Patent Activity in Metropolitan Areas

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Top Five Metropolitan Areas Receiving Utility Patents by Industry, 1990-95

(Number of Patents)

Four

Combined Industries

Chemicals

& Allied Products

(SIC 28)

Industrial Machinery

(SIC 35)

Electronics

(SIC 36)

Instruments

(SIC 38)

New York

(7,674)

New York

(1,886)

New York

(870)

San Francisco

(1,402)

Rochester

(736)

San Francisco

(3,270)

Philadelphia

(992)

San Francisco

(848)

New York

(1,254)

New York

(684)

Boston

(2,160)

San Francisco

(520)

Detroit

(640)

Boston

(729)

Los Angeles

(545)

Los Angeles

(1,992)

Chicago

(471)

Boston

(590)

Los Angeles

(725)

San Francisco

(500)

Chicago

(1,929)

Boston

(357)

Chicago

(566)

Chicago

(686)

Boston

(486)

Percent of all Utility Patents Received by the Top Five Metro Areas

37.2%

47.2%

33.0%

42.1%

44.0%

Table 3

Definition of Variables

POPDEN_s	Average number of persons per square kilometer in a metro area, 1990-95
R&DLABS_s^*	Average number of R&D labs located within a metro area, 1990-95
EMPSIC73_s	Average employment level in business services (SIC 73) within a metro area, 1990-95
EMPCON_i_s	Average location quotient for employment in industry i within a metro area, 1990-95
UNIVDUM_s	Dummy variable indicating whether (=1) or not (=0) any Research I/II or Doctorate I/II universities were located in a metro area, 1990-95
UNIVR&D_i_s	Total level of academic R&D expenditures by Research I/II or Doctorate I/II institutions in fields corresponding to industry i within a metro area, 1990-95 (in thousands of 1992 Dollars)
PATDUM_i_s	Dummy variable indicating whether (=1) or not (=0) a metro area had any firms or individuals receive any utility patents in industry i, 1990-95
PATENT_i_s	Total number of utility patents received by firms and individuals in industry i within a metro area, 1990-95

^*R&D lab data cannot be disaggregated at the industry level. Therefore, R&DLABS is only disaggregated at the metropolitan level.
Table 4

Descriptive Statistics for Patents by Metropolitan Area, 1990-95

(N=273)

Variable

Mean

Std. Dev.

Minimum

Maximum

POPDEN

101.2

103.9

1.9

954.0

R&DLABS

36.8

118.7

0.0

1,322.8

EMPSIC73

19,120.4

48,588.9

211.1

402,672.7

EMPCON₂₈

121.1

218.9

0.0

1,555.8

EMPCON₃₅

104.0

102.9

1.3

694.3

EMPCON₃₆

104.5

149.2

0.0

1,573.0

EMPCON₃₈

82.0

134.4

0.0

1,049.9

UNIVDUM

0.3

0.5

0

1.0

UNIVR&D₂₈

132,420.1

462,262.6

0.0

4,062,545.1

UNIVR&D₃₅

61,406.5

230,820.6

0.0

2,347,983.4

UNIVR&D₃₆

37,883.6

161,325.5

0.0

1,799,800.1

UNIVR&D₃₈

176,196.6

624,531.6

0.0

5,025,893.3

PATDUM₂₈

0.7

0.5

0.0

1.0

PATDUM₃₅

0.9

0.3

0.0

1.0

PATDUM₃₆

0.7

0.5

0.0

1.0

PATDUM₃₈

0.8

0.4

0.0

1.0

PATENT₂₈

32.8

140.5

0.0

1,866

PATENT₃₅

39.0

107.0

0.0

870

PATENT₃₆

41.8

144.4

0.0

1,402

PATENT₃₈

24.5

86.6

0.0

736

Table 5

Probit Equation for Patents – First Step

(N=273)

	Estimated Coefficients (Standard Errors)
Variable	Chemicals & Allied Products	Industrial Machinery	Electronics	Instruments
Constant	-0.6606 (0.1762)	-0.6747 (0.3133)	-0.9624 (0.2364)	-0.9382 (0.2570)
POPDEN	-0.05333 (0.01441)	0.04628 (0.04329)	0.05154 (0.03271)	0.06538* (0.03729)
R&DLABS	0.07719*** (0.2186)	0.09011** (0.04322)	0.08735*** (0.02907)	0.1174*** (0.03812)
EMPSIC73	0.4905 (0.03107)	0.2374** (0.09307)	0.08293* (0.04431)	0.09567* (0.05137)
UNIVDUM	0.7284** (0.2946)	-0.2713 (0.3577)	0.1544 (0.3217)	0.7100* (0.3742)
EMPCON_i	0.01943*** (0.005642)	0.05041*** (0.01939)	0.02841*** (0.009977)	0.02714** (0.01161)
Ln L	-113.4	-70.7	-106.3	-94.6
²	115.0	75.2	112.1	110.5

*Significant at the 10 percent level

**Significant at the 5 percent level

***Significant at the 1 percent level
Table 6

Negative Binomial Equation for Patents – Second Step

(N=257)

	Estimated Coefficients (Standard Errors)
Variable	Chemicals & Allied Products	Industrial Machinery	Electronics	Instruments
Constant	1.2330 (0.2018)	1.6180 (0.1721)	1.8808 (0.1767)	1.1331 (0.1319)
POPDEN	0.07945*** (0.01360)	0.05185*** (0.008408)	0.03495*** (0.01171)	0.04284*** (0.007906)
R&DLABS	0.001892 (0.001985)	-0.002571* (0.001445)	-0.002271 (0.001986)	-0.0006587 (0.001408)
EMPSIC73	0.004672 (0.004256)	0.02025*** (0.003417)	0.02118*** (0.004437)	0.01868*** (0.003019)
UNIVR&D_i	0.005673** (0.002350)	0.01304*** (0.003033)	0.01235* (0.007211)	0.001261 (0.001247)
EMPCON_i	0.02317*** (0.003246)	0.03598*** (0.009126)	0.02989*** (0.005632)	0.03223*** (0.003776)
	1.1563*** (0.1502)	0.9704*** (0.09818)	1.3663*** (0.1775)	0.8436*** (0.1018)
Ln L	-719.6	-954.3	-814.4	-717.0

*Significant at the 10 percent level

**Significant at the 5 percent level

***Significant at the 1 percent level

 Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA 30303.

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1 Utility patents are patents granted for inventions and exclude other patent types such as designs.

2 For example, see Markusen et al. (1986) and Jaffe (1989).

3 Several alternative approaches have been pursued to counter some of the drawbacks of the USPTO concordance. Evenson has been instrumental in developing the Yale Technology Concordance and Wellesley Technology Concordance, which are based on patent examiners’ assignment of industry classifications in the Canadian patent system (Englander et al. 1988; Evenson et al. 1988; Johnson 1999; Johnson and Evenson 1997; Kortum and Putnam 1989, 1997). Scherer (1965a, 1965b) and the NBER group (Bound et al. 1984) have compiled industry-level patent data by aggregating firm level data based on the firms’ primary activities. Others (Griliches et al. 1987; Hall et al. 1988; Scherer 1982b, 1984) have linked individual patent data with other data sources, including the Federal Trade Commission Line of Business survey and financial data for publicly traded firms.

4 The United States had 332 classified metropolitan areas in 1990-95. This analysis excludes 59 of the 332 metro areas (all 58 Primary Metropolitan Statistical Areas (PMSAs) and 1 NECMA that serves as a PMSA substitute) because these areas are located within the 17 CMSAs included in the analysis. The CMSAs more accurately encompass the sphere of activity for these larger urban areas.

5 Hausman et al. (1984) provide the first application of using a count model to estimate innovative activity when investigating the effects of industrial R&D on firms’ patenting behavior using panel data.

6 Overdispersion is evident in the patent data, indicated by the significance of the overdispersion parameter, α. This coincides with previous evidence of overdispersion in patent data (Adams, 2000). The presence of overdispersion maintains the appropriateness of the negative binomial model in this analysis.

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