Women in China Tech: Is It a Man’s World?

The 2019 UN Women In Leadership Conference

China’s internet revolution has birthed some of the world’s most successful technology companies. In area’s from education, e-commerce, and social media, to smartphones and electric vehicles, Chinese firms have elevated the reputation of the country’s tech ecosystem to a global standard. The impressive rise of Chinese entrepreneurs and innovators has been hailed and encouraged by Premier Li Keqiang, who called for “mass entrepreneurship” earlier this year. 

Chairman Mao famously said in 1968, “Women hold up half of the sky”. The ethos of this quote exists today, with Chinese women enjoying greater levels of equality in the workforce than many of their Asian neighbors. However, women in China still face many challenges in terms of gainful employment. For example, at entry level, women make up about 50% of the workforce, but in terms of advancement to senior positions, this proportion decreases significantly. In fact, despite the success of certain Chinese companies, many of which were born in the Internet era and boast strong tech competencies, only 9.4% of the board members of Chinese public companies are women, compared with the global average of 33%. While not in the private sector, the lack of women in leadership roles extends to the public sector as well. For example in the CCP’s Politburo Standing Committee, the leadership group of the Chinese government, only one of the 25 members is a woman, Sun Chunlan. 

Just last week, the 2019 UN Women in Leadership forum was hosted in conjunction with Cheung Kong Graduate School of Business in Beijing. The event provided some valuable context on the status of gender equality both in China and globally. While 50% of entry level employees are female, they have much more trouble rising through the ranks in technology companies, accounting for just around 1% at executive levels. For example, among public Chinese companies, many of which feature a strong technology competency, only 9.4% of board members are female, compared to an average of 33% globally. While China has made great strides in promoting quality in STEM education, why is this not translating into greater equality in the upper echelons of the tech sector. There are increasingly more success stories that buck this trend, including female role models like VIPKID’s Cindy Mi, and Luckin Coffee’s Jenny Qian Zhiya. Women in tech face a myriad of challenges, but they have also defied expectation to make great advances in technology in many unique ways. 

I spoke with Jill Tang, Founder and CEO of LadiesWhoTech, an organization whose missionis to close the status quo by inspiring the gender gap and promote gender diversity and inclusion in STEM industries in China. Headquartered in Shanghai, LadiesWhoTech has connected over 10,000 women in STEM throughout China. Jill Tang provided an optimistic outlook on the future of greater gender equality in China’s tech industry, noting that while China has done well in many aspects, significant challenges remain. 

(Source: LadiesWhoTech)

When asked about the challenges women face when trying to launch businesses, Tang had a fairly optimistic outlook. She commented, “Starting a business in China, for Chinese women, probably is even easier than in lots of other countries since China encourages innovation in tech and entrepreneurship. The main problem is still the stereotyping and unconscious bias which lead to less investment into women led firms or other unequal and unfair resource allocation in both the work place and the startup world.” In fact, only 2.3% of the total VC pot of money ($131 billion in the United States in 2018) goes to women-led businesses. If you include founding teams that include at least one woman, the number still rises only to 10.4%. This disparity is the result of a dearth of women in leadership roles at VC firms, only 11 percent of VCs have women on their investment committee.

I also asked Jill Tang about some of the solutions that can be effective in combating bias in the tech sector and workplace more generally. She said that there are not clear solutions that yield results, but placed significant emphasis on STEM education equality. Some suggested best practices center around more female representation in various STEM arenas. Tang listed, “1) Place more female STEM teachers in primary school 2) Offer more scholarships to girls for STEM majors 3) Update gender biased education materials 4) Hire more women in STEM roles 5) Promote women STEM role models via increased exposure in media 6) Gender neutral parental leave policy so that men can share responsibilities of child rearing and women can work 7) Provide unconscious bias workshops and education 8) Set up ally programs and have men to support, mentor and sponsor women.”

Jill Tang also spoke about the need for unbiased data as technology advances into an era that is dominated by machine learning algorithms and artificial intelligence. Specifically this is relevant to human resources practices in the tech sector. For example, blind interviews or gender neutral job descriptions would be a good place to start. While this would be startling and abhorrent in many Western countries, sometimes job descriptions in China still stipulate “attractive female wanted” or “men only” in their position postings.

Tang comments, “With imbalanced or biased data to train our machines, AI or robots will be biased too. So we should stay from correcting the system and imbalanced data.” Harvard Business Review recently published an article on avoiding gender bias in artificial intelligence, and much of it centered around having a diverse and balanced data set for training the algorithms. In a data-rich environment like China, the objectivity of data is paramount to maintaining fairness in the AI powered products of tomorrow. This not only must the sample data be diverse in its sourcing, but also the humans responsible for labeling the data for the algorithms should come from diverse backgrounds so as to avoid any unconscious bias that exists as the result of the environmental conditioning of a particular demographic. Tang also mentioned that this type of bias for machine learning is not limited to gender, but also to ethnicity as well. 

While progress has been and is continuing to be made, Tang acknowledges that rather than a steadfast set of policies to enact wholesale change, an adjustment of mindset is required to create a more equal environment for women in tech. This, she adds, will take time.