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Gender distinctions in patenting: Does nanotechnology make a difference?

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Abstract

Analyzing the domestic patent records filed with the United State Patent and Trademark Office (USPTO) in the 16-year time period from 1990 to 2005, this study benchmarks the collaboration patterns and gender-specific performance in patenting nanotechnology, a newly emerging field, with those in the general area across all technological fields (thereafter the overall tech area, a proxy of traditional technological fields). Going beyond what has been discovered in a previous study that women’s involvement in patenting is lower than their male peers in nanotechnology, the empirical evidence reported here suggests that the gap to women’s disadvantage was smaller in nanotechnology than in the overall tech area in the studied period. The major finding of this study is that, while more than 90% of patents across fields were from industry where patenting is least likely to be collaborative, nano-patents have more diverse origins (79% from industry and 21 from universities, government, public institutions, and cross-sectoral collaboration) and are more likely to be collaborative outcomes (including those from industry). The profile of nanotechnology patents in terms of workforce sectors has the implication that nanotechnology presents an environment where women are more able to catch collaborative opportunities and engage in patenting. Implications for future research are discussed correspondingly.

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Notes

  1. A large volume of research on gender and scientific productivity has been focusing on publication records (see Cole and Zuckerman 1984; Xie and Shauman 1998; Pripic 2002 for the comprehensive review, and Elsevier 2017 for the latest analyses) and persistently reporting a gender gap to women’s disadvantage. While patenting and nanotechnology are focused as specific research contexts here, subjects of study are expanded from academic scientists to scientists in all workforce sectors. As such, throughout the article, more attention is given to patent related literature despite some references on publication analyses are mentioned as well.

  2. Conceptually, collaboration in S&T refers to joint efforts and continuous interactions of scientists to achieve common goals (Katz and Martin 1997). Practically and methodologically, in both the cited and the present study it refers to being listed as a co-inventor in patent documents.

  3. Nanotechnology is a broad unbrella term encompassing a wide variety of research and innovative efforts dealing with the manipulation of matter at the atomic and molecular scale. Therefore, it is not an independent field or has been assigned a single patent class in any patent system. But the program in Science, Technology, and Innovation Policy (STIP) at Georgia Institute of Technology (Georgia Tech) in Atlanta, Georgia USA has developed a search algorithm. And with the use of the tool, STIP has built mega nanotechnology databases of publications and patents. The search results that constitute these databases have been validated with experts in the field and have formed a base from which many analyses are generated (Youtie et al. 2016).

  4. The overall tech area here does not represent traditional fields in a strict term because it includes the emerging fields as well. However, given that the emergence of some fields is a recent phenomenon and they only account for a small share of scientific outputs such as patents by 2005 (the time window for this study is 1990–2005), it is still reasonable to consider the overall area as a rough representation of traditional fields to make comparisons with nanotechnology.

  5. Women were better represented in the subfield of computer software than in other technological categories in the IT industry, according to Ashcraft and Breitzman’s study (2007); and they were better represented in Human Necessities and Chemistry in Spain according to Mauleón and Bordons (2009).

  6. The rationale behind the comparison between nanotechnology and the overall tech area is as follows: nanotechnology is such an extensive field infusing branches of biology, chemistry, physics and engineering (Islam and Ozcan 2017; Kulzer and Orrit 2004) that it is incomparable with any single traditional field in terms of the knowledge foundation. In addition, the converging trend of nanotechnology, biotechnology, information technology and cognitive technology is anticipated and observed (Tarafdar et al. 2013), suggesting little use in comparing nanotechnology with these emerging and interdisciplinary fields.

  7. These sources include MicroPatent database, US Patent and Trademark Office (USPTO), European Patent Office (EPO), Japanese Patent Office (JPO), World Intellectual Property Office (WIPO), patent offices of Germany, Great Britain, and France, and the EPO’s raw data resources (INPADOC).

  8. The current focus is on the gender pattern among scientists in the US, but patents filed with USPTO may come from other countries. The restriction is applied to minimize the noise from foreign patents.

  9. Here as well as in Fig. 1, there seems difference on the selection criterion: while for the patent database it is used “only those having at least one inventor or assignee located in the US”, for the PATSTAT it is used “only those having at least one inventor and assignee located in the US”. This is because in the former database “inventor(s)’ address” and “assignee’s address” were saved in separate categories and the in latter one all address information was saved in one category “inventor(s)’ and assignee’s address”. In this sense, both selection procedures yielded patent records that have at least one inventor or assignee located in the US, and the comparisons are meaningful.

  10. The list can be downloaded from http://www.heise.de/ct/ftp/07/17/182/. It was developed for a computer program that can help identify gender of popular European, American, Japanese, Indian, and Chinese names.

  11. Before integrating the name list to the existing name database, I removed many duplicated first names as they were coded in different genders in different European countries. Compared to the old name database that contained 2440 unique first names (female 756 and male 1684), the extended one contains 33,468 unique first names (female 16,088 and male 17,380). Many Korean and Chinese names that could not be identified in the previous study have their gender codes in this new name database.

  12. The list of faculty members in Georgia Institute of Technology is available online (http://www.catalog.gatech.edu/general/adminfac/ag.php) and the seemingly Asian names are easily verifiable by checking their background information, usually their undergraduate universities/colleges, available in their personal webpages or CVs.

  13. The matching procedure was only applied to the nanotechnology patent database. The gender information was available in the PATSTAT database stored in Fraunhofer ISI after applying combined name lists from Frietsch and peers (2009) and C’t Magazin for the sex identification. And in extracting patent records from that database, the gender information of inventors were extracted as well.

  14. http://www.gpeters.com/names/baby-names.php.

  15. Only the probability of a name to be female (or male) is larger than 60% did I assign the informed gender to that name; otherwise (around 50% but below 60%), I marked the name as unknown.

  16. Please note that at the inventor level, around 16% of inventors in nanotechnology and around 18% in the overall tech area were not identified. A further check of the unidentified in both datasets found most of them are Asian names (Chinse, Korean, and Japanese). At the patent level, only those with all the team inventors unidentified are excluded, and so the different rates here indicate that a higher share of patents in nanotechnology has all the team inventors unidentified. Despite the different rates, the patents excluded only account for a small share and would not impost significant impacts on patent-level analyses.

  17. To detect whether excluding those patent records with inventors’ gender unidentified would introduce significant biases to the analysis, for both nano-patent data and all patent data I conducted comparisons between identified and unidentified records on most of the analytic variables, namely the distribution over years, average team size, the share of collaborative patents, and the distribution over workforce sectors. The t test and ANOVA results show that statistically significant differences exist for patents across all fields but not for nano-patent data (p < 0.001, detailed results will be provided per request). The significance in the overall patents is understandable given the massive total number, but the fact that only 0.6% of the total patents are excluded for analysis leads me to expect very limited impacts on the final results.

  18. Again, to detect whether the exclusion would introduce any significant biases, I compared excluded with included patents on women’s participation and women’s contribution for both datasets. While the excluded patents in both sets tend to be individual patents (30.1% in nano patents and 38.2% in the overall tech area), the t tests show no statistically significant differences between the excluded and included patents on women’s relative participation (around 21% for nano and around 11% for overall) and women’s relative contribution (0.10 for nano and 0.06 for overall) over the 15 years.

  19. This suggests that the comparisons here are not logically strict. If female scientists do not have an equal status to males in patenting, then it exists a natural tendency that the total number of patents goes larger the gender gap becomes larger. However, the current focus is not to statistically examine the difference but collect empirical evidence about the difference. While this limitation caution the interpretation of the comparative results, the current results are still useful to set the baseline for future verification and development.

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Acknowledgements

This study was supported by the Program in Science, Technology, and Innovation Policy (STIP) at Georgia Institute of Technology. Special gratitude is extended to Philip Shapira, Rainer Frietsch, and Peter Neuhäusler for their insightful suggestions on data use as well as method and framework development. Thanks also go the two anonymous reviewers for their instructive comments. All errors and omissions remain to the author.

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Correspondence to Yu Meng.

Appendices

Appendix 1

Women’s and men’s participation,a 1990–2005

Year

Nanotechnology

Overall

Female

Male

Total

Female

Male

Total

1990

21

167

171

3129

41,474

42,553

1991

23

200

202

3665

44,708

45,921

1992

31

237

239

4050

45,673

46,867

1993

31

224

225

4292

46,601

47,848

1994

32

237

239

4564

49,183

50,392

1995

39

257

262

4706

48,776

50,002

1996

60

295

301

5733

53,354

54,846

1997

50

287

293

6118

53,897

55,442

1998

54

382

391

8224

70,133

72,207

1999

86

443

454

8734

72,841

75,103

2000

126

586

604

8931

73,673

75,959

2001

155

798

821

9547

75,386

77,719

2002

378

1636

1684

9847

74,345

76,694

2003

441

1807

1859

9798

74,700

76,973

2004

551

2260

2327

9133

71,319

73,389

2005

756

3156

3261

8146

61,989

63,773

Overall

2834

12,972

13,333

108,617

958,052

985,688

  1. aNote because a patent team may include both female and male inventors and may be counted once for women’s participation and once for men’s participation in this case, it is possible the sum of women’s and men’s participation in a given year exceeds the total number of patents in that year

Appendix 2

Women and men’s contribution, 1990–2005

Year

Nanotechnology

Overall

Female

Male

Relative ratio

Female

Male

Relative ratio

1990

9.492857

152.7762

0.062

1733.206

37,233.38

0.047

1991

10.65

180.0214

0.059

1992.003

39,943.38

0.050

1992

11.30952

206.8071

0.055

2108.146

40,386.8

0.052

1993

11.78333

195.7619

0.060

2152.335

41,013.78

0.052

1994

12.34524

207.4536

0.060

2240.565

43,079.36

0.052

1995

14.31627

223.0254

0.064

2284.374

42,566.26

0.054

1996

21.39841

250.3571

0.085

2722.752

46,086.3

0.059

1997

21.18333

247.6563

0.086

2852.588

46,177.69

0.062

1998

20.5623

337.4976

0.061

3840.348

59,893.41

0.064

1999

34.78016

361.177

0.096

4104.826

61,769.74

0.066

2000

49.49762

487.6

0.102

4122.769

62,171.73

0.066

2001

62.01575

661.26

0.094

4279.605

62,927.47

0.068

2002

134.72

1346.874

0.100

4315.392

61,559.3

0.070

2003

166.4838

1461.846

0.114

4220.085

61,597.49

0.069

2004

207.8232

1815.53

0.114

3872.43

58,602.61

0.066

2005

271.5225

2502.312

0.109

3376.114

50,627.75

0.067

Overall

1059.884

10,637.96

0.100

50,217.54

815,636.5

0.062

Appendix 3

Comparisons on team size, 1990–2005

Year

Nanotechnology

Overall

Patents involved female inventor(s)

Patents involved male inventor(s)

Patents involved female inventor(s)

Patents involved male inventor(s)

1990

3.24

2.31

2.60

1.86

1991

2.70

2.15

2.68

1.89

1992

3.87

2.26

2.82

1.97

1993

3.13

2.41

2.95

2.04

1994

3.66

2.43

3.05

2.08

1995

4.08

2.65

3.12

2.13

1996

4.00

2.66

3.31

2.22

1997

3.14

2.45

3.35

2.28

1998

3.59

2.41

3.38

2.30

1999

3.60

2.77

3.36

2.33

2000

3.69

2.81

3.47

2.38

2001

3.95

2.85

3.56

2.46

2002

4.43

3.06

3.69

2.51

2003

4.17

2.98

3.78

2.55

2004

4.05

3.01

3.83

2.57

2005

4.26

3.12

3.88

2.61

Overall

4.08

2.91

3.43

2.31

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Meng, Y. Gender distinctions in patenting: Does nanotechnology make a difference?. Scientometrics 114, 971–992 (2018). https://doi.org/10.1007/s11192-017-2607-4

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