Role Invntn US Metro Prodctvty

8/22/2019 Role Invntn US Metro Prodctvty

1/36Electronic copy available at: http://ssrn.com/abstract=2175310

WORKING PAPER

The Role of Invention in U.S. Metropolitan Productivity

Jonathan Rothwell*

Brookings InstitutionMetropolitan Policy Program1775 Massachusetts Ave NW

Washington DC [email protected]

202-797-6314

Jos Lobo

Arizona State UniversitySchool of Sustainability

P.O. Box 875502

Tempe, AZ [email protected]

Deborah Strumsky

University of North Carolina at CharlotteDepartment of Geography and Earth Science

McEniry 4299201 University City Boulevard

Charlotte NC [email protected]

November 2012

*corresponding author; the views expressed here do not necessary reflect those of the BrookingsInstitution or its funders.
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


2/36Electronic copy available at: http://ssrn.com/abstract=2175310

1

Abstract

It is well established in economic literature that innovation and invention are primary engines of

economic growth, but there has been surprisingly little empirical work on the specific

mechanisms, with credit going to such diverse attributes as human capital, population density,

institutions, and geography. Patent records offer a more direct measure of inventive activity at

national and regional scales through an institution that retains at least some of the value of

invention for inventors and their employers. This article examines the relationship between

inventive productivity and economic productivity at the level of U.S. metropolitan economies,

using a new patents database that links inventors to their metropolitan areas. We combine these

data with the economic characteristics of metropolitan areas over the 1980 to 2010 period in a

panel estimation framework and find that metropolitan areas experience higher productivity

growth when they have a large patent portfolio. The effect appears to operate through patent

claims, a putative measure of inventive quality. We conclude that patenting activity is a

distinguishable and important determinant of metropolitan productivity, estimating that a

standard deviation increase in metropolitan claims raises productivity by over one-third of a

standard deviation ten years later. The presence of high-productivity industries, high-tech

workers in a highly educated regional economy, and a large population also contribute to

metropolitan productivity growth apart from whatever contributions they may make to patenting.


3/36

2

1. Introduction

There is broad agreement among economists (a community not know for easily arriving

at consensus) that innovation and invention are the primary engines of economic growth (Aghion

and Howitt, 2008; Barro and Sala-i-Martin, 2003; Jones, 2002; Jones and Romer, 2010; Lucas,

1988; Romer 1986, 1990). Technological change is often manifested in inventionsnew

artifacts, devices, processes, algorithms or materials (Arthur, 2009; Jorgenson 2001; Landes

1998; Mokyr 1990; Rosenberg 1982; Schmookler, 1966). A specific kind of inventive activity,

that which results in the granting of a patent, has become a widely used metric for studying the

knowledge economy (see, for example, Comanor and Scherer, 1969; Griliches, 1981, 1990;

Jaffe and Trajtenberg, 2002; Mansfield, 1986; Scherer, 1965; Scotchmer, 2006). A salient

characteristic of patenting activity in the United States is that it has been an urban phenomenon

(Higgs, 1971; Khan, 2005; Pred, 1966; Sokoloff, 1988; Ullman, 1958), and remains so today

with over 90% of all patents granted by the U.S. Patent Office authored by inventors residing in

metropolitan areas (which we call cities interchangeably in the text that follows).

Consequently patent analysis has also become a well-established framework for investigating

locational aspects of technological change and knowledge spillovers with much effort having

been devoted to elucidating the determinants of urban patenting productivity (see, for example,

Acs, Anselin and Varga, 2002; Audretsch and Feldman, 1996; Bettencourt, Lobo and Strumsky,

2007; Breschi and Lenzi, 2010; Feldman and Florida, 1994; Hunt, Carlino and Chatterjee, 2007;

Jaffe, Trajtenberg and Henderson, 1993; Knudsen et al., 2008; Lobo and Strumsky, 2008;

Sedgley and Elmslie, 2011)

Yet there has been surprisingly little work on the effects of patenting activity on the

productivity of urban economies. Then again there is a paucity of research clearly documenting a


4/36

3

relationship between patenting and economic growth at the national level. Instead, both

internationally and within nations, empirical studies of growth have focused on attributes such as

human capital (Glaeser and Saiz, 2004; Mankiw, Romer, and Weil, 1992) geographic advantages

(Gallup, Sachs, and Mellinger, 1999; Rappaport and Sachs, 2003), and institutions (Acemoglu,

Johnson, and Robinson, 2002 and 2001; Besley and Case, 2003; Glaeser et al., 2004). These

models effectively assume that regions are otherwise identical in their ability to translate these

knowledge-based attributes into innovation and economic growth. Notable exceptions include

papers that have identified a national and state-level growth-enhancing effect from information

technology (IT) production, in addition to IT-use (Daveri and Mascotto, 2006; Gordon 2003;)

and the share of engineers working in the economy (Murphy, Shleifer, and Vishny, 1991).

Invention may lie at the heart of both of these findings.

A few papers, using international data, find that patenting output is associated with

productivity increases but the statistical evidence is weak (Alllred and Park, 2007; Chen, Hu and

Yang, 2010; Crosby, 2000; Falvey, Foster and Greenaway, 2006; Hasan and Tucci, 2010; Hu

and Png, 2009; Park and Ginarte, 1997; Sinha, 2008). A confounding factor is that while

patenting (which can be expected to be highly correlated with private sector R&D expenditures)

might affect growth, it can be growth, instead, which leads to increased R&D investments which

in turn leads to an increase in patenting. In this latter case, economic success ends up promoting

patenting.

Here we examine the relationship between inventive productivity and economic

productivity at the level of U.S. metropolitan economies, and askwhether patenting activity per

se has a discernible effect on metropolitan productivity. Though still a debatable proposition,

there are many reasons to think that patents do capture economically relevant information with


5/36

4

respect to invention (Grilliches 1990; Jaffe and Trajtenberg 2002). A patent is an intellectual

property right granted by a government, in this case that of the United States of America, to an

inventor; it excludes others from making, using, offering for sale, or selling the invention

throughout the United States, or importing the invention into the United States, for a limited time

(generally 20 years from the filing date) in exchange for public disclosure of the invention when

the patent is granted.1 The statutory definition of a patentable invention is that it be novel, non-

obvious (to others skilled in the same field) and useful.2 A patent thus signals the arrival of new

technology. Although it is the case that most patents have been granted to inventions involving

machines or the transformation of one physical substance into another, business methods,

computer programs and algorithms can also be patented.3 Whereas measuring R&D investments

is to measure an input into the process that results in technological change, measuring patents is

a way of tracking the output resulting from R&D efforts.

For thinking about how location-specific patenting can be expected to affect metropolitan

productivity, we find it helpful to use the conceptual framework put forth by Paul Romer (2010)

in discussing how rules interact with the flow of idea to spur economic development.

Technologies, as embodied in patents, are ideas about how to rearrange inanimate objects into

something economically valuable. Abstractly, ideas have two key economic features: non-

excludability, meaning that once an idea is made public it can be freely adopted and distributed

by others, and non-rivalry, meaning that acquisition of an idea (or demand for an idea) does not

reduce its supply. These features mean that the economic value of an idea is not likely to be

1 This right was established over 200 years ago in Article 1, Section 8 of the United States Constitution:

To promote the Progress of Science and useful Arts, by securing for limited Times to Authors andInventors the exclusive Right to their respective Writings and Discoveries.2 35 U.S.C. 101-103 (2000).3 Note, however, that patenting has never been restricted to devices. Consider, as an example, the patentgranted in 1917 to Claurence Saunders for a self serving store which became the model for allsubsequent self-service stores and the modern supermarket (patent # 1,242,872).


6/36

5

captured by its inventors or remain within the confines of the spatial location where it first

germinated. Yet, non-excludability is subject to rules, such as intellectual property law that make

ideas at least partially and temporarily excludable, in the context of economic production. These

laws do not affect the non-rival nature of ideas, which means that the costs of discovering ideas

are fixed and not included in subsequent production, but in the context of excludable protections,

this feature is a benefit to the inventor as well as would-be competitors.

Ideas (or knowledge) can therefore have very large, if temporary, economic value for

inventors and companies. Since inventors and R&D oriented companies are geographically

concentrated (Echeverri and Avala 2009), the economic gains from ideas should also be

geographically concentrated, albeit partially and perhaps only a short period of time. Benefits

from invention are only partially appropriable because of the nonrival and partially non-

excludable nature of ideas. There is strong evidence that technological adoption causes

productivity growth (Brynjolfsson and Hitt. 2003; Bartel, Ichniowski, and Shaw, 2007;

Jorgenson, Ho, and Stiroh. 2008), but only a small fraction of these economic gains are captured

by producers or inventors at the point of sale. This implies that cities will only realize a fraction

of the gains from invention. Still, that small fraction is nontrivial. Toivanen and Vnnen

(2010) find that inventors receive a wage boost of roughly 4-5% within the first four years of a

patents grant, and a much higher increase (as high as 30%) if the patent is highly cited. Likewise,

Bloom and Van Reenen (2002) find that patenting, especially high-quality patents, raise the

productivity of a firm, consistent with other findings that firms benefit from inventing patents

(Lanjouw, Pakes and Putnam, 1998; Hall, Jaffe, and Trajtenberg, 2005).

We recognize various difficulties in clearly teasing out the effects of metropolitan

patenting on local productivity. For one, patenting is related to human capital. Most inventors


7/36

6

have acquired more education than the average person, though only a very small number of

highly educated workers produce patents (Walsh and Nagaoka, 2009; Baumol, Schilling, and

Wolff, 2009). The size and scale of a city (or regional labor market) also determine the degree of

specializationincluding technical specializationthat the area can accommodate (Moretti,

2012; Duranton and Jayet, 2011). Moreover, some industries rely far more heavily on patenting

than others (Graham et al., 2009; USPTO, 2012), and variation in global market scale and the

nature of technology may explain inter-industry variation (Acemoglu and Linn, 2004). Our

theoretical and empirical approach, however, distinguishes patenting from these other

attributeshuman capital, industry, and scale. A metropolitan areas industries provide its

capital, and its workforce provides its human capital. Inventive activity is a purposeful

application of knowledge. Patents represent proprietary technologies (i.e., ideas) that areat

least for a timeuniquely utilized or produced by the patent owners. They can enhance physical

and human capital in the context of market opportunities to innovate.

The discussion is organized as follows. The modeling and estimation framework are

presented in the next section; section three describes the variables used in the econometric

estimations and data sources used in constructing the variables. Section four discusses the

estimation results whiles section five concludes. Anticipating our main findings we find that

metropolitan patenting positively, and distinctively, contributes to an areas economic

productivity.

2. Estimation Model

Patents invented in a metropolitan area embody partially excludable inventions of

potential economic value, and some nontrivial amount of the appropriated value is likely to be

realized and consumed locally. By contrast, human capital, as measured by educational


8/36

7

attainment, is a measure of labor skill, and higher skill results in higher productivity in the

adoption and use of capital, including technology, as well as knowledge, and various labor

techniques (including communication, organization, and other less tangible qualities). In this

sense, we distinguish human capital from inventive capacity, recognizing that inventing or

patenting skills are a distinct and relatively rare manifestation of human capital. This distinction

is sensible because only a tiny fraction of educated workers engage in inventive activity which

generates invention (and these two are by no means synonymous), and human capital, as we

define it and as commonly understood, should add value to production for the reasons just listed.

The distinction we make between patenting (an inventive activity) and human capital

(which is a stock) grounds our decision to treat patenting as a determinant of the technology, or

Total Factor Productivity, term of a production function in a fairly standard growth accounting

exercise (Solow, 1957). Urban population is also included as a determinant, even though labor is

already a factor of production. A compelling theoretical justification for is provided by Adam

Smith (2003/1776) with evidence from Duranton and Jayet (2011): the extent of the market

determines the degree of specialization, and specialization enhances productivity through

learning. To elaborate, large urban populations provide a larger number of services in the non-

traded sector (ranging from day care to administrative and legal services), which allows workers

in the exporting sector to focus on producing and developing products. Agglomeration

economiesby facilitating matching, learning, and sharingalso facilitate productivity growth

through scale effects. In our model, human capital is treated as an additional factor of production,

but could be just as easily thought of as modifying the productivity of labor.

The modeling and estimation frameworkwhich is largely based in Abel, Dey and Gabe

(2012)is as follows. We treat the metropolitan economies of the United States as open


9/36

8

economies sharing common pools of production inputs (see, e.g., Glaeser et al., 1995, Lobo and

Smole, 2002). As a consequence, inter-metropolitan differences in productivity are not explained

in terms of differences in capital per worker, which seems justified given federal laws and

institutions and the fact that capital investments seems to be driven by scale effects which are

included in the model (see Forman, Goldfarb and Greenstein, 2008). We assume further that the

generation of metropolitan output of the ith metropolitan area at time t can be modeled by a

Cobb-Douglass production function so that output (Y) is given by:

, , , , , ,i t i t i t i t i t Y A K L H (1)

whereA is Hicks-neutral technology (often referred to as total factor productivity or TFP),

andK, HandL measure the stock of physical capital, the stock of human capital and the amount

of labor available in a metropolitan area, respectively. The parameters , and represent the

elasticity of output with respect to capital, labor and human capital. The choice of a Cobb-

Douglas production function, with the assumption of constant returns to scale to the rival factors

of production, is justified by the fact that the ratio of metropolitan labor income to metropolitan

total income has remained about 0.7 for all metropolitan areas in the U.S. over the past forty

years for which data is available.

Solving equation (1) for the marginal product of capital and equating it with the

metropolitan rental price of capital yields:

, ,1

, , , ,

, ,

.i t i t t i t i t i t i t i t i t

Y Yr A K L H

K K

(2)

Data on metropolitan stock of physical capital is not readily and reliably reported; by making the

not-too-heroic assumption that the rental price of capital is the same, or nearly the same, across

metropolitan economies we can obtain a capital demand function from equation (2):


10/36

9

, , .i t i t K Yr

(3)

Equation (3) can then be substituted into equation (1):

, , , , .i t i t i t i t Y A Y L H r

(4)

Solving forY, and treating the (/r) term as a constant we get:

11 1 1

, , , , ,i t i t i t i t Y A L H

(5)

from which we can obtain an expression for metropolitan productivity defined as output per

worker:

11 1,, 1 1 1

, , , , ,, ,

.i ti t i t i t i t i t i t i t i t

HYy Y A A h

L L

(6)

Taking the natural logarithm of equation (6) we get our basic estimation equation:

, , ,

1ln ln ln .

1 1i t i t i t y c A h

(7)

with metropolitan human capital measured as the exponential of metropolitan educational

attainment:

, ,exp(% of adult population with a B.A. degree or higher ).i t i t h (8)

We choose an exponential functional form for the human capital measure so that when the

regression equation is estimated a percentage measure (i.e., educational attainment) is not

logarithmically transformed.

Equation (7) states that natural logarithm of metropolitan productivity in a given period is

a function of location-specific technology and human capital. Technology in turn is hypothesized

to be a function of population size (N) and metropolitan patenting (P):


11/36

10

, , , .i t i t i t A P N

(9)

The effects of scale on urban productivity are well documented (Forman, Goldfarb, and

Greenstein, 2008; Philippe-Combes et al, 2012; Puga, 2010; Rosenthal and Strange, 2004).

Recent work has also highlighted the strong and positive relationship between urban (population)

size and inventive productivity (Bettencourt, Lobo and Strumsky, 2007). Patenting activity can

be captured either through output (number of patents) or productivity (patents per worker).

3. Data

Our spatial units of analysis are the 366 Metropolitan Statistical Areas (MSAs) of the

United States. An MSA is defined as a central county with one urbanized areas or more that has

a population of at least 50,000 as well as the adjacent counties having a high degree of social and

economic integration with the central county as measured through commuting flows. An MSA is

in effect a unified labor markets based on county boundaries (we use the 2012 county boundaries

and applied them retrospectively when gathering metropolitan data).4

Metropolitan Gross Domestic Product (GDP) is the metropolitan counterpart to national

GDP and is derived as the sum of GDP originating in all the industries in the metropolitan area.

Data on yearly employment and real metropolitan GDP, measured in chained (2005) dollars, is

provided by Moodys Analytics for the 1980 to 2010 period. Metropolitan area data from

Moodys Analytics uses a consistent definition of metropolitan statistical areas over time (based

on the latest standards). Moodys employment data are based on the Quarterly Census of

Employment and Wages and the Current Employment Statistics from the Bureau of Labor

Statistics, and its MSA output series is based on data from the Bureau of Economic Analysis

4 The county definitions of MSAs are set by the Office of Management and Budget (OMB). For moreinformation on how MSAS go to www.census.gov/population/www/estimates/metroarea.html.


12/36

11

series.Metropolitan productivity is measured as metropolitan GDP divided by total metropolitan

employment.

Human capital is measured as the percentage of adults 25 and older who have obtained at

least a bachelors degree. Practically, bachelors degree or higher educational attainmentis both

easily accessible and has been found to be empirically important (Glaeser and Saiz, 2004;

Baumol, Shilling, and Wolf, 2009; Wadhwa, Freeman, and Rissing, 2008). During the period of

analysis, most workers in high-skilled occupations, including scientists, engineers, investment

bankers, and other professionals have bachelors degrees, and models of human capitals growth-

enhancing effects are based on such workers, as opposed to skilled tradesmen, though that latter

contributed greatly to patenting during the 19th Century (Thomson, 2009). Data on metropolitan

population and human capital was obtained from the Decennial Census (U.S. Census, multiple

years).

Our patent data comes from a newly created database that aggregates detailed

information on all granted patents from the United States Patent and Trademark Office (USPTO)

since 1975 and through 2011.5Every 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 matching procedures are discussed in Marx, Strumsky and Fleming (2009)). By

identifying individual inventors and their place of residence at the time the application for the

patent was filed, each patent and inventor is assigned to a metropolitan area. (We restrict our

5 Section 101 of U.S. Patent Law specifies four categories of inventions or discoveries that are eligible for theprotection of a patent: processes, machines, manufactures and compositions of matter. The United States Patent andTrademark Office (USPTO) grants three types of patents: utility patentsalso referred to as a patents forinventionare issued for the invention of new and useful processes, machines, artifacts, or compositions of

matter; design patents, which are granted for the ornamental design of a functional item; and plant patents which areconferred for new varieties of plants or seeds. 94% of the patents granted by the USPTO are utility patents.


13/36

12

analysis to patents with at least one author who is a U.S. resident.) For purposes of calculating

metropolitan patent counts, a patent with multiple authors is allocated to of each of the distinct

locations in which the authors reside (if several authors reside in the same MSA that location

gets its patent count increased by just one). Patents are counted the year the patent was

successfully applied for so as to measure inventive activity as close as possible to the moment of

invention. The patent database includes information on slightly more than 5 million granted

patents, representing almost 60% of all the patents granted by the patent office since 1790.

The variable patents measures metropolitan patenting output as the number of patents

invented by metropolitan area residents while metropolitan patenting productivity is measured as

patents per worker (defined as patents per one thousand metropolitan workers). We use two

other patenting-related independent variables. Patent claims are the part of a patent that establish

the scope of legal protection granted by the patent; claims describe, in precise and technical

language, what the invention does and how it does it. Citations made to a given patent by other

patents as part of their prior art is often used as an indicator of the quality of an invention, a

characteristic which is otherwise very hard to ascertain using patent data alone (Trajtenberg,

1990).6 We use citations received within eight years of a patents grant as a measure of quality.

We use several control variables in our estimations. To control for scale effects on

productivity we use metropolitanpopulation (data for which is available from the Department of

Commerces Bureau of Economic analysis (BEA)). To distinguish the effects of patenting from

that of employment in high-technology areas we use a measure of high-tech employmentusing

data from Moodys Analytics. Moodys high tech sector consists of 17 distinct 4-digit NAICS

industries deemed especially important to the tech sector. These industries largely overlap with

6By law inventors must identify all publicly information that the inventors drew on and which might berelevant to evaluate a patent's claims of originality. This information, known as prior art consists mainlyof other patented inventions and results reported in the scientific and technical literature.


14/36

13

the manufacturing industries identified by the U.S. Patent Office as patent-intensive industries,

which include computer and electronics manufacturing, chemical manufacturing, machinery

manufacturing, electrical equipment, appliances and components manufacturing, medical

equipment, and miscellaneous manufacturing. The Moodys list add a number of service

industries to this list including software publishing, telecommunications and other information

providers, as well as computer systems design, scientific research and development services, and

medical labs. Note that metropolitan educational attainment and high-technology employment,

measured as a share of total employment, are not that highly correlated: the Pearson correlation

coefficient for the two variables is approximately 0.27 for each decade.

In the estimations we control for industry effects on productivity, and in so doing,

effectively strip the productivity term of any industry specific effects (which would make

making metropolitan productivity conditional on location-specific industry composition).

Industry effects could also be thought of as a separate determinant of metropolitan TFP, as

patents and population certainly affect productivity in different ways for different industries

(consider such geographically clustered industries as manufacturing, finance, and oil and gas

extraction). The variable predicted productivity, used to control for industry effects on

metropolitan productivity, is built using 2-digit level NAICS data for metropolitan area

employment and data on metropolitan Gross Domestic Product. The motivation is to construct an

exogenous productivity effect based on the metropolitan areas sector composition. Productivity

is high and volatile in the oil and gas industry, for example, and depends in part on international

commodity prices, so metropolitan areas with a large share of jobs in that industry will tend to

have inflated productivity if this effect is not considered. To account for this effect, the share of


15/36

14

metropolitan areas employment in each sector was multiplied by U.S. productivity for that sector

(GDP per worker) to arrive at a measure of predicted productivity.

Summary statistics for the principal variables are shown on Table 1. A full time series for

366 metropolitan areas is available for the estimation data, the decennial years of 1980 to 2010.

Over that period, metropolitan areas became larger in population, more educated, more

productive, and more inventive, on a per worker basis and overall. The analysis below will

attempt to sort out the relative importance of each of these trends, with respect to growth in

productivity. Alleviating concerns over possible multicollinearity, the correlation between

patenting activity (measured as either patent counts or patents per worker) and human capital

(percentage of adults 25 and older who have obtained at least a bachelors degree) is in the order

of 0.27 over the 1980 2010 period. Not surprisingly, there is some correlation between an

areas human capital and its patenting capabilities (after all, most inventors have at least a

college degree), but patenting success and high educational attainment are not synonymous: a lot

more is required besides having an educated work force to generate inventions. Also note that

metropolitan patenting (either output or productivity) exhibits much more variability across

metropolitan areas, as indicated by a coefficient of variation value greater than one, than

metropolitan productivity (with an associated coefficient of variability of approximate 0.20), a

consequence of the extreme agglomeration of patenting. This disparity in variability makes it all

the more challenging to econometrically discern the effects of patenting on metropolitan

productivity. A challenge addressed below.

4. Estimation Results


16/36

15

In the estimations both the A and h terms are lagged to avoid endogeneity, though we do

not lag the industry productivity effect, since it is exogenous by construction and current

commodity prices have a strong effect on its importance. We exploit the cross-sectional and time

series nature of the data and implement a panel estimation so as to control for unobserved

heterogeneity. Specifically we use a fixed-effects model (Woolridge, 2010) to control for

location-specific and long-run historic factors that are unchanged during the time period of

analysis, such as natural endowments (like ports), weather, tax and regulatory policies, and

institutional legacies (such as the presence of a research university or national laboratory). The

choice of a fixed-effects framework is further justified by the fact that metropolitan educational

attainment, patenting output and patenting productivity have varying rates of growth which

exhibit very little correlation among them (in the order of 0.12 to 0.19).

In assessing the importance of patents to productivity growth in metropolitan area

economies, the estimations use two measures of patenting: the level of patents (expressed in

natural logarithm) and thousands of patents per worker (also expressed in natural logarithmic

form). We think both are justified: the regressions control for lagged population and productivity,

so, in principle, the coefficient on the number of patents should represent the marginal effect of

one additional patent on productivity growth, conditional on city size and other factors. Yet, it is

possible that the scale effects of patents are somewhat distinct from the scale effects of

population. Moreover, the non-working population is unlikely to patent, so patents per worker

may be a more accurate way of measuring the inventive output of a region. The basic estimation

equation is:

, 1 , 10 2 , 10 3 , 10

4 , 10 5 , 10

,

ln ln ln ln

ln predicted productivity educational attainment

place and decade dummy variables

i t i t i t i t

i t i t

i t

y c patenting Population y

(10)


17/36

16

wherey refers to metropolitan productivity (GDP per worker) at time tand denotes Gaussian

White Noise; dependent variables are lagged 10 years. To dampen the effects of fluctuations the

patenting variables are measured as five-year moving averages. (All the estimations reported

here were done using the Stata software package version 11/SE and the areg procedure). The

estimation results are presented in Table 2 with the model (1) and model (2) differing with

respect to how patenting activity is measured: model (1) uses patenting output while model (2)

uses patents per 10,000 workers as a measure of patenting activity. The coefficients all point in

the hypothesized direction and each are significant. (The impressively highR2values are typical

of panel estimations with a lagged dependent variable.)

To evaluate the relative importance of each of these characteristics the variables were

standardized and the regressions repeated. In so doing, the predicted productivity index (based

on sector concentration) emerges as the most important explanatory variable. This is not

surprising given the huge differences in measured productivity across sectors, which very greatly

in ease of entry and capital costs. From 1980 to 2010, real estate, oil and gas extraction, mining,

utilities, and financial activities averaged output to worker ratios that are more than double the

national average, while such regionally concentrated sectors as leisure and hospitality and health

care and education services were well below average. A doubling of one standard in this index is

associated with a 7.1 percentage point increase in productivity. By comparison, a standard

deviation in productivity growth is 9.2 percentage points.

City population also has a very large effect. Doubling a standard deviation in population

predicts a 6.4 percentage point increase in productivity, conditional on lagged productivity. This

is consistent with a large number of studies finding that population density or city scale causes

higher productivity across countries and cities. Like previous studies (e.g., Harris and Ioannides,


18/36

17

2000), we find that a doubling of population density results in roughly a 6 percentage point

increase in productivity. The regression results remain unchanged whether population size of

population density is used to capture the effects of population on productivity. We focus on

population here since land area is constant for most metro areas since 1980. Our regressions use

both intra-metro and cross-metro variation to reach its conclusions; density, with population in

the same regression, can only explain cross-metro variation, since land area doesnt change much

(given the definition of Metropolitan Statistical Areas).

Patents and human capital are roughly equally important in explaining productivity

across metro areas. In the first specification, doubling a standard deviation in either human

capital or patents is associated with roughly a 2 percentage point increase in the level of

productivity. The patent effect is slightly larger: 1.9 versus 1.7. In the second specification

which uses patents per workerthe human capital measure effect is larger. Comparing a

metropolitan area with twice the standard deviation of another, would predict just 1.2 percentage

points of higher productivity, conditional on lagged productivity. Introducing an interaction

effect between patenting and educational attainmentin case an MSA experienced high growth

rates in both characteristicsdoes not add explanatory power.

To better discern the effects of patenting, one can compare productivity levels across

metro areas of similar size and education rates using 2010 data. Among small metro areas with

very high rates of college attainment (34 percent or better), Boulder, Colorado, Corvallis, Oregon,

Durham, North Carolina and Trenton, New Jersey have high rates of patenting (above 2000 per

million workers, including over 5000 in Boulder and Corvallis) and high productivity (above

$100,000), but other small metro areas with very similar educational attainment rates have much

lower patenting (less than 1000 per worker) and much lower productivity (less than 80,000 per


19/36

18

worker), such as Lawrence, Kansas, Columbia, Missouri, Tallahassee, Florida, and College

Station, Texas. These two groups also have very similar predicted productivity indexes, based

on their sector concentrations.

Likewise, among large metro areas, Atlanta, Baltimore, Seattle, and San Diego have

similar population sizes and levels of education, but the first two are much less productive

ranking 59th and 54th on productivitythan the second two, which rank 19th and 16th

respectively, for 2010. Patents offer an explanation. Atlanta and Baltimore patent less than 1000

patents per worker (average of 2006 to 2010), ranking 98th and 124th, but Seattle and San Diego

patent over 5000 patents per worker, ranking 14th and 17th. Sector-employment differences may

explain Seattle and Baltimore, but Atlanta and San Diego have almost identical predicted

productivity scores.

One obvious methodological limitation is that within sector productivity also varies

widely, especially in manufacturing (though the differences in productivity among

manufacturing industries are surely related to the intensity of R&D expenditure and patenting).

This is dealt with explicitly in the third and fourth columns, as explained below.

The third and fourth models estimated (also shown on Table 2) incorporate an important

control variable: the proportion of the total metropolitan workforce which is employed in the

high technology sector, as defined by Moodys Analytics to include high Intellectual Property

(IP) content and R&D intensive industries. (The predicted productivity control variable used in

the main regressions presented here was built using NAICS sector level data, while the tech-

sector variable includes more detailed industries.) The manufacturing industries included overlap

with those specified by the USPTO as high-patenting.7

7See USPTO, Intellectual Property andthe U.S. Economy: Industries in Focus (Washington DC: 2012).


20/36

19

Inclusion of the high-tech employment variable reduces the size of the coefficients for

patenting, human capital, and population by almost one fifth, but has almost no effect on

predicted productivity, using the sector index. The p-values for the level of patents and the

human capital measure fall from well below 5 percent to between 5 and 6 percent confidence,

reflecting strong multi-collinearity. Nonetheless, as shown in column 4, the patents per worker

variable remains strongly significant below 5 percent confidence levels. Thus, these regression

results provide tentative evidence that the local economic consequences of patenting are distinct

from those of having a highly educated labor force or one employed in tech industries.

Next we consider the well-established fact that not all patents are created equal. Thus, we

introduce a set of regression results which try to control for the presence of high-quality

inventive effort. A seemingly straightforward way to accomplish this is to measure the average

number ofcitations per patent garnered by the patents generated in a metropolitan area or the

average numberclaims per patent (similarly defined but using the number of claims constituting

a patent).8 These regressions are shown as models 1 and 2 on Table 3. Both average claims per

patent and average citations per patent are statistically significant (Table 3 columns 1-2) when

included with the level of patents. The level of patenting, however, become insignificant. These

results do not depend on the inclusion of the hi-tech employment variable in the equation;

moreover, both average quality variables remain significant when the level of patents is removed

from the model.

Citations per patent and claims per patent measure the marginal effect of putting out high

quality patents of any number, but these measures do not account for whether having many

claims or many citations matters in itself (some small-sized low-patenting metropolitan areas

8 Given that time is recorded as the year a patent was successfully applied for, citations received by a patent arecounted within eight years of a patent being granted so as not to bias the measure in favor of patents which havebeen around for longer.


21/36

20

score very highly on average quality or claims). Removing these variables from the regressions

and focusing on claims and citations clarifies the strength of claims.Models 3 and 4, in Table 3,

repeat the regressions from column 3 of Table 1 (the baseline regression with tech employment)

but replace the number of patents with the number of claims or the number of citations.

Claims and citations are effectively alternative measures of invention. Perhaps patents,

per se, are less important than patented claims or patented impact (as measure through citations).

As it happens, each of those measures predicts productivity more than patents when entered in

the estimation equation, suggesting that superior patents yield higher productivity growth than

ordinary patents. Model 5 repeats our baseline regression but adds claims and citations; this

model is essentially a horse race for statistical significance between patents, claims, and citations,

and the level of patent claims is the clear victor. Conditional on claims, having more patents

actually has a negative effect on productivity, as it lowers the number of claims per patent. The

level of citations has no marginal effect alongside claims. In results not shown, the level of

claims is better at predicting growth than average claims per patent when both are in the same

regression. Given these results, Model 3 in Table 3 is our preferred specification. In standardized

terms, it suggests a doubling of a standard deviation in claims is associated with a 2.5 percentage

point increase in productivity.

One surprising outcome of even our preferred model is that human capital and tech-sector

employment are insignificant at the 5% level. Skeptical of this result, we tested the interaction of

the share of bachelors degree graduates in the population with the log of tech sector

employment. This term was strongly significant but did not meaningfully affect the size of the

claims coefficient. With the interaction term in the model, human capital and tech sector

employment become negative and marginally insignificant. We interpret this to mean that tech-


22/36

21

sector workers and highly educated workers contribute to metropolitan productivity growth, but

only when they are both high. The presence of a large tech-sector in a less educated metro does

little to boost marginal productivity growth; similarly, having a highly educated labor force but

no tech sector (e.g. many college town metros) does not contribute to growth, conditional on the

other factors in the model.

We ran additional regressions as robustness checks. We replaced the future level of

productivity as a dependent variable with a growth variable using ln(Productivity)

ln(Productivityt-10) as the new dependent variable and re-estimated Models 1 and 3 of Table 2

and Model 3 in Table 3. Patents continued to be significant in the first but not the second when

tech-sector employment was included. The number of claims continued to be significant.

Finally, we re-ran our preferred model while removing outliers. Observations were

deemed outliers if the error term was large enough, in absolute value, to be at the 95th percentile

of observations in our sample. This resulted in the removal of 64 observations, including two for

San Jose. The effect of patent claims was smaller but still highly significant.9

Overall, the statistical power of patents and especially claims to predict urban

productivity growth strikes us as impressive, and the real effect is likely to be larger. Patenting

almost surely causes growth in moderately high-productivity sectors like information,

professional services, and manufacturing, and specific tech-industries, but our models control for

these effects directly, reducing the marginal effect of patents by almost 30 percentage points. In

addition, there are reasons to believe that much of the value of a patent escapes its regional

economy. The outsourcing of operations from R&D establishments to those outside the

metropolitan areas is a within company phenomena that would mask observed productivity

gains; likewise partial non-excludability and non-rivalry spread the gains outside of the inventors,

9 These results are available upon request.


23/36

22

companies, and metro areas. Expressed otherwise, the national and global effects of patenting on

productivity are likely to be considerably larger than metropolitan level effects.

5. Conclusions

Our investigation was driven by the question of whether patenting activity, distinct from

the presence of high levels of human capital, positively affects metropolitan productivity. By

using a fixed-effects framework, we are able to clearly separate the effects of patenting from

those of educational attainment and persistent metropolitan area characteristics, such as

institutions and geography. While it is the case that most inventors are highly educated, not every

highly educated (or creative) individual invents. Other than sector-effects, the strongest predictor

of metro area productivity growth is population size. The college education rate also has a large

effect, in the presence of tech-sector workers, but so does patenting when measured as the

number of patents or patents per worker. Metro areas with similar attributesin terms of sector

concentrations, human capital, and populationhave faster productivity growth if more patents

are invented in their area. We estimate that a standard deviation increase in patented claims

within a metropolitan area increases the areas productivity ten years later by roughly 39 percent

of a standard deviation in productivity growth.

Despite our efforts to test their robustness, there are reasons to be cautious about these

results. We have assumed that lagging patenting, population, and human capital by ten years

makes them exogenous, at least in a fixed effects panel. Yet, one could imagine omitted variables

that change within states or metropolitan areas that could affect both patenting and productivity,

such as R&D or corporate tax policies or improvements in the quality of the regions research

universities. Further research could explore these possibilities, given the important role research

universities seem to play in invention (Mansfield, 1991), and the possibility that institutional


24/36

23

changes could also matter at the local level (Acemoglu and Dell, 2010). Finally, investments in

technological adoption (rather than R&D) may complement high-skilled, high-patenting

industries, as suggested by recent work, and this could account for some of the positive effects

we observe (Forman, Goldfarb, and Greenstein, 2012).

To summarize, metropolitan areas experience faster productivity growth when they have

a large patent portfolio, and the effect is ever stronger when those patents are of higher quality.

The effect appears to operate through patent claims, a putative measure of inventive quality.

Conditional on the level of claims or claims per patent, an extra patent is not associated with an

increase in productivity. The number of claims granted to inventors living in a metropolitan area

predicts productivity growth better than the number of patents or the number of citations

received. Additionally, the presence of high-tech workers, a highly educated workforce, and a

large population also contribute to metropolitan productivity growth apart from whatever

contributions they may make to patenting and patent claims. These effects, especially from tech

sector workers in an educated labor market, may be capturing non-patented innovations, or they

may reflect returns to knowledge and specialization, realized through higher wage growth and

indirect effects on demand for local services and property (Moretti, 2007 and 2012).

In any case, patented intellectual property claims granted to inventors living in a

metropolitan area have a large additional effect on productivity growth that cannot be explained

by city size, traditional measures of human capital, or industry presence. Apparently regions are

able to capture a significant share of the value of invention, despite its non-rival and only

partially excludable characteristics.


25/36

24

Table 1. Summary Statistics by Decade

Variable Obs. Mean Std.Dev. Min Max

Patents invented by metropolitan area residents, 5-year moving average 363 401 1,272 0 13,311

1000s of Patents per worker, 5-year moving average 363 945 1,515 22 15,254

MSA Productivity in thous 363 87 14 51 162

Predicted MSA Productivity in thous 363 96 18 0 146

Population 363 701,758 1,568,880 55,274 18,900,000

Bachelor's degree or higher attainment rate for population 25 and above 363 0 0 0 1

Employment in High-Tech Sector, 1000s 363 15 42 0 416

Average number of Claims per patent, 5-year moving average 363 14 3 0 25

Average number of Citations received within 8 years per patent, 5-year moving average 363 1 0 0 4

Number of Claims, 5-year moving average 363 6,190 20,871 0 252,379

Number of Citations received within 8 years, 5-year moving average 363 356 1,261 0 14,366

Patents invented by metropolitan area residents, 5-year moving average 363 567 1,741 0 17,529

1000s of Patents per worker, 5-year moving average 363 1,320 1,778 13 16,524



Population 363 634,354 1,469,085 49,832 18,300,000


Employment in Hi-Tech Sector, 1000s 363 17 49 0 480Average number of Claims per patent, 5-year moving average 363 17 4 0 29



Number of Citations received within 8 years, 5-year moving average 363 9,736 36,451 0 451,750

2010

2000


26/36

25

Table 1 (continued). Summary Statistics by Decade.

Variable Obs. Mean Std.Dev. Min Max

Patents invented by metropolitan area residents, 5-year moving average 363 222 659 0 7,850

1000s of Patents per worker, 5-year moving average 363 677 763 21 7,778



Population 363 559,649 1,329,841 28,701 16,800,000


Employment in Hi-Tech Sector, 1000s 363 13 39 0 434

Average number of Claims per patent, 5-year moving average 363 12 3 1 26



Number of Citations received within 8 years, 5-year moving average 363 2,250 7,185 0 78,148

Patents invented by metropolitan area residents, 5-year moving average 363 162 551 0 7,410

1000s of Patents per worker, 5-year moving average 363 554 565 0 4,795



Population 363 501,087 1,228,123 10,913 16,400,000


Employment in Tech Sector, 1000s 363 11 36 0 401Average number of Claims per patent, 5-year moving average 363 9 3 0 22



Number of Citations received within 8 years, 5-year moving average 363 669 2,289 0 29,585

1990

1980


27/36

26

Table 2. Panel Regression of Metropolitan Area Patenting on Metropolitan Area

Productivity, 1980-2010.

Each model includes fixed effects for metropolitan areas. Standard errors in parentheses.

***: p< 0.01, **: p< 0.05, *: p< 0.1.

Model 1 2 3 4

Ln(patents t-10), 5-year moving avg. 0.015** 0.012*

(0.006) (0.006)

Ln(patents per workert-10) 0.016** 0.014**

(0.006) (0.006)

Ln(MSA productivity t-10 ) 0.193*** 0.191*** 0.196*** 0.195***

(0.028) (0.028) (0.028) (0.028)

Ln(predicted MSA productivity t-10) 0.552*** 0.558*** 0.547*** 0.552***

(0.047) (0.047) (0.047) (0.047)

Ln(population t-10) 0.080*** 0.092*** 0.066*** 0.075***(0.021) (0.019) (0.022) (0.021)

Bachelor's degree attainment rate t-10 0.320** 0.332** 0.267* 0.270*

(0.138) (0.136) (0.141) (0.140)

Ln(hi-tech employmentt-10) 0.018* 0.019*

(0.010) (0.010)

Year 1990 -0.059 -0.062

(0.017) (0.017)

Year 2000 0.006 0.066*** 0.005 0.068***

(0.010) (0.008) (0.010) (0.008)

Year 2010 0.059*** 0.062***

(0.017) (0.017)

-0.064 -0.332 0.122 -0.106

Constant (0.364) (0.336) (0.378) (0.357)

1,082 1,082 1,082 1,082

Observations 0.930 0.930 0.930 0.930

Adjusted R-squared 0.92 0.92 0.92 0.92

Dependent variable: ln(MSA productivity t)


28/36

27

Table 3. Panel Regression of Metropolitan Area Patenting Quality on Metropolitan Area

Productivity, 1980-2010.

Each model includes fixed effects for metropolitan areas. Standard errors in parentheses.

***: p< 0.01, **: p< 0.05, *: p< 0.1.

Model 1 2 3 4 5Ln(patents t-10) 0.006 0.009 0.043***

(0.007) (0.006) (0.016)

Ln(average number of claims per patentt-10 ) 0.003***

(0.001)

Ln(average number of citations per patentt-10 ) 0.002**

(0.001)

Ln(number of claims t-10 ) 0.017*** 0.043***

(0.005) (0.012)

Ln(citations receivedt-10 ) 0.012** 0.004

(0.005) (0.008)

Ln(MSA productivity t-10 ) 0.193*** 0.195*** 0.196*** 0.197*** 0.195***

(0.028) (0.028) (0.027) (0.028) (0.028)Ln(predicted MSA productivity t-10) 0.555*** 0.549*** 0.549*** 0.548*** 0.557***

(0.0467) (0.047) (0.047) (0.047) (0.046)

Ln(population t-10) 0.070*** 0.062*** 0.058*** 0.062*** 0.065***

(0.022) (0.022) (0.022) (0.022) (0.022)

Bachelor's degree attainment rate t-10 0.256* 0.206 0.244* 0.247* 0.307**

(0.140) (0.144) (0.139) (0.141) (0.142)

Ln(hi-tech employmentt-10) 0.016 0.017 0.016 0.018* 0.018*

(0.010) (0.010) (0.010) (0.010) (0.010)

Year 2000 0.016 0.006 0.011 0.008 0.018*

-0.011 -0.01 -0.01 -0.01 -0.011

Year 1990 -0.044** -0.055*** -0.051*** -0.051*** -0.035*

(0.018) (0.017) (0.017) (0.018) (0.019)

Constant 0.028 0.173 0.155 0.141 -0.015

(0.376) (0.378) (0.372) (0.376) (0.377)

Observations 1,082 1,082 1,082 1,082 1,082

Adjusted R-squared 0.93 0.93 0.93 0.93 0.93

Dependent variable: Ln(MSA Productivity t )


29/36

28

References

Abel, J.R., Dey, I., Gabe, T.M. 2012. Productivity and density of human capital. Journal of

Regional Science 52, 562-586.

Acemoglu, D., Linn, J., 2004. Market size in innovation: theory and evidence from the

pharmaceutical Industry. The Quarterly Journal of Economics 119, 1049-1090.

Acemoglu, D., Dell, M., 2010. Productivity differences between and within countries.

American Economic Journal: Macroeconomics 2, 169188.

Acemoglu, D., Johnson, S., Robinson, J.A., 2001. The colonial origins of comparative

development: an empirical investigation. American Economic Review 91, 1369-1401.

Acemoglu, D., Johnson, S., Robinson, J.A., 2002. Reversal of fortune: geography and

institutions in the making of the modern world income distribution. Quarterly Journal of

Economics 117, 1231-1294.

Acs, Z., Anselin, L., Varga, A. 2002. Patents and innovation counts as measures of regional

production of new knowledge. Research Policy 31, 1069-1085.

Aghion, P., Howitt, P. W., 2008. The Economics of Growth. The MIT Press, Cambridge, MA.

Allred, B.., Park, W.G., 2007. Patent rights and innovative activity: evidence from national and

firm-level data. Journal of International Business Studies 38, 878-900.

Arthur, B. 2009. The Nature of Technology: What It Is and How It Evolves. Free Press,

New York, NY.

Audretsch, D. B., Feldman, M. P., 1996. R&D spillovers and the geography of innovation

and production. American Economic Review 86, 630-640.

Bartel, A.P., Ichniowski, C., Shaw, K., 2007. How does information technology affect

productivity? Plant-level comparisons of product innovation, process improvement


30/36

29

and worker skills. The Quarterly Journal of Economics 122, 1721-1758.

Barro, R. J., Sala-i-Martin, X. I., 2003. EconomicGrowth. Second ed., The MIT Press,

Cambridge, MA.

Baumol, W. J., Schilling, M. A., Wolff, E. N., 2009. The superstar inventors and entrepreneurs:

how were they educated. Journal of Economics and Management Strategy 18, 711-728.

Besley, T., Case, A., 2003. Political institutions and policy choices: evidence from the United

States. Journal of Economic Literature 41, 7-73.

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.

Bloom, N., Van Reenen, J., 2002. Patents, real options and firm performance. The

Economic Journal 112: 97-116.

Breschi, S., Lenzi, C. 2010. Spatial patterns of inventors mobility: evidence of U.S. urban areas.

Papers in Regional Science 89, 235-250.

Brynjolfsson, E., Hitt, L. M., 2003. Computing productivity: firm-level evidence. Review

of Economics and Statistics 85, 793808.

Chiang-Ping, C., Hu, J.L., Yang, C.H., 2012. Produce patents or journal articles? A cross-

country comparison of R&D productivity change. Scientometrics. DOI:

10.1007/s11192-012-0811-9.

Comanor, W., Scherer, F.M,. 1969. Patent statistics as a measure of technical change. Journal of

Political Economy 77, 392-398.

Crosby, M,. 2000. Patents, innovation, and growth. The Economic Record 26, 255-262.

Daveri, F., Mascotto, F., 2006. The IT revolution across the United States. Review of Income

and Wealth Series 52, 569-602.


31/36

30

Duranton, G and Jayet, H. 2011. Is the Division of Labour Limited by the Extent of the Market?

Evidence from French cities. Journal of Urban Economics 69, 56-71.

Echeverri-Carroll, E., Ayala, S.G., 2009. Wage differentials and the spatial concentration of

high-technology industries. Papers in Regional Science 88, 623-641.

Falvey, R., Foster, N., Greenaway, D. 2006. Intellectual property and economic growth. Review

of Development Economics 10, 700-719.

Feldman, M., Florida, R. 1994. The geographic sources of innovation: technological

infrastructure and product innovation in the United States. Annals of the Association of

American Geographers 84, 210-229.

Forman, C., Goldfarb, A., Greenstein, S. 2008. Understanding the inputs into innovation: do

cities substitute for internal firm resources? Journal of Economics and Management

Strategy 17, 295-316.

Gallup, J.L., Sachs, J.D., Mellinger, A.D. 1999. Geography and economic development.

International Regional Science Review 22, 179-232.

Glaeser, E.L., La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2004. Do institutions cause

growth? Journal of Economic Growth 9, 271-303.

Glaeser, E. L., Saiz, A. 2004. The Rise of the Skilled City. Brookings-Wharton Papers on Urban

Affairs 47- 105.

Glaeser, E.L., Scheinkman, J.A., Shleifer, A., 1995. Economic growth in a cross-section of cities.

Journal of Monetary Economics 36, 117-143.

Gordon, R.J., 2003. Hi-tech Innovation and Productivity Growth: Does Supply Create Its Own

Demand? NBER Working Paper 9437. National Bureau of Economic Research,

Cambridge, MA.


32/36

31

Graham, S.J.H., Merges, R.P., Samuelson, P., Sichelman, T., 2009. High technology

entrepreneurs and the patent system: results of the 2008 Berkeley Patent Survey.

Berkeley Technology Law Journal 24, 255-327.

Griliches, Z. 1981. Market value, R&D and patents. Economics Letters 7, 183-197.

Griliches, Z. 1990. Patent statistics as economic indicators: a survey. Journal of Economic

Literature 28, 1661-1707.

Hall, B.H., Jaffe, A., Trajtenberg, M., 2005. Market value and patent citations. Rand Journal of

Economics36, 16-38.

Harris, T.F., Ioannides, Y.M., 2000. Productivity and metropolitan density. Discussion Papers

Series 0016, Department of Economics, Tufts University.

Hasan, I., Tucci, C.L., 2010. The innovationeconomic growth nexus: global evidence. Research

Policy 39, 12641276.

Higgs, R. 1971. American inventiveness, 1870-1920, Journal of Political Economy 79, 661-667.

Hu, A.G.Z., Png, I.P.L., 2009. Patent Rights and Economic Growth: Cross-Country Evidence.

CELS 2009 4th Annual Conference on Empirical Legal Studies Paper.

(http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1339730)

Hunt, R.M., Carlino, G., Chatterjee, S. 2007. Urban density and the rate of invention. Journal of

Urban Economics 61, 389-419.

Jaffe, A.B., Trajtenberg, M. 2002. Patents, Citations, and Innovations: A Window on the

Knowledge Economy. The MIT Press, Cambridge, MA.

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.


33/36

32

Jones, C. I., 2002. Sources of U.S. economic growth in a world of ideas. American

Economic Review 92, 220-239.

Jones, C. I., Romer, P. M., 2010. The new Kaldor facts: institutions, population, and human

capital. American Economic Journal: Macroeconomics 2, 224-245.

Jorgenson, D. W., 2001. Information technology and the U.S. economy. American Economic

Review 91, 1-32.

Jorgenson, D.W., Ho, M.S., Stiroh, K.J., 2008. Retrospective look at the U.S. productivity

growth resurgence. Journal of Economic Perspectives 22, 3-24.

Khan, Z. 2005. The Democratization of Invention: Patents and Copyrights in American

Economic Development, 1790-1920. Cambridge University Press, New York, NY.

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.

Landes, D. S., 1998. The Wealth and Poverty of Nations: Why Some Are So Rich and Some

So Poor. W. W. Norton & Company, New York, NY.

Lanjouw, J.O., Pakes, A., Putnam, J. 1998. How to count patents and value intellectual property:

the uses of patent renewal and application data. The Journal of Industrial Economics 46,

405-432.

Lobo, J., Smole, D.P. 2002. Stratification and spatial segregation of human capital as

determinants of metropolitan productivity in the United States. Urban Studies 39, 529-

547.

Lobo, J., Strumsky, D. (2008) Metropolitan patenting, inventor agglomeration and social

networks: a tale of two effects. Journal of Urban Economics 63, 871-884.


34/36

33

Lucas, R. E., 1988. On the mechanics of economic development. Journal of Monetary

Economics 22, 3-42.

Mankiw, G., Romer, D., Weil, D. 1992. A contribution to the empirics of economic growth.

Quarterly Journal of Economics 107, 407-437.

Mansfield, E. 1986. Patents and innovation: an empirical study. Management Science 32, 173-

181.

Mansfield, E. 1991. Academic research and industrial innovation. Research Policy 20, 1-12.

Marx, M., Strumsky, D., Fleming, L. 2009. Noncompetes and inventor mobility: specialists,

stars, and the Michigan experiment. Management Science55, 875-889.

Mokyr, J. , 1990. The Lever of Riches: Technological Creativity and Economic Progress.

Oxford University Press, New York, NY.

Moretti, E., 2007. Local multipliers. American Economic Review 100, 1-7.

Moretti, E. 2012. The New Geography of Jobs. Houghton Mifflin, New York, NY.

Murphy, K.M., Shleifer, A., Vishny, R.W. 1991. The allocation of talent: implications for growth.

The Quarterly Journal of Economics 106, 503-530.

Park, W., Ginarte, J. 1997. Intellectual property rights and economic growth. Contemporary

Economic Policy 25, 51-61.

Philippe-Combes, P., Duranton, G., Gobillon, L., Puga, D., Roux, S., 2012. The Productivity

advantages of large cities: distinguishing agglomeration from firm selection.

Econometrica (forthcoming).

Puga, D. 2010. The magnitude and causes of agglomeration economies. Journal of Regional

Science 50, 203-219.

Rappaport., J., Sachs, J.D. 2003. The United States as a coastal nation. Journal Of Economic


35/36

34

Growth 8, 5-46.

Romer, Paul M. 1986. Increasing returns and log-run growth, Journal of Political Economy 94,

1002-1037.

Romer, Paul M. 1990. Endogenous technological change. Journal of Political Economy 98, S71-

S102.

Romer, P.M., 2010. What parts of globalization matter for catch-up growth? American

Economic Review 100, 94-98.

Rosenberg, Nathan, 1982. Inside the Black Box: Technology and Economics. Cambridge

University Press, New York, NY.

Sedgley N, Elmslie B. 2011. Do we still need cities? Evidence on rates of innovation from count

data models of metropolitan statistical area patents. American Journal of Economic

Sociology 70, 86-108.

Sinha, D., 2008. Patents, innovations and economic growth in Japan and South Korea: evidence

from individual country and panel data. Applied Econometrics and International

Development 8, 181-188.

Scherer, F.M., 1965. Firm size, market structure, opportunity, and the output of patented

Inventions. American Economic Review 55, 1097-1125.

Schmookler, Jacob. 1966. Invention and Economic Growth. Harvard University Press,

Cambridge, MA.

Scotchmer, S. 2006. Innovation and Incentives. The MIT Press, Cambridge, MA.

Smith, Adam 2003/1776. The Wealth of Nations. Bantam Books, New York, NY.

Sokoloff, K.L. 1988. Inventive activity in early industrial America: evidence from patent records,

17901846. Journal of Economic History 48, 813-850.


36/36

Solow, R., 1957. Technical change and the aggregate production function. Review of Economics

and Statistics 39, 312-320.

Rosenthal, S., Strange, W., 2004. Evidence on the nature and sources of agglomeration

economies. In J.V. Henderson and J. F. Thisse, ed., Handbook of Regional and Urban

Economics, vol. 4, North Holland.

Thomson, R., 2009. Structures of Change in the Mechanical Age: Technological Innovation in

the United States, 1790 to 1865. Johns Hopkins University Press, Baltimore (MD).

Trajtenberg, M., 1990. A penny for your quotes: patent citations and the value of innovations.

RAND Journal of Economics 21, 172-187.

Toivanen, O., Vnnen, L. 2010. Returns to Inventors. CEPR Discussion Papers 7744, Centre

for Economic Policy Research, London.

Ullman, E. 1958. Regional development and the geography of concentration. Papers and

Proceedings of the Regional Science Association, 4, 179-207.

USPTO, 2012. Intellectual Property and the U.S. Economy: Industries in Focus. United States

Patent anhd Trademark Office, Washington, D.C.

Wahdwa, V., Freeman, R., Rissing, B. 2008. Education and Tech Entrepreneurship. Ewing

Marion Kaufman Foundation, Kansas City.

Walsh, J.P., Nagaoka, S. 2009. Who invents? Evidence from the Japan-U.S. inventor survey.

Discussion Paper 09034, Research Institute of Economy, Trade, and Industry, Tokyo.

Woolridge, J.M. 2010. Econometric analysis of cross section and panel data. The MIT Press,

Cambridge, MA.

Role Invntn US Metro Prodctvty

Documents

Transcript of Role Invntn US Metro Prodctvty