1

Metropolitan Patenting in the United States: Does Intensity Beget Quality? Working Paper Series: Martin Prosperity Research Prepared by: Deborah Strumsky, University of North Carolina at Charlotte José Lobo, Arizona State University March 2011 REF. 2011-MPIWP-009

2

Abstract

Patents have become a widely used metric in studies of the “knowledge economy” and patent analysis has become a well-established framework for investigating locational and spatial aspects of technological change. Much effort has been devoted towards elucidating the determinants of urban patenting productivity. The relationship between metropolitan patenting productivity and the quality of the patenting output generated by metropolitan-based inventors has gone largely unexplored although only high-quality inventions can create prosperity by way of commercialization. It seems plausible to expect that as a community of inventors accumulates experience, the higher the quality of their inventive output should be. In this short note we seek to clarify the strength of the relationship between metropolitan patenting productivity and quality of inventive output. Utilizing data on patents produced by inventors based in U.S. metropolitan areas (over the 1975 to 2009 period) we construct a straightforward measure of patenting productivity, and using data on the number of citations accumulated by patents we construct a measure of patent quality. We find that there is essentially no relationship between increasing patenting productivity and increasing patenting quality. Keywords: patenting productivity, citations, patenting quality *: Corresponding author. Department of Geography and Earth Sciences, University of North Carolina at Charlotte, McEniry 429, 9201 University City Boulevard, Charlotte, NC 28223. 704-687-5934 (ph), 704-687-5966 (fax).

3

1. Introduction Despite many important caveats, patents have become a widely used metric in studies of the “knowledge economy” and technological change (e.g., Acs and Audretsch, 1989; Griliches, 1990; Jaffe et al., 1993; Jaffe and Trajtenberg, 2002). Patent analysis has therefore become a well-established framework for investigating locational and spatial aspects of technological advance with much effort having been devoted elucidating the determinants of urban patenting productivity (see, for example, Acs, Anselin and Varga, 2002; Bettencourt, Lobo and Strumsky, 2007; Hunt, Carlino and Chatterjee, 2007; Knudsen et al., 2008; Lobo and Strumsky, 2008). The relationship between metropolitan patenting productivity and the quality of the patenting output generated by metropolitan-based inventors has gone largely unexplored, yet only high-quality, read useful, inventions can create prosperity by way of commercialization. In this short note we seek to clarify the strength of the relationship between metropolitan patenting productivity and quality of inventive output.

Assessing the economic quality of patented inventions would require the means to track their commercialization or licensing success, data for which is neither comprehensibly nor reliably available. One way of measuring the intellectual quality of patents is through patent citations, that is, the citations made by a patent to other patents.1 The idea behind using patent citation counts as a measure of quality is that a patent cited by many later patents is likely to contain useful ideas or technologies upon which later inventors are building. Studies have established a strong positive relationship between highly cited patents and technological importance, stock market valuations, and firm profitability (Trajtenberg, 1990; Albert, Avery and McAllister, 1991; Karki, 1997; Trajtenberg, Henderson and Jaffe, 1997; Hall, Jaffe and Trajtenberg, 2001, 2005).2 .

It would seem plausible to expect the very well studied phenomenon of “learning by doing”―productivity and quality improvements resulting from regularly repeating the same type of activity3―to also kick-in when it comes to patenting: the greater the experience of patenting a group of individuals accumulates, the higher the quality of the generated output should be. Of course unlike the case of manufacturing, inventors’ experience does not come from repetitively performing a set of routines involved in producing the same output. But the members of an inventive community capable of producing a large number of patents can benefit from each other’s expertise and skills, which could in turn positively affect the quality of their inventive output. But this reasonable expectation runs into the harsh empirics of patent citations: most

1 The United States Patent and Trademark Office (USPTO), and most other patenting legal systems, require the authors of a patent application to disclose any intellectual material (such as previous patented inventions patents and scientific literature) that is pertinent to the determination of patentability, and these disclosures are recorded as citations. A patent examiner may, during the examination of an application submitted to the USPTO, add citations to relevant prior inventions. There thus two sources of citations on a patent: those entered by the inventor and those entered by a patent examiner. 2 There is an analytical danger in quantifying usefulness (and therefore quality) by way of citation counts: a patented invention might very well be a very important commercial or technological success―think the iPhone or silver-based photographic film―without it being used or incorporated in other distinct inventions. Not all important patents are highly cited, nor is every highly cited patent necessarily ground-breaking. 3 For a discussion of learning-by-doing see Arrow (1962), Sheshinski (1967), Anzai and Simon (1979), Fudenberg and Tirole (1983) Young, (1993), and Auerswald et al. (2000).

4

patents are never cited or receive few citations. Over the span 1790 to 2006, the average number of citations received by the almost 8.5 million patents granted by the United States Patent and Trademark Office (USPTO) is 5.4 with a median of 1 (and a value of 14 for the 95th percentile); the distribution of patent citations is extremely left-skewed.4 Of the patents granted since 1975, almost a quarter have not received a single citation with 39% receiving one citation or less. (The mean and median number of citations per patent over the 1975-2010 period are 7.7 and 3, respectively.) It is hard for a patent to get noticed. Does producing more patents increase the likelihood that high quality patents (that is, patents which garner citations) are produced? Is the inventive output of those metropolitan areas with higher patenting productivity (intensity) of higher average quality?

The discussion is organized as follows. The next section describes how metropolitan

patenting productivity and quality are measured and presents summary statistics for the two metrics. The third section quantifies the effect on citations accrued to metropolitan patents of increases in patenting output and the relationship between metropolitan patenting productivity and patenting quality. Section five concludes. Anticipating our principal result, we find a very weak statistical relationship between patenting productivity and patenting quality and that increases in the former do not substantially increase the latter. 2. Metropolitan Patenting Productivity and Quality

Our spatial units of analysis are the Metropolitan Statistical Areas (MSAs) and Micropolitan Areas of the continental United States which together we treat as comprising the urban areas of the U.S.5 If one accepts―with all the necessary caveats—that patents are a useful proxy measure for inventive activity, then the per capita number of patents authored by individuals residing in metropolitan areas is a plausible indicator of the area’s inventiveness. The source of data on granted patents is a database developed by one of the authors (Strumsky); the database was constructed using data directly downloaded from the United States Patent and Trademark Office (USPTO) and utilizing the National Bureau of Economic Research (NBER) patent file (Hall et al., 2001) for data prior to 1998. To count inventions as close as possible to the time the inventive activity took place, we follow the convention of recording granted patents in the year a patent was applied for.

Every patent application and granted patent lists the inventors’ names and home towns; patents do not, however, provide consistent listings of inventor names or unique identifiers for the authors, so matching procedures were used to uniquely identify inventors across time and locations (the database and matching procedures are discussed in detail in Marx, Strumsky and Fleming (2009)). By identifying individual inventors and the place of residence at the time a

4 For our citation counts and summary statistics we include both the citations made by inventors and those added by patent examiners; the citation counts do not exclude “self-citations,” i.e., citations made by a patent to prior patents authored by some of the same inventors, as these citations do constitute a knowledge flow from prior to current inventions. Removing self-citations increases the percentage of patents with no citations to approximately 44%. 5 MSAs and Micropolitan Areas are defined by the U.S. Office of Management and Budget and are standardized county-based areas having at least one urbanized area (with 50,000 or more population in the case of MSAs or at least 10,000, but less than 50,000, in the case of Micropolitan Areas), plus adjacent territory with a high degree of social and economic integration with the core as measured by commuting ties. Both MSAs and Micropolitan Areas are in effect unified labor markets.

5

patent is applied for each patent to a Metropolitan Statistical Area (MSA).6 If a patent has several authors who reside in the same MSA, the metropolitan area’s patent count includes it once; if the authors of a patent reside in different metropolitan areas, the patent is fractionally assigned to the metropolitan areas where the inventors reside. (We restrict our analysis to patents whose authors are U.S. residents.) The variable patents per capita is constructed by dividing the total number of patents successfully applied for within a year by inventors residing in a metropolitan area divided by the area’s total population (the measure is reported per 10,000 inhabitants).7

Citations made to a patent by subsequent patents are counted up to the end of the period

covered by the database (end of 2010). The citation counts includes both references made to prior inventions made by inventors and those added by patent examiners since we are interested in the extent to which any one invention is relevant to later inventive efforts (but the results and conclusions reported do not much change when using only the references to prior inventions made by inventors). It takes time for a patent to accumulate a large number of citations from later patents, but we have found that most citations are accumulated within eight years of a patent being granted. The measure citations per patent is constructed by counting the citations received, from the application year onwards, by patents assigned to a metropolitan area and diving that count by the number of patents generating the citations.

Tables 1 and 2 present the summary statistics for patents per capita and citations per

patent for all urban areas and for Metropolitan Statistical Areas (MSAs), respectively. To dampen the effects of fluctuations the two measures are smoothed over five-year windows; statistics are reported for six windows: 1975-1979, 1980-1984, 1985-1989, 1990-1994, 1995-1994, and 2000-2004 (patents successfully applied for since 2005 have not had as much time to accumulate citations). Not surprisingly both patenting productivity and patenting quality exhibit significant variation across metropolitan areas (as indicated by the coefficients of variation (CoV)), although the level of variability is much greater for the productivity measure. This reflects the fact that it is a lot easier to increase inventive output than increase inventive quality.

3. Increasing returns for patenting quality?

We now turn to some simple regression exercises in order to elucidate the relationship between location-specific patenting output and productivity on the quality of the generated inventions. These regression models are free from endogeneity concerns due to the unidirectionality of causality inherent in the chosen variables (patents accrue citations but citations do not engender inventions) and the time-lag between the granting of a patent and its being cited by later patents.

6 The patent database covers the period 1975 to 2011, and includes about 5.1 million utility patents (with information on over 1.5 million uniquely identified inventors). A utility patent—also referred to as “patents for invention”—is issued for the invention of “new and useful” processes, machines, artifacts, or composition of matter. Approximately 90% of the patents granted by the USPTO are utility patents. 7 Population data for Metropolitan and Micropolitan Areas was obtained from the Commerce Department’s Bureau of Economic Analysis (http://www.bea.gov/regional/reis/default.cfm?selTable=CA1-3&section=2).

6

One way to quantity the relationship between metropolitan patenting output and quality is to examine the scaling relationship between the number of patents produced by a metropolitan inventive community and the number of citations generated by those patents. Specifically, we hypothesize a power-law relationship between the number of patents and the number of citations accrued by the patents:

,i iCitations c Patents (1)

where c is a constant, i indexes urban areas and β is the scaling coefficient. Logarithmically transforming equation (1) we get the estimation equation: ln( ) ln( ) ,i i icitations c patents (2)

where ε is Gaussian White-Noise. Table 3 shows the estimated scaling coefficients for the six data periods (the equation was estimated using data for all urban areas and only data from MSAs). Interpreted as an elasticity, the beta coefficient informs us as to the percentage increase in total citations induced by a 1% increase in patenting output. The estimated coefficients are all modestly greater than one, indicating an almost proportional increase in total citations per one percent increase in output.

What about the relationship between metropolitan inventive intensity (productivity) and citation intensity? To get at this we estimate the following equation: ln ln .i i icitations per patent c patents per capita (3)

The estimation results for equation (3), using data for urban areas and for MSAS only are presented in Table 4. When data is used for all urban areas the amount of variation in patenting quality explained by differences in patenting intensity is small (no more than 17%) while the magnitude of the effect, as revealed through the regression coefficient, is quite miniscule (never mind the significance level). When considering only metropolitan areas (MSAs) the R2 values improve somewhat but the effect on patenting quality of increasing patenting intensity by 1% is no more than 0.2%. (The R2 and coefficient values hardly change when the regression is restricted to include only those metropolitan areas whose output is above a certain threshold; the relationship remains weak even metropolitan areas with high levels of inventive output.) 4. Conclusions Is increasing metropolitan patenting productivity accompanied by an increase in the average quality of the patented inventions? Our results clearly indicate that the answer is “no.” Urban areas which produce more patents and which have higher patenting productivity do not necessarily produce higher quality patents (that is, patents that are cited by other patents). Consider the contrasting cases of Boston, generally considered a patenting powerhouse and Phoenix, the archetypal Sunbelt metropolitan area (and better known for its real estate excesses than for its innovativeness): over the 2000-2004 period Boston-based inventors produced almost three times more patents than inventors in Phoenix, and Boston’s patenting productivity was 2.2

7

times that of Phoenix. Yet the average citations per patent characterizing Boston’s and Phoenix’s inventive outputs were 5.1 and 4.8, respectively. Boston produced more, but on average not necessarily better, patents than Phoenix.

The relationship examined here is that between the inventive productivity of a metropolitan community and the quality of the inventions generated by the whole of that community. The patenting productivity of a metropolitan area’s inventor community could have a positive effect on technology-specific patenting quality. We have also examined the relationship between citations per patent and patents per inventor for 64 patent technology groupings (communications, drugs, biotechnology, computer software, etc.) and we find that the correlation remains very small. Highly productive inventor communities do not necessarily generate highly cited patents.8

Let’s consider patenting as the search for good locations in a space of technological

possibilities (Fleming, 2001; Fleming and Sorenson, 2001). The features of the space make finding a “good” location (i.e., a high-quality invention) very difficult. A good search strategy on a rugged space is to deploy many searchers (Macready et al., 1996), a search strategy made even more attractive in the case of patenting by the fact that over 70% of all inventors patent at most two times. It is therefore to be expected that having more individuals engaged in invention will not result in patenting of higher average quality but can increase the likelihood that good patents will be found. Returning to the examples of Boston and Phoenix, by having more inventors Boston is more likely to generate a feew high-quality patents. And more generally, metropolitan areas with larger inventor communities and higher patenting intensities are likely to agglomerate high-quality patents (but without average quality differing much across urban areas). We will investigate the agglomeration of high-quality patents in our next research effort.

The absence of a strong relationship between output levels, productivity and quality in metropolitan patenting raises some questions about the use of patent counts as a prognosticator of urban prosperity. For many the interest in patents stems from the putative connection between invention and commercialization (itself an antecedent for wealth creation). But the path from invention to commercialization is fraught with difficulties; surely high-quality inventions stand more of a chance of turning into profitable investments (Jaffe and Lerner, 2004; Lerner, 2009). Creating high-quality inventions is much harder than producing inventions deserving of a patent. And while increasing R&D investments can result in higher output and productivity levels, raising the quality of inventive output is much less responsive to higher levels of funding. When it comes to patenting quality, more is not necessarily better. 8 These results are available upon request.

8

References Acs, Z., Anselin, L., Varga, A. (2002) Patents and innovation counts as measures of regional production of new knowledge. Research Policy 31, 1069-1085.

Acs, Z., Audretsch, D. (1989) Patents as a measure of innovative activity. Kylos 42, 171-180. Albert, M.B., Avery, D., Narin, F., McAllister, P. (1991) Direct validation of citation counts as indicators of industrially important patents. Research Policy 20, 251-259. Anzai, Y., Simon, H.A. (1979) The theory of learning by doing. Psychological Review 86, 124-140. Arrow, K. (1962) The economic implications of learning by doing. Review of Economic Studies 28, 155-173. Auerswald, P., Kauffman, S., Lobo, J., Shell, K. (2000) The production recipes approach to modeling technological innovation: an application to learning by doing. Journal of Economic Dynamics and Control 24, 389-450. Bettencourt, L.M.A., Lobo, J., Strumsky, D. (2007) Invention in the city: increasing returns to patenting as a scaling function of metropolitan size. Research Policy 36, 107-120. Fleming, L. (2001) Recombinant uncertainty in technological search. Management Science 47, 117-132. Fleming, L., Sorenson, O. (2001) Technology as a complex adaptive system: evidence from patent data. Research Policy 30, 1019-1039. Fudenberg, D., Tirole, J. (1983) Learning by doing and market performance. Bell Journal 14, 522-530. Griliches, Z. (1990) Patent statistics as economic indicators: a survey. Journal of Economic Literature 28, 1661-1707. Hall, B.H., Jaffe, A., Trajtenberg, M. (2001) The NBER Patent Citations Data File: Lessons, Insights and Methodological Tools. NBER Working Paper 8498. National Bureau of Economic Research, Cambridge, MA. Hall, B.H., Jaffe, A., Trajtenberg, M. (2005) Market value and patent citations. Rand Journal of Economics 36, 16-38. Hunt, R.M., Carlino, G., Chatterjee, S. (2007) Urban density and the rate of invention. Journal of Urban Economics 61, 389-419.

9

Jaffe, A.B., Lerner, J. (2004) Innovation and Its Discontents: How Our Broken Patent System is Endangering Innovation and Progress, and What to Do About It. Princeton, NJ: Princeton University Press. Jaffe, A.B., Trajtenberg, M. (2002) Patents, Citations, and Innovations: A Window on the Knowledge Economy. Cambridge, MA: MIT Press. Jaffe, A.B., Trajtenberg, M., Henderson, R. (1993) Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics 108, 577-598. Karki, M.M.S. (1997) Patent citation analysis: a policy analysis tool. World Patent Information 19, 269-272. Knudsen, B., Florida, R., Stolarick, Gates, G. (2008) Density and creativity in U.S. regions. Annals of the Association of American Geographers 98, 461-478. Lerner, J. (2009) Boulevard of Broken Dreams: Why Public Efforts to Boost Entrepreneurship and Venture Capital Have Failed--and What to Do About It. Princeton, NJ: Princeton University Press. Lobo, J., Strumsky, D. (2008) Metropolitan patenting, inventor agglomeration and social networks: a tale of two effects. Journal of Urban Economics 63, 871-884. Macready, W.G., Siapas, A.G., Kauffman, S.A. 1996. Criticality and parallelism in combinatorial optimization. Science 271, 56-59. Marx, M., Strumsky, D., Fleming, L. (2009) Noncompetes and inventor mobility: specialists, stars, and the Michigan experiment. Management Science 55, 875-889. Sheshinski, E. (1967) Test of the learning-by-doing hypothesis. Review of Economic and Statistics 49, 568-578. Trajtenberg, M. (1990) A penny for your quotes: patent citations and the value of innovations. RAND Journal of Economics, 21, 172-187. Trajtenberg, M., Henderson, R., Jaffe, A. (1997) University versus corporate patents: a window on the basicness of invention. Economics of Innovation and New Technology 5, 19-50. Young, A. (1993) Invention and bounded learning by doing. Journal of Political Economy 101, 443-472.

10

Table 1. Summary statistics for measures of metropolitan patenting productivity and quality. (For Micropolitan Areas and Metropolitan Statistical Areas.)

1975 - 1979 1980 - 1984 1985 - 1989

Patents per Capita

Citations per Patent

Patents per Capita

Citations per Patent

Patents per Capita

Citations per Patent

Mean 5.54 4.67 Mean 5.38 6.14 Mean 5.58 8.43Median 3.19 3.00 Median 2.79 5.48 Median 3.25 4.00Std Dev 11.79 3.65 Std Dev 17.48 4.29 Std Dev 9.44 5.83

CoV 2.13 0.78 CoV 3.25 0.70 CoV 1.69 0.69N 934 934 N 933 933 N 944 944

Correlation 0.03 Correlation 0.07 Correlation 0.09

1990 - 1994 1995 - 1999 2000 - 2004Patents per

CapitaCitations per

PatentPatents per

CapitaCitations per

PatentPatents per

CapitaCitations per

Patent

Mean 7.77 7.57 Mean 10.02 9.47 Mean 11.66 9.94Median 4.59 5.43 Median 4.64 4.00 Median 4.54 3.14Std Dev 12.72 5.41 Std Dev 47.70 5.41 Std Dev 53.39 4.58

CoV 1.64 0.71 CoV 4.76 0.57 CoV 4.58 0.46N 945 945 N 951 951 N 954 954

Correlation 0.13 Correlation 0.18 Correlation 0.11

11

Table 2. Summary statistics for measures of metropolitan patenting productivity and quality. (Only for Metropolitan Statistical Areas.)

1975 - 1979 1980 - 1984 1985 - 1989Patents per

CapitaCitations per

PatentPatents per

CapitaCitations per

PatentPatents per

CapitaCitations per

Patent

Mean 7.19 4.97 Mean 6.89 6.41 Mean 8.46 9.48Median 5.24 3.00 Median 3.61 5.96 Median 5.10 4.00Std Dev 8.67 2.16 Std Dev 8.83 3.15 Std Dev 9.92 3.48

CoV 1.21 0.43 CoV 1.28 0.49 CoV 1.17 0.37N 363 363 N 363 363 N 363 363

Correlation 0.22 Correlation 0.27 Correlation 0.31

1990 - 1994 1995 - 1999 2000 - 2004Patents per

CapitaCitations per

PatentPatents per

CapitaCitations per

PatentPatents per

CapitaCitations per

Patent

Mean 10.23 11.68 Mean 13.04 10.95 Mean 15.16 9.92Median 6.58 3.94 Median 7.56 5.00 Median 7.98 7.04Std Dev 14.06 5.65 Std Dev 16.83 4.39 Std Dev 20.54 3.33

CoV 1.37 0.48 CoV 1.29 0.40 CoV 1.35 0.34N 363 363 N 363 363 N 363

Correlation 0.28 Correlation 0.34 Correlation 0.39

12

Table 3. Regression results for LN(Citations) = c +βLN(Patents).

intercept β R 2 N intercept β R 2 N intercept β R 2 N1.257 1.082 0.93 934 1.383 1.097 0.93 933 1.667 1.117 0.94 944

(0.010) (0.014) (0.006)1.386 1.068 0.96 363 1.417 1.081 0.94 363 1.814 1.094 0.96 363

(0.013) (0.015) (0.015)

intercept β R 2 N intercept β R 2 N intercept β R 2 N1.821 1.124 0.95 945 1.642 1.129 0.97 951 0.727 1.093 0.96 954

(0.007) (0.008) (0.008)1.958 1.098 0.96 363 1.749 1.107 0.96 363 0.777 1.084 0.97 363

(0.011) (0.009) (0.009)

1975 - 1979 1980 - 1984

1990 - 1994 1995 - 1999

1985 - 1989

2000 - 2004

13

Table 4. Regression results for LN(Citations per Patent) = c +αLN(Patents per Capita).

intercept α R 2 N intercept α R 2 N intercept α R 2 N1.414 0.011 0.05 934 2.622 0.074 0.04 933 2.005 0.114 0.09 944

(0.023) (0.041) (0.021)1.454 0.037 0.07 363 2.383 0.118 0.08 363 1.844 0.153 0.12 363

(0.031) (0.052) (0.019)

intercept α R 2 N intercept α R 2 N intercept α R 2 N3.821 0.161 0.15 945 1.814 0.181 0.17 951 0.874 0.144 0.12 954

(0.022) (0.017) (0.015)3.644 0.211 0.19 363 1.957 0.205 0.24 363 0.921 0.149 0.23 363

(0.031) (0.019) (0.015)

1975 - 1979 1980 - 1984

1990 - 1994 1995 - 1999 2000 - 2004

1985 - 1989

Author Bio Deborah Strumsky is Assistant Professor in the Department of Geography and Earth Sciences at the University of North Carolina at Charlotte (dstrumsky@uncc.edu). José Lobo is Associate Research Professor at the W.P. Carey School of Business and School of Human Evolution and Social Change at Arizona State University (jose.lobo@asu.edu) Working Paper Series The MPI is dedicated to producing research that engages individuals, organizations and governments. We strive to make as much research as possible publicly available. Our research focuses on developing data and new insight about the underlying forces that power economic prosperity. It is oriented around three main themes: economic performance, place, and creativity. Disclaimer The views represented in this paper are those of the author and may not necessarily reflect the views of the Martin Prosperity Institute, its affiliates or its funding partners. Any omissions or errors remain the sole responsibility of the author. Any comments or questions regarding the content of this report may be directed to the author.