Statistics Without Borders Webinars
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There were many volunteers for this project including those who visited Haiti two months after the earthquake in 2010 and those who conducted the survey five months after the earthquake. Statistics without Borders conducted a nationally representative sample survey to examine economic impact using a random digit dial sample of mobile phone numbers. We analyzed the anonymized survey data and the questionnaire that they made available for public use. Radical changes in household members occurred among post-earthquake Haitian households. Similar changes of household members that are caused by natural disasters have been associated with long-term psychological well-being in the literature. The survey also provides a rare look at gender discrepancy in employment retention following a natural disaster from a nationally representative survey. While the overall employment rate was down by 50% five months after the earthquake, our findings indicate that households with female heads are at a significantly greater risk of losing employment.
Pre-natal care plays a critical role in material and infant healthcare. The present work seeks to assess a maternal and infant care program administered by Global Community Service Foundation, (GCSF), in the Inle lake area of Myanmar (formerly called Burma), and find ways to expand it. Such expansions includes identification of critical maternal and infant care knowledge gaps among women and health care workers with the objective of communicating those gaps so that they can be addressed. Statistics Without Borders (SwB) members worked closely with the organization to develop a study, which fits the purpose and meets the ultimate objective. This paper discusses the key results and issues of this collaborative work.
The disaster response resources and the analytical resources must work closely together to ensure that the analysis is fit for purpose and meets the ultimate objective. This study discusses the key considerations for such collaboration through an analysis of Twitter data surrounding the 2013 landfall of Typhoon Haiyan in the Philippines.