026-3.069*0.7784.97 [2.450][1.572][2.939][1.713][4.009][6.160] Generality Index-4.547-22.89-12.020.299-25.89-51.33** [14.30][15.89][18.97][12.83][26.17][25.84] Examiner Experience-2.459***-3.831***-2.947***-3.691***-3.041***-2.584*** [0.151][0.182][0.286][0.177][0.196][0.191] Examiner Experience, squared0.00117***0.00352***0.00134***0.00335***0.00166***0.00131*** [0.000131][0.000266][0.000252][0.000310][0.000128][0.000141] Examiner Busyness1.065***1.943***1.041***1.455***1.125***1.090*** [0.0502][0.161][0.0740][0.0766][0.108][0.0804] Assistant Examiner14.63-21.66-53.90***13.8144.5420.06 [14.35][13.32][17.42][19.44][28.65][35.17] Japan18.68-9.631-21.540.897-12.8936.17 [19.31][63.69][29.30][41.27][51.67][54.50] Germany-13.49-26.5218.51-55.44***34.8919.89 [27.46][34.70][23.95][20.85][29.21][36.22] France7.93755.29**-66.5135.6459.96*13.79 [78.36][22.31][51.13][31.25][32.38][39.44] Great Britain4.619-3.23760.75*14.6970.6857.59 [25.23][74.27][33.23][17.36][44.22][57.79] Canada-52.02*-2.167-16.25-0.4822555.65 [27.91][22.45][36.12][21.72][24.62][39.98] Other EPO-32.6430.842.05150.246.28122.8** [20.80][38.88][19.68][36.92][38.66][62.16] Other Country-25.3425.39-8.5828.423-25.8262.59 [25.81][23.46][22.65][24.39][29.20][39.81] Assignee 75th Percentile-47.60*-22.853.761-7.758-16.74-10.91 [26.38][22.93][22.19][32.91][28.13][40.53] Assignee 90th Percentile33.45*-2.19312.59-4.635-10.4518.61 [18.48][20.44][18.15][15.02][23.08][32.35] No Assignee-158.3-40.06-18.26-56.18049.44 [115.6][29.67][42.94][48.54][0][71.76] Examiner binariesYYYYYY Year binariesYYYYYY Assignee TypeYYYYYY Observations1353638762724484415 Adjusted R-squared0.760.850.770.770.810.74 R-squared0.7830.860.7980.790.8330.78 Robust standard errors in brackets p<0.10, ** p<0.05, *** p<0.01. All estimates are OLS. Estimates are based on patents where the first lawyer listed was successfully matched to a registration number. This limitation facilitates comparisons to other regressions where the characteristics of the first lawyer listed on a patent are included.

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106 Table 4-9. Impact of Lawyer Expe rience, Using First Lawyer Listed Agriculture, Food, TextilesGasBiotechnology MiscellaneousDrug & Med COEFFICIENT(1)(2)(3)(4) Lawyer Experience-1.301-0.56-2.324***-1.546** [2.951][1.038][0.750][0.741] Lawyer Experience, squared0.0008870.00006850.00288***0.00103* [0.00131][0.000386][0.000906][0.000542] Lawyer Busyness4.478*1.3410.8260.467 [1.963][1.105][0.589][0.358] Lawyer Exp. Gen. Index-0.4010.013-0.475-0.214 [1.231][0.184][0.316][0.151] Claims-0.138-0.09190.0286-0.393 [5.534][1.903][0.755][1.245] Drawing Pages33.488.4572.5944.179 [76.57][11.00][2.115][5.774] Figures-19.36-0.992-4.406**-1.984 [37.12][7.902][1.717][2.294] Citations Made0.2560.8180.2420.415 [4.070][1.986][0.430][0.756] Citations Received3.87-8.2445.507-3.332 [22.13][6.794][4.680][3.962] Generality Index26.1137.12-1724.27 [165.4][59.97][51.08][35.39] Examiner Experience-2.576-2.604***-4.481***-2.010*** [2.511][0.888][0.412][0.240] Examiner Experience, squared0.002040.001610.00346***0.000767*** [0.00146][0.00112][0.000393][0.0000737] Examiner Busyness0.2060.741*2.442***1.523*** [2.805][0.419][0.391][0.266] Assistant Examiner-318.8136.3-53.8541.89 [236.7][172.3][35.18][37.82] Japan063.58-85.53-136 [0][180.1][103.8][128.8] Germany177348.22-30.97-49.93 [924.5][134.2][138.2][59.67] France-1139-25.07-12.22-330.4 [721.3][377.2][110.1][199.9] Great Britain1354-132.4-71.8442.72 [1203][95.50][96.20][80.77] Canada-343.747.2397.75132.4 [1785][302.7][123.2][94.79] Other EPO114.3140.5-34.44-1.691 [635.8][312.7][76.81][66.11] Other Country991.7-30.26-45.25-84.91 [1236][252.0][98.06][62.02] Assignee 75th Percentile137.1-6.467-44.3938.75 [987.8][280.9][113.0][46.31] Assignee 90th Percentile156.33.274-87.3623.13 [244.0][146.9][83.72][44.44] No Assignee0-893.9*0-1747 [0][481.6][0][1508] Examiner binariesYYYY Lawyer binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations133260882546 Adjusted R-squared0.920.850.830.87 R-squared0.9970.9630.920.96

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107 Table 4-9. Continued Electrical Lighting Nuclear & Xrays Semiconductor Devices Motors, Engines & Parts COEFFICIENT(5)(6)(7)(8) Lawyer Experience-0.319-0.468**-1.038***-0.0709 [0.274][0.219][0.0888][0.159] Lawyer Experience, squared0.000130.000154*0.000347***0.0000723 [0.000104][0.0000860][0.0000311][0.0000494] Lawyer Busyness0.361**0.477***0.377***0.616*** [0.157][0.131][0.0336][0.0954] Lawyer Exp. Gen. Index0.0133-0.151*0.0228-0.0125 [0.0526][0.0901][0.0152][0.0250] Claims0.1132.054**0.243-0.00721 [0.664][1.027][0.296][0.385] Drawing Pages1.202-4.3950.9-1.823 [2.661][4.947][1.082][1.688] Figures-0.08733.739-0.551.942* [0.676][3.120][0.531][1.002] Citations Made0.2923.610.2420.339 [0.390][2.248][0.272][0.484] Citations Received-1.645-8.370**-1.543***-2.913** [2.212][3.360][0.494][1.174] Generality Index-3.53933.22-17.039.342 [23.90][34.51][12.98][12.71] Examiner Experience-2.258***-1.654***-1.460***-2.439*** [0.205][0.249][0.0538][0.266] Examiner Experience, squared0.00094 2***0.000589***0.00053 3***0.00125*** [0.000127][0.000200][0.0000271][0.000162] Examiner Busyness0.644***0.565***0.600***1.016*** [0.0794][0.145][0.0253][0.0671] Assistant Examiner-73.74-7.606-34.67***7.176 [47.48][25.79][10.58][10.98] Japan-141.6**-117.7-36.48-22.51 [60.99][103.0][29.81][22.63] Germany-11.46-52.84-65.72*1.328 [45.05][55.85][39.69][17.15] France92.891438*-26.3523.96 [167.5][736.6][40.16][28.33] Great Britain-71.38-113.8-140.4-18.98 [111.4][73.08][92.94][20.92] Canada-34.521416*33.2-22.27 [99.56][720.4][57.32][29.19] Other EPO-87.50*-69.57-1.83311.82 [52.55][70.38][37.52][22.17] Other Country-25.6399.68-13.11-35.7 [47.67][268.3][21.25][32.17] Assignee 75th Percentile-11.0241.25107.5**35.22 [61.93][72.96][44.94][27.39] Assignee 90th Percentile19.46-17.78-10.67-10.66 [57.18][82.80][35.23][20.73] No Assignee0000 [0][0][0][0] Examiner binariesYYYY Lawyer binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations118557035241758 Adjusted R-squared0.710.860.790.86 R-squared0.8470.9550.8330.918

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108 Table 4-9. Continued Optics Amusement Devices Apparel & Textile Earth Working & WellsHeating Pipes & Joints COEFFICIENT(9)(10)(11)(12)(13)(14) Lawyer Experience-0.861**-0.696-0.0834-0.337-0.7830.498 [0.400][0.534][0.387][0.497][0.805][0.970] Lawyer Experience, squared0.000438***0.000640.0001460.00108***0.000293-0.0000537 [0.000159][0.000460][0.000183][0.000412][0.000285][0.000491] Lawyer Busyness0.542***1.355***0.468***2.583***0.4210.698 [0.193][0.439][0.167][0.572][0.469][0.812] Lawyer Exp. Gen. Index-0.01130.0590.04630.2970.131-0.097 [0.0421][0.192][0.0763][0.260][0.276][0.275] Claims0.776*0.0224-0.3980.756*-0.4285.182 [0.441][0.668][0.768][0.432][2.683][4.013] Drawing Pages-0.9444.05-1.837-1.3915.88111.93 [1.236][2.556][3.346][1.790][10.00][18.36] Figures0.868-0.991.0760.4051.076-1.862 [0.625][1.066][1.673][0.960][5.425][7.073] Citations Made0.9410.992.042**-0.0584-0.01510.428 [0.650][1.303][0.967][0.247][2.295][2.965] Citations Received-6.140**-1.376-5.569-0.6734.57517.16 [2.668][2.674][6.399][2.371][11.60][15.53] Generality Index13.37-54.4618.87-15.19-38.7-52.82 [16.56][39.15][45.18][26.40][72.75][93.81] Examiner Experience-2.077***-3.129***-2.898***-3.052***-2.425***-3.353*** [0.223][0.303][0.316][0.248][0.473][0.633] Examiner Experience, squared0.000939***0.00278***0.00153***0.00281***0.00133***0.00157*** [0.000161][0.000485][0.000215][0.000395][0.000327][0.000361] Examiner Busyness0.882***1.649***0.890***1.475***1.050***1.018*** [0.0678][0.332][0.125][0.142][0.282][0.256] Assistant Examiner-6.144-45.4-21.7634.259.964-43.66 [28.38][43.85][33.30][24.20][110.4][125.3] Japan5564.58-67.57128.1-100.7-47.48 [57.27][109.8][91.84][82.06][178.8][184.1] Germany-30.97261.0**-17.5584.92*-70.93157.8 [49.59][131.6][66.90][49.65][160.5][113.3] France44.160-45.2513.9846.422.69 [127.7][0][70.05][39.49][87.70][280.1] Great Britain47.37-28.1124.914.682101.274.24 [43.92][108.5][80.97][29.54][265.5][126.7] Canada-43.3490.7431.66-3.31658.54105.7 [72.66][89.60][91.91][46.32][104.5][170.8] Other EPO-42.51361.9*-9.654-27.71-109.6241.6 [61.98][209.2][58.61][109.8][189.1][212.7] Other Country65.4796.96-82.1540.95-204-40.68 [90.22][73.46][57.30][46.06][161.5][146.0] Assignee 75th Percentile-68.2417.6436.3157.55-26.71-74.26 [65.66][63.65][53.61][49.54][106.4][135.4] Assignee 90th Percentile11.04-22.09-28.23-21.685.95825.55 [45.17][66.93][41.99][43.26][103.2][90.99] No Assignee-836.5-368.0*-303.737.47080.37 [535.3][191.8][354.3][74.88][0][193.4] Examiner binariesYYYYYY Lawyer binariesYYYYYY Year binariesYYYYYY Assignee TypeYYYYYY Observations1353638762724484415 Adjusted R-squared0.820.90.90.860.830.73 R-squared0.8970.970.9680.9330.9610.946 Robust standard errors in brackets p<0. 10, ** p<0.05, *** p<0.01. All estimates are OLS.

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109 Table 4-10. Estimating Grant Lag, Without Lawyer (Patents Where Most Experienced Lawyer is Known) Agriculture, Food, TextilesGasBiotechnology MiscellaneousDrug & Med COEFFICIENT(1)(2)(3)(4) Claims0.421-0.2960.2820.101 [1.541][0.374][0.536][0.468] Drawing Pages21.90**3.5152.376*-1.261 [10.64][3.002][1.215][2.281] Figures-11.54*-2.036-3.048***1.442 [6.090][2.025][1.102][0.987] Citations Made-1.8890.8620.1140.441 [1.132][0.625][0.303][0.342] Citations Received-5.552-7.896***3.956-2.982 [6.639][2.359][3.201][2.233] Generality Index-39.2828.52*-46.58-4.153 [55.06][15.16][30.22][19.29] Examiner Experience-4.284***-2.516***-5.780***-2.563*** [0.516][0.253][0.229][0.157] Examiner Experience, squared0.00302***0.00133***0.00412***0.000874*** [0.000541][0.000169][0.000240][0.0000571] Examiner Busyness0.921***0.815***2.453***1.345*** [0.256][0.127][0.234][0.127] Assistant Examiner-206.1***64.57-62.23***30.61* [77.51][42.37][23.14][18.13] Japan-267.30.0827-42.74-59.99 [173.3][43.29][61.69][37.81] Germany-118-69.95-26.92-10.62 [123.8][59.72][58.79][23.75] France-7.653-67.99649.2-67.35 [62.78][47.53][519.0][95.61] Great Britain-176.3-78.570.446-33.94 [147.0][55.63][47.41][46.87] Canada-668.9***-50.1384.1446.53* [52.89][40.41][90.14][28.13] Other EPO-219.846.42-16.6816.15 [158.0][55.53][42.92][22.88] Other Country-109.3-61.49**-27.89-29.05 [113.3][30.67][56.05][27.25] Assignee 75th Percentile-8.14219.8458.34-16.08 [46.08][25.27][68.37][18.34] Assignee 90th Percentile-21.63-11.08-115.3**13.89 [41.17][23.96][50.52][15.34] No Assignee0000 [0][0][0][0] Examiner binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations134267906550 Adjusted R-squared0.880.890.760.81 R-squared0.9460.910.8050.836

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110 Table 4-10. Continued Electrical Lighting Nuclear & X rays Semiconductor Devices Motors, Engines & PartsOptics COEFFICIENT(5)(6)(7)(8)(9) Claims0.647*1.283***0.0460.2210.543 [0.376][0.467][0.274][0.248][0.344] Drawing Pages-0.00786-1.617-0.0352-0.661-0.404 [1.558][2.083][0.869][1.018][0.864] Figures0.2110.802-0.1891.252**0.467 [0.480][1.231][0.486][0.532][0.533] Citations Made0.1370.2160.394*0.396*0.345 [0.235][0.383][0.238][0.229][0.494] Citations Received-3.915***-2.825-2.538***-2.763***-6.951*** [1.250][2.311][0.513][0.908][2.385] Generality Index2-10.13-14.14.712-4.175 [12.48][13.30][9.007][7.280][14.24] Examiner Experience-2.506***-2.394***-2.098***-2.516***-2.451*** [0.118][0.361][0.0528][0.112][0.151] Examiner Experience, squared0.000996***0.00118***0.000793***0.00126***0.00117*** [0.0000808][0.000308][0.0000288][0.0000837][0.000130] Examiner Busyness0.793***0.904***0.784***1.088***1.064*** [0.0489][0.169][0.0251][0.0377][0.0500] Assistant Examiner-45.7910.12-33.70***-0.074614.91 [28.08][12.80][10.78][6.905][14.36] Japan-23.5740.37-0.708-10.518.09 [27.58][30.43][20.33][13.67][19.04] Germany-3.2318.321-34.47*-2.908-12.94 [26.19][22.97][19.12][10.75][27.37] France95.2883.11***14.931.1439.091 [91.52][30.25][22.68][20.39][78.17] Great Britain-29.29-20.54-39.4814.424.604 [36.29][28.38][43.56][18.70][25.23] Canada-3.519-34.16-4.1793.963-51.48** [27.14][33.10][39.45][16.09][25.44] Other EPO-13.5811.9933.93*2.711-32.38 [17.54][31.66][19.59][11.98][20.67] Other Country-30.5629.7-40.85***-25.57-24.32 [21.61][28.78][13.03][15.93][25.44] Assignee 75th Percentile-24.63-10.7831.35-10.74-48.35* [21.40][25.54][38.51][11.85][26.11] Assignee 90th Percentile31.97**-5.5965.3911.9433.52* [13.77][20.33][29.07][10.03][18.46] No Assignee0-84.92000 [0][65.05][0][0][0] Examiner binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations1195582358117701365 Adjusted R-squared0.720.810.710.850.76 R-squared0.740.8360.7270.8670.782

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111 Table 4-10. Continued Amusement Devices Apparel & Textile Earth Working & WellsHeating Pipes & Joints COEFFICIENT(10)(11)(12)(13)(14) Claims0.4880.4450.3031.413**2.159*** [0.344][0.473][0.329][0.694][0.834] Drawing Pages-1.493-4.202*-2.1620.0308-0.328 [1.159][2.205][1.476][3.961][3.877] Figures0.6771.2540.8942.3290.57 [0.527][1.175][0.848][2.946][1.524] Citations Made0.950**0.655-0.08840.974-0.392 [0.480][0.607][0.209][0.950][0.903] Citations Received-2.33-4.045-3.018*-0.7254.775 [1.572][2.933][1.717][4.241][6.063] Generality Index-22.93-11.81-1.67-5.544-49.81** [15.89][18.97][12.76][31.60][24.99] Examiner Experience-3.831***-2.948***-3.699***-3.115***-2.589*** [0.182][0.286][0.176][0.211][0.189] Examiner Experience, squared0.00352***0.00134***0.00336***0.00172***0.00131*** [0.000266][0.000252][0.000309][0.000139][0.000140] Examiner Busyness1.943***1.042***1.458***1.150***1.094*** [0.161][0.0740][0.0762][0.111][0.0786] Assistant Examiner-21.7-53.14***14.2347.11*19.88 [13.32][17.38][19.45][26.73][34.99] Japan-9.675-22.89.483-30.238.93 [63.69][29.21][36.25][53.64][52.98] Germany-26.5617.57-53.97**39.7820.29 [34.70][23.91][20.90][30.14][35.96] France55.33**-66.6935.0365.99**12.45 [22.29][51.24][31.26][31.56][38.93] Great Britain-3.27348.8615.5167.1556.5 [74.26][30.57][17.43][45.07][57.76] Canada-2.3-16.59-3.59828.0354.77 [22.41][36.11][21.55][24.08][39.92] Other EPO30.711.24952.1647.58122.7** [38.89][19.61][37.08][41.89][61.91] Other Country25.37-9.2748.22-22.1862.47 [23.47][22.55][24.33][29.53][39.64] Assignee 75th Percentile-22.843.573-8.299-18.99-6.724 [22.92][22.14][32.81][27.68][39.83] Assignee 90th Percentile-2.18212.32-5.078-6.74216.48 [20.44][18.11][14.98][23.12][32.08] No Assignee-40.5521.83-33.45-113.8***0 [26.97][23.66][29.93][26.72][0] Examiner binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations639765726487421 Adjusted R-squared0.850.770.770.790.74 R-squared0.860.7980.7910.8230.781 Robust standard errors in brackets p<0.10, ** p<0.05, *** p<0.01. All estimates are OLS. Estimates are based on patents where the most experienced lawyer listed on a patent was successfully matched to a regist ration number. This limitation facilitates comparisons to other regressions where the ch aracteristics of the most experienced are included.

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112 Table 4-11. Impact of Lawyer Experi ence, Most Experienced Lawyer Listed Agriculture, Food, TextilesG asBiotechnology MiscellaneousDrug & Med COEFFICIENT(1)(2)(3)(4) Lawyer Experience-3.9270.04-1.960***-1.315** [4.561][0.897][0.569][0.540] Lawyer Experience, squared0.00186-0.0001970.00270***0.000957** [0.00192][0.000362][0.000734][0.000444] Lawyer Busyness1.5241.0061.099**0.512 [1.967][0.794][0.556][0.399] Lawyer Exp. Gen. Index0.0649-0.0209-0.635**-0.106 [1.195][0.170][0.294][0.164] Claims4.330.2310.0298-0.547 [6.983][1.579][0.802][1.127] Drawing Pages-17.495.6523.400**6.243 [94.61][8.429][1.725][5.108] Figures4.7660.58-3.819**-2.116 [44.25][6.242][1.542][2.234] Citations Made-3.9170.7-0.1490.229 [5.194][1.701][0.376][0.714] Citations Received-4.872-10.41*6.51-3.599 [32.22][6.210][4.219][3.332] Generality Index54.9442.4613.2623.86 [331.2][58.93][47.65][35.19] Examiner Experience-1.323-2.649***-4.431***-1.813*** [3.350][0.871][0.372][0.204] Examiner Experience, squared0.0008730.001760.00333***0.000699*** [0.00242][0.00113][0.000343][0.0000608] Examiner Busyness0.1970.812**2.348***1.442*** [3.161][0.404][0.320][0.243] Assistant Examiner-455.181.1-46.892.266 [1314][150.9][30.00][30.40] Japan057.15-108.7-183 [0][167.1][86.40][113.9] Germany968.629.37-124.4-60.82 [1509][144.9][128.4][53.34] France111.6-161-40.89-354.2 [171.7][371.8][98.63][219.4] Great Britain231-145.4*-65.2325.7 [2378][83.62][81.79][72.73] Canada-916.6-29.3714.29209.6** [1721][154.7][99.07][92.18] Other EPO178.28.94-98.866.881 [1009][307.1][70.29][65.69] Other Country1765-69.03-66.42-117.6 [3587][231.0][88.20][81.93] Assignee 75th Percentile1130-81.32-55.28-21.11 [1326][245.0][113.9][43.99] Assignee 90th Percentile-288.425.76-105.657.43 [853.2][144.7][77.52][45.82] No Assignee0000 [0][0][0][0] Examiner binariesYYYY Lawyer binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations134267906550 Adjusted R-squared0.80.850.850.9 R-squared0.990.960.9250.967

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113 Table 4-11. Continued Electrical Lighting Nuclear & X-rays Semiconductor Devices Motors, Engines & PartsOptics COEFFICIENT(5)(6)(7)(8)(9) Lawyer Experience-0.469*-0.527**-1.115***-0.336-0.843** [0.252][0.212][0.0644][0.216][0.345] Lawyer Experience, squared0.000183*0.000156**0.000357***0.0001130.000420*** [0.000101][0.0000758][0.0000225][0.0000687][0.000134] Lawyer Busyness0.288*0.246*0.374***0.487***0.529*** [0.148][0.126][0.0259][0.0644][0.157] Lawyer Exp. Gen. Index0.000136-0.03970.0148-0.0109-0.0206 [0.0503][0.0693][0.0120][0.0227][0.0343] Claims0.1792.441**0.353-0.1690.796* [0.636][0.997][0.252][0.371][0.422] Drawing Pages0.639-4.9650.297-2.292-1.298 [2.560][4.292][0.943][1.881][1.237] Figures0.0573.318-0.1961.7631.025* [0.631][2.701][0.488][1.091][0.616] Citations Made0.2283.2180.1570.4040.797 [0.399][2.226][0.227][0.401][0.627] Citations Received-1.419-8.683**-1.354***-2.507**-6.316** [2.138][3.582][0.467][1.076][2.674] Generality Index-7.5820.69-19.984.71820.54 [23.18][31.22][12.66][10.72][16.44] Examiner Experience-2.186***-1.568***-1.296***-2.284***-1.961*** [0.203][0.219][0.0492][0.112][0.190] Examiner Experience, squared0.000917***0.000552***0.000469***0.00117***0.000866*** [0.000126][0.000165][0.0000236][0.0000918][0.000138] Examiner Busyness0.654***0.478***0.564***1.015***0.883*** [0.0772][0.117][0.0241][0.0433][0.0665] Assistant Examiner-68.41-0.609-34.76***9.2684.383 [45.94][23.68][9.600][10.07][25.59] Japan-86.36-91.37-29.8-17.0456.34 [69.10][86.75][25.27][22.08][56.17] Germany4.385-39.89-30.284.364-30.32 [45.52][64.60][37.46][16.67][50.91] France130.1466.2***45.3420.0541.56 [168.5][78.89][32.52][30.69][121.3] Great Britain-65-5.558-99.7-20.5149.27 [113.8][104.3][86.20][15.56][39.02] Canada-39.45150.9*39.59-20.3-35.2 [96.55][87.83][58.88][31.24][53.40] Other EPO-90.57*-45.2519.178.798-43.96 [52.21][82.06][33.57][20.87][60.89] Other Country-19.0880.87-4.156-44.4980.81 [46.35][151.5][19.66][32.87][84.29] Assignee 75th Percentile-31.67-10.23108.8**22.08-26.5 [63.22][60.64][45.82][26.32][57.60] Assignee 90th Percentile26.8949.61-6.952-7.334-9.651 [55.47][64.26][34.30][20.13][39.45] No Assignee0-57.05000 [0][152.2][0][0][0] Examiner binariesYYYYY Lawyer binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations1195582358117701365 Adjusted R-squared0.720.910.820.870.86 R-squared0.8450.9660.8570.9230.915

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114 Table 4-11. Continued Amusement Devices Apparel & Textile Earth Working & WellsHeatingPipes & Joints COEFFICIENT(10)(11)(12)(13)(14) Lawyer Experience-0.374-0.207-0.52-1.2820.526 [0.566][0.405][0.522][0.869][0.850] Lawyer Experience, squared0.0003850.0001780.00119***0.000419-0.000237 [0.000470][0.000199][0.000417][0.000297][0.000451] Lawyer Busyness1.377***0.386***2.574***0.3110.519 [0.414][0.133][0.562][0.407][0.645] Lawyer Exp. Gen. Index0.0870.05540.3960.219-0.0267 [0.180][0.0764][0.278][0.280][0.262] Claims0.117-0.5430.812*-1.3754.332 [0.602][0.798][0.443][2.571][3.491] Drawing Pages3.859-2.643-0.7355.16811.44 [2.466][3.399][1.723][10.38][17.51] Figures-1.1771.4460.01542.787-2.114 [0.974][1.687][0.905][5.732][6.244] Citations Made0.9461.935**-0.02420.1840.494 [1.245][0.886][0.245][2.081][2.816] Citations Received-1.078-5.742-0.6492.77718.5 [2.721][6.114][2.307][10.42][14.79] Generality Index-53.4315.63-13.97-26.33-71.64 [38.45][44.51][27.75][68.59][80.56] Examiner Experience-3.062***-2.921***-2.917***-2.394***-3.077*** [0.282][0.310][0.260][0.473][0.549] Examiner Experience, squared0.00261***0.00154***0.00274***0.00135***0.00145*** [0.000461][0.000207][0.000409][0.000309][0.000320] Examiner Busyness1.560***0.895***1.473***1.022***1.119*** [0.316][0.125][0.142][0.256][0.260] Assistant Examiner-50.68-24.9122.8531.4-50.14 [40.94][31.59][22.26][102.6][105.8] Japan113.7-71.392.88-83.6-51.42 [112.6][88.51][85.45][152.1][173.3] Germany246.0*-19.8862.16108.1154.8 [135.1][65.92][53.68][227.3][113.0] France0-48.021.58483.184.167 [0][71.13][38.18][78.72][256.0] Great Britain-62.7717.92-10.0260.23102.4 [108.1][87.55][30.29][231.6][128.9] Canada128.552.4816.5340.4675 [141.5][84.86][43.82][88.84][134.3] Other EPO-55.13-12.578742***116.5279.9 [179.1][58.57][2082][215.7][199.1] Other Country128.4-75.9435.08-214.283.36 [85.51][56.24][43.03][134.0][135.1] Assignee 75th Percentile9.47717.0448.8-6.783-103 [63.24][51.69][47.60][95.06][121.4] Assignee 90th Percentile-29.58-18.02-22.35-0.58654.69 [66.06][41.70][44.46][93.90][79.12] No Assignee-12.54-65.66-37.18-252.30 [42.82][44.16][48.30][182.8][0] Examiner binariesYYYYY Lawyer binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations639765726487421 Adjusted R-squared0.910.890.860.820.74 R-squared0.9710.9670.9310.9560.941 Robust standard errors in brackets p<0.10, ** p<0.05, *** p<0.01. All estimates are OLS. The interaction term between lawyer e xperience and the generality index is meant to capture the effect lawyer experience has on broadening the breadth of patent protection.

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115 Table 4-12. Impact of Examiner Experien ce, Controlling for First Listed Lawyer Agriculture, Food, TextilesGasBiotechnology MiscellaneousDrug & Med COEFFICIENT(1)(2)(3)(4) Low Lawyer Exp. (First Lawyer)1152842.1***662.8***434.2*** [650.5][55.70][194.1][139.2] Low Examiner Experience00-77.870 [0][0][339.9][0] Examiner Busyness1.9291.261***1.902***1.309*** [1.512][0.359][0.412][0.422] Assistant Examiner-790.2101.4-33.93-47.42 [539.9][181.1][52.15][51.47] Low Lawyer Exp. (First) Examiner Exp.00-27.610 [0][0][192.8][0] Low Examiner Exp Generality Index00483.5*0 [0][0][246.3][0] Claims2.4470.8870.522-1.259 [7.370][1.610][1.003][1.868] Drawing Pages48.66-1.2716.914**11.84 [58.81][10.65][2.978][10.18] Figures-10.28-3.469-4.427*-2.581 [31.46][7.091][2.388][4.768] Citations Made-1.142-1.924-0.4240.176 [5.630][1.765][0.916][1.343] Citations Received13.13-8.6020.592-10.20* [40.98][6.425][5.831][5.348] Generality Index-213.867.46-51.5458.89 [384.2][61.75][72.83][55.19] Japan0-2.341-121.9193.1 [0][97.30][208.0][159.2] Germany-1247**-72.62-143.79.25 [569.4][228.0][178.6][109.3] France282.7296.4-27.18-338.3 [988.6][374.4][140.9][259.5] Great Britain-1575**-264.7***40.18327.7*** [525.4][71.41][131.9][123.1] Canada-590.8-166.141.59707.5*** [1574][203.1][141.9][172.3] Other EPO-987.3**367.9-6.948171.7* [363.5][370.1][125.5][96.07] Other Country2661-413.8212.9-108.4 [1607][284.7][184.8][93.55] Assignee 75th Percentile12888.794-114.65.844 [852.6][205.2][144.5][88.87] Assignee 90th Percentile-475.9-163.8-56.06178.4** [393.8][158.4][102.2][82.74] No Assignee0001881*** [0][0][0][318.0] Examiner binariesYYYY Lawyer binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations133260882546 Adjusted R-squared0.690.780.660.64 R-squared0.9720.9430.8340.887

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116 Table 4-12. Continued Electrical Lighting Nuclear & X-rays Semiconductor Devices Motors, Engines & PartsOptics COEFFICIENT(5)(6)(7)(8)(9) Low Lawyer Exp. (First Lawyer)609.0***2011*517.3***260.3**-201.8 [114.8][1178][192.0][115.9][202.4] Low Examiner Experience-9.844819.6***-41.2901044*** [271.6][230.2][123.6][0][158.9] Examiner Busyness0.470***1.068***0.420***1.278***0.840*** [0.110][0.208][0.0400][0.0936][0.0884] Assistant Examiner-44.118.261-30.60*-5.83118.38 [47.75][37.14][17.45][19.33][38.69] Low Lawyer Exp. (First) Examiner Exp.0-189080.630730.0*** [0][1188][193.5][0][233.3] Low Examiner Exp Generality Index0-296.3-1.2010-28.15 [0][427.7][150.3][0][155.7] Claims0.3443.992**0.8740.5851.617** [1.002][1.954][0.545][0.729][0.806] Drawing Pages0.197-6.218-1.422-1.832-0.929 [3.319][5.481][1.790][3.161][2.379] Figures0.05963.9861.1361.430.987 [1.013][3.441][0.762][1.861][1.276] Citations Made2.357***6.988*1.084**1.445*0.183 [0.678][3.558][0.458][0.770][1.089] Citations Received1.127-3.36-2.669***-1.086-2.957 [3.172][5.467][0.835][2.077][4.545] Generality Index6.89621.311.630.44*51.98** [28.37][42.53][15.21][16.68][24.88] Japan-53.62-24.13-10.08-27.8873.67 [101.7][122.6][61.11][44.16][80.63] Germany33.37-84.28-14.88-1.078-3.639 [62.68][70.83][58.76][34.50][66.58] France329.2*2181*-212.2***18.34215 [177.5][1115][59.01][63.37][148.5] Great Britain-6.433-43.71-167.4-20.1135.71 [116.2][130.7][112.4][45.91][73.69] Canada20.481952*84.63-88.68-42.08 [154.5][1125][79.48][71.07][134.5] Other EPO-73.94-156.6*12.5437.6620.04 [63.99][87.95][53.00][43.92][94.68] Other Country-46.67261.2-75.94**-66.47-38.96 [118.6][351.5][38.19][65.01][144.6] Assignee 75th Percentile-27.41-4.015207.6***-5.841-174.2 [111.6][144.4][68.43][46.48][127.9] Assignee 90th Percentile42.5912.25-94.78*-1783.31 [81.27][174.7][49.78][35.05][72.95] No Assignee000018.35 [0][0][0][0][309.9] Examiner binariesYYYYY Lawyer binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations1185570352417581353 Adjusted R-squared0.420.630.430.540.56 R-squared0.690.8820.5530.7210.742

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117 Table 4-12. Continued Amusement Devices Apparel & Textile Earth Working & WellsHeatingPipes & Joints COEFFICIENT(10)(11)(12)(13)(14) Low Lawyer Exp. (First Lawyer)1029***260.9***210.5***291.3416.3 [340.6][94.06][54.53][239.6][255.1] Low Examiner Experience-620.9***00540.90 [139.4][0][0][814.5][0] Examiner Busyness1.741***1.658***1.505***1.108**0.755** [0.268][0.223][0.222][0.473][0.328] Assistant Examiner-151.3***-116.1*-22.29-76.1810.4 [57.69][60.69][50.79][143.3][177.7] Low Lawyer Exp. (First) Examiner Exp.-871.8***0000 [320.4][0][0][0][0] Low Examiner Exp Generality Index00000 [0][0][0][0][0] Claims-0.2380.9223.789***-0.4891.811 [1.112][1.562][1.034][4.442][5.702] Drawing Pages5.905-3.985-1.66-3.5995.655 [4.269][6.804][4.015][16.61][28.97] Figures-1.4650.0749-0.1917.757-0.561 [1.483][3.150][2.072][9.116][10.46] Citations Made1.7042.3460.8111.5523.016 [2.514][2.296][0.516][3.390][4.587] Citations Received2.854-2.8290.3521.799.219 [6.128][9.580][4.092][16.69][25.36] Generality Index-101.3*47.5850.34-100.510.37 [56.87][50.27][37.47][90.81][117.1] Japan292.4*-8.559226.5*-46.66-216.8 [164.2][136.5][136.4][365.7][290.1] Germany626.2***-31.94-82.75198.4105.8 [218.4][118.6][120.1][347.1][199.7] France808.2**-88.9233.3457.36-354.8 [323.6][176.8][71.37][178.2][490.2] Great Britain13.3229.7154.14214.3194 [200.1][150.7][54.64][399.6][203.3] Canada14.18-5.944104.540.0690.74 [112.8][160.0][111.9][192.9][261.7] Other EPO913.4**-75.22-124.787.22455.8 [352.9][101.0][230.6][375.0][369.2] Other Country170.3-146.9*4.079268-151 [191.6][80.57][76.80][184.1][218.5] Assignee 75th Percentile96.5877.7486.66-135.311.57 [88.05][79.17][78.62][326.9][205.5] Assignee 90th Percentile-11028.08-3.569126.7153.5 [83.17][68.99][67.06][245.2][130.8] No Assignee-731.8**400.689.840389 [350.9][447.4][148.7][0][300.0] Examiner binariesYYYYY Lawyer binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations638762724484415 Adjusted R-squared0.670.690.460.640.29 R-squared0.8960.9010.7380.9160.851 Robust standard errors in brackets p<0.10, ** p<0.05, *** p<0.01. All estimates are OLS.

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118 Table 4-13. Impact of Examiner Experience, Controlling for Most Experienced Lawyer Agriculture, Food, TextilesGasBiotechnology MiscellaneousDrug & Med COEFFICIENT(1)(2)(3)(4) Low Lawyer Experience (Most Exp. Lawyer)-176.7835.8***666.4***278.9** [350.3][51.09][209.6][128.1] Low Examiner Experience-1240793.7*246.60 [726.2][461.9][191.4][0] Examiner Busyness2.0881.264***2.081***1.352*** [1.343][0.323][0.391][0.441] Assistant Examiner-632.3*-121.1-16.85-65.55 [314.9][122.3][48.00][48.78] Low Lawyer Exp. (Most) Low Examiner Exp.0025.330 [0][0][208.7][0] Examiner Exp. Generality Index00455.4*0 [0][0][260.8][0] Claims2.9620.2910.4020.104 [7.444][1.474][1.051][1.762] Drawing Pages45.26-1.2243.3869.049 [57.05][8.852][2.286][9.094] Figures-9.429-2.751-2.694-1.941 [30.63][5.489][2.091][4.463] Citations Made-1.052-1.476-0.689-0.0633 [5.128][1.634][0.744][1.323] Citations Received10.3-8.1252.36-9.72 [34.62][5.833][5.123][5.938] Generality Index-203.170.25-36.5962.31 [365.9][52.67][66.57][56.93] Japan010.8-137.984.48 [0][87.81][172.2][162.0] Germany-1186**-63.39-272.93.177 [502.8][215.1][178.3][101.4] France-5.989286.9-154.2-423.3* [164.3][339.2][122.4][255.5] Great Britain-1135**-249.8***131.6260.9** [470.2][59.24][113.8][125.3] Canada340.2-25.1827.02630.6*** [863.0][160.8][110.0][164.7] Other EPO-958.7**376.9-93.17131.4 [346.3][334.1][113.6][107.6] Other Country-772.4-197.3125.8-158.6 [624.8][151.7][154.3][112.9] Assignee 75th Percentile-357.960.14-188.9-3.761 [590.9][185.8][145.0][92.50] Assignee 90th Percentile25.44-162.4-47.39163.5* [346.4][145.4][97.41][94.79] No Assignee0000 [0][0][0][0] Examiner binariesYYYY Lawyer binariesYYYY Year binariesYYYY Assignee TypeYYYY Observations134267906550 Adjusted R-squared0.710.80.690.66 R-squared0.9710.9410.840.888

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119 Table 4-13. Continued Electrical Lighting Nuclear & X-rays Semiconductor Devices Motors, Engines & PartsOptics COEFFICIENT(5)(6)(7)(8)(9) Low Lawyer Experience (Most Exp. Lawyer)624.1***254.7182.3247.6**439.8* [126.7][340.7][122.9][122.8][263.5] Low Examiner Experience123.9239.9-1751***0-315.2 [179.3][171.3][217.6][0][302.4] Examiner Busyness0.512***0.981***0.430***1.345***0.854*** [0.103][0.186][0.0412][0.0934][0.0877] Assistant Examiner-46.02-6.188-47.37***-2.39124.71 [45.48][37.07][17.99][18.64][36.84] Low Lawyer Exp. (Most) Low Examiner Exp.0-152.7370.7***077.25 [0][363.3][133.4][0][260.3] Examiner Exp. Generality Index0-208.8-99.670-62.19 [0][327.9][144.2][0][164.4] Claims0.5573.131*1.004*0.4071.206 [0.952][1.717][0.526][0.763][0.794] Drawing Pages-0.79-9.567*-1.012-2.126-0.594 [3.288][5.424][1.750][3.298][2.322] Figures0.3755.731*1.368*0.8990.86 [1.010][3.302][0.747][2.014][1.239] Citations Made2.418***6.300*1.236***1.564**-0.099 [0.675][3.558][0.472][0.791][1.005] Citations Received1.756-1.96-3.049***-0.345-5.947 [3.140][5.008][0.876][1.874][4.550] Generality Index-0.10249.9911.5923.3379.27*** [26.89][43.19][15.53][15.10][24.41] Japan-79.9912.0815.55-33.2683.55 [94.19][112.9][73.01][44.02][90.34] Germany43.99-44.49-12.894.0020.842 [62.37][85.70][57.41][33.62][67.68] France387.9**707.7***-132.9**-1.188222.5 [175.4][87.87][52.13][56.61][147.5] Great Britain-24.5878.51-110.5-13.0279.77 [115.3][121.6][106.2][40.52][66.93] Canada30.11195.791.13-88.6262.73 [147.4][166.9][72.04][71.95][103.7] Other EPO-84.49-82.07-0.18937.0612.51 [61.74][107.4][51.32][41.05][95.21] Other Country-34.56113.3-78.46**-64.0511.55 [117.6][230.6][37.39][67.25][152.4] Assignee 75th Percentile-95.36 -44.67190.2***-9.501-80.77 [111.0][130.5][67.43][46.50][118.2] Assignee 90th Percentile1 12.652.76-75.52*-15.9656.22 [82.78][153.8][45.45][34.49][72.21] No Assignee0-32.35000 [0][202.2][0][0][0] Examiner binariesYYYYY Lawyer binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations1195582358117701365 Adjusted R-squared0.430.770.420.560.6 R-squared0.6830.9160.5350.7290.76

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120 Table 4-13. Continued Amusement Devices Apparel & Textile Earth Working & WellsHeating Pipes & Joints COEFFICIENT(10)(11)(12)(13)(14) Low Lawyer Experience (Most Exp. Lawyer)108.1297.9***153.9**406.5*408.1* [85.20][94.24][65.94][207.7][231.3] Low Examiner Experience-648.6***00829.90 [139.9][0][0][561.1][0] Examiner Busyness1.732***1.636***1.538***1.170**0.869*** [0.263][0.202][0.216][0.459][0.309] Assistant Examiner-110.5**-124.9**-49.56-60.36-5.706 [53.09][56.01][51.54][134.4][162.1] Low Lawyer Exp. (Most) Low Examiner Exp.00000 [0][0][0][0][0] Examiner Exp. Generality Index00000 [0][0][0][0][0] Claims-0.3740.8963.322***-6.0981.154 [1.088][1.505][0.953][5.074][5.059] Drawing Pages5.294-1.309-0.572.6888.168 [3.738][6.392][3.944][16.70][26.55] Figures-1.438-1.397-0.5076.488-4.099 [1.500][2.888][1.986][9.672][9.128] Citations Made1.1042.9160.5794.5682.319 [2.447][1.887][0.490][3.808][4.379] Citations Received3.752-4.7171.24715.1712.34 [5.859][9.335][4.170][14.67][22.94] Generality Index-104.9*47.442.83-69.58-32.01 [56.92][48.58][40.86][99.69][107.0] Japan320.3*-16.91170.5207.3-289.6 [179.8][125.1][135.8][207.8][277.4] Germany576.5***-42.36-83.86485.885.1 [187.7][108.7][122.7][352.4][185.1] France0-64.2313.13123.3-392.5 [0][161.9][56.88][179.8][428.5] Great Britain-12.93 21.7116.65258.5182.1 [158.9][145.4][51.88][315.3][191.3] Canada46.7827.32123.4-12811.83 [155.9][158.4][101.7][229.9][232.1] Other EPO220.7-70.62-57.08467.9375.6 [264.1][96.10][226.9][353.2][335.7] Other Country180.5-125.9*3.637124.7-142.9 [238.1][73.57][77.11][147.8][194.4] Assignee 75th Percentil e90.1990.5379.13-69.79.204 [81.79][82.79][73.18][266.1][193.1] Assignee 90th Percentile-95.97-7.19-25.189.45104.2 [75.46][72.16][69.67][206.7][128.3] No Assignee-87.14-22.3416.07-413.40 [86.95][82.69][73.81][324.7][0] Examiner binariesYYYYY Lawyer binariesYYYYY Year binariesYYYYY Assignee TypeYYYYY Observations639765726487421 Adjusted R-squared0.710.710.460.590.29 R-squared0.9080.9050.7360.8940.833 Robust standard errors in brackets p<0.10, ** p<0.05, *** p<0.01. All estimates are OLS.

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121Table 4-14. Predicted Impact of Lawyer Quality, First Lawyer Technology Subcategory Lawyer Experience Lawyer Experience, squared Lawyer Exp. Gen. Index Lawyer ExperienceGenerality Agriculture, Food, Textiles -1.300.0009-0.401050.00.00-1.2-60.6 Gas -0.560.00010.013046.00.00-0.6-25.5 Biotechnology -2.320.0029-0.475032.00.00-2.1-68.5 Miscellaneous-Drug & Med -1.550.0010-0.214039.00.00-1.5-57.2 Electrical Lighting -0.320.00010.013362.00.00-0.3-18.8 Nuclear & X-rays -0.470.0002-0.151050.00.00-0.5-22.6 Semiconductor Devices -1.040.00030.0228112.00.32-1.0-106.7 Motors, Engines & Parts -0.070.0001-0.012576.00.00-0.1-4.6 Optics -0.860.0004-0.011367.00.00-0.8-53.8 Amusement Devices -0.700.00060.059033.00.00-0.7-21.6 Apparel & Textile -0.080.00010.046349.50.00-0.1-3.4 Earth Working & Wells -0.340.00110.297037.00.00-0.3-9.5 Heating -0.780.00030.131046.50.00-0.8-35.1 Pipes & Joints 0.50-0.0001-0.097050.00.000.524.6 Estimated Total Impact (Days) CoefficientsSample Medians Estimated Marginal Impact (Days) Coefficients estimates are from Table 4-9. The marginal impact is calculated as the first deri vative of Equation 4-2 with resp ect to Lawyer Experience.

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122Table 4-15. Predicted Impact of Lawyer Quality, Most Experienced Lawyer Technology Subcategory Lawyer Experience Lawyer Experience, squared Lawyer Exp. Gen. Index Lawyer ExperienceGenerality Agriculture, Food, Textiles -3.930.00190.064950.00.00-3.7-187.1 Gas 0.04-0.0002-0.020946.00.000.01.0 Biotechnology -1.960.0027-0.635032.00.00-1.8-57.2 Miscellaneous-Drug & Med -1.320.0010-0.106039.00.00-1.2-48.4 Electrical Lighting -0.470.00020.000162.00.00-0.4-27.7 Nuclear & X-rays -0.530.0002-0.039750.00.00-0.5-25.6 Semiconductor Devices -1.120.00040.0148112.00.29-1.0-115.4 Motors, Engines & Parts -0.340.0001-0.010976.00.00-0.3-24.2 Optics -0.840.0004-0.020667.00.00-0.8-52.7 Amusement Devices -0.370.00040.087033.00.00-0.3-11.5 Apparel & Textile -0.210.00020.055449.50.00-0.2-9.4 Earth Working & Wells -0.520.00120.396037.00.00-0.4-16.0 Heating -1.280.00040.219046.50.00-1.2-57.8 Pipes & Joints 0.53-0.0002-0.026750.00.000.525.1 Estimated Total Impact (Days) CoefficientsSample Medians Estimated Marginal Impact (Days) Coefficients estimates are from Table 4-11. The marginal impact is calculated as the first deri vative of Equation 4-2 with res pect to Lawyer Experience.

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123 CHAPTER 5 CONCLUSION This body of research was motivated by two premises. First, that technological change is important to an economy. S econd, that intermediaries may facilitate technological change. The intersection betw een these two statements was studied here, both theoretically and empirically. As a w hole, this dissertati on makes two general contributions: Intermediaries may facilitate technologi cal change, but they will not replace the importance of creative ideas spurred by scientific inquiry and competition. The impact of intermediaries on technol ogical change is not uniform across all industries. In addition to the overall dissertation, each essay makes separate and specific contributions to the literature on technol ogical change. The second chapter of the dissertation updated an existing model of endogenous economic growth to include a finance sector. In the model, the finance sect or acted as a filter of good entrepreneurs and as a means of acquiring capital. It was show n that the finance sector, both as filter and provider of research funds, facilitates econom ic growth. The third chapter maintained focus on the financial intermediary and built upon the conclusions of the previous chapter. While not directly testing the theory, the empirical study did evaluate the impact of prudent investor laws with priors similar to the theory of the second chapter. The regression analysis in the third essay shows that prudent investor laws had no impact on the venture capital, R&D, or technological chan ge. The fourth chapter of the dissertation extends the treatment of intermediaries beyond the finance sector. It considers the role

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124 that patent attorneys have in getting a patent application a pproved. The regression results presented in that chapter showed that lawyer characteristics are an important determinant of the grant lag for a patent. The results confirm that, contrary to existing theories on patents, patent approval is a process of nego tiation between the lawyer and the examiner. That fact may have profound public policy imp lications since small firms are unlikely to be able to be able to distinguish good lawy ers from bad ones, or could afford the good lawyers even if they knew who they were. As is often the case, this research spurs additional questions worth exploring. From a theoretical perspective, it might be valuab le to extend chapter two’s model to include asymmetric information between the entrepre neur and finance sect or. The model might also be generalized, a la Li (2003), to incl ude non-unitary elasticities of substitution. Further, the theoretical sket ch of bargaining between patent examiners and lawyers contained in the fourth chapter should be formalized. Once formalized, more empirical explorations on the relationship could be made, including whether or not experienced lawyers are able to get invalid patents approved. It might also be possible to explore how the relationship between lawyers and examiner s changes over time or if other factors, such as gender, race, or creed affect patent approval outcomes. Finally, it might also be used to evaluate the efficacy of incentives provided to examiners a nd suggest better ones. Additional data would be helpful for each of these extensions. New data may also allow a more thorough exploration into the impact of prudent investor laws on technological change. Much of the variation in R&D, ve nture capital, and innovation may be lost due to the aggregate nature of the data.

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125 APPENDIX A PROOFS OF PROPOSITIONS Proof of Proposition 2-1: Consider the equilibrium for an arbitrar y industry where the combined payoff must be offset by the combined cost of attempting to innovate. we t v we t v t X Ae t weH t v t X t AeH t weH t v t t weH t t v t ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( 0 ) ( 1 ) ( ) ( Proof of Proposition 2-2: To solve for the growth rate of the stock market value consider a firm’s equilibrium employment choice at a point in time. At each point in time the firm maximizes expected revenues, tv(t)-we In equilibrium the expected re venues are exactly offset by labor expenditures so that tv(t) = we Given the definition of (t) and taking logs and differentiating it is clear that ) ( ) ( ) ( ) ( t X t X t v t v At each point in time an entrepreneur choos es its optimal level of labor (e) so that the expenditures on inputs are equal to the expected stock ma rket value of the innovation: we t v t ) ( ) ( Since ) ( / ) ( t X Ae t

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126 e e A t wX t v ) ( ) ( Taking logs and differentiating w.r.t. t : 0 ) ( ) ( 0 ) ( ) ( ln ) ( ln ln ) ( ln t X t X t v t v A t X w t v Thus we can see that, in equilibrium, ) ( ) ( ) ( ) ( t X t X t v t v n t X t X t N t N t X t X t x t x ) ( ) ( 0 ) ( ) ( ) ( ) ( ) ( ) ( From (16) ) ( ) ( ) ( ) ( t X t X t H t H Proofs to Propositions 2-3 through 2-5 rely on the sign of the following parts to determine the sign of the entire first order c ondition. For brevity they are only mentioned here: 0 1 0 n 0 1 0 ) 1 ( ) ( 1 n n n n n n n n Proof of Proposition 2-3: 1 ) 1 ( ˆ2n e f Aef x > 0

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127Proof of Proposition 2-4: 1 ) 1 ( ˆ2n e f Ae f x < 0 Since 0 ) 1 ( ) 1 ( 1 n n n n n n n 1 ) 1 ( ˆ2n e f Af e x > 0 0 ˆ f c 0 ˆ e c Proof of Proposition 2-5: 2)) 1 ( )( ( ) ) 1 ( 1 )( 1 ( ˆ n e f n Ae x > 0 )) 1 ( )( ( ) 1 ( ˆ n e f e A x > 0

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128 APPENDIX B THE BLUNDELL-BOND ESTIMATOR Analyses based on panel data are a combin ation of time-series and cross-sectional analyses in that the data follow several units of interest (i.e. states, individuals, etc.) over time. Given this length and breadth, empiri cal work using panel data can account for differences across units of interest and how each unit may (or may not) change over time. Since units of interest are observed over se veral periods, it is possible to use a lagged dependent variable to help estimate the depe ndent variable in the current period. For example, the current level of population likel y depends on the level of population in the previous year. This causal relationship can be exploited in estimation, especially when paired with other dependent variables that ar e of interest. Even if there is no causal relationship, a lagged level of the dependent va riable will likely be highly correlated with the current level. That is, the same factors that led R&D investment in California to be $48 billion in 1999 may also lead to sim ilar levels of investment in 2000. Inclusion of a lagged dependent variable is not without its problems since there will likely be time-invariant characteristics (ic ) for each unit of observation (i.e. gender, ability, endowment of natural resources, etc. ). If left unaccounted for in regressions, these characteristics are included as part of the error term. it u i c it it it y it y where 1

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129 Since ic is time-invariant it will appear in both ity and 1ity which, given the dynamic nature of the model, means correlation between the error term it and the lagged dependent variable. This correlation causes estimates of to be biased. Non-dynamic models often use the first differences estimator to account for this bias because the timeinvariant component is eliminated.28 1 2 1 1 1 2 1 1, and Given it it it it it it it i it it it i it itu u y y y y u c y y u c y y However, this causes additional problems in the dynamic setting because now 1itu appears in 1it ity y and 2 1 it ity y meaning the independent variable and the error term are correlated and coefficient estimates are still biased. Econometricians are able to deal with this problem through the use of instrumental variables. Instrumental vari ables are variables correlated with the endogenous variable, 2 1 it ity y but uncorrelated with the error term. The correlation between the endogenous variable and the instrument(s) ma y then be exploited to estimate the coefficients properly, assuming no other problems (such as serial correlation) exist. The difficulty in this approach is finding suitable instruments. Anderson and Hsiao (1981) suggested that lags of the dependent variable are suitable instruments for the difference (i.e. 1ity is a good instrument for 1it ity y ). Arellano and Bond (1991) implemented this in to a GMM framework in both one-step and two-step variants where the tw o-step estimation is more efficient. In addition to the estimator, they created test st atistics to assure that sec ond-order serial correlation does 28 Other methods, such as pooled OLS, random effect s, and fixed effects estima tors are similarly biased (Cameron and Trivedi (2005, pp.764-765)).

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130 not exist, since that would violate the indepe ndence of the instrument and the error term. A Sargan test of over-identifyi ng restrictions was al so created. The interpretation of both statistics will be discussed in more detail later. The Arellano-Bond estimator and test statistics have been implemented in STATA’s xtabond command. The set of instruments suggested by Are llano and Bond (1991) was criticized for being too weak. Ahn and Schmidt (1995) a nd Blundell and Bond (1998) extended the set of instruments to include lagged levels of the dependent variable. By including more instruments, the estimator becomes stronger a nd more efficient. The estimator proposed by Blundell and Bond is shown to be better in cases where the number of time periods is small and the coefficient on the lagged differe nce is high. This scenario is common in microeconomic applications. The “system GMM” estimator of Blundell and Bond was later incorporated into STATA in the user written module, xta bond2 (Roodman (2005)). The xtabond2 module incorporates both the system GMM and the Arellano-Bond “difference GMM” estimators for both one-step and two-step estimation. Windmeijer (2005) showed that the asymptotic standard errors for the two-step system GMM are severely biased downward and proposed an adjustment that can make th e robust two-step variant better than the robust one-step. This adjustment is include d in the xtabond2 module. In addition, the module includes the Arellano-Bond test for seri al correlation and the Sargan (for one-step non-robust estimation) or Hansen (for robust on e-step and all two-st ep estimation) test statistics of over-ident ifying restrictions. First order serial correlation in dynamic m odels is expected given the error term (see above) however, second-orde r serial correlation would cause the GMM estimator to

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131 be inconsistent. The null hypothesis for the Are llano-Bond test statistic is that there is no serial correlation. Thus, for the system G MM estimator to be accu rately applied, the first-order test statistic should reject the nu ll (low p-value) while the second-order test statistic should fail to reject the null (high p-value). A ppropriate use of the BlundellBond estimator also requires th at the model not be over-identif ied. That is, the number of restrictions on the system cannot exceed thos e required for exact identification. The Sargan and Hansen test statistics test for this with null hypotheses that the model is not over-identified. A low Sargan or Hansen test statistic (high p-value) is preferred as the null cannot be rejected.

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132 APPENDIX C INSTRUMENTAL VARIABLES AND THE ENDOGENEITY OF LAWYER CHARACTERISTICS As discussed in chapter four, the choice of lawyer is endogenous to the inventor or assignee. Unobserved variables may guide th is choice and, in turn, may affect the coefficient estimates. One method for deal ing with these problems is the use of instrumental variables. Initially, three diffe rent potential instrumental variables were considered. To be valid, instrumental vari ables must satisfy two conditions: 1. have strong correlation with the e ndogenous regressor(s); 2. be uncorrelated with the error term. In addition x z has to be full rank. The first instrument considered is the number of patents granted by the USPTO since the lawyer first appeared on a patent application. Th is should be correlated with experience since they are both increasing over time. It might also satisfy the second condition assuming the number of patents gr anted by the USPTO is not simply a linear transformation of the lawyer’s experience. Th is appears to be true when considering the entire set of known lawyers. As Figure C-1 shows, the number of u tility patents granted by the USPTO has been growing at an increas ing rate. The black trend-line underscores the fact that this is a nonlinear trend. Thus, the instrument appears to be valid. Further investigation into th e lawyers that represent patent s during the period 1996-2000 showed that most registered with the USPTO in the early 1990’s. Therefore the trend in patenting from 1970-2000 is not the one that should be considered. Instead, the trend from 19902000 more accurately represents the relationship between the da te a lawyer first appears

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133 on a successful patent application and th e number of patents granted by the USPTO during that time. That relationship was found to be linear, implying that the instrument does not satisfy the second condition. Another instrumental variable considered was the distance between the zip code of the first inventor and the lawy er’s zip code. Distance between a lawyer and his/her client might be correlated with the choice of the lawyer since minimal distance may contribute to oversight and communication between the client and the lawyer. Further, the distance between the first inventor and the lawyer s hould be uncorrelated with the grant lag since it should have no impact on negotiations taking place between the lawyer and the examiner. Other papers, such as Card (1995) have used distance as an instrumental variable with reasonable success. Unfortunate ly, few patent applications included the zip code of the inventors. Once other factors we re taken into consideration (i.e. missing data, selected technologies, selected years, etc.) less than 200 patents were available to be analyzed—not enough to reliably instrument for the impact of patent attorneys.29 The final instrumental variable that was considered was the date of OED examinations. Immediately following an exam, there is an increase in the number of untested lawyers. If lawyers were picked at random, then the probability of choosing a low quality lawyer was higher immediately follo wing an exam than it was in the days and weeks before an OED exam. This influx of inexperienced and potentially low quality lawyers will likely cause the assignee or invent or to choose an experienced lawyer so as not to be stuck with substandard representati on. As time passes, and the ability of newly admitted lawyers becomes known, the new cohort of lawyers are less likely to be 29 Research using the addresses of the lawyer and first inventor and Geographic Information Systems (GIS) software may have better success. Unfortunately, GIS software was not available to the researcher.

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134 discriminated against. Therefore, the e xperience of the lawyer chosen should be correlated with the date of the OED exams, and the first condition is satisfied. The second condition is also satisfied since the OED exams occurred at regular intervals for the period analyzed, either annually or bi -annually. Table C-1 shows the dates and passing rates for exams administered between 1991 and 2003. On average, approximately 1,100 new lawyers entered the pool of registered lawyers after each exam. Since the register maintained by OED c ontains approximately 30,000 lawyers, the fluctuation in the number of lawyers is small, likely making this a weak instrument. Regressions were run for Semiconductor patents to test the instrumental variable. Both the number of days before and the number of da ys after an exam were considered. In all cases the partial R-squared and partial F statistics indicate the instruments are weak, and may therefore be potentially more biased than the OLS estimates. Given the limited data on lawyers and the invalidity or weakness of potential instrumental variables, only OLS estimates are included in chapter four.

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135 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 1970197519801985199019952000 Figure C-1. Number of Utility Pate nts Granted, Annually, by the USPTO

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136 Table C-1. OED Examinati on Dates and Passing Rates Exam Date Number of People Taking ExamPercent Passed August 21, 1991.. April 8, 1992.. October 14, 1992.. April 21, 1993.. October 13, 1993.. April 13, 1994.. November 2, 1994.. May 3, 1995.. August 28, 1996.. November 1, 1996.. August 27, 19973,16258% August 26, 19982,76237% April 21, 19991,57153% November 3, 19991,53266% April 12, 20001,43252% October 18, 20002,02137% April 18, 20011,91147% October 17, 20011,95858% April 17, 20021,93157% October 16, 20021,95672% April 15, 20031,82549% October 15, 20033,16258% The number of people taking th e exam and the pass rates for exams administered from 1991-1997 were not available.

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142 Schumpeter, J., 1950. Capitalism, socialism, and democracy. Harper & Brothers, New York, NY. Scotchemer, S., 2004. Innovation and incentives. MIT Press, Cambridge, MA. Segerstrom, P., 1998. Endogenous growth w ithout scale effects, American Economic Review 88, 1290-1210. Shaw, E., 1973. Financial deepening in economic development. Oxford University Press, New York, NY. Shattuck, M. A., 1951. The development of the prudent man rule for fiduciary investment in the United States in the tw entieth century, Ohio State Law Journal 12, 491-521. Solow, R., 1956. A contribution to the theo ry of economic growth, Quarterly Journal of Economics 70, 65-94. Sorenson, O., and T. E. Stuart, 2001. Syndi cation networks and th e special distribution of venture capital investments, Amer ican Journal of Sociology 106, 1546-1588. Thompson, P., and M. Fox-Kean, 2005. Pate nt citations and the geography of knowledge spillovers: A reassessment, American Economic Review 95, 450-460. Trajtenberg, M., R. Henderson and A. B. Ja ffe, 1997. University versus corporate patents: A window on the basicness of invention, Economics of Innovation and New Technology 5, 19-50. University of Pennsylvania. Uniform Prudent Investor act, http://www.law.upenn.edu/bll/ ulc/fnact99/1990s/upia94.htm. U.S. Department of Comme rce, U.S. Patent and Trademark Office. Weekly bibliographic raw data, ftp: //ftp.uspto.gov/pub/patdata. U.S. Department of Commerce, U.S. Pate nt and Trademark Office, 2001. Manual of Patent Examining Procedur es, (Revised May 2004) 8th Edition. Windmeijer, F., 2005. A finite sample correct ion for the variance of linear efficient twostep GMM estimators, Journa l of Econometrics 126, 25-51. Young, A., 1998. Growth without scale eff ects, The Journal of Political Economy 106, 41-63. Zook, M. A., 2002. Grounded capital: Vent ure financing and the geography of the internet industry, 1994-2000, Journa l of Economic Geography 2, 151-177.

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143 BIOGRAPHICAL SKETCH Kevin W. Christensen was born in Alexandria Virginia, in 1976. He graduated in 1998 with a Bachelor of Science degree in ec onomics from James Madison University in Harrisonburg, Virginia. He also minored in As ian studies. Prior to graduate school, he held positions at the Washington, DC, offices of National Economic Research Associates (NERA) and Ernst & Young, LLP. In 2001, he left Ernst & Young to attend graduate school at the University of Flor ida. He received a Master of Arts in economics from the University of Florida in 2004.


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Permanent Link: http://ufdc.ufl.edu/UFE0014643/00001

Material Information

Title: Essays on Technological Change
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014643:00001

Permanent Link: http://ufdc.ufl.edu/UFE0014643/00001

Material Information

Title: Essays on Technological Change
Physical Description: Mixed Material
Copyright Date: 2008

Record Information

Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
System ID: UFE0014643:00001


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Full Text











ESSAYS ON TECHNOLOGICAL CHANGE


By

KEVIN W. CHRISTENSEN












A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Kevin W. Christensen





























To my family. Without their love and support none of this would have been possible.
















ACKNOWLEDGMENTS

I would like to thank my advisors, Elias Dinopoulos and Chunrong Ai, for their

help with my research. James Seale, Jr. and Doug Waldo were also helpful in developing

this finished product. David Figlio, Sarah Hamersma, Jonathan Hamilton, Larry Kenny,

and other professors in the Economics Department were generous with their time and

input. Special thanks go to committee member and graduate coordinator Steven Slutsky.

Professor Slutsky's insights, guidance, and advice were valuable at all stages of my

graduate career.

The successful completion of my dissertation would not have been possible without

my family and friends. My parents, Homer and Charlene Christensen, my sister Michele

Bach-Hansen, and her husband Scott, were unwavering in their love and support. My

nieces, Madison and Kayla, provided me with much needed distraction, entertainment,

and joy. Friends in Virginia, Florida, and elsewhere were always available when I

needed them. Finally, I am very grateful to Burgin Uinel. She believed in me when I was

sure no one else did.





















TABLE OF CONTENTS


page

ACKNOWLEDGMENT S .............. .................... iv


LI ST OF T ABLE S ................. ................. vii........ ....


LIST OF FIGURES .............. .................... ix


AB S TRAC T ..... ._ ................. ............_........x


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


2 A MODEL OF ENTREPRENEURSHIP AND SCALE-INVARIANT GROWTH....4


2.1 Introduction ......._................ ...............4. ....
2.2 Previous Literature .................. ............ ...............5......
2.2.1 Endogenous Growth Literature ................. ............_ ........ .......5
2.2.2 Finance and Growth Literature: Theory .............. .....................
2.3 The M odel ................. ......... .. ...............9......
2.3.1 Consumer Utility ................. ......... ... ...............9. ....
2.3.2 Competition, Prices, and Profits ..............._ .............._ ........ ...11
2.3.3 Innovation ..............._ ................. 13........ ....
2.3.4 Finance Sector ................. ...............15......... ....
2.3.5 Financial Intermediation ......... .............._ ........_ ............1
2.3.6 Stock M market ................._ .......... ...............17.....
2.3.7 Financial Sector Equilibrium ..............._ ...............18 ........._....
2.3.8 Labor M market .............. ...............18....
2.4 Balanced Growth Equilibrium ..............._ ........ ........_ ............1
2.4.1 Transitional Dynamics ..............._ ...............20 ......... ....
2.4.2 Economic Growth ..............._ ...............22 ......... ....
2.4.3 Comparative Statics .............. ...............23....
2.5 Conclusions and Extensions .............. ...............25....


3 THE EFFECT OF PRUDENT INVESTOR LAWS ON INNOVATION .................30


3.1 Introduction ................. ...............30........... ....
3.2 Background ................. ......... ...............32.......
3.2.1 Prudence of Investment ................. ...............32................












3.2.2 Previous Literature ................. ...............34........... ....
3.3 Data and Empirical Methodology ................. ...............36........... ...
3.3.1 D ata ................... ............ ...............37.......
3.3.2 Empirical Methodology .............. ...............39...
3.4 Tests of Exogeneity & Benchmark Regressions ................. ......................42
3.4.1 State Innovative Output and the Timing of Adoption ................... ........42
3.4.2 Evidence from a Long Difference............... ...............4
3.5 Prudent Investor Laws & Innovation............... ...............4
3.5.1 Indirect Investments: Venture Capital .............. .....................4
3.5.2 Direct Investments: R&D Expenditures .............. ....................4
3.5.3 Alternative Mechanisms .............. ...............48....
3.6 Conclusion .............. ...............49....


4 DO PATENT ATTORNEY S MATTER? ............. ...............64.....


4.1 Introduction ................. ...............64........_. ....
4.2 Literature Review ...._._. ................. ......._._. .........6
4.2.1 Theoretical Models............... ...............65.
4.2.2 Previous Empirical Analyses ................. ..............................67
4.3 Sources and Descriptive Statistics .............. ...............69....
4.3.1 Data Sources .............. ...............69....
4.3.2 Description of Variables ........._..._ ...._.... ...._.__ ................ 71
4.4 Empirical Methodology .............. ...............77....
4.4.1 Regression Specifications .............. ...............77....
4.4.2 Endogeneity of Lawyer Choice ................. ..............................78
4.5 Re sults ................. ............ .................8 1....
4.5.1 Estimated Impact of Lawyers ................. ............. ......... .......81
4.5.2 Examiner Experience and Generality .............. ..... ............... 8
4.5.3 Examiner Effects ................... ............ ...............84......
4.5.4 Experience as a Proxy for Quality .............. ...............85....
4.6 Conclusion .............. ...............86....


5 CONCLUSION................ ..............12


APPENDIX


A PROOFS OF PROPOSITIONS ................. ...............125...............


B THE BLUNDELL-B OND ESTIMATOR ........_.......... __. ............... 128 ....


C INSTRUMENTAL VARIABLES AND THE ENDOGENEITY OF LAWYER
CHARACTERI ST IC S ............_...... ..............1 2.....


REFERENCE S .............. ...............137....


BIOGRAPHICAL SKETCH ............_...... ...............143...




















LIST OF TABLES


Table pg

2- 1. C omparative Stati c s................. ...............29...._.__ ..

3-1. Correlation Matrix ..........._.._ ....... ...............51...

3-2. Descriptive Statistics .............. ...............52....

3-3. Descriptive Statistics by Year............... ...............53..

3-4. Year of Adoption of UPIA (or equivalent) ...._ ......_____ ...... ......__......5

3-5. Comparison of Adopters and Non Adopters ...........__...... ..........__......55

3-6. The Timing of Adoption ...........__..... .___ ...............57..

3 -7. Impact of Prudent Investor Laws over Long Difference.............__ ..........___.....5 8

3 -8. Estimates of the Impact on Venture Capital Investments in a State ........................59

3 -9. Estimates of the Impact on R&D Expenditures in a State ................. ................ ...60

3 -10. Estimates of the Impact on Citation Weighted Patent Counts in a State ........._.......62

4-1. Variable Descriptions .............. ...............88....

4-2. Technology Subcategories Descriptions .............. ...............89....

4-3. Count of Unique Occurrences, by Subcategory, When Identification of First or
Most Experienced Lawyer is Known ................. ...............90........... ...

4-4. Averages by Subcategory, When Identification of First or Most Experienced
Lawyer is Known .............. ...............91....

4-5. Number of Patents by Country and Subcategory, When Identification of First or
Most Experienced Lawyer is known ................. ...............93........... ...

4-6. Correlation between Grant Lag and Independent Variables, by Technology
Sub category ................. ...............94.......... ......










4-7. Impact of Representation by Patent Attorney or Agent .............. ....................9

4-8. Estimating Grant Lag, Without Lawyer (Patents Where First Lawyer is Known) .103

4-9. Impact of Lawyer Experience, Using First Lawyer Listed ................ ..................106

4-10. Estimating Grant Lag, Without Lawyer (Patents Where Most Experienced
Lawyer is Known) ................. ...............109................

4-1 1. Impact of Lawyer Experience, Most Experienced Lawyer Listed ................... .....1 12

4-12. Impact of Examiner Experience, Controlling for First Listed Lawyer. .................1 15

4-14. Predicted Impact of Lawyer Quality, First Lawyer ................. ............ .........121

4-15. Predicted Impact of Lawyer Quality, Most Experienced Lawyer ................... ...... 122

C-1. OED Examination Dates and Passing Rates............... ...............136.

















LIST OF FIGURES


Figure pg

2-1. Equilibrium Conditions for Model .............. ...............27....

2-2. Stability of Balanced-Growth Equilibrium............... ..............2

C-1. Number of Utility Patents Granted, Annually, by the USPTO .............. ..............13 5
















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ESSAYS ON TECHNOLOGICAL CHANGE

By

Kevin W. Christensen

August 2006

Chair: Elias Dinopoulos
Cochair: Chunrong Ai
Major Department: Economics

My dissertation consists of three essays on the economics of technological change.

The first essay develops a theoretical model that describes how financial intermediaries

may influence economic growth. Previous theoretical models on the topic predict that

economies with larger populations will grow at faster rates, something which has not

been empirically supported. This paper corrects this "scale effects" issue by extending an

existing model of economic growth, without scale effects, to include a Einance sector.

The Einancial intermediary evaluates potential entrepreneurs and their ex ante potential

for being an entrepreneur. Upon receiving a positive rating from the intermediary, the

entrepreneur receives money for R&D which, in turn, may lead to successful innovation.

Changes in the steady-state growth rate are explained by shifts in parameter values.

In a spirit similar to the first essay, the second essay considers the impact the

adoption of prudent investor laws had on innovation. These laws were primarily adopted

by states in the late 1990's and expanded the scope of investment options available to










financial intermediaries to include new and untried enterprises and venture capital.

Various specifications using state-by-industry patent counts, venture capital

disbursements by state, and R&D expenditures were used to test whether these laws

affected technological change. The empirical results show that, contrary to previous

evidence, prudent investor laws had only a small effect on technological change. This

suggests that the impact of financial intermediaries on economic growth may be bounded.

The final essay explores the role that another intermediary has on technological

change. As active participants in the patenting process, patent attorneys are involved in

writing and defending the claims on an application (among other things) and thus can

help to establish the scope of patent protection. This chapter explores the value added of

patent attorneys by looking at how more experienced lawyers affect the time between

filing of an application and the date a patent is granted. It has been found that more

experienced attorneys can reduce the grant lag, but the reduction depends on the

invention's technology. This research is the first that considers the role of attorneys in

the patent process.















CHAPTER 1
INTTRODUCTION

Technological change affects every sub-discipline of economics. Micro-

economists may explore the role of research and development in competition. They

might also explore the role that technology plays in determining industry composition.

Labor economists may be concerned with increased worker productivity as a result of

new machinery and equipment. Public economists may consider the role of the Internet

in increasing test scores among minorities and the poor. Research on international trade

may estimate the impact and flow of international technology spillovers. The

overarching theme of these scenarios is that technological change is generally good for an

economy.l Nowhere in economics is that made clearer than in the literature on economic

growth. As Schumpeter said,

The fundamental impulse that sets and keeps the capitalist engine in motion comes
from the new consumers' goods, the new methods of production or transportation,
the new markets, the new forms of industrial organization that capitalist enterprise
creates....This kind of competition is...the powerful lever that in the long run
expands output and brings down prices.... (1950, pp. 83-85)

This idea is found in theoretical models, such as Solow (1956) and Romer (1986), which

provide a framework for understanding how advancements in technology can positively

affect economic growth. However, theoretical models require assumptions that abstract

from the real world and can assume away some features to facilitate understanding or





SPolitical economists, such as Karl Marx, may disagree with this assertion.










computation. One oft-ignored element is the role of intermediaries in facilitating

innovation.

Economic theory provides two primary reasons for the existence of intermediaries:

cost and information. Intermediaries specialize in a particular field and therefore have

capabilities and knowledge that more generalized firms (e.g. a manufacturing company)

may not have. It is not that the firms could not acquire these capabilities but that, by

specializing, the intermediary is better informed and may provide the services cheaper

than a general firm could achieve alone. Given this, use of an intermediary causes

efficiencies that may, in turn, lead to an increased rate of innovation.

This dissertation explores the intersection between technological change and

intermediaries. Specifically it considers the role that of financial and legal intermediaries

have on facilitating technological change. The second chapter of the dissertation presents

a theoretical model that outlines the role of financial intermediaries in fostering economic

growth through technological change. The third chapter empirically tests the effect a law

change affecting the types of investments financial intermediaries could be made.

Combined, these essays show that financial intermediaries can positively affect

technological change but are not necessarily guaranteed to do so since financial

intermediaries are complements to, not substitutes for, other processes such as research

and development or entrepreneurial initiatives. The fourth chapter considers the role of

patent attorneys in the patent approval process. Using a unique dataset on patent

attorneys it is shown that patent attorneys can affect the time in which a patent is

approved. This is in stark contrast to previous empirical and theoretical literature on the






3


topic which holds the view that a patent examiner works independent of other factors.

The final chapter summarizes the findings from each the previous three chapters.















CHAPTER 2
A MODEL OF ENTREPRENEURSHIP AND SCALE-INVARIANT GROWTH

2.1 Introduction

As early as Schumpeter (1934), the finance sector was proposed as an important

component in the growth process. Bencivenga and Smith (1991), Greenwood and

Javanovic (1990), and King and Levine (1993b) later formalized that proposition within

the context of endogenous growth theory.2,3 Beck and Levine (2004), Benhabib and

Spiegel (2000), and Rajan and Zingales (1998), among others, have empirically shown

that the finance sector plays an important role in fostering economic growth. They

continue a long line of empirical papers evaluating the relationship (see Levine (2004) for

a review of empirical and theoretical papers). Compared to empirical research,

theoretical work on the finance-growth hypothesis has slowed. As a result, some

innovations in endogenous growth theory remain outside the finance-growth literature.

One of the most significant omissions is the treatment of population as a variable

changing over time rather than as a parameter.

Earlier models of endogenous growth incorporated the undesirable property of

scale effects. These models predict that as population increases, the long-run rate of

growth also increases, implying, ceterus paribus, larger economies grow at faster rates.

For some time, this was considered to be a strength to the theory as growth in population

2 Other, more classical references in the literature are Goldsmith (1969), McKinnon (1973) and Shaw
(1973).

3 Romer (1986), Grossman and Helpman (1991), and Aghion and Howitt (1992) are major contributors to
the endogenous growth literature.









was deemed analogous to globalization. However, time series tests by Jones (1995a,

1995b) showed that growth had remained roughly constant regardless of scale of

population, contradicting these models. As a result, theorists began to develop second-

generation models of endogenous growth that had growing population. In spite of this

innovation, no previous paper modeling the relationship between Einance and growth has

been updated to account for the scale effects issue and a disconnect remains between the

Einance-growth theories and the most state of the art endogenous growth models.

This chapter attempts to bridge that gap by combining a second-generation model

of endogenous growth with the King and Levine (1993b) Einance sector. The general

equilibrium model presented here includes growing population and is shown to have a

balanced growth equilibrium that is saddlepath stable. Fluctuations in parameter values

explain changes in the growth rate of the economy. The rest of the chapter proceeds as

follows. The next section of the chapter reviews the relevant literature. The third section

introduces the equilibrium conditions for consumers, producers, and Einancial

intermediaries. These conditions are used to specify the balanced growth, transitional

dynamics, and comparative statics presented in the fourth section. The Einal section

offers conclusions, limitations, and proposes extensions for the model.

2.2 Previous Literature

2.2.1 Endogenous Growth Literature

In the late 1990's three papers, Young (1998), Howitt (1999), and Segerstrom

(1998) were published as the core of the second generation endogenous growth models,

each with a distinct answer to scale effects. Young introduced the idea that both

horizontal and vertical product competition offsets the scale effects problem. More firms

producing at the same level of quality but with different varieties will reduce the spoils










available to any one producer. As a result, growth does not reach the high levels it did in

first-generation models. Howitt translated Young's original idea into a more traditional

Schumpetarian growth model. In doing so, he reintroduced the result that R&D subsidies

provide a positive impact on growth which was lacking in Young's original model.

Segerstrom's model used a quality ladder approach and an R&D difficulty index to offset

the impact of larger population size. The difficulty index removes the population scale

effects while at the same time explaining why R&D employment has increased without a

commensurate increase in innovation. Neither of the other two models explains this

phenomenon. Segerstrom's model also allows for a positive impact of R&D subsidies on

growth.

2.2.2 Finance and Growth Literature: Theory

Theoretical models on the finance-growth relationship are varied in their scope and

use of endogenous growth fundamentals. Bencivenga and Smith (1991) consider how a

developing finance sector alters the composition of consumer savings using a three period

overlapping generations model. As in other models, the introduction of a finance sector

increases the accumulation of capital. It is shown that these changes do not occur as a

result of changes in savings behavior but instead are a direct result of the intermediary

efficiently allocating consumer savings. Greenwood and Javanovic (1990) also consider

an evolving finance sector with endogenous improvement in production inputs. In their

model, the finance sector matures as the income of the population increases. Higher rates

of savings drive the finance sector (and economy) forward in the development process.

As the finance sector evolves, the rate of return on capital increases. This increased

return is what drives growth in the economy. Therefore a country with a more mature

finance sector would have a higher level of growth than a relatively less-mature









economy. Each of these models utilizes an AK endogenous growth model as a starting

point where capital accumulation is determined endogenously.

Unlike the previous two papers, King and Levine (1993b) use Aghion and Howitt' s

(1992) endogenous growth model as its foundation and does not consider an evolving

finance sector. Instead, the maturity of the finance sector is treated as given and its

impact on the introduction of intermediate products is evaluated. The financial

intermediary acts as a filter of prospective entrepreneurs that seek financing. Only

projects presented by skillful entrepreneurs will be able to obtain funding. Those

individuals without a positive rating cannot compete for the next innovation and instead

become production workers. Another change from the previously mentioned models is

the introduction of a stock market which accumulates consumer savings and provides

revenue to fund entrepreneurial ventures through the initial offering of stocks.

A paper by Morales (2003) is the most recent known paper modeling the finance-

growth relationship.4 It is based on Howitt and Aghion's study (1998) which

incorporates a "leading-edge technology parameter" that provides a similar function to

Segerstrom's (1998) R&D difficulty index and uses both capital and labor as factors of

production. Two elements make their paper unique from other finance-growth models.

First, the model introduces capital as an input in production. By including capital, both

capital accumulation and technological change lead to economic growth. The second

element is the inclusion of moral hazard between the financial intermediary and the

researchers. In spite of the presence of moral hazard, her results show a positive


SAghion, Angeletos, Banerjee, and Manova (21 *4) considers a related (but not identical) issue of how
volatility affects technological change. The model developed considers the investments of finitely lived
entrepreneurs but assumes the number of entrepreneurs is constant over time.










relationship between the finance sector and the success rate of proj ects and research.

While the addition of capital and moral hazard are significant contributions, the scale

effects issue remains. Therefore the steady-state analysis presented in her paper is only

stable for a set population not for one growing over time.

This chapter presents a model of growth without scale effects by combining King

and Levine's (1993b) finance sector with Segerstrom's (1998) model to obtain a general

equilibrium model that has a balanced growth equilibrium and is saddlepath stable.

Similar to Morales (2003), the model shows a positive relationship between finance and

economic growth spurred through technological change. Unlike Morales' work, labor is

the only input and any potential moral hazard is assumed away. To combine the King

and Levine and Segerstrom models, several changes have been made. In the models

mentioned above, technological advancement improves intermediate goods that are used

to produce a single consumable product. The model presented in here utilizes product,

rather than process innovations. That is, rather than multiple inputs for one final product

there are multiple final goods. Entrepreneurs compete to innovate to the next quality

level of the final good in a specific industry.' Economic growth is observed through

increases in consumer utility which is affected by the quality and the quantity of the

goods consumed. The second important change is the endogenous treatment of

entrepreneurial competitors. This is done to reflect how the growing population (and

increased consumer demand) affects the number of entrepreneurs competing for the next


SOne advantage of this structure is that it fits well with the empirical observations of Hellman and Puri
(2000) where a venture capitalist is likely to invest in a technology that is pushing out the technological
frontier as opposed to one creating horizontal product innovation. The predicted direction of this model
(but not necessarily the magnitude) can help in understanding the role venture capital plays in economic
growth.









innovation. Finally, only a portion of R&D employment directly affects the innovation

rate whereas Segerstrom's model attributes all non-production workers as R&D labor.

Since the model introduces a Einancial sector, labor allocated to the financial intermediary

is not growth promoting and does not per se impact the innovation probability. However

their importance to the growth process will be highlighted later in the chapter.

2.3 The Model

The description of the model begins with a discussion of consumer preferences.

Once the consumer equilibrium condition is established the producer side, innovation

process, and the role of Einancial intermediaries are developed. This section concludes by

elaborating on the labor market. These market equilibrium conditions will be used in the

fourth section to estimate the balanced growth equilibrium values of per capital

consumption and per capital R&D difficulty.

2.3.1 Consumer Utility

The model uses dynastic families as outlined by Barro and Sala-i-Martin (2001)

and used by Segerstrom (1998). Dynastic families choose to maximize the utility of all

family members over an infinite horizon. That is, current family members are altruistic

towards their current and future relatives and make consumption choices with them in

mind. By using the dynastic family assumption the model bypasses the problems of

finitely lived people and allows for a single and unified utility function to be maximized.

Assuming that each individual has an identical utility function, the utility equation is the

product of the individual discounted utility and the population for the entire economy

summed over an infinite horizon.









Each individual in the economy has a discounted utility function equal to


Se t In[u(t)]dt where p is the discount factor and u(t) is the subutility. Population at


time t is N(t) = e"' when initial population is normalized to 1 and the exogenous growth

rate of population is n (births minus deaths). For optimization purposes p is assumed to

be greater than n.6 The simplified product of these components over an infinite horizon is


U = e "" InBu(t)]dt (2-1)


Product quality and consumer demand are introduced in the subutility. Quality

levels are sequential so an industry cannot produce the j+1 quality product without the j

quality product already having been discovered. Each industry can produce goods of

different qualities at the same time. Once price accounts for quality differences, each

product within the same industry substitutes perfectly. The subutility function is defined




Inu o)] In I d(j, m, t) m. (2-2)


The quality of product j is denoted by A' where the parameter Ai represents the step

size of innovation. As Ai increases, the difference between the quality of the new good

and the old good increases. Since product quality improves with each innovation, Ai must

be strictly greater than 1. Quantity demanded by an individual consumer is denoted by

dcj, mi, t) for a particular quality (j) and industry (0i) at a point in time (t). The total affect



6 See Barro and Sala-i-Martin (2001, p. 67) for a thorough explanation of this restriction and the
transversality condition.









of consumption on utility is simply the product of the quality and demand summed across

all industries, which are indexed along a continuum from 0 to 1.

At every point in time consumers choose the amount to spend on an industry's

product. Given a unitary elasticity of substitution between goods of differing qualities,


per capital demands at a point in time are d = c per capital consumption divided by the


price of the good.' To break ties, it is assumed that the consumer purchases the more

advanced quality product. Consumers only choose c(t) and treat prices and qualities as

given so over time, per capital consumption may vary. Taking this into account and

substituting the demands as noted above into the subutility function, maximizing (2-3) is

equivalent to maximizing (2-1).


ea r".' In c(t)dt (2-3)


The family's optimal consumption is bounded by the growth of per capital assets,

ci(t). Consumer assets change due to wages w(t), stock market dividends r(t)a(t),

consumption c(t), and division of assets among new family members na(t). Therefore,

the constraint for the maximization of utility above is ci(t) = w + r(t)a(t) c(t) na(t) .

Solving the dynamic constrained maximization problem yields


= r(t) p. (2-4)


2.3.2 Competition, Prices, and Profits

Consider the only possible competitive case where there are two firms in an

industry each producing different qualities, j and j+1. The producer of the cutting edge


SLi (2003) extends Segerstrom's (1998) model to account for non-unitary elasticities of substitution.









technology, j+1, is called the quality leader and the other firm is referred to as the quality

follower. Consumers are indifferent between the qualities if the effect on utility is the

same for either good. That is if, ii d( j, mi, t) = il""d( j + 1, mi, t) Recall that demands are

equal to per capital consumption divided by price and that consumers allocate the amount

of consumption to an industry, not a specific quality. Given this, the equivalent price

indifference equation is p~,, = Alp, Assuming Bertrand competition prevails in all

industries, the quality follower sets its price at the lowest possible level, the marginal cost

of production. Since labor is the only input and one unit of labor is required to produce

one unit of output the price of the quality leader is p, = lw This is the case for all

quality leaders regardless of industry. Assuming consumers prefer the quality leader's

product when formally indifferent, the quality leader is the sole producer in equilibrium

given the contestable market. Since this will be true for all industries and qualities, the

prevailing market price for the economy is

p = Alw (2-5)

The profits of the quality leader are equal to the price-cost margin of each product

times the number of products sold. Since consumers are assumed to have identical

utilities the demands for each individual are also the same, implying that market demand

for a specific time, quality and industry is DOi, mi, t)=N(t)dyi, mi, t). The profit equation

for the sole producer may therefore be simplified to


Fot)= Ntc*,t) (2-6)

The profits earned by the quality leader are greater than zero by definition of N(t),

c(t), w, and ii. It is the desire for these profits that leads to innovation.









2.3.3 Innovation

Each innovation attempt may advance only one step beyond the current quality

level and successful innovation is far from certain. Each attempt is governed by a

Poisson process where the probability of innovation increases with the amount of labor

used in R&D. In this model there are two components of R&D labor: the researchers

and the Einancial intermediary's employees. Unlike in Segerstrom's (1998) model, not all

R&D employees affect the rate of growth of innovation. The labor used by the Einancial

intermediary does not directly advance research so only researcher's labor, e, increases

the probability of success. It is possible that multiple entrepreneurs in the same industry

may be positively rated by the intermediary so that there is more than one competitor for

the next quality step. The endogenous variable H~im, t) represents the number of

entrepreneurs that attempt to innovate in that industry at each point in time. Even though

Financial intermediary employees and R&D workers are represented by parameters, the

number of competitors and therefore the total number of employees will grow over time.

It is plausible to think that early stage advancements are easier than later stage

advancements. That is, simpler innovations take no time whereas more complex

innovations require extensive testing, or perhaps even a lengthier review process by

government agencies. As time passes and the industry moves up the quality ladder the

probability of successfully innovating decreases. To account for this, the innovation

probability uses an industry specific R&D difficulty index, X~im, t), which increases over

time but affects the innovation probability negatively. In spite of the growth in

competitors it is possible that innovation may stay constant or decrease depending on if it









is dominated by the R&D difficulty index. The probability that an industry innovates to

the next quality level is

Ae
O(, t) = H(m, t), (2-7)
X(mi, t)

where A is a productivity parameter.

Assumption 2-1: The R&D difficulty index increases at a rate equal to

X(mi, t)
= pe(mi, t), where the parameter puE (0,1]. This implies that complexity of
X(mi, t)

products rises as firms become more innovative.

There are no spillovers across quality levels. A veteran participant in the jth patent

race now competing in the j+1~st race has no advantage over a relative newcomer. Any

participants in the patent race for the jth quality must start from the beginning of the

process to reach the j+1~st quality level so there are no spillovers between previous and

current research nor is there any spillover between researchers in the same patent race.

Thus, the industry innovation rate is the product of individual competitor probabilities,

(m,t), multiplied by the number of competitors so that, O(mi, t) = #(mi, t)H(mi, t) .

Each attempt requires a different request for startup capital by entrepreneurs from

investors. The return on investment to these investors is the expected profits from the

sale of the product. Given the competitive makeup of all industries, the profits associated

with one quality level disappear when the next innovation occurs. If the current industry

quality leader attempts to innovate twice to advance two steps up the quality ladder, the

leader becomes indebted to two cohorts of investors. Further, by innovating to the j+1

quality, the firm eliminates the demand for its j quality product and thus cuts off revenues

from that product and dilutes the shares contrary to the interest of its original set of










investors (a similar concept to Myers and Majluf (1984)). This business stealing outcome

and the inability to repay two cohorts of investors are the reasons each entrepreneur will

only choose to advance one quality rung at a time. Therefore, while possible, it is not

desirable for an entrepreneur to attempt two successive levels of innovation. Further

advances in quality must come from outside the firm.

2.3.4 Finance Sector

As mentioned previously, profits are the incentive for innovation, but there are

steps that must be taken before these profits are realized. The Einance sector is composed

of two related areas, a financial intermediary and a stock market. The symbiotic

relationship between the two areas is critical to technological change and thus economic

growth. In the model, the intermediary provides a means of assurance to investors by

rating each entrepreneur and entrepreneurial venture as either good or bad. Upon a

positive rating, the startup capital necessary to participate in a patent race is provided

through the stock market. Only positively rated firms will receive startup capital and be

able to attempt to innovate.

In the real world, an intermediary provides more than just a rating. Startup capital

to an entrepreneurial proj ect is invested with the expectation that there will be a return on

that investment. During the time between investment and realization, the intermediary

firm may provide strategic advice, monitoring, or lower the learning curve for new

entrepreneurs (in the context of venture capital, see Hellmann and Puri (2000, p. 960).

Finally, by investing in a company, a financial intermediary firm sends a signal to future

investors that the project, while risky, has potential. The model presented here eliminates

the financial and mentoring responsibilities of the intermediary and focuses solely on the

signaling aspect. However, the productivity parameter in the innovation probability









could be interpreted as the value added impact from mentoring. The sole proactive

responsibility of the intermediary in the model is to provide assurance to stock market

investors. The rating guarantees that the proj ect can succeed but does not guarantee that

it will be the first to succeed.

2.3.5 Financial Intermediation

It is assumed that individuals posses traits that will make them successful with a

probability a. The intermediary can reveal a potential entrepreneur's ability, with

certainty, by investigating the individual at a cost of units of labor. In equilibrium, the

maximum value an intermediary is willing to invest on a rating for an individual proj ect

is the expected value of the proposed entrepreneurial project. The structure of the model

is such that each equivalent quality step results in the same amount of profit regardless of

industry. It is possible that multiple entrepreneurs in the same industry may be positively

rated by the intermediary so that there are multiple competitors for the next quality step;

however, each potential entrepreneur is considered on a case by case basis. With q

representing the expected discounted value of the entrepreneurial venture, the equilibrium

conditions for a financial intermediary are

aq = wf (2-8)

q = #(co~, t)py(t) we (2-9)

With the perfectly competitive labor market w is the same wage as in the

production side of the model. The stock market value of a firm is represented by v(t).

Proposition 2-1: The expression for equilibrium of one firm, q, is equivalent to

the industry equilibrium condition. See Appendix A for proof.









Combining these equations and solving for v(t) yields the financial intermediary

equilibrium condition.


v(t)= w( +e (2-10

The structure of the model is such that each equivalent quality step results in the

same amount of profit during the same time period, regardless of industry. The financial

intermediary has no incentive to prefer some industries to others since profits are the

same across industries. Due to the symmetric nature of the financial intermediary,

profits, and price equilibria, the rest of the model focuses on the general case where the

innovation rate and entrepreneurial competition is the same across all industries. As a

result, the industry component of all functions from this point on is dropped.

2.3.6 Stock Market

After being rated, an entrepreneur may seek funding via the stock market to start a

new business. This funding is used to pay for R&D that will hopefully lead to an

innovation. The securities issued for new firms compete with those from other industries

and with stocks from already established quality leaders. When making her investment

choices, a rational consumer will make comparisons to a perfectly riskless asset with a

rate of return r(t)dt for a time segment dt. In equilibrium, the expected rate of return for

new stock must be equal to the rate of return on the riskless asset. The expected stock

value of the new firm is equal to the realized dividends plus the expected capital gains for

the time segment dt. The expected value is adjusted downward since the future value

disappears when the next product innovation occurs. The equilibrium condition for the









v(t) 0
O(t)dt)dt O(t)dt = r(t)dt .
v(t)


time segment dtf is therefore d~t) + f)(1_
v(t) v(t)

Taking the limit as dt approaches zero it follo~


r(t) + (t)-
v(t)


v(t)


(2-11)


As in Segerstrom (1998), the growth rate of the stock market value of monopoly

9(t) X(t)
profits must be equal to the growth rate of the R&D difficulty index,
v(t) X(t)

One implication of the stock market equilibrium is that as R&D difficulty

increases, the stock market value increases which corresponds with more investment.

This model has a decreasing per dollar impact of financial capital on innovation as time

progresses so that more capital is needed over time to keep innovation probability the

same.

2.3.7 Financial Sector Equilibrium

When both the stock market and intermediary are in equilibrium the entire finance

sector is in equilibrium. Recalling Assumption 2-1, the R&D equilibrium condition may

now be solved. Where x(t), which equals, X(t) N(t), is per capital R&D difficulty.


wx(t) f Aww
+ e = .(2-12)
Aep a [rt) + O(t)(1- p)]l

2.3.8 Labor Market

Employees have two choices of employment; they may work either in the

manufacturing or R&D sectors. Since the wages in these two sectors are the same,


SSee Appendix A for proof.









workers are indifferent between these two j obs. Given full employment, N(t) is the sum

of manufacturing labor (N"id(t)) and R&D labor (NRDt), which includes financial

intermediary labor). The manufacturing labor is equal to the market demand summed

across the total number of industries since it was assumed that each unit of labor supplies

one unit of output.


N~()=j c(t)N(t)il c(t)N(t)il
N" (t = d (2-13)


On a per proj ect basis, entrepreneurial employment can be found in the financial

intermediary condition. Multiplying this by the number of competing entrepreneurial

firms in each industry and summing across all industries yields


NR (t = a + e H(t 5e + e -H(t). (2-14)

Given full employment, the resource constraint for the economy is equivalent to

1= +x~t)- +e, ( 1~ (2-15)
Av a Ae

2.4 Balanced Growth Equilibrium

Now consider the balanced growth equilibrium where all endogenous variables

grow at a constant but not necessarily identical rate. Using (2-7) the balanced growth

innovation rate 0 is

H(t)
e = H~t)(2-16)


H (t)
Proposition 2-2: In the balanced growth equilibrium H(t) See Appendix A

for proof.









It is intuitive that the number of competitors should grow at the same rate of

population since as population grows the set of potential entrepreneurs grows

proportionally (due to a being a parameter). Given Proposition 2-2 and equation 2-16 the


balanced growth rate of innovation is (D = n Using this, c.(t)/c(t) = 0 ,the wage as


numeraire, and both the resource (2-15) and R&D (2-12) conditions, the balanced growth

values of 2 and c^ may be solved for explicitly. The results are graphically represented

in Figure 2-1. Only the positive quadrant is considered since per capital consumption and

R&D difficulty only have values greater than or equal to zero. The R&D constraint is

upward sloping because increased R&D increases quality of goods. The increase in

quality is translated to increased per capital consumption due to decreases in quality

adjusted prices. The vertical intercept of the resource condition is ii. As the per capital

R&D difficulty increases, this signals an increase in required assets needed to innovate to

maintain the same level of industry innovation. As a result, labor resources are shifted

away from manufacturing jobs. With fewer products manufactured, per capital

consumption must decrease, implying a downward sloping resource condition. The two

lines intersect at a unique point identifying equilibrium values, 2 and c .

Ae al-l -1 p
x = (2-17)
(f + ea)Gn(1 p + p[A 1D+ ~pp

A~pp~n-n)
c = (2-18)
(n(1- pu + p[il -1D+ pp)

2.4.1 Transitional Dynamics

Since this is a dynamic model, it must be shown that over time the economy can

converge to the equilibrium values stated above when out of equilibrium. To formulate









the first differential equation recall that x(t) = X(t) N(t). Using this and Assumption 2-1,

x(t)/x (t) = pe(t) n is obtained. The industry innovation probability is solved by using

the resource equilibrium condition (2-15). The resulting differential equation for per

capital R&D difficulty is

Aepu c(t) 2-9

+e 1 i

In balanced growth it is assumed that x = 0. Transforming the above equation by

solving for c(t) yields one that is identical to the equilibrium resource condition. It has

already been shown that it is downward sloping with a vertical intercept of Ai.

The per capital consumption differential equation is derived using the maximization

of consumer utility, (2-4). The riskless rate of return is substituted by using the R&D

equilibrium condition. The result of this is

Aec(t) p ct
c(t) = -~>(~ p ct -C 1- c(1- p) pct) (2-20)


By following the balanced growth assumptions the above equation is found to be

upward sloping with a vertical intercept is A(1- pu)/(A pu). This is strictly below the

vertical intercept of (2-19) since Ai > 1 and pue (0,1]. Therefore the two equations

intersect at point E in Figure 2-2.

Increases in x(t) affect (2-19) positively so positive changes will lead to larger x

and negative changes will lead to a reduced x -. This affect is identified by the horizontal

arrows in Figure 2-2. Likewise, changes in c(t) will have an effect on (2-20). In this case

increases in c(t) will result in a decrease of c with the opposite being true for decreases

in c(t). Figure 2-2 shows this effect with the vertical path arrows. As the figure shows,









there exists a saddlepath where the model will transition from out of equilibrium to the

balanced-growth values as described in (2-17) and (2-18).

2.4.2 Economic Growth

The final term of balanced growth to be concerned with is the overall rate of

growth in the economy. This is defined as the rate of growth in consumer utility which is

calculated by using the log of the subutilty function. Substituting in consumer demands

for the highest quality product leaves


logu~t)c t)
logut) =log loga -jb m, tde (2- 21)


The last term of the above equation represents the sum of all quality levels across

all industries multiplied by log ii. The sum of quality levels is analogous to the sum of


innovations that have occurred to date, m(t) = jmr)dv Summing this term across the


number of industries results in the total number of innovations in the economy. Finally,

remember that balanced growth implies that c(t) is constant over time. Differentiating (2-

21) with respect to time yields the growth rate of consumer utility, g. Sub stituting

O(t) = n/pu into the equation to get the balanced equilibrium growth rate of the economy,


g u nlg A (2-2)
upu

While it appears that this is the same balanced growth equilibrium growth rate as in

Segerstrom (1998), there are three main differences. First the productivity parameter pu

has been constrained to be less than or equal to one. Second, the growth rate n, refers to

the growth in entrepreneurs, not the overall population. Finally, the elimination of the

financial intermediary from the economy will result in no innovation. Without










innovation, it is impossible for the economy to grow which highlights the pivotal role of

the financial sector in long term growth.

2.4.3 Comparative Statics

Changes in the parameters will have different affects on the equilibrium values of x

and c. A summary of all the first order conditions for the equilibrium values of a~ and C

appears in Table 2-1. This section focuses on some of the more important results of the

model .

Proposition 2-3: Increases in the probability of being a successful entrepreneur

will increase the amount of per capital innovation.

Changes in the probability a can be a result of advanced educational attainment or

worker training. A better trained work force will increase the likelihood that any

potential entrepreneur will have the skills necessary to innovate. While the model does

not include R&D subsidies like other Schumpetarian models, other government actions

such as student loans, government grants, and increased funding to higher education will

induce a higher a and thus higher growth. Likewise more flexible standards on

evaluating a potential entrepreneur will result in more innovation. The clarification of the

"prudent man" clause can be seen as a loosening of regulations which then allowed more

potential proj ects to be viewed as good investments (see Kortum and Lerner (2000)).

Proposition 2-4: Increases in required financial intermediary employment will

decrease per capital R&D difficulty while increased use of researchers will increase per

capital R&D difficulty. Neither researcher nor financial intermediary employment levels

affect per capital consumption.










An increase in the number of financial intermediary employees signals an increased

cost of evaluation of each entrepreneurial proj ect. The number of positively rated

proj ects will decrease since a higher threshold of earnings is required to offset the

increased rating cost. Since research labor increases the innovation probability it is clear

that an increase in the number of researchers will lead to an increase in innovation and

therefore R&D difficulty, ceterus paribus. Consumers allocate their per capital

consumption independently of prices and quality. Therefore any changes in parameters

that solely affect production will have no effect on per capital consumption. A similar

argument can be made for increased costs.

Corollary 2-1: Increased costs of evaluating an entrepreneur will reduce the

amount of capital provided by investors.

From the model, it is clear that entrepreneurial proj ects require startup capital.

Fewer acceptable entrepreneurial projects leads to less startup capital provided. In

addition, increased investor skepticism may result in increased costs of evaluation. The

recent accounting scandals and dot-com "shake-out" can be put forth as examples that

would increase investor skepticism. These events also corresponded with decreased

levels of investment in new proj ects. Of course, more research must be done to firmly

establish a causal relationship.

Proposition 2-5: Positive productivity shocks through the parameters C1 and A will

positively impact R&D difficulty.

The affect pu has on R&D difficulty comes directly from Assumption 2-1. Further,

increases in A will increase the probability of innovation. As innovation becomes faster,

the R&D difficulty increases. Illustrations of these can be found through the Internet and










increased diffusion of computers. The transmission of information across the Internet has

increased the productivity by decreased time lags and costs. The automation of various

processes through computers is reflected as changes the parameter pu.

2.5 Conclusions and Extensions

This chapter develops a general equilibrium model to explain the relationship

between the finance sector and economic growth. It makes several improvements on the

previous literature. First, the King and Levine (1993b) model of financial intermediaries

has been updated to adjust for population scale effects. Second, the model uses product

rather than process innovations. Third, unlike Segerstrom (1998), not all R&D labor

makes direct contributions to innovation since some must be allocated to the financial

intermediary. As a result of these changes, the model is able to evaluate the impact of the

financial intermediary on economic growth without relying on the level of population--

something that has been empirically shown to lead to inaccurate growth rate predictions.

By removing the scale effects property, a barrier inhibiting the understanding of the

finance-growth relationship is likewise removed.

To highlight the model's intuitive appeal it has been shown that steady-state growth

is affected by changes in the growth rate of entrepreneurs--a direct consequence of how

the finance sector is included in the model. The significance of the finance sector on

economic growth is highlighted in the way all innovations are funded. Without funding

from an intermediary, there could be no innovation. The model also underscores the

importance of education and other factors since they increase potential entrepreneurial

success and thus, innovation. Through parameter shifts, the model is also able to explain

several recent events. Increases in investor doubt, represented by increases in evaluation










cost, will lead to a decrease in the level of investment. Increases in monitoring (or the

effectiveness of monitoring) increase the amount of innovation taking place in the

economy. In each case the predicted outcome matches the actual outcome as experienced

in the United States during the early part of the decade. Accounting scandals were

followed by a decrease in investment and economic growth. Increased concern by

financial intermediaries over their investments excesses led to increased success.

In addition to the above results, the model provides the foundation for several

extensions that would increase awareness of the contribution a finance sector makes to

economic growth. Currently the model assumes perfect information by the intermediary

after the period of evaluation. Incorporating asymmetric information would prove to be

valuable addition to this line of research. The contribution of venture capital to economic

growth has been considered in recent papers by Kortum and Lerner (2000) and Hellman

and Puri (2000). Although King and Levine (1993b) cite the venture capital process as a

motivation for their model, to directly translate the results of their model and this one as

the impact of venture capital would be an exaggeration of the impact of venture capital

investments. Adding multiple financial intermediaries to the existing framework would

advance the understanding of venture capital's role in economic growth.














R&D Condition


Condition


Figure 2-1. Equilibrium Conditions for Model



























Figure 2-2. Stability of Balanced-Growth Equilibrium






29


Table 2-1. Comparative Statics
x c


Comparative statics are the partial derivative of the equilibrium values (Equations 2-17
and 2-18) with respect to the parameters stated above. By assumption, pue (0,1]. If C1=1,
then 89/8p and 88/8p both equal zero but all other effects are the same.















CHAPTER 3
THE EFFECT OF PRUDENT INVESTOR LAWS ON INNOVATION

3.1 Introduction

As early as 1985, but predominately in the mid-1990s, states began to adopt new

laws that govern investments made by Eiduciaries. The laws were direct enactments of

the Uniform Prudent Investor Act (University of Pennsylvania) as drafted by the National

Conference of Commissioners on Uniform State Laws or only marginally different. The

primary goals of the laws were to introduce modern portfolio theory as it applies to the

prudence of investments, remove restrictions on specific Einancial instruments, and to

allow delegation of management. Previous empirical investigations on related subjects

have shown that laws regulating Eiduciaries affect their portfolio holdings, that previous

reductions of prudence standards for pensions led to increased investment in venture

capital, and that the Einance sector can positively affect technological change and

economic growth. Many theoretical models have also shown the positive affect of

Financial intermediaries on technological change. These previous theoretical and

empirical findings suggest that the changes in prudent investor laws should alter the

composition of investments and lead to increased technological progress. Few papers

have considered the impact of prudent man or prudent investor laws. Of those that do,

none consider the impact of prudent investor laws on something other than equities. This

chapter considers the impact of the law on innovation by considering its impact on

venture capital, R&D, and patenting and therefore provides a significant expansion to the

existing literature.









A variety of regression specifications and models are used to determine empirically

the effect of the new prudence regime. The basic specification considers how R&D,

venture capital investment, and patenting are affected after a state adopts the law. These

regressions ignore the complexity of spillovers across states and how timing of adoption

affects the outcomes. Other regressions included variables that identify neighboring

states that had adopted prudent investor laws in order to account for any interstate

spillovers. Regressions were run on state quartiles since the effect of the law change

should be different for states with different shares of venture capital, R&D, or patenting

or for those states that adopted the law later. The maj ority of evidence shows venture

capital, R&D expenditures, and innovation (as proxied by citation weighted patent

counts) were unaffected by the new prudence standards. In regressions for states in the

bottom quartiles of R&D and patenting there is a slight positive effect, but nothing is

found for venture capital. While previous research has shown changes to the prudence

standards of pension funds had a significant impact on innovation, given the small size of

the impact, the same cannot be said for funds held it trust by banks. The results presented

here provide a significant contribution to the literature on the finance-growth nexus by

showing the effectiveness of financial intermediaries in fostering innovation is bounded

and that not all policy reforms in the United States favorable to financial intermediaries

are equally capable of fostering innovation.

In section two of this chapter, the background of prudent investor laws are

reviewed to underscore the consequences of adopting the new prudence standards. It

continues by summarizing relevant literature studying prudent man, prudent investor, and

the relationship between finance and technological change. The third section reviews the









data sources and empirical methodology. Section four presents benchmark analyses to

provide the intuitive foundation for the main empirical results. It also considers tests the

potential endogeneity of the prudent investor law. Section five presents the main

empirical results of the impact on venture capital, R&D expenditures, and patenting using

detailed data and more advanced techniques. The final section offers conclusions and

implications of the findings.

3.2 Background

3.2.1 Prudence of Investmentl

Laws governing the investments offiduciaries have long been based on the abstract

notion of prudence. The origins of this yardstick date back to 1830 in Harvard College v.

Amory. The opinion from the Massachusetts Supreme Judicial Court for that case stated

"[a trustee] is to observe how men of prudence, discretion and intelligence manage their

own affairs" and then act similarly (Longstreth (1986, p. 12)). In spite of being around

for nearly a century, the prudent man rule was not the rule of law for a maj ority of states

until the mid-1940's. Prior to that, many states chose a more strict approach by

specifying lists of acceptable investments for trusts.2

In the middle of the 20th century, the Model Prudent Investment Act (MPIA),

backed by the American Banking Association was adopted by many states. The MPIA

contained language, nearly verbatim, from the original Harvard College v. Amory ruling

and made 'prudence' the standard in evaluating investments. Rather than specifying a list

of investments that were acceptable, the prudence of an investment was to be decided on



SThis sub-section draws from Longstreth (1986), Shattuck (1951), and Langbein (1995).

2 See Shattuck (1951, p. 502-504) for a detailed classification of state statutes pre- and post-1940.









a case by case basis. The MPIA did, however, explicitly forbid the use of investments

such as those in "new and untried enterprise" which were deemed speculative and

therefore imprudent (Langbein 1995). In spite of these apparently more liberal rules, the

interpretation by courts and subsequent cases of precedence led to rules just as restrictive

as legal lists (see Restatement of the Law (1992)).

The relative impotence of some fiduciaries caused by prudent man laws and their

interpretation led to the creation of the Uniform Prudent Investor Act (UPIA). The law

made five substantive changes to the evaluation of"prudence" and management of

investments. One of the more significant changes was the inclusion of modern portfolio

theory in gauging prudence:

A trustee's investment and management decisions respecting individual assets must
be evaluated not in isolation but in the context of the trust portfolio as a whole and
as a part of an overall investment strategy having risk and return obj ectives
reasonably suited to the trust. (University of Pennsylvania @ 2(b))

The UPIA further stated that no type of investment can be categorically deemed as

imprudent.

If the predictions hold true, the adoption of prudent investor laws will have a large

impact on venture capital investment, R&D expenditures, and innovation. According to

Flow of Funds data from the Federal Reserve,3 COmmercial banks in the U. S. held an

average of $1,358.64 billion in treasury securities, municipal securities, corporate

equities, or mutual fund shares between 1995 and 2004. Much of the investment was

made using money held in trust. It is unlikely that the adoption of prudent investor laws

will cause a complete shift away from these asset types to just venture capital or new and

untried enterprises. However, even a shift of 5% would result in an additional $67 billion

3 Board of Governors of the Federal Reserve System (p. 61), line 5 minus line 10.









investment in R&D-about $12 billion more than was spent on R&D in California in

2000. While banks are directly under the jurisdiction of UPIA it is possible that the law' s

impact is broader. Survey evidence from Longstreth (1986) suggests that pension fund

managers were also constrained in their investment choices by prudent man regulations.

This is in spite of the fact that pension funds have had more liberal prudence guidelines in

place since 1979. One explanation for this is that case law and precedents based on

Restatement of the Law (1959) were common while the same could not be said for more

recent laws governing other fiduciaries. Trustees were thus limiting their investments to

some degree in order to protect themselves from uncertain litigation outcomes

(Longstreth, 1986). With prudent investor guidelines, this uncertainty may fade and

other fiduciaries may use a different investment strategy.

3.2.2 Previous Literature

Previous studies by Del Guercio (1996) and Gompers and Metrick (2001) explore

the impact of the restrictive prudent man guidelines. Del Guercio examines the impact

prudent man laws have on the management of equities by institutional investors. She

observes different reactions by bank and mutual fund managers. While bank managers

are more likely to shift their portfolios toward stocks "viewed by the courts as prudent,"

mutual fund managers do not. It concludes that by forcing intermediaries to protect their

liability, these laws alter incentives and lead managers to act in ways contrary to the best

interest of clients. Gompers and Metrick consider prudent man's effect on institutional

investor behavior and equity prices. They find some evidence that prudent man

regulations affect the ownership of stocks in favor of older and larger firms. In other

words, those investments that are less risky or more prudent. More recently, Hankins,

Flannery, and Nimalendran (2005) investigate the impact of lessening the prudence









standard on equity holdings. They find that the adoption of prudent investor laws affects

institutional portfolios in the predicted direction. Since prudent investor laws weaken the

duty of fiduciaries, there is a shifting away from dividend paying stocks to more risky

assets. This finding is a logical corollary to Del Guercio's and Gompers and Metrick's

analyses. Gompers and Lerner (1998) investigate the changes in the levels of

investments made by venture capital firms from 1972 to 1994. Their approach considers

fluctuations in investment levels as a result of shifts in the supply and demand for venture

capital. One of the more significant shifts in the supply of venture capital resulted from

the 1979 ERISA clarification which allowed pension funds to invest in venture capital.

The Department of Labor' s clarification and the UPIA redefine prudence similarly so the

enactment of prudent investor laws may also be considered a positive shift in the supply

of venture capital. Finally, Kortum and Lerner (2000) investigate the contribution of

venture capital investment to innovation. They find that venture capital dollars are about

three times more likely to lead to innovations than other forms of R&D financing. Thus,

even a small shift in the supply of venture capital investment resulting from UPIA should

significantly increase innovation. The estimate is based on the results of reduced form

and structural regressions, as well as regressions that use the 1979 ERISA clarification as

an instrumental variable to account for the potential endogeneity of venture capital

investment.

The question posed by Kortum and Lerner (2000) is slightly different than the one

posed here. Rather than considering the role of venture capital dollars, this essay

considers the impact of a law that should have affected the type of investments banks

could make with funds held in trust. Thus, this paper tests the impact of the law change









on venture capital, R&D, and innovation rather than testing the contribution of the entire

venture capital industry on innovation. The different tack also implies that the same

concern over the endogeneity of venture capital found in Kortum and Lerner do not

necessarily apply here. In spite these differences, the theoretical model developed in

their paper can be used to illustrate how prudent investor laws should increase the amount

of venture capital as well as the total innovative effort.

Kortum and Lerner (2000) assume that there are costs associated with venture

capital funds (i.e. screening opportunities, recruiting managers, etc.) that may affect the

type of proj ects funded by venture capitalists. As these costs are reduced, more and more

proj ects are capable of being funded by the venture capitalist rather than by traditional

corporate R&D. Kortum and Lerner motivate their use of the ERISA clarification by

stating it "lowered the cost of funds to venture capitalists" (p. 685) which, in their model,

was shown to increase both the total innovative effort and the ratio of venture capital to

corporate R&D. Since the ERISA clarification and prudent investor laws are similar, the

adoption of the new prudence standard is predicted to reduce the cost of funds and thus

lead to an increased share of venture capital and more patenting.

3.3 Data and Empirical Methodology

When taking the previous literature as a whole, the predicted effect of prudent

investor laws on innovation is clear. Less stringent guidelines should cause a shift away

from established assets in favor of other investments, including those in new and

innovative companies. The possibility of such a shift was underscored in Restatement of

the Law (1992) which specifically mentions venture capital funds and new and untried

enterprises as newly acceptable investments under the updated prudence regime. It is

through these two mechanisms that the adoption of prudent investor laws should cause









innovation. The aforementioned theories suggest that money will be used for R&D

purposes which will then lead to innovation. To test the impact of prudent investor laws

on innovation it is necessary to consider the adoption's impact on venture capital, R&D,

and patenting. By doing so the empirical analysis may also test the viability of the above

theories. If patenting were affected, but not R&D or venture capital, then an alternative

and unknown mechanism is causing a change in innovation.

3.3.1 Data

Data on R&D expenditures in a state for the years 1993-2002 is taken from a

survey from the National Science Foundation (National Science Foundation). The time-

period used is limited due to inconsistencies in survey methodology and missing data.

Prior to 1993, new sampling was not done for each survey year. For these non-sampling

years, the National Science Foundation interpolated data from the previous sample. After

1993, sampling was done every year the survey was given. This methodological break

causes pre- and post-1993 survey data to be inconsistent. To bypass this problem, only

data from 1993 forward was used. Another cause for concern is that surveys were not

given annually until 1997, so data for 1994 and 1996 are not available. Further, some

state R&D information was not made publicly available due to privacy restrictions. This

is more likely to affect smaller states since there is anonymity in numbers. Instead of

interpolating for missing values (and perhaps introducing errors in the dataset) data was

taken as-is and missing data caused the exclusion of that observation in regressions. The

dollar values are in billions of 2000 dollars. They were deflated by the implicit GDP

deflator as provided by the Bureau of Economic Analysis.

Venture capital data is from the MoneyTreeThl (PriceWaterhouseCoopers, et al.)

survey jointly sponsored by PriceWaterhouseCoopers, Thomson Venture Economics, and










the National Venture Capital Association. The survey collects information on venture

capital investments made within a state for each quarter starting in 1995. For this

analysis, total annual venture capital disbursements in each state for 1995-2001 were

used. The investment amounts were deflated similarly to R&D expenditures and are in

millions of dollars. There is no indication that the venture capital survey suffers from the

same methodological inconsistencies as the R&D survey, however, the original dataset

does not distinguish between zero investment and unknown investment in a state. If the

survey does not report a dollar value for investment for a particular state in a particular

year then that state is excluded from regressions.

Patent statistics are used as a proxy for innovation. In summarizing previous

research on patents, Griliches (1990) describes how patents weighted by citations may be

a better proxy for innovation than raw patent counts. Patent data from the NBER patent

database was updated by Bronwyn Hall (Hall). Her updated dataset contains detailed

information on patents granted from 1963 to 2002. Due to data limitations on application

year and suspect patent information for 2001-2002, only successful patents applied for

from 1990-2000 are considered.4 The citation weighted patent counts were then summed

to get patent counts by state (including Washington, D.C.) and year of application.

Patents were assigned to a state based on the location of the first inventor listed on the

patent application.' Patent applications from foreign individuals were dropped and



4 Patent information for later years in the database is unreliable due to the time lag between applying for
and being granted a patent. Since the database is based on information on granted patents it is likely that
2001 and 2002 patent information is understated as some applications that are patent-worthy have yet to be
awarded a patent.

5 It is common to assign patents to states in this manner. Other approaches to identifying geograpluc origin
exist: most coming from the literature on geographic knowledge spillovers (see Thompson and Fox-Kean
(2005)).









records with no state information or with values of 'US', 'APO', or US territories were

eliminated. Tables 3-1 through 3-3 provide summary statistics for the data used.

Adoption of prudent investor laws are represented by an indicator variable which

takes on the value of one if a prudent investor law is active in that state during that year.

As shown in Table 3-4, the adoption of prudent investor laws occurred, by and large,

after 1995. By the end of 2000, forty states including Washington, D.C. adopted the

UJPIA. It is possible that investment originating in one state may spillover to neighboring

states. To account for this possibility some regressions included a variable representing

the percent of neighboring states that adopted prudent investor laws. It is plausible that

the neighborhood effects are not linear, i.e., over time the spillovers from states may

increase or decrease exponentially. Select regressions include the squared percent of

neighboring states to account for this possibility. Interstate spillovers were also

accounted for by limiting the regressions to a subset of states.

3.3.2 Empirical Methodology

The empirical analysis uses several different specifications. The fourth section of

this chapter presents the results of a hazard model used to explore the determinants for

the timing of adoption. It also presents estimates on the impact of adoption using a long

difference. The hazard model specification (Equation 3-1) considers how patenting,

among other things, affects the adoption of prudent investor laws and can provide a check

on whether the adoption of these laws is endogenous to the innovativeness of a state.

InLAGADOPTi = a(t) +XiP + i (3-1)

The dependent variable is the difference between the year of adoption and 1980

(the start of the sample). The vector of independent variables, X;, includes average










number of patents for each state from 1980-1989, average Americans for Democratic

Action (ADA) scores for U. S. senators from 1979-1997 (ADA), state average median

income from 1984-1989, and average size of the state's finance sector as a percent of

gross state product. More detail on the sources and predicted signs are provided in

section four of the chapter.

Long differences are also estimated and the results are presented in section four.

These regressions are included to provide a basic understanding of how prudent investor

regimes affected innovative inputs and outputs without having to account for yearly

fluctuations. Each regression uses the specification:

1t~,l ,, -1, = ao + a, Yt+s-k,l t-k,l ]+02 [t+s,l Ptol ]+ 2S + er so -6t,I i, (3 -2)

where Y may be R&D, venture capital, or raw patent counts. The lag for venture capital

and patenting was one year (k=1) but since R&D data is available every other year the

lag was two years (k=2). The difference in prudent investor variables may be zero or 1

but never -1 since no state changed back to a prudent man regime. Venture capital

information was not available for all states for the same years. For example, the level of

venture capital investment in Texas was known for 1995 through 2004 but was only

known for North Dakota for 1997 through 2004, therefore the number of years the

difference encompasses may be different by state. A vector of indicator variables, S,

were included to account for these issues. The vector includes indicator variables for

differences of 7 years, 8, years, and 9 years. Since North Dakota data uses a seven year

difference, the indicator variable representing seven years was equal to 1 and all others

were equal to zero. For Texas, the indicator variable representing nine years was equal to

1 and all others were equal to zero. Other states were similarly recorded.










The main analysis in section Hyve uses the specification:

Y,, = oY,,_z + P,PI,,z + L'P + e,,. (3 -3)

As before, the dependent variable Y may be venture capital, R&D expenditures, or

citation weighted patent counts. A vector of variables on the adoption characteristics of

neighboring states is represented by L. These include year indicator variables, the

percentage of neighboring states that adopted prudent investor laws, and that percentage

squared, although not all regressions included all variables. As was true for the long

difference, R&D data requires that the dependent variable is lagged two years while the

other data uses only a one year lag. The regressions use the Blundell-Bond estimator as

implemented in the xtabond2 module for STATA.6 This estimation procedure was

necessary due to the lagged dependent variable on the right-hand side of Equation 3-3

and the potential for time-invariant characteristics The Blundell-Bond estimator (known

elsewhere as GNINBB Or B-B) uses the first difference of the empirical specification to

remove time-invariant characteristics. It then uses the lagged levels of the dependent

variable as an instrumental variable for the lagged difference of the dependent variable.

Since these instruments alone may not be effective, the estimator also uses lagged

differences as instrumental variables for lagged levels. For this research the endogenous

variables are the prudent investor indicator, neighbor state effects, and the lagged

dependent variable. Year effects are also included in both the dynamic panel regressions

and as exogenous instruments. Tables 3-7 through 3-9 report the results for these

regressions. In addition to coefficient estimates, the tables include p-values for the

Arellano-Bond test for autocorrelation and the Sargan/Hansen test for overidentification.

6 MOre information on the xtabond2 module may be found in Roodman (2005). Appendix B provides a
more thorough treatment of the Blundell-Bond estimator and the reasons why it was preferred over OLS.









For the Blundell-Bond estimates to be consistent, second order serial correlation must not

exist (Arellano-Bond statistic rej ects the null) and the model must not be overidentified

(Sargan/Hansen test statistic must rej ect the null of overidentification). Regressions are

one-step GMM unless otherwise noted.

The sample was divided into quartiles and regressions were run separately for

quartiles of venture capital, R&D, and citation-weighted patent counts. By dividing the

sample into quartiles of the respective dependent variable, it is possible to see how the

law affected states with different sized venture capital, R&D, or innovative output. Only

the top quartile and bottom quartile are presented. The sample was also broken into

quartiles based on the adoption of the law. It is likely that the first states to adopt the law

should be those where the impact, if any, is the greatest. Regressions for the bottom

quartile of adopters were also included to estimate the potential differences prudent

investor laws had across states.

3.4 Tests of Exogeneity & Benchmark Regressions

3.4.1 State Innovative Output and the Timing of Adoption

The UPIA was available for adoption by all states at the same time yet the adoption

of the law was not uniform across states. That some states adopted the law earlier than

others may be exploited as a means of understanding who considers the law important. It

also allows a check of whether or not highly patenting states adopted the law first. If they

did, then there is an endogeneity problem. The specification in Equation 3-1 was used

for all survival analyses.

Estimating survival functions was complicated due to right-censoring and ties.

Data was right censored after 2000 since data on patents is incomplete after that year.

Several ties occurred in the data since several states also adopted the law in the same year









which may affect estimation. Proportional hazards models were used for all estimates to

account for these problems. Due to the small sample size and low computational cost,

exact estimation methods for ties were used (for more information see Allison (1995, p.

127)). The a(t) term is eliminated by using the proportional hazards model.

Americans for Democratic Action scores were used as a proxy for a state's political

leanings. They were taken from Francis and Kenny (1999, pp. 88-89). If a state had a

senator from both parties, the average of the two scores was taken. For states that only

elected members of one party, the ADA score used was the score of that party. The

predicted sign of the ADA coefficient is not obvious, ex ante. On one hand, more liberal

states may feel that prudent investor adoption may only benefit the wealthy. Therefore a

higher ADA score will lead to a longer lag in adoption. On the other hand, more liberal

states are also those with higher levels of income so they may be more likely to adopt

prudent investor laws. To make this relationship more clear, median income is also

included in some of the regressions. Finally, if the finance sector is a large part of gross

state product it is more important to adopt favorable legislation and do so early.

Therefore the coefficient for this variable is expected to be positive.

Averages of patents across the 1980's were used for several reasons. With the

exception of Delaware, no state adopted prudent investor laws prior to 1992. Therefore,

including information from the 1990's may lead to false precision since adoption of the

laws may have affected rates of innovation.7 Adoption of the law is unlikely to be a

reaction to one year' s worth of patent statistics. If patenting did play a role in the

decision to adopt, it is more likely that historical patent rates were considered. Finally,

SAn indicator variable for Delaware was also considered due to its unique nature. It was not, however,
statistically significant.










checks of the data confirmed that despite fluctuations in the amount of patenting, the

ranking of states patenting outcomes did not change much. Since survival analysis uses

rank order, the average is an acceptable method of approximation.

Table 3-6 shows the results of the regressions. Regardless of specification, the

predictive power of a state's average patenting on adoption is indistinguishable from

zero. The coefficient with the most explanatory power belongs to the relative size of the

Einance sector in a state, which is not surprising. An industry with large economic

significance is likely to have the political capital necessary to influence legislative action.

The ADA score coefficient is statistically significant when median income or the size of

the finance sector is excluded. Like average patents, median income is not statistically

significant in any regression. That patenting is not significant suggests that the potential

innovation effects were not a consideration in adopting the law. Thus, the prudent

investor law is assumed to be exogenous in the regressions presented in section five.

3.4.2 Evidence from a Long Difference

Before considering the affect on annual data, it is instructive to see if the change of

the dependent variable over a long period is influenced by the prudent investor law. If

the regressions on the long difference indicate a statistically significant impact then the

impact of the law change seen in more detailed regressions should likewise be large.

However, if the coefficient estimates are not significant, then any impact of the prudence

regime change should be small or nonexistent. The regression results are presented in

Table 3-7.

The change of prudence regimes appears to have no impact on the change in

venture capital, research expenditures, or patenting. Based on the R-squared, the venture

capital and patenting regressions appear to fit the data well. The regression on R&D










expenditures is not as well estimated, this is undoubtedly a result of the every-other-year

nature of the data. These basic regressions are the first (of much) evidence to suggest

that prudent investor laws have not had the predicted affect on innovation.

3.5 Prudent Investor Laws & Innovation

3.5.1 Indirect Investments: Venture Capital

The most obvious mechanism by which prudent investor laws should affect

innovation is through venture capital. Banks invest in venture capital funds which in turn

invest in innovative companies. Therefore changes in investments by banks will have an

indirect affect on innovation. Investment in venture capital causes several difficulties for

the analysis. Unlike R&D, there is no intuitive or legal reason that money originating in

one state must stay within that state's borders. In fact, Florida and Smith (1993) Einds

that venture capital investment gravitates towards regions with an established venture

capital sector. If this is the case, then Minnesota' s adoption of a prudent investor law, for

example, may not impact the amount of venture capital invested in that state. Instead

there may be a flow of money to California. Fortunately, Florida and Smith's findings

also suggest a method on how to deal with the problem of interstate flows. Just as

investments in a state with a small venture capital sector will flow outside of the borders,

the strong pull of a large venture capital sector in a state should keep investments inside

its borders. That is, Minnesota may have trouble keeping its venture capital investment

within its borders but California likely does not. A state with a small venture capital

sector may likewise Eind a negative or zero effect of the prudent investor law.

To consider this issue, states were divided into quartiles based on the size of their

venture capital sectors. The first quartile (states with large venture capital sector) and the

fourth quartile (states with the smallest venture capital sector) were considered










separately. The results are presented in Table 3-8. As predicted, the bottom states

(fourth quartile) observed no impact. Surprisingly however, neither did the top venture

capital states. Another way of dividing the sample is to look at when they adopted the

prudent investor law. That is, whether the state was among the earliest or among the

latest adopters of the new prudence regime. It was not possible to test the impact on the

early adopters, since the data on venture capital begins several years after they adopted

the law implying no variation in the prudent investor indicator variable. It was possible

to consider the late adopters. As predicted (regressions 7 through 9 of Table 3-8), the

coefficient on the prudent investor indicator has a negative sign. Without complementary

evidence from the set of early adopting states it is difficult to interpret the results on the

late adopters. It might be that, as late adopters, these states were crowded out of

collecting venture capital investment. This may have less to do with the prudent investor

law than to do with the timing of adoption; a causal versus correlation issue.

Confirmation from regressions on R&D or citation weighted patent counts are necessary

before drawing any firm conclusions.

3.5.2 Direct Investments: R&D Expenditures

In addition to venture capital investment, the UPIA allows direct investment by

banks to new and untried enterprises. This would represent a direct investment of funds

held in trust by banks that may encourage innovation. In this regard, banks are likely to

act as a venture capitalist would. A large body of research on venture capital (Lerner

(1995), Sorenson & Stuart (2001), and Zook (2002), among others) shows that venture

capital investment is local. That is, venture capital funds invest in firms that are close to

the offices of the venture capitalist in order to provide better guidance to the (perhaps)

uninitiated entrepreneur and protect their investment. If banks are directly investing in










new and untried enterprises then it is logical to assume they follow the same strategy for

similar reasons, therefore the spillover problem seen with venture capital likely does not

affect direct investment by banks.

Of course "local" does not imply "intrastate" since many investments may cross a

state's borders, although, they will be confined to neighboring states. Most regressions

include the percentage of neighboring states that have adopted prudent investor laws to

capture these effects. Percentage terms are needed since not all states are bordered by the

same number of states. Inclusion of the neighbor state impacts causes the exclusion of

Hawaii and Alaska from regressions.

Estimates of the prudent investor law' s impact on direct investment may be found

in Table 3-9. Ignoring regressions (1), (4), and (10) due to the low p-value of the Hansen

statistic, the measured impact of the prudent investor law on R&D is mixed. As with

venture capital, the larger level impact should be found in the Top R&D states, states

with the highest amount of per capital expenditures. In percentage terms, those states with

the least amount ofR&D expenditures should see gains. The latter is confirmed in

regression (6) of Table 3-9. When accounting for interstate spillovers, there is a positive

and statistically significant effect of prudent investor laws for the bottom R&D states.

However, their appears to be no impact of the prudent investor law on the top R&D

states.

As before, the data was divided into quartiles based on when each state adopted the

UJPIA. The evidence for the early adopters is ambiguous since, each specification leads

to a different result: positive, negative, or zero. For late the estimates are based on the

third quartile since, for the fourth quartile, the prudent investor indicator remains









constant. Again, ignoring regression (10) due to the poor Hansen statistic, there appears

to be no impact of the prudent investor law on the R&D of late adopters.


3.5.3 Alternative Mechanisms

The theories cited in section two indicate that prudent investor laws will impact

innovation through the mechanisms of venture capital and R&D. The preceding analysis

tested these mechanisms for any change that could be associated with the adoption of

prudent investor laws and found some weak supporting evidence, but only for the late

adopters of the law for venture capital or those states with the lowest R&D output.

Neither of these findings shows a strong positive impact on innovation that previous

theoretical and empirical work predict. This final section considers the impact on

innovation through any mechanism, not just venture capital or R&D.

Citation weighted patent counts for each state are used to estimate the impact of the

prudent investor law on innovation through any means. As before, states were analyzed,

by quartiles, based on the amount of innovation that takes place within that state' s

borders and the timing of adoption. The analysis for late adopters is based on the third

quartile due to no variation in the prudent investor indicator for the fourth quartile. For

all but one regression, the results indicate the prudent investor laws had no effect on

innovation. The sole exception, regression (5) of Table 3-10, shows that the prudent

investor law had a small, positive impact on innovation for the least patenting states.

This is treated as confirmation of the R&D results in the previous section. If the bottom

R&D states were positively affected, then the likely consequence of this is that patenting

in bottom states would also increase. This is the only evidence that suggests that prudent

investor laws had any effect on innovation. That the impact is weak and not in the states










expected suggests that previous results considering the impact of venture capital on

innovation are likely overstated.

3.6 Conclusion

Prior empirical and theoretical research suggests that the adoption of prudent

investor laws should have an unambiguously positive effect on innovation. The evidence

presented here, by and large, shows otherwise. While some regressions suggest a small

positive impact, most estimates indicate that prudent investor laws have had no impact on

venture capital, R&D expenditures, or innovation. The latter results can be interpreted

either optimistically or pessimistically. The optimistic scenario is that fiduciaries have

yet to fully extend their investments to the point they are legally allowed. This view

suggests that states that have adopted prudent investor laws should experience

technological change and economic growth sometime in the future. The pessimistic view

interprets the results as evidence that prudent investor laws do not affect technological

change. That is, prudence guidelines for Eiduciaries were already sufficient to promote

growth before prudent investor laws were enacted.

It is likely that the latter scenario is the correct description of events. Under the

optimistic view, investment is constrained while case law is established. If this were true,

investments, including those in equities, would not be impacted until well after the

adoption of these laws. This is contradicted by Hankins et al. (2005) who show equity

investments have already been affected by prudent investor laws. The pessimistic view is

consistent with previous empirical findings on prudent investor laws but runs counter to

prior theoretical and empirical work. The results presented here suggest that not all laws

affecting investment decisions will lead to changes in technological change, for good or

ill. Further, not all Eiduciaries will be able to positively impact innovation.









This research, as with most, does not close the door on further investigations on the

topic. Estimates on the impact of prudent investor laws would greatly benefit from

micro-level data on financial intermediary investments. The detailed data could expand

the analysis to include systems of equations rather than changes in the equilibria.
































Due to variation in the time series available for data, correlations are based on 1990-2000 values.


Table 3-1. Correlation Matrix


Citation Weighted Patent Counts (WPC)
WPC, lagged 1 year
WPC, lagged 2 years
Log of deflated VC disburmsents (VC)
VC, lagged one year
VC, lagged two years
Log of deflated R&D expenditures (R&D)
R&D, lagged one year
R&D, lagged two years
Prudent Investor Indicator (PI)
PI, lagged 1 year
PI, lagged 2 years


WPCt WPCtz WPCt-2 VCt
1.0000
0.9745 1.0000


VCtz VCt-2 R&Dt R&Dtz R&Dt-2 PIt PItz PIt-2


0.9567
0.3922
0.3795
0.4069
0.4945
0.4741
0.4809
0.0641
0.0782
0.1171


0.9964
0.4795
0.4678
0.4871
0.5619
0.5411
0.5472
0.0950
0.1077
0.1371


1.0000
0.5050
0.4924
0.5091
0.5702
0.5507
0.5561
0.0996
0.1162
0.1427


1.0000
0.9099
0.8907
0.7484
0.7532
0.7633
0.0828
0.0240
-0.0404


1.0000
0.8907 1.0000
0.7403 0.8336
0.7566 0.8357
0.7603 0.8238
0.0445 0.0089
0.0054 -0.0097
-0.0450 -0.0533


1.0000
0.9834
0.9651
0.0963
0.0005
-0.0090


1.0000
0.9767 1.0000
0.0876 0.1018 1.0000
-0.0337 -0.0184 0.7671 1.0000
-0.0584 -0.0656 0.6852 0.8932 1.0000










Table 3-2. Descriptive Statistics
Std.
Variable Obs Mean Dev. Min Max
Prudent Investor Indicator 561 0.299 0.458 0 1
Log, Deflated VC disbursments 280 4.427 2.304 -3.080 10.667
Log, Deflated R&D expenditures 343 -0.147 1.919 -6.178 3.8236
Citation Weighted Patent Counts 561 6395.18 13090 3 106580
Descriptive statistics are based on 1990-2000 data.













Table 3-3. Descriptive Statistics by Year
Citation Weighted Patent Counts
Standard


Lon, Deflated VC disbursments


Log, Deflated R&D Expenditures
Standard
Mean Median Deviation


Standard
Mean Median Deviation


Year Mean Median Deviation P
1990 10,047 4,283 15,798
1991 9,734 4,111 15,600
1992 9,809 4,098 16,496
1993 9,281 3,760 16,362
1994 9,044 3,371 16,122
1995 8,425 3,170 15,492
1996 6,223 2,298 11,954
1997 4,555 1,536 8,713
1998 2,152 786 4,027
1999 890 308 1,648
2000 185 72 324
2001
Descriptive statistics are based on 1990-2000 data.


-0.38


-0.21


-0.09
-0.08
0.15
-0.06


-0.11


0.08


0.14
0.31
0.32
0.19


1.91


1.88


1.78
2.06
1.76
1.96


3.47
3.78
3.96
4.44
5.16
5.61
4.84


3.53
3.86
3.88
4.66
5.44
5.91
5.12


2.09
2.13
2.14
2.16
2.18
2.41
2.21










Table 3-4. Year of Adoption of UPIA (or equivalent)
Year of
State Adoption State
Delaware 1985 Alaska
Illinois 1992 Vermont
Florida 1993 Washington, D.C.
Maryland 1994 Massachusetts
New York 1995 New Hampshire
South Dakota 1995 Ohio
Colorado 1995 Indiana
New Mexico 1995 Wyoming
Oregon 1995 Pennsylvania
Utah 1995 North Carolina
Washington 1995 Virginia
Oklahoma 1995 Michigan
California 1996 Iowa
Arizona 1996 Kansas
Montana 1996 South Carolina
Rhode Island 1996 Tennessee
West Virginia 1996 Nevada
Nebraska 1997 Texas
Connecticut 1997
Arkansas 1997
Minnesota 1997
Hawaii 1997
New Jersey 1997
Idaho 1997
Maine 1997
North Dakota 1997
Source: Hankins et al. (2005)


Year of
Adoption
1998
1998
1999
1999
1999
1999
1999
1999
2000
2000
2000
2000
2000
2000
2001
2002
2003
2004












Table 3-5. Comparison of Adopters and Non Adopters


Adooters


No. of
States
Adopting
1
1
1
1
8
5
9
2
6
6
1
1
1
1


Avg. Med.
Household
Income
$22,980
33,615
32,490
34,862
33,985
34,263
36,782
39,330
40,966
42,627
43,145
42,969
44,249


Avg. Industrial
R&D
Expenditures
$1,077
3,427
3,651
3,007
2,513
3,588
2,970
2,767
2,729
2,897
2,844
2,801
2,743
2,822


Adoption
Year
1985
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004


Cumulative
Adopters
1
2
3
4
12
17
26
28
34
40
41
42
43
44


Avg.
Population
618,280
6,162,678
8,713,017
7,865,153
5,827,223
6,498,299
5,237,148
4,953,199
4,870,142
5,361,696
5,381,061
5,439,859
5,414,360
5,851,535


Avg. Size of
Finance Sector
$2,893
34,750
44,978
42,612
36,770
42,193
13,966
13,890
14,093
15,531
16,128
16,679
17,686
20,406


Avg. GSP
$13,222
163,510
214,290
205,654
164,516
191,395
165,383
163,523
170,184
189,869
193,815
200,525
207,290
236,413












Table 3-5. Continued
Non Adopters
Avg. Med. Avg. Industrial
Adoption Household Avg. Avg. Size of R&D
Year Income Population Avg. GSP Finance Sector Expenditures
1985 $23,177 4,746,110 $82,836 $14,034 $2,585
1992 30,463 4,953,150 119,334 21,540 2,520
1993 31,147 4,825,907 121,054 21,814 2,487
1994 32,213 4,869,498 128,572 22,611 2,517
1995 33,761 4,945,554 134,834 22,606 2,569
1996 35,551 4,551,691 129,586 20,419 2,039
1997 36,419 5,264,710 157,522 9,297 2,125
1998 37,603 5,719,932 178,305 10,966 2,298
1999 38,822 6,300,352 200,875 11,805 2,208
2000 38,620 6, 156,757 195,849 10,842 1,314
2001 38,839 6,447,856 211,176 12,139 1,371
2002 39,553 6,607,460 221,133 13,073 1,412
2003 39,021 7,246,438 251,296 15,300 1,548
2004 .5,169,695 180,490 10,648 882
Industrial R&D expenditures is for 1995 data only since annual data is not always available. They are reported in thousands of current
dollars. GSP and Finance sector data pre- and post-1997 are inconsistent due to switch to NAICS classifications. GSP & Size of
Finance Sector are in millions of current dollars. Median Income is in current dollars.













Table 3-6. The Timing of Adoption


Proportional Hazard
(4) (5)


(1)

0.00016
(0.0001)


(2)

0.0001
(0.0002)


0.01864
(0.0082)


(3)

0.0000419
(0.0002)


0.01306
(0.0089)


0.0000391
(0.0000)


Avg. Patenting
(1980-1989)


-0.0000842
(0.0002)


-0.0000748
(0.0002)


-0.0000844
(0.0017)


-0.0000745
(0.0002)


ADA score "


0.00159
(0.0097)


0.00746
(0.0098)


Avg. Median Income
(1984-1989)


Finance Sector as a
Percent of GSP



Obs.
Censored obs.


Likelihood Ratio
Score


-0.000003426
(0.0000)


-5.4477E-06
(0.0000)


20.74362
(5.6103)


19.92661
(6.9538)


21.26685
(7.8835)


20.68383
(8.6529)


51
7


1.062
1.187


50
7


6.102
6.347


50
7


7.9365
8.4676


14.4335
15.1175


14.2455
15.0002


14.4424
15.3021


14.2975
15.1154


Wald 1.179 6.266 8.2254 14.6295 14.4011 14.6167 14.3869
LAGADOPT is dependent variable. Standard errors are reported in parentheses.
a Inclusion of ADA scores decreases the number of observations by 1 since the District of Columbia does not have Congressmen.













Table 3-7. Impact of Prudent Investor Laws over Long Difference


Raw
Patent
Counts
(3)
4.8
[24.4875]
0.7554***
[0.0254]
-45.8805
[40.3746]


Venture
Capital


R&D
(2)
-0.4202
[0.3090]
1.5838***
[0.3603]
0.3647
[0.3393]


Constant


-0.4956
[0.3743]
0.9338***
[0.0165]
-39.0688
[3 6342]

-5.3980**
[2.0989]
126.5767
[82.3239]
13.4023
[25.3304]


Lagged Difference Yr+sl-rft-,


Lagged Prudent Investor Difference PI,,s_1-PI,;

Indicator Variables: Sizes of s
s= 7

s=8

s=9


Ob servati ons 50 51 51
R-squared 0.98 0.71 0.98
The dependent variable is Yt+s-Yt, where Y is Venture Capital Investments, R&D Expenditures, or Raw Patent Counts for each state.
R&D expenditures and Raw Patent Counts were available for all states for the same period of time. Sizes of s were included in the
V.C. regression due to differences in the availability of data across states.Robust standard errors in brackets. p<0. 10, ** p<0.05, ***
p<0.01















Table 3-8. Estimates of the Impact on Venture Capital Investments in a State
Top VC States (1st Quartile) Bottom VC States (4th Quartile) Late Adopters (4th Quartile)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Log of deflated VC investment (lagged one year) 0.9664*** 0.9695*** 0.9693*** 0.4118*** 0.5411*** 0.5414*** 0.8797*** 0.9362*** 0.9357***
[0.0263] [0.0249] [0.0264] [0.1399] [0.1412] [0.1418] [0.0662] [0.0522] [0.0543]
Prudent Investor Indicator (lagged one year) -0.023 -0.0393 -0.0389 0.3889 0.1548 0.1558 -0.4352 -0.4561* -0.4563*
[0.0364] [0.0261] [0.0325] [0.5042] [0.4768] [0.4729] [0.2747] [0.2534] [0.2594]
Percent of neighboring states adopted (lagged one year) 0.0151 0.0203 0.0895 -0.2875 0.1983 0.3048
[0.0555] [0.1913] [0.5840] [2.0950] [0.1322] [0.6160]
Percent of neighboring states adopted, squared (lagged one year) -0.0053 0.3192 -0.1293
[0.1767] [1.9806] [0.6409]
Year Indicator Variables Y Y Y Y Y Y Y Y Y

Observations 72 72 72 48 42 42 60 60 60
Number of states 12 12 12 12 10 10 10 10 10

Hansen statistic (P-value) 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Arellano-Bond, AR(1) (P-value) 0.049 0.049 0.050 0.359 0.222 0.221 0.076 0.084 0.084
Arellano-Bond, AR(2) (P-value) 0.240 0.233 0.236 0.383 0.107 0.102 0.809 0.835 0.837
* p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are in brackets. Regressions were run using the Blundell-Bond dynamic
panel estimator. The symbol 'Jf' indicates a two-step robust GMM estimation. All other regressions are robust one-step regressions.
Alaska and Hawaii are dropped when including data on neighboring states. Top VC States (1st Quartile): Illinois, Florida, New York,
Colorado, Washington, California, New Jersey, Massachusetts, Pennsylvania, Virginia, and Texas. Bottom VC States (4th Quartile):
South Dakota, New Mexico, Montana, West Virginia, Arkansas, Hawaii, Idaho, North Dakota, Alaska, Vermont, Wyoming, Iowa, and
Nevada. Late Adopters (4th Quartile): Delaware, Illinois, Florida, Maryland, New York, South Dakota, Colorado, New Mexico,
Oregon, Utah, Washington, and Oklahoma. Late Adopters (4th Quartile): Pennsylvania, North Carolina, Virginia, Michigan, Iowa,
Kansas, South Carolina, Tennessee, Nevada, and Texas.













Table 3-9. Estimates of the Impact on R&D Expenditures in a State
Top R&D States (1st Quartile) Bottom R&D States (4th Quartile)
(1) (2) (3)t (4)t (5)t (6)t
Log of deflated R&D expenditures (lagged two years) 1.1801*** 0.9248*** 1.0988*** 0.7670*** 0.6148*** 0.8146***
[0.1404] [0.0942] [0.1056] [0.1878] [0.1820] [0.2332]
Prudent Investor Indicator (lagged one year) -0.414 0.0597 -0.1708 1.1097* 0.3402 0.9719**
[0.2719] [0.1160] [0.3157] [0.6489] [0.3061] [0.4326]
Percent of neighboring states adopted (lagged one year) 0.0073 0.1723 -0.009 0.9747
[0.1404] [0.7885] [0.4505] [2.8315]
Percent of neighboring states adopted, squared (lagged one year) -0.3338 -0.6081
[0.7441] [3.1410]
Year Indicator Variables Y Y Y Y Y Y

Observations 48 48 48 49 43 43
Number of states 12 12 12 13 11 11

Hansen statistic (P-value) 0.503 0.998 0.999 0.53 0.92 1
Arellano-Bond, AR(1) (P-value)
Arellano-Bond, AR(2) (P-value)













Table 3-9. Continued
Early Adopters (1st Quartile) Late Adopters (3rd Quartile)
(7)t (8) (9) (10)1 (11)? (12)
Log of deflated R&D expenditures (lagged two years) 0.7061*** 0.8472*** 0.8594*** 0.8317*** 0.8053*** 0.9209***
[0.0714] [0.0343] [0.0346] [0.0641] [0.0691] [0.0647]
Prudent Investor Indicator (lagged one year) 0.2822** -0.6323** -0.4427 -0.2947** -0.0068 0.2988
[0.1114] [0.3033] [0.7028] [0.1168] [0.2409] [0.3620]
Percent of neighboring states adopted (lagged one year) 0.0924 -0.6519 -0.7924 -1.7842*
[0.3670] [2.4100] [1.7390] [1.0233]
Percent of neighboring states adopted, squared (lagged one year) 0.4223 1.7835
[2.9749] [1.2786]
Year Indicator Variables Y Y Y Y Y Y

Observations 48 48 48 45 39 39
Number of states 12 12 12 12 10 10

Hansen statistic (P-value) 0.842 0.998 1.000 0.477 0.998 1.000
Arellano-Bond, AR(1) (P-value)
Arellano-Bond, AR(2) (P-value)
*F p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are in brackets. Regressions were run using the Blundell-Bond dynamic
panel estimator. The symbol 'tf' indicates a two-step robust GMM estimation. All other regressions are robust one-step regressions.
Alaska and Hawaii are dropped when including data on neighboring states. The regressions for the Late Adopters (4th Quartile) are
not reported because there is no change in the prudent investor indicator variable. Late Adopters (3rd Quartile) were included instead.
Top R&D States (1st Quartile): Illinois, New York, Washington, California, Connecticut, Minnesota, New Jersey, Massachusetts,
Ohio, Pennsylvania, Michigan, and Texas. Bottom R&D States (4th Quartile): South Dakota, Montana, West Virginia, Nebraska,
Arkansas, Hawaii, Maine, North Dakota, Alaska, Vermont, Washington,















Table 3-10. Estimates of the Impact on Citation Weighted Patent Counts in a State
Top Patenting States (1st Quartile) Bottom Patenting States (4th Quartile)
(1)t (2) (3)t (4)t (5)t (6)t
Citation Weighted Patent Counts (lagged one year) 0.8221*** 0.8847*** -0.2708 0.6544* 0.0859 0.0523
[0.0518] [0.0059] [0.9829] [0.3778] [0.3800] [0.4678]
Prudent Investor Indicator (lagged one year) -1960.1743 -1981.2384 -146977.1501 127.4741 260.3926* 298.8881
[1,985.7316] [1,572.3731] [124,809.3084] [746.4490] [155.6210] [206.0809]
Percent of neighboring states adopted (lagged one year) -3101.5074 112319.2625 232.87 -254.71
[2,345.8993] [92,315.6510] [226.8410] [1,303.2966]
Percent of neighboring states adopted, squared (lagged one year) -391398.7295 512.31
[324,730.7970] [1,449.3961]
Year Indicator Variables Y Y Y Y Y Y

Observations 120 120 120 130 110 110
Number of states 12 12 12 13 11 11

Hansen statistic (P-value) 1.000 1.000 1.000 1.000 1.000 1.000
Arellano-Bond, AR(1) (P-value) 0.074 0.134 0.539 0.061 0.079 0.163
Arellano-Bond, AR(2) (P-value) 0.12 0.201 0.794 0.858 0.097 0.292














Table 3-10. Continued
Early Adopters (1st Quartile) Late Adopters (3th Quartile)
(7) (8) (9) (10) (11) (12)
Citation Weighted Patent Counts (lagged one year) 0.8162*** 0.8236*** 0.8260*** 0.8535*** 0.8636*** 0.8605***
[0.0146] [0.0159] [0.0189] [0.0047] [0.0137] [0.0084]
Prudent Investor Indicator (lagged one year) -588.0025 -625.1823 -645.9265 -486.5116 -925.3682 -963.4427
[661.2821] [628.0003] [613.8107] [759.1179] [860.0614] [894.0843]
Percent of neighboring states adopted (lagged one year) 1,194.2446*** 2366.0929 54.445 1042.0041
[400.3876] [1,448.2099] [790.8376] [1,514.9252]
Percent of neighboring states adopted, squared (lagged one year) -1458.6684 -1,076.40
[1,742.1348] [1,653.2968]
Year Indicator Variables Y Y Y Y Y Y

Observations 120 120 120 120 100 100
Number of states 12 12 12 12 10 10

Hansen statistic (P-value) 1.000 1.000 1.000 1.000 1.000 1.000
Arellano-Bond, AR(1) (P-value) 0.176 0.181 0.173 0.118 0.123 0.127
Arellano-Bond, AR(2) (P-value) 0.233 0.246 0.241 0.225 0.213 0.213
*F p<0.10, ** p<0.05, *** p<0.01. Robust standard errors are in brackets. Regressions were run using the Blundell-Bond dynamic
panel estimator. The symbol 'tf' indicates a two-step robust GMM estimation. All other regressions are robust one-step regressions.
The regressions for the Late Adopters (4th Quartile) are not reported because there is no change in the prudent investor indicator
variable. Late Adopters (3rd Quartile) were included instead. Alaska and Hawaii are dropped when including data on neighboring
states. Top Patenting States (1st Quartile): Illinois, Florida, New York, California, Connecticut, Minnesota, New Jersey,
Massachusetts, Ohio, Pennsylvania, Michigan, and Texas. Bottom Patenting States (4th Quartile): South Dakota, Montana, West
Virginia, Nebraska, Arkansas, Hawaii, Maine, North Dakota, Alaska, Washington, D.C., Wyoming, and Nevada. Early Adopters (1st
Quartile): Delaware, Illinois, Florida, Maryland, New York, South Dakota, Colorado, New Mexico, Oregon, Utah, Washington, and
Oklahoma. Late Adopters (3rd Quartile): Arkansas, Minnesota, Hawaii, New Jersey, Alaska, Vermont, Washington, D.C.,
Massachusetts, New Hampshire, Ohio, Indiana, and Wyoming.















CHAPTER 4
DO PATENT ATTORNEYS MATTER?

4.1 Introduction

The recent patent infringement lawsuit between Research In Motion and NTP has

increased public awareness of problems at the United States Patent and Trademark Office

(USPTO) and the various issues that currently burden patent policy in this country.

While the general public has only recently become aware of the problems, economists

have studied these issues for several years, with research on the topic generally falling

under two categories. The first group of research analyzes the impact of legal changes

that occurred during the 1980's and 1990's (see Jaffe (2000) for a good overview). The

second group examines institutional problems within the USPTO, specifically the

incentives for and effectiveness of the corps of examiners. One common element ignored

in the literature is the role of the patent attorney in the patent approval process.

While economists ignore lawyers, inventors tend to rely upon them during the

application and examination stages (to say nothing of during litigation). In assembling an

application, lawyers may search for prior art, make citations, and write claims to assure

that applications comply with USPTO regulations and to increase the odds a patent will

be granted. They are also the primary contact during the application process and may

meet with examiners, in person, to discuss an application. If inventors are right, and

lawyers provide an invaluable service, then economists have ignored an important

component of the patenting process. If lawyers are as unimportant as implied by

previous research, then inventors are wasting thousands of dollars by hiring one. Thus, to










fully understand the economics of patents it is important to be able to answer the

question, "Do patent attorneys matter?"

This chapter is the first to address the question by investigating how lawyer

characteristics affect a patent application's grant lag--the number of days between the

date an application is filed and the date a patent is granted. To do so, a unique dataset

that contains the names of lawyers representing the patent application was collected.

Additional lawyer characteristics, such as experience and busyness, were calculated to

help quantify and isolate the impact. The empirical analysis shows that lawyers do affect

a patent' s grant lag, but the magnitude of the impact differs by an invention' s technology.

Another finding is that the estimated impact of examiner experience on the grant lag is

different when lawyer characteristics are included than when they are excluded. This is

an important discovery since previous studies have suggested that differences in

examiner experience affect the quality of patents granted.

The rest of the chapter is as follows. The next section motivates the study with a

review of the relevant literature. In the third section, the data and variables are described.

The fourth section presents the empirical methodology and the fifth section discusses the

findings of the research. The final section concludes.

4.2 Literature Review

4.2.1 Theoretical Models

Theoretical models such as Segerstrom (1998), Denicolo (1996) underscore that the

timing of an innovation or patent is important. Firms compete in R&D in order to

develop the next best quality of a product and are compensated with monopoly profits

until the next quality innovation is discovered. In patent races, such as these, it is

imperative that researchers be the first to discover a new quality, or else they cannot









recoup their investment in R&D. Denicolo's model is particularly intriguing in that it

estimates the optimal breadth and length of patent protection. While the length of patent

protection in the United States is statutory, the breadth can potentially be influenced by a

lawyer representing the patent application. To extend Denicolo's thinking by including

legal intermediaries is, in truth, stepping beyond the original model, since having

representation implies there is some sort of negotiation and asymmetric information.

These elements are not present in the current literature on patent races or even some

criticisms of the USPTO (e.g. Griliches (1989) and Merges (1999)). They are, however,

included in models of bargaining.

Bargaining games of alternating offers, such as those in Osborne and Rubinstein

(1990), also motivate the analysis and the importance of timing. In these models, two

players bargain over the allocation of a pie of finite size and negotiations continue until

an agreement is reached. Each player prefers to possess a larger piece of the pie, which

has adverse effects on the size of the pie possessed by his/her opponent. The "driving

force of the model" is that each player has strong preferences over how long it takes to

find a solution. In the full information case an agreement between the two players is

immediately reached. When one player is uninformed, the equilibrium may not occur

immediately since "each player may try to deduce from his opponent' s moves the private

information that the opponent possesses." (p. 91) Regardless of when the agreement is

reached, the uninformed player receives a smaller allocation of the pie compared to the

fully informed player.

With this understanding, it is possible to draw a complete analogy to patent

negotiation; where breadth of protection is the pie to be divided (breadth of zero implies a










rej ected patent) and the lawyer and examiner are the players. It is clear why the owner of

an invention (and therefore lawyer) may prefer a broader patent since they may be used

to deter competition (see Scotchmer (2004)), increase revenue from licensing (Kamien

(1992) and Scotchmer), or facilitate cross-licensing agreements (in the context of

semiconductors, see Hall and Ziedonis (2001)). What is not necessarily clear is why

examiners may care about the size of the pie, since they do not directly benefit from their

allocation. Examiners are agents for society and future inventors. They are therefore

obligated to withhold patent breadth from the current inventor for use by future

inventors.' If the lawyer is the fully informed party (or, by extension, the one with the

most skill) the pie will be divided in his or her favor and potentially done so quicker than

under the opposite scenario. This chapter tests the accuracy of both predictions.

4.2.2 Previous Empirical Analyses

Much of the existing empirical literature on U. S. patent policy deals with legal

issues and the various causes and consequences of increased patent litigation. Even

though this research considers the role of lawyers, literature on litigation is not helpful

since these studies consider events after a patent is granted rather than during the

examination process. Papers that consider institutional issues at the USPTO provide

significant guidance on the topic. Three of the most significant papers in this area are

Cockburn, Kortum, and Stern (2003, CKS), King (2003), and Popp, Juhl, and Johnson

(2004, PJJ).2



SMerges (1999) argues that the incentives provided to examiners are not necessarily in line with the
demands of society.

2 MergeS (1999) criticizes the experience of examiners and suggests reforms but does not provide
supporting empirical evidence.










CKS (2003) explore how differences between patent examiners affect the quality of

granted patents. Their data is based on 182 patents that were examined by the Court of

Appeals for the Federal Circuit. In addition to patent data they collected information on

the examiners that processed the patent application. The examiner data includes tenure

with the USPTO, actual time spent evaluating a patent, size of examiner' s docket, and

total number of patents the examiner reviewed. They cautiously conclude that examiner

discretion, but not workload or examiner experience, may affect the court' s decision to

rule it invalid.

King (2003) uses USPTO Time and Activity Reports to explore the examination

process and patent quality. He finds that examiner hours and examiner actions have been

relatively constant over time, in spite of the increased number of patent applications

submitted to the USPTO. He also shows that other measures of examiner quality, such as

pendency, have declined. These findings reinforce the notion that patent examiners are

extremely important to the patenting process. Of particular interest for this essay is

King's finding that as the number of examiner actions per patent increase, so does the

probability of approval. As King puts it, "this is consistent with examiners interacting

with inventors to improve the application and resulting patent award, a manifestation of

examination quality." (p. 65) While this tacitly supports this essay's hypothesis, he is

cautious since poor inventions may also require additional help from examiners.

PJJ (2004) explore who is most affected by long grant lags, not the causes of the

grant lag. Controlling for characteristics of the application, assignee, country of origin,

and industry (among others) they find that the primary driver of grant lag is the

technological field of the invention. They find that patent applications from the










biotechnology and computer sectors are most likely to have a longer grant lag. Given

their interests on effect rather than causality, examiner characteristics are omitted from

the analysis.

These three empirical papers help to motivate the empirical methodology for this

research. Following PJJ's (2004) example, grant lag is included as the dependent

variable.3 In addition, some of the variables (e.g. country indicators, number of Eigures,

and number of drawing pages) used by PJJ are included in the analysis. Based on the

Endings of CKS (2003) and King (2003), this analysis includes measures of examiner

experience and busyness. These data and their sources are discussed in more detail in the

following section.

4.3 Sources and Descriptive Statistics

4.3.1 Data Sources

The primary source for patent data is the NBER patent citations data-file (Hall,

Jaffe, Trajtenberg, 2001) which has recently been updated by Bronwyn Hall (Hall). The

dataset was expanded to include information from the public-use Hiles made available by

the USPTO (U. S. Department of Commerce). The new information included the full

application date, number of drawing pages, number of Eigures, the name of the primary

examiner and assistant examiner (if present), and the names of lawyers) representing the

patent.4 The names of examiners and lawyers extracted from the USPTO files were not

consistently formatted. On some patents, the individual is identified by surname and first


3 Due to data limitations, the other dependent variables used in King (2003) and CKS (2003) could not be
considered here.

4 IH Some cases the name of the law firm was listed on the patent, not the name of the lawyerss.
Unfortunately, due to mergers between firms and inconsistencies in how the law firm name was entered,
patents that list the law firm name are of limited usefulness.










and middle initials. Other patents may exclude the middle initial and print out the full

first name. Still others may include "Jr.," "III," "Esq.," or a maiden name. These

irregularities had to be corrected in order to properly estimate the contribution of lawyers

and examiners.

The USPTO requires that all attorneys and agents register with the Office of

Enrollment and Discipline (OED) before being allowed to prosecute a patent application.

Upon registration, lawyers are issued a registration number that is to be used on all

official correspondence with the USPTO. Since registration numbers are awarded only

once, it is possible to match the names that appear on a patent with those that appear on

the OED's register, regardless of format or typographical errors. A list of lawyer names

and registration numbers were compiled through various searches on the USPTO's

website.5 Names on a patent that could not be confidently matched with a registration

number were marked and not included in the analysis.

Compared to lawyers, examiner identification was slightly more difficult since the

USPTO does not have a list of registration numbers or other unique identifier publicly

available. As a result, identification of examiners can solely be based on the names

printed on the patent. To identify examiners, each unique primary examiner name was

assigned an identification number. A slight misspelling, e.g. inclusion of suffix or middle


5 A list of registered lawyers currently active is available for download from the OED's webpage. The list
of active registrants was expanded by searching the USPTO's Patent Application Information Retrieval
website (PAIR) and collecting registration numbers and names that do not appear in the active roster.
Registration numbers and names were also found via a document identifying attorneys and agents that did
not respond to an OED survey. The lowest known registration number-name pairing in the 13,388 and the
highest known is 56,991. The compiled roster includes 31,673 names and registration numbers. This set of
names and registration numbers were then matched to those names listed in the expanded patent dataset.
Care was taken to assure that matching was as accurate as possible. If the accuracy of a match was in
question due to misspellings, an additional search of PAIR was made. If a match could not be verified the
lawyer was not assigned a registration number. Lawyers with known duplicate names were not included in
regressions.










initial, may cause several different identification numbers for the same lawyer. To

account for these issues, several different sets of examiner identification numbers were

created and each set differed by its sensitivity to typographical errors, use or ignorance of

"Jr." or "Sr.," or inclusion of middle initial. Regressions were run using both sets of IDs

and the results did not significantly change. The results presented here use the least

tolerant IDs. Assistant examiners were not similarly identified since not all patents have

an assistant examiner and, if they do, work done by assistant examiners is subj ect to

approval by the primary examiner. An indicator variable was used to denote the presence

of an assistant examiner working on the patent application.

4.3.2 Description of Variables

This research uses data on patents granted in either 1999 or 2000 to quantify the

impact that legal intermediaries have on the grant lag--the number of days between when

an application was filed and a patent was approved.6 Many other factors besides lawyer

characteristics may affect a patent' s grant lag. These include application, invention,

country, examiner, and other characteristics as described in Table 4-1. Of these,

application, invention, and country characteristics are those most commonly included in

previous research. The effect of the number of figures on the grant lag is ambiguous. In

some cases, more figures may imply more clarity, making the examination of a patent

quicker and easier. At the same time, more figures may increase the amount of work an

examiner has to do since he or she must assure that what is claimed in the patent matches

the figures and that the figures are clear. Similarly, the number of drawing pages may


6 In 1995, the United States changed the length of a patent from seventeen years after the grant of a patent
to a length of twenty years after the application date. Successful applications filed after June 8, 1995 were
awarded the twenty year term. Only patents that were applied after that date were included in the dataset to
avoid issues caused by this change.









increase or decrease the grant lag. More pages may mean more clarity, but they might

also require more work by the examiner. The number of claims should have a positive

impact on the grant lag. Each claim must be evaluated by the examiner in the context of

the invention, but also based on prior art. As the number of claims increases, so should

the amount of time the examiner has to spend on the patent. An added benefit to

including claims is that it may capture the complexity of an invention. A more complex

invention will likely have more claims, and thus lead to an increase in the grant lag. The

number of citations will likely also have a positive impact on the grant lag, since each

must be checked by the examiner.

The contribution of an invention to society or an industry may also play a role in

the time a patent is reviewed, however importance is difficult to quantify. Two measures,

a generality index and citations received, are included in the regressions to attempt to

capture variation in grant lag associated with importance. Each was included in Hall's

updated patent citations dataset (Hall). The generality index is calculated as a

Herfindahl-type index and evaluates how important the patent was to inventions of other

technologies. A high index implies that the patent was cited by patents from a broad

range of technologies. It is expected that patents with a higher generality index will have

a reduced grant lag since important inventions likely have little prior art to make

comparisons. An important invention may also be cited more by other patents. To also

account for an invention's importance the number of citations received from other patents

is also included in regressions.

Who owns a patent may also affect the grant lag since some firms may prefer

shorter lags than others; however, computational constraints prohibited the inclusion of










assignee indicator variables to fully capture the assignee effects. Another, less

burdensome measure included in regressions is the type of the assignee, i.e. company,

government, individual, or unassigned. Since companies are subject to competitive

pressure they may strongly prefer a shorter lag whereas a government may solely be

interested in receiving a patent and not necessarily the timing of a patent. Assignees may

also differ in their ability to pay a lawyer and it is likely that larger companies have more

money available to hire patent attorneys. Financial data for assignees was not available

but it is possible to account for assignee size by looking at the number of patents the

assignee has owned over time, and how large that number is compared to other firms.

Regressions include two indicator variables, Assignee Percentile (75) and Assignee

Percentile (90), to indicate if the assignee was in the 75th or the 90th percentile of all

assignees in terms of patent ownership, respectively. It is expected that the signs of these

variables will be negative. Since not all patents are assigned, an indicator variable is

included that equals one if the patent was assigned and zero otherwise. Another variable

included is an indicator of the grant year. This is included to capture general USPTO

effects as well as trends in patenting and innovation. Many international inventors Eile

through the World Intellectual Property Organization (WIPO) and applications are later

transferred to the USPTO. The date of application extracted from the patent files may

reflect the date it was Hiled with WIPO and not with the USPTO. Therefore, international

patents may have a longer grant lag than domestic patents. To account for these issues, a

set of indicator variables denoting the country of origin was included in regressions. In

addition to capturing measurement error they may also capture communication problems

between an examiner and the inventor.










Like CKS (2003) and King (2003), this essay includes examiner characteristics, but

there is an important difference between their measure of experience and the one used

here. In CKS and King, examiner experience is the actual number of patents each

examiner reviewed during his or her tenure at the USPTO. Here, examiner experience is

based on the number of patents the primary examiner reviewed since mid-1 996. Since

data prior to 1996 is not available, there is imprecision in the calculations of examiner

experience. For this reason only patents granted in 1999 or 2000 were included in

regressions.' It is assumed that, over time, experience estimates will reach a level

proportional to their true levels. Even if this is not true, experience prior to mid-1996 is

essentially fixed and should be captured in the set of examiner indicator variables

included in all regressions. The impact of experience on the grant lag is likely nonlinear.

Early in an examiner' s career there is significant learning by doing and they become

more efficient at processing patenting applications over time. However it is likely that,

beyond a certain point, experience of the examiner does not improve performance. To

account for this nonlinearity, an experience squared term was included in regressions.

It is possible that a large examiner workload will cause delays in the patent

approval process. In addition to examiner experience and examiner indicator variables,

regressions also include a measure of examiner busyness. Examiner busyness is a count

of the number of patent applications that are currently part of an examiner' s docket. The

count is for all patents granted from mid-1996 to 2002 that have overlapping application

dates and/or overlapping grant dates.




SPatents granted in 2001 and 2002 are not included because generality measures are biased. See Hall,
Jaffe, and Trajtenberg (2001, p. 21-23) for more details.









To analyze the impact of lawyers on patent applications this essay considers two

separate measures of lawyer influence. The first measure is an indicator variable that is

equal to one if there was a lawyer listed on the patent application. Here the presence, but

not the skill, of a lawyer is measured. This general measure of lawyer impact also allows

patents that list a law firm (as opposed to a just a lawyer) to be included in the

regressions. The cost associated with inclusiveness is that an indicator variable cannot

capture the effect of an individual lawyer' s skill. This shortcoming leads to the second

measure of lawyer influence-individual lawyer attributes.

Skill at patent prosecution can be both dynamic and static. Dynamic skill implies

acquired knowledge or learning by doing. Static skill can be the result of intelligence,

education, personality, or other characteristics that are time invariant and inherent to the

lawyer, or at least did not change after passing the OED examination. The second set of

regressions account for both static and dynamic lawyer attributes by including a set of

lawyer indicator variables, lawyer experience, and lawyer busyness. As with examiner

experience, lawyer experience may be nonlinear. Lawyer busyness and experience were

calculated in the same manner as examiner busyness and experience. Beyond a certain

point, additional experience may not make a lawyer more efficient at getting a patent

approved. Therefore regressions also include lawyer experience squared. Since these

characteristics are individual specific effects, patents that list the law firm were not

included in the analysis.

Patent applications often have more than one lawyer working on them. As such, it

would be difficult to completely disaggregate the impact of each individual lawyer on the

patent application. It is assumed that one of the lawyers is the lead lawyer who directs or










guides the work of the other lawyers listed on the patent. For the purposes of this study

the lead lawyer is either the first lawyer listed on the patent or the most experienced

lawyer out of the entire set of lawyers.8 Each is considered separately.

One factor that may be an important determinant of grant lag, but not yet been

discussed is the technology of an invention. As Cohen et al. (2000) and Cohen et al.

(2002) point out, different industries prefer different forms of intellectual property

protection.9 Technology type may also affect the impact of legal representation and

lawyer experience. To account for both grant lag and lawyer issues, separate regressions

are run for fourteen different technology subcategories. The subcategories are based on

the Patent Classification System as of December 31i, 1999 as presented in Appendix 1 of

Hall, Jaffe, and Trajtenberg (2001). Table 4-2 lists the fourteen selected subcategories

and the top three assignees for each subcategory for patents granted during the 1999-2000

window. The chosen technology subcategories intentionally cover a wide range of

products including golf equipment, semiconductors, and agricultural products. This

facilitates comparisons to the entire set of technologies.

Tables 4-3 and 4-4 provide summary statistics of selected independent variables.

The statistics are separated by technology subcategory to underscore the potential

differences across technologies. The information is based on patents where the identity

of either the first lawyer or most experienced lawyer is known. Note that there is

considerable variance in the number of lawyers, examiners, and patents for each

SThe assertion that the first lawyer is the lead lawyer is dubious if lawyer names are listed alphabetically
on patents. Visual inspection of the data does not suggest this pattern is consistently used across patents. It
makes sense that the most experienced lawyer be the lead lawyer since, as with most other positions, more
success typically leads to promotions.

9 Another example is Levin et al. (1987), however the survey occurred before significant reforms of U.S.
patent policy.









subcategory. Significant differences also exist between technology types for the average

number of Eigures and lawyer experience. In spite of these differences, there is

remarkable similarity in the average number of claims, average grant lag, and citations

received across technologies.

The number of international patent applications has increased recently and it is

necessary to control for impacts unique to the country of origin. Table 4-5 shows the

number of patents originating in each country for each technology subcategory. Not

surprisingly, Japan and Germany inventors appear most often in Semiconductor Devices

(Subcategory 46), Motors, Engines & Parts (53), and Optics (54). Table 4-6 presents the

correlations between independent variables and the dependent variable, grant lag, by

technology subcategory.

4.4 Empirical Methodology

4.4.1 Regression Specifications

Several different measures of lawyer impact are considered, each requiring a

different specification. The first measure of lawyer impact is simply an indicator of

having a patent lawyer listed on the patent. These regressions use the specification:

grantlag, = a,, + a,,Represent + a,,Represent x Genderality Index + X' a,, (4-1)

where Xis a vector of independent variables (see Table 4-1 for descriptions) and a~

is the vector of coefficients for those regressors, for patents in technology subcategory i.

This simple specification establishes a baseline understanding of lawyer impact without

being concerned with lawyer heterogeneity caused by differences in experience,

education, or skill. As a consequence of this, all patents for each technology type are









included in regressions, even if those patents list a law firm name as opposed to

individual lawyer names.

The baseline specification assumes all lawyers are on equal footing. In reality

lawyers may differ in a variety of ways including education, experience, personality,

nationality, and gender. All of these differences may, in some way, affect the ability of

the lawyer in representing a patent. To estimate the impact of these factors another set of

regressions are run for each of the fourteen industries. These regressions have the

specification:

grantlag = A, + AExperience + P,,Experience2 + P,,Lawyer Busyness
,(4-2)
+ A4Experience Generality Index + Y' /

where Experience is the number of patents a lawyer previously represented, Lawyer

Busyness is the number of other patents the lawyer is representing at the same time, and

Y is the same vector of regressors as in X of Equation 4-1 plus a set of lawyer indicator

variables. B is the vector of coefficient estimates for Y. Since lawyer specific variables

are used, those patents that did not hire a lawyer and those that listed a law firm were not

included. To account for the two different definitions of lead lawyer, two separate sets of

regressions were run. The first set defined the lead lawyer as the first lawyer listed on the

patent. The second considered the most experienced lawyer as the lead lawyer.

4.4.2 Endogeneity of Lawyer Choice

Before moving forward, it is necessary to acknowledge the potential endogeneity of

lawyer characteristics. Lawyers are chosen by the applicant and are expected to achieve

an outcome consistent with the applicant' s desires. Thus the application' s treatment by

lawyer may depend on the applicant's expectations of the grant lag. If the applicant










expects a patent to take too long, he or she may choose a lawyer to reduce the grant lag.

Similarly, if the grant lag is too short (due to limited breadth or potential rej section of the

application) then an experienced lawyer will be hired to extend the grant lag. Under

either case, there may be an unknown variable guiding the choice of the lawyer and that

omitted variable may be biasing the coefficient estimates.10

One possible unobserved variable that could affect lawyer selection is the value of

the invention to the applicant. An invention may be of high value if it is of significant

importance to a product the inventor or assignee wishes to sell. It may also be of high

value if the patent would be an important piece of a patent portfolio or patent wall. If the

applicant values the patent highly, then the expected grant lag (regardless of how many

days it actually is) is too long, and they will hire an experienced lawyer to reduce the

grant lag. Under this scenario, the unobserved variable causes coefficient estimates to be

biased upwards. Another unobserved variable that may affect lawyer selection is the

invention's merit to be patented. The USPTO requires that inventions must be unique,

novel, and non-obvious for them to be patentable, all of which are unobservable to the

researcher. However, recent criticisms of the USPTO have revolved around the issuance

of patents of questionable validity. Most notably the NTP patents on wireless email.ll If

an invention is of questionable merit, then it likely requires more time and a better lawyer

to convince the examiner that it is a worthy invention. From this perspective, a shorter



"' An alternative argument that is less problematic for the analysis is that lawyer choice is endogenous but
lawyer characteristics, such as experience, are predetermined. The subtle distinction implies that
endogeneity bias does not exist and that the coefficient estimates presented here are correct.

11Other popular examples include the peanut butter and jelly sandwich (Patent No. 6,004,596: represented
by Vickers, Daniels, and Young), Method of Exercising a Cat (5,443,036; represented by Epstein, Edell &
Retzer), and Method of Swinging on a Swing (6,368,227; represented by Peter L. Olson, the 5 year old
inventor' fatherr.










expected grant lag would be associated with the choice of an experienced lawyer and

coefficient estimates would be biased downwards.

The common approach to dealing with endogeneity is to find instrumental variables

for all endogenous regressors (i.e. lawyer experience, experience squared, and the

complete set of lawyer indicator variables) and exploit the variation of the instrument to

get at the true contribution of the endogenous variables.

While the data on patent characteristics is rich, the data on lawyers is limited.

Aside from name, experience, and registration number, very little is known about a

particular lawyer. This paucity of data limits the set of potential instrumental variables.

In regressions not included in this chapter, assignee experience, dates for the OED

examination and the number of patents granted by the USPTO since a lawyer first

appeared on a patent were tested as potential instruments.12 Without exception, the

available instruments were invalid or weak. Since weak instruments may be just as bad

(or worse) than the problems of endogeneity, only OLS estimates are presented.

Even though OLS estimates may be biased, knowing the direction of the bias may

facilitate interpretation. In the preceding discussion two potential omitted variables,

value and merit, showed the bias may be positive or negative, respectively. However it is

likely that invention value is more powerful than an invention's merit. Hiring a lawyer

imposes an additional cost (about $10,000 according to Barton (2000)) on the inventor or

assignee. If these costs could not be offset by additional gains, no lawyer would be hired.

Arguably, high value inventions provide a better means to offset the cost of a lawyer than

meritless inventions. Therefore, it is assumed that the endogeneity bias, if it exists, is


'2 See Appendix C for a more thorough discussion of the instrumental variables considered.










positive and that the coefficients are conservative estimates of the true contribution of a

lawyer.

4.5 Results

4.5.1 Estimated Impact of Lawyers

Table 4-7 presents the results of regressions using the specification in Equation 4-1.

Regressions that exclude the representation indicator are also presented in the table to

highlight the contribution of representation. All regressions include application,

invention, country, examiner, and other variables as described in Table 4-1. Note that the

first and second regressions in the table are for patents in all fourteen technology

subcategories. When considering all subcategories, the impact of representation is fairly

small; having a lawyer will decrease grant lag by approximately ten days. While the

impact is small, the estimate is highly statistically significant. Based on the technology

specific regressions, the driving force behind the result for all technologies appears to be

Nuclear and X-ray inventions. The presence of a legal intermediary for patents of this

technology apparently reduces the grant lag by about one month. Lawyers also appear to

have a statistically signification impact in Optics and Apparel & Textiles. Interestingly,

including the representation indicator does not significantly affect the coefficients on any

of the examiner relevant variables (examiner experience, examiner experiences squared,

and examiner busyness). Regressions with the representation indicator variable also

included an interaction term between representation and the generality index to test the

hypothesis that representation may increase the scope of patent protection. If the

hypothesis is true, the coefficient for this interaction term should be statistically

significant and positive. None of the selected industries confirm this relationship. The










signs and magnitudes of examiner relevant coefficients are consistent with findings from

CKS (2003) and King (2003).

Table 4-8 presents the regression results for patents where the identity of the first

lawyer is known but the regressions do not include lawyer effects. Table 4-9 presents the

results of regressions on the same set of patents but includes the experience, experience

squared, and busyness of the first lawyer listed on the application. Again, inclusion of

lawyer characteristics causes the exclusion of patents with no representation or those that

only list a law firm. When accounting for lawyer specific characteristics, the impact of

the lawyer is not the same across technology subcategories. The grant lag for patents in

biotechnology (subcategory 33), Miscellaneous Drug & Medical (39), Nuclear and X-

rays (44), Semiconductors (46), and Optics (54) appear to be affected by lawyer

experience. In these technologies, increases in lawyer experience will reduce the time it

takes for a patent to be approved. Since the estimates for lawyer experience squared are

generally positive and significant, there appears to be decreasing returns to lawyer

experience.

Regressions included an interaction term for lawyer experience and generality to

again test the hypothesis that experienced lawyers are fighting for more breadth. As

before, the coefficient for this term is predicted to be statistically significant and positive.

In Nuclear and X-ray, the only technology subcategory that the coefficient was

statistically significant, the sign is negative. This appears to rej ect one of the predictions

of bargaining theory as outlined in sub-section 4.2. 1, however caution is merited in its

interpretation. The generality index may indicate the importance of an invention to an

industry and therefore may be an imperfect measure of breadth. The importance of an









invention is unaffected by the lawyer as it is determined during the research and

development stage and not during the prosecution of a patent application. A better

measure of breadth is needed to fully (and accurately) test the predictions of bargaining

theory as they pertain to the patent approval process. Another Einding is that, as lawyers

become more busy, the grant lag increases. This is true in most industries, even those

that are not impacted by differences in lawyer experience.

The same specification was used on patents where the most experienced lawyer

was identified (see Tables 4-10 and 4-11). The results are very similar to those based on

first lawyer characteristics. A likely cause of the similarity is the large number of patents

with only one lawyer. In these cases, the characteristics of the first and most experienced

lawyers are the same. Be that as it may, the regressions using most experienced lawyers

further emphasizes that patent attorneys are significant in the patent approval process.

4.5.2 Examiner Experience and Generality

Regressions in the previous sub-section included an interaction between lawyer

experience and a patent' s generality index to test if, as bargaining theory would suggest,

more experienced patent attorneys attempt to expand the scope of patent protection. The

results indicate weak evidence against the prediction. Another, prediction of bargaining

theory is that experienced examiners would decrease the breadth of a patent. Thus, the

coefficient of an interaction term between examiner experience and the generality index

is predicted to have a negative sign. Recognizing the caveat that the generality index is a

flawed measure of patent breadth there still may be some information gained from testing

this hypothesis.

Regressions in this sub-section use the specification










grantlalg, = Tio + 7tl LowLawExp + 7i2 LowExamExp + 7ta Examiner Busyness
+ Ti4 LowLawExp* LowExamExp + Tis LowExamExp Generality Index + Y' }t (4-3)

The main difference between equations (4-3) and (4-2) is the treatment of experience.

An indicator of low experience is used in these regressions rather than a count of the

number of previous patents represented or reviewed. Lawyers (examiners) that appear on

less than 10 (104) patents--the median experience for all lawyers (examiners)--are

considered to be low experience. The median was based on the highest amount of

experience gained for each lawyer (examiner), through 2002, regardless of technology

type. In addition, to the examiner-generality interaction, lawyer and examiner experience

indicators are likewise interacted.

Tables 4-12 and 4-13 present the results of these regressions using the experience

of the first lawyer or most experienced lawyer, respectively. As before, low lawyer

experience increases the grant lag in most cases. The impact of examiner experience is

less clear. In some cases low experience increases the grant lag, while in other

technology subcategories, the grant lag is decreased. This is in contrast to the evidence in

the previous subsection where examiner experience clearly decreased the grant lag. The

coefficient of the interaction term between examiner experience and the generality index

is rarely statistically significant. In the one case that it is statistically significant

(biotechnology), the sign is the opposite of the prediction. This again provides some

indication that bargaining may not be taking place, however further investigation with a

better measure of patent breadth is warranted.

4.5.3 Examiner Effects

As noted earlier, several papers have been extremely critical of examiners and their

incentives. The criticisms have been motivated, in part, by showing that differences in










examiner experience are a factor in the patent approval process. The results of this

inquiry confirm these findings. However, without exception, the coefficients for

examiner experience are lower (more negative) when not controlling for lawyer

characteristics than when they are included in regressions. For nuclear, x-ray, and

semiconductor patents, the coefficient for examiner experience is nearly half as large

when lawyers are included. This further suggests that examiner differences are important

but that these differences are overstated when not accounting for lawyer effects. In other

words, previous research that does not include lawyer effects has an omitted variable

problem.

4.5.4 Experience as a Proxy for Quality

It might be reasonable to interpret lawyer experience as a proxy for quality since

low quality lawyers are unlikely to continue to attract business. On the other hand, high

quality lawyers will likely attract a large amount of business and have higher levels of

busyness. Tables 4-14 and 4-15 summarize the impact of the median lawyer based on

coefficient estimates from subsection 4.5.2. The impact of lawyer quality, as proxied by

experience, is significantly different across technology types. Semiconductor

technologies experience the largest reduction in the grant lag. There, lawyer quality is

associated with reductions in the grant lag by approximately five months. In Pipes &

Joints and Gas technologies, lawyer quality (experience) appears to increase the grant lag.

Based on the descriptive information provided in Tables 4-3 through 4-6, there is no

obvious explanation for the result. It is therefore likely that these technologies are

intrinsically different than the others considered in this analysis and may be interesting

case studies for future research.










The evidence in Tables 4-14 and 4-15 confirms the findings of Cohen et al. (2000)

and Cohen et al. (2002). In those papers, survey evidence shows that product innovations

are more likely to use patents as a means of protection than are process innovations.

They also find that some industries (e.g. machine tools, medical equipment, and drugs)

place more of an emphasis on patents than others (e.g. food, textiles, and

printing/publi shing). Cohen et al. (2000) also examine the possibility that several

different mechanisms are combined to appropriate intellectual property. Respondents

that indicated a high regard for patents also stressed the importance of lead time; further

evidence that timing and patents are important and that lawyers should be an important

input in the patent approval process.

4.6 Conclusion

This research is the first to consider the impact that patent attorneys and agents

have on patents. Using an original dataset, it was shown that lawyers do affect the

pendency of a patent application, although the impact depends on an invention's

technology. In addition to the results on lawyer impacts, the essay reconfirms and

extends previous work on patents. Specifically, results provide further evidence that

different industries value patents differently and examiner experience does affect the

patent process. Importantly, including lawyer characteristics in regressions decreases the

importance of examiner experience. This suggests that previous research that do not

account for lawyer effects may overstate the problems associated with inexperienced

exammners .

While the results are promising, the endogeneity of lawyers tempers their

applicability. A structural model on the role of lawyers could account for this problem

and lead to further understanding of the relationship between lawyers and examiners.










Bargaining games, such as those presented in Osborne and Rubinstein (1990) would be a

good starting point for such a model. More detailed data on lawyer characteristics may

yield stronger instrumental variables that also may account for endogeneity. Better data

on experience could allow more years of data to be included in the analysis. Finally, this

research does not explore repeated interactions between lawyers and examiners nor does

it estimate any differences between attorneys and agents (i.e. those with or without legal

training). These would be interesting extensions to research on patent attorneys.











Description


Table 4-1. Variable Descriptions
Variable
Application
Claims
Drawing Pages
Figures
Citations Made
Invention
Citations Received
Generality Index
Country
Japan
Germany
France
Great Britain
Canada
Other EPO Countries
Other Countries
Examiner
Examiner Experience

Examiner Experience, squared
Examiner Busyness

Assistant Examiner

Lawyer
Represented

Lawyer Experience

Lawyer Experience, squared
Lawyer Busyness


Number of Claims by Patent
Number of Pages for Figures included in file
Number of Figures included in file
Number of citations made to other patents

Number of citations received by other patents
Herfindahl-like index of generality

Patents from Japanese inventors
Patents from German inventors
Patents from French inventors
Patents from British inventors
Patents from Canadian inventors
Patents from other European Patent Office countries
Patents from other countries (not U.S.)

Number of patents reviewed by primary examiner
since mid-1996
Square of Examiner experience
Number of other patents primary examiner is working
on at the same time
Indicator variable if assistant examiner is used
(1 if true, else 0)

Indicator equal to one if lawyer or law firm was listed
on application
Number of patents represented by lawyer (either first
or most experienced) since mid-1996
Square of Lawyer Experience
Number of other patents the lawyer (first or most
experienced) is working on at the same time


Other Variables
Examiner binaries Set of indicator variables for Primary examiner
Lawyer binaries Set of indicator variables for lawyer
(either first or most experienced)
Year binaries Indicator for year patent was granted (2000=1)
Unassinged Whether or not the patent was assigned
Assignee Type Type of Assignee (individuals, corporations, etc.)
Assignee Percentile (75)a Indicator if assignee is in the 75th percentile of all
assignees, by patent ownership
Assignee Percentile (90)a Indicator if assignee is in the 90th percentile of all
assignees, by patent ownership
a Calculation based on all patents granted from 1976-2000














Table 4-2. Technology Sub categories Descriptions
Subcategory ID Subcateogory Description Largest Patent Holders by Category
11 Agriculture, Food, Textiles American Cyanamid Company, Procter & Gamble Company, and Ciba Specialty Chemicals
Corporation
13 Gas Air Products And Chemicals, Inc., Boc Group, Inc., and Praxair Technology, Inc.
33 Biotechnology Incyte Pharmaceuticals, Inc., Novo Nordisk A/S, and Smithkline Beecham Corporation
39 Miscellaneous-Drug & Med St. Jude Medical, Inc., Sulzer Orthopedics Inc., and 3M Innovative Properties Company
42 Electrical Lighting Motorola, Inc., U.S. Philips Corporation, and Philips Electronics North America Corp.

44 Nuclear & X-rays General Electric Company, Siemens Aktiengesellschaft, and U.S. Philips Corporation
46 Semiconductor Devices Advanced Micro Devices, Inc., International Business Machines Corporation, & Taiwan
Semiconductor Manufacturing Co., Ltd.
53 Motors, Engines & Parts Caterpillar Inc., Robert Bosch Gmbh, and Ford Global Technologies, Inc.
54 Optics Eastman Kodak Company, Fuji Photo Optical Co. Ltd., and Xerox Corporation
62 Amusement Devices Callaway Golf Company, Mattel Inc., and Walker Digital, LLC
63 Apparel & Textile Lindauer Dornier Gesellschaft Mbh, Sipra Patententwicklungs- Und
Beteiligungsgesellschaft Mbh, and Zinser Textilmaschinen Gmbh
64 Earth Working & Wells Schlumberger Technology Corporation, Weatherford/Lamb, Inc., and Halliburton Energy
Services, Inc.
66 Heating Babcock & Wilcox Company, Eastman Kodak Company, and IBM Corporation
67 Pipes & Joints Caterpillar Inc., Dayco Products, Inc., and General Electric Company
Source: Hall, Jaffe, and Trajtenberg (2001)