Mobile Phone Access and Usage and the Impacts on Women’s Well-Being in India

Faculty Member: 

This Research Assistantship position is being offered in association with the Yale Economic Growth Center’s (EGC) pilot Data Science for Development Initiative.

Proposal Description:

Access to digital technology is shaping how poor communities in low- and middle-income countries (LMICs) navigate pandemic life and the nature of their post-pandemic recovery. Growing smartphone access has allowed for unprecedented opportunities to access information, markets and services related to improving health behaviors. However, the net impact of access to mobile phones and the internet on information - and, ultimately, equity, health, and well-being – remains unclear, especially for women. Gender gaps in digital literacy remain particularly high in South Asia, in large part due to long-held beliefs that it is inappropriate for women to access mobile technology. Second, the internet is often a source of misogynistic messages that could reinforce gender-conservative views and discourage women’s mobile phone. For women who do use mobile Internet, there is reason to believe women may have a heightened risk of vulnerability to mis- and disinformation. Finally, greater social isolation among women, relative to men, may lower their ability to cross-check or validate information sources.

Policy responses are hampered by scant evidence on the causal impact of mobile technology access and usage on women’s behavior and gender-related beliefs relevant to women’s broader social and economic roles. The Tobin fellow will work with Pande and co-authors on supporting data collection, processing and analysis for a set of papers that examine how mobile phone access and use impacts women’s well-being.

Requisite Skills and Qualifications:

Tobin RAs will help with literature reviews, support ongoing surveys and, depending on skill set, write code to clean survey data, scrape data and conduct initial analysis. Skill and experience with econometrics software such as R or STATA to run econometric analysis, as well as Python skills, is valuable. Successful fellows will be detail oriented and able to work independently.

Award: 
  • Nastaran Moghimi
  • Lili Cerise