Working with a new dataset every week helped me hone and further sharpen my data skills. Combining my undergraduate training in Statistics with HKS coursework in Economics and Data Visualization, I developed a master code to analyze different datasets and get comparable results for the same metric (tool). This enabled the team to test the efficacy of the tools in different contexts and fine tune their measuring capabilities. It also gave the team an accurate sense of the sensitivity and stability of the tools with respect to sampling fluctuations and errors.
A particular study in Rwanda that I analyzed provided interesting insights about gender norms and social conditioning prevalent in the region. Data showed how for the same amount of work (hours), women expected lesser wages than men. Women also believed that it was okay for men to work more hours and earn more income, even if it meant that they would be able to dedicate lesser time for family and household responsibilities. Whereas, both men and women believed that if a woman is offered a job with a high pay that would cause her to dedicate less time for household responsibilities, she should not take up that job. Having predominantly worked in the South Asian context, it was interesting for me to observe how gender norms transcend geographies to negatively impact the lives of women across the globe. It also helped the government understand peoples’ income generating decisions to then specifically tailor policies that can increase female labor force participation in the country.
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