Institutions and female entrepreneurship

This paper compares the impact of institutions on men and women’s decisions to establish new business start-ups between 2001 and 2006. We use data from the Global Entrepreneurship Monitor survey (GEM) which cover at least 2,000 individuals per year in each of up to 55 countries and have merged it with country-level data, from the World Bank, Economist Intelligence Unit, Polity IV and the Heritage Foundation. We find that women are less likely to undertake entrepreneurial activity in countries where the state sector is larger, but the rule of law is not generally found to have gender-specific effects. However, more detailed institutional components of discrimination against women, in particular, restrictions on freedom of movement away from home, make it less likely for women to have high entrepreneurial aspirations in terms of employment growth, even if their entry into entrepreneurial activities, including self-employment, is not affected by this.

A computational approach to measuring the correlation between expertise and social media influence for celebrities on microblogs

Existing approaches of social influence analysis usually focus on how to develop effective algorithms to quantize users' influence scores. They rarely consider a person's expertise levels which are arguably important to influence measures. In this paper, we propose a computational approach to measuring the correlation between expertise and social media influence, and we take a new perspective to understand social media influence by incorporating expertise into influence analysis. We carefully constructed a large dataset of 13,684 Chinese celebrities from Sina Weibo (literally 'Sina microblogging'). We found that there is a strong correlation between expertise levels and social media influence scores. In addition, different expertise levels showed influence variation patterns: high-expertise celebrities have stronger influence on the 'audience' in their expertise domains.