SDG 5 and the Gender Gap in Standardization: Empirical Evidence From Germany
Abstract
:1. Introduction
- To what extent are women represented in standardization committees?
- Why (if so) are women under-represented?
2. Literature and Theory
2.1. Initiatives on the Consideration of Gender in Standards
prevailing gender norms present barriers to women’s participation in the development of standards [and] dominance of male representation in standard-setting affects the way that standards are produced, with insufficient consideration of women specificities in the deliverables.([41], pp. 2–3)
2.2. Perspectives on Gender in Standardization
the presence of diversity balances biases, which thereby contributes to the generation of alternative perspectives and experiences for exploring new problems.
[scientific] publications of mixed teams, i.e., with a high share of female authors, receive higher citation rates than homogeneous teams [and] women have higher citation rates than men.
- general unfairness and gender inequality,
- lowered input-legitimacy,
- and, possibly, a suboptimal utilization of output-enhancing effects related to the diversity of standardization committees.
2.3. Causes of Female Under-Representation in Standardization
[men are] more heavily represented in [higher paid] engineering/technology and physical/mathematical sciences, whereas women had a much larger share of graduates in [lower paid] language studies and humanities, creative arts, and education.([65], p. 3)
2.4. Hypotheses
3. Materials and Methods
3.1. Data
3.2. Models
4. Results
4.1. Regional Differences
4.2. Size
4.3. Education
4.4. SSOs
4.5. Regression
5. Discussion
6. Conclusions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
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Variable | Women = 1 (%) | Obs. | Mean | Std. Dev. | ||
---|---|---|---|---|---|---|
dependent | Logistic regression model. Equal to 1 if an organization is represented by at least one female expert, otherwise 0 | 16.7 | 8134 | |||
dependent | Fractional logit model. Fraction of female experts affiliated with organization | |||||
independent | Cert., Test. Labs | 42.0 | 317 | |||
Public | 39.0 | 136 | ||||
Food | 35.5 | 62 | ||||
Associations | 32.6 | 221 | ||||
Science, Edu. | 31.1 | 325 | ||||
Chem., Pharma. | 24.2 | 541 | ||||
Energy, Water, Oil | 21.9 | 269 | ||||
Logistics, Mobility | 21.6 | 125 | ||||
Automotive Eng. | 20.7 | 309 | ||||
Medical Eng. | 19.6 | 560 | ||||
Clothing, Textiles | 18.9 | 334 | ||||
Service | 11.4 | 1827 | ||||
Mechanical Eng. | 11.2 | 1326 | ||||
Metal Prod. | 11.0 | 619 | ||||
Electrical Eng. | 10.8 | 753 | ||||
Trade | 10.5 | 229 | ||||
Construction | 7.5 | 361 | ||||
Primary | 14.3 | 888 | ||||
Secondary | 15.2 | 4246 | ||||
Tertiary | 19.4 | 3180 | ||||
Berlin | 31.4 | 388 | ||||
East Germany | 20.7 | 666 | ||||
West Germany | 15.6 | 7260 | ||||
Number of employees | 8134 | 2649 | 21,771 | |||
Micro: <10 | 10.9 | 1364 | ||||
Small: 10–49 | 12.8 | 1718 | ||||
Medium: 50–249 | 14.7 | 2345 | ||||
Large: 250–999 | 18.3 | 1605 | ||||
Larger:≥1000 | 30.2 | 1282 | ||||
control | Total number of experts affiliated with organization (experts = employees active in standardization) | 8134 | 3.5 | 8.5 |
Models | Logistic Regression | Fract. Logit | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | ||
0.028 *** | 0.028 *** | 0.028 *** | 0.027 *** | 0.000 ** | ||
(0.001) | (0.001) | (0.001) | (0.001) | (0.000) | ||
0.005 ** | 0.007 *** | 0.007 *** | 0.007 *** | 0.003 ** | ||
(0.002) | (0.002) | (0.002) | (0.002) | (0.001) | ||
Secondary | 0.015 | 0.016 | ||||
baseline Primary | (0.011) | (0.011) | ||||
Tertiary | 0.071 *** | 0.064 *** | ||||
(0.012) | (0.012) | |||||
Berlin | 0.099 *** | 0.087 *** | 0.044 *** | |||
baseline west | (0.021) | (0.020) | (0.013) | |||
East | 0.054 *** | 0.045 ** | 0.029 ** | |||
(0.015) | (0.015) | (0.010) | ||||
Associations | 0.121 *** | 0.077 *** | ||||
baseline Service | (0.030) | (0.019) | ||||
Automotive Eng. | −0.017 | −0.017 | ||||
(0.019) | (0.011) | |||||
Chem., Pharma. | 0.067 *** | 0.050 *** | ||||
(0.019) | (0.014) | |||||
Clothing, Textiles | 0.066 ** | 0.051 ** | ||||
(0.023) | (0.018) | |||||
Construction | −0.057 *** | −0.037 *** | ||||
(0.017) | (0.011) | |||||
Electrical Eng. | −0.066 *** | −0.036 *** | ||||
(0.013) | (0.008) | |||||
Energy, Water, Oil | −0.009 | 0.000 | ||||
(0.021) | (0.014) | |||||
Food | 0.211 *** | 0.180 *** | ||||
(0.057) | (0.048) | |||||
Logistics, Mobility | 0.006 | 0.023 | ||||
(0.031) | (0.023) | |||||
Mechanical Eng. | −0.052 *** | −0.036 *** | ||||
(0.012) | (0.007) | |||||
Medical Eng. | 0.028 | 0.011 | ||||
(0.017) | (0.011) | |||||
Metal Production | −0.039 ** | −0.025 ** | ||||
(0.015) | (0.009) | |||||
Public | 0.066 | 0.062 ** | ||||
(0.035) | (0.022) | |||||
Cert., Testing Labs. | 0.172 *** | 0.120 *** | ||||
(0.027) | (0.019) | |||||
Science, Education | 0.055 * | 0.041 ** | ||||
(0.022) | (0.015) | |||||
Trade | −0.025 | −0.014 | ||||
(0.023) | (0.016) | |||||
Const | −2.744 *** | −3.195 *** | −3.267 *** | −2.952 *** | −2.782 *** | |
(0.081) | (0.138) | (0.139) | (0.099) | (0.104) | ||
Observations | 8314 | 8314 | 8314 | 8314 | 8314 | |
Pseudo | 0.159 | 0.167 | 0.172 | 0.200 | 0.047 | |
AIC | 6322 | 6270 | 6233 | 6053 | 4515 |
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Heß, P. SDG 5 and the Gender Gap in Standardization: Empirical Evidence From Germany. Sustainability 2020, 12, 8699. https://doi.org/10.3390/su12208699
Heß P. SDG 5 and the Gender Gap in Standardization: Empirical Evidence From Germany. Sustainability. 2020; 12(20):8699. https://doi.org/10.3390/su12208699
Chicago/Turabian StyleHeß, Philipp. 2020. "SDG 5 and the Gender Gap in Standardization: Empirical Evidence From Germany" Sustainability 12, no. 20: 8699. https://doi.org/10.3390/su12208699